diff --git a/.idea/.name b/.idea/.name
index e96ba93..c5e1413 100644
--- a/.idea/.name
+++ b/.idea/.name
@@ -1 +1 @@
-GUI_main.py
\ No newline at end of file
+CLI_main.py
\ No newline at end of file
diff --git a/.idea/workspace.xml b/.idea/workspace.xml
index 562e9fc..638ef5b 100644
--- a/.idea/workspace.xml
+++ b/.idea/workspace.xml
@@ -4,10 +4,7 @@
-
-
-
-
+
@@ -66,7 +63,7 @@
-
+
diff --git a/BETA_E_Model_T&T.ipynb b/BETA_E_Model_T&T.ipynb
index a11f22c..9988e0e 100644
--- a/BETA_E_Model_T&T.ipynb
+++ b/BETA_E_Model_T&T.ipynb
@@ -22,7 +22,7 @@
},
{
"cell_type": "code",
- "execution_count": 2,
+ "execution_count": 1,
"metadata": {
"ExecuteTime": {
"end_time": "2023-12-28T02:27:44.939427800Z",
@@ -46,7 +46,7 @@
},
{
"cell_type": "code",
- "execution_count": 3,
+ "execution_count": 2,
"metadata": {
"ExecuteTime": {
"end_time": "2023-12-28T02:27:47.128539500Z",
@@ -134,7 +134,7 @@
},
{
"cell_type": "code",
- "execution_count": 4,
+ "execution_count": 3,
"metadata": {
"notebookRunGroups": {
"groupValue": "123"
@@ -169,7 +169,7 @@
},
{
"cell_type": "code",
- "execution_count": 5,
+ "execution_count": 4,
"metadata": {
"ExecuteTime": {
"end_time": "2023-12-28T02:27:47.139048Z",
@@ -215,7 +215,7 @@
},
{
"cell_type": "code",
- "execution_count": 6,
+ "execution_count": 5,
"metadata": {
"ExecuteTime": {
"end_time": "2023-12-28T02:27:48.287855100Z",
@@ -732,7 +732,7 @@
},
{
"cell_type": "code",
- "execution_count": 7,
+ "execution_count": 6,
"metadata": {
"ExecuteTime": {
"end_time": "2023-12-28T02:31:27.380088800Z",
@@ -775,7 +775,7 @@
},
{
"cell_type": "code",
- "execution_count": 8,
+ "execution_count": 7,
"metadata": {
"notebookRunGroups": {
"groupValue": "3"
@@ -926,7 +926,7 @@
},
{
"cell_type": "code",
- "execution_count": 11,
+ "execution_count": 8,
"metadata": {
"ExecuteTime": {
"end_time": "2023-12-27T17:34:12.077394600Z",
@@ -946,14 +946,14 @@
"Freezing 0 layers in the base model...\n",
"Percentage of the base model that is frozen: 0.00%\n",
"Total model layers: 814\n",
- "Model: \"model_1\"\n",
+ "Model: \"model\"\n",
"_____________________________________________________________________________________________________________\n",
" Layer (type) Output Shape Param # Connected to Trainable \n",
"=============================================================================================================\n",
- " input_2 (InputLayer) [(None, 224, 224, 3 0 [] Y \n",
+ " input_1 (InputLayer) [(None, 224, 224, 3 0 [] Y \n",
" )] \n",
" \n",
- " stem_conv (Conv2D) (None, 112, 112, 64 1728 ['input_2[0][0]'] Y \n",
+ " stem_conv (Conv2D) (None, 112, 112, 64 1728 ['input_1[0][0]'] Y \n",
" ) \n",
" \n",
" stem_bn (BatchNormalization) (None, 112, 112, 64 256 ['stem_conv[0][0]'] Y \n",
@@ -3122,13 +3122,13 @@
" # GlobalAveragePooling2D\n",
" base_model_FT = GlobalAveragePooling2D(name=\"FC_INPUT_Avg-Pooling\")(base_model.output)\n",
" # Dense\n",
- " Dense_L1 = Dense(512, activation=\"relu\", kernel_regularizer=l2(0.0026), name=\"FC_C_Dense-L1-512\")(base_model_FT)\n",
+ " Dense_L1 = Dense(512, activation=\"relu\", kernel_regularizer=l2(0.0016), name=\"FC_C_Dense-L1-512\")(base_model_FT)\n",
" # Dropout\n",
" Dropout_L1 = Dropout(0.125, name=\"FC_C_Dropout-L1-0.1\")(Dense_L1)\n",
" # BatchNormalization\n",
" BatchNorm_L2 = BatchNormalization(name=\"FC_C_Avg-BatchNormalization-L1\")(Dropout_L1)\n",
" # Dense\n",
- " Dense_L2 = Dense(256, activation=\"relu\", kernel_regularizer=l2(0.0015), name=\"FC_C_Dense-L2-512\")(BatchNorm_L2)\n",
+ " Dense_L2 = Dense(256, activation=\"relu\", kernel_regularizer=l2(0.0005), name=\"FC_C_Dense-L2-512\")(BatchNorm_L2)\n",
" # BatchNormalization\n",
" BatchNorm_L3 = BatchNormalization(name=\"FC_C_Avg-BatchNormalization-L2\")(Dense_L2)\n",
" # Dense\n",
@@ -3636,9 +3636,1143 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 13,
"metadata": {},
- "outputs": [],
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Creating the model...\n",
+ "Total model layers: 10\n",
+ "Model: \"model\"\n",
+ "____________________________________________________________________________\n",
+ " Layer (type) Output Shape Param # Trainable \n",
+ "============================================================================\n",
+ " input_3 (InputLayer) [(None, 224, 224, 3)] 0 Y \n",
+ " \n",
+ " convnext_xlarge (Functional (None, None, None, 2048) 34814796 Y \n",
+ " ) 8 \n",
+ "|¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯|\n",
+ "| input_2 (InputLayer) [(None, None, None, 3)] 0 Y |\n",
+ "| |\n",
+ "| convnext_xlarge_prestem_nor (None, None, None, 3) 0 Y |\n",
+ "| malization (Normalization) |\n",
+ "| |\n",
+ "| convnext_xlarge_stem (Seque (None, None, None, 256) 13056 Y |\n",
+ "| ntial) |\n",
+ "||¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯||\n",
+ "|| convnext_xlarge_stem_conv ( (None, None, None, 256) 12544 Y ||\n",
+ "|| Conv2D) ||\n",
+ "|| ||\n",
+ "|| convnext_xlarge_stem_layern (None, None, None, 256) 512 Y ||\n",
+ "|| orm (LayerNormalization) ||\n",
+ "|¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯|\n",
+ "| convnext_xlarge_stage_0_blo (None, None, None, 256) 12800 Y |\n",
+ "| ck_0_depthwise_conv (Conv2D |\n",
+ "| ) |\n",
+ "| |\n",
+ "| convnext_xlarge_stage_0_blo (None, None, None, 256) 512 Y |\n",
+ "| ck_0_layernorm (LayerNormal |\n",
+ "| ization) |\n",
+ "| |\n",
+ "| convnext_xlarge_stage_0_blo (None, None, None, 1024) 263168 Y |\n",
+ "| ck_0_pointwise_conv_1 (Dens |\n",
+ "| e) |\n",
+ "| |\n",
+ "| convnext_xlarge_stage_0_blo (None, None, None, 1024) 0 Y |\n",
+ "| ck_0_gelu (Activation) |\n",
+ "| |\n",
+ "| convnext_xlarge_stage_0_blo (None, None, None, 256) 262400 Y |\n",
+ "| ck_0_pointwise_conv_2 (Dens |\n",
+ "| e) |\n",
+ "| |\n",
+ "| convnext_xlarge_stage_0_blo (None, None, None, 256) 256 Y |\n",
+ "| ck_0_layer_scale (LayerScal |\n",
+ "| e) |\n",
+ "| |\n",
+ "| convnext_xlarge_stage_0_blo (None, None, None, 256) 0 Y |\n",
+ "| ck_0_identity (Activation) |\n",
+ "| |\n",
+ "| tf.__operators__.add (TFOpL (None, None, None, 256) 0 Y |\n",
+ "| ambda) |\n",
+ "| |\n",
+ "| convnext_xlarge_stage_0_blo (None, None, None, 256) 12800 Y |\n",
+ "| ck_1_depthwise_conv (Conv2D |\n",
+ "| ) |\n",
+ "| |\n",
+ "| convnext_xlarge_stage_0_blo (None, None, None, 256) 512 Y |\n",
+ "| ck_1_layernorm (LayerNormal |\n",
+ "| ization) |\n",
+ "| |\n",
+ "| convnext_xlarge_stage_0_blo (None, None, None, 1024) 263168 Y |\n",
+ "| ck_1_pointwise_conv_1 (Dens |\n",
+ "| e) |\n",
+ "| |\n",
+ "| convnext_xlarge_stage_0_blo (None, None, None, 1024) 0 Y |\n",
+ "| ck_1_gelu (Activation) |\n",
+ "| |\n",
+ "| convnext_xlarge_stage_0_blo (None, None, None, 256) 262400 Y |\n",
+ "| ck_1_pointwise_conv_2 (Dens |\n",
+ "| e) |\n",
+ "| |\n",
+ "| convnext_xlarge_stage_0_blo (None, None, None, 256) 256 Y |\n",
+ "| ck_1_layer_scale (LayerScal |\n",
+ "| e) |\n",
+ "| |\n",
+ "| convnext_xlarge_stage_0_blo (None, None, None, 256) 0 Y |\n",
+ "| ck_1_identity (Activation) |\n",
+ "| |\n",
+ "| tf.__operators__.add_1 (TFO (None, None, None, 256) 0 Y |\n",
+ "| pLambda) |\n",
+ "| |\n",
+ "| convnext_xlarge_stage_0_blo (None, None, None, 256) 12800 Y |\n",
+ "| ck_2_depthwise_conv (Conv2D |\n",
+ "| ) |\n",
+ "| |\n",
+ "| convnext_xlarge_stage_0_blo (None, None, None, 256) 512 Y |\n",
+ "| ck_2_layernorm (LayerNormal |\n",
+ "| ization) |\n",
+ "| |\n",
+ "| convnext_xlarge_stage_0_blo (None, None, None, 1024) 263168 Y |\n",
+ "| ck_2_pointwise_conv_1 (Dens |\n",
+ "| e) |\n",
+ "| |\n",
+ "| convnext_xlarge_stage_0_blo (None, None, None, 1024) 0 Y |\n",
+ "| ck_2_gelu (Activation) |\n",
+ "| |\n",
+ "| convnext_xlarge_stage_0_blo (None, None, None, 256) 262400 Y |\n",
+ "| ck_2_pointwise_conv_2 (Dens |\n",
+ "| e) |\n",
+ "| |\n",
+ "| convnext_xlarge_stage_0_blo (None, None, None, 256) 256 Y |\n",
+ "| ck_2_layer_scale (LayerScal |\n",
+ "| e) |\n",
+ "| |\n",
+ "| convnext_xlarge_stage_0_blo (None, None, None, 256) 0 Y |\n",
+ "| ck_2_identity (Activation) |\n",
+ "| |\n",
+ "| tf.__operators__.add_2 (TFO (None, None, None, 256) 0 Y |\n",
+ "| pLambda) |\n",
+ "| |\n",
+ "| convnext_xlarge_downsamplin (None, None, None, 512) 525312 Y |\n",
+ "| g_block_0 (Sequential) |\n",
+ "||¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯||\n",
+ "|| convnext_xlarge_downsamplin (None, None, None, 256) 512 Y ||\n",
+ "|| g_layernorm_0 (LayerNormali ||\n",
+ "|| zation) ||\n",
+ "|| ||\n",
+ "|| convnext_xlarge_downsamplin (None, None, None, 512) 524800 Y ||\n",
+ "|| g_conv_0 (Conv2D) ||\n",
+ "|¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯|\n",
+ "| convnext_xlarge_stage_1_blo (None, None, None, 512) 25600 Y |\n",
+ "| ck_0_depthwise_conv (Conv2D |\n",
+ "| ) |\n",
+ "| |\n",
+ "| convnext_xlarge_stage_1_blo (None, None, None, 512) 1024 Y |\n",
+ "| ck_0_layernorm (LayerNormal |\n",
+ "| ization) |\n",
+ "| |\n",
+ "| convnext_xlarge_stage_1_blo (None, None, None, 2048) 1050624 Y |\n",
+ "| ck_0_pointwise_conv_1 (Dens |\n",
+ "| e) |\n",
+ "| |\n",
+ "| convnext_xlarge_stage_1_blo (None, None, None, 2048) 0 Y |\n",
+ "| ck_0_gelu (Activation) |\n",
+ "| |\n",
+ "| convnext_xlarge_stage_1_blo (None, None, None, 512) 1049088 Y |\n",
+ "| ck_0_pointwise_conv_2 (Dens |\n",
+ "| e) |\n",
+ "| |\n",
+ "| convnext_xlarge_stage_1_blo (None, None, None, 512) 512 Y |\n",
+ "| ck_0_layer_scale (LayerScal |\n",
+ "| e) |\n",
+ "| |\n",
+ "| convnext_xlarge_stage_1_blo (None, None, None, 512) 0 Y |\n",
+ "| ck_0_identity (Activation) |\n",
+ "| |\n",
+ "| tf.__operators__.add_3 (TFO (None, None, None, 512) 0 Y |\n",
+ "| pLambda) |\n",
+ "| |\n",
+ "| convnext_xlarge_stage_1_blo (None, None, None, 512) 25600 Y |\n",
+ "| ck_1_depthwise_conv (Conv2D |\n",
+ "| ) |\n",
+ "| |\n",
+ "| convnext_xlarge_stage_1_blo (None, None, None, 512) 1024 Y |\n",
+ "| ck_1_layernorm (LayerNormal |\n",
+ "| ization) |\n",
+ "| |\n",
+ "| convnext_xlarge_stage_1_blo (None, None, None, 2048) 1050624 Y |\n",
+ "| ck_1_pointwise_conv_1 (Dens |\n",
+ "| e) |\n",
+ "| |\n",
+ "| convnext_xlarge_stage_1_blo (None, None, None, 2048) 0 Y |\n",
+ "| ck_1_gelu (Activation) |\n",
+ "| |\n",
+ "| convnext_xlarge_stage_1_blo (None, None, None, 512) 1049088 Y |\n",
+ "| ck_1_pointwise_conv_2 (Dens |\n",
+ "| e) |\n",
+ "| |\n",
+ "| convnext_xlarge_stage_1_blo (None, None, None, 512) 512 Y |\n",
+ "| ck_1_layer_scale (LayerScal |\n",
+ "| e) |\n",
+ "| |\n",
+ "| convnext_xlarge_stage_1_blo (None, None, None, 512) 0 Y |\n",
+ "| ck_1_identity (Activation) |\n",
+ "| |\n",
+ "| tf.__operators__.add_4 (TFO (None, None, None, 512) 0 Y |\n",
+ "| pLambda) |\n",
+ "| |\n",
+ "| convnext_xlarge_stage_1_blo (None, None, None, 512) 25600 Y |\n",
+ "| ck_2_depthwise_conv (Conv2D |\n",
+ "| ) |\n",
+ "| |\n",
+ "| convnext_xlarge_stage_1_blo (None, None, None, 512) 1024 Y |\n",
+ "| ck_2_layernorm (LayerNormal |\n",
+ "| ization) |\n",
+ "| |\n",
+ "| convnext_xlarge_stage_1_blo (None, None, None, 2048) 1050624 Y |\n",
+ "| ck_2_pointwise_conv_1 (Dens |\n",
+ "| e) |\n",
+ "| |\n",
+ "| convnext_xlarge_stage_1_blo (None, None, None, 2048) 0 Y |\n",
+ "| ck_2_gelu (Activation) |\n",
+ "| |\n",
+ "| convnext_xlarge_stage_1_blo (None, None, None, 512) 1049088 Y |\n",
+ "| ck_2_pointwise_conv_2 (Dens |\n",
+ "| e) |\n",
+ "| |\n",
+ "| convnext_xlarge_stage_1_blo (None, None, None, 512) 512 Y |\n",
+ "| ck_2_layer_scale (LayerScal |\n",
+ "| e) |\n",
+ "| |\n",
+ "| convnext_xlarge_stage_1_blo (None, None, None, 512) 0 Y |\n",
+ "| ck_2_identity (Activation) |\n",
+ "| |\n",
+ "| tf.__operators__.add_5 (TFO (None, None, None, 512) 0 Y |\n",
+ "| pLambda) |\n",
+ "| |\n",
+ "| convnext_xlarge_downsamplin (None, None, None, 1024) 2099200 Y |\n",
+ "| g_block_1 (Sequential) |\n",
+ "||¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯||\n",
+ "|| convnext_xlarge_downsamplin (None, None, None, 512) 1024 Y ||\n",
+ "|| g_layernorm_1 (LayerNormali ||\n",
+ "|| zation) ||\n",
+ "|| ||\n",
+ "|| convnext_xlarge_downsamplin (None, None, None, 1024) 2098176 Y ||\n",
+ "|| g_conv_1 (Conv2D) ||\n",
+ "|¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯|\n",
+ "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 51200 Y |\n",
+ "| ck_0_depthwise_conv (Conv2D |\n",
+ "| ) |\n",
+ "| |\n",
+ "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 2048 Y |\n",
+ "| ck_0_layernorm (LayerNormal |\n",
+ "| ization) |\n",
+ "| |\n",
+ "| convnext_xlarge_stage_2_blo (None, None, None, 4096) 4198400 Y |\n",
+ "| ck_0_pointwise_conv_1 (Dens |\n",
+ "| e) |\n",
+ "| |\n",
+ "| convnext_xlarge_stage_2_blo (None, None, None, 4096) 0 Y |\n",
+ "| ck_0_gelu (Activation) |\n",
+ "| |\n",
+ "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 4195328 Y |\n",
+ "| ck_0_pointwise_conv_2 (Dens |\n",
+ "| e) |\n",
+ "| |\n",
+ "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 1024 Y |\n",
+ "| ck_0_layer_scale (LayerScal |\n",
+ "| e) |\n",
+ "| |\n",
+ "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 0 Y |\n",
+ "| ck_0_identity (Activation) |\n",
+ "| |\n",
+ "| tf.__operators__.add_6 (TFO (None, None, None, 1024) 0 Y |\n",
+ "| pLambda) |\n",
+ "| |\n",
+ "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 51200 Y |\n",
+ "| ck_1_depthwise_conv (Conv2D |\n",
+ "| ) |\n",
+ "| |\n",
+ "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 2048 Y |\n",
+ "| ck_1_layernorm (LayerNormal |\n",
+ "| ization) |\n",
+ "| |\n",
+ "| convnext_xlarge_stage_2_blo (None, None, None, 4096) 4198400 Y |\n",
+ "| ck_1_pointwise_conv_1 (Dens |\n",
+ "| e) |\n",
+ "| |\n",
+ "| convnext_xlarge_stage_2_blo (None, None, None, 4096) 0 Y |\n",
+ "| ck_1_gelu (Activation) |\n",
+ "| |\n",
+ "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 4195328 Y |\n",
+ "| ck_1_pointwise_conv_2 (Dens |\n",
+ "| e) |\n",
+ "| |\n",
+ "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 1024 Y |\n",
+ "| ck_1_layer_scale (LayerScal |\n",
+ "| e) |\n",
+ "| |\n",
+ "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 0 Y |\n",
+ "| ck_1_identity (Activation) |\n",
+ "| |\n",
+ "| tf.__operators__.add_7 (TFO (None, None, None, 1024) 0 Y |\n",
+ "| pLambda) |\n",
+ "| |\n",
+ "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 51200 Y |\n",
+ "| ck_2_depthwise_conv (Conv2D |\n",
+ "| ) |\n",
+ "| |\n",
+ "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 2048 Y |\n",
+ "| ck_2_layernorm (LayerNormal |\n",
+ "| ization) |\n",
+ "| |\n",
+ "| convnext_xlarge_stage_2_blo (None, None, None, 4096) 4198400 Y |\n",
+ "| ck_2_pointwise_conv_1 (Dens |\n",
+ "| e) |\n",
+ "| |\n",
+ "| convnext_xlarge_stage_2_blo (None, None, None, 4096) 0 Y |\n",
+ "| ck_2_gelu (Activation) |\n",
+ "| |\n",
+ "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 4195328 Y |\n",
+ "| ck_2_pointwise_conv_2 (Dens |\n",
+ "| e) |\n",
+ "| |\n",
+ "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 1024 Y |\n",
+ "| ck_2_layer_scale (LayerScal |\n",
+ "| e) |\n",
+ "| |\n",
+ "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 0 Y |\n",
+ "| ck_2_identity (Activation) |\n",
+ "| |\n",
+ "| tf.__operators__.add_8 (TFO (None, None, None, 1024) 0 Y |\n",
+ "| pLambda) |\n",
+ "| |\n",
+ "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 51200 Y |\n",
+ "| ck_3_depthwise_conv (Conv2D |\n",
+ "| ) |\n",
+ "| |\n",
+ "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 2048 Y |\n",
+ "| ck_3_layernorm (LayerNormal |\n",
+ "| ization) |\n",
+ "| |\n",
+ "| convnext_xlarge_stage_2_blo (None, None, None, 4096) 4198400 Y |\n",
+ "| ck_3_pointwise_conv_1 (Dens |\n",
+ "| e) |\n",
+ "| |\n",
+ "| convnext_xlarge_stage_2_blo (None, None, None, 4096) 0 Y |\n",
+ "| ck_3_gelu (Activation) |\n",
+ "| |\n",
+ "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 4195328 Y |\n",
+ "| ck_3_pointwise_conv_2 (Dens |\n",
+ "| e) |\n",
+ "| |\n",
+ "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 1024 Y |\n",
+ "| ck_3_layer_scale (LayerScal |\n",
+ "| e) |\n",
+ "| |\n",
+ "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 0 Y |\n",
+ "| ck_3_identity (Activation) |\n",
+ "| |\n",
+ "| tf.__operators__.add_9 (TFO (None, None, None, 1024) 0 Y |\n",
+ "| pLambda) |\n",
+ "| |\n",
+ "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 51200 Y |\n",
+ "| ck_4_depthwise_conv (Conv2D |\n",
+ "| ) |\n",
+ "| |\n",
+ "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 2048 Y |\n",
+ "| ck_4_layernorm (LayerNormal |\n",
+ "| ization) |\n",
+ "| |\n",
+ "| convnext_xlarge_stage_2_blo (None, None, None, 4096) 4198400 Y |\n",
+ "| ck_4_pointwise_conv_1 (Dens |\n",
+ "| e) |\n",
+ "| |\n",
+ "| convnext_xlarge_stage_2_blo (None, None, None, 4096) 0 Y |\n",
+ "| ck_4_gelu (Activation) |\n",
+ "| |\n",
+ "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 4195328 Y |\n",
+ "| ck_4_pointwise_conv_2 (Dens |\n",
+ "| e) |\n",
+ "| |\n",
+ "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 1024 Y |\n",
+ "| ck_4_layer_scale (LayerScal |\n",
+ "| e) |\n",
+ "| |\n",
+ "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 0 Y |\n",
+ "| ck_4_identity (Activation) |\n",
+ "| |\n",
+ "| tf.__operators__.add_10 (TF (None, None, None, 1024) 0 Y |\n",
+ "| OpLambda) |\n",
+ "| |\n",
+ "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 51200 Y |\n",
+ "| ck_5_depthwise_conv (Conv2D |\n",
+ "| ) |\n",
+ "| |\n",
+ "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 2048 Y |\n",
+ "| ck_5_layernorm (LayerNormal |\n",
+ "| ization) |\n",
+ "| |\n",
+ "| convnext_xlarge_stage_2_blo (None, None, None, 4096) 4198400 Y |\n",
+ "| ck_5_pointwise_conv_1 (Dens |\n",
+ "| e) |\n",
+ "| |\n",
+ "| convnext_xlarge_stage_2_blo (None, None, None, 4096) 0 Y |\n",
+ "| ck_5_gelu (Activation) |\n",
+ "| |\n",
+ "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 4195328 Y |\n",
+ "| ck_5_pointwise_conv_2 (Dens |\n",
+ "| e) |\n",
+ "| |\n",
+ "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 1024 Y |\n",
+ "| ck_5_layer_scale (LayerScal |\n",
+ "| e) |\n",
+ "| |\n",
+ "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 0 Y |\n",
+ "| ck_5_identity (Activation) |\n",
+ "| |\n",
+ "| tf.__operators__.add_11 (TF (None, None, None, 1024) 0 Y |\n",
+ "| OpLambda) |\n",
+ "| |\n",
+ "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 51200 Y |\n",
+ "| ck_6_depthwise_conv (Conv2D |\n",
+ "| ) |\n",
+ "| |\n",
+ "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 2048 Y |\n",
+ "| ck_6_layernorm (LayerNormal |\n",
+ "| ization) |\n",
+ "| |\n",
+ "| convnext_xlarge_stage_2_blo (None, None, None, 4096) 4198400 Y |\n",
+ "| ck_6_pointwise_conv_1 (Dens |\n",
+ "| e) |\n",
+ "| |\n",
+ "| convnext_xlarge_stage_2_blo (None, None, None, 4096) 0 Y |\n",
+ "| ck_6_gelu (Activation) |\n",
+ "| |\n",
+ "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 4195328 Y |\n",
+ "| ck_6_pointwise_conv_2 (Dens |\n",
+ "| e) |\n",
+ "| |\n",
+ "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 1024 Y |\n",
+ "| ck_6_layer_scale (LayerScal |\n",
+ "| e) |\n",
+ "| |\n",
+ "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 0 Y |\n",
+ "| ck_6_identity (Activation) |\n",
+ "| |\n",
+ "| tf.__operators__.add_12 (TF (None, None, None, 1024) 0 Y |\n",
+ "| OpLambda) |\n",
+ "| |\n",
+ "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 51200 Y |\n",
+ "| ck_7_depthwise_conv (Conv2D |\n",
+ "| ) |\n",
+ "| |\n",
+ "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 2048 Y |\n",
+ "| ck_7_layernorm (LayerNormal |\n",
+ "| ization) |\n",
+ "| |\n",
+ "| convnext_xlarge_stage_2_blo (None, None, None, 4096) 4198400 Y |\n",
+ "| ck_7_pointwise_conv_1 (Dens |\n",
+ "| e) |\n",
+ "| |\n",
+ "| convnext_xlarge_stage_2_blo (None, None, None, 4096) 0 Y |\n",
+ "| ck_7_gelu (Activation) |\n",
+ "| |\n",
+ "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 4195328 Y |\n",
+ "| ck_7_pointwise_conv_2 (Dens |\n",
+ "| e) |\n",
+ "| |\n",
+ "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 1024 Y |\n",
+ "| ck_7_layer_scale (LayerScal |\n",
+ "| e) |\n",
+ "| |\n",
+ "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 0 Y |\n",
+ "| ck_7_identity (Activation) |\n",
+ "| |\n",
+ "| tf.__operators__.add_13 (TF (None, None, None, 1024) 0 Y |\n",
+ "| OpLambda) |\n",
+ "| |\n",
+ "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 51200 Y |\n",
+ "| ck_8_depthwise_conv (Conv2D |\n",
+ "| ) |\n",
+ "| |\n",
+ "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 2048 Y |\n",
+ "| ck_8_layernorm (LayerNormal |\n",
+ "| ization) |\n",
+ "| |\n",
+ "| convnext_xlarge_stage_2_blo (None, None, None, 4096) 4198400 Y |\n",
+ "| ck_8_pointwise_conv_1 (Dens |\n",
+ "| e) |\n",
+ "| |\n",
+ "| convnext_xlarge_stage_2_blo (None, None, None, 4096) 0 Y |\n",
+ "| ck_8_gelu (Activation) |\n",
+ "| |\n",
+ "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 4195328 Y |\n",
+ "| ck_8_pointwise_conv_2 (Dens |\n",
+ "| e) |\n",
+ "| |\n",
+ "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 1024 Y |\n",
+ "| ck_8_layer_scale (LayerScal |\n",
+ "| e) |\n",
+ "| |\n",
+ "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 0 Y |\n",
+ "| ck_8_identity (Activation) |\n",
+ "| |\n",
+ "| tf.__operators__.add_14 (TF (None, None, None, 1024) 0 Y |\n",
+ "| OpLambda) |\n",
+ "| |\n",
+ "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 51200 Y |\n",
+ "| ck_9_depthwise_conv (Conv2D |\n",
+ "| ) |\n",
+ "| |\n",
+ "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 2048 Y |\n",
+ "| ck_9_layernorm (LayerNormal |\n",
+ "| ization) |\n",
+ "| |\n",
+ "| convnext_xlarge_stage_2_blo (None, None, None, 4096) 4198400 Y |\n",
+ "| ck_9_pointwise_conv_1 (Dens |\n",
+ "| e) |\n",
+ "| |\n",
+ "| convnext_xlarge_stage_2_blo (None, None, None, 4096) 0 Y |\n",
+ "| ck_9_gelu (Activation) |\n",
+ "| |\n",
+ "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 4195328 Y |\n",
+ "| ck_9_pointwise_conv_2 (Dens |\n",
+ "| e) |\n",
+ "| |\n",
+ "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 1024 Y |\n",
+ "| ck_9_layer_scale (LayerScal |\n",
+ "| e) |\n",
+ "| |\n",
+ "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 0 Y |\n",
+ "| ck_9_identity (Activation) |\n",
+ "| |\n",
+ "| tf.__operators__.add_15 (TF (None, None, None, 1024) 0 Y |\n",
+ "| OpLambda) |\n",
+ "| |\n",
+ "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 51200 Y |\n",
+ "| ck_10_depthwise_conv (Conv2 |\n",
+ "| D) |\n",
+ "| |\n",
+ "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 2048 Y |\n",
+ "| ck_10_layernorm (LayerNorma |\n",
+ "| lization) |\n",
+ "| |\n",
+ "| convnext_xlarge_stage_2_blo (None, None, None, 4096) 4198400 Y |\n",
+ "| ck_10_pointwise_conv_1 (Den |\n",
+ "| se) |\n",
+ "| |\n",
+ "| convnext_xlarge_stage_2_blo (None, None, None, 4096) 0 Y |\n",
+ "| ck_10_gelu (Activation) |\n",
+ "| |\n",
+ "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 4195328 Y |\n",
+ "| ck_10_pointwise_conv_2 (Den |\n",
+ "| se) |\n",
+ "| |\n",
+ "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 1024 Y |\n",
+ "| ck_10_layer_scale (LayerSca |\n",
+ "| le) |\n",
+ "| |\n",
+ "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 0 Y |\n",
+ "| ck_10_identity (Activation) |\n",
+ "| |\n",
+ "| tf.__operators__.add_16 (TF (None, None, None, 1024) 0 Y |\n",
+ "| OpLambda) |\n",
+ "| |\n",
+ "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 51200 Y |\n",
+ "| ck_11_depthwise_conv (Conv2 |\n",
+ "| D) |\n",
+ "| |\n",
+ "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 2048 Y |\n",
+ "| ck_11_layernorm (LayerNorma |\n",
+ "| lization) |\n",
+ "| |\n",
+ "| convnext_xlarge_stage_2_blo (None, None, None, 4096) 4198400 Y |\n",
+ "| ck_11_pointwise_conv_1 (Den |\n",
+ "| se) |\n",
+ "| |\n",
+ "| convnext_xlarge_stage_2_blo (None, None, None, 4096) 0 Y |\n",
+ "| ck_11_gelu (Activation) |\n",
+ "| |\n",
+ "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 4195328 Y |\n",
+ "| ck_11_pointwise_conv_2 (Den |\n",
+ "| se) |\n",
+ "| |\n",
+ "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 1024 Y |\n",
+ "| ck_11_layer_scale (LayerSca |\n",
+ "| le) |\n",
+ "| |\n",
+ "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 0 Y |\n",
+ "| ck_11_identity (Activation) |\n",
+ "| |\n",
+ "| tf.__operators__.add_17 (TF (None, None, None, 1024) 0 Y |\n",
+ "| OpLambda) |\n",
+ "| |\n",
+ "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 51200 Y |\n",
+ "| ck_12_depthwise_conv (Conv2 |\n",
+ "| D) |\n",
+ "| |\n",
+ "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 2048 Y |\n",
+ "| ck_12_layernorm (LayerNorma |\n",
+ "| lization) |\n",
+ "| |\n",
+ "| convnext_xlarge_stage_2_blo (None, None, None, 4096) 4198400 Y |\n",
+ "| ck_12_pointwise_conv_1 (Den |\n",
+ "| se) |\n",
+ "| |\n",
+ "| convnext_xlarge_stage_2_blo (None, None, None, 4096) 0 Y |\n",
+ "| ck_12_gelu (Activation) |\n",
+ "| |\n",
+ "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 4195328 Y |\n",
+ "| ck_12_pointwise_conv_2 (Den |\n",
+ "| se) |\n",
+ "| |\n",
+ "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 1024 Y |\n",
+ "| ck_12_layer_scale (LayerSca |\n",
+ "| le) |\n",
+ "| |\n",
+ "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 0 Y |\n",
+ "| ck_12_identity (Activation) |\n",
+ "| |\n",
+ "| tf.__operators__.add_18 (TF (None, None, None, 1024) 0 Y |\n",
+ "| OpLambda) |\n",
+ "| |\n",
+ "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 51200 Y |\n",
+ "| ck_13_depthwise_conv (Conv2 |\n",
+ "| D) |\n",
+ "| |\n",
+ "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 2048 Y |\n",
+ "| ck_13_layernorm (LayerNorma |\n",
+ "| lization) |\n",
+ "| |\n",
+ "| convnext_xlarge_stage_2_blo (None, None, None, 4096) 4198400 Y |\n",
+ "| ck_13_pointwise_conv_1 (Den |\n",
+ "| se) |\n",
+ "| |\n",
+ "| convnext_xlarge_stage_2_blo (None, None, None, 4096) 0 Y |\n",
+ "| ck_13_gelu (Activation) |\n",
+ "| |\n",
+ "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 4195328 Y |\n",
+ "| ck_13_pointwise_conv_2 (Den |\n",
+ "| se) |\n",
+ "| |\n",
+ "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 1024 Y |\n",
+ "| ck_13_layer_scale (LayerSca |\n",
+ "| le) |\n",
+ "| |\n",
+ "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 0 Y |\n",
+ "| ck_13_identity (Activation) |\n",
+ "| |\n",
+ "| tf.__operators__.add_19 (TF (None, None, None, 1024) 0 Y |\n",
+ "| OpLambda) |\n",
+ "| |\n",
+ "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 51200 Y |\n",
+ "| ck_14_depthwise_conv (Conv2 |\n",
+ "| D) |\n",
+ "| |\n",
+ "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 2048 Y |\n",
+ "| ck_14_layernorm (LayerNorma |\n",
+ "| lization) |\n",
+ "| |\n",
+ "| convnext_xlarge_stage_2_blo (None, None, None, 4096) 4198400 Y |\n",
+ "| ck_14_pointwise_conv_1 (Den |\n",
+ "| se) |\n",
+ "| |\n",
+ "| convnext_xlarge_stage_2_blo (None, None, None, 4096) 0 Y |\n",
+ "| ck_14_gelu (Activation) |\n",
+ "| |\n",
+ "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 4195328 Y |\n",
+ "| ck_14_pointwise_conv_2 (Den |\n",
+ "| se) |\n",
+ "| |\n",
+ "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 1024 Y |\n",
+ "| ck_14_layer_scale (LayerSca |\n",
+ "| le) |\n",
+ "| |\n",
+ "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 0 Y |\n",
+ "| ck_14_identity (Activation) |\n",
+ "| |\n",
+ "| tf.__operators__.add_20 (TF (None, None, None, 1024) 0 Y |\n",
+ "| OpLambda) |\n",
+ "| |\n",
+ "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 51200 Y |\n",
+ "| ck_15_depthwise_conv (Conv2 |\n",
+ "| D) |\n",
+ "| |\n",
+ "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 2048 Y |\n",
+ "| ck_15_layernorm (LayerNorma |\n",
+ "| lization) |\n",
+ "| |\n",
+ "| convnext_xlarge_stage_2_blo (None, None, None, 4096) 4198400 Y |\n",
+ "| ck_15_pointwise_conv_1 (Den |\n",
+ "| se) |\n",
+ "| |\n",
+ "| convnext_xlarge_stage_2_blo (None, None, None, 4096) 0 Y |\n",
+ "| ck_15_gelu (Activation) |\n",
+ "| |\n",
+ "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 4195328 Y |\n",
+ "| ck_15_pointwise_conv_2 (Den |\n",
+ "| se) |\n",
+ "| |\n",
+ "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 1024 Y |\n",
+ "| ck_15_layer_scale (LayerSca |\n",
+ "| le) |\n",
+ "| |\n",
+ "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 0 Y |\n",
+ "| ck_15_identity (Activation) |\n",
+ "| |\n",
+ "| tf.__operators__.add_21 (TF (None, None, None, 1024) 0 Y |\n",
+ "| OpLambda) |\n",
+ "| |\n",
+ "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 51200 Y |\n",
+ "| ck_16_depthwise_conv (Conv2 |\n",
+ "| D) |\n",
+ "| |\n",
+ "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 2048 Y |\n",
+ "| ck_16_layernorm (LayerNorma |\n",
+ "| lization) |\n",
+ "| |\n",
+ "| convnext_xlarge_stage_2_blo (None, None, None, 4096) 4198400 Y |\n",
+ "| ck_16_pointwise_conv_1 (Den |\n",
+ "| se) |\n",
+ "| |\n",
+ "| convnext_xlarge_stage_2_blo (None, None, None, 4096) 0 Y |\n",
+ "| ck_16_gelu (Activation) |\n",
+ "| |\n",
+ "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 4195328 Y |\n",
+ "| ck_16_pointwise_conv_2 (Den |\n",
+ "| se) |\n",
+ "| |\n",
+ "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 1024 Y |\n",
+ "| ck_16_layer_scale (LayerSca |\n",
+ "| le) |\n",
+ "| |\n",
+ "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 0 Y |\n",
+ "| ck_16_identity (Activation) |\n",
+ "| |\n",
+ "| tf.__operators__.add_22 (TF (None, None, None, 1024) 0 Y |\n",
+ "| OpLambda) |\n",
+ "| |\n",
+ "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 51200 Y |\n",
+ "| ck_17_depthwise_conv (Conv2 |\n",
+ "| D) |\n",
+ "| |\n",
+ "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 2048 Y |\n",
+ "| ck_17_layernorm (LayerNorma |\n",
+ "| lization) |\n",
+ "| |\n",
+ "| convnext_xlarge_stage_2_blo (None, None, None, 4096) 4198400 Y |\n",
+ "| ck_17_pointwise_conv_1 (Den |\n",
+ "| se) |\n",
+ "| |\n",
+ "| convnext_xlarge_stage_2_blo (None, None, None, 4096) 0 Y |\n",
+ "| ck_17_gelu (Activation) |\n",
+ "| |\n",
+ "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 4195328 Y |\n",
+ "| ck_17_pointwise_conv_2 (Den |\n",
+ "| se) |\n",
+ "| |\n",
+ "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 1024 Y |\n",
+ "| ck_17_layer_scale (LayerSca |\n",
+ "| le) |\n",
+ "| |\n",
+ "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 0 Y |\n",
+ "| ck_17_identity (Activation) |\n",
+ "| |\n",
+ "| tf.__operators__.add_23 (TF (None, None, None, 1024) 0 Y |\n",
+ "| OpLambda) |\n",
+ "| |\n",
+ "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 51200 Y |\n",
+ "| ck_18_depthwise_conv (Conv2 |\n",
+ "| D) |\n",
+ "| |\n",
+ "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 2048 Y |\n",
+ "| ck_18_layernorm (LayerNorma |\n",
+ "| lization) |\n",
+ "| |\n",
+ "| convnext_xlarge_stage_2_blo (None, None, None, 4096) 4198400 Y |\n",
+ "| ck_18_pointwise_conv_1 (Den |\n",
+ "| se) |\n",
+ "| |\n",
+ "| convnext_xlarge_stage_2_blo (None, None, None, 4096) 0 Y |\n",
+ "| ck_18_gelu (Activation) |\n",
+ "| |\n",
+ "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 4195328 Y |\n",
+ "| ck_18_pointwise_conv_2 (Den |\n",
+ "| se) |\n",
+ "| |\n",
+ "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 1024 Y |\n",
+ "| ck_18_layer_scale (LayerSca |\n",
+ "| le) |\n",
+ "| |\n",
+ "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 0 Y |\n",
+ "| ck_18_identity (Activation) |\n",
+ "| |\n",
+ "| tf.__operators__.add_24 (TF (None, None, None, 1024) 0 Y |\n",
+ "| OpLambda) |\n",
+ "| |\n",
+ "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 51200 Y |\n",
+ "| ck_19_depthwise_conv (Conv2 |\n",
+ "| D) |\n",
+ "| |\n",
+ "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 2048 Y |\n",
+ "| ck_19_layernorm (LayerNorma |\n",
+ "| lization) |\n",
+ "| |\n",
+ "| convnext_xlarge_stage_2_blo (None, None, None, 4096) 4198400 Y |\n",
+ "| ck_19_pointwise_conv_1 (Den |\n",
+ "| se) |\n",
+ "| |\n",
+ "| convnext_xlarge_stage_2_blo (None, None, None, 4096) 0 Y |\n",
+ "| ck_19_gelu (Activation) |\n",
+ "| |\n",
+ "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 4195328 Y |\n",
+ "| ck_19_pointwise_conv_2 (Den |\n",
+ "| se) |\n",
+ "| |\n",
+ "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 1024 Y |\n",
+ "| ck_19_layer_scale (LayerSca |\n",
+ "| le) |\n",
+ "| |\n",
+ "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 0 Y |\n",
+ "| ck_19_identity (Activation) |\n",
+ "| |\n",
+ "| tf.__operators__.add_25 (TF (None, None, None, 1024) 0 Y |\n",
+ "| OpLambda) |\n",
+ "| |\n",
+ "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 51200 Y |\n",
+ "| ck_20_depthwise_conv (Conv2 |\n",
+ "| D) |\n",
+ "| |\n",
+ "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 2048 Y |\n",
+ "| ck_20_layernorm (LayerNorma |\n",
+ "| lization) |\n",
+ "| |\n",
+ "| convnext_xlarge_stage_2_blo (None, None, None, 4096) 4198400 Y |\n",
+ "| ck_20_pointwise_conv_1 (Den |\n",
+ "| se) |\n",
+ "| |\n",
+ "| convnext_xlarge_stage_2_blo (None, None, None, 4096) 0 Y |\n",
+ "| ck_20_gelu (Activation) |\n",
+ "| |\n",
+ "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 4195328 Y |\n",
+ "| ck_20_pointwise_conv_2 (Den |\n",
+ "| se) |\n",
+ "| |\n",
+ "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 1024 Y |\n",
+ "| ck_20_layer_scale (LayerSca |\n",
+ "| le) |\n",
+ "| |\n",
+ "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 0 Y |\n",
+ "| ck_20_identity (Activation) |\n",
+ "| |\n",
+ "| tf.__operators__.add_26 (TF (None, None, None, 1024) 0 Y |\n",
+ "| OpLambda) |\n",
+ "| |\n",
+ "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 51200 Y |\n",
+ "| ck_21_depthwise_conv (Conv2 |\n",
+ "| D) |\n",
+ "| |\n",
+ "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 2048 Y |\n",
+ "| ck_21_layernorm (LayerNorma |\n",
+ "| lization) |\n",
+ "| |\n",
+ "| convnext_xlarge_stage_2_blo (None, None, None, 4096) 4198400 Y |\n",
+ "| ck_21_pointwise_conv_1 (Den |\n",
+ "| se) |\n",
+ "| |\n",
+ "| convnext_xlarge_stage_2_blo (None, None, None, 4096) 0 Y |\n",
+ "| ck_21_gelu (Activation) |\n",
+ "| |\n",
+ "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 4195328 Y |\n",
+ "| ck_21_pointwise_conv_2 (Den |\n",
+ "| se) |\n",
+ "| |\n",
+ "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 1024 Y |\n",
+ "| ck_21_layer_scale (LayerSca |\n",
+ "| le) |\n",
+ "| |\n",
+ "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 0 Y |\n",
+ "| ck_21_identity (Activation) |\n",
+ "| |\n",
+ "| tf.__operators__.add_27 (TF (None, None, None, 1024) 0 Y |\n",
+ "| OpLambda) |\n",
+ "| |\n",
+ "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 51200 Y |\n",
+ "| ck_22_depthwise_conv (Conv2 |\n",
+ "| D) |\n",
+ "| |\n",
+ "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 2048 Y |\n",
+ "| ck_22_layernorm (LayerNorma |\n",
+ "| lization) |\n",
+ "| |\n",
+ "| convnext_xlarge_stage_2_blo (None, None, None, 4096) 4198400 Y |\n",
+ "| ck_22_pointwise_conv_1 (Den |\n",
+ "| se) |\n",
+ "| |\n",
+ "| convnext_xlarge_stage_2_blo (None, None, None, 4096) 0 Y |\n",
+ "| ck_22_gelu (Activation) |\n",
+ "| |\n",
+ "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 4195328 Y |\n",
+ "| ck_22_pointwise_conv_2 (Den |\n",
+ "| se) |\n",
+ "| |\n",
+ "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 1024 Y |\n",
+ "| ck_22_layer_scale (LayerSca |\n",
+ "| le) |\n",
+ "| |\n",
+ "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 0 Y |\n",
+ "| ck_22_identity (Activation) |\n",
+ "| |\n",
+ "| tf.__operators__.add_28 (TF (None, None, None, 1024) 0 Y |\n",
+ "| OpLambda) |\n",
+ "| |\n",
+ "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 51200 Y |\n",
+ "| ck_23_depthwise_conv (Conv2 |\n",
+ "| D) |\n",
+ "| |\n",
+ "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 2048 Y |\n",
+ "| ck_23_layernorm (LayerNorma |\n",
+ "| lization) |\n",
+ "| |\n",
+ "| convnext_xlarge_stage_2_blo (None, None, None, 4096) 4198400 Y |\n",
+ "| ck_23_pointwise_conv_1 (Den |\n",
+ "| se) |\n",
+ "| |\n",
+ "| convnext_xlarge_stage_2_blo (None, None, None, 4096) 0 Y |\n",
+ "| ck_23_gelu (Activation) |\n",
+ "| |\n",
+ "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 4195328 Y |\n",
+ "| ck_23_pointwise_conv_2 (Den |\n",
+ "| se) |\n",
+ "| |\n",
+ "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 1024 Y |\n",
+ "| ck_23_layer_scale (LayerSca |\n",
+ "| le) |\n",
+ "| |\n",
+ "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 0 Y |\n",
+ "| ck_23_identity (Activation) |\n",
+ "| |\n",
+ "| tf.__operators__.add_29 (TF (None, None, None, 1024) 0 Y |\n",
+ "| OpLambda) |\n",
+ "| |\n",
+ "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 51200 Y |\n",
+ "| ck_24_depthwise_conv (Conv2 |\n",
+ "| D) |\n",
+ "| |\n",
+ "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 2048 Y |\n",
+ "| ck_24_layernorm (LayerNorma |\n",
+ "| lization) |\n",
+ "| |\n",
+ "| convnext_xlarge_stage_2_blo (None, None, None, 4096) 4198400 Y |\n",
+ "| ck_24_pointwise_conv_1 (Den |\n",
+ "| se) |\n",
+ "| |\n",
+ "| convnext_xlarge_stage_2_blo (None, None, None, 4096) 0 Y |\n",
+ "| ck_24_gelu (Activation) |\n",
+ "| |\n",
+ "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 4195328 Y |\n",
+ "| ck_24_pointwise_conv_2 (Den |\n",
+ "| se) |\n",
+ "| |\n",
+ "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 1024 Y |\n",
+ "| ck_24_layer_scale (LayerSca |\n",
+ "| le) |\n",
+ "| |\n",
+ "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 0 Y |\n",
+ "| ck_24_identity (Activation) |\n",
+ "| |\n",
+ "| tf.__operators__.add_30 (TF (None, None, None, 1024) 0 Y |\n",
+ "| OpLambda) |\n",
+ "| |\n",
+ "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 51200 Y |\n",
+ "| ck_25_depthwise_conv (Conv2 |\n",
+ "| D) |\n",
+ "| |\n",
+ "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 2048 Y |\n",
+ "| ck_25_layernorm (LayerNorma |\n",
+ "| lization) |\n",
+ "| |\n",
+ "| convnext_xlarge_stage_2_blo (None, None, None, 4096) 4198400 Y |\n",
+ "| ck_25_pointwise_conv_1 (Den |\n",
+ "| se) |\n",
+ "| |\n",
+ "| convnext_xlarge_stage_2_blo (None, None, None, 4096) 0 Y |\n",
+ "| ck_25_gelu (Activation) |\n",
+ "| |\n",
+ "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 4195328 Y |\n",
+ "| ck_25_pointwise_conv_2 (Den |\n",
+ "| se) |\n",
+ "| |\n",
+ "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 1024 Y |\n",
+ "| ck_25_layer_scale (LayerSca |\n",
+ "| le) |\n",
+ "| |\n",
+ "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 0 Y |\n",
+ "| ck_25_identity (Activation) |\n",
+ "| |\n",
+ "| tf.__operators__.add_31 (TF (None, None, None, 1024) 0 Y |\n",
+ "| OpLambda) |\n",
+ "| |\n",
+ "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 51200 Y |\n",
+ "| ck_26_depthwise_conv (Conv2 |\n",
+ "| D) |\n",
+ "| |\n",
+ "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 2048 Y |\n",
+ "| ck_26_layernorm (LayerNorma |\n",
+ "| lization) |\n",
+ "| |\n",
+ "| convnext_xlarge_stage_2_blo (None, None, None, 4096) 4198400 Y |\n",
+ "| ck_26_pointwise_conv_1 (Den |\n",
+ "| se) |\n",
+ "| |\n",
+ "| convnext_xlarge_stage_2_blo (None, None, None, 4096) 0 Y |\n",
+ "| ck_26_gelu (Activation) |\n",
+ "| |\n",
+ "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 4195328 Y |\n",
+ "| ck_26_pointwise_conv_2 (Den |\n",
+ "| se) |\n",
+ "| |\n",
+ "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 1024 Y |\n",
+ "| ck_26_layer_scale (LayerSca |\n",
+ "| le) |\n",
+ "| |\n",
+ "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 0 Y |\n",
+ "| ck_26_identity (Activation) |\n",
+ "| |\n",
+ "| tf.__operators__.add_32 (TF (None, None, None, 1024) 0 Y |\n",
+ "| OpLambda) |\n",
+ "| |\n",
+ "| convnext_xlarge_downsamplin (None, None, None, 2048) 8392704 Y |\n",
+ "| g_block_2 (Sequential) |\n",
+ "||¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯||\n",
+ "|| convnext_xlarge_downsamplin (None, None, None, 1024) 2048 Y ||\n",
+ "|| g_layernorm_2 (LayerNormali ||\n",
+ "|| zation) ||\n",
+ "|| ||\n",
+ "|| convnext_xlarge_downsamplin (None, None, None, 2048) 8390656 Y ||\n",
+ "|| g_conv_2 (Conv2D) ||\n",
+ "|¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯|\n",
+ "| convnext_xlarge_stage_3_blo (None, None, None, 2048) 102400 Y |\n",
+ "| ck_0_depthwise_conv (Conv2D |\n",
+ "| ) |\n",
+ "| |\n",
+ "| convnext_xlarge_stage_3_blo (None, None, None, 2048) 4096 Y |\n",
+ "| ck_0_layernorm (LayerNormal |\n",
+ "| ization) |\n",
+ "| |\n",
+ "| convnext_xlarge_stage_3_blo (None, None, None, 8192) 16785408 Y |\n",
+ "| ck_0_pointwise_conv_1 (Dens |\n",
+ "| e) |\n",
+ "| |\n",
+ "| convnext_xlarge_stage_3_blo (None, None, None, 8192) 0 Y |\n",
+ "| ck_0_gelu (Activation) |\n",
+ "| |\n",
+ "| convnext_xlarge_stage_3_blo (None, None, None, 2048) 16779264 Y |\n",
+ "| ck_0_pointwise_conv_2 (Dens |\n",
+ "| e) |\n",
+ "| |\n",
+ "| convnext_xlarge_stage_3_blo (None, None, None, 2048) 2048 Y |\n",
+ "| ck_0_layer_scale (LayerScal |\n",
+ "| e) |\n",
+ "| |\n",
+ "| convnext_xlarge_stage_3_blo (None, None, None, 2048) 0 Y |\n",
+ "| ck_0_identity (Activation) |\n",
+ "| |\n",
+ "| tf.__operators__.add_33 (TF (None, None, None, 2048) 0 Y |\n",
+ "| OpLambda) |\n",
+ "| |\n",
+ "| convnext_xlarge_stage_3_blo (None, None, None, 2048) 102400 Y |\n",
+ "| ck_1_depthwise_conv (Conv2D |\n",
+ "| ) |\n",
+ "| |\n",
+ "| convnext_xlarge_stage_3_blo (None, None, None, 2048) 4096 Y |\n",
+ "| ck_1_layernorm (LayerNormal |\n",
+ "| ization) |\n",
+ "| |\n",
+ "| convnext_xlarge_stage_3_blo (None, None, None, 8192) 16785408 Y |\n",
+ "| ck_1_pointwise_conv_1 (Dens |\n",
+ "| e) |\n",
+ "| |\n",
+ "| convnext_xlarge_stage_3_blo (None, None, None, 8192) 0 Y |\n",
+ "| ck_1_gelu (Activation) |\n",
+ "| |\n",
+ "| convnext_xlarge_stage_3_blo (None, None, None, 2048) 16779264 Y |\n",
+ "| ck_1_pointwise_conv_2 (Dens |\n",
+ "| e) |\n",
+ "| |\n",
+ "| convnext_xlarge_stage_3_blo (None, None, None, 2048) 2048 Y |\n",
+ "| ck_1_layer_scale (LayerScal |\n",
+ "| e) |\n",
+ "| |\n",
+ "| convnext_xlarge_stage_3_blo (None, None, None, 2048) 0 Y |\n",
+ "| ck_1_identity (Activation) |\n",
+ "| |\n",
+ "| tf.__operators__.add_34 (TF (None, None, None, 2048) 0 Y |\n",
+ "| OpLambda) |\n",
+ "| |\n",
+ "| convnext_xlarge_stage_3_blo (None, None, None, 2048) 102400 Y |\n",
+ "| ck_2_depthwise_conv (Conv2D |\n",
+ "| ) |\n",
+ "| |\n",
+ "| convnext_xlarge_stage_3_blo (None, None, None, 2048) 4096 Y |\n",
+ "| ck_2_layernorm (LayerNormal |\n",
+ "| ization) |\n",
+ "| |\n",
+ "| convnext_xlarge_stage_3_blo (None, None, None, 8192) 16785408 Y |\n",
+ "| ck_2_pointwise_conv_1 (Dens |\n",
+ "| e) |\n",
+ "| |\n",
+ "| convnext_xlarge_stage_3_blo (None, None, None, 8192) 0 Y |\n",
+ "| ck_2_gelu (Activation) |\n",
+ "| |\n",
+ "| convnext_xlarge_stage_3_blo (None, None, None, 2048) 16779264 Y |\n",
+ "| ck_2_pointwise_conv_2 (Dens |\n",
+ "| e) |\n",
+ "| |\n",
+ "| convnext_xlarge_stage_3_blo (None, None, None, 2048) 2048 Y |\n",
+ "| ck_2_layer_scale (LayerScal |\n",
+ "| e) |\n",
+ "| |\n",
+ "| convnext_xlarge_stage_3_blo (None, None, None, 2048) 0 Y |\n",
+ "| ck_2_identity (Activation) |\n",
+ "| |\n",
+ "| tf.__operators__.add_35 (TF (None, None, None, 2048) 0 Y |\n",
+ "| OpLambda) |\n",
+ "| |\n",
+ "| layer_normalization (LayerN (None, None, None, 2048) 4096 Y |\n",
+ "| ormalization) |\n",
+ "¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯\n",
+ " global_average_pooling2d (G (None, 2048) 0 Y \n",
+ " lobalAveragePooling2D) \n",
+ " \n",
+ " dense (Dense) (None, 512) 1049088 Y \n",
+ " \n",
+ " dropout (Dropout) (None, 512) 0 Y \n",
+ " \n",
+ " batch_normalization (BatchN (None, 512) 2048 Y \n",
+ " ormalization) \n",
+ " \n",
+ " dense_1 (Dense) (None, 512) 262656 Y \n",
+ " \n",
+ " batch_normalization_1 (Batc (None, 512) 2048 Y \n",
+ " hNormalization) \n",
+ " \n",
+ " dense_2 (Dense) (None, 128) 65664 Y \n",
+ " \n",
+ " dense_3 (Dense) (None, 2) 258 Y \n",
+ " \n",
+ "============================================================================\n",
+ "Total params: 349,529,730\n",
+ "Trainable params: 349,527,682\n",
+ "Non-trainable params: 2,048\n",
+ "____________________________________________________________________________\n",
+ "done.\n"
+ ]
+ }
+ ],
"source": [
"from keras.applications import ConvNeXtXLarge\n",
"from keras.layers import Lambda\n",
@@ -3667,7 +4801,7 @@
" Dense_L3 = Dense(128, activation=\"relu\")(BatchNorm_L3)\n",
" predictions = Dense(2, activation=\"softmax\")(Dense_L3)\n",
"\n",
- " model_EfficientNetB7_NS = Model(inputs=input, outputs=predictions)\n",
+ " model_EfficientNetB7_NS = Model(inputs=x, outputs=predictions)\n",
" print(\"Total model layers: \", len(model_EfficientNetB7_NS.layers))\n",
" # OPT/compile\n",
" opt = SGD(momentum=0.9) # noqa: F405\n",
@@ -6184,7 +7318,7 @@
},
{
"cell_type": "code",
- "execution_count": 14,
+ "execution_count": 9,
"metadata": {
"ExecuteTime": {
"end_time": "2023-12-28T07:04:23.573633300Z",
@@ -6199,20 +7333,20 @@
"Training the model...\n",
"\u001b[0;33m\n",
"Setup Verbose:\u001b[0m\n",
- "\u001b[0m\u001b[0m\u001b[0;36mExperiment name: \u001b[0m\u001b[0;32m[SGD-M-92-New_y2024_m03_d23-h17_m04_s11]\u001b[0m\u001b[0;36m...\u001b[0m\n",
- "\u001b[0m\u001b[0m\u001b[0;36mSetting TensorBoard Log dir to \u001b[0m\u001b[0;32m[logs/fit/SGD-M-92-New_y2024_m03_d23-h17_m04_s11]\u001b[0m\u001b[0;36m...\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;36mExperiment name: \u001b[0m\u001b[0;32m[_y2024_m03_d29-h20_m44_s53]\u001b[0m\u001b[0;36m...\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;36mSetting TensorBoard Log dir to \u001b[0m\u001b[0;32m[logs/fit/_y2024_m03_d29-h20_m44_s53]\u001b[0m\u001b[0;36m...\u001b[0m\n",
"\u001b[0m\u001b[0m\u001b[0;36mUse_extended_tensorboard \u001b[0m\u001b[0;32m[False]\u001b[0m\u001b[0;36m.\u001b[0m\n",
"\u001b[0m\u001b[0m\u001b[0;36mDebug_OUTPUT_DPS \u001b[0m\u001b[0;32m[True]\u001b[0m\u001b[0;36m.\u001b[0m\n",
"\u001b[0m\u001b[0m\u001b[0;36mUse_OneCycleLr \u001b[0m\u001b[0;32m[False]\u001b[0m\u001b[0;36m.\u001b[0m\n",
"\u001b[0m\u001b[0m\u001b[0;36mUsing the modified ReduceLROnPlateau.\u001b[0m\n",
"\u001b[0m\u001b[0m\u001b[0;36mOneCycleLr_UFTS \u001b[0m\u001b[0;32m[False]\u001b[0m\u001b[0;36m.\u001b[0m\n",
- "\u001b[0m\u001b[0m\u001b[0;33mOptimizer: \u001b[0m\u001b[0;32mSGD\u001b[0m\n",
- "\u001b[0;36mParameters:\u001b[0m\n",
- " -- name: SGD\n",
- " -- learning_rate: 0.3310000002384186\n",
- " -- decay: 0.0\n",
- " -- momentum: 0.9200000166893005\n",
- " -- nesterov: False\n",
+ "\u001b[0m\u001b[0m\u001b[0;36mOptimizer: \u001b[0m\u001b[0;32mSGD\u001b[0m\n",
+ "\u001b[0;36m Parameters:\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;36m -- \u001b[0m\u001b[0;96mname: \u001b[0m\u001b[0;32mSGD\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;36m -- \u001b[0m\u001b[0;96mlearning_rate: \u001b[0m\u001b[0;32m0.009999999776482582\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;36m -- \u001b[0m\u001b[0;96mdecay: \u001b[0m\u001b[0;32m0.0\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;36m -- \u001b[0m\u001b[0;96mmomentum: \u001b[0m\u001b[0;32m0.8899999856948853\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;36m -- \u001b[0m\u001b[0;96mnesterov: \u001b[0m\u001b[0;32mFalse\u001b[0m\n",
"\u001b[0;33mSetup Verbose END.\u001b[0m\n",
"\u001b[0m\n",
"\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m1\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m489 (TSEC: 0)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Stage 1]\u001b[0m\n",
@@ -6222,33 +7356,34 @@
"\u001b[0;33m- ImageDataGenerator fit done.\u001b[0m\n",
"\u001b[0;33m- Augmenting Image Data...\u001b[0m\n",
"\u001b[0;33m- Normalizing Image Data...\u001b[0m\n",
- "\u001b[0m\u001b[0m\u001b[0;31m- Debug DP Sample dir: \u001b[0m\u001b[0;32mSamples/TSR_SUB_400_y2024_m03_d23-h17_m05_s07\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;31m- Debug DP Sample dir: \u001b[0m\u001b[0;32mSamples/TSR_SUB_400_y2024_m03_d29-h20_m45_s50\u001b[0m\n",
"\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n",
"\u001b[0;32mTraining on subset...\u001b[0m\n",
"Epoch 1/6\n",
- "256/256 [==============================] - 89s 279ms/step - loss: nan - accuracy: 0.5042 - val_loss: nan - val_accuracy: 0.3750 - lr: 0.3310\n",
+ "256/256 [==============================] - 94s 289ms/step - loss: 2.1378 - accuracy: 0.6372 - val_loss: 1.8907 - val_accuracy: 0.7372 - lr: 0.0100\n",
"Epoch 2/6\n",
- "256/256 [==============================] - 69s 267ms/step - loss: nan - accuracy: 0.5049 - val_loss: nan - val_accuracy: 0.3750 - lr: 0.3310\n",
+ "256/256 [==============================] - 70s 273ms/step - loss: 1.7242 - accuracy: 0.8125 - val_loss: 1.4988 - val_accuracy: 0.8494 - lr: 0.0100\n",
"Epoch 3/6\n",
- "256/256 [==============================] - 68s 264ms/step - loss: nan - accuracy: 0.5049 - val_loss: nan - val_accuracy: 0.3750 - lr: 0.3310\n",
+ "256/256 [==============================] - 71s 278ms/step - loss: 1.4643 - accuracy: 0.8684 - val_loss: 1.3688 - val_accuracy: 0.8638 - lr: 0.0100\n",
"Epoch 4/6\n",
- "256/256 [==============================] - 70s 274ms/step - loss: nan - accuracy: 0.5049 - val_loss: nan - val_accuracy: 0.3750 - lr: 0.3310\n",
+ "256/256 [==============================] - 72s 281ms/step - loss: 1.2522 - accuracy: 0.8992 - val_loss: 1.2234 - val_accuracy: 0.8381 - lr: 0.0100\n",
"Epoch 5/6\n",
- "256/256 [==============================] - 71s 275ms/step - loss: nan - accuracy: 0.5049 - val_loss: nan - val_accuracy: 0.3750 - lr: 0.3310\n",
+ "256/256 [==============================] - 73s 284ms/step - loss: 1.1065 - accuracy: 0.9102 - val_loss: 1.1080 - val_accuracy: 0.9038 - lr: 0.0100\n",
"Epoch 6/6\n",
- "256/256 [==============================] - 69s 268ms/step - loss: nan - accuracy: 0.5049 - val_loss: nan - val_accuracy: 0.3750 - lr: 0.3310\n",
+ "256/256 [==============================] - 72s 281ms/step - loss: 0.9423 - accuracy: 0.9287 - val_loss: 0.9623 - val_accuracy: 0.8750 - lr: 0.0100\n",
"\u001b[0;32mSubset training done.\u001b[0m\n",
"\u001b[0;33mLoading the best weights...\u001b[0m\n",
- "\u001b[0;31mERROR: Failed to load weights. Error: max() arg is an empty sequence\u001b[0m\n",
- "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.3750\u001b[0m\n",
- "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32mnan\u001b[0m\n",
- "\u001b[0m\u001b[0m\u001b[0;32mImproved model accuracy from 0.000000 to 0.375000. \u001b[0m\u001b[0;96mSaving model.\u001b[0m\n",
+ "\u001b[0;33mLoading weights from file cache\\model_SUB_checkpoint-005-0.9038.h5...\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9038\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m1.0990\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;32mImproved model accuracy from 0.000000 to 0.903846. \u001b[0m\u001b[0;96mSaving model.\u001b[0m\n",
"\u001b[0m\u001b[0m\u001b[0;36mSaving full model H5 format...\u001b[0m\n",
- "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from inf. Not saving model.\u001b[0m\n",
- "\u001b[0m\u001b[0m\u001b[0;32m(GPU-MEM)\u001b[0m\u001b[0;36m----[free: 4.67GB, used: 19.33GB, total, 24.00GB]\u001b[0m\n",
- "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m509.52 \u001b[0m\u001b[0;36msec\u001b[0m\n",
- "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m436.98 \u001b[0m\u001b[0;36msec\u001b[0m\n",
- "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m72.54 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;32mImproved model loss from inf to 1.09902048. \u001b[0m\u001b[0;96mSaving model.\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;36mSaving full model H5 format...\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;32m(GPU-MEM)\u001b[0m\u001b[0;36m----[free: 1.95GB, used: 22.05GB, total, 24.00GB]\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m531.25 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m453.88 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m77.36 \u001b[0m\u001b[0;36msec\u001b[0m\n",
"\u001b[0;36m<---------------------------------------|Epoch [1] END|--------------------------------------->\u001b[0m\n",
"\u001b[0m\n",
"\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m2\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m489 (TSEC: 6)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Stage 1]\u001b[0m\n",
@@ -6259,28 +7394,29 @@
"\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n",
"\u001b[0;32mTraining on subset...\u001b[0m\n",
"Epoch 7/12\n",
- "256/256 [==============================] - 74s 276ms/step - loss: nan - accuracy: 0.5093 - val_loss: nan - val_accuracy: 0.3750 - lr: 0.3310\n",
+ "256/256 [==============================] - 77s 282ms/step - loss: 1.0754 - accuracy: 0.8726 - val_loss: 0.9810 - val_accuracy: 0.8910 - lr: 0.0100\n",
"Epoch 8/12\n",
- "256/256 [==============================] - 71s 275ms/step - loss: nan - accuracy: 0.5093 - val_loss: nan - val_accuracy: 0.3750 - lr: 0.3310\n",
+ "256/256 [==============================] - 69s 270ms/step - loss: 0.9141 - accuracy: 0.9070 - val_loss: 1.0838 - val_accuracy: 0.7997 - lr: 0.0100\n",
"Epoch 9/12\n",
- "256/256 [==============================] - 70s 273ms/step - loss: nan - accuracy: 0.5093 - val_loss: nan - val_accuracy: 0.3750 - lr: 0.3310\n",
+ "256/256 [==============================] - 70s 271ms/step - loss: 0.8049 - accuracy: 0.9192 - val_loss: 0.8255 - val_accuracy: 0.8814 - lr: 0.0100\n",
"Epoch 10/12\n",
- "256/256 [==============================] - 71s 276ms/step - loss: nan - accuracy: 0.5093 - val_loss: nan - val_accuracy: 0.3750 - lr: 0.3310\n",
+ "256/256 [==============================] - 69s 269ms/step - loss: 0.6986 - accuracy: 0.9326 - val_loss: 0.7473 - val_accuracy: 0.8894 - lr: 0.0100\n",
"Epoch 11/12\n",
- "256/256 [==============================] - 72s 279ms/step - loss: nan - accuracy: 0.5093 - val_loss: nan - val_accuracy: 0.3750 - lr: 0.3310\n",
+ "256/256 [==============================] - 69s 270ms/step - loss: 0.6178 - accuracy: 0.9426 - val_loss: 0.9366 - val_accuracy: 0.8397 - lr: 0.0100\n",
"Epoch 12/12\n",
- "256/256 [==============================] - 71s 275ms/step - loss: nan - accuracy: 0.5093 - val_loss: nan - val_accuracy: 0.3750 - lr: 0.3310\n",
+ "256/256 [==============================] - 69s 269ms/step - loss: 0.5278 - accuracy: 0.9568 - val_loss: 1.1692 - val_accuracy: 0.8478 - lr: 0.0100\n",
"\u001b[0;32mSubset training done.\u001b[0m\n",
"\u001b[0;33mLoading the best weights...\u001b[0m\n",
- "\u001b[0;31mERROR: Failed to load weights. Error: max() arg is an empty sequence\u001b[0m\n",
- "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.3750\u001b[0m\n",
- "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32mnan\u001b[0m\n",
- "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.3750000000. Not saving model.\u001b[0m\n",
- "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from inf. Not saving model.\u001b[0m\n",
- "\u001b[0m\u001b[0m\u001b[0;32m(GPU-MEM)\u001b[0m\u001b[0;36m----[free: 4.69GB, used: 19.30GB, total, 24.00GB]\u001b[0m\n",
- "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m489.68 \u001b[0m\u001b[0;36msec\u001b[0m\n",
- "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m429.41 \u001b[0m\u001b[0;36msec\u001b[0m\n",
- "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m60.27 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0;33mLoading weights from file cache\\model_SUB_checkpoint-007-0.8910.h5...\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.8910\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.9810\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9038461447. Not saving model.\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;32mImproved model loss from 1.09902048 to 0.98102009. \u001b[0m\u001b[0;96mSaving model.\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;36mSaving full model H5 format...\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;32m(GPU-MEM)\u001b[0m\u001b[0;36m----[free: 5.14GB, used: 18.86GB, total, 24.00GB]\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m509.46 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m424.94 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m84.52 \u001b[0m\u001b[0;36msec\u001b[0m\n",
"\u001b[0;36m<---------------------------------------|Epoch [2] END|--------------------------------------->\u001b[0m\n",
"\u001b[0m\n",
"\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m3\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m489 (TSEC: 12)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Stage 1]\u001b[0m\n",
@@ -6291,9 +7427,2961 @@
"\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n",
"\u001b[0;32mTraining on subset...\u001b[0m\n",
"Epoch 13/18\n",
- "256/256 [==============================] - 75s 276ms/step - loss: nan - accuracy: 0.5127 - val_loss: nan - val_accuracy: 0.3750 - lr: 0.3310\n",
+ "256/256 [==============================] - 74s 275ms/step - loss: 0.9580 - accuracy: 0.8875 - val_loss: 0.8333 - val_accuracy: 0.9199 - lr: 0.0100\n",
"Epoch 14/18\n",
- " 18/256 [=>............................] - ETA: 56s - loss: nan - accuracy: 0.5312\n",
+ "256/256 [==============================] - 69s 269ms/step - loss: 0.8153 - accuracy: 0.9146 - val_loss: 0.8155 - val_accuracy: 0.8878 - lr: 0.0100\n",
+ "Epoch 15/18\n",
+ "256/256 [==============================] - 69s 271ms/step - loss: 0.7287 - accuracy: 0.9175 - val_loss: 0.7287 - val_accuracy: 0.9167 - lr: 0.0100\n",
+ "Epoch 16/18\n",
+ "256/256 [==============================] - 69s 270ms/step - loss: 0.6085 - accuracy: 0.9424 - val_loss: 0.6945 - val_accuracy: 0.9054 - lr: 0.0100\n",
+ "Epoch 17/18\n",
+ "256/256 [==============================] - 69s 271ms/step - loss: 0.5522 - accuracy: 0.9500 - val_loss: 0.6496 - val_accuracy: 0.8830 - lr: 0.0100\n",
+ "Epoch 18/18\n",
+ "256/256 [==============================] - 69s 270ms/step - loss: 0.4957 - accuracy: 0.9553 - val_loss: 0.5994 - val_accuracy: 0.9199 - lr: 0.0100\n",
+ "\u001b[0;32mSubset training done.\u001b[0m\n",
+ "\u001b[0;33mLoading the best weights...\u001b[0m\n",
+ "\u001b[0;33mLoading weights from file cache\\model_SUB_checkpoint-013-0.9199.h5...\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9199\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.8333\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;32mImproved model accuracy from 0.903846 to 0.919872. \u001b[0m\u001b[0;96mSaving model.\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;36mSaving full model H5 format...\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;32mImproved model loss from 0.98102009 to 0.83334637. \u001b[0m\u001b[0;96mSaving model.\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;36mSaving full model H5 format...\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;32m(GPU-MEM)\u001b[0m\u001b[0;36m----[free: 5.13GB, used: 18.87GB, total, 24.00GB]\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m491.59 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m422.14 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m69.45 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0;36m<---------------------------------------|Epoch [3] END|--------------------------------------->\u001b[0m\n",
+ "\u001b[0m\n",
+ "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m4\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m489 (TSEC: 18)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Stage 1]\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+ "\u001b[0;33mPreparing train data...\u001b[0m\n",
+ "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n",
+ "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+ "\u001b[0;32mTraining on subset...\u001b[0m\n",
+ "Epoch 19/24\n",
+ "256/256 [==============================] - 74s 275ms/step - loss: 0.8811 - accuracy: 0.8813 - val_loss: 0.8378 - val_accuracy: 0.9103 - lr: 0.0100\n",
+ "Epoch 20/24\n",
+ "256/256 [==============================] - 69s 269ms/step - loss: 0.7593 - accuracy: 0.9089 - val_loss: 0.8316 - val_accuracy: 0.8526 - lr: 0.0100\n",
+ "Epoch 21/24\n",
+ "256/256 [==============================] - 70s 272ms/step - loss: 0.6531 - accuracy: 0.9248 - val_loss: 0.6264 - val_accuracy: 0.9327 - lr: 0.0100\n",
+ "Epoch 22/24\n",
+ "256/256 [==============================] - 70s 272ms/step - loss: 0.5809 - accuracy: 0.9392 - val_loss: 0.5873 - val_accuracy: 0.9375 - lr: 0.0100\n",
+ "Epoch 23/24\n",
+ "256/256 [==============================] - 69s 270ms/step - loss: 0.5031 - accuracy: 0.9480 - val_loss: 0.5634 - val_accuracy: 0.9343 - lr: 0.0100\n",
+ "Epoch 24/24\n",
+ "256/256 [==============================] - 69s 269ms/step - loss: 0.4328 - accuracy: 0.9570 - val_loss: 0.5751 - val_accuracy: 0.8846 - lr: 0.0100\n",
+ "\u001b[0;32mSubset training done.\u001b[0m\n",
+ "\u001b[0;33mLoading the best weights...\u001b[0m\n",
+ "\u001b[0;33mLoading weights from file cache\\model_SUB_checkpoint-022-0.9375.h5...\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9375\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.5873\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;32mImproved model accuracy from 0.919872 to 0.937500. \u001b[0m\u001b[0;96mSaving model.\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;36mSaving full model H5 format...\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;32mImproved model loss from 0.83334637 to 0.58728427. \u001b[0m\u001b[0;96mSaving model.\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;36mSaving full model H5 format...\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;32m(GPU-MEM)\u001b[0m\u001b[0;36m----[free: 5.13GB, used: 18.87GB, total, 24.00GB]\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m492.19 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m422.44 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m69.75 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0;36m<---------------------------------------|Epoch [4] END|--------------------------------------->\u001b[0m\n",
+ "\u001b[0m\n",
+ "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m5\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m489 (TSEC: 24)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Stage 1]\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+ "\u001b[0;33mPreparing train data...\u001b[0m\n",
+ "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n",
+ "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+ "\u001b[0;32mTraining on subset...\u001b[0m\n",
+ "Epoch 25/30\n",
+ "256/256 [==============================] - 75s 277ms/step - loss: 0.6212 - accuracy: 0.9043 - val_loss: 0.7914 - val_accuracy: 0.8670 - lr: 0.0100\n",
+ "Epoch 26/30\n",
+ "256/256 [==============================] - 70s 273ms/step - loss: 0.5334 - accuracy: 0.9290 - val_loss: 0.5001 - val_accuracy: 0.9311 - lr: 0.0100\n",
+ "Epoch 27/30\n",
+ "256/256 [==============================] - 70s 271ms/step - loss: 0.4541 - accuracy: 0.9397 - val_loss: 0.5195 - val_accuracy: 0.9135 - lr: 0.0100\n",
+ "Epoch 28/30\n",
+ "256/256 [==============================] - 69s 270ms/step - loss: 0.4093 - accuracy: 0.9451 - val_loss: 0.5215 - val_accuracy: 0.9087 - lr: 0.0100\n",
+ "Epoch 29/30\n",
+ "256/256 [==============================] - 70s 274ms/step - loss: 0.3499 - accuracy: 0.9597 - val_loss: 0.4166 - val_accuracy: 0.9375 - lr: 0.0100\n",
+ "Epoch 30/30\n",
+ "256/256 [==============================] - 69s 269ms/step - loss: 0.3113 - accuracy: 0.9634 - val_loss: 0.4444 - val_accuracy: 0.9151 - lr: 0.0100\n",
+ "\u001b[0;32mSubset training done.\u001b[0m\n",
+ "\u001b[0;33mLoading the best weights...\u001b[0m\n",
+ "\u001b[0;33mLoading weights from file cache\\model_SUB_checkpoint-029-0.9375.h5...\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9375\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.4166\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9375000000. Not saving model.\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;32mImproved model loss from 0.58728427 to 0.41660872. \u001b[0m\u001b[0;96mSaving model.\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;36mSaving full model H5 format...\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;32m(GPU-MEM)\u001b[0m\u001b[0;36m----[free: 5.13GB, used: 18.87GB, total, 24.00GB]\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m493.68 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m424.53 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m69.15 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0;36m<---------------------------------------|Epoch [5] END|--------------------------------------->\u001b[0m\n",
+ "\u001b[0m\n",
+ "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m6\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m489 (TSEC: 30)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Stage 1]\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+ "\u001b[0;33mPreparing train data...\u001b[0m\n",
+ "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n",
+ "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+ "\u001b[0;32mTraining on subset...\u001b[0m\n",
+ "Epoch 31/36\n",
+ "256/256 [==============================] - 75s 276ms/step - loss: 0.4474 - accuracy: 0.9150 - val_loss: 0.3971 - val_accuracy: 0.9375 - lr: 0.0100\n",
+ "Epoch 32/36\n",
+ "256/256 [==============================] - 69s 270ms/step - loss: 0.3720 - accuracy: 0.9348 - val_loss: 0.3943 - val_accuracy: 0.9295 - lr: 0.0100\n",
+ "Epoch 33/36\n",
+ "256/256 [==============================] - 70s 274ms/step - loss: 0.3061 - accuracy: 0.9551 - val_loss: 0.3369 - val_accuracy: 0.9487 - lr: 0.0100\n",
+ "Epoch 34/36\n",
+ "256/256 [==============================] - 70s 271ms/step - loss: 0.2716 - accuracy: 0.9651 - val_loss: 0.3636 - val_accuracy: 0.9375 - lr: 0.0100\n",
+ "Epoch 35/36\n",
+ "256/256 [==============================] - 70s 271ms/step - loss: 0.2348 - accuracy: 0.9690 - val_loss: 0.3595 - val_accuracy: 0.9407 - lr: 0.0100\n",
+ "Epoch 36/36\n",
+ "256/256 [==============================] - 70s 271ms/step - loss: 0.2161 - accuracy: 0.9714 - val_loss: 0.3430 - val_accuracy: 0.9359 - lr: 0.0100\n",
+ "\u001b[0;32mSubset training done.\u001b[0m\n",
+ "\u001b[0;33mLoading the best weights...\u001b[0m\n",
+ "\u001b[0;33mLoading weights from file cache\\model_SUB_checkpoint-033-0.9487.h5...\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9487\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.3369\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;32mImproved model accuracy from 0.937500 to 0.948718. \u001b[0m\u001b[0;96mSaving model.\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;36mSaving full model H5 format...\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;32mImproved model loss from 0.41660872 to 0.33694357. \u001b[0m\u001b[0;96mSaving model.\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;36mSaving full model H5 format...\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;32m(GPU-MEM)\u001b[0m\u001b[0;36m----[free: 5.13GB, used: 18.87GB, total, 24.00GB]\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m496.62 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m424.14 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m72.47 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0;36m<---------------------------------------|Epoch [6] END|--------------------------------------->\u001b[0m\n",
+ "\u001b[0m\n",
+ "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m7\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m489 (TSEC: 36)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Stage 1]\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+ "\u001b[0;33mPreparing train data...\u001b[0m\n",
+ "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n",
+ "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+ "\u001b[0;32mTraining on subset...\u001b[0m\n",
+ "Epoch 37/42\n",
+ "256/256 [==============================] - 75s 277ms/step - loss: 0.3834 - accuracy: 0.9062 - val_loss: 0.3117 - val_accuracy: 0.9471 - lr: 0.0100\n",
+ "Epoch 38/42\n",
+ "256/256 [==============================] - 70s 271ms/step - loss: 0.3038 - accuracy: 0.9417 - val_loss: 0.3520 - val_accuracy: 0.9423 - lr: 0.0100\n",
+ "Epoch 39/42\n",
+ "256/256 [==============================] - 70s 272ms/step - loss: 0.2586 - accuracy: 0.9529 - val_loss: 0.3165 - val_accuracy: 0.9295 - lr: 0.0100\n",
+ "Epoch 40/42\n",
+ "256/256 [==============================] - 70s 272ms/step - loss: 0.2164 - accuracy: 0.9651 - val_loss: 1.0490 - val_accuracy: 0.7901 - lr: 0.0100\n",
+ "Epoch 41/42\n",
+ "256/256 [==============================] - 70s 271ms/step - loss: 0.1917 - accuracy: 0.9736 - val_loss: 0.3480 - val_accuracy: 0.9135 - lr: 0.0100\n",
+ "Epoch 42/42\n",
+ "256/256 [==============================] - 70s 271ms/step - loss: 0.1689 - accuracy: 0.9761 - val_loss: 0.2705 - val_accuracy: 0.9375 - lr: 0.0100\n",
+ "\u001b[0;32mSubset training done.\u001b[0m\n",
+ "\u001b[0;33mLoading the best weights...\u001b[0m\n",
+ "\u001b[0;33mLoading weights from file cache\\model_SUB_checkpoint-037-0.9471.h5...\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9471\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.3118\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9487179518. Not saving model.\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;32mImproved model loss from 0.33694357 to 0.31175852. \u001b[0m\u001b[0;96mSaving model.\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;36mSaving full model H5 format...\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;32m(GPU-MEM)\u001b[0m\u001b[0;36m----[free: 5.13GB, used: 18.87GB, total, 24.00GB]\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m496.05 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m424.58 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m71.48 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0;36m<---------------------------------------|Epoch [7] END|--------------------------------------->\u001b[0m\n",
+ "\u001b[0m\n",
+ "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m8\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m489 (TSEC: 42)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Stage 1]\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+ "\u001b[0;33mPreparing train data...\u001b[0m\n",
+ "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n",
+ "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+ "\u001b[0;32mTraining on subset...\u001b[0m\n",
+ "Epoch 43/48\n",
+ "256/256 [==============================] - 75s 277ms/step - loss: 0.3506 - accuracy: 0.9248 - val_loss: 0.3114 - val_accuracy: 0.9439 - lr: 0.0100\n",
+ "Epoch 44/48\n",
+ "256/256 [==============================] - 70s 274ms/step - loss: 0.3039 - accuracy: 0.9392 - val_loss: 0.3033 - val_accuracy: 0.9503 - lr: 0.0100\n",
+ "Epoch 45/48\n",
+ "256/256 [==============================] - 70s 271ms/step - loss: 0.2514 - accuracy: 0.9565 - val_loss: 0.3239 - val_accuracy: 0.9167 - lr: 0.0100\n",
+ "Epoch 46/48\n",
+ "256/256 [==============================] - 69s 271ms/step - loss: 0.2171 - accuracy: 0.9617 - val_loss: 0.2641 - val_accuracy: 0.9423 - lr: 0.0100\n",
+ "Epoch 47/48\n",
+ "256/256 [==============================] - 70s 271ms/step - loss: 0.1880 - accuracy: 0.9717 - val_loss: 0.3846 - val_accuracy: 0.9054 - lr: 0.0100\n",
+ "Epoch 48/48\n",
+ "256/256 [==============================] - 70s 271ms/step - loss: 0.1528 - accuracy: 0.9807 - val_loss: 0.2730 - val_accuracy: 0.9375 - lr: 0.0100\n",
+ "\u001b[0;32mSubset training done.\u001b[0m\n",
+ "\u001b[0;33mLoading the best weights...\u001b[0m\n",
+ "\u001b[0;33mLoading weights from file cache\\model_SUB_checkpoint-044-0.9503.h5...\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9503\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.3033\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;32mImproved model accuracy from 0.948718 to 0.950321. \u001b[0m\u001b[0;96mSaving model.\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;36mSaving full model H5 format...\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;32mImproved model loss from 0.31175852 to 0.30326003. \u001b[0m\u001b[0;96mSaving model.\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;36mSaving full model H5 format...\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;32m(GPU-MEM)\u001b[0m\u001b[0;36m----[free: 5.13GB, used: 18.87GB, total, 24.00GB]\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m498.87 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m424.45 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m74.42 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0;36m<---------------------------------------|Epoch [8] END|--------------------------------------->\u001b[0m\n",
+ "\u001b[0m\n",
+ "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m9\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m489 (TSEC: 48)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Stage 1]\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+ "\u001b[0;33mPreparing train data...\u001b[0m\n",
+ "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n",
+ "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+ "\u001b[0;32mTraining on subset...\u001b[0m\n",
+ "Epoch 49/54\n",
+ "256/256 [==============================] - 75s 279ms/step - loss: 0.3169 - accuracy: 0.9275 - val_loss: 0.3260 - val_accuracy: 0.9215 - lr: 0.0100\n",
+ "Epoch 50/54\n",
+ "256/256 [==============================] - 70s 271ms/step - loss: 0.2677 - accuracy: 0.9438 - val_loss: 0.3534 - val_accuracy: 0.9006 - lr: 0.0100\n",
+ "Epoch 51/54\n",
+ "256/256 [==============================] - 71s 276ms/step - loss: 0.2187 - accuracy: 0.9622 - val_loss: 0.3277 - val_accuracy: 0.9295 - lr: 0.0100\n",
+ "Epoch 52/54\n",
+ "256/256 [==============================] - 70s 271ms/step - loss: 0.1777 - accuracy: 0.9702 - val_loss: 0.4656 - val_accuracy: 0.9119 - lr: 0.0100\n",
+ "Epoch 53/54\n",
+ "256/256 [==============================] - 71s 275ms/step - loss: 0.1573 - accuracy: 0.9761 - val_loss: 0.2278 - val_accuracy: 0.9487 - lr: 0.0100\n",
+ "Epoch 54/54\n",
+ "256/256 [==============================] - 70s 272ms/step - loss: 0.1485 - accuracy: 0.9785 - val_loss: 0.5326 - val_accuracy: 0.8317 - lr: 0.0100\n",
+ "\u001b[0;32mSubset training done.\u001b[0m\n",
+ "\u001b[0;33mLoading the best weights...\u001b[0m\n",
+ "\u001b[0;33mLoading weights from file cache\\model_SUB_checkpoint-053-0.9487.h5...\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9487\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.2279\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9503205419. Not saving model.\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;32mImproved model loss from 0.30326003 to 0.22785568. \u001b[0m\u001b[0;96mSaving model.\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;36mSaving full model H5 format...\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;32m(GPU-MEM)\u001b[0m\u001b[0;36m----[free: 5.13GB, used: 18.87GB, total, 24.00GB]\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m497.91 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m427.05 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m70.86 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0;36m<---------------------------------------|Epoch [9] END|--------------------------------------->\u001b[0m\n",
+ "\u001b[0m\n",
+ "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m10\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m489 (TSEC: 54)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Stage 1]\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+ "\u001b[0;33mPreparing train data...\u001b[0m\n",
+ "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n",
+ "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+ "\u001b[0;32mTraining on subset...\u001b[0m\n",
+ "Epoch 55/60\n",
+ "256/256 [==============================] - 75s 277ms/step - loss: 0.2935 - accuracy: 0.9275 - val_loss: 0.2426 - val_accuracy: 0.9535 - lr: 0.0100\n",
+ "Epoch 56/60\n",
+ "256/256 [==============================] - 70s 271ms/step - loss: 0.2239 - accuracy: 0.9478 - val_loss: 0.2476 - val_accuracy: 0.9455 - lr: 0.0100\n",
+ "Epoch 57/60\n",
+ "256/256 [==============================] - 70s 271ms/step - loss: 0.1874 - accuracy: 0.9607 - val_loss: 0.3168 - val_accuracy: 0.9183 - lr: 0.0100\n",
+ "Epoch 58/60\n",
+ "256/256 [==============================] - 70s 271ms/step - loss: 0.1435 - accuracy: 0.9746 - val_loss: 0.2209 - val_accuracy: 0.9407 - lr: 0.0100\n",
+ "Epoch 59/60\n",
+ "256/256 [==============================] - 70s 271ms/step - loss: 0.1247 - accuracy: 0.9778 - val_loss: 0.2745 - val_accuracy: 0.9487 - lr: 0.0100\n",
+ "Epoch 60/60\n",
+ "256/256 [==============================] - 70s 272ms/step - loss: 0.1104 - accuracy: 0.9810 - val_loss: 0.2360 - val_accuracy: 0.9487 - lr: 0.0100\n",
+ "\u001b[0;32mSubset training done.\u001b[0m\n",
+ "\u001b[0;33mLoading the best weights...\u001b[0m\n",
+ "\u001b[0;33mLoading weights from file cache\\model_SUB_checkpoint-055-0.9535.h5...\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9535\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.2426\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;32mImproved model accuracy from 0.950321 to 0.953526. \u001b[0m\u001b[0;96mSaving model.\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;36mSaving full model H5 format...\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.2278556824. Not saving model.\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;32m(GPU-MEM)\u001b[0m\u001b[0;36m----[free: 5.13GB, used: 18.87GB, total, 24.00GB]\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m496.06 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m424.46 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m71.60 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0;36m<---------------------------------------|Epoch [10] END|--------------------------------------->\u001b[0m\n",
+ "\u001b[0m\n",
+ "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m11\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m489 (TSEC: 60)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Stage 1]\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+ "\u001b[0;33mPreparing train data...\u001b[0m\n",
+ "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n",
+ "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+ "\u001b[0;32mTraining on subset...\u001b[0m\n",
+ "Epoch 61/66\n",
+ "256/256 [==============================] - 75s 277ms/step - loss: 0.2740 - accuracy: 0.9314 - val_loss: 0.2571 - val_accuracy: 0.9519 - lr: 0.0100\n",
+ "Epoch 62/66\n",
+ "256/256 [==============================] - 70s 274ms/step - loss: 0.2121 - accuracy: 0.9539 - val_loss: 0.2049 - val_accuracy: 0.9551 - lr: 0.0100\n",
+ "Epoch 63/66\n",
+ "256/256 [==============================] - 70s 275ms/step - loss: 0.1764 - accuracy: 0.9653 - val_loss: 0.1865 - val_accuracy: 0.9615 - lr: 0.0100\n",
+ "Epoch 64/66\n",
+ "256/256 [==============================] - 70s 271ms/step - loss: 0.1430 - accuracy: 0.9741 - val_loss: 0.2043 - val_accuracy: 0.9471 - lr: 0.0100\n",
+ "Epoch 65/66\n",
+ "256/256 [==============================] - 70s 272ms/step - loss: 0.1236 - accuracy: 0.9753 - val_loss: 0.2097 - val_accuracy: 0.9583 - lr: 0.0100\n",
+ "Epoch 66/66\n",
+ "256/256 [==============================] - 70s 272ms/step - loss: 0.1056 - accuracy: 0.9819 - val_loss: 0.2676 - val_accuracy: 0.9535 - lr: 0.0100\n",
+ "\u001b[0;32mSubset training done.\u001b[0m\n",
+ "\u001b[0;33mLoading the best weights...\u001b[0m\n",
+ "\u001b[0;33mLoading weights from file cache\\model_SUB_checkpoint-063-0.9615.h5...\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9615\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.1865\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;32mImproved model accuracy from 0.953526 to 0.961538. \u001b[0m\u001b[0;96mSaving model.\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;36mSaving full model H5 format...\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;32mImproved model loss from 0.22785568 to 0.18650842. \u001b[0m\u001b[0;96mSaving model.\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;36mSaving full model H5 format...\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;32m(GPU-MEM)\u001b[0m\u001b[0;36m----[free: 5.13GB, used: 18.87GB, total, 24.00GB]\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m501.19 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m426.43 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m74.76 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0;36m<---------------------------------------|Epoch [11] END|--------------------------------------->\u001b[0m\n",
+ "\u001b[0m\n",
+ "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m12\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m489 (TSEC: 66)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Stage 1]\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+ "\u001b[0;33mPreparing train data...\u001b[0m\n",
+ "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n",
+ "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+ "\u001b[0;32mTraining on subset...\u001b[0m\n",
+ "Epoch 67/72\n",
+ "256/256 [==============================] - 76s 279ms/step - loss: 0.2568 - accuracy: 0.9346 - val_loss: 0.2103 - val_accuracy: 0.9519 - lr: 0.0100\n",
+ "Epoch 68/72\n",
+ "256/256 [==============================] - 71s 277ms/step - loss: 0.2037 - accuracy: 0.9536 - val_loss: 0.2022 - val_accuracy: 0.9535 - lr: 0.0100\n",
+ "Epoch 69/72\n",
+ "256/256 [==============================] - 71s 275ms/step - loss: 0.1751 - accuracy: 0.9573 - val_loss: 0.1843 - val_accuracy: 0.9551 - lr: 0.0100\n",
+ "Epoch 70/72\n",
+ "256/256 [==============================] - 71s 275ms/step - loss: 0.1368 - accuracy: 0.9707 - val_loss: 0.1812 - val_accuracy: 0.9599 - lr: 0.0100\n",
+ "Epoch 71/72\n",
+ "256/256 [==============================] - 70s 272ms/step - loss: 0.1159 - accuracy: 0.9780 - val_loss: 0.2526 - val_accuracy: 0.9407 - lr: 0.0100\n",
+ "Epoch 72/72\n",
+ "256/256 [==============================] - 70s 274ms/step - loss: 0.1069 - accuracy: 0.9795 - val_loss: 0.2513 - val_accuracy: 0.9231 - lr: 0.0100\n",
+ "\u001b[0;32mSubset training done.\u001b[0m\n",
+ "\u001b[0;33mLoading the best weights...\u001b[0m\n",
+ "\u001b[0;33mLoading weights from file cache\\model_SUB_checkpoint-070-0.9599.h5...\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9599\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.1812\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9615384340. Not saving model.\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;32mImproved model loss from 0.18650842 to 0.18118645. \u001b[0m\u001b[0;96mSaving model.\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;36mSaving full model H5 format...\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;32m(GPU-MEM)\u001b[0m\u001b[0;36m----[free: 5.13GB, used: 18.87GB, total, 24.00GB]\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m503.89 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m429.46 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m74.43 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0;36m<---------------------------------------|Epoch [12] END|--------------------------------------->\u001b[0m\n",
+ "\u001b[0m\n",
+ "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m13\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m489 (TSEC: 72)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Stage 1]\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+ "\u001b[0;33mPreparing train data...\u001b[0m\n",
+ "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n",
+ "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+ "\u001b[0;32mTraining on subset...\u001b[0m\n",
+ "Epoch 73/78\n",
+ "256/256 [==============================] - 75s 279ms/step - loss: 0.2414 - accuracy: 0.9294 - val_loss: 0.1788 - val_accuracy: 0.9599 - lr: 0.0100\n",
+ "Epoch 74/78\n",
+ "256/256 [==============================] - 70s 273ms/step - loss: 0.1754 - accuracy: 0.9548 - val_loss: 0.2104 - val_accuracy: 0.9439 - lr: 0.0100\n",
+ "Epoch 75/78\n",
+ "256/256 [==============================] - 70s 273ms/step - loss: 0.1465 - accuracy: 0.9670 - val_loss: 0.1927 - val_accuracy: 0.9471 - lr: 0.0100\n",
+ "Epoch 76/78\n",
+ "256/256 [==============================] - 70s 273ms/step - loss: 0.1156 - accuracy: 0.9741 - val_loss: 0.1797 - val_accuracy: 0.9535 - lr: 0.0100\n",
+ "Epoch 77/78\n",
+ "256/256 [==============================] - 70s 273ms/step - loss: 0.0964 - accuracy: 0.9812 - val_loss: 0.3014 - val_accuracy: 0.9423 - lr: 0.0100\n",
+ "Epoch 78/78\n",
+ "256/256 [==============================] - 70s 273ms/step - loss: 0.0891 - accuracy: 0.9807 - val_loss: 0.1904 - val_accuracy: 0.9423 - lr: 0.0100\n",
+ "\u001b[0;32mSubset training done.\u001b[0m\n",
+ "\u001b[0;33mLoading the best weights...\u001b[0m\n",
+ "\u001b[0;33mLoading weights from file cache\\model_SUB_checkpoint-073-0.9599.h5...\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9599\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.1788\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9615384340. Not saving model.\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;32mImproved model loss from 0.18118645 to 0.17884569. \u001b[0m\u001b[0;96mSaving model.\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;36mSaving full model H5 format...\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;32m(GPU-MEM)\u001b[0m\u001b[0;36m----[free: 5.14GB, used: 18.86GB, total, 24.00GB]\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m502.74 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m426.94 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m75.80 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0;36m<---------------------------------------|Epoch [13] END|--------------------------------------->\u001b[0m\n",
+ "\u001b[0m\n",
+ "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m14\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m489 (TSEC: 78)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Stage 1]\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+ "\u001b[0;33mPreparing train data...\u001b[0m\n",
+ "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n",
+ "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+ "\u001b[0;32mTraining on subset...\u001b[0m\n",
+ "Epoch 79/84\n",
+ "256/256 [==============================] - 75s 277ms/step - loss: 0.2316 - accuracy: 0.9380 - val_loss: 0.2060 - val_accuracy: 0.9407 - lr: 0.0100\n",
+ "Epoch 80/84\n",
+ "256/256 [==============================] - 71s 275ms/step - loss: 0.1715 - accuracy: 0.9580 - val_loss: 0.1804 - val_accuracy: 0.9503 - lr: 0.0100\n",
+ "Epoch 81/84\n",
+ "256/256 [==============================] - 70s 271ms/step - loss: 0.1476 - accuracy: 0.9646 - val_loss: 0.3042 - val_accuracy: 0.9087 - lr: 0.0100\n",
+ "Epoch 82/84\n",
+ "256/256 [==============================] - 70s 272ms/step - loss: 0.1162 - accuracy: 0.9746 - val_loss: 0.2478 - val_accuracy: 0.9279 - lr: 0.0100\n",
+ "Epoch 83/84\n",
+ "256/256 [==============================] - 70s 274ms/step - loss: 0.0961 - accuracy: 0.9824 - val_loss: 0.1980 - val_accuracy: 0.9599 - lr: 0.0100\n",
+ "Epoch 84/84\n",
+ "256/256 [==============================] - 70s 272ms/step - loss: 0.0851 - accuracy: 0.9861 - val_loss: 0.2208 - val_accuracy: 0.9535 - lr: 0.0100\n",
+ "\u001b[0;32mSubset training done.\u001b[0m\n",
+ "\u001b[0;33mLoading the best weights...\u001b[0m\n",
+ "\u001b[0;33mLoading weights from file cache\\model_SUB_checkpoint-083-0.9599.h5...\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9599\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.1980\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9615384340. Not saving model.\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1788456887. Not saving model.\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;32m(GPU-MEM)\u001b[0m\u001b[0;36m----[free: 5.18GB, used: 18.82GB, total, 24.00GB]\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m500.63 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m426.28 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m74.35 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0;36m<---------------------------------------|Epoch [14] END|--------------------------------------->\u001b[0m\n",
+ "\u001b[0m\n",
+ "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m15\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m489 (TSEC: 84)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Stage 1]\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+ "\u001b[0;33mPreparing train data...\u001b[0m\n",
+ "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n",
+ "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+ "\u001b[0;32mTraining on subset...\u001b[0m\n",
+ "Epoch 85/90\n",
+ "256/256 [==============================] - 75s 277ms/step - loss: 0.2205 - accuracy: 0.9375 - val_loss: 0.1953 - val_accuracy: 0.9487 - lr: 0.0100\n",
+ "Epoch 86/90\n",
+ "256/256 [==============================] - 69s 271ms/step - loss: 0.1596 - accuracy: 0.9578 - val_loss: 0.2314 - val_accuracy: 0.9471 - lr: 0.0100\n",
+ "Epoch 87/90\n",
+ "256/256 [==============================] - 70s 271ms/step - loss: 0.1303 - accuracy: 0.9661 - val_loss: 0.2036 - val_accuracy: 0.9471 - lr: 0.0100\n",
+ "Epoch 88/90\n",
+ "256/256 [==============================] - 70s 271ms/step - loss: 0.0946 - accuracy: 0.9812 - val_loss: 0.2871 - val_accuracy: 0.9311 - lr: 0.0100\n",
+ "Epoch 89/90\n",
+ "256/256 [==============================] - 69s 270ms/step - loss: 0.0762 - accuracy: 0.9836 - val_loss: 0.5652 - val_accuracy: 0.8686 - lr: 0.0100\n",
+ "Epoch 90/90\n",
+ "256/256 [==============================] - 70s 270ms/step - loss: 0.0751 - accuracy: 0.9851 - val_loss: 0.4255 - val_accuracy: 0.9006 - lr: 0.0100\n",
+ "\u001b[0;32mSubset training done.\u001b[0m\n",
+ "\u001b[0;33mLoading the best weights...\u001b[0m\n",
+ "\u001b[0;33mLoading weights from file cache\\model_SUB_checkpoint-085-0.9487.h5...\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9487\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.1953\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9615384340. Not saving model.\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1788456887. Not saving model.\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;32m(GPU-MEM)\u001b[0m\u001b[0;36m----[free: 5.18GB, used: 18.82GB, total, 24.00GB]\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m499.26 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m423.69 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m75.57 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0;36m<---------------------------------------|Epoch [15] END|--------------------------------------->\u001b[0m\n",
+ "\u001b[0m\n",
+ "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m16\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m489 (TSEC: 90)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Stage 1]\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+ "\u001b[0;33mPreparing train data...\u001b[0m\n",
+ "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n",
+ "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+ "\u001b[0;32mTraining on subset...\u001b[0m\n",
+ "Epoch 91/96\n",
+ "256/256 [==============================] - 76s 280ms/step - loss: 0.2224 - accuracy: 0.9333 - val_loss: 0.2097 - val_accuracy: 0.9471 - lr: 0.0100\n",
+ "Epoch 92/96\n",
+ "256/256 [==============================] - 71s 276ms/step - loss: 0.1542 - accuracy: 0.9565 - val_loss: 0.1671 - val_accuracy: 0.9599 - lr: 0.0100\n",
+ "Epoch 93/96\n",
+ "256/256 [==============================] - 70s 273ms/step - loss: 0.1241 - accuracy: 0.9712 - val_loss: 0.2496 - val_accuracy: 0.9199 - lr: 0.0100\n",
+ "Epoch 94/96\n",
+ "256/256 [==============================] - 70s 273ms/step - loss: 0.0903 - accuracy: 0.9802 - val_loss: 0.1669 - val_accuracy: 0.9583 - lr: 0.0100\n",
+ "Epoch 95/96\n",
+ "256/256 [==============================] - 70s 274ms/step - loss: 0.0800 - accuracy: 0.9841 - val_loss: 0.1707 - val_accuracy: 0.9599 - lr: 0.0100\n",
+ "Epoch 96/96\n",
+ "256/256 [==============================] - 70s 273ms/step - loss: 0.0814 - accuracy: 0.9822 - val_loss: 0.2124 - val_accuracy: 0.9455 - lr: 0.0100\n",
+ "\u001b[0;32mSubset training done.\u001b[0m\n",
+ "\u001b[0;33mLoading the best weights...\u001b[0m\n",
+ "\u001b[0;33mLoading weights from file cache\\model_SUB_checkpoint-092-0.9599.h5...\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9599\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.1671\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9615384340. Not saving model.\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;32mImproved model loss from 0.17884569 to 0.16713950. \u001b[0m\u001b[0;96mSaving model.\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;36mSaving full model H5 format...\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;32m(GPU-MEM)\u001b[0m\u001b[0;36m----[free: 5.18GB, used: 18.82GB, total, 24.00GB]\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m506.35 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m428.29 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m78.06 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0;36m<---------------------------------------|Epoch [16] END|--------------------------------------->\u001b[0m\n",
+ "\u001b[0m\n",
+ "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m17\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m489 (TSEC: 96)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Stage 1]\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+ "\u001b[0;33mPreparing train data...\u001b[0m\n",
+ "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n",
+ "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+ "\u001b[0;32mTraining on subset...\u001b[0m\n",
+ "Epoch 97/102\n",
+ "256/256 [==============================] - 76s 281ms/step - loss: 0.2000 - accuracy: 0.9419 - val_loss: 0.1803 - val_accuracy: 0.9455 - lr: 0.0100\n",
+ "Epoch 98/102\n",
+ "256/256 [==============================] - 70s 272ms/step - loss: 0.1603 - accuracy: 0.9570 - val_loss: 0.1950 - val_accuracy: 0.9391 - lr: 0.0100\n",
+ "Epoch 99/102\n",
+ "256/256 [==============================] - 71s 278ms/step - loss: 0.1286 - accuracy: 0.9688 - val_loss: 0.1884 - val_accuracy: 0.9471 - lr: 0.0100\n",
+ "Epoch 100/102\n",
+ "256/256 [==============================] - 70s 272ms/step - loss: 0.1045 - accuracy: 0.9773 - val_loss: 0.2358 - val_accuracy: 0.9311 - lr: 0.0100\n",
+ "Epoch 101/102\n",
+ "256/256 [==============================] - 70s 274ms/step - loss: 0.0909 - accuracy: 0.9807 - val_loss: 0.2543 - val_accuracy: 0.9359 - lr: 0.0100\n",
+ "Epoch 102/102\n",
+ "256/256 [==============================] - 70s 273ms/step - loss: 0.0815 - accuracy: 0.9836 - val_loss: 0.2290 - val_accuracy: 0.9247 - lr: 0.0100\n",
+ "\u001b[0;32mSubset training done.\u001b[0m\n",
+ "\u001b[0;33mLoading the best weights...\u001b[0m\n",
+ "\u001b[0;33mLoading weights from file cache\\model_SUB_checkpoint-099-0.9471.h5...\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9471\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.1884\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9615384340. Not saving model.\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1671395004. Not saving model.\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;32m(GPU-MEM)\u001b[0m\u001b[0;36m----[free: 5.18GB, used: 18.82GB, total, 24.00GB]\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m506.73 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m428.74 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m78.00 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0;36m<---------------------------------------|Epoch [17] END|--------------------------------------->\u001b[0m\n",
+ "\u001b[0m\n",
+ "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m18\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m489 (TSEC: 102)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Stage 1]\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+ "\u001b[0;33mPreparing train data...\u001b[0m\n",
+ "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n",
+ "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+ "\u001b[0;32mTraining on subset...\u001b[0m\n",
+ "Epoch 103/108\n",
+ "256/256 [==============================] - 76s 280ms/step - loss: 0.2191 - accuracy: 0.9355 - val_loss: 0.2022 - val_accuracy: 0.9439 - lr: 0.0100\n",
+ "Epoch 104/108\n",
+ "256/256 [==============================] - 71s 276ms/step - loss: 0.1620 - accuracy: 0.9548 - val_loss: 0.1848 - val_accuracy: 0.9471 - lr: 0.0100\n",
+ "Epoch 105/108\n",
+ "256/256 [==============================] - 70s 274ms/step - loss: 0.1338 - accuracy: 0.9631 - val_loss: 0.2283 - val_accuracy: 0.9279 - lr: 0.0100\n",
+ "Epoch 106/108\n",
+ "256/256 [==============================] - 71s 276ms/step - loss: 0.1021 - accuracy: 0.9741 - val_loss: 0.1688 - val_accuracy: 0.9551 - lr: 0.0100\n",
+ "Epoch 107/108\n",
+ "256/256 [==============================] - 70s 275ms/step - loss: 0.0819 - accuracy: 0.9819 - val_loss: 0.1969 - val_accuracy: 0.9455 - lr: 0.0100\n",
+ "Epoch 108/108\n",
+ "256/256 [==============================] - 70s 273ms/step - loss: 0.0776 - accuracy: 0.9841 - val_loss: 0.2636 - val_accuracy: 0.8990 - lr: 0.0100\n",
+ "\u001b[0;32mSubset training done.\u001b[0m\n",
+ "\u001b[0;33mLoading the best weights...\u001b[0m\n",
+ "\u001b[0;33mLoading weights from file cache\\model_SUB_checkpoint-106-0.9551.h5...\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9535\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.1688\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9615384340. Not saving model.\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1671395004. Not saving model.\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;32m(GPU-MEM)\u001b[0m\u001b[0;36m----[free: 5.18GB, used: 18.82GB, total, 24.00GB]\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m508.41 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m429.53 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m78.89 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0;36m<---------------------------------------|Epoch [18] END|--------------------------------------->\u001b[0m\n",
+ "\u001b[0m\n",
+ "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m19\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m489 (TSEC: 108)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Stage 1]\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+ "\u001b[0;33mPreparing train data...\u001b[0m\n",
+ "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n",
+ "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+ "\u001b[0;32mTraining on subset...\u001b[0m\n",
+ "Epoch 109/114\n",
+ "256/256 [==============================] - 76s 281ms/step - loss: 0.1952 - accuracy: 0.9424 - val_loss: 0.1801 - val_accuracy: 0.9487 - lr: 0.0100\n",
+ "Epoch 110/114\n",
+ "256/256 [==============================] - 71s 278ms/step - loss: 0.1472 - accuracy: 0.9558 - val_loss: 0.1730 - val_accuracy: 0.9503 - lr: 0.0100\n",
+ "Epoch 111/114\n",
+ "256/256 [==============================] - 70s 274ms/step - loss: 0.1048 - accuracy: 0.9722 - val_loss: 0.4313 - val_accuracy: 0.8670 - lr: 0.0100\n",
+ "Epoch 112/114\n",
+ "256/256 [==============================] - 70s 274ms/step - loss: 0.0790 - accuracy: 0.9790 - val_loss: 0.2087 - val_accuracy: 0.9391 - lr: 0.0100\n",
+ "Epoch 113/114\n",
+ "256/256 [==============================] - 70s 274ms/step - loss: 0.0691 - accuracy: 0.9861 - val_loss: 0.1956 - val_accuracy: 0.9471 - lr: 0.0100\n",
+ "Epoch 114/114\n",
+ "256/256 [==============================] - 70s 274ms/step - loss: 0.0683 - accuracy: 0.9849 - val_loss: 0.1962 - val_accuracy: 0.9423 - lr: 0.0100\n",
+ "\u001b[0;32mSubset training done.\u001b[0m\n",
+ "\u001b[0;33mLoading the best weights...\u001b[0m\n",
+ "\u001b[0;33mLoading weights from file cache\\model_SUB_checkpoint-110-0.9503.h5...\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9503\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.1730\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9615384340. Not saving model.\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1671395004. Not saving model.\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;32m(GPU-MEM)\u001b[0m\u001b[0;36m----[free: 5.18GB, used: 18.82GB, total, 24.00GB]\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m510.54 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m430.22 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m80.32 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0;36m<---------------------------------------|Epoch [19] END|--------------------------------------->\u001b[0m\n",
+ "\u001b[0m\n",
+ "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m20\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m489 (TSEC: 114)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Stage 1]\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+ "\u001b[0;33mPreparing train data...\u001b[0m\n",
+ "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n",
+ "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+ "\u001b[0;32mTraining on subset...\u001b[0m\n",
+ "Epoch 115/120\n",
+ "256/256 [==============================] - 76s 279ms/step - loss: 0.1874 - accuracy: 0.9419 - val_loss: 0.1975 - val_accuracy: 0.9375 - lr: 0.0100\n",
+ "Epoch 116/120\n",
+ "256/256 [==============================] - 70s 274ms/step - loss: 0.1582 - accuracy: 0.9531 - val_loss: 0.2660 - val_accuracy: 0.9263 - lr: 0.0100\n",
+ "Epoch 117/120\n",
+ "256/256 [==============================] - 70s 273ms/step - loss: 0.1196 - accuracy: 0.9690 - val_loss: 0.2343 - val_accuracy: 0.9327 - lr: 0.0100\n",
+ "Epoch 118/120\n",
+ "256/256 [==============================] - 71s 276ms/step - loss: 0.0896 - accuracy: 0.9792 - val_loss: 0.1768 - val_accuracy: 0.9471 - lr: 0.0100\n",
+ "Epoch 119/120\n",
+ "256/256 [==============================] - 71s 276ms/step - loss: 0.0643 - accuracy: 0.9858 - val_loss: 0.1641 - val_accuracy: 0.9615 - lr: 0.0100\n",
+ "Epoch 120/120\n",
+ "256/256 [==============================] - 70s 273ms/step - loss: 0.0559 - accuracy: 0.9863 - val_loss: 0.1774 - val_accuracy: 0.9471 - lr: 0.0100\n",
+ "\u001b[0;32mSubset training done.\u001b[0m\n",
+ "\u001b[0;33mLoading the best weights...\u001b[0m\n",
+ "\u001b[0;33mLoading weights from file cache\\model_SUB_checkpoint-119-0.9615.h5...\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9615\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.1641\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9615384340. Not saving model.\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;32mImproved model loss from 0.16713950 to 0.16408551. \u001b[0m\u001b[0;96mSaving model.\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;36mSaving full model H5 format...\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;32m(GPU-MEM)\u001b[0m\u001b[0;36m----[free: 5.19GB, used: 18.81GB, total, 24.00GB]\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m513.32 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m429.12 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m84.20 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0;36m<---------------------------------------|Epoch [20] END|--------------------------------------->\u001b[0m\n",
+ "\u001b[0m\n",
+ "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m21\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m489 (TSEC: 120)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Stage 1]\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+ "\u001b[0;33mPreparing train data...\u001b[0m\n",
+ "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n",
+ "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+ "\u001b[0;32mTraining on subset...\u001b[0m\n",
+ "Epoch 121/126\n",
+ "256/256 [==============================] - 76s 280ms/step - loss: 0.1836 - accuracy: 0.9478 - val_loss: 0.1733 - val_accuracy: 0.9439 - lr: 0.0100\n",
+ "Epoch 122/126\n",
+ "256/256 [==============================] - 70s 274ms/step - loss: 0.1378 - accuracy: 0.9592 - val_loss: 0.1685 - val_accuracy: 0.9439 - lr: 0.0100\n",
+ "Epoch 123/126\n",
+ "256/256 [==============================] - ETA: 0s - loss: 0.0880 - accuracy: 0.9766\n",
+ "Epoch 123: ReduceLROnPlateau reducing learning rate to 0.009819999780505895.\n",
+ "256/256 [==============================] - 70s 274ms/step - loss: 0.0880 - accuracy: 0.9766 - val_loss: 0.3659 - val_accuracy: 0.9022 - lr: 0.0100\n",
+ "Epoch 124/126\n",
+ "256/256 [==============================] - 70s 274ms/step - loss: 0.0708 - accuracy: 0.9817 - val_loss: 0.1868 - val_accuracy: 0.9423 - lr: 0.0098\n",
+ "Epoch 125/126\n",
+ "256/256 [==============================] - 70s 274ms/step - loss: 0.0506 - accuracy: 0.9890 - val_loss: 0.2319 - val_accuracy: 0.9407 - lr: 0.0098\n",
+ "Epoch 126/126\n",
+ "256/256 [==============================] - 70s 274ms/step - loss: 0.0529 - accuracy: 0.9868 - val_loss: 0.2361 - val_accuracy: 0.9391 - lr: 0.0098\n",
+ "\u001b[0;32mSubset training done.\u001b[0m\n",
+ "\u001b[0;33mLoading the best weights...\u001b[0m\n",
+ "\u001b[0;33mLoading weights from file cache\\model_SUB_checkpoint-121-0.9439.h5...\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9439\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.1734\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9615384340. Not saving model.\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1640855074. Not saving model.\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;32m(GPU-MEM)\u001b[0m\u001b[0;36m----[free: 5.17GB, used: 18.83GB, total, 24.00GB]\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m511.89 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m428.85 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m83.04 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0;36m<---------------------------------------|Epoch [21] END|--------------------------------------->\u001b[0m\n",
+ "\u001b[0m\n",
+ "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m22\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m489 (TSEC: 126)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Stage 1]\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+ "\u001b[0;33mPreparing train data...\u001b[0m\n",
+ "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n",
+ "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+ "\u001b[0;32mTraining on subset...\u001b[0m\n",
+ "Epoch 127/132\n",
+ "256/256 [==============================] - 76s 280ms/step - loss: 0.1919 - accuracy: 0.9417 - val_loss: 0.2013 - val_accuracy: 0.9487 - lr: 0.0098\n",
+ "Epoch 128/132\n",
+ "256/256 [==============================] - 71s 278ms/step - loss: 0.1440 - accuracy: 0.9563 - val_loss: 0.1692 - val_accuracy: 0.9503 - lr: 0.0098\n",
+ "Epoch 129/132\n",
+ "256/256 [==============================] - 70s 273ms/step - loss: 0.1061 - accuracy: 0.9709 - val_loss: 0.1920 - val_accuracy: 0.9391 - lr: 0.0098\n",
+ "Epoch 130/132\n",
+ "256/256 [==============================] - 70s 274ms/step - loss: 0.0866 - accuracy: 0.9783 - val_loss: 0.2752 - val_accuracy: 0.9167 - lr: 0.0098\n",
+ "Epoch 131/132\n",
+ "256/256 [==============================] - 70s 273ms/step - loss: 0.0707 - accuracy: 0.9834 - val_loss: 0.4520 - val_accuracy: 0.9038 - lr: 0.0098\n",
+ "Epoch 132/132\n",
+ "256/256 [==============================] - 70s 274ms/step - loss: 0.0596 - accuracy: 0.9868 - val_loss: 0.2635 - val_accuracy: 0.9359 - lr: 0.0098\n",
+ "\u001b[0;32mSubset training done.\u001b[0m\n",
+ "\u001b[0;33mLoading the best weights...\u001b[0m\n",
+ "\u001b[0;33mLoading weights from file cache\\model_SUB_checkpoint-128-0.9503.h5...\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9503\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.1692\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9615384340. Not saving model.\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1640855074. Not saving model.\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;32m(GPU-MEM)\u001b[0m\u001b[0;36m----[free: 5.18GB, used: 18.82GB, total, 24.00GB]\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m513.73 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m429.30 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m84.43 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0;36m<---------------------------------------|Epoch [22] END|--------------------------------------->\u001b[0m\n",
+ "\u001b[0m\n",
+ "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m23\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m489 (TSEC: 132)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Stage 1]\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+ "\u001b[0;33mPreparing train data...\u001b[0m\n",
+ "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n",
+ "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+ "\u001b[0;32mTraining on subset...\u001b[0m\n",
+ "Epoch 133/138\n",
+ "256/256 [==============================] - 76s 281ms/step - loss: 0.1859 - accuracy: 0.9434 - val_loss: 0.1613 - val_accuracy: 0.9519 - lr: 0.0098\n",
+ "Epoch 134/138\n",
+ "256/256 [==============================] - 70s 275ms/step - loss: 0.1337 - accuracy: 0.9622 - val_loss: 0.1738 - val_accuracy: 0.9487 - lr: 0.0098\n",
+ "Epoch 135/138\n",
+ "256/256 [==============================] - 71s 274ms/step - loss: 0.1014 - accuracy: 0.9734 - val_loss: 0.1834 - val_accuracy: 0.9375 - lr: 0.0098\n",
+ "Epoch 136/138\n",
+ "256/256 [==============================] - 71s 275ms/step - loss: 0.0863 - accuracy: 0.9761 - val_loss: 0.2131 - val_accuracy: 0.9327 - lr: 0.0098\n",
+ "Epoch 137/138\n",
+ "256/256 [==============================] - 71s 274ms/step - loss: 0.0600 - accuracy: 0.9868 - val_loss: 0.1950 - val_accuracy: 0.9391 - lr: 0.0098\n",
+ "Epoch 138/138\n",
+ "256/256 [==============================] - 71s 275ms/step - loss: 0.0604 - accuracy: 0.9858 - val_loss: 0.2959 - val_accuracy: 0.9279 - lr: 0.0098\n",
+ "\u001b[0;32mSubset training done.\u001b[0m\n",
+ "\u001b[0;33mLoading the best weights...\u001b[0m\n",
+ "\u001b[0;33mLoading weights from file cache\\model_SUB_checkpoint-133-0.9519.h5...\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9519\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.1613\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9615384340. Not saving model.\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;32mImproved model loss from 0.16408551 to 0.16128203. \u001b[0m\u001b[0;96mSaving model.\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;36mSaving full model H5 format...\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;32m(GPU-MEM)\u001b[0m\u001b[0;36m----[free: 5.18GB, used: 18.82GB, total, 24.00GB]\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m516.63 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m430.13 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m86.50 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0;36m<---------------------------------------|Epoch [23] END|--------------------------------------->\u001b[0m\n",
+ "\u001b[0m\n",
+ "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m24\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m489 (TSEC: 138)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Stage 1]\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+ "\u001b[0;33mPreparing train data...\u001b[0m\n",
+ "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n",
+ "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+ "\u001b[0;32mTraining on subset...\u001b[0m\n",
+ "Epoch 139/144\n",
+ "256/256 [==============================] - 76s 282ms/step - loss: 0.1792 - accuracy: 0.9421 - val_loss: 0.2648 - val_accuracy: 0.9167 - lr: 0.0098\n",
+ "Epoch 140/144\n",
+ "256/256 [==============================] - 71s 279ms/step - loss: 0.1274 - accuracy: 0.9597 - val_loss: 0.1855 - val_accuracy: 0.9295 - lr: 0.0098\n",
+ "Epoch 141/144\n",
+ "256/256 [==============================] - 72s 279ms/step - loss: 0.0982 - accuracy: 0.9697 - val_loss: 0.1819 - val_accuracy: 0.9439 - lr: 0.0098\n",
+ "Epoch 142/144\n",
+ "256/256 [==============================] - 71s 276ms/step - loss: 0.0807 - accuracy: 0.9780 - val_loss: 0.2425 - val_accuracy: 0.9423 - lr: 0.0098\n",
+ "Epoch 143/144\n",
+ "256/256 [==============================] - 71s 275ms/step - loss: 0.0618 - accuracy: 0.9851 - val_loss: 0.3697 - val_accuracy: 0.8878 - lr: 0.0098\n",
+ "Epoch 144/144\n",
+ "256/256 [==============================] - 71s 276ms/step - loss: 0.0544 - accuracy: 0.9866 - val_loss: 0.2781 - val_accuracy: 0.9359 - lr: 0.0098\n",
+ "\u001b[0;32mSubset training done.\u001b[0m\n",
+ "\u001b[0;33mLoading the best weights...\u001b[0m\n",
+ "\u001b[0;33mLoading weights from file cache\\model_SUB_checkpoint-141-0.9439.h5...\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9439\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.1820\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9615384340. Not saving model.\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1612820327. Not saving model.\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;32m(GPU-MEM)\u001b[0m\u001b[0;36m----[free: 5.19GB, used: 18.81GB, total, 24.00GB]\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m518.02 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m432.96 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m85.06 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0;36m<---------------------------------------|Epoch [24] END|--------------------------------------->\u001b[0m\n",
+ "\u001b[0m\n",
+ "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m25\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m489 (TSEC: 144)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Stage 1]\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+ "\u001b[0;33mPreparing train data...\u001b[0m\n",
+ "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n",
+ "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+ "\u001b[0;32mTraining on subset...\u001b[0m\n",
+ "Epoch 145/150\n",
+ "256/256 [==============================] - 76s 280ms/step - loss: 0.1798 - accuracy: 0.9436 - val_loss: 0.1756 - val_accuracy: 0.9407 - lr: 0.0098\n",
+ "Epoch 146/150\n",
+ "256/256 [==============================] - 71s 277ms/step - loss: 0.1337 - accuracy: 0.9600 - val_loss: 0.1838 - val_accuracy: 0.9615 - lr: 0.0098\n",
+ "Epoch 147/150\n",
+ "256/256 [==============================] - 70s 274ms/step - loss: 0.0957 - accuracy: 0.9753 - val_loss: 0.1846 - val_accuracy: 0.9567 - lr: 0.0098\n",
+ "Epoch 148/150\n",
+ "256/256 [==============================] - 71s 274ms/step - loss: 0.0823 - accuracy: 0.9788 - val_loss: 0.2423 - val_accuracy: 0.9439 - lr: 0.0098\n",
+ "Epoch 149/150\n",
+ "256/256 [==============================] - 70s 274ms/step - loss: 0.0595 - accuracy: 0.9846 - val_loss: 0.2196 - val_accuracy: 0.9503 - lr: 0.0098\n",
+ "Epoch 150/150\n",
+ "256/256 [==============================] - 71s 275ms/step - loss: 0.0646 - accuracy: 0.9846 - val_loss: 0.1869 - val_accuracy: 0.9391 - lr: 0.0098\n",
+ "\u001b[0;32mSubset training done.\u001b[0m\n",
+ "\u001b[0;33mLoading the best weights...\u001b[0m\n",
+ "\u001b[0;33mLoading weights from file cache\\model_SUB_checkpoint-146-0.9615.h5...\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9615\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.1839\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9615384340. Not saving model.\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1612820327. Not saving model.\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;32m(GPU-MEM)\u001b[0m\u001b[0;36m----[free: 5.19GB, used: 18.81GB, total, 24.00GB]\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m516.77 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m430.03 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m86.75 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0;36m<---------------------------------------|Epoch [25] END|--------------------------------------->\u001b[0m\n",
+ "\u001b[0m\n",
+ "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m26\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m489 (TSEC: 150)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Stage 2]\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+ "\u001b[0;33mPreparing train data...\u001b[0m\n",
+ "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n",
+ "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+ "\u001b[0;32mTraining on subset...\u001b[0m\n",
+ "Epoch 151/156\n",
+ "256/256 [==============================] - 76s 282ms/step - loss: 0.1690 - accuracy: 0.9475 - val_loss: 0.1507 - val_accuracy: 0.9567 - lr: 0.0098\n",
+ "Epoch 152/156\n",
+ "256/256 [==============================] - 70s 274ms/step - loss: 0.1301 - accuracy: 0.9624 - val_loss: 0.2251 - val_accuracy: 0.9038 - lr: 0.0098\n",
+ "Epoch 153/156\n",
+ "256/256 [==============================] - 71s 276ms/step - loss: 0.1059 - accuracy: 0.9702 - val_loss: 0.2136 - val_accuracy: 0.9183 - lr: 0.0098\n",
+ "Epoch 154/156\n",
+ "256/256 [==============================] - 71s 275ms/step - loss: 0.0606 - accuracy: 0.9868 - val_loss: 0.3920 - val_accuracy: 0.8942 - lr: 0.0098\n",
+ "Epoch 155/156\n",
+ "256/256 [==============================] - 71s 276ms/step - loss: 0.0563 - accuracy: 0.9861 - val_loss: 0.1865 - val_accuracy: 0.9423 - lr: 0.0098\n",
+ "Epoch 156/156\n",
+ "256/256 [==============================] - 70s 274ms/step - loss: 0.0489 - accuracy: 0.9871 - val_loss: 0.2106 - val_accuracy: 0.9487 - lr: 0.0098\n",
+ "\u001b[0;32mSubset training done.\u001b[0m\n",
+ "\u001b[0;33mLoading the best weights...\u001b[0m\n",
+ "\u001b[0;33mLoading weights from file cache\\model_SUB_checkpoint-151-0.9567.h5...\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9567\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.1507\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9615384340. Not saving model.\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;32mImproved model loss from 0.16128203 to 0.15071724. \u001b[0m\u001b[0;96mSaving model.\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;36mSaving full model H5 format...\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;32m(GPU-MEM)\u001b[0m\u001b[0;36m----[free: 5.19GB, used: 18.81GB, total, 24.00GB]\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m524.27 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m430.46 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m93.81 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0;36m<---------------------------------------|Epoch [26] END|--------------------------------------->\u001b[0m\n",
+ "\u001b[0m\n",
+ "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m27\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m489 (TSEC: 156)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Stage 2]\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+ "\u001b[0;33mPreparing train data...\u001b[0m\n",
+ "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n",
+ "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+ "\u001b[0;32mTraining on subset...\u001b[0m\n",
+ "Epoch 157/162\n",
+ "256/256 [==============================] - 76s 283ms/step - loss: 0.1683 - accuracy: 0.9421 - val_loss: 0.1730 - val_accuracy: 0.9423 - lr: 0.0098\n",
+ "Epoch 158/162\n",
+ "256/256 [==============================] - 71s 277ms/step - loss: 0.1310 - accuracy: 0.9622 - val_loss: 0.2105 - val_accuracy: 0.9439 - lr: 0.0098\n",
+ "Epoch 159/162\n",
+ "256/256 [==============================] - 70s 274ms/step - loss: 0.0979 - accuracy: 0.9705 - val_loss: 0.2328 - val_accuracy: 0.9183 - lr: 0.0098\n",
+ "Epoch 160/162\n",
+ "256/256 [==============================] - 70s 274ms/step - loss: 0.0757 - accuracy: 0.9795 - val_loss: 0.2100 - val_accuracy: 0.9343 - lr: 0.0098\n",
+ "Epoch 161/162\n",
+ "256/256 [==============================] - 71s 275ms/step - loss: 0.0616 - accuracy: 0.9822 - val_loss: 0.2751 - val_accuracy: 0.9423 - lr: 0.0098\n",
+ "Epoch 162/162\n",
+ "256/256 [==============================] - 71s 278ms/step - loss: 0.0492 - accuracy: 0.9849 - val_loss: 0.2698 - val_accuracy: 0.9471 - lr: 0.0098\n",
+ "\u001b[0;32mSubset training done.\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{0.9471}, \u001b[0m\u001b[0;33mloss{0.1730}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9615}, loss{0.1507}]\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9471\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.2698\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9615384340. Not saving model.\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1507172436. Not saving model.\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;32m(GPU-MEM)\u001b[0m\u001b[0;36m----[free: 5.19GB, used: 18.81GB, total, 24.00GB]\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m521.31 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m431.35 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m89.96 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0;36m<---------------------------------------|Epoch [27] END|--------------------------------------->\u001b[0m\n",
+ "\u001b[0m\n",
+ "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m28\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m489 (TSEC: 162)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Stage 2]\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+ "\u001b[0;33mPreparing train data...\u001b[0m\n",
+ "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n",
+ "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+ "\u001b[0;32mTraining on subset...\u001b[0m\n",
+ "Epoch 163/168\n",
+ "256/256 [==============================] - 77s 283ms/step - loss: 0.1891 - accuracy: 0.9412 - val_loss: 0.2367 - val_accuracy: 0.9279 - lr: 0.0098\n",
+ "Epoch 164/168\n",
+ "256/256 [==============================] - 71s 275ms/step - loss: 0.1229 - accuracy: 0.9609 - val_loss: 0.2379 - val_accuracy: 0.8814 - lr: 0.0098\n",
+ "Epoch 165/168\n",
+ "256/256 [==============================] - 72s 280ms/step - loss: 0.0927 - accuracy: 0.9749 - val_loss: 0.1685 - val_accuracy: 0.9407 - lr: 0.0098\n",
+ "Epoch 166/168\n",
+ "256/256 [==============================] - 70s 274ms/step - loss: 0.0695 - accuracy: 0.9792 - val_loss: 0.2267 - val_accuracy: 0.9071 - lr: 0.0098\n",
+ "Epoch 167/168\n",
+ "256/256 [==============================] - 72s 279ms/step - loss: 0.0488 - accuracy: 0.9885 - val_loss: 0.1868 - val_accuracy: 0.9503 - lr: 0.0098\n",
+ "Epoch 168/168\n",
+ "256/256 [==============================] - 71s 275ms/step - loss: 0.0394 - accuracy: 0.9922 - val_loss: 0.2207 - val_accuracy: 0.9439 - lr: 0.0098\n",
+ "\u001b[0;32mSubset training done.\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{0.9503}, \u001b[0m\u001b[0;33mloss{0.1685}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9615}, loss{0.1507}]\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9439\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.2207\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9615384340. Not saving model.\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1507172436. Not saving model.\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;32m(GPU-MEM)\u001b[0m\u001b[0;36m----[free: 5.18GB, used: 18.82GB, total, 24.00GB]\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m521.12 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m433.13 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m87.99 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0;36m<---------------------------------------|Epoch [28] END|--------------------------------------->\u001b[0m\n",
+ "\u001b[0m\n",
+ "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m29\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m489 (TSEC: 168)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Stage 2]\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+ "\u001b[0;33mPreparing train data...\u001b[0m\n",
+ "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n",
+ "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+ "\u001b[0;32mTraining on subset...\u001b[0m\n",
+ "Epoch 169/174\n",
+ "256/256 [==============================] - 77s 283ms/step - loss: 0.1920 - accuracy: 0.9395 - val_loss: 0.1970 - val_accuracy: 0.9183 - lr: 0.0098\n",
+ "Epoch 170/174\n",
+ "256/256 [==============================] - 71s 278ms/step - loss: 0.1269 - accuracy: 0.9636 - val_loss: 0.1754 - val_accuracy: 0.9455 - lr: 0.0098\n",
+ "Epoch 171/174\n",
+ "256/256 [==============================] - 70s 275ms/step - loss: 0.0881 - accuracy: 0.9753 - val_loss: 0.2025 - val_accuracy: 0.9423 - lr: 0.0098\n",
+ "Epoch 172/174\n",
+ "256/256 [==============================] - 70s 274ms/step - loss: 0.0649 - accuracy: 0.9812 - val_loss: 0.2053 - val_accuracy: 0.9263 - lr: 0.0098\n",
+ "Epoch 173/174\n",
+ "256/256 [==============================] - 71s 275ms/step - loss: 0.0409 - accuracy: 0.9890 - val_loss: 0.2178 - val_accuracy: 0.9327 - lr: 0.0098\n",
+ "Epoch 174/174\n",
+ "256/256 [==============================] - 70s 274ms/step - loss: 0.0601 - accuracy: 0.9810 - val_loss: 0.2450 - val_accuracy: 0.9311 - lr: 0.0098\n",
+ "\u001b[0;32mSubset training done.\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{0.9455}, \u001b[0m\u001b[0;33mloss{0.1754}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9615}, loss{0.1507}]\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9311\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.2450\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9615384340. Not saving model.\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1507172436. Not saving model.\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;32m(GPU-MEM)\u001b[0m\u001b[0;36m----[free: 5.18GB, used: 18.82GB, total, 24.00GB]\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m519.28 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m431.00 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m88.28 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0;36m<---------------------------------------|Epoch [29] END|--------------------------------------->\u001b[0m\n",
+ "\u001b[0m\n",
+ "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m30\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m489 (TSEC: 174)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Stage 2]\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+ "\u001b[0;33mPreparing train data...\u001b[0m\n",
+ "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n",
+ "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+ "\u001b[0;32mTraining on subset...\u001b[0m\n",
+ "Epoch 175/180\n",
+ "256/256 [==============================] - 77s 284ms/step - loss: 0.1802 - accuracy: 0.9473 - val_loss: 0.1820 - val_accuracy: 0.9503 - lr: 0.0098\n",
+ "Epoch 176/180\n",
+ "256/256 [==============================] - 71s 275ms/step - loss: 0.1165 - accuracy: 0.9634 - val_loss: 0.2613 - val_accuracy: 0.9263 - lr: 0.0098\n",
+ "Epoch 177/180\n",
+ "256/256 [==============================] - 71s 276ms/step - loss: 0.0847 - accuracy: 0.9751 - val_loss: 0.2826 - val_accuracy: 0.9247 - lr: 0.0098\n",
+ "Epoch 178/180\n",
+ "256/256 [==============================] - 71s 275ms/step - loss: 0.0618 - accuracy: 0.9819 - val_loss: 0.2232 - val_accuracy: 0.9295 - lr: 0.0098\n",
+ "Epoch 179/180\n",
+ "256/256 [==============================] - 71s 277ms/step - loss: 0.0464 - accuracy: 0.9858 - val_loss: 0.6330 - val_accuracy: 0.8718 - lr: 0.0098\n",
+ "Epoch 180/180\n",
+ "256/256 [==============================] - 71s 276ms/step - loss: 0.0441 - accuracy: 0.9873 - val_loss: 0.3597 - val_accuracy: 0.9247 - lr: 0.0098\n",
+ "\u001b[0;32mSubset training done.\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{0.9503}, \u001b[0m\u001b[0;33mloss{0.1820}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9615}, loss{0.1507}]\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9247\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.3597\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9615384340. Not saving model.\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1507172436. Not saving model.\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;32m(GPU-MEM)\u001b[0m\u001b[0;36m----[free: 5.19GB, used: 18.81GB, total, 24.00GB]\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m524.83 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m432.33 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m92.50 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0;36m<---------------------------------------|Epoch [30] END|--------------------------------------->\u001b[0m\n",
+ "\u001b[0m\n",
+ "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m31\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m489 (TSEC: 180)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Stage 2]\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+ "\u001b[0;33mPreparing train data...\u001b[0m\n",
+ "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n",
+ "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+ "\u001b[0;32mTraining on subset...\u001b[0m\n",
+ "Epoch 181/186\n",
+ "256/256 [==============================] - 77s 283ms/step - loss: 0.1875 - accuracy: 0.9390 - val_loss: 0.3153 - val_accuracy: 0.8798 - lr: 0.0098\n",
+ "Epoch 182/186\n",
+ "256/256 [==============================] - 72s 279ms/step - loss: 0.1251 - accuracy: 0.9646 - val_loss: 0.2459 - val_accuracy: 0.9215 - lr: 0.0098\n",
+ "Epoch 183/186\n",
+ "256/256 [==============================] - 72s 279ms/step - loss: 0.0922 - accuracy: 0.9729 - val_loss: 0.1697 - val_accuracy: 0.9439 - lr: 0.0098\n",
+ "Epoch 184/186\n",
+ "256/256 [==============================] - 71s 275ms/step - loss: 0.0712 - accuracy: 0.9814 - val_loss: 0.4005 - val_accuracy: 0.8926 - lr: 0.0098\n",
+ "Epoch 185/186\n",
+ "256/256 [==============================] - 71s 276ms/step - loss: 0.0475 - accuracy: 0.9875 - val_loss: 0.2829 - val_accuracy: 0.9311 - lr: 0.0098\n",
+ "Epoch 186/186\n",
+ "256/256 [==============================] - 71s 276ms/step - loss: 0.0462 - accuracy: 0.9895 - val_loss: 0.2588 - val_accuracy: 0.9231 - lr: 0.0098\n",
+ "\u001b[0;32mSubset training done.\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{0.9439}, \u001b[0m\u001b[0;33mloss{0.1697}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9615}, loss{0.1507}]\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9231\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.2588\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9615384340. Not saving model.\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1507172436. Not saving model.\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;32m(GPU-MEM)\u001b[0m\u001b[0;36m----[free: 5.19GB, used: 18.81GB, total, 24.00GB]\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m527.27 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m433.39 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m93.88 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0;36m<---------------------------------------|Epoch [31] END|--------------------------------------->\u001b[0m\n",
+ "\u001b[0m\n",
+ "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m32\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m489 (TSEC: 186)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Stage 2]\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+ "\u001b[0;33mPreparing train data...\u001b[0m\n",
+ "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n",
+ "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+ "\u001b[0;32mTraining on subset...\u001b[0m\n",
+ "Epoch 187/192\n",
+ "256/256 [==============================] - 78s 286ms/step - loss: 0.1648 - accuracy: 0.9492 - val_loss: 0.2811 - val_accuracy: 0.9087 - lr: 0.0098\n",
+ "Epoch 188/192\n",
+ "256/256 [==============================] - ETA: 0s - loss: 0.1060 - accuracy: 0.9678\n",
+ "Epoch 188: ReduceLROnPlateau reducing learning rate to 0.009643239349126816.\n",
+ "256/256 [==============================] - 72s 279ms/step - loss: 0.1060 - accuracy: 0.9678 - val_loss: 0.2874 - val_accuracy: 0.9135 - lr: 0.0098\n",
+ "Epoch 189/192\n",
+ "256/256 [==============================] - 72s 279ms/step - loss: 0.0762 - accuracy: 0.9807 - val_loss: 0.1937 - val_accuracy: 0.9343 - lr: 0.0096\n",
+ "Epoch 190/192\n",
+ "256/256 [==============================] - 71s 276ms/step - loss: 0.0565 - accuracy: 0.9854 - val_loss: 0.3148 - val_accuracy: 0.9263 - lr: 0.0096\n",
+ "Epoch 191/192\n",
+ "256/256 [==============================] - 71s 276ms/step - loss: 0.0482 - accuracy: 0.9880 - val_loss: 0.2285 - val_accuracy: 0.9006 - lr: 0.0096\n",
+ "Epoch 192/192\n",
+ "256/256 [==============================] - 72s 279ms/step - loss: 0.0387 - accuracy: 0.9895 - val_loss: 0.2529 - val_accuracy: 0.9375 - lr: 0.0096\n",
+ "\u001b[0;32mSubset training done.\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{0.9375}, \u001b[0m\u001b[0;33mloss{0.1937}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9615}, loss{0.1507}]\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9375\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.2529\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9615384340. Not saving model.\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1507172436. Not saving model.\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;32m(GPU-MEM)\u001b[0m\u001b[0;36m----[free: 5.18GB, used: 18.82GB, total, 24.00GB]\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m528.81 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m435.34 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m93.47 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0;36m<---------------------------------------|Epoch [32] END|--------------------------------------->\u001b[0m\n",
+ "\u001b[0m\n",
+ "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m33\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m489 (TSEC: 192)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Stage 2]\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+ "\u001b[0;33mPreparing train data...\u001b[0m\n",
+ "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n",
+ "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+ "\u001b[0;32mTraining on subset...\u001b[0m\n",
+ "Epoch 193/198\n",
+ "256/256 [==============================] - 77s 284ms/step - loss: 0.1693 - accuracy: 0.9453 - val_loss: 0.1848 - val_accuracy: 0.9343 - lr: 0.0096\n",
+ "Epoch 194/198\n",
+ "256/256 [==============================] - 71s 275ms/step - loss: 0.1097 - accuracy: 0.9668 - val_loss: 0.1614 - val_accuracy: 0.9343 - lr: 0.0096\n",
+ "Epoch 195/198\n",
+ "256/256 [==============================] - 71s 277ms/step - loss: 0.0705 - accuracy: 0.9771 - val_loss: 0.2779 - val_accuracy: 0.9247 - lr: 0.0096\n",
+ "Epoch 196/198\n",
+ "256/256 [==============================] - 72s 280ms/step - loss: 0.0583 - accuracy: 0.9824 - val_loss: 0.2878 - val_accuracy: 0.9407 - lr: 0.0096\n",
+ "Epoch 197/198\n",
+ "256/256 [==============================] - 71s 277ms/step - loss: 0.0482 - accuracy: 0.9868 - val_loss: 0.3605 - val_accuracy: 0.9247 - lr: 0.0096\n",
+ "Epoch 198/198\n",
+ "256/256 [==============================] - 71s 276ms/step - loss: 0.0361 - accuracy: 0.9885 - val_loss: 0.2136 - val_accuracy: 0.9391 - lr: 0.0096\n",
+ "\u001b[0;32mSubset training done.\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{0.9407}, \u001b[0m\u001b[0;33mloss{0.1614}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9615}, loss{0.1507}]\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9391\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.2137\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9615384340. Not saving model.\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1507172436. Not saving model.\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;32m(GPU-MEM)\u001b[0m\u001b[0;36m----[free: 5.18GB, used: 18.82GB, total, 24.00GB]\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m528.40 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m433.79 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m94.62 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0;36m<---------------------------------------|Epoch [33] END|--------------------------------------->\u001b[0m\n",
+ "\u001b[0m\n",
+ "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m34\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m489 (TSEC: 198)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Stage 2]\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+ "\u001b[0;33mPreparing train data...\u001b[0m\n",
+ "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n",
+ "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+ "\u001b[0;32mTraining on subset...\u001b[0m\n",
+ "Epoch 199/204\n",
+ "256/256 [==============================] - 77s 285ms/step - loss: 0.1925 - accuracy: 0.9436 - val_loss: 0.1574 - val_accuracy: 0.9519 - lr: 0.0096\n",
+ "Epoch 200/204\n",
+ "256/256 [==============================] - 71s 276ms/step - loss: 0.1273 - accuracy: 0.9609 - val_loss: 0.2147 - val_accuracy: 0.9407 - lr: 0.0096\n",
+ "Epoch 201/204\n",
+ "256/256 [==============================] - 71s 276ms/step - loss: 0.0876 - accuracy: 0.9717 - val_loss: 0.2848 - val_accuracy: 0.9022 - lr: 0.0096\n",
+ "Epoch 202/204\n",
+ "256/256 [==============================] - 71s 276ms/step - loss: 0.0617 - accuracy: 0.9822 - val_loss: 0.2692 - val_accuracy: 0.9327 - lr: 0.0096\n",
+ "Epoch 203/204\n",
+ "256/256 [==============================] - 71s 276ms/step - loss: 0.0496 - accuracy: 0.9861 - val_loss: 0.1747 - val_accuracy: 0.9503 - lr: 0.0096\n",
+ "Epoch 204/204\n",
+ "256/256 [==============================] - 71s 276ms/step - loss: 0.0379 - accuracy: 0.9900 - val_loss: 0.2128 - val_accuracy: 0.9407 - lr: 0.0096\n",
+ "\u001b[0;32mSubset training done.\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{0.9519}, \u001b[0m\u001b[0;33mloss{0.1574}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9615}, loss{0.1507}]\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9407\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.2128\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9615384340. Not saving model.\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1507172436. Not saving model.\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;32m(GPU-MEM)\u001b[0m\u001b[0;36m----[free: 5.19GB, used: 18.81GB, total, 24.00GB]\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m527.84 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m432.60 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m95.24 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0;36m<---------------------------------------|Epoch [34] END|--------------------------------------->\u001b[0m\n",
+ "\u001b[0m\n",
+ "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m35\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m489 (TSEC: 204)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Stage 2]\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+ "\u001b[0;33mPreparing train data...\u001b[0m\n",
+ "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n",
+ "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+ "\u001b[0;32mTraining on subset...\u001b[0m\n",
+ "Epoch 205/210\n",
+ "256/256 [==============================] - 77s 285ms/step - loss: 0.1705 - accuracy: 0.9463 - val_loss: 0.3243 - val_accuracy: 0.9103 - lr: 0.0096\n",
+ "Epoch 206/210\n",
+ "256/256 [==============================] - 72s 279ms/step - loss: 0.1079 - accuracy: 0.9634 - val_loss: 0.2504 - val_accuracy: 0.9359 - lr: 0.0096\n",
+ "Epoch 207/210\n",
+ "256/256 [==============================] - 71s 276ms/step - loss: 0.0722 - accuracy: 0.9766 - val_loss: 0.3823 - val_accuracy: 0.9119 - lr: 0.0096\n",
+ "Epoch 208/210\n",
+ "256/256 [==============================] - 72s 280ms/step - loss: 0.0482 - accuracy: 0.9863 - val_loss: 0.2689 - val_accuracy: 0.9375 - lr: 0.0096\n",
+ "Epoch 209/210\n",
+ "256/256 [==============================] - 72s 281ms/step - loss: 0.0357 - accuracy: 0.9900 - val_loss: 0.2405 - val_accuracy: 0.9423 - lr: 0.0096\n",
+ "Epoch 210/210\n",
+ "256/256 [==============================] - 71s 276ms/step - loss: 0.0374 - accuracy: 0.9885 - val_loss: 0.3618 - val_accuracy: 0.9423 - lr: 0.0096\n",
+ "\u001b[0;32mSubset training done.\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{0.9423}, \u001b[0m\u001b[0;33mloss{0.2405}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9615}, loss{0.1507}]\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9423\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.3618\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9615384340. Not saving model.\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1507172436. Not saving model.\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;32m(GPU-MEM)\u001b[0m\u001b[0;36m----[free: 5.19GB, used: 18.81GB, total, 24.00GB]\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m530.09 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m435.61 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m94.48 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0;36m<---------------------------------------|Epoch [35] END|--------------------------------------->\u001b[0m\n",
+ "\u001b[0m\n",
+ "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m36\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m489 (TSEC: 210)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Stage 2]\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+ "\u001b[0;33mPreparing train data...\u001b[0m\n",
+ "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n",
+ "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+ "\u001b[0;32mTraining on subset...\u001b[0m\n",
+ "Epoch 211/216\n",
+ "256/256 [==============================] - 77s 285ms/step - loss: 0.1826 - accuracy: 0.9414 - val_loss: 0.2124 - val_accuracy: 0.9359 - lr: 0.0096\n",
+ "Epoch 212/216\n",
+ "256/256 [==============================] - 71s 276ms/step - loss: 0.1050 - accuracy: 0.9666 - val_loss: 0.2501 - val_accuracy: 0.9327 - lr: 0.0096\n",
+ "Epoch 213/216\n",
+ "256/256 [==============================] - 72s 282ms/step - loss: 0.0695 - accuracy: 0.9780 - val_loss: 0.2009 - val_accuracy: 0.9423 - lr: 0.0096\n",
+ "Epoch 214/216\n",
+ "256/256 [==============================] - 71s 277ms/step - loss: 0.0432 - accuracy: 0.9868 - val_loss: 0.1850 - val_accuracy: 0.9423 - lr: 0.0096\n",
+ "Epoch 215/216\n",
+ "256/256 [==============================] - 72s 280ms/step - loss: 0.0376 - accuracy: 0.9890 - val_loss: 0.1988 - val_accuracy: 0.9535 - lr: 0.0096\n",
+ "Epoch 216/216\n",
+ "256/256 [==============================] - 71s 276ms/step - loss: 0.0287 - accuracy: 0.9939 - val_loss: 0.2456 - val_accuracy: 0.9439 - lr: 0.0096\n",
+ "\u001b[0;32mSubset training done.\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{0.9535}, \u001b[0m\u001b[0;33mloss{0.1850}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9615}, loss{0.1507}]\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9439\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.2456\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9615384340. Not saving model.\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1507172436. Not saving model.\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;32m(GPU-MEM)\u001b[0m\u001b[0;36m----[free: 5.19GB, used: 18.81GB, total, 24.00GB]\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m533.03 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m435.18 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m97.85 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0;36m<---------------------------------------|Epoch [36] END|--------------------------------------->\u001b[0m\n",
+ "\u001b[0m\n",
+ "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m37\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m489 (TSEC: 216)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Stage 2]\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+ "\u001b[0;33mPreparing train data...\u001b[0m\n",
+ "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n",
+ "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+ "\u001b[0;32mTraining on subset...\u001b[0m\n",
+ "Epoch 217/222\n",
+ "256/256 [==============================] - 77s 284ms/step - loss: 0.1854 - accuracy: 0.9436 - val_loss: 0.1820 - val_accuracy: 0.9423 - lr: 0.0096\n",
+ "Epoch 218/222\n",
+ "256/256 [==============================] - 71s 277ms/step - loss: 0.1210 - accuracy: 0.9619 - val_loss: 0.1830 - val_accuracy: 0.9407 - lr: 0.0096\n",
+ "Epoch 219/222\n",
+ "256/256 [==============================] - 71s 277ms/step - loss: 0.0846 - accuracy: 0.9763 - val_loss: 0.5565 - val_accuracy: 0.8590 - lr: 0.0096\n",
+ "Epoch 220/222\n",
+ "256/256 [==============================] - 71s 277ms/step - loss: 0.0700 - accuracy: 0.9812 - val_loss: 0.2388 - val_accuracy: 0.9423 - lr: 0.0096\n",
+ "Epoch 221/222\n",
+ "256/256 [==============================] - 71s 276ms/step - loss: 0.0406 - accuracy: 0.9895 - val_loss: 0.2682 - val_accuracy: 0.9343 - lr: 0.0096\n",
+ "Epoch 222/222\n",
+ "256/256 [==============================] - 71s 276ms/step - loss: 0.0396 - accuracy: 0.9912 - val_loss: 0.2547 - val_accuracy: 0.9423 - lr: 0.0096\n",
+ "\u001b[0;32mSubset training done.\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{0.9423}, \u001b[0m\u001b[0;33mloss{0.1820}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9615}, loss{0.1507}]\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9423\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.2547\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9615384340. Not saving model.\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1507172436. Not saving model.\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;32m(GPU-MEM)\u001b[0m\u001b[0;36m----[free: 5.18GB, used: 18.82GB, total, 24.00GB]\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m530.61 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m433.35 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m97.26 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0;36m<---------------------------------------|Epoch [37] END|--------------------------------------->\u001b[0m\n",
+ "\u001b[0m\n",
+ "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m38\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m489 (TSEC: 222)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Stage 2]\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+ "\u001b[0;33mPreparing train data...\u001b[0m\n",
+ "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n",
+ "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+ "\u001b[0;32mTraining on subset...\u001b[0m\n",
+ "Epoch 223/228\n",
+ "256/256 [==============================] - 77s 285ms/step - loss: 0.1648 - accuracy: 0.9492 - val_loss: 0.1929 - val_accuracy: 0.9359 - lr: 0.0096\n",
+ "Epoch 224/228\n",
+ "256/256 [==============================] - 72s 281ms/step - loss: 0.1032 - accuracy: 0.9680 - val_loss: 0.2054 - val_accuracy: 0.9391 - lr: 0.0096\n",
+ "Epoch 225/228\n",
+ "256/256 [==============================] - 71s 276ms/step - loss: 0.0676 - accuracy: 0.9827 - val_loss: 0.2465 - val_accuracy: 0.9327 - lr: 0.0096\n",
+ "Epoch 226/228\n",
+ "256/256 [==============================] - 72s 279ms/step - loss: 0.0484 - accuracy: 0.9873 - val_loss: 0.2819 - val_accuracy: 0.9407 - lr: 0.0096\n",
+ "Epoch 227/228\n",
+ "256/256 [==============================] - 71s 277ms/step - loss: 0.0391 - accuracy: 0.9883 - val_loss: 0.3457 - val_accuracy: 0.9343 - lr: 0.0096\n",
+ "Epoch 228/228\n",
+ "256/256 [==============================] - 71s 278ms/step - loss: 0.0236 - accuracy: 0.9941 - val_loss: 0.3316 - val_accuracy: 0.9263 - lr: 0.0096\n",
+ "\u001b[0;32mSubset training done.\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{0.9407}, \u001b[0m\u001b[0;33mloss{0.1929}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9615}, loss{0.1507}]\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9263\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.3316\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9615384340. Not saving model.\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1507172436. Not saving model.\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;32m(GPU-MEM)\u001b[0m\u001b[0;36m----[free: 5.19GB, used: 18.81GB, total, 24.00GB]\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m534.80 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m435.83 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m98.97 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0;36m<---------------------------------------|Epoch [38] END|--------------------------------------->\u001b[0m\n",
+ "\u001b[0m\n",
+ "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m39\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m489 (TSEC: 228)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Stage 2]\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+ "\u001b[0;33mPreparing train data...\u001b[0m\n",
+ "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n",
+ "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+ "\u001b[0;32mTraining on subset...\u001b[0m\n",
+ "Epoch 229/234\n",
+ "256/256 [==============================] - 77s 285ms/step - loss: 0.1775 - accuracy: 0.9451 - val_loss: 0.1967 - val_accuracy: 0.9231 - lr: 0.0096\n",
+ "Epoch 230/234\n",
+ "256/256 [==============================] - 72s 280ms/step - loss: 0.1063 - accuracy: 0.9644 - val_loss: 0.2430 - val_accuracy: 0.9343 - lr: 0.0096\n",
+ "Epoch 231/234\n",
+ "256/256 [==============================] - 71s 277ms/step - loss: 0.0785 - accuracy: 0.9775 - val_loss: 0.1958 - val_accuracy: 0.9279 - lr: 0.0096\n",
+ "Epoch 232/234\n",
+ "256/256 [==============================] - 71s 276ms/step - loss: 0.0495 - accuracy: 0.9863 - val_loss: 0.3232 - val_accuracy: 0.9311 - lr: 0.0096\n",
+ "Epoch 233/234\n",
+ "256/256 [==============================] - 72s 281ms/step - loss: 0.0378 - accuracy: 0.9888 - val_loss: 0.2346 - val_accuracy: 0.9439 - lr: 0.0096\n",
+ "Epoch 234/234\n",
+ "256/256 [==============================] - 71s 277ms/step - loss: 0.0411 - accuracy: 0.9888 - val_loss: 0.4498 - val_accuracy: 0.8926 - lr: 0.0096\n",
+ "\u001b[0;32mSubset training done.\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{0.9439}, \u001b[0m\u001b[0;33mloss{0.1958}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9615}, loss{0.1507}]\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.8926\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.4497\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9615384340. Not saving model.\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1507172436. Not saving model.\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;32m(GPU-MEM)\u001b[0m\u001b[0;36m----[free: 5.17GB, used: 18.83GB, total, 24.00GB]\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m534.70 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m435.67 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m99.02 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0;36m<---------------------------------------|Epoch [39] END|--------------------------------------->\u001b[0m\n",
+ "\u001b[0m\n",
+ "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m40\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m489 (TSEC: 234)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Stage 2]\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+ "\u001b[0;33mPreparing train data...\u001b[0m\n",
+ "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n",
+ "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+ "\u001b[0;32mTraining on subset...\u001b[0m\n",
+ "Epoch 235/240\n",
+ "256/256 [==============================] - 77s 285ms/step - loss: 0.1779 - accuracy: 0.9412 - val_loss: 0.2640 - val_accuracy: 0.9022 - lr: 0.0096\n",
+ "Epoch 236/240\n",
+ "256/256 [==============================] - 72s 280ms/step - loss: 0.1238 - accuracy: 0.9592 - val_loss: 0.2169 - val_accuracy: 0.9391 - lr: 0.0096\n",
+ "Epoch 237/240\n",
+ "256/256 [==============================] - 71s 278ms/step - loss: 0.0842 - accuracy: 0.9756 - val_loss: 0.4989 - val_accuracy: 0.9071 - lr: 0.0096\n",
+ "Epoch 238/240\n",
+ "256/256 [==============================] - 71s 278ms/step - loss: 0.0577 - accuracy: 0.9854 - val_loss: 0.4745 - val_accuracy: 0.8926 - lr: 0.0096\n",
+ "Epoch 239/240\n",
+ "256/256 [==============================] - 71s 277ms/step - loss: 0.0469 - accuracy: 0.9866 - val_loss: 0.2976 - val_accuracy: 0.9279 - lr: 0.0096\n",
+ "Epoch 240/240\n",
+ "256/256 [==============================] - 71s 277ms/step - loss: 0.0304 - accuracy: 0.9937 - val_loss: 0.4848 - val_accuracy: 0.9103 - lr: 0.0096\n",
+ "\u001b[0;32mSubset training done.\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{0.9391}, \u001b[0m\u001b[0;33mloss{0.2169}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9615}, loss{0.1507}]\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9103\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.4847\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9615384340. Not saving model.\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1507172436. Not saving model.\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;32m(GPU-MEM)\u001b[0m\u001b[0;36m----[free: 5.18GB, used: 18.82GB, total, 24.00GB]\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m536.43 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m435.41 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m101.02 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0;36m<---------------------------------------|Epoch [40] END|--------------------------------------->\u001b[0m\n",
+ "\u001b[0m\n",
+ "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m41\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m489 (TSEC: 240)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Stage 2]\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+ "\u001b[0;33mPreparing train data...\u001b[0m\n",
+ "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n",
+ "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+ "\u001b[0;32mTraining on subset...\u001b[0m\n",
+ "Epoch 241/246\n",
+ "256/256 [==============================] - 77s 286ms/step - loss: 0.1722 - accuracy: 0.9431 - val_loss: 0.3465 - val_accuracy: 0.9022 - lr: 0.0096\n",
+ "Epoch 242/246\n",
+ "256/256 [==============================] - 72s 279ms/step - loss: 0.1058 - accuracy: 0.9678 - val_loss: 0.2628 - val_accuracy: 0.9295 - lr: 0.0096\n",
+ "Epoch 243/246\n",
+ "256/256 [==============================] - 71s 275ms/step - loss: 0.0724 - accuracy: 0.9773 - val_loss: 0.2259 - val_accuracy: 0.9263 - lr: 0.0096\n",
+ "Epoch 244/246\n",
+ "256/256 [==============================] - 71s 276ms/step - loss: 0.0526 - accuracy: 0.9856 - val_loss: 0.4048 - val_accuracy: 0.9071 - lr: 0.0096\n",
+ "Epoch 245/246\n",
+ "256/256 [==============================] - 71s 275ms/step - loss: 0.0383 - accuracy: 0.9893 - val_loss: 0.3190 - val_accuracy: 0.9231 - lr: 0.0096\n",
+ "Epoch 246/246\n",
+ "256/256 [==============================] - 71s 276ms/step - loss: 0.0287 - accuracy: 0.9922 - val_loss: 0.5040 - val_accuracy: 0.9215 - lr: 0.0096\n",
+ "\u001b[0;32mSubset training done.\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{0.9295}, \u001b[0m\u001b[0;33mloss{0.2259}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9615}, loss{0.1507}]\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9215\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.5041\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9615384340. Not saving model.\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1507172436. Not saving model.\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;32m(GPU-MEM)\u001b[0m\u001b[0;36m----[free: 5.17GB, used: 18.82GB, total, 24.00GB]\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m532.98 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m432.80 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m100.18 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0;36m<---------------------------------------|Epoch [41] END|--------------------------------------->\u001b[0m\n",
+ "\u001b[0m\n",
+ "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m42\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m489 (TSEC: 246)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Stage 2]\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+ "\u001b[0;33m└───Shuffling data...\u001b[0m\n",
+ "\u001b[0;33mPreparing train data...\u001b[0m\n",
+ "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n",
+ "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;31m- Debug DP Sample dir: \u001b[0m\u001b[0;32mSamples/TSR_SUB_400_y2024_m03_d30-h02_m38_s03\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+ "\u001b[0;32mTraining on subset...\u001b[0m\n",
+ "Epoch 247/252\n",
+ "256/256 [==============================] - 77s 285ms/step - loss: 0.1686 - accuracy: 0.9458 - val_loss: 0.2969 - val_accuracy: 0.9295 - lr: 0.0096\n",
+ "Epoch 248/252\n",
+ "256/256 [==============================] - 72s 281ms/step - loss: 0.0966 - accuracy: 0.9727 - val_loss: 0.2599 - val_accuracy: 0.9423 - lr: 0.0096\n",
+ "Epoch 249/252\n",
+ "256/256 [==============================] - 71s 278ms/step - loss: 0.0879 - accuracy: 0.9775 - val_loss: 0.2880 - val_accuracy: 0.9199 - lr: 0.0096\n",
+ "Epoch 250/252\n",
+ "256/256 [==============================] - 71s 277ms/step - loss: 0.0486 - accuracy: 0.9863 - val_loss: 0.2426 - val_accuracy: 0.9391 - lr: 0.0096\n",
+ "Epoch 251/252\n",
+ "256/256 [==============================] - 71s 277ms/step - loss: 0.0358 - accuracy: 0.9917 - val_loss: 0.2913 - val_accuracy: 0.9263 - lr: 0.0096\n",
+ "Epoch 252/252\n",
+ "256/256 [==============================] - 71s 277ms/step - loss: 0.0311 - accuracy: 0.9915 - val_loss: 0.2226 - val_accuracy: 0.9407 - lr: 0.0096\n",
+ "\u001b[0;32mSubset training done.\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{0.9423}, \u001b[0m\u001b[0;33mloss{0.2226}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9615}, loss{0.1507}]\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9407\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.2226\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9615384340. Not saving model.\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1507172436. Not saving model.\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;32m(GPU-MEM)\u001b[0m\u001b[0;36m----[free: 5.18GB, used: 18.82GB, total, 24.00GB]\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m550.51 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m435.23 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m115.28 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0;36m<---------------------------------------|Epoch [42] END|--------------------------------------->\u001b[0m\n",
+ "\u001b[0m\n",
+ "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m43\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m489 (TSEC: 252)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Stage 2]\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+ "\u001b[0;33mPreparing train data...\u001b[0m\n",
+ "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n",
+ "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+ "\u001b[0;32mTraining on subset...\u001b[0m\n",
+ "Epoch 253/258\n",
+ "256/256 [==============================] - ETA: 0s - loss: 0.1562 - accuracy: 0.9495\n",
+ "Epoch 253: ReduceLROnPlateau reducing learning rate to 0.009469660667702556.\n",
+ "256/256 [==============================] - 78s 287ms/step - loss: 0.1562 - accuracy: 0.9495 - val_loss: 0.1962 - val_accuracy: 0.9279 - lr: 0.0096\n",
+ "Epoch 254/258\n",
+ "256/256 [==============================] - 71s 277ms/step - loss: 0.0941 - accuracy: 0.9719 - val_loss: 0.2699 - val_accuracy: 0.9103 - lr: 0.0095\n",
+ "Epoch 255/258\n",
+ "256/256 [==============================] - 71s 277ms/step - loss: 0.0692 - accuracy: 0.9788 - val_loss: 0.3234 - val_accuracy: 0.9103 - lr: 0.0095\n",
+ "Epoch 256/258\n",
+ "256/256 [==============================] - 71s 277ms/step - loss: 0.0420 - accuracy: 0.9895 - val_loss: 0.3635 - val_accuracy: 0.9151 - lr: 0.0095\n",
+ "Epoch 257/258\n",
+ "256/256 [==============================] - 72s 281ms/step - loss: 0.0337 - accuracy: 0.9910 - val_loss: 0.3884 - val_accuracy: 0.9295 - lr: 0.0095\n",
+ "Epoch 258/258\n",
+ "256/256 [==============================] - 71s 277ms/step - loss: 0.0294 - accuracy: 0.9919 - val_loss: 0.4633 - val_accuracy: 0.9167 - lr: 0.0095\n",
+ "\u001b[0;32mSubset training done.\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{0.9295}, \u001b[0m\u001b[0;33mloss{0.1962}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9615}, loss{0.1507}]\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9167\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.4633\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9615384340. Not saving model.\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1507172436. Not saving model.\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;32m(GPU-MEM)\u001b[0m\u001b[0;36m----[free: 5.17GB, used: 18.83GB, total, 24.00GB]\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m537.96 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m435.55 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m102.41 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0;36m<---------------------------------------|Epoch [43] END|--------------------------------------->\u001b[0m\n",
+ "\u001b[0m\n",
+ "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m44\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m489 (TSEC: 258)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Stage 2]\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+ "\u001b[0;33mPreparing train data...\u001b[0m\n",
+ "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n",
+ "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+ "\u001b[0;32mTraining on subset...\u001b[0m\n",
+ "Epoch 259/264\n",
+ "256/256 [==============================] - 78s 286ms/step - loss: 0.1649 - accuracy: 0.9478 - val_loss: 0.2313 - val_accuracy: 0.9343 - lr: 0.0095\n",
+ "Epoch 260/264\n",
+ "256/256 [==============================] - 72s 280ms/step - loss: 0.1053 - accuracy: 0.9668 - val_loss: 0.1970 - val_accuracy: 0.9391 - lr: 0.0095\n",
+ "Epoch 261/264\n",
+ "256/256 [==============================] - 71s 277ms/step - loss: 0.0735 - accuracy: 0.9790 - val_loss: 0.2894 - val_accuracy: 0.9103 - lr: 0.0095\n",
+ "Epoch 262/264\n",
+ "256/256 [==============================] - 71s 276ms/step - loss: 0.0540 - accuracy: 0.9861 - val_loss: 0.3597 - val_accuracy: 0.9215 - lr: 0.0095\n",
+ "Epoch 263/264\n",
+ "256/256 [==============================] - 71s 278ms/step - loss: 0.0364 - accuracy: 0.9922 - val_loss: 0.2845 - val_accuracy: 0.9343 - lr: 0.0095\n",
+ "Epoch 264/264\n",
+ "256/256 [==============================] - 71s 277ms/step - loss: 0.0347 - accuracy: 0.9912 - val_loss: 0.3006 - val_accuracy: 0.9327 - lr: 0.0095\n",
+ "\u001b[0;32mSubset training done.\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{0.9391}, \u001b[0m\u001b[0;33mloss{0.1970}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9615}, loss{0.1507}]\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9327\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.3006\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9615384340. Not saving model.\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1507172436. Not saving model.\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;32m(GPU-MEM)\u001b[0m\u001b[0;36m----[free: 5.18GB, used: 18.82GB, total, 24.00GB]\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m539.13 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m435.24 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m103.88 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0;36m<---------------------------------------|Epoch [44] END|--------------------------------------->\u001b[0m\n",
+ "\u001b[0m\n",
+ "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m45\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m489 (TSEC: 264)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Stage 2]\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+ "\u001b[0;33mPreparing train data...\u001b[0m\n",
+ "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n",
+ "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+ "\u001b[0;32mTraining on subset...\u001b[0m\n",
+ "Epoch 265/270\n",
+ "256/256 [==============================] - 78s 286ms/step - loss: 0.1549 - accuracy: 0.9541 - val_loss: 0.3054 - val_accuracy: 0.9295 - lr: 0.0095\n",
+ "Epoch 266/270\n",
+ "256/256 [==============================] - 72s 281ms/step - loss: 0.0943 - accuracy: 0.9709 - val_loss: 0.2693 - val_accuracy: 0.9327 - lr: 0.0095\n",
+ "Epoch 267/270\n",
+ "256/256 [==============================] - 72s 281ms/step - loss: 0.0682 - accuracy: 0.9802 - val_loss: 0.2379 - val_accuracy: 0.9343 - lr: 0.0095\n",
+ "Epoch 268/270\n",
+ "256/256 [==============================] - 72s 280ms/step - loss: 0.0489 - accuracy: 0.9868 - val_loss: 0.1810 - val_accuracy: 0.9423 - lr: 0.0095\n",
+ "Epoch 269/270\n",
+ "256/256 [==============================] - 71s 278ms/step - loss: 0.0295 - accuracy: 0.9917 - val_loss: 0.4588 - val_accuracy: 0.9006 - lr: 0.0095\n",
+ "Epoch 270/270\n",
+ "256/256 [==============================] - 71s 277ms/step - loss: 0.0214 - accuracy: 0.9951 - val_loss: 0.4001 - val_accuracy: 0.9119 - lr: 0.0095\n",
+ "\u001b[0;32mSubset training done.\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{0.9423}, \u001b[0m\u001b[0;33mloss{0.1810}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9615}, loss{0.1507}]\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9119\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.4001\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9615384340. Not saving model.\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1507172436. Not saving model.\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;32m(GPU-MEM)\u001b[0m\u001b[0;36m----[free: 5.18GB, used: 18.82GB, total, 24.00GB]\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m543.38 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m437.16 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m106.22 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0;36m<---------------------------------------|Epoch [45] END|--------------------------------------->\u001b[0m\n",
+ "\u001b[0m\n",
+ "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m46\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m489 (TSEC: 270)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Stage 2]\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+ "\u001b[0;33mPreparing train data...\u001b[0m\n",
+ "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n",
+ "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+ "\u001b[0;32mTraining on subset...\u001b[0m\n",
+ "Epoch 271/276\n",
+ "256/256 [==============================] - 78s 286ms/step - loss: 0.1875 - accuracy: 0.9353 - val_loss: 0.1975 - val_accuracy: 0.9423 - lr: 0.0095\n",
+ "Epoch 272/276\n",
+ "256/256 [==============================] - 71s 277ms/step - loss: 0.1151 - accuracy: 0.9612 - val_loss: 0.2110 - val_accuracy: 0.9327 - lr: 0.0095\n",
+ "Epoch 273/276\n",
+ "256/256 [==============================] - 71s 278ms/step - loss: 0.0753 - accuracy: 0.9773 - val_loss: 0.1985 - val_accuracy: 0.9423 - lr: 0.0095\n",
+ "Epoch 274/276\n",
+ "256/256 [==============================] - 71s 277ms/step - loss: 0.0634 - accuracy: 0.9802 - val_loss: 0.2084 - val_accuracy: 0.9343 - lr: 0.0095\n",
+ "Epoch 275/276\n",
+ "256/256 [==============================] - 71s 279ms/step - loss: 0.0407 - accuracy: 0.9868 - val_loss: 0.2229 - val_accuracy: 0.9407 - lr: 0.0095\n",
+ "Epoch 276/276\n",
+ "256/256 [==============================] - 72s 278ms/step - loss: 0.0294 - accuracy: 0.9922 - val_loss: 0.2511 - val_accuracy: 0.9423 - lr: 0.0095\n",
+ "\u001b[0;32mSubset training done.\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{0.9423}, \u001b[0m\u001b[0;33mloss{0.1975}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9615}, loss{0.1507}]\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9423\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.2511\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9615384340. Not saving model.\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1507172436. Not saving model.\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;32m(GPU-MEM)\u001b[0m\u001b[0;36m----[free: 5.18GB, used: 18.82GB, total, 24.00GB]\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m539.92 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m435.51 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m104.41 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0;36m<---------------------------------------|Epoch [46] END|--------------------------------------->\u001b[0m\n",
+ "\u001b[0m\n",
+ "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m47\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m489 (TSEC: 276)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Stage 2]\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+ "\u001b[0;33mPreparing train data...\u001b[0m\n",
+ "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n",
+ "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+ "\u001b[0;32mTraining on subset...\u001b[0m\n",
+ "Epoch 277/282\n",
+ "256/256 [==============================] - 77s 285ms/step - loss: 0.1688 - accuracy: 0.9482 - val_loss: 0.1570 - val_accuracy: 0.9471 - lr: 0.0095\n",
+ "Epoch 278/282\n",
+ "256/256 [==============================] - 71s 277ms/step - loss: 0.0959 - accuracy: 0.9692 - val_loss: 0.1613 - val_accuracy: 0.9375 - lr: 0.0095\n",
+ "Epoch 279/282\n",
+ "256/256 [==============================] - 71s 278ms/step - loss: 0.0664 - accuracy: 0.9802 - val_loss: 0.1662 - val_accuracy: 0.9359 - lr: 0.0095\n",
+ "Epoch 280/282\n",
+ "256/256 [==============================] - 71s 278ms/step - loss: 0.0480 - accuracy: 0.9856 - val_loss: 0.2022 - val_accuracy: 0.9343 - lr: 0.0095\n",
+ "Epoch 281/282\n",
+ "256/256 [==============================] - 71s 278ms/step - loss: 0.0376 - accuracy: 0.9893 - val_loss: 0.2449 - val_accuracy: 0.9327 - lr: 0.0095\n",
+ "Epoch 282/282\n",
+ "256/256 [==============================] - 71s 277ms/step - loss: 0.0293 - accuracy: 0.9924 - val_loss: 0.2447 - val_accuracy: 0.9391 - lr: 0.0095\n",
+ "\u001b[0;32mSubset training done.\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{0.9471}, \u001b[0m\u001b[0;33mloss{0.1570}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9615}, loss{0.1507}]\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9391\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.2447\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9615384340. Not saving model.\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1507172436. Not saving model.\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;32m(GPU-MEM)\u001b[0m\u001b[0;36m----[free: 5.16GB, used: 18.84GB, total, 24.00GB]\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m540.71 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m434.45 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m106.26 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0;36m<---------------------------------------|Epoch [47] END|--------------------------------------->\u001b[0m\n",
+ "\u001b[0m\n",
+ "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m48\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m489 (TSEC: 282)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Stage 2]\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+ "\u001b[0;33mPreparing train data...\u001b[0m\n",
+ "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n",
+ "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+ "\u001b[0;32mTraining on subset...\u001b[0m\n",
+ "Epoch 283/288\n",
+ "256/256 [==============================] - 77s 285ms/step - loss: 0.1552 - accuracy: 0.9521 - val_loss: 0.2704 - val_accuracy: 0.9295 - lr: 0.0095\n",
+ "Epoch 284/288\n",
+ "256/256 [==============================] - 71s 276ms/step - loss: 0.1001 - accuracy: 0.9688 - val_loss: 0.2616 - val_accuracy: 0.9295 - lr: 0.0095\n",
+ "Epoch 285/288\n",
+ "256/256 [==============================] - 71s 277ms/step - loss: 0.0536 - accuracy: 0.9817 - val_loss: 0.4002 - val_accuracy: 0.9135 - lr: 0.0095\n",
+ "Epoch 286/288\n",
+ "256/256 [==============================] - 71s 276ms/step - loss: 0.0378 - accuracy: 0.9888 - val_loss: 0.3390 - val_accuracy: 0.9215 - lr: 0.0095\n",
+ "Epoch 287/288\n",
+ "256/256 [==============================] - 71s 277ms/step - loss: 0.0190 - accuracy: 0.9954 - val_loss: 0.5053 - val_accuracy: 0.8862 - lr: 0.0095\n",
+ "Epoch 288/288\n",
+ "256/256 [==============================] - 71s 277ms/step - loss: 0.0199 - accuracy: 0.9939 - val_loss: 0.4386 - val_accuracy: 0.9103 - lr: 0.0095\n",
+ "\u001b[0;32mSubset training done.\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{0.9295}, \u001b[0m\u001b[0;33mloss{0.2616}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9615}, loss{0.1507}]\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9103\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.4385\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9615384340. Not saving model.\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1507172436. Not saving model.\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;32m(GPU-MEM)\u001b[0m\u001b[0;36m----[free: 5.17GB, used: 18.83GB, total, 24.00GB]\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m538.49 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m433.36 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m105.13 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0;36m<---------------------------------------|Epoch [48] END|--------------------------------------->\u001b[0m\n",
+ "\u001b[0m\n",
+ "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m49\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m489 (TSEC: 288)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Stage 2]\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+ "\u001b[0;33mPreparing train data...\u001b[0m\n",
+ "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n",
+ "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+ "\u001b[0;32mTraining on subset...\u001b[0m\n",
+ "Epoch 289/294\n",
+ "256/256 [==============================] - 78s 287ms/step - loss: 0.1457 - accuracy: 0.9519 - val_loss: 0.2238 - val_accuracy: 0.9279 - lr: 0.0095\n",
+ "Epoch 290/294\n",
+ "256/256 [==============================] - 72s 282ms/step - loss: 0.0863 - accuracy: 0.9753 - val_loss: 0.2242 - val_accuracy: 0.9343 - lr: 0.0095\n",
+ "Epoch 291/294\n",
+ "256/256 [==============================] - 72s 279ms/step - loss: 0.0620 - accuracy: 0.9812 - val_loss: 0.2938 - val_accuracy: 0.9231 - lr: 0.0095\n",
+ "Epoch 292/294\n",
+ "256/256 [==============================] - 71s 278ms/step - loss: 0.0400 - accuracy: 0.9880 - val_loss: 0.3755 - val_accuracy: 0.9054 - lr: 0.0095\n",
+ "Epoch 293/294\n",
+ "256/256 [==============================] - 72s 279ms/step - loss: 0.0354 - accuracy: 0.9900 - val_loss: 0.4175 - val_accuracy: 0.9103 - lr: 0.0095\n",
+ "Epoch 294/294\n",
+ "256/256 [==============================] - 71s 278ms/step - loss: 0.0219 - accuracy: 0.9941 - val_loss: 0.4969 - val_accuracy: 0.9054 - lr: 0.0095\n",
+ "\u001b[0;32mSubset training done.\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{0.9343}, \u001b[0m\u001b[0;33mloss{0.2238}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9615}, loss{0.1507}]\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9054\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.4968\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9615384340. Not saving model.\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1507172436. Not saving model.\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;32m(GPU-MEM)\u001b[0m\u001b[0;36m----[free: 5.17GB, used: 18.83GB, total, 24.00GB]\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m546.42 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m437.29 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m109.13 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0;36m<---------------------------------------|Epoch [49] END|--------------------------------------->\u001b[0m\n",
+ "\u001b[0m\n",
+ "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m50\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m489 (TSEC: 294)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Stage 2]\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+ "\u001b[0;33mPreparing train data...\u001b[0m\n",
+ "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n",
+ "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+ "\u001b[0;32mTraining on subset...\u001b[0m\n",
+ "Epoch 295/300\n",
+ "256/256 [==============================] - 78s 286ms/step - loss: 0.1733 - accuracy: 0.9436 - val_loss: 0.2319 - val_accuracy: 0.9295 - lr: 0.0095\n",
+ "Epoch 296/300\n",
+ "256/256 [==============================] - 72s 281ms/step - loss: 0.1085 - accuracy: 0.9646 - val_loss: 0.2159 - val_accuracy: 0.9311 - lr: 0.0095\n",
+ "Epoch 297/300\n",
+ "256/256 [==============================] - 72s 282ms/step - loss: 0.0659 - accuracy: 0.9795 - val_loss: 0.2045 - val_accuracy: 0.9343 - lr: 0.0095\n",
+ "Epoch 298/300\n",
+ "256/256 [==============================] - 72s 281ms/step - loss: 0.0421 - accuracy: 0.9885 - val_loss: 0.1744 - val_accuracy: 0.9407 - lr: 0.0095\n",
+ "Epoch 299/300\n",
+ "256/256 [==============================] - 73s 283ms/step - loss: 0.0349 - accuracy: 0.9902 - val_loss: 0.3020 - val_accuracy: 0.9439 - lr: 0.0095\n",
+ "Epoch 300/300\n",
+ "256/256 [==============================] - 72s 282ms/step - loss: 0.0290 - accuracy: 0.9924 - val_loss: 0.3009 - val_accuracy: 0.9455 - lr: 0.0095\n",
+ "\u001b[0;32mSubset training done.\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{0.9455}, \u001b[0m\u001b[0;33mloss{0.1744}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9615}, loss{0.1507}]\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9455\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.3008\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9615384340. Not saving model.\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1507172436. Not saving model.\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;32m(GPU-MEM)\u001b[0m\u001b[0;36m----[free: 5.17GB, used: 18.83GB, total, 24.00GB]\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m546.17 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m440.89 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m105.28 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0;36m<---------------------------------------|Epoch [50] END|--------------------------------------->\u001b[0m\n",
+ "\u001b[0m\n",
+ "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m51\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m489 (TSEC: 300)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Stage 2]\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+ "\u001b[0;33mPreparing train data...\u001b[0m\n",
+ "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n",
+ "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+ "\u001b[0;32mTraining on subset...\u001b[0m\n",
+ "Epoch 301/306\n",
+ "256/256 [==============================] - 78s 288ms/step - loss: 0.1551 - accuracy: 0.9495 - val_loss: 0.1855 - val_accuracy: 0.9487 - lr: 0.0095\n",
+ "Epoch 302/306\n",
+ "256/256 [==============================] - 72s 279ms/step - loss: 0.0895 - accuracy: 0.9719 - val_loss: 0.2617 - val_accuracy: 0.9359 - lr: 0.0095\n",
+ "Epoch 303/306\n",
+ "256/256 [==============================] - 71s 278ms/step - loss: 0.0615 - accuracy: 0.9844 - val_loss: 0.2679 - val_accuracy: 0.9359 - lr: 0.0095\n",
+ "Epoch 304/306\n",
+ "256/256 [==============================] - 71s 278ms/step - loss: 0.0486 - accuracy: 0.9863 - val_loss: 0.2134 - val_accuracy: 0.9359 - lr: 0.0095\n",
+ "Epoch 305/306\n",
+ "256/256 [==============================] - 72s 279ms/step - loss: 0.0334 - accuracy: 0.9912 - val_loss: 0.2021 - val_accuracy: 0.9295 - lr: 0.0095\n",
+ "Epoch 306/306\n",
+ "256/256 [==============================] - 72s 279ms/step - loss: 0.0224 - accuracy: 0.9944 - val_loss: 0.2594 - val_accuracy: 0.9343 - lr: 0.0095\n",
+ "\u001b[0;32mSubset training done.\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{0.9487}, \u001b[0m\u001b[0;33mloss{0.1855}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9615}, loss{0.1507}]\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9343\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.2593\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9615384340. Not saving model.\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1507172436. Not saving model.\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;32m(GPU-MEM)\u001b[0m\u001b[0;36m----[free: 5.17GB, used: 18.83GB, total, 24.00GB]\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m545.63 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m436.74 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m108.89 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0;36m<---------------------------------------|Epoch [51] END|--------------------------------------->\u001b[0m\n",
+ "\u001b[0m\n",
+ "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m52\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m489 (TSEC: 306)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Stage 2]\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+ "\u001b[0;33mPreparing train data...\u001b[0m\n",
+ "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n",
+ "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+ "\u001b[0;32mTraining on subset...\u001b[0m\n",
+ "Epoch 307/312\n",
+ "256/256 [==============================] - 78s 287ms/step - loss: 0.1859 - accuracy: 0.9370 - val_loss: 0.2198 - val_accuracy: 0.9327 - lr: 0.0095\n",
+ "Epoch 308/312\n",
+ "256/256 [==============================] - 71s 278ms/step - loss: 0.1066 - accuracy: 0.9648 - val_loss: 0.2533 - val_accuracy: 0.9247 - lr: 0.0095\n",
+ "Epoch 309/312\n",
+ "256/256 [==============================] - 72s 279ms/step - loss: 0.0696 - accuracy: 0.9780 - val_loss: 0.3515 - val_accuracy: 0.9263 - lr: 0.0095\n",
+ "Epoch 310/312\n",
+ "256/256 [==============================] - 71s 278ms/step - loss: 0.0519 - accuracy: 0.9836 - val_loss: 0.2943 - val_accuracy: 0.9311 - lr: 0.0095\n",
+ "Epoch 311/312\n",
+ "256/256 [==============================] - 72s 279ms/step - loss: 0.0390 - accuracy: 0.9900 - val_loss: 0.3001 - val_accuracy: 0.9311 - lr: 0.0095\n",
+ "Epoch 312/312\n",
+ "256/256 [==============================] - 71s 278ms/step - loss: 0.0265 - accuracy: 0.9937 - val_loss: 0.4250 - val_accuracy: 0.9295 - lr: 0.0095\n",
+ "\u001b[0;32mSubset training done.\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{0.9327}, \u001b[0m\u001b[0;33mloss{0.2198}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9615}, loss{0.1507}]\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9295\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.4250\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9615384340. Not saving model.\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1507172436. Not saving model.\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;32m(GPU-MEM)\u001b[0m\u001b[0;36m----[free: 5.17GB, used: 18.83GB, total, 24.00GB]\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m547.27 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m436.31 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m110.95 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0;36m<---------------------------------------|Epoch [52] END|--------------------------------------->\u001b[0m\n",
+ "\u001b[0m\n",
+ "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m53\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m489 (TSEC: 312)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Stage 2]\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+ "\u001b[0;33mPreparing train data...\u001b[0m\n",
+ "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n",
+ "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+ "\u001b[0;32mTraining on subset...\u001b[0m\n",
+ "Epoch 313/318\n",
+ "256/256 [==============================] - 78s 287ms/step - loss: 0.1569 - accuracy: 0.9460 - val_loss: 0.2276 - val_accuracy: 0.9375 - lr: 0.0095\n",
+ "Epoch 314/318\n",
+ "256/256 [==============================] - 72s 282ms/step - loss: 0.0949 - accuracy: 0.9717 - val_loss: 0.2151 - val_accuracy: 0.9407 - lr: 0.0095\n",
+ "Epoch 315/318\n",
+ "256/256 [==============================] - 72s 278ms/step - loss: 0.0694 - accuracy: 0.9800 - val_loss: 0.2282 - val_accuracy: 0.9391 - lr: 0.0095\n",
+ "Epoch 316/318\n",
+ "256/256 [==============================] - 71s 278ms/step - loss: 0.0497 - accuracy: 0.9868 - val_loss: 0.2215 - val_accuracy: 0.9375 - lr: 0.0095\n",
+ "Epoch 317/318\n",
+ "256/256 [==============================] - 72s 280ms/step - loss: 0.0354 - accuracy: 0.9900 - val_loss: 0.2642 - val_accuracy: 0.9311 - lr: 0.0095\n",
+ "Epoch 318/318\n",
+ "256/256 [==============================] - ETA: 0s - loss: 0.0274 - accuracy: 0.9937\n",
+ "Epoch 318: ReduceLROnPlateau reducing learning rate to 0.009299207033589482.\n",
+ "256/256 [==============================] - 71s 278ms/step - loss: 0.0274 - accuracy: 0.9937 - val_loss: 0.2980 - val_accuracy: 0.9263 - lr: 0.0095\n",
+ "\u001b[0;32mSubset training done.\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{0.9407}, \u001b[0m\u001b[0;33mloss{0.2151}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9615}, loss{0.1507}]\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9263\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.2981\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9615384340. Not saving model.\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1507172436. Not saving model.\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;32m(GPU-MEM)\u001b[0m\u001b[0;36m----[free: 5.18GB, used: 18.82GB, total, 24.00GB]\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m550.42 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m437.37 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m113.05 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0;36m<---------------------------------------|Epoch [53] END|--------------------------------------->\u001b[0m\n",
+ "\u001b[0m\n",
+ "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m54\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m489 (TSEC: 318)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Stage 2]\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+ "\u001b[0;33mPreparing train data...\u001b[0m\n",
+ "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n",
+ "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+ "\u001b[0;32mTraining on subset...\u001b[0m\n",
+ "Epoch 319/324\n",
+ "256/256 [==============================] - 78s 287ms/step - loss: 0.1510 - accuracy: 0.9531 - val_loss: 0.1710 - val_accuracy: 0.9407 - lr: 0.0093\n",
+ "Epoch 320/324\n",
+ "256/256 [==============================] - 72s 281ms/step - loss: 0.0903 - accuracy: 0.9714 - val_loss: 0.1643 - val_accuracy: 0.9423 - lr: 0.0093\n",
+ "Epoch 321/324\n",
+ "256/256 [==============================] - 72s 279ms/step - loss: 0.0601 - accuracy: 0.9824 - val_loss: 0.2904 - val_accuracy: 0.9327 - lr: 0.0093\n",
+ "Epoch 322/324\n",
+ "256/256 [==============================] - 72s 278ms/step - loss: 0.0391 - accuracy: 0.9893 - val_loss: 0.2208 - val_accuracy: 0.9391 - lr: 0.0093\n",
+ "Epoch 323/324\n",
+ "256/256 [==============================] - 72s 282ms/step - loss: 0.0324 - accuracy: 0.9915 - val_loss: 0.2139 - val_accuracy: 0.9439 - lr: 0.0093\n",
+ "Epoch 324/324\n",
+ "256/256 [==============================] - 71s 278ms/step - loss: 0.0207 - accuracy: 0.9939 - val_loss: 0.2766 - val_accuracy: 0.9407 - lr: 0.0093\n",
+ "\u001b[0;32mSubset training done.\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{0.9439}, \u001b[0m\u001b[0;33mloss{0.1643}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9615}, loss{0.1507}]\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9407\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.2766\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9615384340. Not saving model.\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1507172436. Not saving model.\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;32m(GPU-MEM)\u001b[0m\u001b[0;36m----[free: 5.18GB, used: 18.82GB, total, 24.00GB]\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m550.43 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m438.08 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m112.35 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0;36m<---------------------------------------|Epoch [54] END|--------------------------------------->\u001b[0m\n",
+ "\u001b[0m\n",
+ "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m55\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m489 (TSEC: 324)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Stage 2]\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+ "\u001b[0;33mPreparing train data...\u001b[0m\n",
+ "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n",
+ "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+ "\u001b[0;32mTraining on subset...\u001b[0m\n",
+ "Epoch 325/330\n",
+ "256/256 [==============================] - 78s 287ms/step - loss: 0.1381 - accuracy: 0.9590 - val_loss: 0.1834 - val_accuracy: 0.9391 - lr: 0.0093\n",
+ "Epoch 326/330\n",
+ "256/256 [==============================] - 72s 282ms/step - loss: 0.0842 - accuracy: 0.9736 - val_loss: 0.2042 - val_accuracy: 0.9407 - lr: 0.0093\n",
+ "Epoch 327/330\n",
+ "256/256 [==============================] - 72s 279ms/step - loss: 0.0535 - accuracy: 0.9854 - val_loss: 0.3113 - val_accuracy: 0.9199 - lr: 0.0093\n",
+ "Epoch 328/330\n",
+ "256/256 [==============================] - 72s 281ms/step - loss: 0.0324 - accuracy: 0.9924 - val_loss: 0.2379 - val_accuracy: 0.9439 - lr: 0.0093\n",
+ "Epoch 329/330\n",
+ "256/256 [==============================] - 72s 283ms/step - loss: 0.0262 - accuracy: 0.9937 - val_loss: 0.1793 - val_accuracy: 0.9487 - lr: 0.0093\n",
+ "Epoch 330/330\n",
+ "256/256 [==============================] - 71s 278ms/step - loss: 0.0233 - accuracy: 0.9941 - val_loss: 0.2066 - val_accuracy: 0.9455 - lr: 0.0093\n",
+ "\u001b[0;32mSubset training done.\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{0.9487}, \u001b[0m\u001b[0;33mloss{0.1793}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9615}, loss{0.1507}]\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9455\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.2066\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9615384340. Not saving model.\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1507172436. Not saving model.\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;32m(GPU-MEM)\u001b[0m\u001b[0;36m----[free: 5.18GB, used: 18.82GB, total, 24.00GB]\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m549.02 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m439.02 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m110.00 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0;36m<---------------------------------------|Epoch [55] END|--------------------------------------->\u001b[0m\n",
+ "\u001b[0m\n",
+ "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m56\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m489 (TSEC: 330)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Stage 2]\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+ "\u001b[0;33mPreparing train data...\u001b[0m\n",
+ "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n",
+ "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+ "\u001b[0;32mTraining on subset...\u001b[0m\n",
+ "Epoch 331/336\n",
+ "256/256 [==============================] - 78s 286ms/step - loss: 0.1498 - accuracy: 0.9478 - val_loss: 0.1643 - val_accuracy: 0.9455 - lr: 0.0093\n",
+ "Epoch 332/336\n",
+ "256/256 [==============================] - 72s 281ms/step - loss: 0.0890 - accuracy: 0.9705 - val_loss: 0.1541 - val_accuracy: 0.9471 - lr: 0.0093\n",
+ "Epoch 333/336\n",
+ "256/256 [==============================] - 72s 279ms/step - loss: 0.0583 - accuracy: 0.9802 - val_loss: 0.2413 - val_accuracy: 0.9359 - lr: 0.0093\n",
+ "Epoch 334/336\n",
+ "256/256 [==============================] - 72s 279ms/step - loss: 0.0374 - accuracy: 0.9866 - val_loss: 0.3158 - val_accuracy: 0.9375 - lr: 0.0093\n",
+ "Epoch 335/336\n",
+ "256/256 [==============================] - 72s 279ms/step - loss: 0.0314 - accuracy: 0.9912 - val_loss: 0.2443 - val_accuracy: 0.9407 - lr: 0.0093\n",
+ "Epoch 336/336\n",
+ "256/256 [==============================] - 72s 279ms/step - loss: 0.0226 - accuracy: 0.9934 - val_loss: 0.2873 - val_accuracy: 0.9295 - lr: 0.0093\n",
+ "\u001b[0;32mSubset training done.\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{0.9471}, \u001b[0m\u001b[0;33mloss{0.1541}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9615}, loss{0.1507}]\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9295\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.2872\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9615384340. Not saving model.\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1507172436. Not saving model.\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;32m(GPU-MEM)\u001b[0m\u001b[0;36m----[free: 5.16GB, used: 18.84GB, total, 24.00GB]\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m551.47 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m437.88 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m113.59 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0;36m<---------------------------------------|Epoch [56] END|--------------------------------------->\u001b[0m\n",
+ "\u001b[0m\n",
+ "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m57\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m489 (TSEC: 336)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Stage 2]\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+ "\u001b[0;33mPreparing train data...\u001b[0m\n",
+ "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n",
+ "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+ "\u001b[0;32mTraining on subset...\u001b[0m\n",
+ "Epoch 337/342\n",
+ "256/256 [==============================] - 78s 288ms/step - loss: 0.1644 - accuracy: 0.9434 - val_loss: 0.1689 - val_accuracy: 0.9471 - lr: 0.0093\n",
+ "Epoch 338/342\n",
+ "256/256 [==============================] - 72s 279ms/step - loss: 0.0994 - accuracy: 0.9683 - val_loss: 0.1996 - val_accuracy: 0.9391 - lr: 0.0093\n",
+ "Epoch 339/342\n",
+ "256/256 [==============================] - 71s 278ms/step - loss: 0.0547 - accuracy: 0.9817 - val_loss: 0.2180 - val_accuracy: 0.9439 - lr: 0.0093\n",
+ "Epoch 340/342\n",
+ "256/256 [==============================] - 71s 277ms/step - loss: 0.0324 - accuracy: 0.9888 - val_loss: 0.3856 - val_accuracy: 0.9135 - lr: 0.0093\n",
+ "Epoch 341/342\n",
+ "256/256 [==============================] - 71s 278ms/step - loss: 0.0305 - accuracy: 0.9907 - val_loss: 0.3454 - val_accuracy: 0.9391 - lr: 0.0093\n",
+ "Epoch 342/342\n",
+ "256/256 [==============================] - 71s 278ms/step - loss: 0.0255 - accuracy: 0.9939 - val_loss: 0.4204 - val_accuracy: 0.9215 - lr: 0.0093\n",
+ "\u001b[0;32mSubset training done.\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{0.9471}, \u001b[0m\u001b[0;33mloss{0.1689}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9615}, loss{0.1507}]\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9215\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.4204\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9615384340. Not saving model.\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1507172436. Not saving model.\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;32m(GPU-MEM)\u001b[0m\u001b[0;36m----[free: 5.18GB, used: 18.82GB, total, 24.00GB]\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m550.62 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m436.75 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m113.87 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0;36m<---------------------------------------|Epoch [57] END|--------------------------------------->\u001b[0m\n",
+ "\u001b[0m\n",
+ "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m58\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m489 (TSEC: 342)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Stage 2]\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+ "\u001b[0;33mPreparing train data...\u001b[0m\n",
+ "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n",
+ "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+ "\u001b[0;32mTraining on subset...\u001b[0m\n",
+ "Epoch 343/348\n",
+ "256/256 [==============================] - 78s 287ms/step - loss: 0.1762 - accuracy: 0.9375 - val_loss: 0.2056 - val_accuracy: 0.9183 - lr: 0.0093\n",
+ "Epoch 344/348\n",
+ "256/256 [==============================] - 72s 282ms/step - loss: 0.1109 - accuracy: 0.9626 - val_loss: 0.1948 - val_accuracy: 0.9311 - lr: 0.0093\n",
+ "Epoch 345/348\n",
+ "256/256 [==============================] - 72s 282ms/step - loss: 0.0762 - accuracy: 0.9780 - val_loss: 0.2274 - val_accuracy: 0.9343 - lr: 0.0093\n",
+ "Epoch 346/348\n",
+ "256/256 [==============================] - 72s 279ms/step - loss: 0.0495 - accuracy: 0.9851 - val_loss: 0.2694 - val_accuracy: 0.9311 - lr: 0.0093\n",
+ "Epoch 347/348\n",
+ "256/256 [==============================] - 72s 282ms/step - loss: 0.0362 - accuracy: 0.9895 - val_loss: 0.2763 - val_accuracy: 0.9423 - lr: 0.0093\n",
+ "Epoch 348/348\n",
+ "256/256 [==============================] - 72s 279ms/step - loss: 0.0209 - accuracy: 0.9946 - val_loss: 0.2977 - val_accuracy: 0.9391 - lr: 0.0093\n",
+ "\u001b[0;32mSubset training done.\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{0.9423}, \u001b[0m\u001b[0;33mloss{0.1948}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9615}, loss{0.1507}]\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9391\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.2976\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9615384340. Not saving model.\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1507172436. Not saving model.\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;32m(GPU-MEM)\u001b[0m\u001b[0;36m----[free: 5.17GB, used: 18.82GB, total, 24.00GB]\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m552.41 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m439.31 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m113.10 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0;36m<---------------------------------------|Epoch [58] END|--------------------------------------->\u001b[0m\n",
+ "\u001b[0m\n",
+ "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m59\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m489 (TSEC: 348)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Stage 2]\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+ "\u001b[0;33mPreparing train data...\u001b[0m\n",
+ "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n",
+ "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+ "\u001b[0;32mTraining on subset...\u001b[0m\n",
+ "Epoch 349/354\n",
+ "256/256 [==============================] - 78s 288ms/step - loss: 0.1428 - accuracy: 0.9558 - val_loss: 0.2571 - val_accuracy: 0.9151 - lr: 0.0093\n",
+ "Epoch 350/354\n",
+ "256/256 [==============================] - 72s 282ms/step - loss: 0.0798 - accuracy: 0.9736 - val_loss: 0.2159 - val_accuracy: 0.9535 - lr: 0.0093\n",
+ "Epoch 351/354\n",
+ "256/256 [==============================] - 72s 279ms/step - loss: 0.0534 - accuracy: 0.9836 - val_loss: 0.2903 - val_accuracy: 0.9263 - lr: 0.0093\n",
+ "Epoch 352/354\n",
+ "256/256 [==============================] - 71s 278ms/step - loss: 0.0337 - accuracy: 0.9927 - val_loss: 0.2496 - val_accuracy: 0.9423 - lr: 0.0093\n",
+ "Epoch 353/354\n",
+ "256/256 [==============================] - 72s 278ms/step - loss: 0.0310 - accuracy: 0.9924 - val_loss: 0.2418 - val_accuracy: 0.9407 - lr: 0.0093\n",
+ "Epoch 354/354\n",
+ "256/256 [==============================] - 71s 278ms/step - loss: 0.0266 - accuracy: 0.9922 - val_loss: 0.3734 - val_accuracy: 0.9311 - lr: 0.0093\n",
+ "\u001b[0;32mSubset training done.\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{0.9535}, \u001b[0m\u001b[0;33mloss{0.2159}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9615}, loss{0.1507}]\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9311\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.3735\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9615384340. Not saving model.\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1507172436. Not saving model.\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;32m(GPU-MEM)\u001b[0m\u001b[0;36m----[free: 5.18GB, used: 18.82GB, total, 24.00GB]\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m550.96 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m437.79 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m113.17 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0;36m<---------------------------------------|Epoch [59] END|--------------------------------------->\u001b[0m\n",
+ "\u001b[0m\n",
+ "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m60\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m489 (TSEC: 354)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Stage 2]\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+ "\u001b[0;33mPreparing train data...\u001b[0m\n",
+ "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n",
+ "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+ "\u001b[0;32mTraining on subset...\u001b[0m\n",
+ "Epoch 355/360\n",
+ "256/256 [==============================] - 78s 286ms/step - loss: 0.1507 - accuracy: 0.9507 - val_loss: 0.1681 - val_accuracy: 0.9359 - lr: 0.0093\n",
+ "Epoch 356/360\n",
+ "256/256 [==============================] - 72s 282ms/step - loss: 0.0962 - accuracy: 0.9705 - val_loss: 0.2051 - val_accuracy: 0.9391 - lr: 0.0093\n",
+ "Epoch 357/360\n",
+ "256/256 [==============================] - 72s 282ms/step - loss: 0.0647 - accuracy: 0.9788 - val_loss: 0.2295 - val_accuracy: 0.9423 - lr: 0.0093\n",
+ "Epoch 358/360\n",
+ "256/256 [==============================] - 72s 282ms/step - loss: 0.0327 - accuracy: 0.9905 - val_loss: 0.2502 - val_accuracy: 0.9455 - lr: 0.0093\n",
+ "Epoch 359/360\n",
+ "256/256 [==============================] - 72s 279ms/step - loss: 0.0246 - accuracy: 0.9937 - val_loss: 0.2248 - val_accuracy: 0.9439 - lr: 0.0093\n",
+ "Epoch 360/360\n",
+ "256/256 [==============================] - 72s 279ms/step - loss: 0.0268 - accuracy: 0.9944 - val_loss: 0.2514 - val_accuracy: 0.9391 - lr: 0.0093\n",
+ "\u001b[0;32mSubset training done.\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{0.9455}, \u001b[0m\u001b[0;33mloss{0.1681}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9615}, loss{0.1507}]\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9391\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.2514\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9615384340. Not saving model.\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1507172436. Not saving model.\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;32m(GPU-MEM)\u001b[0m\u001b[0;36m----[free: 5.18GB, used: 18.82GB, total, 24.00GB]\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m555.71 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m439.11 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m116.60 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0;36m<---------------------------------------|Epoch [60] END|--------------------------------------->\u001b[0m\n",
+ "\u001b[0m\n",
+ "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m61\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m489 (TSEC: 360)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Stage 2]\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+ "\u001b[0;33mPreparing train data...\u001b[0m\n",
+ "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n",
+ "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+ "\u001b[0;32mTraining on subset...\u001b[0m\n",
+ "Epoch 361/366\n",
+ "256/256 [==============================] - 78s 287ms/step - loss: 0.1526 - accuracy: 0.9478 - val_loss: 0.1712 - val_accuracy: 0.9375 - lr: 0.0093\n",
+ "Epoch 362/366\n",
+ "256/256 [==============================] - 73s 283ms/step - loss: 0.0930 - accuracy: 0.9712 - val_loss: 0.1701 - val_accuracy: 0.9487 - lr: 0.0093\n",
+ "Epoch 363/366\n",
+ "256/256 [==============================] - 72s 279ms/step - loss: 0.0547 - accuracy: 0.9846 - val_loss: 0.3175 - val_accuracy: 0.9295 - lr: 0.0093\n",
+ "Epoch 364/366\n",
+ "256/256 [==============================] - 71s 278ms/step - loss: 0.0312 - accuracy: 0.9895 - val_loss: 0.2525 - val_accuracy: 0.9407 - lr: 0.0093\n",
+ "Epoch 365/366\n",
+ "256/256 [==============================] - 72s 279ms/step - loss: 0.0218 - accuracy: 0.9941 - val_loss: 0.3255 - val_accuracy: 0.9327 - lr: 0.0093\n",
+ "Epoch 366/366\n",
+ "256/256 [==============================] - 72s 279ms/step - loss: 0.0187 - accuracy: 0.9954 - val_loss: 0.2214 - val_accuracy: 0.9439 - lr: 0.0093\n",
+ "\u001b[0;32mSubset training done.\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{0.9487}, \u001b[0m\u001b[0;33mloss{0.1701}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9615}, loss{0.1507}]\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9439\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.2214\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9615384340. Not saving model.\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1507172436. Not saving model.\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;32m(GPU-MEM)\u001b[0m\u001b[0;36m----[free: 5.18GB, used: 18.82GB, total, 24.00GB]\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m552.81 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m437.90 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m114.90 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0;36m<---------------------------------------|Epoch [61] END|--------------------------------------->\u001b[0m\n",
+ "\u001b[0m\n",
+ "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m62\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m489 (TSEC: 366)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Stage 2]\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+ "\u001b[0;33mPreparing train data...\u001b[0m\n",
+ "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n",
+ "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+ "\u001b[0;32mTraining on subset...\u001b[0m\n",
+ "Epoch 367/372\n",
+ "256/256 [==============================] - 78s 287ms/step - loss: 0.1570 - accuracy: 0.9517 - val_loss: 0.2226 - val_accuracy: 0.9263 - lr: 0.0093\n",
+ "Epoch 368/372\n",
+ "256/256 [==============================] - 73s 283ms/step - loss: 0.1003 - accuracy: 0.9690 - val_loss: 0.2266 - val_accuracy: 0.9327 - lr: 0.0093\n",
+ "Epoch 369/372\n",
+ "256/256 [==============================] - 72s 280ms/step - loss: 0.0663 - accuracy: 0.9817 - val_loss: 0.3376 - val_accuracy: 0.9167 - lr: 0.0093\n",
+ "Epoch 370/372\n",
+ "256/256 [==============================] - 71s 279ms/step - loss: 0.0470 - accuracy: 0.9844 - val_loss: 0.2824 - val_accuracy: 0.9151 - lr: 0.0093\n",
+ "Epoch 371/372\n",
+ "256/256 [==============================] - 72s 280ms/step - loss: 0.0282 - accuracy: 0.9912 - val_loss: 0.3004 - val_accuracy: 0.9167 - lr: 0.0093\n",
+ "Epoch 372/372\n",
+ "256/256 [==============================] - 72s 279ms/step - loss: 0.0243 - accuracy: 0.9944 - val_loss: 0.2254 - val_accuracy: 0.9327 - lr: 0.0093\n",
+ "\u001b[0;32mSubset training done.\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{0.9327}, \u001b[0m\u001b[0;33mloss{0.2226}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9615}, loss{0.1507}]\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9327\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.2254\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9615384340. Not saving model.\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1507172436. Not saving model.\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;32m(GPU-MEM)\u001b[0m\u001b[0;36m----[free: 5.18GB, used: 18.82GB, total, 24.00GB]\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m551.46 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m438.57 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m112.89 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0;36m<---------------------------------------|Epoch [62] END|--------------------------------------->\u001b[0m\n",
+ "\u001b[0m\n",
+ "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m63\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m489 (TSEC: 372)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Stage 2]\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+ "\u001b[0;33mPreparing train data...\u001b[0m\n",
+ "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n",
+ "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+ "\u001b[0;32mTraining on subset...\u001b[0m\n",
+ "Epoch 373/378\n",
+ "256/256 [==============================] - 78s 288ms/step - loss: 0.1464 - accuracy: 0.9521 - val_loss: 0.3296 - val_accuracy: 0.9135 - lr: 0.0093\n",
+ "Epoch 374/378\n",
+ "256/256 [==============================] - 73s 283ms/step - loss: 0.0846 - accuracy: 0.9758 - val_loss: 0.3144 - val_accuracy: 0.9167 - lr: 0.0093\n",
+ "Epoch 375/378\n",
+ "256/256 [==============================] - 72s 279ms/step - loss: 0.0528 - accuracy: 0.9841 - val_loss: 0.2769 - val_accuracy: 0.9151 - lr: 0.0093\n",
+ "Epoch 376/378\n",
+ "256/256 [==============================] - 72s 279ms/step - loss: 0.0304 - accuracy: 0.9897 - val_loss: 0.4158 - val_accuracy: 0.9071 - lr: 0.0093\n",
+ "Epoch 377/378\n",
+ "256/256 [==============================] - 73s 284ms/step - loss: 0.0290 - accuracy: 0.9937 - val_loss: 0.2375 - val_accuracy: 0.9215 - lr: 0.0093\n",
+ "Epoch 378/378\n",
+ "256/256 [==============================] - 73s 283ms/step - loss: 0.0267 - accuracy: 0.9927 - val_loss: 0.1940 - val_accuracy: 0.9407 - lr: 0.0093\n",
+ "\u001b[0;32mSubset training done.\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{0.9407}, \u001b[0m\u001b[0;33mloss{0.1940}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9615}, loss{0.1507}]\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9407\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.1940\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9615384340. Not saving model.\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1507172436. Not saving model.\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;32m(GPU-MEM)\u001b[0m\u001b[0;36m----[free: 5.18GB, used: 18.82GB, total, 24.00GB]\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m555.86 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m440.61 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m115.25 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0;36m<---------------------------------------|Epoch [63] END|--------------------------------------->\u001b[0m\n",
+ "\u001b[0m\n",
+ "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m64\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m489 (TSEC: 378)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Stage 2]\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+ "\u001b[0;33mPreparing train data...\u001b[0m\n",
+ "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n",
+ "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+ "\u001b[0;32mTraining on subset...\u001b[0m\n",
+ "Epoch 379/384\n",
+ "256/256 [==============================] - 79s 289ms/step - loss: 0.1658 - accuracy: 0.9478 - val_loss: 0.1683 - val_accuracy: 0.9311 - lr: 0.0093\n",
+ "Epoch 380/384\n",
+ "256/256 [==============================] - 73s 283ms/step - loss: 0.1027 - accuracy: 0.9697 - val_loss: 0.1709 - val_accuracy: 0.9327 - lr: 0.0093\n",
+ "Epoch 381/384\n",
+ "256/256 [==============================] - 73s 283ms/step - loss: 0.0663 - accuracy: 0.9802 - val_loss: 0.1952 - val_accuracy: 0.9423 - lr: 0.0093\n",
+ "Epoch 382/384\n",
+ "256/256 [==============================] - 72s 279ms/step - loss: 0.0364 - accuracy: 0.9895 - val_loss: 0.2132 - val_accuracy: 0.9279 - lr: 0.0093\n",
+ "Epoch 383/384\n",
+ "256/256 [==============================] - ETA: 0s - loss: 0.0272 - accuracy: 0.9941\n",
+ "Epoch 383: ReduceLROnPlateau reducing learning rate to 0.009131821744143963.\n",
+ "256/256 [==============================] - 72s 280ms/step - loss: 0.0272 - accuracy: 0.9941 - val_loss: 0.2627 - val_accuracy: 0.9151 - lr: 0.0093\n",
+ "Epoch 384/384\n",
+ "256/256 [==============================] - 72s 279ms/step - loss: 0.0305 - accuracy: 0.9937 - val_loss: 0.2253 - val_accuracy: 0.9263 - lr: 0.0091\n",
+ "\u001b[0;32mSubset training done.\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{0.9423}, \u001b[0m\u001b[0;33mloss{0.1683}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9615}, loss{0.1507}]\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9263\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.2253\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9615384340. Not saving model.\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1507172436. Not saving model.\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;32m(GPU-MEM)\u001b[0m\u001b[0;36m----[free: 5.17GB, used: 18.82GB, total, 24.00GB]\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m557.97 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m440.50 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m117.47 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0;36m<---------------------------------------|Epoch [64] END|--------------------------------------->\u001b[0m\n",
+ "\u001b[0m\n",
+ "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m65\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m489 (TSEC: 384)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Stage 2]\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+ "\u001b[0;33mPreparing train data...\u001b[0m\n",
+ "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n",
+ "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+ "\u001b[0;32mTraining on subset...\u001b[0m\n",
+ "Epoch 385/390\n",
+ "256/256 [==============================] - 78s 287ms/step - loss: 0.1475 - accuracy: 0.9512 - val_loss: 0.1970 - val_accuracy: 0.9247 - lr: 0.0091\n",
+ "Epoch 386/390\n",
+ "256/256 [==============================] - 72s 282ms/step - loss: 0.0880 - accuracy: 0.9719 - val_loss: 0.2173 - val_accuracy: 0.9375 - lr: 0.0091\n",
+ "Epoch 387/390\n",
+ "256/256 [==============================] - 72s 280ms/step - loss: 0.0646 - accuracy: 0.9817 - val_loss: 0.1910 - val_accuracy: 0.9375 - lr: 0.0091\n",
+ "Epoch 388/390\n",
+ "256/256 [==============================] - 72s 280ms/step - loss: 0.0267 - accuracy: 0.9937 - val_loss: 0.3254 - val_accuracy: 0.9311 - lr: 0.0091\n",
+ "Epoch 389/390\n",
+ "256/256 [==============================] - 72s 280ms/step - loss: 0.0243 - accuracy: 0.9934 - val_loss: 0.2513 - val_accuracy: 0.9359 - lr: 0.0091\n",
+ "Epoch 390/390\n",
+ "256/256 [==============================] - 72s 280ms/step - loss: 0.0185 - accuracy: 0.9941 - val_loss: 0.2746 - val_accuracy: 0.9295 - lr: 0.0091\n",
+ "\u001b[0;32mSubset training done.\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{0.9375}, \u001b[0m\u001b[0;33mloss{0.1910}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9615}, loss{0.1507}]\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9295\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.2746\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9615384340. Not saving model.\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1507172436. Not saving model.\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;32m(GPU-MEM)\u001b[0m\u001b[0;36m----[free: 5.18GB, used: 18.82GB, total, 24.00GB]\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m555.02 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m438.76 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m116.27 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0;36m<---------------------------------------|Epoch [65] END|--------------------------------------->\u001b[0m\n",
+ "\u001b[0m\n",
+ "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m66\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m489 (TSEC: 390)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Stage 2]\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+ "\u001b[0;33mPreparing train data...\u001b[0m\n",
+ "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n",
+ "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+ "\u001b[0;32mTraining on subset...\u001b[0m\n",
+ "Epoch 391/396\n",
+ "256/256 [==============================] - 78s 288ms/step - loss: 0.1466 - accuracy: 0.9553 - val_loss: 0.1735 - val_accuracy: 0.9311 - lr: 0.0091\n",
+ "Epoch 392/396\n",
+ "256/256 [==============================] - 72s 279ms/step - loss: 0.0834 - accuracy: 0.9744 - val_loss: 0.2050 - val_accuracy: 0.9311 - lr: 0.0091\n",
+ "Epoch 393/396\n",
+ "256/256 [==============================] - 72s 280ms/step - loss: 0.0528 - accuracy: 0.9849 - val_loss: 0.2451 - val_accuracy: 0.9247 - lr: 0.0091\n",
+ "Epoch 394/396\n",
+ "256/256 [==============================] - 72s 282ms/step - loss: 0.0319 - accuracy: 0.9907 - val_loss: 0.2601 - val_accuracy: 0.9407 - lr: 0.0091\n",
+ "Epoch 395/396\n",
+ "256/256 [==============================] - 72s 280ms/step - loss: 0.0238 - accuracy: 0.9944 - val_loss: 0.3207 - val_accuracy: 0.9311 - lr: 0.0091\n",
+ "Epoch 396/396\n",
+ "256/256 [==============================] - 73s 283ms/step - loss: 0.0176 - accuracy: 0.9961 - val_loss: 0.2425 - val_accuracy: 0.9455 - lr: 0.0091\n",
+ "\u001b[0;32mSubset training done.\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{0.9455}, \u001b[0m\u001b[0;33mloss{0.1735}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9615}, loss{0.1507}]\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9439\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.2558\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9615384340. Not saving model.\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1507172436. Not saving model.\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;32m(GPU-MEM)\u001b[0m\u001b[0;36m----[free: 5.17GB, used: 18.82GB, total, 24.00GB]\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m561.06 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m439.89 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m121.16 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0;36m<---------------------------------------|Epoch [66] END|--------------------------------------->\u001b[0m\n",
+ "\u001b[0m\n",
+ "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m67\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m489 (TSEC: 396)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Stage 2]\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+ "\u001b[0;33mPreparing train data...\u001b[0m\n",
+ "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n",
+ "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+ "\u001b[0;32mTraining on subset...\u001b[0m\n",
+ "Epoch 397/402\n",
+ "256/256 [==============================] - 78s 288ms/step - loss: 0.1413 - accuracy: 0.9556 - val_loss: 0.2070 - val_accuracy: 0.9215 - lr: 0.0091\n",
+ "Epoch 398/402\n",
+ "256/256 [==============================] - 72s 282ms/step - loss: 0.0911 - accuracy: 0.9719 - val_loss: 0.1711 - val_accuracy: 0.9407 - lr: 0.0091\n",
+ "Epoch 399/402\n",
+ "256/256 [==============================] - 72s 281ms/step - loss: 0.0450 - accuracy: 0.9868 - val_loss: 0.2181 - val_accuracy: 0.9263 - lr: 0.0091\n",
+ "Epoch 400/402\n",
+ "256/256 [==============================] - 72s 279ms/step - loss: 0.0310 - accuracy: 0.9922 - val_loss: 0.1772 - val_accuracy: 0.9375 - lr: 0.0091\n",
+ "Epoch 401/402\n",
+ "256/256 [==============================] - 73s 283ms/step - loss: 0.0230 - accuracy: 0.9941 - val_loss: 0.1968 - val_accuracy: 0.9471 - lr: 0.0091\n",
+ "Epoch 402/402\n",
+ "256/256 [==============================] - 72s 279ms/step - loss: 0.0228 - accuracy: 0.9944 - val_loss: 0.2389 - val_accuracy: 0.9263 - lr: 0.0091\n",
+ "\u001b[0;32mSubset training done.\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{0.9471}, \u001b[0m\u001b[0;33mloss{0.1711}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9615}, loss{0.1507}]\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9263\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.2390\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9615384340. Not saving model.\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1507172436. Not saving model.\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;32m(GPU-MEM)\u001b[0m\u001b[0;36m----[free: 5.18GB, used: 18.82GB, total, 24.00GB]\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m558.20 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m439.77 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m118.43 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0;36m<---------------------------------------|Epoch [67] END|--------------------------------------->\u001b[0m\n",
+ "\u001b[0m\n",
+ "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m68\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m489 (TSEC: 402)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Stage 2]\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+ "\u001b[0;33mPreparing train data...\u001b[0m\n",
+ "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n",
+ "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+ "\u001b[0;32mTraining on subset...\u001b[0m\n",
+ "Epoch 403/408\n",
+ "256/256 [==============================] - 78s 288ms/step - loss: 0.1415 - accuracy: 0.9543 - val_loss: 0.1678 - val_accuracy: 0.9423 - lr: 0.0091\n",
+ "Epoch 404/408\n",
+ "256/256 [==============================] - 72s 280ms/step - loss: 0.0974 - accuracy: 0.9717 - val_loss: 0.2233 - val_accuracy: 0.9167 - lr: 0.0091\n",
+ "Epoch 405/408\n",
+ "256/256 [==============================] - 72s 280ms/step - loss: 0.0535 - accuracy: 0.9839 - val_loss: 0.1927 - val_accuracy: 0.9279 - lr: 0.0091\n",
+ "Epoch 406/408\n",
+ "256/256 [==============================] - 72s 279ms/step - loss: 0.0391 - accuracy: 0.9883 - val_loss: 0.2091 - val_accuracy: 0.9279 - lr: 0.0091\n",
+ "Epoch 407/408\n",
+ "256/256 [==============================] - 72s 281ms/step - loss: 0.0260 - accuracy: 0.9924 - val_loss: 0.2332 - val_accuracy: 0.9295 - lr: 0.0091\n",
+ "Epoch 408/408\n",
+ "256/256 [==============================] - 72s 279ms/step - loss: 0.0215 - accuracy: 0.9949 - val_loss: 0.2343 - val_accuracy: 0.9279 - lr: 0.0091\n",
+ "\u001b[0;32mSubset training done.\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{0.9423}, \u001b[0m\u001b[0;33mloss{0.1678}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9615}, loss{0.1507}]\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9279\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.2343\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9615384340. Not saving model.\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1507172436. Not saving model.\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;32m(GPU-MEM)\u001b[0m\u001b[0;36m----[free: 5.18GB, used: 18.82GB, total, 24.00GB]\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m558.04 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m438.78 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m119.26 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0;36m<---------------------------------------|Epoch [68] END|--------------------------------------->\u001b[0m\n",
+ "\u001b[0m\n",
+ "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m69\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m489 (TSEC: 408)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Stage 2]\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+ "\u001b[0;33mPreparing train data...\u001b[0m\n",
+ "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n",
+ "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+ "\u001b[0;32mTraining on subset...\u001b[0m\n",
+ "Epoch 409/414\n",
+ "256/256 [==============================] - 79s 289ms/step - loss: 0.1623 - accuracy: 0.9475 - val_loss: 0.1975 - val_accuracy: 0.9391 - lr: 0.0091\n",
+ "Epoch 410/414\n",
+ "256/256 [==============================] - 73s 283ms/step - loss: 0.1023 - accuracy: 0.9653 - val_loss: 0.1627 - val_accuracy: 0.9471 - lr: 0.0091\n",
+ "Epoch 411/414\n",
+ "256/256 [==============================] - 72s 281ms/step - loss: 0.0608 - accuracy: 0.9824 - val_loss: 0.2578 - val_accuracy: 0.9199 - lr: 0.0091\n",
+ "Epoch 412/414\n",
+ "256/256 [==============================] - 72s 278ms/step - loss: 0.0401 - accuracy: 0.9888 - val_loss: 0.2376 - val_accuracy: 0.9375 - lr: 0.0091\n",
+ "Epoch 413/414\n",
+ "256/256 [==============================] - 72s 280ms/step - loss: 0.0334 - accuracy: 0.9902 - val_loss: 0.2274 - val_accuracy: 0.9359 - lr: 0.0091\n",
+ "Epoch 414/414\n",
+ "256/256 [==============================] - 72s 279ms/step - loss: 0.0218 - accuracy: 0.9941 - val_loss: 0.2506 - val_accuracy: 0.9311 - lr: 0.0091\n",
+ "\u001b[0;32mSubset training done.\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{0.9471}, \u001b[0m\u001b[0;33mloss{0.1627}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9615}, loss{0.1507}]\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9311\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.2506\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9615384340. Not saving model.\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1507172436. Not saving model.\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;32m(GPU-MEM)\u001b[0m\u001b[0;36m----[free: 5.18GB, used: 18.82GB, total, 24.00GB]\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m559.65 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m439.56 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m120.09 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0;36m<---------------------------------------|Epoch [69] END|--------------------------------------->\u001b[0m\n",
+ "\u001b[0m\n",
+ "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m70\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m489 (TSEC: 414)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Stage 2]\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+ "\u001b[0;33mPreparing train data...\u001b[0m\n",
+ "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n",
+ "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+ "\u001b[0;32mTraining on subset...\u001b[0m\n",
+ "Epoch 415/420\n",
+ "256/256 [==============================] - 78s 289ms/step - loss: 0.1488 - accuracy: 0.9524 - val_loss: 0.1494 - val_accuracy: 0.9423 - lr: 0.0091\n",
+ "Epoch 416/420\n",
+ "256/256 [==============================] - 73s 283ms/step - loss: 0.0901 - accuracy: 0.9719 - val_loss: 0.1522 - val_accuracy: 0.9439 - lr: 0.0091\n",
+ "Epoch 417/420\n",
+ "256/256 [==============================] - 73s 284ms/step - loss: 0.0483 - accuracy: 0.9863 - val_loss: 0.1585 - val_accuracy: 0.9455 - lr: 0.0091\n",
+ "Epoch 418/420\n",
+ "256/256 [==============================] - 72s 280ms/step - loss: 0.0380 - accuracy: 0.9880 - val_loss: 0.2341 - val_accuracy: 0.9263 - lr: 0.0091\n",
+ "Epoch 419/420\n",
+ "256/256 [==============================] - 73s 284ms/step - loss: 0.0260 - accuracy: 0.9922 - val_loss: 0.1966 - val_accuracy: 0.9487 - lr: 0.0091\n",
+ "Epoch 420/420\n",
+ "256/256 [==============================] - 72s 279ms/step - loss: 0.0216 - accuracy: 0.9937 - val_loss: 0.3120 - val_accuracy: 0.9135 - lr: 0.0091\n",
+ "\u001b[0;32mSubset training done.\u001b[0m\n",
+ "\u001b[0;33mLoading the best weights...\u001b[0m\n",
+ "\u001b[0;33mLoading weights from file cache\\model_SUB_checkpoint-419-0.9487.h5...\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9487\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.1967\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9615384340. Not saving model.\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1507172436. Not saving model.\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;32m(GPU-MEM)\u001b[0m\u001b[0;36m----[free: 5.18GB, used: 18.82GB, total, 24.00GB]\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m564.62 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m441.50 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m123.11 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0;36m<---------------------------------------|Epoch [70] END|--------------------------------------->\u001b[0m\n",
+ "\u001b[0m\n",
+ "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m71\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m489 (TSEC: 420)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Stage 2]\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+ "\u001b[0;33mPreparing train data...\u001b[0m\n",
+ "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n",
+ "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+ "\u001b[0;32mTraining on subset...\u001b[0m\n",
+ "Epoch 421/426\n",
+ "256/256 [==============================] - 78s 288ms/step - loss: 0.1333 - accuracy: 0.9587 - val_loss: 0.1808 - val_accuracy: 0.9407 - lr: 0.0091\n",
+ "Epoch 422/426\n",
+ "256/256 [==============================] - 72s 279ms/step - loss: 0.0725 - accuracy: 0.9785 - val_loss: 0.2205 - val_accuracy: 0.9263 - lr: 0.0091\n",
+ "Epoch 423/426\n",
+ "256/256 [==============================] - 72s 280ms/step - loss: 0.0480 - accuracy: 0.9873 - val_loss: 0.2139 - val_accuracy: 0.9311 - lr: 0.0091\n",
+ "Epoch 424/426\n",
+ "256/256 [==============================] - 72s 280ms/step - loss: 0.0302 - accuracy: 0.9927 - val_loss: 0.2470 - val_accuracy: 0.9311 - lr: 0.0091\n",
+ "Epoch 425/426\n",
+ "256/256 [==============================] - 72s 279ms/step - loss: 0.0242 - accuracy: 0.9939 - val_loss: 0.3428 - val_accuracy: 0.8958 - lr: 0.0091\n",
+ "Epoch 426/426\n",
+ "256/256 [==============================] - 72s 280ms/step - loss: 0.0226 - accuracy: 0.9937 - val_loss: 0.2293 - val_accuracy: 0.9359 - lr: 0.0091\n",
+ "\u001b[0;32mSubset training done.\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{0.9407}, \u001b[0m\u001b[0;33mloss{0.1808}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9615}, loss{0.1507}]\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9359\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.2293\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9615384340. Not saving model.\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1507172436. Not saving model.\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;32m(GPU-MEM)\u001b[0m\u001b[0;36m----[free: 5.18GB, used: 18.82GB, total, 24.00GB]\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m561.24 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m438.60 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m122.64 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0;36m<---------------------------------------|Epoch [71] END|--------------------------------------->\u001b[0m\n",
+ "\u001b[0m\n",
+ "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m72\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m489 (TSEC: 426)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Stage 2]\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+ "\u001b[0;33mPreparing train data...\u001b[0m\n",
+ "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n",
+ "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+ "\u001b[0;32mTraining on subset...\u001b[0m\n",
+ "Epoch 427/432\n",
+ "256/256 [==============================] - 78s 288ms/step - loss: 0.1330 - accuracy: 0.9551 - val_loss: 0.1782 - val_accuracy: 0.9375 - lr: 0.0091\n",
+ "Epoch 428/432\n",
+ "256/256 [==============================] - 72s 280ms/step - loss: 0.0870 - accuracy: 0.9707 - val_loss: 0.2001 - val_accuracy: 0.9231 - lr: 0.0091\n",
+ "Epoch 429/432\n",
+ "256/256 [==============================] - 72s 280ms/step - loss: 0.0539 - accuracy: 0.9814 - val_loss: 0.2954 - val_accuracy: 0.9215 - lr: 0.0091\n",
+ "Epoch 430/432\n",
+ "256/256 [==============================] - 72s 280ms/step - loss: 0.0305 - accuracy: 0.9929 - val_loss: 0.3820 - val_accuracy: 0.9151 - lr: 0.0091\n",
+ "Epoch 431/432\n",
+ "256/256 [==============================] - 72s 280ms/step - loss: 0.0232 - accuracy: 0.9956 - val_loss: 0.2771 - val_accuracy: 0.9279 - lr: 0.0091\n",
+ "Epoch 432/432\n",
+ "256/256 [==============================] - 72s 280ms/step - loss: 0.0206 - accuracy: 0.9941 - val_loss: 0.2467 - val_accuracy: 0.9199 - lr: 0.0091\n",
+ "\u001b[0;32mSubset training done.\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{0.9375}, \u001b[0m\u001b[0;33mloss{0.1782}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9615}, loss{0.1507}]\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9199\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.2466\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9615384340. Not saving model.\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1507172436. Not saving model.\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;32m(GPU-MEM)\u001b[0m\u001b[0;36m----[free: 5.16GB, used: 18.84GB, total, 24.00GB]\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m562.22 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m438.66 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m123.56 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0;36m<---------------------------------------|Epoch [72] END|--------------------------------------->\u001b[0m\n",
+ "\u001b[0m\n",
+ "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m73\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m489 (TSEC: 432)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Stage 2]\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+ "\u001b[0;33mPreparing train data...\u001b[0m\n",
+ "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n",
+ "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+ "\u001b[0;32mTraining on subset...\u001b[0m\n",
+ "Epoch 433/438\n",
+ "256/256 [==============================] - 79s 289ms/step - loss: 0.1536 - accuracy: 0.9478 - val_loss: 0.2038 - val_accuracy: 0.9311 - lr: 0.0091\n",
+ "Epoch 434/438\n",
+ "256/256 [==============================] - 72s 280ms/step - loss: 0.0833 - accuracy: 0.9751 - val_loss: 0.2294 - val_accuracy: 0.9087 - lr: 0.0091\n",
+ "Epoch 435/438\n",
+ "256/256 [==============================] - 72s 282ms/step - loss: 0.0488 - accuracy: 0.9866 - val_loss: 0.2332 - val_accuracy: 0.9151 - lr: 0.0091\n",
+ "Epoch 436/438\n",
+ "256/256 [==============================] - 72s 280ms/step - loss: 0.0327 - accuracy: 0.9897 - val_loss: 0.2499 - val_accuracy: 0.9167 - lr: 0.0091\n",
+ "Epoch 437/438\n",
+ "256/256 [==============================] - 72s 280ms/step - loss: 0.0235 - accuracy: 0.9927 - val_loss: 0.3192 - val_accuracy: 0.9119 - lr: 0.0091\n",
+ "Epoch 438/438\n",
+ "256/256 [==============================] - 72s 279ms/step - loss: 0.0188 - accuracy: 0.9944 - val_loss: 0.2932 - val_accuracy: 0.9167 - lr: 0.0091\n",
+ "\u001b[0;32mSubset training done.\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{0.9311}, \u001b[0m\u001b[0;33mloss{0.2038}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9615}, loss{0.1507}]\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9167\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.2931\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9615384340. Not saving model.\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1507172436. Not saving model.\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;32m(GPU-MEM)\u001b[0m\u001b[0;36m----[free: 5.18GB, used: 18.82GB, total, 24.00GB]\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m561.86 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m439.36 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m122.50 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0;36m<---------------------------------------|Epoch [73] END|--------------------------------------->\u001b[0m\n",
+ "\u001b[0m\n",
+ "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m74\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m489 (TSEC: 438)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Stage 2]\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+ "\u001b[0;33mPreparing train data...\u001b[0m\n",
+ "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n",
+ "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+ "\u001b[0;32mTraining on subset...\u001b[0m\n",
+ "Epoch 439/444\n",
+ "256/256 [==============================] - 78s 289ms/step - loss: 0.1456 - accuracy: 0.9500 - val_loss: 0.2129 - val_accuracy: 0.9022 - lr: 0.0091\n",
+ "Epoch 440/444\n",
+ "256/256 [==============================] - 73s 283ms/step - loss: 0.0840 - accuracy: 0.9722 - val_loss: 0.3310 - val_accuracy: 0.9071 - lr: 0.0091\n",
+ "Epoch 441/444\n",
+ "256/256 [==============================] - 73s 284ms/step - loss: 0.0483 - accuracy: 0.9849 - val_loss: 0.3311 - val_accuracy: 0.9151 - lr: 0.0091\n",
+ "Epoch 442/444\n",
+ "256/256 [==============================] - 73s 283ms/step - loss: 0.0315 - accuracy: 0.9915 - val_loss: 0.2708 - val_accuracy: 0.9231 - lr: 0.0091\n",
+ "Epoch 443/444\n",
+ "256/256 [==============================] - 72s 281ms/step - loss: 0.0227 - accuracy: 0.9951 - val_loss: 0.2968 - val_accuracy: 0.9231 - lr: 0.0091\n",
+ "Epoch 444/444\n",
+ "256/256 [==============================] - 72s 280ms/step - loss: 0.0191 - accuracy: 0.9961 - val_loss: 0.2900 - val_accuracy: 0.9183 - lr: 0.0091\n",
+ "\u001b[0;32mSubset training done.\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{0.9231}, \u001b[0m\u001b[0;33mloss{0.2129}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9615}, loss{0.1507}]\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9183\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.2900\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9615384340. Not saving model.\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1507172436. Not saving model.\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;32m(GPU-MEM)\u001b[0m\u001b[0;36m----[free: 5.17GB, used: 18.83GB, total, 24.00GB]\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m569.56 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m442.04 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m127.52 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0;36m<---------------------------------------|Epoch [74] END|--------------------------------------->\u001b[0m\n",
+ "\u001b[0m\n",
+ "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m75\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m489 (TSEC: 444)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Stage 2]\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+ "\u001b[0;33mPreparing train data...\u001b[0m\n",
+ "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n",
+ "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+ "\u001b[0;32mTraining on subset...\u001b[0m\n",
+ "Epoch 445/450\n",
+ "256/256 [==============================] - 79s 289ms/step - loss: 0.1509 - accuracy: 0.9526 - val_loss: 0.2378 - val_accuracy: 0.9006 - lr: 0.0091\n",
+ "Epoch 446/450\n",
+ "256/256 [==============================] - 73s 284ms/step - loss: 0.0915 - accuracy: 0.9707 - val_loss: 0.2024 - val_accuracy: 0.9199 - lr: 0.0091\n",
+ "Epoch 447/450\n",
+ "256/256 [==============================] - 72s 281ms/step - loss: 0.0512 - accuracy: 0.9819 - val_loss: 0.3725 - val_accuracy: 0.9135 - lr: 0.0091\n",
+ "Epoch 448/450\n",
+ "256/256 [==============================] - ETA: 0s - loss: 0.0394 - accuracy: 0.9893\n",
+ "Epoch 448: ReduceLROnPlateau reducing learning rate to 0.008967449011281133.\n",
+ "256/256 [==============================] - 72s 281ms/step - loss: 0.0394 - accuracy: 0.9893 - val_loss: 0.2490 - val_accuracy: 0.9119 - lr: 0.0091\n",
+ "Epoch 449/450\n",
+ "256/256 [==============================] - 73s 284ms/step - loss: 0.0200 - accuracy: 0.9951 - val_loss: 0.2879 - val_accuracy: 0.9231 - lr: 0.0090\n",
+ "Epoch 450/450\n",
+ "256/256 [==============================] - 72s 281ms/step - loss: 0.0201 - accuracy: 0.9954 - val_loss: 0.3820 - val_accuracy: 0.9119 - lr: 0.0090\n",
+ "\u001b[0;32mSubset training done.\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{0.9231}, \u001b[0m\u001b[0;33mloss{0.2024}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9615}, loss{0.1507}]\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9119\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.3820\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9615384340. Not saving model.\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1507172436. Not saving model.\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;32m(GPU-MEM)\u001b[0m\u001b[0;36m----[free: 5.17GB, used: 18.83GB, total, 24.00GB]\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m564.96 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m441.96 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m122.99 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0;36m<---------------------------------------|Epoch [75] END|--------------------------------------->\u001b[0m\n",
+ "\u001b[0m\n",
+ "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m76\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m489 (TSEC: 450)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Stage 2]\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+ "\u001b[0;33mPreparing train data...\u001b[0m\n",
+ "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n",
+ "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+ "\u001b[0;32mTraining on subset...\u001b[0m\n",
+ "Epoch 451/456\n",
+ "256/256 [==============================] - 78s 288ms/step - loss: 0.1432 - accuracy: 0.9556 - val_loss: 0.3285 - val_accuracy: 0.8990 - lr: 0.0090\n",
+ "Epoch 452/456\n",
+ "256/256 [==============================] - 73s 284ms/step - loss: 0.0862 - accuracy: 0.9749 - val_loss: 0.2752 - val_accuracy: 0.9119 - lr: 0.0090\n",
+ "Epoch 453/456\n",
+ "256/256 [==============================] - 73s 284ms/step - loss: 0.0580 - accuracy: 0.9849 - val_loss: 0.2599 - val_accuracy: 0.9215 - lr: 0.0090\n",
+ "Epoch 454/456\n",
+ "256/256 [==============================] - 72s 279ms/step - loss: 0.0491 - accuracy: 0.9873 - val_loss: 0.3314 - val_accuracy: 0.9199 - lr: 0.0090\n",
+ "Epoch 455/456\n",
+ "256/256 [==============================] - 72s 280ms/step - loss: 0.0426 - accuracy: 0.9885 - val_loss: 0.4193 - val_accuracy: 0.9103 - lr: 0.0090\n",
+ "Epoch 456/456\n",
+ "256/256 [==============================] - 72s 279ms/step - loss: 0.0298 - accuracy: 0.9927 - val_loss: 0.3315 - val_accuracy: 0.9183 - lr: 0.0090\n",
+ "\u001b[0;32mSubset training done.\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{0.9215}, \u001b[0m\u001b[0;33mloss{0.2599}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9615}, loss{0.1507}]\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9199\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.3315\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9615384340. Not saving model.\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1507172436. Not saving model.\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;32m(GPU-MEM)\u001b[0m\u001b[0;36m----[free: 5.17GB, used: 18.83GB, total, 24.00GB]\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m565.54 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m440.57 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m124.97 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0;36m<---------------------------------------|Epoch [76] END|--------------------------------------->\u001b[0m\n",
+ "\u001b[0m\n",
+ "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m77\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m489 (TSEC: 456)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Stage 2]\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+ "\u001b[0;33mPreparing train data...\u001b[0m\n",
+ "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n",
+ "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+ "\u001b[0;32mTraining on subset...\u001b[0m\n",
+ "Epoch 457/462\n",
+ "256/256 [==============================] - 78s 288ms/step - loss: 0.1317 - accuracy: 0.9607 - val_loss: 0.1910 - val_accuracy: 0.9359 - lr: 0.0090\n",
+ "Epoch 458/462\n",
+ "256/256 [==============================] - 72s 279ms/step - loss: 0.0786 - accuracy: 0.9778 - val_loss: 0.2189 - val_accuracy: 0.9231 - lr: 0.0090\n",
+ "Epoch 459/462\n",
+ "256/256 [==============================] - 72s 281ms/step - loss: 0.0537 - accuracy: 0.9846 - val_loss: 0.2701 - val_accuracy: 0.9071 - lr: 0.0090\n",
+ "Epoch 460/462\n",
+ "256/256 [==============================] - 72s 279ms/step - loss: 0.0304 - accuracy: 0.9919 - val_loss: 0.2740 - val_accuracy: 0.9279 - lr: 0.0090\n",
+ "Epoch 461/462\n",
+ "256/256 [==============================] - 72s 281ms/step - loss: 0.0240 - accuracy: 0.9937 - val_loss: 0.3064 - val_accuracy: 0.9263 - lr: 0.0090\n",
+ "Epoch 462/462\n",
+ "256/256 [==============================] - 72s 280ms/step - loss: 0.0166 - accuracy: 0.9951 - val_loss: 0.3085 - val_accuracy: 0.9311 - lr: 0.0090\n",
+ "\u001b[0;32mSubset training done.\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{0.9359}, \u001b[0m\u001b[0;33mloss{0.1910}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9615}, loss{0.1507}]\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9311\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.3085\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9615384340. Not saving model.\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1507172436. Not saving model.\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;32m(GPU-MEM)\u001b[0m\u001b[0;36m----[free: 5.18GB, used: 18.82GB, total, 24.00GB]\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m562.59 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m439.06 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m123.53 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0;36m<---------------------------------------|Epoch [77] END|--------------------------------------->\u001b[0m\n",
+ "\u001b[0m\n",
+ "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m78\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m489 (TSEC: 462)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Stage 2]\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+ "\u001b[0;33mPreparing train data...\u001b[0m\n",
+ "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n",
+ "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+ "\u001b[0;32mTraining on subset...\u001b[0m\n",
+ "Epoch 463/468\n",
+ "256/256 [==============================] - 79s 290ms/step - loss: 0.1341 - accuracy: 0.9551 - val_loss: 0.2453 - val_accuracy: 0.8894 - lr: 0.0090\n",
+ "Epoch 464/468\n",
+ "256/256 [==============================] - 73s 284ms/step - loss: 0.0742 - accuracy: 0.9746 - val_loss: 0.2062 - val_accuracy: 0.9199 - lr: 0.0090\n",
+ "Epoch 465/468\n",
+ "256/256 [==============================] - 73s 284ms/step - loss: 0.0496 - accuracy: 0.9841 - val_loss: 0.2408 - val_accuracy: 0.9295 - lr: 0.0090\n",
+ "Epoch 466/468\n",
+ "256/256 [==============================] - 73s 284ms/step - loss: 0.0233 - accuracy: 0.9927 - val_loss: 0.2731 - val_accuracy: 0.9311 - lr: 0.0090\n",
+ "Epoch 467/468\n",
+ "256/256 [==============================] - 73s 282ms/step - loss: 0.0219 - accuracy: 0.9949 - val_loss: 0.2976 - val_accuracy: 0.9295 - lr: 0.0090\n",
+ "Epoch 468/468\n",
+ "256/256 [==============================] - 72s 281ms/step - loss: 0.0160 - accuracy: 0.9954 - val_loss: 0.3426 - val_accuracy: 0.9263 - lr: 0.0090\n",
+ "\u001b[0;32mSubset training done.\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{0.9311}, \u001b[0m\u001b[0;33mloss{0.2062}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9615}, loss{0.1507}]\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9263\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.3425\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9615384340. Not saving model.\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1507172436. Not saving model.\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;32m(GPU-MEM)\u001b[0m\u001b[0;36m----[free: 5.18GB, used: 18.82GB, total, 24.00GB]\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m566.24 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m443.29 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m122.95 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0;36m<---------------------------------------|Epoch [78] END|--------------------------------------->\u001b[0m\n",
+ "\u001b[0m\n",
+ "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m79\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m489 (TSEC: 468)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Stage 2]\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+ "\u001b[0;33mPreparing train data...\u001b[0m\n",
+ "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n",
+ "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+ "\u001b[0;32mTraining on subset...\u001b[0m\n",
+ "Epoch 469/474\n",
+ "256/256 [==============================] - 78s 288ms/step - loss: 0.1446 - accuracy: 0.9548 - val_loss: 0.1887 - val_accuracy: 0.9311 - lr: 0.0090\n",
+ "Epoch 470/474\n",
+ "256/256 [==============================] - 73s 283ms/step - loss: 0.0811 - accuracy: 0.9758 - val_loss: 0.1896 - val_accuracy: 0.9407 - lr: 0.0090\n",
+ "Epoch 471/474\n",
+ "256/256 [==============================] - 72s 281ms/step - loss: 0.0573 - accuracy: 0.9822 - val_loss: 0.2068 - val_accuracy: 0.9359 - lr: 0.0090\n",
+ "Epoch 472/474\n",
+ "256/256 [==============================] - 72s 280ms/step - loss: 0.0319 - accuracy: 0.9912 - val_loss: 0.2018 - val_accuracy: 0.9343 - lr: 0.0090\n",
+ "Epoch 473/474\n",
+ "256/256 [==============================] - 73s 285ms/step - loss: 0.0297 - accuracy: 0.9915 - val_loss: 0.2370 - val_accuracy: 0.9423 - lr: 0.0090\n",
+ "Epoch 474/474\n",
+ "256/256 [==============================] - 72s 280ms/step - loss: 0.0218 - accuracy: 0.9958 - val_loss: 0.2261 - val_accuracy: 0.9423 - lr: 0.0090\n",
+ "\u001b[0;32mSubset training done.\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{0.9423}, \u001b[0m\u001b[0;33mloss{0.1887}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9615}, loss{0.1507}]\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9423\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.2261\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9615384340. Not saving model.\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1507172436. Not saving model.\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;32m(GPU-MEM)\u001b[0m\u001b[0;36m----[free: 5.18GB, used: 18.82GB, total, 24.00GB]\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m570.84 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m441.02 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m129.82 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0;36m<---------------------------------------|Epoch [79] END|--------------------------------------->\u001b[0m\n",
+ "\u001b[0m\n",
+ "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m80\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m489 (TSEC: 474)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Stage 2]\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+ "\u001b[0;33mPreparing train data...\u001b[0m\n",
+ "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n",
+ "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+ "\u001b[0;32mTraining on subset...\u001b[0m\n",
+ "Epoch 475/480\n",
+ "256/256 [==============================] - 79s 291ms/step - loss: 0.1372 - accuracy: 0.9600 - val_loss: 0.1696 - val_accuracy: 0.9407 - lr: 0.0090\n",
+ "Epoch 476/480\n",
+ "256/256 [==============================] - 73s 283ms/step - loss: 0.0866 - accuracy: 0.9739 - val_loss: 0.1879 - val_accuracy: 0.9487 - lr: 0.0090\n",
+ "Epoch 477/480\n",
+ "256/256 [==============================] - 72s 281ms/step - loss: 0.0508 - accuracy: 0.9839 - val_loss: 0.2168 - val_accuracy: 0.9439 - lr: 0.0090\n",
+ "Epoch 478/480\n",
+ "256/256 [==============================] - 72s 280ms/step - loss: 0.0376 - accuracy: 0.9883 - val_loss: 0.2498 - val_accuracy: 0.9439 - lr: 0.0090\n",
+ "Epoch 479/480\n",
+ "256/256 [==============================] - 72s 281ms/step - loss: 0.0249 - accuracy: 0.9941 - val_loss: 0.2011 - val_accuracy: 0.9343 - lr: 0.0090\n",
+ "Epoch 480/480\n",
+ "256/256 [==============================] - 72s 281ms/step - loss: 0.0199 - accuracy: 0.9963 - val_loss: 0.2483 - val_accuracy: 0.9295 - lr: 0.0090\n",
+ "\u001b[0;32mSubset training done.\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{0.9487}, \u001b[0m\u001b[0;33mloss{0.1696}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9615}, loss{0.1507}]\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9295\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.2483\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9615384340. Not saving model.\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1507172436. Not saving model.\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;32m(GPU-MEM)\u001b[0m\u001b[0;36m----[free: 5.17GB, used: 18.82GB, total, 24.00GB]\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m568.81 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m441.35 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m127.46 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0;36m<---------------------------------------|Epoch [80] END|--------------------------------------->\u001b[0m\n",
+ "\u001b[0m\n",
+ "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m81\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m489 (TSEC: 480)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Stage 2]\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+ "\u001b[0;33mPreparing train data...\u001b[0m\n",
+ "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n",
+ "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+ "\u001b[0;32mTraining on subset...\u001b[0m\n",
+ "Epoch 481/486\n",
+ "256/256 [==============================] - 78s 288ms/step - loss: 0.1453 - accuracy: 0.9512 - val_loss: 0.2448 - val_accuracy: 0.9231 - lr: 0.0090\n",
+ "Epoch 482/486\n",
+ "256/256 [==============================] - 72s 282ms/step - loss: 0.0865 - accuracy: 0.9734 - val_loss: 0.5154 - val_accuracy: 0.9119 - lr: 0.0090\n",
+ "Epoch 483/486\n",
+ "256/256 [==============================] - 73s 284ms/step - loss: 0.0515 - accuracy: 0.9846 - val_loss: 0.2043 - val_accuracy: 0.9375 - lr: 0.0090\n",
+ "Epoch 484/486\n",
+ "256/256 [==============================] - 73s 284ms/step - loss: 0.0302 - accuracy: 0.9917 - val_loss: 0.1861 - val_accuracy: 0.9423 - lr: 0.0090\n",
+ "Epoch 485/486\n",
+ "256/256 [==============================] - 72s 280ms/step - loss: 0.0303 - accuracy: 0.9910 - val_loss: 0.6762 - val_accuracy: 0.8990 - lr: 0.0090\n",
+ "Epoch 486/486\n",
+ "256/256 [==============================] - 72s 280ms/step - loss: 0.0413 - accuracy: 0.9907 - val_loss: 0.3812 - val_accuracy: 0.9054 - lr: 0.0090\n",
+ "\u001b[0;32mSubset training done.\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{0.9423}, \u001b[0m\u001b[0;33mloss{0.1861}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9615}, loss{0.1507}]\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9038\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.4067\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9615384340. Not saving model.\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1507172436. Not saving model.\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;32m(GPU-MEM)\u001b[0m\u001b[0;36m----[free: 5.18GB, used: 18.82GB, total, 24.00GB]\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m571.00 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m441.56 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m129.43 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0;36m<---------------------------------------|Epoch [81] END|--------------------------------------->\u001b[0m\n",
+ "\u001b[0m\n",
+ "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m82\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m489 (TSEC: 486)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Stage 2]\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+ "\u001b[0;33mPreparing train data...\u001b[0m\n",
+ "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n",
+ "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+ "\u001b[0;32mTraining on subset...\u001b[0m\n",
+ "Epoch 487/492\n",
+ "256/256 [==============================] - 79s 289ms/step - loss: 0.1381 - accuracy: 0.9534 - val_loss: 0.2291 - val_accuracy: 0.9183 - lr: 0.0090\n",
+ "Epoch 488/492\n",
+ "256/256 [==============================] - 72s 280ms/step - loss: 0.0866 - accuracy: 0.9744 - val_loss: 0.2882 - val_accuracy: 0.8894 - lr: 0.0090\n",
+ "Epoch 489/492\n",
+ "256/256 [==============================] - 72s 282ms/step - loss: 0.0542 - accuracy: 0.9829 - val_loss: 0.3209 - val_accuracy: 0.9006 - lr: 0.0090\n",
+ "Epoch 490/492\n",
+ "256/256 [==============================] - 72s 281ms/step - loss: 0.0381 - accuracy: 0.9883 - val_loss: 0.6577 - val_accuracy: 0.8782 - lr: 0.0090\n",
+ "Epoch 491/492\n",
+ "256/256 [==============================] - 72s 281ms/step - loss: 0.0260 - accuracy: 0.9924 - val_loss: 0.6497 - val_accuracy: 0.8926 - lr: 0.0090\n",
+ "Epoch 492/492\n",
+ "256/256 [==============================] - 72s 280ms/step - loss: 0.0223 - accuracy: 0.9939 - val_loss: 1.7101 - val_accuracy: 0.7997 - lr: 0.0090\n",
+ "\u001b[0;32mSubset training done.\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{0.9183}, \u001b[0m\u001b[0;33mloss{0.2291}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9615}, loss{0.1507}]\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.7965\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m1.7177\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9615384340. Not saving model.\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1507172436. Not saving model.\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;32m(GPU-MEM)\u001b[0m\u001b[0;36m----[free: 5.17GB, used: 18.82GB, total, 24.00GB]\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m568.85 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m440.24 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m128.62 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0;36m<---------------------------------------|Epoch [82] END|--------------------------------------->\u001b[0m\n",
+ "\u001b[0m\n",
+ "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m83\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m489 (TSEC: 492)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Stage 2]\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+ "\u001b[0;33mPreparing train data...\u001b[0m\n",
+ "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n",
+ "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+ "\u001b[0;32mTraining on subset...\u001b[0m\n",
+ "Epoch 493/498\n",
+ "256/256 [==============================] - 79s 289ms/step - loss: 0.1347 - accuracy: 0.9539 - val_loss: 1.3282 - val_accuracy: 0.8141 - lr: 0.0090\n",
+ "Epoch 494/498\n",
+ "256/256 [==============================] - 72s 280ms/step - loss: 0.0738 - accuracy: 0.9780 - val_loss: 1.7828 - val_accuracy: 0.8013 - lr: 0.0090\n",
+ "Epoch 495/498\n",
+ "256/256 [==============================] - 72s 282ms/step - loss: 0.0451 - accuracy: 0.9863 - val_loss: 2.3645 - val_accuracy: 0.8109 - lr: 0.0090\n",
+ "Epoch 496/498\n",
+ "256/256 [==============================] - 73s 285ms/step - loss: 0.0307 - accuracy: 0.9924 - val_loss: 0.8665 - val_accuracy: 0.8734 - lr: 0.0090\n",
+ "Epoch 497/498\n",
+ "256/256 [==============================] - 73s 285ms/step - loss: 0.0228 - accuracy: 0.9949 - val_loss: 0.3687 - val_accuracy: 0.9215 - lr: 0.0090\n",
+ "Epoch 498/498\n",
+ "256/256 [==============================] - 73s 285ms/step - loss: 0.0248 - accuracy: 0.9937 - val_loss: 0.3038 - val_accuracy: 0.9359 - lr: 0.0090\n",
+ "\u001b[0;32mSubset training done.\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{0.9359}, \u001b[0m\u001b[0;33mloss{0.3038}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9615}, loss{0.1507}]\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9359\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.3038\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9615384340. Not saving model.\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1507172436. Not saving model.\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;32m(GPU-MEM)\u001b[0m\u001b[0;36m----[free: 5.18GB, used: 18.82GB, total, 24.00GB]\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m569.57 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m443.66 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m125.92 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0;36m<---------------------------------------|Epoch [83] END|--------------------------------------->\u001b[0m\n",
+ "\u001b[0m\n",
+ "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m84\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m489 (TSEC: 498)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Stage 2]\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+ "\u001b[0;33m└───Shuffling data...\u001b[0m\n",
+ "\u001b[0;33mPreparing train data...\u001b[0m\n",
+ "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n",
+ "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;31m- Debug DP Sample dir: \u001b[0m\u001b[0;32mSamples/TSR_SUB_400_y2024_m03_d30-h09_m07_s13\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+ "\u001b[0;32mTraining on subset...\u001b[0m\n",
+ "Epoch 499/504\n",
+ "256/256 [==============================] - 79s 288ms/step - loss: 0.1438 - accuracy: 0.9543 - val_loss: 0.2068 - val_accuracy: 0.9295 - lr: 0.0090\n",
+ "Epoch 500/504\n",
+ "256/256 [==============================] - 72s 280ms/step - loss: 0.0814 - accuracy: 0.9758 - val_loss: 0.3795 - val_accuracy: 0.9054 - lr: 0.0090\n",
+ "Epoch 501/504\n",
+ "256/256 [==============================] - 72s 280ms/step - loss: 0.0593 - accuracy: 0.9827 - val_loss: 0.6725 - val_accuracy: 0.8942 - lr: 0.0090\n",
+ "Epoch 502/504\n",
+ "256/256 [==============================] - 72s 280ms/step - loss: 0.0305 - accuracy: 0.9927 - val_loss: 0.4958 - val_accuracy: 0.9151 - lr: 0.0090\n",
+ "Epoch 503/504\n",
+ "256/256 [==============================] - 72s 281ms/step - loss: 0.0261 - accuracy: 0.9939 - val_loss: 0.3201 - val_accuracy: 0.9279 - lr: 0.0090\n",
+ "Epoch 504/504\n",
+ "256/256 [==============================] - 72s 281ms/step - loss: 0.0251 - accuracy: 0.9941 - val_loss: 0.5018 - val_accuracy: 0.9151 - lr: 0.0090\n",
+ "\u001b[0;32mSubset training done.\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{0.9295}, \u001b[0m\u001b[0;33mloss{0.2068}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9615}, loss{0.1507}]\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9151\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.4984\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9615384340. Not saving model.\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1507172436. Not saving model.\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;32m(GPU-MEM)\u001b[0m\u001b[0;36m----[free: 5.18GB, used: 18.82GB, total, 24.00GB]\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m581.46 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m439.90 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m141.55 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0;36m<---------------------------------------|Epoch [84] END|--------------------------------------->\u001b[0m\n",
+ "\u001b[0m\n",
+ "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m85\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m489 (TSEC: 504)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Stage 2]\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+ "\u001b[0;33mPreparing train data...\u001b[0m\n",
+ "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n",
+ "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+ "\u001b[0;32mTraining on subset...\u001b[0m\n",
+ "Epoch 505/510\n",
+ "256/256 [==============================] - 78s 288ms/step - loss: 0.1284 - accuracy: 0.9575 - val_loss: 0.9978 - val_accuracy: 0.8830 - lr: 0.0090\n",
+ "Epoch 506/510\n",
+ "256/256 [==============================] - 73s 284ms/step - loss: 0.0736 - accuracy: 0.9758 - val_loss: 0.7478 - val_accuracy: 0.8910 - lr: 0.0090\n",
+ "Epoch 507/510\n",
+ "256/256 [==============================] - 73s 285ms/step - loss: 0.0506 - accuracy: 0.9856 - val_loss: 0.5992 - val_accuracy: 0.9054 - lr: 0.0090\n",
+ "Epoch 508/510\n",
+ "256/256 [==============================] - 72s 281ms/step - loss: 0.0228 - accuracy: 0.9939 - val_loss: 0.7798 - val_accuracy: 0.8782 - lr: 0.0090\n",
+ "Epoch 509/510\n",
+ "256/256 [==============================] - 72s 282ms/step - loss: 0.0185 - accuracy: 0.9958 - val_loss: 0.7670 - val_accuracy: 0.8750 - lr: 0.0090\n",
+ "Epoch 510/510\n",
+ "256/256 [==============================] - 72s 281ms/step - loss: 0.0102 - accuracy: 0.9976 - val_loss: 0.8993 - val_accuracy: 0.8670 - lr: 0.0090\n",
+ "\u001b[0;32mSubset training done.\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{0.9054}, \u001b[0m\u001b[0;33mloss{0.5992}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9615}, loss{0.1507}]\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.8670\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.8991\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9615384340. Not saving model.\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1507172436. Not saving model.\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;32m(GPU-MEM)\u001b[0m\u001b[0;36m----[free: 5.18GB, used: 18.82GB, total, 24.00GB]\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m568.18 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m442.62 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m125.56 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0;36m<---------------------------------------|Epoch [85] END|--------------------------------------->\u001b[0m\n",
+ "\u001b[0m\n",
+ "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m86\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m489 (TSEC: 510)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Stage 2]\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+ "\u001b[0;33mPreparing train data...\u001b[0m\n",
+ "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n",
+ "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+ "\u001b[0;32mTraining on subset...\u001b[0m\n",
+ "Epoch 511/516\n",
+ "256/256 [==============================] - 79s 289ms/step - loss: 0.1551 - accuracy: 0.9495 - val_loss: 0.3373 - val_accuracy: 0.8862 - lr: 0.0090\n",
+ "Epoch 512/516\n",
+ "256/256 [==============================] - 72s 281ms/step - loss: 0.0868 - accuracy: 0.9727 - val_loss: 0.8053 - val_accuracy: 0.8814 - lr: 0.0090\n",
+ "Epoch 513/516\n",
+ "256/256 [==============================] - ETA: 0s - loss: 0.0534 - accuracy: 0.9841\n",
+ "Epoch 513: ReduceLROnPlateau reducing learning rate to 0.008806034876033663.\n",
+ "256/256 [==============================] - 73s 286ms/step - loss: 0.0534 - accuracy: 0.9841 - val_loss: 0.2876 - val_accuracy: 0.9167 - lr: 0.0090\n",
+ "Epoch 514/516\n",
+ "256/256 [==============================] - 72s 282ms/step - loss: 0.0333 - accuracy: 0.9900 - val_loss: 0.6914 - val_accuracy: 0.8878 - lr: 0.0088\n",
+ "Epoch 515/516\n",
+ "256/256 [==============================] - 73s 283ms/step - loss: 0.0242 - accuracy: 0.9932 - val_loss: 0.5452 - val_accuracy: 0.8942 - lr: 0.0088\n",
+ "Epoch 516/516\n",
+ "256/256 [==============================] - 72s 281ms/step - loss: 0.0241 - accuracy: 0.9934 - val_loss: 0.7726 - val_accuracy: 0.8638 - lr: 0.0088\n",
+ "\u001b[0;32mSubset training done.\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{0.9167}, \u001b[0m\u001b[0;33mloss{0.2876}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9615}, loss{0.1507}]\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.8638\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.7727\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9615384340. Not saving model.\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1507172436. Not saving model.\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;32m(GPU-MEM)\u001b[0m\u001b[0;36m----[free: 5.18GB, used: 18.82GB, total, 24.00GB]\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m574.58 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m442.74 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m131.84 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0;36m<---------------------------------------|Epoch [86] END|--------------------------------------->\u001b[0m\n",
+ "\u001b[0m\n",
+ "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m87\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m489 (TSEC: 516)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Stage 2]\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+ "\u001b[0;33mPreparing train data...\u001b[0m\n",
+ "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n",
+ "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+ "\u001b[0;32mTraining on subset...\u001b[0m\n",
+ "Epoch 517/522\n",
+ "256/256 [==============================] - 79s 291ms/step - loss: 0.1453 - accuracy: 0.9541 - val_loss: 0.4466 - val_accuracy: 0.8974 - lr: 0.0088\n",
+ "Epoch 518/522\n",
+ "256/256 [==============================] - 72s 281ms/step - loss: 0.0795 - accuracy: 0.9761 - val_loss: 0.5019 - val_accuracy: 0.8926 - lr: 0.0088\n",
+ "Epoch 519/522\n",
+ "256/256 [==============================] - 72s 281ms/step - loss: 0.0578 - accuracy: 0.9841 - val_loss: 0.6239 - val_accuracy: 0.8622 - lr: 0.0088\n",
+ "Epoch 520/522\n",
+ "256/256 [==============================] - 73s 284ms/step - loss: 0.0289 - accuracy: 0.9915 - val_loss: 0.4784 - val_accuracy: 0.9006 - lr: 0.0088\n",
+ "Epoch 521/522\n",
+ "256/256 [==============================] - 72s 282ms/step - loss: 0.0198 - accuracy: 0.9927 - val_loss: 0.8253 - val_accuracy: 0.8846 - lr: 0.0088\n",
+ "Epoch 522/522\n",
+ "256/256 [==============================] - 72s 281ms/step - loss: 0.0190 - accuracy: 0.9954 - val_loss: 1.0015 - val_accuracy: 0.8702 - lr: 0.0088\n",
+ "\u001b[0;32mSubset training done.\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{0.9006}, \u001b[0m\u001b[0;33mloss{0.4466}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9615}, loss{0.1507}]\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.8702\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m1.0208\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9615384340. Not saving model.\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1507172436. Not saving model.\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;32m(GPU-MEM)\u001b[0m\u001b[0;36m----[free: 5.18GB, used: 18.82GB, total, 24.00GB]\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m573.36 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m441.95 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m131.40 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0;36m<---------------------------------------|Epoch [87] END|--------------------------------------->\u001b[0m\n",
+ "\u001b[0m\n",
+ "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m88\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m489 (TSEC: 522)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Stage 2]\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+ "\u001b[0;33mPreparing train data...\u001b[0m\n",
+ "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n",
+ "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+ "\u001b[0;32mTraining on subset...\u001b[0m\n",
+ "Epoch 523/528\n",
+ "256/256 [==============================] - 79s 289ms/step - loss: 0.1377 - accuracy: 0.9536 - val_loss: 0.3953 - val_accuracy: 0.9038 - lr: 0.0088\n",
+ "Epoch 524/528\n",
+ "256/256 [==============================] - 72s 281ms/step - loss: 0.0610 - accuracy: 0.9807 - val_loss: 0.4758 - val_accuracy: 0.9038 - lr: 0.0088\n",
+ "Epoch 525/528\n",
+ "256/256 [==============================] - 73s 284ms/step - loss: 0.0381 - accuracy: 0.9873 - val_loss: 0.5061 - val_accuracy: 0.9087 - lr: 0.0088\n",
+ "Epoch 526/528\n",
+ "256/256 [==============================] - 72s 280ms/step - loss: 0.0293 - accuracy: 0.9917 - val_loss: 0.8461 - val_accuracy: 0.8990 - lr: 0.0088\n",
+ "Epoch 527/528\n",
+ "256/256 [==============================] - 73s 284ms/step - loss: 0.0155 - accuracy: 0.9958 - val_loss: 0.4623 - val_accuracy: 0.9183 - lr: 0.0088\n",
+ "Epoch 528/528\n",
+ "256/256 [==============================] - 73s 284ms/step - loss: 0.0247 - accuracy: 0.9941 - val_loss: 0.2015 - val_accuracy: 0.9535 - lr: 0.0088\n",
+ "\u001b[0;32mSubset training done.\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{0.9535}, \u001b[0m\u001b[0;33mloss{0.2015}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9615}, loss{0.1507}]\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9535\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.2014\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9615384340. Not saving model.\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1507172436. Not saving model.\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;32m(GPU-MEM)\u001b[0m\u001b[0;36m----[free: 5.18GB, used: 18.82GB, total, 24.00GB]\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m572.14 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m442.48 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m129.67 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0;36m<---------------------------------------|Epoch [88] END|--------------------------------------->\u001b[0m\n",
+ "\u001b[0m\n",
+ "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m89\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m489 (TSEC: 528)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Stage 2]\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+ "\u001b[0;33mPreparing train data...\u001b[0m\n",
+ "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n",
+ "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+ "\u001b[0;32mTraining on subset...\u001b[0m\n",
+ "Epoch 529/534\n",
+ "256/256 [==============================] - 79s 290ms/step - loss: 0.1402 - accuracy: 0.9553 - val_loss: 0.3024 - val_accuracy: 0.9359 - lr: 0.0088\n",
+ "Epoch 530/534\n",
+ "256/256 [==============================] - 72s 281ms/step - loss: 0.0776 - accuracy: 0.9746 - val_loss: 0.7350 - val_accuracy: 0.9151 - lr: 0.0088\n",
+ "Epoch 531/534\n",
+ "256/256 [==============================] - 72s 281ms/step - loss: 0.0541 - accuracy: 0.9839 - val_loss: 1.0363 - val_accuracy: 0.8894 - lr: 0.0088\n",
+ "Epoch 532/534\n",
+ "256/256 [==============================] - 73s 283ms/step - loss: 0.0306 - accuracy: 0.9917 - val_loss: 2.3302 - val_accuracy: 0.8574 - lr: 0.0088\n",
+ "Epoch 533/534\n",
+ "256/256 [==============================] - 72s 282ms/step - loss: 0.0208 - accuracy: 0.9946 - val_loss: 2.3128 - val_accuracy: 0.8462 - lr: 0.0088\n",
+ "Epoch 534/534\n",
+ "256/256 [==============================] - 72s 281ms/step - loss: 0.0186 - accuracy: 0.9954 - val_loss: 1.8001 - val_accuracy: 0.8654 - lr: 0.0088\n",
+ "\u001b[0;32mSubset training done.\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{0.9359}, \u001b[0m\u001b[0;33mloss{0.3024}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9615}, loss{0.1507}]\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.8654\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m1.7998\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9615384340. Not saving model.\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1507172436. Not saving model.\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;32m(GPU-MEM)\u001b[0m\u001b[0;36m----[free: 5.18GB, used: 18.82GB, total, 24.00GB]\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m572.27 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m441.16 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m131.11 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0;36m<---------------------------------------|Epoch [89] END|--------------------------------------->\u001b[0m\n",
+ "\u001b[0m\n",
+ "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m90\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m489 (TSEC: 534)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Stage 2]\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+ "\u001b[0;33mPreparing train data...\u001b[0m\n",
+ "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n",
+ "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+ "\u001b[0;32mTraining on subset...\u001b[0m\n",
+ "Epoch 535/540\n",
+ "256/256 [==============================] - 79s 290ms/step - loss: 0.1489 - accuracy: 0.9570 - val_loss: 0.4613 - val_accuracy: 0.8846 - lr: 0.0088\n",
+ "Epoch 536/540\n",
+ "256/256 [==============================] - 73s 285ms/step - loss: 0.0844 - accuracy: 0.9763 - val_loss: 0.1915 - val_accuracy: 0.9375 - lr: 0.0088\n",
+ "Epoch 537/540\n",
+ "256/256 [==============================] - 72s 282ms/step - loss: 0.0488 - accuracy: 0.9880 - val_loss: 0.2034 - val_accuracy: 0.9295 - lr: 0.0088\n",
+ "Epoch 538/540\n",
+ "256/256 [==============================] - 72s 282ms/step - loss: 0.0293 - accuracy: 0.9932 - val_loss: 0.5313 - val_accuracy: 0.9054 - lr: 0.0088\n",
+ "Epoch 539/540\n",
+ "256/256 [==============================] - 73s 283ms/step - loss: 0.0214 - accuracy: 0.9951 - val_loss: 0.4062 - val_accuracy: 0.9183 - lr: 0.0088\n",
+ "Epoch 540/540\n",
+ "256/256 [==============================] - 72s 281ms/step - loss: 0.0269 - accuracy: 0.9934 - val_loss: 0.5009 - val_accuracy: 0.9183 - lr: 0.0088\n",
+ "\u001b[0;32mSubset training done.\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{0.9375}, \u001b[0m\u001b[0;33mloss{0.1915}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9615}, loss{0.1507}]\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9183\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.5005\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9615384340. Not saving model.\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1507172436. Not saving model.\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;32m(GPU-MEM)\u001b[0m\u001b[0;36m----[free: 5.18GB, used: 18.82GB, total, 24.00GB]\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m575.44 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m442.71 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m132.73 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0;36m<---------------------------------------|Epoch [90] END|--------------------------------------->\u001b[0m\n",
+ "\u001b[0m\n",
+ "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m91\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m489 (TSEC: 540)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Stage 2]\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+ "\u001b[0;33mPreparing train data...\u001b[0m\n",
+ "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n",
+ "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+ "\u001b[0;32mTraining on subset...\u001b[0m\n",
+ "Epoch 541/546\n",
+ "256/256 [==============================] - 79s 291ms/step - loss: 0.1381 - accuracy: 0.9539 - val_loss: 0.1409 - val_accuracy: 0.9503 - lr: 0.0088\n",
+ "Epoch 542/546\n",
+ "256/256 [==============================] - 72s 281ms/step - loss: 0.0859 - accuracy: 0.9731 - val_loss: 0.1610 - val_accuracy: 0.9455 - lr: 0.0088\n",
+ "Epoch 543/546\n",
+ "256/256 [==============================] - 73s 283ms/step - loss: 0.0549 - accuracy: 0.9832 - val_loss: 0.1565 - val_accuracy: 0.9471 - lr: 0.0088\n",
+ "Epoch 544/546\n",
+ "256/256 [==============================] - 72s 281ms/step - loss: 0.0320 - accuracy: 0.9912 - val_loss: 0.2411 - val_accuracy: 0.9199 - lr: 0.0088\n",
+ "Epoch 545/546\n",
+ "256/256 [==============================] - 73s 283ms/step - loss: 0.0251 - accuracy: 0.9944 - val_loss: 0.2242 - val_accuracy: 0.9279 - lr: 0.0088\n",
+ "Epoch 546/546\n",
+ "256/256 [==============================] - 72s 282ms/step - loss: 0.0158 - accuracy: 0.9961 - val_loss: 0.3142 - val_accuracy: 0.9247 - lr: 0.0088\n",
+ "\u001b[0;32mSubset training done.\u001b[0m\n",
+ "\u001b[0;33mLoading the best weights...\u001b[0m\n",
+ "\u001b[0;33mLoading weights from file cache\\model_SUB_checkpoint-541-0.9503.h5...\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9503\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.1409\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9615384340. Not saving model.\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;32mImproved model loss from 0.15071724 to 0.14092456. \u001b[0m\u001b[0;96mSaving model.\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;36mSaving full model H5 format...\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;32m(GPU-MEM)\u001b[0m\u001b[0;36m----[free: 5.17GB, used: 18.82GB, total, 24.00GB]\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m580.86 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m442.09 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m138.76 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0;36m<---------------------------------------|Epoch [91] END|--------------------------------------->\u001b[0m\n",
+ "\u001b[0m\n",
+ "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m92\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m489 (TSEC: 546)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Stage 2]\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+ "\u001b[0;33mPreparing train data...\u001b[0m\n",
+ "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n",
+ "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+ "\u001b[0;32mTraining on subset...\u001b[0m\n",
+ "Epoch 547/552\n",
+ "256/256 [==============================] - 79s 290ms/step - loss: 0.1398 - accuracy: 0.9526 - val_loss: 0.4507 - val_accuracy: 0.9071 - lr: 0.0088\n",
+ "Epoch 548/552\n",
+ "256/256 [==============================] - 73s 285ms/step - loss: 0.0904 - accuracy: 0.9714 - val_loss: 0.4890 - val_accuracy: 0.9183 - lr: 0.0088\n",
+ "Epoch 549/552\n",
+ "256/256 [==============================] - 73s 286ms/step - loss: 0.0518 - accuracy: 0.9824 - val_loss: 0.4768 - val_accuracy: 0.9199 - lr: 0.0088\n",
+ "Epoch 550/552\n",
+ "256/256 [==============================] - 73s 282ms/step - loss: 0.0311 - accuracy: 0.9912 - val_loss: 1.0640 - val_accuracy: 0.8862 - lr: 0.0088\n",
+ "Epoch 551/552\n",
+ "256/256 [==============================] - 73s 282ms/step - loss: 0.0231 - accuracy: 0.9932 - val_loss: 0.8470 - val_accuracy: 0.8974 - lr: 0.0088\n",
+ "Epoch 552/552\n",
+ "256/256 [==============================] - 73s 283ms/step - loss: 0.0222 - accuracy: 0.9951 - val_loss: 0.8176 - val_accuracy: 0.9135 - lr: 0.0088\n",
+ "\u001b[0;32mSubset training done.\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{0.9199}, \u001b[0m\u001b[0;33mloss{0.4507}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9615}, loss{0.1409}]\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9135\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.8165\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9615384340. Not saving model.\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1409245580. Not saving model.\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;32m(GPU-MEM)\u001b[0m\u001b[0;36m----[free: 5.18GB, used: 18.82GB, total, 24.00GB]\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m578.78 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m443.96 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m134.82 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0;36m<---------------------------------------|Epoch [92] END|--------------------------------------->\u001b[0m\n",
+ "\u001b[0m\n",
+ "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m93\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m489 (TSEC: 552)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Stage 2]\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+ "\u001b[0;33mPreparing train data...\u001b[0m\n",
+ "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n",
+ "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+ "\u001b[0;32mTraining on subset...\u001b[0m\n",
+ "Epoch 553/558\n",
+ "256/256 [==============================] - 79s 290ms/step - loss: 0.1370 - accuracy: 0.9578 - val_loss: 0.4271 - val_accuracy: 0.9199 - lr: 0.0088\n",
+ "Epoch 554/558\n",
+ "256/256 [==============================] - 73s 285ms/step - loss: 0.0709 - accuracy: 0.9800 - val_loss: 0.2865 - val_accuracy: 0.9247 - lr: 0.0088\n",
+ "Epoch 555/558\n",
+ "256/256 [==============================] - 72s 282ms/step - loss: 0.0485 - accuracy: 0.9873 - val_loss: 0.5766 - val_accuracy: 0.8990 - lr: 0.0088\n",
+ "Epoch 556/558\n",
+ "256/256 [==============================] - 72s 281ms/step - loss: 0.0308 - accuracy: 0.9934 - val_loss: 0.3557 - val_accuracy: 0.9199 - lr: 0.0088\n",
+ "Epoch 557/558\n",
+ "256/256 [==============================] - 73s 286ms/step - loss: 0.0189 - accuracy: 0.9946 - val_loss: 0.3058 - val_accuracy: 0.9343 - lr: 0.0088\n",
+ "Epoch 558/558\n",
+ "256/256 [==============================] - 72s 282ms/step - loss: 0.0183 - accuracy: 0.9968 - val_loss: 0.2928 - val_accuracy: 0.9327 - lr: 0.0088\n",
+ "\u001b[0;32mSubset training done.\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{0.9343}, \u001b[0m\u001b[0;33mloss{0.2865}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9615}, loss{0.1409}]\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9327\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.2928\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9615384340. Not saving model.\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1409245580. Not saving model.\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;32m(GPU-MEM)\u001b[0m\u001b[0;36m----[free: 5.18GB, used: 18.82GB, total, 24.00GB]\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m576.39 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m443.67 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m132.72 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0;36m<---------------------------------------|Epoch [93] END|--------------------------------------->\u001b[0m\n",
+ "\u001b[0m\n",
+ "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m94\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m489 (TSEC: 558)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Stage 2]\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+ "\u001b[0;33mPreparing train data...\u001b[0m\n",
+ "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n",
+ "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+ "\u001b[0;32mTraining on subset...\u001b[0m\n",
+ "Epoch 559/564\n",
+ "256/256 [==============================] - 79s 289ms/step - loss: 0.1468 - accuracy: 0.9463 - val_loss: 0.3421 - val_accuracy: 0.8990 - lr: 0.0088\n",
+ "Epoch 560/564\n",
+ "256/256 [==============================] - 73s 284ms/step - loss: 0.0839 - accuracy: 0.9719 - val_loss: 0.3228 - val_accuracy: 0.9247 - lr: 0.0088\n",
+ "Epoch 561/564\n",
+ "256/256 [==============================] - 72s 281ms/step - loss: 0.0465 - accuracy: 0.9854 - val_loss: 0.2343 - val_accuracy: 0.9231 - lr: 0.0088\n",
+ "Epoch 562/564\n",
+ "256/256 [==============================] - 72s 279ms/step - loss: 0.0293 - accuracy: 0.9902 - val_loss: 0.4216 - val_accuracy: 0.9151 - lr: 0.0088\n",
+ "Epoch 563/564\n",
+ "256/256 [==============================] - 73s 284ms/step - loss: 0.0215 - accuracy: 0.9939 - val_loss: 0.3030 - val_accuracy: 0.9263 - lr: 0.0088\n",
+ "Epoch 564/564\n",
+ "256/256 [==============================] - 73s 284ms/step - loss: 0.0165 - accuracy: 0.9956 - val_loss: 0.2694 - val_accuracy: 0.9327 - lr: 0.0088\n",
+ "\u001b[0;32mSubset training done.\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{0.9327}, \u001b[0m\u001b[0;33mloss{0.2343}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9615}, loss{0.1409}]\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9327\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.2693\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9615384340. Not saving model.\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1409245580. Not saving model.\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;32m(GPU-MEM)\u001b[0m\u001b[0;36m----[free: 5.18GB, used: 18.82GB, total, 24.00GB]\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m574.38 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m442.23 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m132.15 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0;36m<---------------------------------------|Epoch [94] END|--------------------------------------->\u001b[0m\n",
+ "\u001b[0m\n",
+ "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m95\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m489 (TSEC: 564)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Stage 2]\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+ "\u001b[0;33mPreparing train data...\u001b[0m\n",
+ "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n",
+ "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+ "\u001b[0;32mTraining on subset...\u001b[0m\n",
+ "Epoch 565/570\n",
+ "256/256 [==============================] - 79s 290ms/step - loss: 0.1450 - accuracy: 0.9534 - val_loss: 0.2132 - val_accuracy: 0.9311 - lr: 0.0088\n",
+ "Epoch 566/570\n",
+ "256/256 [==============================] - 73s 285ms/step - loss: 0.0841 - accuracy: 0.9724 - val_loss: 0.2178 - val_accuracy: 0.9343 - lr: 0.0088\n",
+ "Epoch 567/570\n",
+ "256/256 [==============================] - 72s 282ms/step - loss: 0.0584 - accuracy: 0.9822 - val_loss: 0.3858 - val_accuracy: 0.9167 - lr: 0.0088\n",
+ "Epoch 568/570\n",
+ "256/256 [==============================] - 72s 282ms/step - loss: 0.0284 - accuracy: 0.9924 - val_loss: 0.2594 - val_accuracy: 0.9343 - lr: 0.0088\n",
+ "Epoch 569/570\n",
+ "256/256 [==============================] - 72s 282ms/step - loss: 0.0303 - accuracy: 0.9917 - val_loss: 0.7569 - val_accuracy: 0.8894 - lr: 0.0088\n",
+ "Epoch 570/570\n",
+ "256/256 [==============================] - 72s 282ms/step - loss: 0.0161 - accuracy: 0.9968 - val_loss: 1.0834 - val_accuracy: 0.8910 - lr: 0.0088\n",
+ "\u001b[0;32mSubset training done.\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{0.9343}, \u001b[0m\u001b[0;33mloss{0.2132}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9615}, loss{0.1409}]\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.8910\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m1.0829\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9615384340. Not saving model.\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1409245580. Not saving model.\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;32m(GPU-MEM)\u001b[0m\u001b[0;36m----[free: 5.18GB, used: 18.82GB, total, 24.00GB]\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m576.87 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m442.54 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m134.34 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0;36m<---------------------------------------|Epoch [95] END|--------------------------------------->\u001b[0m\n",
+ "\u001b[0m\n",
+ "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m96\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m489 (TSEC: 570)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Stage 2]\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+ "\u001b[0;33mPreparing train data...\u001b[0m\n",
+ "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n",
+ "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+ "\u001b[0;32mTraining on subset...\u001b[0m\n",
+ "Epoch 571/576\n",
+ "256/256 [==============================] - 79s 289ms/step - loss: 0.1359 - accuracy: 0.9592 - val_loss: 0.8639 - val_accuracy: 0.8333 - lr: 0.0088\n",
+ "Epoch 572/576\n",
+ "256/256 [==============================] - 73s 283ms/step - loss: 0.0774 - accuracy: 0.9768 - val_loss: 0.9179 - val_accuracy: 0.8526 - lr: 0.0088\n",
+ "Epoch 573/576\n",
+ "256/256 [==============================] - 72s 281ms/step - loss: 0.0396 - accuracy: 0.9885 - val_loss: 1.5042 - val_accuracy: 0.8381 - lr: 0.0088\n",
+ "Epoch 574/576\n",
+ "256/256 [==============================] - ETA: 0s - loss: 0.0300 - accuracy: 0.9919\n",
"KeyboardInterrupt. (Training stopped)\n",
"Training done.\n",
"\n"
@@ -6316,7 +10404,7 @@
"subset_size = 4096 # subset_size: Size of each training subset. Common values: 512, 1024, 2048, 3200, 4096, 5846, 8192.\n",
"Conf_batch_size_REV2 = 16 # Conf_batch_size_REV2: Batch size.\n",
"RES_Train = False # RES_Train: Resume training if True.\n",
- "STR_M = 0.86 # STR_M: Starting momentum.\n",
+ "STR_M = 0.89 # STR_M: Starting momentum.\n",
"STR_LR = 0.01 # STR_LR: Starting learning rate.\n",
"MAX_LR = 0.01 # MAX_LR: Maximum learning rate.\n",
"DEC_LR = 0.00005 # DEC_LR: Learning rate decay.\n",
@@ -6651,7 +10739,7 @@
" \"early_stopping\": Use_ES_ONSUBT,\n",
" \"tensorboard_callback\": True,\n",
" \"confusion_matrix_callback\": Show_confusion_matrix_tensorBoard,\n",
- " \"TerminateOnNaN_callback\": True\n",
+ " \"TerminateOnNaN_callback\": True,\n",
"}\n",
"# MAIN\n",
"print(\"Training the model...\")\n",
@@ -6689,7 +10777,7 @@
" [\"cyan\", \"green\", \"cyan\"],\n",
" advanced_mode=True,\n",
")\n",
- "print_optimizer_info(model) # noqa: F405\n",
+ "print_optimizer_info(model) # noqa: F405\n",
"# warnings\n",
"P_warning(\"[RES_Train -> True].\") if RES_Train else None # noqa: F405\n",
"P_warning(\"[TerminateOnHighTemp_M -> False] GPU temperature protection is OFF\") if not TerminateOnHighTemp_M else None # noqa: F405\n",
diff --git a/Model_T&T.ipynb b/Model_T&T.ipynb
index 2fc54a5..9988e0e 100644
--- a/Model_T&T.ipynb
+++ b/Model_T&T.ipynb
@@ -29,7 +29,7 @@
"start_time": "2023-12-28T02:27:44.923095500Z"
},
"notebookRunGroups": {
- "groupValue": "21"
+ "groupValue": "213"
}
},
"outputs": [],
@@ -53,7 +53,7 @@
"start_time": "2023-12-28T02:27:44.940432900Z"
},
"notebookRunGroups": {
- "groupValue": "12"
+ "groupValue": "123"
}
},
"outputs": [],
@@ -87,11 +87,12 @@
"from adabelief_tf import AdaBeliefOptimizer # noqa: F401\n",
"\n",
"# from tensorflow_addons.optimizers import Yogi\n",
+ "import keras.backend as K # noqa: F401\n",
"from imblearn.over_sampling import SMOTE\n",
"from keras.regularizers import l2\n",
"from keras.models import load_model\n",
"from keras.callbacks import EarlyStopping\n",
- "from keras.callbacks import TensorBoard, LambdaCallback\n",
+ "from keras.callbacks import TensorBoard, LambdaCallback, TerminateOnNaN\n",
"from keras.utils import to_categorical\n",
"from keras.callbacks import (\n",
" ModelCheckpoint,\n",
@@ -136,7 +137,7 @@
"execution_count": 3,
"metadata": {
"notebookRunGroups": {
- "groupValue": "12"
+ "groupValue": "123"
}
},
"outputs": [],
@@ -175,7 +176,7 @@
"start_time": "2023-12-28T02:27:47.116546100Z"
},
"notebookRunGroups": {
- "groupValue": "12"
+ "groupValue": "123"
}
},
"outputs": [],
@@ -221,7 +222,7 @@
"start_time": "2023-12-28T02:27:48.252944800Z"
},
"notebookRunGroups": {
- "groupValue": "12"
+ "groupValue": "123"
}
},
"outputs": [
@@ -229,13 +230,13 @@
"name": "stdout",
"output_type": "stream",
"text": [
- "\n"
+ "\n"
]
}
],
"source": [
"SAVE_TYPE = \"H5\"\n",
- "Use_mixed_float16 = True\n",
+ "Use_mixed_float16 = False\n",
"# Other\n",
"if Use_mixed_float16:\n",
" tf.keras.mixed_precision.set_global_policy(\"mixed_float16\")\n",
@@ -271,19 +272,7 @@
"text": [
"\u001b[0;33mUsing Def IDG...\u001b[0m\n",
"Found 23681 images belonging to 2 classes.\n",
- "\u001b[0;33mLoading all images and labels into memory...\u001b[0m\n",
- "\u001b[0;33mMaking categorical data...\u001b[0m\n",
- "\u001b[0m\u001b[0m\u001b[0;33mGenerating augmented data \u001b[0m\u001b[0;36m[\u001b[0m\u001b[0;32mADBD: \u001b[0m\u001b[0;31m0\u001b[0m\u001b[0;36m]\u001b[0m\u001b[0;33m...\u001b[0m\n",
- "\u001b[0;33mNormalizing image data...\u001b[0m\n",
- "\u001b[0m\u001b[0m\u001b[0mData type: \u001b[0m\u001b[0;32mfloat16\u001b[0m\n",
- "\u001b[0m\u001b[0m\u001b[0mRGB Range: \u001b[0m\u001b[0;34mMin = 0.0\u001b[0m\u001b[0m | \u001b[0m\u001b[0;31mMax = 1.0\u001b[0m\n",
- "\u001b[0m\u001b[0m\u001b[0mLabel ratio: \u001b[0m\u001b[0;31m49.35% PNEUMONIA \u001b[0m\u001b[0;35m| \u001b[0m\u001b[0;32m50.65% NORMAL\u001b[0m\n",
- "\u001b[0;33mSetting LNTS...\u001b[0m\n",
- "\u001b[0m\u001b[0m\u001b[0mOriginal num_samples: \u001b[0m\u001b[0;32m23681\u001b[0m\n",
- "\u001b[0;33mshuffling data...\u001b[0m\n",
- "\u001b[0;33mSaving TS...\u001b[0m\n",
- "\u001b[0m\u001b[0m\u001b[0mSample dir: \u001b[0m\u001b[0;32mSamples/TSR400_y2024_m03_d19-h14_m25_s08\u001b[0m\n",
- "\u001b[0;32mDone.\u001b[0m\n"
+ "\u001b[0;33mLoading all images and labels into memory...\u001b[0m\n"
]
}
],
@@ -719,7 +708,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "## Save EV Dataset"
+ "### Save EV Dataset"
]
},
{
@@ -728,64 +717,92 @@
"metadata": {},
"outputs": [],
"source": [
- "np.save(f\"Database\\\\Test\\\\Data\\\\x_val{SL_EX}.npy\", x_val)\n",
- "np.save(f\"Database\\\\Test\\\\Data\\\\y_val{SL_EX}.npy\", y_val)\n",
- "np.save(f\"Database\\\\Test\\\\Data\\\\x_test{SL_EX}.npy\", x_test)\n",
- "np.save(f\"Database\\\\Test\\\\Data\\\\y_test{SL_EX}.npy\", y_test)"
+ "np.save(f\"Database\\\\Temp\\\\Test\\\\Data\\\\x_val{SL_EX}.npy\", x_val)\n",
+ "np.save(f\"Database\\\\Temp\\\\Test\\\\Data\\\\y_val{SL_EX}.npy\", y_val)\n",
+ "np.save(f\"Database\\\\Temp\\\\Test\\\\Data\\\\x_test{SL_EX}.npy\", x_test)\n",
+ "np.save(f\"Database\\\\Temp\\\\Test\\\\Data\\\\y_test{SL_EX}.npy\", y_test)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
- "## Load EV Dataset"
+ "### Load EV Dataset"
]
},
{
"cell_type": "code",
- "execution_count": 7,
+ "execution_count": 6,
"metadata": {
"ExecuteTime": {
"end_time": "2023-12-28T02:31:27.380088800Z",
"start_time": "2023-12-28T02:31:27.270860200Z"
},
"notebookRunGroups": {
- "groupValue": "1"
+ "groupValue": "13"
}
},
"outputs": [],
"source": [
- "x_val = np.load(f\"Database\\\\Test\\\\Data\\\\x_val{SL_EX}.npy\")\n",
- "y_val = np.load(f\"Database\\\\Test\\\\Data\\\\y_val{SL_EX}.npy\")\n",
- "x_test = np.load(f\"Database\\\\Test\\\\Data\\\\x_test{SL_EX}.npy\")\n",
- "y_test = np.load(f\"Database\\\\Test\\\\Data\\\\y_test{SL_EX}.npy\")"
+ "x_val = np.load(f\"Database\\\\Temp\\\\Test\\\\Data\\\\x_val{SL_EX}.npy\")\n",
+ "y_val = np.load(f\"Database\\\\Temp\\\\Test\\\\Data\\\\y_val{SL_EX}.npy\")\n",
+ "x_test = np.load(f\"Database\\\\Temp\\\\Test\\\\Data\\\\x_test{SL_EX}.npy\")\n",
+ "y_test = np.load(f\"Database\\\\Temp\\\\Test\\\\Data\\\\y_test{SL_EX}.npy\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
- "## Data Analyzation"
+ "### Save Main Dataset"
]
},
{
"cell_type": "code",
- "execution_count": 27,
+ "execution_count": null,
"metadata": {},
- "outputs": [
- {
- "data": {
- "image/png": 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",
- "text/plain": [
- ""
- ]
- },
- "metadata": {},
- "output_type": "display_data"
+ "outputs": [],
+ "source": [
+ "np.savez(f\"Database\\\\Temp\\\\Train\\\\Data\\\\train{SL_EX}.npz\", x_train=x_train, y_train=y_train)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Load Main Dataset"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "metadata": {
+ "notebookRunGroups": {
+ "groupValue": "3"
}
- ],
+ },
+ "outputs": [],
+ "source": [
+ "x_train, y_train = (\n",
+ " np.load(f\"Database\\\\Temp\\\\Train\\\\Data\\\\train{SL_EX}.npz\")[\"x_train\"],\n",
+ " np.load(f\"Database\\\\Temp\\\\Train\\\\Data\\\\train{SL_EX}.npz\")[\"y_train\"],\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Data Analyzation"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
"source": [
- "fig, axes = plt.subplots(4, 8, figsize=(20, 15))\n",
+ "fig, axes = plt.subplots(4, 8, figsize=(15, 15))\n",
"\n",
"for i, ax in enumerate(axes.flat):\n",
" ax.imshow((x_train[i] * 255).astype(\"uint8\"))\n",
@@ -909,7 +926,7 @@
},
{
"cell_type": "code",
- "execution_count": 10,
+ "execution_count": 8,
"metadata": {
"ExecuteTime": {
"end_time": "2023-12-27T17:34:12.077394600Z",
@@ -3105,13 +3122,13 @@
" # GlobalAveragePooling2D\n",
" base_model_FT = GlobalAveragePooling2D(name=\"FC_INPUT_Avg-Pooling\")(base_model.output)\n",
" # Dense\n",
- " Dense_L1 = Dense(512, activation=\"relu\", kernel_regularizer=l2(0.0066), name=\"FC_C_Dense-L1-512\")(base_model_FT)\n",
+ " Dense_L1 = Dense(512, activation=\"relu\", kernel_regularizer=l2(0.0016), name=\"FC_C_Dense-L1-512\")(base_model_FT)\n",
" # Dropout\n",
" Dropout_L1 = Dropout(0.125, name=\"FC_C_Dropout-L1-0.1\")(Dense_L1)\n",
" # BatchNormalization\n",
" BatchNorm_L2 = BatchNormalization(name=\"FC_C_Avg-BatchNormalization-L1\")(Dropout_L1)\n",
" # Dense\n",
- " Dense_L2 = Dense(256, activation=\"relu\", kernel_regularizer=l2(0.0045), name=\"FC_C_Dense-L2-512\")(BatchNorm_L2)\n",
+ " Dense_L2 = Dense(256, activation=\"relu\", kernel_regularizer=l2(0.0005), name=\"FC_C_Dense-L2-512\")(BatchNorm_L2)\n",
" # BatchNormalization\n",
" BatchNorm_L3 = BatchNormalization(name=\"FC_C_Avg-BatchNormalization-L2\")(Dense_L2)\n",
" # Dense\n",
@@ -3123,12 +3140,13 @@
" model_EfficientNetB7_NS = Model(inputs=base_model.input, outputs=predictions)\n",
" print(\"Total model layers: \", len(model_EfficientNetB7_NS.layers))\n",
" # OPT/compile\n",
- " # opt = SGD(momentum=0.95, nesterov=False) # noqa: F405\n",
+ " opt = SGD(momentum=0.89, nesterov=False) # noqa: F405\n",
" # opt = Nadam() # noqa: F405\n",
" # opt = Adamax() # noqa: F405\n",
+ " # opt = Adam(amsgrad=True) # noqa: F405\n",
" # opt = RMSprop(momentum=0.9) # noqa: F405\n",
" # opt = Adagrad() # noqa: F405\n",
- " opt = AdaBeliefOptimizer(epsilon=1e-7, rectify=False, weight_decay=5e-4, print_change_log=False, amsgrad=False) # noqa: F405\n",
+ " # opt = AdaBeliefOptimizer(epsilon=1e-7, rectify=False, weight_decay=5e-4, print_change_log=False, amsgrad=True) # noqa: F405\n",
" # opt = Yogi() # noqa: F405\n",
" model_EfficientNetB7_NS.compile(\n",
" optimizer=opt, loss=\"categorical_crossentropy\", metrics=[\"accuracy\"]\n",
@@ -3260,5464 +3278,53 @@
},
{
"cell_type": "code",
- "execution_count": 28,
+ "execution_count": null,
"metadata": {},
- "outputs": [
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "c:\\Users\\aydin\\Desktop\\Pneumonia AI Dev\\venv\\lib\\site-packages\\keras\\initializers\\initializers_v2.py:120: UserWarning: The initializer VarianceScaling is unseeded and being called multiple times, which will return identical values each time (even if the initializer is unseeded). Please update your code to provide a seed to the initializer, or avoid using the same initalizer instance more than once.\n",
- " warnings.warn(\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- ">>>> Load pretrained from: C:\\Users\\aydin\\.keras\\models/efficientnetv2\\efficientnetv2-xl-21k-ft1k.h5\n",
- "Model: \"model\"\n",
- "_____________________________________________________________________________________________________________\n",
- " Layer (type) Output Shape Param # Connected to Trainable \n",
- "=============================================================================================================\n",
- " input_1 (InputLayer) [(None, 224, 224, 3 0 [] Y \n",
- " )] \n",
- " \n",
- " stem_conv (Conv2D) (None, 112, 112, 32 864 ['input_1[0][0]'] Y \n",
- " ) \n",
- " \n",
- " stem_bn (BatchNormalization) (None, 112, 112, 32 128 ['stem_conv[0][0]'] Y \n",
- " ) \n",
- " \n",
- " stem_swish (Activation) (None, 112, 112, 32 0 ['stem_bn[0][0]'] Y \n",
- " ) \n",
- " \n",
- " stack_0_block0_fu_conv (Conv2D (None, 112, 112, 32 9216 ['stem_swish[0][0]'] Y \n",
- " ) ) \n",
- " \n",
- " stack_0_block0_fu_bn (BatchNor (None, 112, 112, 32 128 ['stack_0_block0_fu_conv[0][0]' Y \n",
- " malization) ) ] \n",
- " \n",
- " stack_0_block0_fu_swish (Activ (None, 112, 112, 32 0 ['stack_0_block0_fu_bn[0][0]'] Y \n",
- " ation) ) \n",
- " \n",
- " add (Add) (None, 112, 112, 32 0 ['stem_swish[0][0]', Y \n",
- " ) 'stack_0_block0_fu_swish[0][0] \n",
- " '] \n",
- " \n",
- " stack_0_block1_fu_conv (Conv2D (None, 112, 112, 32 9216 ['add[0][0]'] Y \n",
- " ) ) \n",
- " \n",
- " stack_0_block1_fu_bn (BatchNor (None, 112, 112, 32 128 ['stack_0_block1_fu_conv[0][0]' Y \n",
- " malization) ) ] \n",
- " \n",
- " stack_0_block1_fu_swish (Activ (None, 112, 112, 32 0 ['stack_0_block1_fu_bn[0][0]'] Y \n",
- " ation) ) \n",
- " \n",
- " add_1 (Add) (None, 112, 112, 32 0 ['add[0][0]', Y \n",
- " ) 'stack_0_block1_fu_swish[0][0] \n",
- " '] \n",
- " \n",
- " stack_0_block2_fu_conv (Conv2D (None, 112, 112, 32 9216 ['add_1[0][0]'] Y \n",
- " ) ) \n",
- " \n",
- " stack_0_block2_fu_bn (BatchNor (None, 112, 112, 32 128 ['stack_0_block2_fu_conv[0][0]' Y \n",
- " malization) ) ] \n",
- " \n",
- " stack_0_block2_fu_swish (Activ (None, 112, 112, 32 0 ['stack_0_block2_fu_bn[0][0]'] Y \n",
- " ation) ) \n",
- " \n",
- " add_2 (Add) (None, 112, 112, 32 0 ['add_1[0][0]', Y \n",
- " ) 'stack_0_block2_fu_swish[0][0] \n",
- " '] \n",
- " \n",
- " stack_0_block3_fu_conv (Conv2D (None, 112, 112, 32 9216 ['add_2[0][0]'] Y \n",
- " ) ) \n",
- " \n",
- " stack_0_block3_fu_bn (BatchNor (None, 112, 112, 32 128 ['stack_0_block3_fu_conv[0][0]' Y \n",
- " malization) ) ] \n",
- " \n",
- " stack_0_block3_fu_swish (Activ (None, 112, 112, 32 0 ['stack_0_block3_fu_bn[0][0]'] Y \n",
- " ation) ) \n",
- " \n",
- " add_3 (Add) (None, 112, 112, 32 0 ['add_2[0][0]', Y \n",
- " ) 'stack_0_block3_fu_swish[0][0] \n",
- " '] \n",
- " \n",
- " stack_1_block0_sortcut_conv (C (None, 56, 56, 128) 36864 ['add_3[0][0]'] Y \n",
- " onv2D) \n",
- " \n",
- " stack_1_block0_sortcut_bn (Bat (None, 56, 56, 128) 512 ['stack_1_block0_sortcut_conv[0 Y \n",
- " chNormalization) ][0]'] \n",
- " \n",
- " stack_1_block0_sortcut_swish ( (None, 56, 56, 128) 0 ['stack_1_block0_sortcut_bn[0][ Y \n",
- " Activation) 0]'] \n",
- " \n",
- " stack_1_block0_MB_pw_conv (Con (None, 56, 56, 64) 8192 ['stack_1_block0_sortcut_swish[ Y \n",
- " v2D) 0][0]'] \n",
- " \n",
- " stack_1_block0_MB_pw_bn (Batch (None, 56, 56, 64) 256 ['stack_1_block0_MB_pw_conv[0][ Y \n",
- " Normalization) 0]'] \n",
- " \n",
- " stack_1_block1_sortcut_conv (C (None, 56, 56, 256) 147456 ['stack_1_block0_MB_pw_bn[0][0] Y \n",
- " onv2D) '] \n",
- " \n",
- " stack_1_block1_sortcut_bn (Bat (None, 56, 56, 256) 1024 ['stack_1_block1_sortcut_conv[0 Y \n",
- " chNormalization) ][0]'] \n",
- " \n",
- " stack_1_block1_sortcut_swish ( (None, 56, 56, 256) 0 ['stack_1_block1_sortcut_bn[0][ Y \n",
- " Activation) 0]'] \n",
- " \n",
- " stack_1_block1_MB_pw_conv (Con (None, 56, 56, 64) 16384 ['stack_1_block1_sortcut_swish[ Y \n",
- " v2D) 0][0]'] \n",
- " \n",
- " stack_1_block1_MB_pw_bn (Batch (None, 56, 56, 64) 256 ['stack_1_block1_MB_pw_conv[0][ Y \n",
- " Normalization) 0]'] \n",
- " \n",
- " add_4 (Add) (None, 56, 56, 64) 0 ['stack_1_block0_MB_pw_bn[0][0] Y \n",
- " ', \n",
- " 'stack_1_block1_MB_pw_bn[0][0] \n",
- " '] \n",
- " \n",
- " stack_1_block2_sortcut_conv (C (None, 56, 56, 256) 147456 ['add_4[0][0]'] Y \n",
- " onv2D) \n",
- " \n",
- " stack_1_block2_sortcut_bn (Bat (None, 56, 56, 256) 1024 ['stack_1_block2_sortcut_conv[0 Y \n",
- " chNormalization) ][0]'] \n",
- " \n",
- " stack_1_block2_sortcut_swish ( (None, 56, 56, 256) 0 ['stack_1_block2_sortcut_bn[0][ Y \n",
- " Activation) 0]'] \n",
- " \n",
- " stack_1_block2_MB_pw_conv (Con (None, 56, 56, 64) 16384 ['stack_1_block2_sortcut_swish[ Y \n",
- " v2D) 0][0]'] \n",
- " \n",
- " stack_1_block2_MB_pw_bn (Batch (None, 56, 56, 64) 256 ['stack_1_block2_MB_pw_conv[0][ Y \n",
- " Normalization) 0]'] \n",
- " \n",
- " add_5 (Add) (None, 56, 56, 64) 0 ['add_4[0][0]', Y \n",
- " 'stack_1_block2_MB_pw_bn[0][0] \n",
- " '] \n",
- " \n",
- " stack_1_block3_sortcut_conv (C (None, 56, 56, 256) 147456 ['add_5[0][0]'] Y \n",
- " onv2D) \n",
- " \n",
- " stack_1_block3_sortcut_bn (Bat (None, 56, 56, 256) 1024 ['stack_1_block3_sortcut_conv[0 Y \n",
- " chNormalization) ][0]'] \n",
- " \n",
- " stack_1_block3_sortcut_swish ( (None, 56, 56, 256) 0 ['stack_1_block3_sortcut_bn[0][ Y \n",
- " Activation) 0]'] \n",
- " \n",
- " stack_1_block3_MB_pw_conv (Con (None, 56, 56, 64) 16384 ['stack_1_block3_sortcut_swish[ Y \n",
- " v2D) 0][0]'] \n",
- " \n",
- " stack_1_block3_MB_pw_bn (Batch (None, 56, 56, 64) 256 ['stack_1_block3_MB_pw_conv[0][ Y \n",
- " Normalization) 0]'] \n",
- " \n",
- " add_6 (Add) (None, 56, 56, 64) 0 ['add_5[0][0]', Y \n",
- " 'stack_1_block3_MB_pw_bn[0][0] \n",
- " '] \n",
- " \n",
- " stack_1_block4_sortcut_conv (C (None, 56, 56, 256) 147456 ['add_6[0][0]'] Y \n",
- " onv2D) \n",
- " \n",
- " stack_1_block4_sortcut_bn (Bat (None, 56, 56, 256) 1024 ['stack_1_block4_sortcut_conv[0 Y \n",
- " chNormalization) ][0]'] \n",
- " \n",
- " stack_1_block4_sortcut_swish ( (None, 56, 56, 256) 0 ['stack_1_block4_sortcut_bn[0][ Y \n",
- " Activation) 0]'] \n",
- " \n",
- " stack_1_block4_MB_pw_conv (Con (None, 56, 56, 64) 16384 ['stack_1_block4_sortcut_swish[ Y \n",
- " v2D) 0][0]'] \n",
- " \n",
- " stack_1_block4_MB_pw_bn (Batch (None, 56, 56, 64) 256 ['stack_1_block4_MB_pw_conv[0][ Y \n",
- " Normalization) 0]'] \n",
- " \n",
- " add_7 (Add) (None, 56, 56, 64) 0 ['add_6[0][0]', Y \n",
- " 'stack_1_block4_MB_pw_bn[0][0] \n",
- " '] \n",
- " \n",
- " stack_1_block5_sortcut_conv (C (None, 56, 56, 256) 147456 ['add_7[0][0]'] Y \n",
- " onv2D) \n",
- " \n",
- " stack_1_block5_sortcut_bn (Bat (None, 56, 56, 256) 1024 ['stack_1_block5_sortcut_conv[0 Y \n",
- " chNormalization) ][0]'] \n",
- " \n",
- " stack_1_block5_sortcut_swish ( (None, 56, 56, 256) 0 ['stack_1_block5_sortcut_bn[0][ Y \n",
- " Activation) 0]'] \n",
- " \n",
- " stack_1_block5_MB_pw_conv (Con (None, 56, 56, 64) 16384 ['stack_1_block5_sortcut_swish[ Y \n",
- " v2D) 0][0]'] \n",
- " \n",
- " stack_1_block5_MB_pw_bn (Batch (None, 56, 56, 64) 256 ['stack_1_block5_MB_pw_conv[0][ Y \n",
- " Normalization) 0]'] \n",
- " \n",
- " add_8 (Add) (None, 56, 56, 64) 0 ['add_7[0][0]', Y \n",
- " 'stack_1_block5_MB_pw_bn[0][0] \n",
- " '] \n",
- " \n",
- " stack_1_block6_sortcut_conv (C (None, 56, 56, 256) 147456 ['add_8[0][0]'] Y \n",
- " onv2D) \n",
- " \n",
- " stack_1_block6_sortcut_bn (Bat (None, 56, 56, 256) 1024 ['stack_1_block6_sortcut_conv[0 Y \n",
- " chNormalization) ][0]'] \n",
- " \n",
- " stack_1_block6_sortcut_swish ( (None, 56, 56, 256) 0 ['stack_1_block6_sortcut_bn[0][ Y \n",
- " Activation) 0]'] \n",
- " \n",
- " stack_1_block6_MB_pw_conv (Con (None, 56, 56, 64) 16384 ['stack_1_block6_sortcut_swish[ Y \n",
- " v2D) 0][0]'] \n",
- " \n",
- " stack_1_block6_MB_pw_bn (Batch (None, 56, 56, 64) 256 ['stack_1_block6_MB_pw_conv[0][ Y \n",
- " Normalization) 0]'] \n",
- " \n",
- " add_9 (Add) (None, 56, 56, 64) 0 ['add_8[0][0]', Y \n",
- " 'stack_1_block6_MB_pw_bn[0][0] \n",
- " '] \n",
- " \n",
- " stack_1_block7_sortcut_conv (C (None, 56, 56, 256) 147456 ['add_9[0][0]'] Y \n",
- " onv2D) \n",
- " \n",
- " stack_1_block7_sortcut_bn (Bat (None, 56, 56, 256) 1024 ['stack_1_block7_sortcut_conv[0 Y \n",
- " chNormalization) ][0]'] \n",
- " \n",
- " stack_1_block7_sortcut_swish ( (None, 56, 56, 256) 0 ['stack_1_block7_sortcut_bn[0][ Y \n",
- " Activation) 0]'] \n",
- " \n",
- " stack_1_block7_MB_pw_conv (Con (None, 56, 56, 64) 16384 ['stack_1_block7_sortcut_swish[ Y \n",
- " v2D) 0][0]'] \n",
- " \n",
- " stack_1_block7_MB_pw_bn (Batch (None, 56, 56, 64) 256 ['stack_1_block7_MB_pw_conv[0][ Y \n",
- " Normalization) 0]'] \n",
- " \n",
- " add_10 (Add) (None, 56, 56, 64) 0 ['add_9[0][0]', Y \n",
- " 'stack_1_block7_MB_pw_bn[0][0] \n",
- " '] \n",
- " \n",
- " stack_2_block0_sortcut_conv (C (None, 28, 28, 256) 147456 ['add_10[0][0]'] Y \n",
- " onv2D) \n",
- " \n",
- " stack_2_block0_sortcut_bn (Bat (None, 28, 28, 256) 1024 ['stack_2_block0_sortcut_conv[0 Y \n",
- " chNormalization) ][0]'] \n",
- " \n",
- " stack_2_block0_sortcut_swish ( (None, 28, 28, 256) 0 ['stack_2_block0_sortcut_bn[0][ Y \n",
- " Activation) 0]'] \n",
- " \n",
- " stack_2_block0_MB_pw_conv (Con (None, 28, 28, 96) 24576 ['stack_2_block0_sortcut_swish[ Y \n",
- " v2D) 0][0]'] \n",
- " \n",
- " stack_2_block0_MB_pw_bn (Batch (None, 28, 28, 96) 384 ['stack_2_block0_MB_pw_conv[0][ Y \n",
- " Normalization) 0]'] \n",
- " \n",
- " stack_2_block1_sortcut_conv (C (None, 28, 28, 384) 331776 ['stack_2_block0_MB_pw_bn[0][0] Y \n",
- " onv2D) '] \n",
- " \n",
- " stack_2_block1_sortcut_bn (Bat (None, 28, 28, 384) 1536 ['stack_2_block1_sortcut_conv[0 Y \n",
- " chNormalization) ][0]'] \n",
- " \n",
- " stack_2_block1_sortcut_swish ( (None, 28, 28, 384) 0 ['stack_2_block1_sortcut_bn[0][ Y \n",
- " Activation) 0]'] \n",
- " \n",
- " stack_2_block1_MB_pw_conv (Con (None, 28, 28, 96) 36864 ['stack_2_block1_sortcut_swish[ Y \n",
- " v2D) 0][0]'] \n",
- " \n",
- " stack_2_block1_MB_pw_bn (Batch (None, 28, 28, 96) 384 ['stack_2_block1_MB_pw_conv[0][ Y \n",
- " Normalization) 0]'] \n",
- " \n",
- " add_11 (Add) (None, 28, 28, 96) 0 ['stack_2_block0_MB_pw_bn[0][0] Y \n",
- " ', \n",
- " 'stack_2_block1_MB_pw_bn[0][0] \n",
- " '] \n",
- " \n",
- " stack_2_block2_sortcut_conv (C (None, 28, 28, 384) 331776 ['add_11[0][0]'] Y \n",
- " onv2D) \n",
- " \n",
- " stack_2_block2_sortcut_bn (Bat (None, 28, 28, 384) 1536 ['stack_2_block2_sortcut_conv[0 Y \n",
- " chNormalization) ][0]'] \n",
- " \n",
- " stack_2_block2_sortcut_swish ( (None, 28, 28, 384) 0 ['stack_2_block2_sortcut_bn[0][ Y \n",
- " Activation) 0]'] \n",
- " \n",
- " stack_2_block2_MB_pw_conv (Con (None, 28, 28, 96) 36864 ['stack_2_block2_sortcut_swish[ Y \n",
- " v2D) 0][0]'] \n",
- " \n",
- " stack_2_block2_MB_pw_bn (Batch (None, 28, 28, 96) 384 ['stack_2_block2_MB_pw_conv[0][ Y \n",
- " Normalization) 0]'] \n",
- " \n",
- " add_12 (Add) (None, 28, 28, 96) 0 ['add_11[0][0]', Y \n",
- " 'stack_2_block2_MB_pw_bn[0][0] \n",
- " '] \n",
- " \n",
- " stack_2_block3_sortcut_conv (C (None, 28, 28, 384) 331776 ['add_12[0][0]'] Y \n",
- " onv2D) \n",
- " \n",
- " stack_2_block3_sortcut_bn (Bat (None, 28, 28, 384) 1536 ['stack_2_block3_sortcut_conv[0 Y \n",
- " chNormalization) ][0]'] \n",
- " \n",
- " stack_2_block3_sortcut_swish ( (None, 28, 28, 384) 0 ['stack_2_block3_sortcut_bn[0][ Y \n",
- " Activation) 0]'] \n",
- " \n",
- " stack_2_block3_MB_pw_conv (Con (None, 28, 28, 96) 36864 ['stack_2_block3_sortcut_swish[ Y \n",
- " v2D) 0][0]'] \n",
- " \n",
- " stack_2_block3_MB_pw_bn (Batch (None, 28, 28, 96) 384 ['stack_2_block3_MB_pw_conv[0][ Y \n",
- " Normalization) 0]'] \n",
- " \n",
- " add_13 (Add) (None, 28, 28, 96) 0 ['add_12[0][0]', Y \n",
- " 'stack_2_block3_MB_pw_bn[0][0] \n",
- " '] \n",
- " \n",
- " stack_2_block4_sortcut_conv (C (None, 28, 28, 384) 331776 ['add_13[0][0]'] Y \n",
- " onv2D) \n",
- " \n",
- " stack_2_block4_sortcut_bn (Bat (None, 28, 28, 384) 1536 ['stack_2_block4_sortcut_conv[0 Y \n",
- " chNormalization) ][0]'] \n",
- " \n",
- " stack_2_block4_sortcut_swish ( (None, 28, 28, 384) 0 ['stack_2_block4_sortcut_bn[0][ Y \n",
- " Activation) 0]'] \n",
- " \n",
- " stack_2_block4_MB_pw_conv (Con (None, 28, 28, 96) 36864 ['stack_2_block4_sortcut_swish[ Y \n",
- " v2D) 0][0]'] \n",
- " \n",
- " stack_2_block4_MB_pw_bn (Batch (None, 28, 28, 96) 384 ['stack_2_block4_MB_pw_conv[0][ Y \n",
- " Normalization) 0]'] \n",
- " \n",
- " add_14 (Add) (None, 28, 28, 96) 0 ['add_13[0][0]', Y \n",
- " 'stack_2_block4_MB_pw_bn[0][0] \n",
- " '] \n",
- " \n",
- " stack_2_block5_sortcut_conv (C (None, 28, 28, 384) 331776 ['add_14[0][0]'] Y \n",
- " onv2D) \n",
- " \n",
- " stack_2_block5_sortcut_bn (Bat (None, 28, 28, 384) 1536 ['stack_2_block5_sortcut_conv[0 Y \n",
- " chNormalization) ][0]'] \n",
- " \n",
- " stack_2_block5_sortcut_swish ( (None, 28, 28, 384) 0 ['stack_2_block5_sortcut_bn[0][ Y \n",
- " Activation) 0]'] \n",
- " \n",
- " stack_2_block5_MB_pw_conv (Con (None, 28, 28, 96) 36864 ['stack_2_block5_sortcut_swish[ Y \n",
- " v2D) 0][0]'] \n",
- " \n",
- " stack_2_block5_MB_pw_bn (Batch (None, 28, 28, 96) 384 ['stack_2_block5_MB_pw_conv[0][ Y \n",
- " Normalization) 0]'] \n",
- " \n",
- " add_15 (Add) (None, 28, 28, 96) 0 ['add_14[0][0]', Y \n",
- " 'stack_2_block5_MB_pw_bn[0][0] \n",
- " '] \n",
- " \n",
- " stack_2_block6_sortcut_conv (C (None, 28, 28, 384) 331776 ['add_15[0][0]'] Y \n",
- " onv2D) \n",
- " \n",
- " stack_2_block6_sortcut_bn (Bat (None, 28, 28, 384) 1536 ['stack_2_block6_sortcut_conv[0 Y \n",
- " chNormalization) ][0]'] \n",
- " \n",
- " stack_2_block6_sortcut_swish ( (None, 28, 28, 384) 0 ['stack_2_block6_sortcut_bn[0][ Y \n",
- " Activation) 0]'] \n",
- " \n",
- " stack_2_block6_MB_pw_conv (Con (None, 28, 28, 96) 36864 ['stack_2_block6_sortcut_swish[ Y \n",
- " v2D) 0][0]'] \n",
- " \n",
- " stack_2_block6_MB_pw_bn (Batch (None, 28, 28, 96) 384 ['stack_2_block6_MB_pw_conv[0][ Y \n",
- " Normalization) 0]'] \n",
- " \n",
- " add_16 (Add) (None, 28, 28, 96) 0 ['add_15[0][0]', Y \n",
- " 'stack_2_block6_MB_pw_bn[0][0] \n",
- " '] \n",
- " \n",
- " stack_2_block7_sortcut_conv (C (None, 28, 28, 384) 331776 ['add_16[0][0]'] Y \n",
- " onv2D) \n",
- " \n",
- " stack_2_block7_sortcut_bn (Bat (None, 28, 28, 384) 1536 ['stack_2_block7_sortcut_conv[0 Y \n",
- " chNormalization) ][0]'] \n",
- " \n",
- " stack_2_block7_sortcut_swish ( (None, 28, 28, 384) 0 ['stack_2_block7_sortcut_bn[0][ Y \n",
- " Activation) 0]'] \n",
- " \n",
- " stack_2_block7_MB_pw_conv (Con (None, 28, 28, 96) 36864 ['stack_2_block7_sortcut_swish[ Y \n",
- " v2D) 0][0]'] \n",
- " \n",
- " stack_2_block7_MB_pw_bn (Batch (None, 28, 28, 96) 384 ['stack_2_block7_MB_pw_conv[0][ Y \n",
- " Normalization) 0]'] \n",
- " \n",
- " add_17 (Add) (None, 28, 28, 96) 0 ['add_16[0][0]', Y \n",
- " 'stack_2_block7_MB_pw_bn[0][0] \n",
- " '] \n",
- " \n",
- " stack_3_block0_sortcut_conv (C (None, 28, 28, 384) 36864 ['add_17[0][0]'] Y \n",
- " onv2D) \n",
- " \n",
- " stack_3_block0_sortcut_bn (Bat (None, 28, 28, 384) 1536 ['stack_3_block0_sortcut_conv[0 Y \n",
- " chNormalization) ][0]'] \n",
- " \n",
- " stack_3_block0_sortcut_swish ( (None, 28, 28, 384) 0 ['stack_3_block0_sortcut_bn[0][ Y \n",
- " Activation) 0]'] \n",
- " \n",
- " stack_3_block0_MB_dw_ (Depthwi (None, 14, 14, 384) 3456 ['stack_3_block0_sortcut_swish[ Y \n",
- " seConv2D) 0][0]'] \n",
- " \n",
- " stack_3_block0_MB_dw_bn (Batch (None, 14, 14, 384) 1536 ['stack_3_block0_MB_dw_[0][0]'] Y \n",
- " Normalization) \n",
- " \n",
- " stack_3_block0_MB_dw_swish (Ac (None, 14, 14, 384) 0 ['stack_3_block0_MB_dw_bn[0][0] Y \n",
- " tivation) '] \n",
- " \n",
- " tf.math.reduce_mean (TFOpLambd (None, 1, 1, 384) 0 ['stack_3_block0_MB_dw_swish[0] Y \n",
- " a) [0]'] \n",
- " \n",
- " stack_3_block0_se_1_conv (Conv (None, 1, 1, 24) 9240 ['tf.math.reduce_mean[0][0]'] Y \n",
- " 2D) \n",
- " \n",
- " activation (Activation) (None, 1, 1, 24) 0 ['stack_3_block0_se_1_conv[0][0 Y \n",
- " ]'] \n",
- " \n",
- " stack_3_block0_se_2_conv (Conv (None, 1, 1, 384) 9600 ['activation[0][0]'] Y \n",
- " 2D) \n",
- " \n",
- " activation_1 (Activation) (None, 1, 1, 384) 0 ['stack_3_block0_se_2_conv[0][0 Y \n",
- " ]'] \n",
- " \n",
- " multiply (Multiply) (None, 14, 14, 384) 0 ['stack_3_block0_MB_dw_swish[0] Y \n",
- " [0]', \n",
- " 'activation_1[0][0]'] \n",
- " \n",
- " stack_3_block0_MB_pw_conv (Con (None, 14, 14, 192) 73728 ['multiply[0][0]'] Y \n",
- " v2D) \n",
- " \n",
- " stack_3_block0_MB_pw_bn (Batch (None, 14, 14, 192) 768 ['stack_3_block0_MB_pw_conv[0][ Y \n",
- " Normalization) 0]'] \n",
- " \n",
- " stack_3_block1_sortcut_conv (C (None, 14, 14, 768) 147456 ['stack_3_block0_MB_pw_bn[0][0] Y \n",
- " onv2D) '] \n",
- " \n",
- " stack_3_block1_sortcut_bn (Bat (None, 14, 14, 768) 3072 ['stack_3_block1_sortcut_conv[0 Y \n",
- " chNormalization) ][0]'] \n",
- " \n",
- " stack_3_block1_sortcut_swish ( (None, 14, 14, 768) 0 ['stack_3_block1_sortcut_bn[0][ Y \n",
- " Activation) 0]'] \n",
- " \n",
- " stack_3_block1_MB_dw_ (Depthwi (None, 14, 14, 768) 6912 ['stack_3_block1_sortcut_swish[ Y \n",
- " seConv2D) 0][0]'] \n",
- " \n",
- " stack_3_block1_MB_dw_bn (Batch (None, 14, 14, 768) 3072 ['stack_3_block1_MB_dw_[0][0]'] Y \n",
- " Normalization) \n",
- " \n",
- " stack_3_block1_MB_dw_swish (Ac (None, 14, 14, 768) 0 ['stack_3_block1_MB_dw_bn[0][0] Y \n",
- " tivation) '] \n",
- " \n",
- " tf.math.reduce_mean_1 (TFOpLam (None, 1, 1, 768) 0 ['stack_3_block1_MB_dw_swish[0] Y \n",
- " bda) [0]'] \n",
- " \n",
- " stack_3_block1_se_1_conv (Conv (None, 1, 1, 48) 36912 ['tf.math.reduce_mean_1[0][0]'] Y \n",
- " 2D) \n",
- " \n",
- " activation_2 (Activation) (None, 1, 1, 48) 0 ['stack_3_block1_se_1_conv[0][0 Y \n",
- " ]'] \n",
- " \n",
- " stack_3_block1_se_2_conv (Conv (None, 1, 1, 768) 37632 ['activation_2[0][0]'] Y \n",
- " 2D) \n",
- " \n",
- " activation_3 (Activation) (None, 1, 1, 768) 0 ['stack_3_block1_se_2_conv[0][0 Y \n",
- " ]'] \n",
- " \n",
- " multiply_1 (Multiply) (None, 14, 14, 768) 0 ['stack_3_block1_MB_dw_swish[0] Y \n",
- " [0]', \n",
- " 'activation_3[0][0]'] \n",
- " \n",
- " stack_3_block1_MB_pw_conv (Con (None, 14, 14, 192) 147456 ['multiply_1[0][0]'] Y \n",
- " v2D) \n",
- " \n",
- " stack_3_block1_MB_pw_bn (Batch (None, 14, 14, 192) 768 ['stack_3_block1_MB_pw_conv[0][ Y \n",
- " Normalization) 0]'] \n",
- " \n",
- " add_18 (Add) (None, 14, 14, 192) 0 ['stack_3_block0_MB_pw_bn[0][0] Y \n",
- " ', \n",
- " 'stack_3_block1_MB_pw_bn[0][0] \n",
- " '] \n",
- " \n",
- " stack_3_block2_sortcut_conv (C (None, 14, 14, 768) 147456 ['add_18[0][0]'] Y \n",
- " onv2D) \n",
- " \n",
- " stack_3_block2_sortcut_bn (Bat (None, 14, 14, 768) 3072 ['stack_3_block2_sortcut_conv[0 Y \n",
- " chNormalization) ][0]'] \n",
- " \n",
- " stack_3_block2_sortcut_swish ( (None, 14, 14, 768) 0 ['stack_3_block2_sortcut_bn[0][ Y \n",
- " Activation) 0]'] \n",
- " \n",
- " stack_3_block2_MB_dw_ (Depthwi (None, 14, 14, 768) 6912 ['stack_3_block2_sortcut_swish[ Y \n",
- " seConv2D) 0][0]'] \n",
- " \n",
- " stack_3_block2_MB_dw_bn (Batch (None, 14, 14, 768) 3072 ['stack_3_block2_MB_dw_[0][0]'] Y \n",
- " Normalization) \n",
- " \n",
- " stack_3_block2_MB_dw_swish (Ac (None, 14, 14, 768) 0 ['stack_3_block2_MB_dw_bn[0][0] Y \n",
- " tivation) '] \n",
- " \n",
- " tf.math.reduce_mean_2 (TFOpLam (None, 1, 1, 768) 0 ['stack_3_block2_MB_dw_swish[0] Y \n",
- " bda) [0]'] \n",
- " \n",
- " stack_3_block2_se_1_conv (Conv (None, 1, 1, 48) 36912 ['tf.math.reduce_mean_2[0][0]'] Y \n",
- " 2D) \n",
- " \n",
- " activation_4 (Activation) (None, 1, 1, 48) 0 ['stack_3_block2_se_1_conv[0][0 Y \n",
- " ]'] \n",
- " \n",
- " stack_3_block2_se_2_conv (Conv (None, 1, 1, 768) 37632 ['activation_4[0][0]'] Y \n",
- " 2D) \n",
- " \n",
- " activation_5 (Activation) (None, 1, 1, 768) 0 ['stack_3_block2_se_2_conv[0][0 Y \n",
- " ]'] \n",
- " \n",
- " multiply_2 (Multiply) (None, 14, 14, 768) 0 ['stack_3_block2_MB_dw_swish[0] Y \n",
- " [0]', \n",
- " 'activation_5[0][0]'] \n",
- " \n",
- " stack_3_block2_MB_pw_conv (Con (None, 14, 14, 192) 147456 ['multiply_2[0][0]'] Y \n",
- " v2D) \n",
- " \n",
- " stack_3_block2_MB_pw_bn (Batch (None, 14, 14, 192) 768 ['stack_3_block2_MB_pw_conv[0][ Y \n",
- " Normalization) 0]'] \n",
- " \n",
- " add_19 (Add) (None, 14, 14, 192) 0 ['add_18[0][0]', Y \n",
- " 'stack_3_block2_MB_pw_bn[0][0] \n",
- " '] \n",
- " \n",
- " stack_3_block3_sortcut_conv (C (None, 14, 14, 768) 147456 ['add_19[0][0]'] Y \n",
- " onv2D) \n",
- " \n",
- " stack_3_block3_sortcut_bn (Bat (None, 14, 14, 768) 3072 ['stack_3_block3_sortcut_conv[0 Y \n",
- " chNormalization) ][0]'] \n",
- " \n",
- " stack_3_block3_sortcut_swish ( (None, 14, 14, 768) 0 ['stack_3_block3_sortcut_bn[0][ Y \n",
- " Activation) 0]'] \n",
- " \n",
- " stack_3_block3_MB_dw_ (Depthwi (None, 14, 14, 768) 6912 ['stack_3_block3_sortcut_swish[ Y \n",
- " seConv2D) 0][0]'] \n",
- " \n",
- " stack_3_block3_MB_dw_bn (Batch (None, 14, 14, 768) 3072 ['stack_3_block3_MB_dw_[0][0]'] Y \n",
- " Normalization) \n",
- " \n",
- " stack_3_block3_MB_dw_swish (Ac (None, 14, 14, 768) 0 ['stack_3_block3_MB_dw_bn[0][0] Y \n",
- " tivation) '] \n",
- " \n",
- " tf.math.reduce_mean_3 (TFOpLam (None, 1, 1, 768) 0 ['stack_3_block3_MB_dw_swish[0] Y \n",
- " bda) [0]'] \n",
- " \n",
- " stack_3_block3_se_1_conv (Conv (None, 1, 1, 48) 36912 ['tf.math.reduce_mean_3[0][0]'] Y \n",
- " 2D) \n",
- " \n",
- " activation_6 (Activation) (None, 1, 1, 48) 0 ['stack_3_block3_se_1_conv[0][0 Y \n",
- " ]'] \n",
- " \n",
- " stack_3_block3_se_2_conv (Conv (None, 1, 1, 768) 37632 ['activation_6[0][0]'] Y \n",
- " 2D) \n",
- " \n",
- " activation_7 (Activation) (None, 1, 1, 768) 0 ['stack_3_block3_se_2_conv[0][0 Y \n",
- " ]'] \n",
- " \n",
- " multiply_3 (Multiply) (None, 14, 14, 768) 0 ['stack_3_block3_MB_dw_swish[0] Y \n",
- " [0]', \n",
- " 'activation_7[0][0]'] \n",
- " \n",
- " stack_3_block3_MB_pw_conv (Con (None, 14, 14, 192) 147456 ['multiply_3[0][0]'] Y \n",
- " v2D) \n",
- " \n",
- " stack_3_block3_MB_pw_bn (Batch (None, 14, 14, 192) 768 ['stack_3_block3_MB_pw_conv[0][ Y \n",
- " Normalization) 0]'] \n",
- " \n",
- " add_20 (Add) (None, 14, 14, 192) 0 ['add_19[0][0]', Y \n",
- " 'stack_3_block3_MB_pw_bn[0][0] \n",
- " '] \n",
- " \n",
- " stack_3_block4_sortcut_conv (C (None, 14, 14, 768) 147456 ['add_20[0][0]'] Y \n",
- " onv2D) \n",
- " \n",
- " stack_3_block4_sortcut_bn (Bat (None, 14, 14, 768) 3072 ['stack_3_block4_sortcut_conv[0 Y \n",
- " chNormalization) ][0]'] \n",
- " \n",
- " stack_3_block4_sortcut_swish ( (None, 14, 14, 768) 0 ['stack_3_block4_sortcut_bn[0][ Y \n",
- " Activation) 0]'] \n",
- " \n",
- " stack_3_block4_MB_dw_ (Depthwi (None, 14, 14, 768) 6912 ['stack_3_block4_sortcut_swish[ Y \n",
- " seConv2D) 0][0]'] \n",
- " \n",
- " stack_3_block4_MB_dw_bn (Batch (None, 14, 14, 768) 3072 ['stack_3_block4_MB_dw_[0][0]'] Y \n",
- " Normalization) \n",
- " \n",
- " stack_3_block4_MB_dw_swish (Ac (None, 14, 14, 768) 0 ['stack_3_block4_MB_dw_bn[0][0] Y \n",
- " tivation) '] \n",
- " \n",
- " tf.math.reduce_mean_4 (TFOpLam (None, 1, 1, 768) 0 ['stack_3_block4_MB_dw_swish[0] Y \n",
- " bda) [0]'] \n",
- " \n",
- " stack_3_block4_se_1_conv (Conv (None, 1, 1, 48) 36912 ['tf.math.reduce_mean_4[0][0]'] Y \n",
- " 2D) \n",
- " \n",
- " activation_8 (Activation) (None, 1, 1, 48) 0 ['stack_3_block4_se_1_conv[0][0 Y \n",
- " ]'] \n",
- " \n",
- " stack_3_block4_se_2_conv (Conv (None, 1, 1, 768) 37632 ['activation_8[0][0]'] Y \n",
- " 2D) \n",
- " \n",
- " activation_9 (Activation) (None, 1, 1, 768) 0 ['stack_3_block4_se_2_conv[0][0 Y \n",
- " ]'] \n",
- " \n",
- " multiply_4 (Multiply) (None, 14, 14, 768) 0 ['stack_3_block4_MB_dw_swish[0] Y \n",
- " [0]', \n",
- " 'activation_9[0][0]'] \n",
- " \n",
- " stack_3_block4_MB_pw_conv (Con (None, 14, 14, 192) 147456 ['multiply_4[0][0]'] Y \n",
- " v2D) \n",
- " \n",
- " stack_3_block4_MB_pw_bn (Batch (None, 14, 14, 192) 768 ['stack_3_block4_MB_pw_conv[0][ Y \n",
- " Normalization) 0]'] \n",
- " \n",
- " add_21 (Add) (None, 14, 14, 192) 0 ['add_20[0][0]', Y \n",
- " 'stack_3_block4_MB_pw_bn[0][0] \n",
- " '] \n",
- " \n",
- " stack_3_block5_sortcut_conv (C (None, 14, 14, 768) 147456 ['add_21[0][0]'] Y \n",
- " onv2D) \n",
- " \n",
- " stack_3_block5_sortcut_bn (Bat (None, 14, 14, 768) 3072 ['stack_3_block5_sortcut_conv[0 Y \n",
- " chNormalization) ][0]'] \n",
- " \n",
- " stack_3_block5_sortcut_swish ( (None, 14, 14, 768) 0 ['stack_3_block5_sortcut_bn[0][ Y \n",
- " Activation) 0]'] \n",
- " \n",
- " stack_3_block5_MB_dw_ (Depthwi (None, 14, 14, 768) 6912 ['stack_3_block5_sortcut_swish[ Y \n",
- " seConv2D) 0][0]'] \n",
- " \n",
- " stack_3_block5_MB_dw_bn (Batch (None, 14, 14, 768) 3072 ['stack_3_block5_MB_dw_[0][0]'] Y \n",
- " Normalization) \n",
- " \n",
- " stack_3_block5_MB_dw_swish (Ac (None, 14, 14, 768) 0 ['stack_3_block5_MB_dw_bn[0][0] Y \n",
- " tivation) '] \n",
- " \n",
- " tf.math.reduce_mean_5 (TFOpLam (None, 1, 1, 768) 0 ['stack_3_block5_MB_dw_swish[0] Y \n",
- " bda) [0]'] \n",
- " \n",
- " stack_3_block5_se_1_conv (Conv (None, 1, 1, 48) 36912 ['tf.math.reduce_mean_5[0][0]'] Y \n",
- " 2D) \n",
- " \n",
- " activation_10 (Activation) (None, 1, 1, 48) 0 ['stack_3_block5_se_1_conv[0][0 Y \n",
- " ]'] \n",
- " \n",
- " stack_3_block5_se_2_conv (Conv (None, 1, 1, 768) 37632 ['activation_10[0][0]'] Y \n",
- " 2D) \n",
- " \n",
- " activation_11 (Activation) (None, 1, 1, 768) 0 ['stack_3_block5_se_2_conv[0][0 Y \n",
- " ]'] \n",
- " \n",
- " multiply_5 (Multiply) (None, 14, 14, 768) 0 ['stack_3_block5_MB_dw_swish[0] Y \n",
- " [0]', \n",
- " 'activation_11[0][0]'] \n",
- " \n",
- " stack_3_block5_MB_pw_conv (Con (None, 14, 14, 192) 147456 ['multiply_5[0][0]'] Y \n",
- " v2D) \n",
- " \n",
- " stack_3_block5_MB_pw_bn (Batch (None, 14, 14, 192) 768 ['stack_3_block5_MB_pw_conv[0][ Y \n",
- " Normalization) 0]'] \n",
- " \n",
- " add_22 (Add) (None, 14, 14, 192) 0 ['add_21[0][0]', Y \n",
- " 'stack_3_block5_MB_pw_bn[0][0] \n",
- " '] \n",
- " \n",
- " stack_3_block6_sortcut_conv (C (None, 14, 14, 768) 147456 ['add_22[0][0]'] Y \n",
- " onv2D) \n",
- " \n",
- " stack_3_block6_sortcut_bn (Bat (None, 14, 14, 768) 3072 ['stack_3_block6_sortcut_conv[0 Y \n",
- " chNormalization) ][0]'] \n",
- " \n",
- " stack_3_block6_sortcut_swish ( (None, 14, 14, 768) 0 ['stack_3_block6_sortcut_bn[0][ Y \n",
- " Activation) 0]'] \n",
- " \n",
- " stack_3_block6_MB_dw_ (Depthwi (None, 14, 14, 768) 6912 ['stack_3_block6_sortcut_swish[ Y \n",
- " seConv2D) 0][0]'] \n",
- " \n",
- " stack_3_block6_MB_dw_bn (Batch (None, 14, 14, 768) 3072 ['stack_3_block6_MB_dw_[0][0]'] Y \n",
- " Normalization) \n",
- " \n",
- " stack_3_block6_MB_dw_swish (Ac (None, 14, 14, 768) 0 ['stack_3_block6_MB_dw_bn[0][0] Y \n",
- " tivation) '] \n",
- " \n",
- " tf.math.reduce_mean_6 (TFOpLam (None, 1, 1, 768) 0 ['stack_3_block6_MB_dw_swish[0] Y \n",
- " bda) [0]'] \n",
- " \n",
- " stack_3_block6_se_1_conv (Conv (None, 1, 1, 48) 36912 ['tf.math.reduce_mean_6[0][0]'] Y \n",
- " 2D) \n",
- " \n",
- " activation_12 (Activation) (None, 1, 1, 48) 0 ['stack_3_block6_se_1_conv[0][0 Y \n",
- " ]'] \n",
- " \n",
- " stack_3_block6_se_2_conv (Conv (None, 1, 1, 768) 37632 ['activation_12[0][0]'] Y \n",
- " 2D) \n",
- " \n",
- " activation_13 (Activation) (None, 1, 1, 768) 0 ['stack_3_block6_se_2_conv[0][0 Y \n",
- " ]'] \n",
- " \n",
- " multiply_6 (Multiply) (None, 14, 14, 768) 0 ['stack_3_block6_MB_dw_swish[0] Y \n",
- " [0]', \n",
- " 'activation_13[0][0]'] \n",
- " \n",
- " stack_3_block6_MB_pw_conv (Con (None, 14, 14, 192) 147456 ['multiply_6[0][0]'] Y \n",
- " v2D) \n",
- " \n",
- " stack_3_block6_MB_pw_bn (Batch (None, 14, 14, 192) 768 ['stack_3_block6_MB_pw_conv[0][ Y \n",
- " Normalization) 0]'] \n",
- " \n",
- " add_23 (Add) (None, 14, 14, 192) 0 ['add_22[0][0]', Y \n",
- " 'stack_3_block6_MB_pw_bn[0][0] \n",
- " '] \n",
- " \n",
- " stack_3_block7_sortcut_conv (C (None, 14, 14, 768) 147456 ['add_23[0][0]'] Y \n",
- " onv2D) \n",
- " \n",
- " stack_3_block7_sortcut_bn (Bat (None, 14, 14, 768) 3072 ['stack_3_block7_sortcut_conv[0 Y \n",
- " chNormalization) ][0]'] \n",
- " \n",
- " stack_3_block7_sortcut_swish ( (None, 14, 14, 768) 0 ['stack_3_block7_sortcut_bn[0][ Y \n",
- " Activation) 0]'] \n",
- " \n",
- " stack_3_block7_MB_dw_ (Depthwi (None, 14, 14, 768) 6912 ['stack_3_block7_sortcut_swish[ Y \n",
- " seConv2D) 0][0]'] \n",
- " \n",
- " stack_3_block7_MB_dw_bn (Batch (None, 14, 14, 768) 3072 ['stack_3_block7_MB_dw_[0][0]'] Y \n",
- " Normalization) \n",
- " \n",
- " stack_3_block7_MB_dw_swish (Ac (None, 14, 14, 768) 0 ['stack_3_block7_MB_dw_bn[0][0] Y \n",
- " tivation) '] \n",
- " \n",
- " tf.math.reduce_mean_7 (TFOpLam (None, 1, 1, 768) 0 ['stack_3_block7_MB_dw_swish[0] Y \n",
- " bda) [0]'] \n",
- " \n",
- " stack_3_block7_se_1_conv (Conv (None, 1, 1, 48) 36912 ['tf.math.reduce_mean_7[0][0]'] Y \n",
- " 2D) \n",
- " \n",
- " activation_14 (Activation) (None, 1, 1, 48) 0 ['stack_3_block7_se_1_conv[0][0 Y \n",
- " ]'] \n",
- " \n",
- " stack_3_block7_se_2_conv (Conv (None, 1, 1, 768) 37632 ['activation_14[0][0]'] Y \n",
- " 2D) \n",
- " \n",
- " activation_15 (Activation) (None, 1, 1, 768) 0 ['stack_3_block7_se_2_conv[0][0 Y \n",
- " ]'] \n",
- " \n",
- " multiply_7 (Multiply) (None, 14, 14, 768) 0 ['stack_3_block7_MB_dw_swish[0] Y \n",
- " [0]', \n",
- " 'activation_15[0][0]'] \n",
- " \n",
- " stack_3_block7_MB_pw_conv (Con (None, 14, 14, 192) 147456 ['multiply_7[0][0]'] Y \n",
- " v2D) \n",
- " \n",
- " stack_3_block7_MB_pw_bn (Batch (None, 14, 14, 192) 768 ['stack_3_block7_MB_pw_conv[0][ Y \n",
- " Normalization) 0]'] \n",
- " \n",
- " add_24 (Add) (None, 14, 14, 192) 0 ['add_23[0][0]', Y \n",
- " 'stack_3_block7_MB_pw_bn[0][0] \n",
- " '] \n",
- " \n",
- " stack_3_block8_sortcut_conv (C (None, 14, 14, 768) 147456 ['add_24[0][0]'] Y \n",
- " onv2D) \n",
- " \n",
- " stack_3_block8_sortcut_bn (Bat (None, 14, 14, 768) 3072 ['stack_3_block8_sortcut_conv[0 Y \n",
- " chNormalization) ][0]'] \n",
- " \n",
- " stack_3_block8_sortcut_swish ( (None, 14, 14, 768) 0 ['stack_3_block8_sortcut_bn[0][ Y \n",
- " Activation) 0]'] \n",
- " \n",
- " stack_3_block8_MB_dw_ (Depthwi (None, 14, 14, 768) 6912 ['stack_3_block8_sortcut_swish[ Y \n",
- " seConv2D) 0][0]'] \n",
- " \n",
- " stack_3_block8_MB_dw_bn (Batch (None, 14, 14, 768) 3072 ['stack_3_block8_MB_dw_[0][0]'] Y \n",
- " Normalization) \n",
- " \n",
- " stack_3_block8_MB_dw_swish (Ac (None, 14, 14, 768) 0 ['stack_3_block8_MB_dw_bn[0][0] Y \n",
- " tivation) '] \n",
- " \n",
- " tf.math.reduce_mean_8 (TFOpLam (None, 1, 1, 768) 0 ['stack_3_block8_MB_dw_swish[0] Y \n",
- " bda) [0]'] \n",
- " \n",
- " stack_3_block8_se_1_conv (Conv (None, 1, 1, 48) 36912 ['tf.math.reduce_mean_8[0][0]'] Y \n",
- " 2D) \n",
- " \n",
- " activation_16 (Activation) (None, 1, 1, 48) 0 ['stack_3_block8_se_1_conv[0][0 Y \n",
- " ]'] \n",
- " \n",
- " stack_3_block8_se_2_conv (Conv (None, 1, 1, 768) 37632 ['activation_16[0][0]'] Y \n",
- " 2D) \n",
- " \n",
- " activation_17 (Activation) (None, 1, 1, 768) 0 ['stack_3_block8_se_2_conv[0][0 Y \n",
- " ]'] \n",
- " \n",
- " multiply_8 (Multiply) (None, 14, 14, 768) 0 ['stack_3_block8_MB_dw_swish[0] Y \n",
- " [0]', \n",
- " 'activation_17[0][0]'] \n",
- " \n",
- " stack_3_block8_MB_pw_conv (Con (None, 14, 14, 192) 147456 ['multiply_8[0][0]'] Y \n",
- " v2D) \n",
- " \n",
- " stack_3_block8_MB_pw_bn (Batch (None, 14, 14, 192) 768 ['stack_3_block8_MB_pw_conv[0][ Y \n",
- " Normalization) 0]'] \n",
- " \n",
- " add_25 (Add) (None, 14, 14, 192) 0 ['add_24[0][0]', Y \n",
- " 'stack_3_block8_MB_pw_bn[0][0] \n",
- " '] \n",
- " \n",
- " stack_3_block9_sortcut_conv (C (None, 14, 14, 768) 147456 ['add_25[0][0]'] Y \n",
- " onv2D) \n",
- " \n",
- " stack_3_block9_sortcut_bn (Bat (None, 14, 14, 768) 3072 ['stack_3_block9_sortcut_conv[0 Y \n",
- " chNormalization) ][0]'] \n",
- " \n",
- " stack_3_block9_sortcut_swish ( (None, 14, 14, 768) 0 ['stack_3_block9_sortcut_bn[0][ Y \n",
- " Activation) 0]'] \n",
- " \n",
- " stack_3_block9_MB_dw_ (Depthwi (None, 14, 14, 768) 6912 ['stack_3_block9_sortcut_swish[ Y \n",
- " seConv2D) 0][0]'] \n",
- " \n",
- " stack_3_block9_MB_dw_bn (Batch (None, 14, 14, 768) 3072 ['stack_3_block9_MB_dw_[0][0]'] Y \n",
- " Normalization) \n",
- " \n",
- " stack_3_block9_MB_dw_swish (Ac (None, 14, 14, 768) 0 ['stack_3_block9_MB_dw_bn[0][0] Y \n",
- " tivation) '] \n",
- " \n",
- " tf.math.reduce_mean_9 (TFOpLam (None, 1, 1, 768) 0 ['stack_3_block9_MB_dw_swish[0] Y \n",
- " bda) [0]'] \n",
- " \n",
- " stack_3_block9_se_1_conv (Conv (None, 1, 1, 48) 36912 ['tf.math.reduce_mean_9[0][0]'] Y \n",
- " 2D) \n",
- " \n",
- " activation_18 (Activation) (None, 1, 1, 48) 0 ['stack_3_block9_se_1_conv[0][0 Y \n",
- " ]'] \n",
- " \n",
- " stack_3_block9_se_2_conv (Conv (None, 1, 1, 768) 37632 ['activation_18[0][0]'] Y \n",
- " 2D) \n",
- " \n",
- " activation_19 (Activation) (None, 1, 1, 768) 0 ['stack_3_block9_se_2_conv[0][0 Y \n",
- " ]'] \n",
- " \n",
- " multiply_9 (Multiply) (None, 14, 14, 768) 0 ['stack_3_block9_MB_dw_swish[0] Y \n",
- " [0]', \n",
- " 'activation_19[0][0]'] \n",
- " \n",
- " stack_3_block9_MB_pw_conv (Con (None, 14, 14, 192) 147456 ['multiply_9[0][0]'] Y \n",
- " v2D) \n",
- " \n",
- " stack_3_block9_MB_pw_bn (Batch (None, 14, 14, 192) 768 ['stack_3_block9_MB_pw_conv[0][ Y \n",
- " Normalization) 0]'] \n",
- " \n",
- " add_26 (Add) (None, 14, 14, 192) 0 ['add_25[0][0]', Y \n",
- " 'stack_3_block9_MB_pw_bn[0][0] \n",
- " '] \n",
- " \n",
- " stack_3_block10_sortcut_conv ( (None, 14, 14, 768) 147456 ['add_26[0][0]'] Y \n",
- " Conv2D) \n",
- " \n",
- " stack_3_block10_sortcut_bn (Ba (None, 14, 14, 768) 3072 ['stack_3_block10_sortcut_conv[ Y \n",
- " tchNormalization) 0][0]'] \n",
- " \n",
- " stack_3_block10_sortcut_swish (None, 14, 14, 768) 0 ['stack_3_block10_sortcut_bn[0] Y \n",
- " (Activation) [0]'] \n",
- " \n",
- " stack_3_block10_MB_dw_ (Depthw (None, 14, 14, 768) 6912 ['stack_3_block10_sortcut_swish Y \n",
- " iseConv2D) [0][0]'] \n",
- " \n",
- " stack_3_block10_MB_dw_bn (Batc (None, 14, 14, 768) 3072 ['stack_3_block10_MB_dw_[0][0]' Y \n",
- " hNormalization) ] \n",
- " \n",
- " stack_3_block10_MB_dw_swish (A (None, 14, 14, 768) 0 ['stack_3_block10_MB_dw_bn[0][0 Y \n",
- " ctivation) ]'] \n",
- " \n",
- " tf.math.reduce_mean_10 (TFOpLa (None, 1, 1, 768) 0 ['stack_3_block10_MB_dw_swish[0 Y \n",
- " mbda) ][0]'] \n",
- " \n",
- " stack_3_block10_se_1_conv (Con (None, 1, 1, 48) 36912 ['tf.math.reduce_mean_10[0][0]' Y \n",
- " v2D) ] \n",
- " \n",
- " activation_20 (Activation) (None, 1, 1, 48) 0 ['stack_3_block10_se_1_conv[0][ Y \n",
- " 0]'] \n",
- " \n",
- " stack_3_block10_se_2_conv (Con (None, 1, 1, 768) 37632 ['activation_20[0][0]'] Y \n",
- " v2D) \n",
- " \n",
- " activation_21 (Activation) (None, 1, 1, 768) 0 ['stack_3_block10_se_2_conv[0][ Y \n",
- " 0]'] \n",
- " \n",
- " multiply_10 (Multiply) (None, 14, 14, 768) 0 ['stack_3_block10_MB_dw_swish[0 Y \n",
- " ][0]', \n",
- " 'activation_21[0][0]'] \n",
- " \n",
- " stack_3_block10_MB_pw_conv (Co (None, 14, 14, 192) 147456 ['multiply_10[0][0]'] Y \n",
- " nv2D) \n",
- " \n",
- " stack_3_block10_MB_pw_bn (Batc (None, 14, 14, 192) 768 ['stack_3_block10_MB_pw_conv[0] Y \n",
- " hNormalization) [0]'] \n",
- " \n",
- " add_27 (Add) (None, 14, 14, 192) 0 ['add_26[0][0]', Y \n",
- " 'stack_3_block10_MB_pw_bn[0][0 \n",
- " ]'] \n",
- " \n",
- " stack_3_block11_sortcut_conv ( (None, 14, 14, 768) 147456 ['add_27[0][0]'] Y \n",
- " Conv2D) \n",
- " \n",
- " stack_3_block11_sortcut_bn (Ba (None, 14, 14, 768) 3072 ['stack_3_block11_sortcut_conv[ Y \n",
- " tchNormalization) 0][0]'] \n",
- " \n",
- " stack_3_block11_sortcut_swish (None, 14, 14, 768) 0 ['stack_3_block11_sortcut_bn[0] Y \n",
- " (Activation) [0]'] \n",
- " \n",
- " stack_3_block11_MB_dw_ (Depthw (None, 14, 14, 768) 6912 ['stack_3_block11_sortcut_swish Y \n",
- " iseConv2D) [0][0]'] \n",
- " \n",
- " stack_3_block11_MB_dw_bn (Batc (None, 14, 14, 768) 3072 ['stack_3_block11_MB_dw_[0][0]' Y \n",
- " hNormalization) ] \n",
- " \n",
- " stack_3_block11_MB_dw_swish (A (None, 14, 14, 768) 0 ['stack_3_block11_MB_dw_bn[0][0 Y \n",
- " ctivation) ]'] \n",
- " \n",
- " tf.math.reduce_mean_11 (TFOpLa (None, 1, 1, 768) 0 ['stack_3_block11_MB_dw_swish[0 Y \n",
- " mbda) ][0]'] \n",
- " \n",
- " stack_3_block11_se_1_conv (Con (None, 1, 1, 48) 36912 ['tf.math.reduce_mean_11[0][0]' Y \n",
- " v2D) ] \n",
- " \n",
- " activation_22 (Activation) (None, 1, 1, 48) 0 ['stack_3_block11_se_1_conv[0][ Y \n",
- " 0]'] \n",
- " \n",
- " stack_3_block11_se_2_conv (Con (None, 1, 1, 768) 37632 ['activation_22[0][0]'] Y \n",
- " v2D) \n",
- " \n",
- " activation_23 (Activation) (None, 1, 1, 768) 0 ['stack_3_block11_se_2_conv[0][ Y \n",
- " 0]'] \n",
- " \n",
- " multiply_11 (Multiply) (None, 14, 14, 768) 0 ['stack_3_block11_MB_dw_swish[0 Y \n",
- " ][0]', \n",
- " 'activation_23[0][0]'] \n",
- " \n",
- " stack_3_block11_MB_pw_conv (Co (None, 14, 14, 192) 147456 ['multiply_11[0][0]'] Y \n",
- " nv2D) \n",
- " \n",
- " stack_3_block11_MB_pw_bn (Batc (None, 14, 14, 192) 768 ['stack_3_block11_MB_pw_conv[0] Y \n",
- " hNormalization) [0]'] \n",
- " \n",
- " add_28 (Add) (None, 14, 14, 192) 0 ['add_27[0][0]', Y \n",
- " 'stack_3_block11_MB_pw_bn[0][0 \n",
- " ]'] \n",
- " \n",
- " stack_3_block12_sortcut_conv ( (None, 14, 14, 768) 147456 ['add_28[0][0]'] Y \n",
- " Conv2D) \n",
- " \n",
- " stack_3_block12_sortcut_bn (Ba (None, 14, 14, 768) 3072 ['stack_3_block12_sortcut_conv[ Y \n",
- " tchNormalization) 0][0]'] \n",
- " \n",
- " stack_3_block12_sortcut_swish (None, 14, 14, 768) 0 ['stack_3_block12_sortcut_bn[0] Y \n",
- " (Activation) [0]'] \n",
- " \n",
- " stack_3_block12_MB_dw_ (Depthw (None, 14, 14, 768) 6912 ['stack_3_block12_sortcut_swish Y \n",
- " iseConv2D) [0][0]'] \n",
- " \n",
- " stack_3_block12_MB_dw_bn (Batc (None, 14, 14, 768) 3072 ['stack_3_block12_MB_dw_[0][0]' Y \n",
- " hNormalization) ] \n",
- " \n",
- " stack_3_block12_MB_dw_swish (A (None, 14, 14, 768) 0 ['stack_3_block12_MB_dw_bn[0][0 Y \n",
- " ctivation) ]'] \n",
- " \n",
- " tf.math.reduce_mean_12 (TFOpLa (None, 1, 1, 768) 0 ['stack_3_block12_MB_dw_swish[0 Y \n",
- " mbda) ][0]'] \n",
- " \n",
- " stack_3_block12_se_1_conv (Con (None, 1, 1, 48) 36912 ['tf.math.reduce_mean_12[0][0]' Y \n",
- " v2D) ] \n",
- " \n",
- " activation_24 (Activation) (None, 1, 1, 48) 0 ['stack_3_block12_se_1_conv[0][ Y \n",
- " 0]'] \n",
- " \n",
- " stack_3_block12_se_2_conv (Con (None, 1, 1, 768) 37632 ['activation_24[0][0]'] Y \n",
- " v2D) \n",
- " \n",
- " activation_25 (Activation) (None, 1, 1, 768) 0 ['stack_3_block12_se_2_conv[0][ Y \n",
- " 0]'] \n",
- " \n",
- " multiply_12 (Multiply) (None, 14, 14, 768) 0 ['stack_3_block12_MB_dw_swish[0 Y \n",
- " ][0]', \n",
- " 'activation_25[0][0]'] \n",
- " \n",
- " stack_3_block12_MB_pw_conv (Co (None, 14, 14, 192) 147456 ['multiply_12[0][0]'] Y \n",
- " nv2D) \n",
- " \n",
- " stack_3_block12_MB_pw_bn (Batc (None, 14, 14, 192) 768 ['stack_3_block12_MB_pw_conv[0] Y \n",
- " hNormalization) [0]'] \n",
- " \n",
- " add_29 (Add) (None, 14, 14, 192) 0 ['add_28[0][0]', Y \n",
- " 'stack_3_block12_MB_pw_bn[0][0 \n",
- " ]'] \n",
- " \n",
- " stack_3_block13_sortcut_conv ( (None, 14, 14, 768) 147456 ['add_29[0][0]'] Y \n",
- " Conv2D) \n",
- " \n",
- " stack_3_block13_sortcut_bn (Ba (None, 14, 14, 768) 3072 ['stack_3_block13_sortcut_conv[ Y \n",
- " tchNormalization) 0][0]'] \n",
- " \n",
- " stack_3_block13_sortcut_swish (None, 14, 14, 768) 0 ['stack_3_block13_sortcut_bn[0] Y \n",
- " (Activation) [0]'] \n",
- " \n",
- " stack_3_block13_MB_dw_ (Depthw (None, 14, 14, 768) 6912 ['stack_3_block13_sortcut_swish Y \n",
- " iseConv2D) [0][0]'] \n",
- " \n",
- " stack_3_block13_MB_dw_bn (Batc (None, 14, 14, 768) 3072 ['stack_3_block13_MB_dw_[0][0]' Y \n",
- " hNormalization) ] \n",
- " \n",
- " stack_3_block13_MB_dw_swish (A (None, 14, 14, 768) 0 ['stack_3_block13_MB_dw_bn[0][0 Y \n",
- " ctivation) ]'] \n",
- " \n",
- " tf.math.reduce_mean_13 (TFOpLa (None, 1, 1, 768) 0 ['stack_3_block13_MB_dw_swish[0 Y \n",
- " mbda) ][0]'] \n",
- " \n",
- " stack_3_block13_se_1_conv (Con (None, 1, 1, 48) 36912 ['tf.math.reduce_mean_13[0][0]' Y \n",
- " v2D) ] \n",
- " \n",
- " activation_26 (Activation) (None, 1, 1, 48) 0 ['stack_3_block13_se_1_conv[0][ Y \n",
- " 0]'] \n",
- " \n",
- " stack_3_block13_se_2_conv (Con (None, 1, 1, 768) 37632 ['activation_26[0][0]'] Y \n",
- " v2D) \n",
- " \n",
- " activation_27 (Activation) (None, 1, 1, 768) 0 ['stack_3_block13_se_2_conv[0][ Y \n",
- " 0]'] \n",
- " \n",
- " multiply_13 (Multiply) (None, 14, 14, 768) 0 ['stack_3_block13_MB_dw_swish[0 Y \n",
- " ][0]', \n",
- " 'activation_27[0][0]'] \n",
- " \n",
- " stack_3_block13_MB_pw_conv (Co (None, 14, 14, 192) 147456 ['multiply_13[0][0]'] Y \n",
- " nv2D) \n",
- " \n",
- " stack_3_block13_MB_pw_bn (Batc (None, 14, 14, 192) 768 ['stack_3_block13_MB_pw_conv[0] Y \n",
- " hNormalization) [0]'] \n",
- " \n",
- " add_30 (Add) (None, 14, 14, 192) 0 ['add_29[0][0]', Y \n",
- " 'stack_3_block13_MB_pw_bn[0][0 \n",
- " ]'] \n",
- " \n",
- " stack_3_block14_sortcut_conv ( (None, 14, 14, 768) 147456 ['add_30[0][0]'] Y \n",
- " Conv2D) \n",
- " \n",
- " stack_3_block14_sortcut_bn (Ba (None, 14, 14, 768) 3072 ['stack_3_block14_sortcut_conv[ Y \n",
- " tchNormalization) 0][0]'] \n",
- " \n",
- " stack_3_block14_sortcut_swish (None, 14, 14, 768) 0 ['stack_3_block14_sortcut_bn[0] Y \n",
- " (Activation) [0]'] \n",
- " \n",
- " stack_3_block14_MB_dw_ (Depthw (None, 14, 14, 768) 6912 ['stack_3_block14_sortcut_swish Y \n",
- " iseConv2D) [0][0]'] \n",
- " \n",
- " stack_3_block14_MB_dw_bn (Batc (None, 14, 14, 768) 3072 ['stack_3_block14_MB_dw_[0][0]' Y \n",
- " hNormalization) ] \n",
- " \n",
- " stack_3_block14_MB_dw_swish (A (None, 14, 14, 768) 0 ['stack_3_block14_MB_dw_bn[0][0 Y \n",
- " ctivation) ]'] \n",
- " \n",
- " tf.math.reduce_mean_14 (TFOpLa (None, 1, 1, 768) 0 ['stack_3_block14_MB_dw_swish[0 Y \n",
- " mbda) ][0]'] \n",
- " \n",
- " stack_3_block14_se_1_conv (Con (None, 1, 1, 48) 36912 ['tf.math.reduce_mean_14[0][0]' Y \n",
- " v2D) ] \n",
- " \n",
- " activation_28 (Activation) (None, 1, 1, 48) 0 ['stack_3_block14_se_1_conv[0][ Y \n",
- " 0]'] \n",
- " \n",
- " stack_3_block14_se_2_conv (Con (None, 1, 1, 768) 37632 ['activation_28[0][0]'] Y \n",
- " v2D) \n",
- " \n",
- " activation_29 (Activation) (None, 1, 1, 768) 0 ['stack_3_block14_se_2_conv[0][ Y \n",
- " 0]'] \n",
- " \n",
- " multiply_14 (Multiply) (None, 14, 14, 768) 0 ['stack_3_block14_MB_dw_swish[0 Y \n",
- " ][0]', \n",
- " 'activation_29[0][0]'] \n",
- " \n",
- " stack_3_block14_MB_pw_conv (Co (None, 14, 14, 192) 147456 ['multiply_14[0][0]'] Y \n",
- " nv2D) \n",
- " \n",
- " stack_3_block14_MB_pw_bn (Batc (None, 14, 14, 192) 768 ['stack_3_block14_MB_pw_conv[0] Y \n",
- " hNormalization) [0]'] \n",
- " \n",
- " add_31 (Add) (None, 14, 14, 192) 0 ['add_30[0][0]', Y \n",
- " 'stack_3_block14_MB_pw_bn[0][0 \n",
- " ]'] \n",
- " \n",
- " stack_3_block15_sortcut_conv ( (None, 14, 14, 768) 147456 ['add_31[0][0]'] Y \n",
- " Conv2D) \n",
- " \n",
- " stack_3_block15_sortcut_bn (Ba (None, 14, 14, 768) 3072 ['stack_3_block15_sortcut_conv[ Y \n",
- " tchNormalization) 0][0]'] \n",
- " \n",
- " stack_3_block15_sortcut_swish (None, 14, 14, 768) 0 ['stack_3_block15_sortcut_bn[0] Y \n",
- " (Activation) [0]'] \n",
- " \n",
- " stack_3_block15_MB_dw_ (Depthw (None, 14, 14, 768) 6912 ['stack_3_block15_sortcut_swish Y \n",
- " iseConv2D) [0][0]'] \n",
- " \n",
- " stack_3_block15_MB_dw_bn (Batc (None, 14, 14, 768) 3072 ['stack_3_block15_MB_dw_[0][0]' Y \n",
- " hNormalization) ] \n",
- " \n",
- " stack_3_block15_MB_dw_swish (A (None, 14, 14, 768) 0 ['stack_3_block15_MB_dw_bn[0][0 Y \n",
- " ctivation) ]'] \n",
- " \n",
- " tf.math.reduce_mean_15 (TFOpLa (None, 1, 1, 768) 0 ['stack_3_block15_MB_dw_swish[0 Y \n",
- " mbda) ][0]'] \n",
- " \n",
- " stack_3_block15_se_1_conv (Con (None, 1, 1, 48) 36912 ['tf.math.reduce_mean_15[0][0]' Y \n",
- " v2D) ] \n",
- " \n",
- " activation_30 (Activation) (None, 1, 1, 48) 0 ['stack_3_block15_se_1_conv[0][ Y \n",
- " 0]'] \n",
- " \n",
- " stack_3_block15_se_2_conv (Con (None, 1, 1, 768) 37632 ['activation_30[0][0]'] Y \n",
- " v2D) \n",
- " \n",
- " activation_31 (Activation) (None, 1, 1, 768) 0 ['stack_3_block15_se_2_conv[0][ Y \n",
- " 0]'] \n",
- " \n",
- " multiply_15 (Multiply) (None, 14, 14, 768) 0 ['stack_3_block15_MB_dw_swish[0 Y \n",
- " ][0]', \n",
- " 'activation_31[0][0]'] \n",
- " \n",
- " stack_3_block15_MB_pw_conv (Co (None, 14, 14, 192) 147456 ['multiply_15[0][0]'] Y \n",
- " nv2D) \n",
- " \n",
- " stack_3_block15_MB_pw_bn (Batc (None, 14, 14, 192) 768 ['stack_3_block15_MB_pw_conv[0] Y \n",
- " hNormalization) [0]'] \n",
- " \n",
- " add_32 (Add) (None, 14, 14, 192) 0 ['add_31[0][0]', Y \n",
- " 'stack_3_block15_MB_pw_bn[0][0 \n",
- " ]'] \n",
- " \n",
- " stack_4_block0_sortcut_conv (C (None, 14, 14, 1152 221184 ['add_32[0][0]'] Y \n",
- " onv2D) ) \n",
- " \n",
- " stack_4_block0_sortcut_bn (Bat (None, 14, 14, 1152 4608 ['stack_4_block0_sortcut_conv[0 Y \n",
- " chNormalization) ) ][0]'] \n",
- " \n",
- " stack_4_block0_sortcut_swish ( (None, 14, 14, 1152 0 ['stack_4_block0_sortcut_bn[0][ Y \n",
- " Activation) ) 0]'] \n",
- " \n",
- " stack_4_block0_MB_dw_ (Depthwi (None, 14, 14, 1152 10368 ['stack_4_block0_sortcut_swish[ Y \n",
- " seConv2D) ) 0][0]'] \n",
- " \n",
- " stack_4_block0_MB_dw_bn (Batch (None, 14, 14, 1152 4608 ['stack_4_block0_MB_dw_[0][0]'] Y \n",
- " Normalization) ) \n",
- " \n",
- " stack_4_block0_MB_dw_swish (Ac (None, 14, 14, 1152 0 ['stack_4_block0_MB_dw_bn[0][0] Y \n",
- " tivation) ) '] \n",
- " \n",
- " tf.math.reduce_mean_16 (TFOpLa (None, 1, 1, 1152) 0 ['stack_4_block0_MB_dw_swish[0] Y \n",
- " mbda) [0]'] \n",
- " \n",
- " stack_4_block0_se_1_conv (Conv (None, 1, 1, 48) 55344 ['tf.math.reduce_mean_16[0][0]' Y \n",
- " 2D) ] \n",
- " \n",
- " activation_32 (Activation) (None, 1, 1, 48) 0 ['stack_4_block0_se_1_conv[0][0 Y \n",
- " ]'] \n",
- " \n",
- " stack_4_block0_se_2_conv (Conv (None, 1, 1, 1152) 56448 ['activation_32[0][0]'] Y \n",
- " 2D) \n",
- " \n",
- " activation_33 (Activation) (None, 1, 1, 1152) 0 ['stack_4_block0_se_2_conv[0][0 Y \n",
- " ]'] \n",
- " \n",
- " multiply_16 (Multiply) (None, 14, 14, 1152 0 ['stack_4_block0_MB_dw_swish[0] Y \n",
- " ) [0]', \n",
- " 'activation_33[0][0]'] \n",
- " \n",
- " stack_4_block0_MB_pw_conv (Con (None, 14, 14, 256) 294912 ['multiply_16[0][0]'] Y \n",
- " v2D) \n",
- " \n",
- " stack_4_block0_MB_pw_bn (Batch (None, 14, 14, 256) 1024 ['stack_4_block0_MB_pw_conv[0][ Y \n",
- " Normalization) 0]'] \n",
- " \n",
- " stack_4_block1_sortcut_conv (C (None, 14, 14, 1536 393216 ['stack_4_block0_MB_pw_bn[0][0] Y \n",
- " onv2D) ) '] \n",
- " \n",
- " stack_4_block1_sortcut_bn (Bat (None, 14, 14, 1536 6144 ['stack_4_block1_sortcut_conv[0 Y \n",
- " chNormalization) ) ][0]'] \n",
- " \n",
- " stack_4_block1_sortcut_swish ( (None, 14, 14, 1536 0 ['stack_4_block1_sortcut_bn[0][ Y \n",
- " Activation) ) 0]'] \n",
- " \n",
- " stack_4_block1_MB_dw_ (Depthwi (None, 14, 14, 1536 13824 ['stack_4_block1_sortcut_swish[ Y \n",
- " seConv2D) ) 0][0]'] \n",
- " \n",
- " stack_4_block1_MB_dw_bn (Batch (None, 14, 14, 1536 6144 ['stack_4_block1_MB_dw_[0][0]'] Y \n",
- " Normalization) ) \n",
- " \n",
- " stack_4_block1_MB_dw_swish (Ac (None, 14, 14, 1536 0 ['stack_4_block1_MB_dw_bn[0][0] Y \n",
- " tivation) ) '] \n",
- " \n",
- " tf.math.reduce_mean_17 (TFOpLa (None, 1, 1, 1536) 0 ['stack_4_block1_MB_dw_swish[0] Y \n",
- " mbda) [0]'] \n",
- " \n",
- " stack_4_block1_se_1_conv (Conv (None, 1, 1, 64) 98368 ['tf.math.reduce_mean_17[0][0]' Y \n",
- " 2D) ] \n",
- " \n",
- " activation_34 (Activation) (None, 1, 1, 64) 0 ['stack_4_block1_se_1_conv[0][0 Y \n",
- " ]'] \n",
- " \n",
- " stack_4_block1_se_2_conv (Conv (None, 1, 1, 1536) 99840 ['activation_34[0][0]'] Y \n",
- " 2D) \n",
- " \n",
- " activation_35 (Activation) (None, 1, 1, 1536) 0 ['stack_4_block1_se_2_conv[0][0 Y \n",
- " ]'] \n",
- " \n",
- " multiply_17 (Multiply) (None, 14, 14, 1536 0 ['stack_4_block1_MB_dw_swish[0] Y \n",
- " ) [0]', \n",
- " 'activation_35[0][0]'] \n",
- " \n",
- " stack_4_block1_MB_pw_conv (Con (None, 14, 14, 256) 393216 ['multiply_17[0][0]'] Y \n",
- " v2D) \n",
- " \n",
- " stack_4_block1_MB_pw_bn (Batch (None, 14, 14, 256) 1024 ['stack_4_block1_MB_pw_conv[0][ Y \n",
- " Normalization) 0]'] \n",
- " \n",
- " add_33 (Add) (None, 14, 14, 256) 0 ['stack_4_block0_MB_pw_bn[0][0] Y \n",
- " ', \n",
- " 'stack_4_block1_MB_pw_bn[0][0] \n",
- " '] \n",
- " \n",
- " stack_4_block2_sortcut_conv (C (None, 14, 14, 1536 393216 ['add_33[0][0]'] Y \n",
- " onv2D) ) \n",
- " \n",
- " stack_4_block2_sortcut_bn (Bat (None, 14, 14, 1536 6144 ['stack_4_block2_sortcut_conv[0 Y \n",
- " chNormalization) ) ][0]'] \n",
- " \n",
- " stack_4_block2_sortcut_swish ( (None, 14, 14, 1536 0 ['stack_4_block2_sortcut_bn[0][ Y \n",
- " Activation) ) 0]'] \n",
- " \n",
- " stack_4_block2_MB_dw_ (Depthwi (None, 14, 14, 1536 13824 ['stack_4_block2_sortcut_swish[ Y \n",
- " seConv2D) ) 0][0]'] \n",
- " \n",
- " stack_4_block2_MB_dw_bn (Batch (None, 14, 14, 1536 6144 ['stack_4_block2_MB_dw_[0][0]'] Y \n",
- " Normalization) ) \n",
- " \n",
- " stack_4_block2_MB_dw_swish (Ac (None, 14, 14, 1536 0 ['stack_4_block2_MB_dw_bn[0][0] Y \n",
- " tivation) ) '] \n",
- " \n",
- " tf.math.reduce_mean_18 (TFOpLa (None, 1, 1, 1536) 0 ['stack_4_block2_MB_dw_swish[0] Y \n",
- " mbda) [0]'] \n",
- " \n",
- " stack_4_block2_se_1_conv (Conv (None, 1, 1, 64) 98368 ['tf.math.reduce_mean_18[0][0]' Y \n",
- " 2D) ] \n",
- " \n",
- " activation_36 (Activation) (None, 1, 1, 64) 0 ['stack_4_block2_se_1_conv[0][0 Y \n",
- " ]'] \n",
- " \n",
- " stack_4_block2_se_2_conv (Conv (None, 1, 1, 1536) 99840 ['activation_36[0][0]'] Y \n",
- " 2D) \n",
- " \n",
- " activation_37 (Activation) (None, 1, 1, 1536) 0 ['stack_4_block2_se_2_conv[0][0 Y \n",
- " ]'] \n",
- " \n",
- " multiply_18 (Multiply) (None, 14, 14, 1536 0 ['stack_4_block2_MB_dw_swish[0] Y \n",
- " ) [0]', \n",
- " 'activation_37[0][0]'] \n",
- " \n",
- " stack_4_block2_MB_pw_conv (Con (None, 14, 14, 256) 393216 ['multiply_18[0][0]'] Y \n",
- " v2D) \n",
- " \n",
- " stack_4_block2_MB_pw_bn (Batch (None, 14, 14, 256) 1024 ['stack_4_block2_MB_pw_conv[0][ Y \n",
- " Normalization) 0]'] \n",
- " \n",
- " add_34 (Add) (None, 14, 14, 256) 0 ['add_33[0][0]', Y \n",
- " 'stack_4_block2_MB_pw_bn[0][0] \n",
- " '] \n",
- " \n",
- " stack_4_block3_sortcut_conv (C (None, 14, 14, 1536 393216 ['add_34[0][0]'] Y \n",
- " onv2D) ) \n",
- " \n",
- " stack_4_block3_sortcut_bn (Bat (None, 14, 14, 1536 6144 ['stack_4_block3_sortcut_conv[0 Y \n",
- " chNormalization) ) ][0]'] \n",
- " \n",
- " stack_4_block3_sortcut_swish ( (None, 14, 14, 1536 0 ['stack_4_block3_sortcut_bn[0][ Y \n",
- " Activation) ) 0]'] \n",
- " \n",
- " stack_4_block3_MB_dw_ (Depthwi (None, 14, 14, 1536 13824 ['stack_4_block3_sortcut_swish[ Y \n",
- " seConv2D) ) 0][0]'] \n",
- " \n",
- " stack_4_block3_MB_dw_bn (Batch (None, 14, 14, 1536 6144 ['stack_4_block3_MB_dw_[0][0]'] Y \n",
- " Normalization) ) \n",
- " \n",
- " stack_4_block3_MB_dw_swish (Ac (None, 14, 14, 1536 0 ['stack_4_block3_MB_dw_bn[0][0] Y \n",
- " tivation) ) '] \n",
- " \n",
- " tf.math.reduce_mean_19 (TFOpLa (None, 1, 1, 1536) 0 ['stack_4_block3_MB_dw_swish[0] Y \n",
- " mbda) [0]'] \n",
- " \n",
- " stack_4_block3_se_1_conv (Conv (None, 1, 1, 64) 98368 ['tf.math.reduce_mean_19[0][0]' Y \n",
- " 2D) ] \n",
- " \n",
- " activation_38 (Activation) (None, 1, 1, 64) 0 ['stack_4_block3_se_1_conv[0][0 Y \n",
- " ]'] \n",
- " \n",
- " stack_4_block3_se_2_conv (Conv (None, 1, 1, 1536) 99840 ['activation_38[0][0]'] Y \n",
- " 2D) \n",
- " \n",
- " activation_39 (Activation) (None, 1, 1, 1536) 0 ['stack_4_block3_se_2_conv[0][0 Y \n",
- " ]'] \n",
- " \n",
- " multiply_19 (Multiply) (None, 14, 14, 1536 0 ['stack_4_block3_MB_dw_swish[0] Y \n",
- " ) [0]', \n",
- " 'activation_39[0][0]'] \n",
- " \n",
- " stack_4_block3_MB_pw_conv (Con (None, 14, 14, 256) 393216 ['multiply_19[0][0]'] Y \n",
- " v2D) \n",
- " \n",
- " stack_4_block3_MB_pw_bn (Batch (None, 14, 14, 256) 1024 ['stack_4_block3_MB_pw_conv[0][ Y \n",
- " Normalization) 0]'] \n",
- " \n",
- " add_35 (Add) (None, 14, 14, 256) 0 ['add_34[0][0]', Y \n",
- " 'stack_4_block3_MB_pw_bn[0][0] \n",
- " '] \n",
- " \n",
- " stack_4_block4_sortcut_conv (C (None, 14, 14, 1536 393216 ['add_35[0][0]'] Y \n",
- " onv2D) ) \n",
- " \n",
- " stack_4_block4_sortcut_bn (Bat (None, 14, 14, 1536 6144 ['stack_4_block4_sortcut_conv[0 Y \n",
- " chNormalization) ) ][0]'] \n",
- " \n",
- " stack_4_block4_sortcut_swish ( (None, 14, 14, 1536 0 ['stack_4_block4_sortcut_bn[0][ Y \n",
- " Activation) ) 0]'] \n",
- " \n",
- " stack_4_block4_MB_dw_ (Depthwi (None, 14, 14, 1536 13824 ['stack_4_block4_sortcut_swish[ Y \n",
- " seConv2D) ) 0][0]'] \n",
- " \n",
- " stack_4_block4_MB_dw_bn (Batch (None, 14, 14, 1536 6144 ['stack_4_block4_MB_dw_[0][0]'] Y \n",
- " Normalization) ) \n",
- " \n",
- " stack_4_block4_MB_dw_swish (Ac (None, 14, 14, 1536 0 ['stack_4_block4_MB_dw_bn[0][0] Y \n",
- " tivation) ) '] \n",
- " \n",
- " tf.math.reduce_mean_20 (TFOpLa (None, 1, 1, 1536) 0 ['stack_4_block4_MB_dw_swish[0] Y \n",
- " mbda) [0]'] \n",
- " \n",
- " stack_4_block4_se_1_conv (Conv (None, 1, 1, 64) 98368 ['tf.math.reduce_mean_20[0][0]' Y \n",
- " 2D) ] \n",
- " \n",
- " activation_40 (Activation) (None, 1, 1, 64) 0 ['stack_4_block4_se_1_conv[0][0 Y \n",
- " ]'] \n",
- " \n",
- " stack_4_block4_se_2_conv (Conv (None, 1, 1, 1536) 99840 ['activation_40[0][0]'] Y \n",
- " 2D) \n",
- " \n",
- " activation_41 (Activation) (None, 1, 1, 1536) 0 ['stack_4_block4_se_2_conv[0][0 Y \n",
- " ]'] \n",
- " \n",
- " multiply_20 (Multiply) (None, 14, 14, 1536 0 ['stack_4_block4_MB_dw_swish[0] Y \n",
- " ) [0]', \n",
- " 'activation_41[0][0]'] \n",
- " \n",
- " stack_4_block4_MB_pw_conv (Con (None, 14, 14, 256) 393216 ['multiply_20[0][0]'] Y \n",
- " v2D) \n",
- " \n",
- " stack_4_block4_MB_pw_bn (Batch (None, 14, 14, 256) 1024 ['stack_4_block4_MB_pw_conv[0][ Y \n",
- " Normalization) 0]'] \n",
- " \n",
- " add_36 (Add) (None, 14, 14, 256) 0 ['add_35[0][0]', Y \n",
- " 'stack_4_block4_MB_pw_bn[0][0] \n",
- " '] \n",
- " \n",
- " stack_4_block5_sortcut_conv (C (None, 14, 14, 1536 393216 ['add_36[0][0]'] Y \n",
- " onv2D) ) \n",
- " \n",
- " stack_4_block5_sortcut_bn (Bat (None, 14, 14, 1536 6144 ['stack_4_block5_sortcut_conv[0 Y \n",
- " chNormalization) ) ][0]'] \n",
- " \n",
- " stack_4_block5_sortcut_swish ( (None, 14, 14, 1536 0 ['stack_4_block5_sortcut_bn[0][ Y \n",
- " Activation) ) 0]'] \n",
- " \n",
- " stack_4_block5_MB_dw_ (Depthwi (None, 14, 14, 1536 13824 ['stack_4_block5_sortcut_swish[ Y \n",
- " seConv2D) ) 0][0]'] \n",
- " \n",
- " stack_4_block5_MB_dw_bn (Batch (None, 14, 14, 1536 6144 ['stack_4_block5_MB_dw_[0][0]'] Y \n",
- " Normalization) ) \n",
- " \n",
- " stack_4_block5_MB_dw_swish (Ac (None, 14, 14, 1536 0 ['stack_4_block5_MB_dw_bn[0][0] Y \n",
- " tivation) ) '] \n",
- " \n",
- " tf.math.reduce_mean_21 (TFOpLa (None, 1, 1, 1536) 0 ['stack_4_block5_MB_dw_swish[0] Y \n",
- " mbda) [0]'] \n",
- " \n",
- " stack_4_block5_se_1_conv (Conv (None, 1, 1, 64) 98368 ['tf.math.reduce_mean_21[0][0]' Y \n",
- " 2D) ] \n",
- " \n",
- " activation_42 (Activation) (None, 1, 1, 64) 0 ['stack_4_block5_se_1_conv[0][0 Y \n",
- " ]'] \n",
- " \n",
- " stack_4_block5_se_2_conv (Conv (None, 1, 1, 1536) 99840 ['activation_42[0][0]'] Y \n",
- " 2D) \n",
- " \n",
- " activation_43 (Activation) (None, 1, 1, 1536) 0 ['stack_4_block5_se_2_conv[0][0 Y \n",
- " ]'] \n",
- " \n",
- " multiply_21 (Multiply) (None, 14, 14, 1536 0 ['stack_4_block5_MB_dw_swish[0] Y \n",
- " ) [0]', \n",
- " 'activation_43[0][0]'] \n",
- " \n",
- " stack_4_block5_MB_pw_conv (Con (None, 14, 14, 256) 393216 ['multiply_21[0][0]'] Y \n",
- " v2D) \n",
- " \n",
- " stack_4_block5_MB_pw_bn (Batch (None, 14, 14, 256) 1024 ['stack_4_block5_MB_pw_conv[0][ Y \n",
- " Normalization) 0]'] \n",
- " \n",
- " add_37 (Add) (None, 14, 14, 256) 0 ['add_36[0][0]', Y \n",
- " 'stack_4_block5_MB_pw_bn[0][0] \n",
- " '] \n",
- " \n",
- " stack_4_block6_sortcut_conv (C (None, 14, 14, 1536 393216 ['add_37[0][0]'] Y \n",
- " onv2D) ) \n",
- " \n",
- " stack_4_block6_sortcut_bn (Bat (None, 14, 14, 1536 6144 ['stack_4_block6_sortcut_conv[0 Y \n",
- " chNormalization) ) ][0]'] \n",
- " \n",
- " stack_4_block6_sortcut_swish ( (None, 14, 14, 1536 0 ['stack_4_block6_sortcut_bn[0][ Y \n",
- " Activation) ) 0]'] \n",
- " \n",
- " stack_4_block6_MB_dw_ (Depthwi (None, 14, 14, 1536 13824 ['stack_4_block6_sortcut_swish[ Y \n",
- " seConv2D) ) 0][0]'] \n",
- " \n",
- " stack_4_block6_MB_dw_bn (Batch (None, 14, 14, 1536 6144 ['stack_4_block6_MB_dw_[0][0]'] Y \n",
- " Normalization) ) \n",
- " \n",
- " stack_4_block6_MB_dw_swish (Ac (None, 14, 14, 1536 0 ['stack_4_block6_MB_dw_bn[0][0] Y \n",
- " tivation) ) '] \n",
- " \n",
- " tf.math.reduce_mean_22 (TFOpLa (None, 1, 1, 1536) 0 ['stack_4_block6_MB_dw_swish[0] Y \n",
- " mbda) [0]'] \n",
- " \n",
- " stack_4_block6_se_1_conv (Conv (None, 1, 1, 64) 98368 ['tf.math.reduce_mean_22[0][0]' Y \n",
- " 2D) ] \n",
- " \n",
- " activation_44 (Activation) (None, 1, 1, 64) 0 ['stack_4_block6_se_1_conv[0][0 Y \n",
- " ]'] \n",
- " \n",
- " stack_4_block6_se_2_conv (Conv (None, 1, 1, 1536) 99840 ['activation_44[0][0]'] Y \n",
- " 2D) \n",
- " \n",
- " activation_45 (Activation) (None, 1, 1, 1536) 0 ['stack_4_block6_se_2_conv[0][0 Y \n",
- " ]'] \n",
- " \n",
- " multiply_22 (Multiply) (None, 14, 14, 1536 0 ['stack_4_block6_MB_dw_swish[0] Y \n",
- " ) [0]', \n",
- " 'activation_45[0][0]'] \n",
- " \n",
- " stack_4_block6_MB_pw_conv (Con (None, 14, 14, 256) 393216 ['multiply_22[0][0]'] Y \n",
- " v2D) \n",
- " \n",
- " stack_4_block6_MB_pw_bn (Batch (None, 14, 14, 256) 1024 ['stack_4_block6_MB_pw_conv[0][ Y \n",
- " Normalization) 0]'] \n",
- " \n",
- " add_38 (Add) (None, 14, 14, 256) 0 ['add_37[0][0]', Y \n",
- " 'stack_4_block6_MB_pw_bn[0][0] \n",
- " '] \n",
- " \n",
- " stack_4_block7_sortcut_conv (C (None, 14, 14, 1536 393216 ['add_38[0][0]'] Y \n",
- " onv2D) ) \n",
- " \n",
- " stack_4_block7_sortcut_bn (Bat (None, 14, 14, 1536 6144 ['stack_4_block7_sortcut_conv[0 Y \n",
- " chNormalization) ) ][0]'] \n",
- " \n",
- " stack_4_block7_sortcut_swish ( (None, 14, 14, 1536 0 ['stack_4_block7_sortcut_bn[0][ Y \n",
- " Activation) ) 0]'] \n",
- " \n",
- " stack_4_block7_MB_dw_ (Depthwi (None, 14, 14, 1536 13824 ['stack_4_block7_sortcut_swish[ Y \n",
- " seConv2D) ) 0][0]'] \n",
- " \n",
- " stack_4_block7_MB_dw_bn (Batch (None, 14, 14, 1536 6144 ['stack_4_block7_MB_dw_[0][0]'] Y \n",
- " Normalization) ) \n",
- " \n",
- " stack_4_block7_MB_dw_swish (Ac (None, 14, 14, 1536 0 ['stack_4_block7_MB_dw_bn[0][0] Y \n",
- " tivation) ) '] \n",
- " \n",
- " tf.math.reduce_mean_23 (TFOpLa (None, 1, 1, 1536) 0 ['stack_4_block7_MB_dw_swish[0] Y \n",
- " mbda) [0]'] \n",
- " \n",
- " stack_4_block7_se_1_conv (Conv (None, 1, 1, 64) 98368 ['tf.math.reduce_mean_23[0][0]' Y \n",
- " 2D) ] \n",
- " \n",
- " activation_46 (Activation) (None, 1, 1, 64) 0 ['stack_4_block7_se_1_conv[0][0 Y \n",
- " ]'] \n",
- " \n",
- " stack_4_block7_se_2_conv (Conv (None, 1, 1, 1536) 99840 ['activation_46[0][0]'] Y \n",
- " 2D) \n",
- " \n",
- " activation_47 (Activation) (None, 1, 1, 1536) 0 ['stack_4_block7_se_2_conv[0][0 Y \n",
- " ]'] \n",
- " \n",
- " multiply_23 (Multiply) (None, 14, 14, 1536 0 ['stack_4_block7_MB_dw_swish[0] Y \n",
- " ) [0]', \n",
- " 'activation_47[0][0]'] \n",
- " \n",
- " stack_4_block7_MB_pw_conv (Con (None, 14, 14, 256) 393216 ['multiply_23[0][0]'] Y \n",
- " v2D) \n",
- " \n",
- " stack_4_block7_MB_pw_bn (Batch (None, 14, 14, 256) 1024 ['stack_4_block7_MB_pw_conv[0][ Y \n",
- " Normalization) 0]'] \n",
- " \n",
- " add_39 (Add) (None, 14, 14, 256) 0 ['add_38[0][0]', Y \n",
- " 'stack_4_block7_MB_pw_bn[0][0] \n",
- " '] \n",
- " \n",
- " stack_4_block8_sortcut_conv (C (None, 14, 14, 1536 393216 ['add_39[0][0]'] Y \n",
- " onv2D) ) \n",
- " \n",
- " stack_4_block8_sortcut_bn (Bat (None, 14, 14, 1536 6144 ['stack_4_block8_sortcut_conv[0 Y \n",
- " chNormalization) ) ][0]'] \n",
- " \n",
- " stack_4_block8_sortcut_swish ( (None, 14, 14, 1536 0 ['stack_4_block8_sortcut_bn[0][ Y \n",
- " Activation) ) 0]'] \n",
- " \n",
- " stack_4_block8_MB_dw_ (Depthwi (None, 14, 14, 1536 13824 ['stack_4_block8_sortcut_swish[ Y \n",
- " seConv2D) ) 0][0]'] \n",
- " \n",
- " stack_4_block8_MB_dw_bn (Batch (None, 14, 14, 1536 6144 ['stack_4_block8_MB_dw_[0][0]'] Y \n",
- " Normalization) ) \n",
- " \n",
- " stack_4_block8_MB_dw_swish (Ac (None, 14, 14, 1536 0 ['stack_4_block8_MB_dw_bn[0][0] Y \n",
- " tivation) ) '] \n",
- " \n",
- " tf.math.reduce_mean_24 (TFOpLa (None, 1, 1, 1536) 0 ['stack_4_block8_MB_dw_swish[0] Y \n",
- " mbda) [0]'] \n",
- " \n",
- " stack_4_block8_se_1_conv (Conv (None, 1, 1, 64) 98368 ['tf.math.reduce_mean_24[0][0]' Y \n",
- " 2D) ] \n",
- " \n",
- " activation_48 (Activation) (None, 1, 1, 64) 0 ['stack_4_block8_se_1_conv[0][0 Y \n",
- " ]'] \n",
- " \n",
- " stack_4_block8_se_2_conv (Conv (None, 1, 1, 1536) 99840 ['activation_48[0][0]'] Y \n",
- " 2D) \n",
- " \n",
- " activation_49 (Activation) (None, 1, 1, 1536) 0 ['stack_4_block8_se_2_conv[0][0 Y \n",
- " ]'] \n",
- " \n",
- " multiply_24 (Multiply) (None, 14, 14, 1536 0 ['stack_4_block8_MB_dw_swish[0] Y \n",
- " ) [0]', \n",
- " 'activation_49[0][0]'] \n",
- " \n",
- " stack_4_block8_MB_pw_conv (Con (None, 14, 14, 256) 393216 ['multiply_24[0][0]'] Y \n",
- " v2D) \n",
- " \n",
- " stack_4_block8_MB_pw_bn (Batch (None, 14, 14, 256) 1024 ['stack_4_block8_MB_pw_conv[0][ Y \n",
- " Normalization) 0]'] \n",
- " \n",
- " add_40 (Add) (None, 14, 14, 256) 0 ['add_39[0][0]', Y \n",
- " 'stack_4_block8_MB_pw_bn[0][0] \n",
- " '] \n",
- " \n",
- " stack_4_block9_sortcut_conv (C (None, 14, 14, 1536 393216 ['add_40[0][0]'] Y \n",
- " onv2D) ) \n",
- " \n",
- " stack_4_block9_sortcut_bn (Bat (None, 14, 14, 1536 6144 ['stack_4_block9_sortcut_conv[0 Y \n",
- " chNormalization) ) ][0]'] \n",
- " \n",
- " stack_4_block9_sortcut_swish ( (None, 14, 14, 1536 0 ['stack_4_block9_sortcut_bn[0][ Y \n",
- " Activation) ) 0]'] \n",
- " \n",
- " stack_4_block9_MB_dw_ (Depthwi (None, 14, 14, 1536 13824 ['stack_4_block9_sortcut_swish[ Y \n",
- " seConv2D) ) 0][0]'] \n",
- " \n",
- " stack_4_block9_MB_dw_bn (Batch (None, 14, 14, 1536 6144 ['stack_4_block9_MB_dw_[0][0]'] Y \n",
- " Normalization) ) \n",
- " \n",
- " stack_4_block9_MB_dw_swish (Ac (None, 14, 14, 1536 0 ['stack_4_block9_MB_dw_bn[0][0] Y \n",
- " tivation) ) '] \n",
- " \n",
- " tf.math.reduce_mean_25 (TFOpLa (None, 1, 1, 1536) 0 ['stack_4_block9_MB_dw_swish[0] Y \n",
- " mbda) [0]'] \n",
- " \n",
- " stack_4_block9_se_1_conv (Conv (None, 1, 1, 64) 98368 ['tf.math.reduce_mean_25[0][0]' Y \n",
- " 2D) ] \n",
- " \n",
- " activation_50 (Activation) (None, 1, 1, 64) 0 ['stack_4_block9_se_1_conv[0][0 Y \n",
- " ]'] \n",
- " \n",
- " stack_4_block9_se_2_conv (Conv (None, 1, 1, 1536) 99840 ['activation_50[0][0]'] Y \n",
- " 2D) \n",
- " \n",
- " activation_51 (Activation) (None, 1, 1, 1536) 0 ['stack_4_block9_se_2_conv[0][0 Y \n",
- " ]'] \n",
- " \n",
- " multiply_25 (Multiply) (None, 14, 14, 1536 0 ['stack_4_block9_MB_dw_swish[0] Y \n",
- " ) [0]', \n",
- " 'activation_51[0][0]'] \n",
- " \n",
- " stack_4_block9_MB_pw_conv (Con (None, 14, 14, 256) 393216 ['multiply_25[0][0]'] Y \n",
- " v2D) \n",
- " \n",
- " stack_4_block9_MB_pw_bn (Batch (None, 14, 14, 256) 1024 ['stack_4_block9_MB_pw_conv[0][ Y \n",
- " Normalization) 0]'] \n",
- " \n",
- " add_41 (Add) (None, 14, 14, 256) 0 ['add_40[0][0]', Y \n",
- " 'stack_4_block9_MB_pw_bn[0][0] \n",
- " '] \n",
- " \n",
- " stack_4_block10_sortcut_conv ( (None, 14, 14, 1536 393216 ['add_41[0][0]'] Y \n",
- " Conv2D) ) \n",
- " \n",
- " stack_4_block10_sortcut_bn (Ba (None, 14, 14, 1536 6144 ['stack_4_block10_sortcut_conv[ Y \n",
- " tchNormalization) ) 0][0]'] \n",
- " \n",
- " stack_4_block10_sortcut_swish (None, 14, 14, 1536 0 ['stack_4_block10_sortcut_bn[0] Y \n",
- " (Activation) ) [0]'] \n",
- " \n",
- " stack_4_block10_MB_dw_ (Depthw (None, 14, 14, 1536 13824 ['stack_4_block10_sortcut_swish Y \n",
- " iseConv2D) ) [0][0]'] \n",
- " \n",
- " stack_4_block10_MB_dw_bn (Batc (None, 14, 14, 1536 6144 ['stack_4_block10_MB_dw_[0][0]' Y \n",
- " hNormalization) ) ] \n",
- " \n",
- " stack_4_block10_MB_dw_swish (A (None, 14, 14, 1536 0 ['stack_4_block10_MB_dw_bn[0][0 Y \n",
- " ctivation) ) ]'] \n",
- " \n",
- " tf.math.reduce_mean_26 (TFOpLa (None, 1, 1, 1536) 0 ['stack_4_block10_MB_dw_swish[0 Y \n",
- " mbda) ][0]'] \n",
- " \n",
- " stack_4_block10_se_1_conv (Con (None, 1, 1, 64) 98368 ['tf.math.reduce_mean_26[0][0]' Y \n",
- " v2D) ] \n",
- " \n",
- " activation_52 (Activation) (None, 1, 1, 64) 0 ['stack_4_block10_se_1_conv[0][ Y \n",
- " 0]'] \n",
- " \n",
- " stack_4_block10_se_2_conv (Con (None, 1, 1, 1536) 99840 ['activation_52[0][0]'] Y \n",
- " v2D) \n",
- " \n",
- " activation_53 (Activation) (None, 1, 1, 1536) 0 ['stack_4_block10_se_2_conv[0][ Y \n",
- " 0]'] \n",
- " \n",
- " multiply_26 (Multiply) (None, 14, 14, 1536 0 ['stack_4_block10_MB_dw_swish[0 Y \n",
- " ) ][0]', \n",
- " 'activation_53[0][0]'] \n",
- " \n",
- " stack_4_block10_MB_pw_conv (Co (None, 14, 14, 256) 393216 ['multiply_26[0][0]'] Y \n",
- " nv2D) \n",
- " \n",
- " stack_4_block10_MB_pw_bn (Batc (None, 14, 14, 256) 1024 ['stack_4_block10_MB_pw_conv[0] Y \n",
- " hNormalization) [0]'] \n",
- " \n",
- " add_42 (Add) (None, 14, 14, 256) 0 ['add_41[0][0]', Y \n",
- " 'stack_4_block10_MB_pw_bn[0][0 \n",
- " ]'] \n",
- " \n",
- " stack_4_block11_sortcut_conv ( (None, 14, 14, 1536 393216 ['add_42[0][0]'] Y \n",
- " Conv2D) ) \n",
- " \n",
- " stack_4_block11_sortcut_bn (Ba (None, 14, 14, 1536 6144 ['stack_4_block11_sortcut_conv[ Y \n",
- " tchNormalization) ) 0][0]'] \n",
- " \n",
- " stack_4_block11_sortcut_swish (None, 14, 14, 1536 0 ['stack_4_block11_sortcut_bn[0] Y \n",
- " (Activation) ) [0]'] \n",
- " \n",
- " stack_4_block11_MB_dw_ (Depthw (None, 14, 14, 1536 13824 ['stack_4_block11_sortcut_swish Y \n",
- " iseConv2D) ) [0][0]'] \n",
- " \n",
- " stack_4_block11_MB_dw_bn (Batc (None, 14, 14, 1536 6144 ['stack_4_block11_MB_dw_[0][0]' Y \n",
- " hNormalization) ) ] \n",
- " \n",
- " stack_4_block11_MB_dw_swish (A (None, 14, 14, 1536 0 ['stack_4_block11_MB_dw_bn[0][0 Y \n",
- " ctivation) ) ]'] \n",
- " \n",
- " tf.math.reduce_mean_27 (TFOpLa (None, 1, 1, 1536) 0 ['stack_4_block11_MB_dw_swish[0 Y \n",
- " mbda) ][0]'] \n",
- " \n",
- " stack_4_block11_se_1_conv (Con (None, 1, 1, 64) 98368 ['tf.math.reduce_mean_27[0][0]' Y \n",
- " v2D) ] \n",
- " \n",
- " activation_54 (Activation) (None, 1, 1, 64) 0 ['stack_4_block11_se_1_conv[0][ Y \n",
- " 0]'] \n",
- " \n",
- " stack_4_block11_se_2_conv (Con (None, 1, 1, 1536) 99840 ['activation_54[0][0]'] Y \n",
- " v2D) \n",
- " \n",
- " activation_55 (Activation) (None, 1, 1, 1536) 0 ['stack_4_block11_se_2_conv[0][ Y \n",
- " 0]'] \n",
- " \n",
- " multiply_27 (Multiply) (None, 14, 14, 1536 0 ['stack_4_block11_MB_dw_swish[0 Y \n",
- " ) ][0]', \n",
- " 'activation_55[0][0]'] \n",
- " \n",
- " stack_4_block11_MB_pw_conv (Co (None, 14, 14, 256) 393216 ['multiply_27[0][0]'] Y \n",
- " nv2D) \n",
- " \n",
- " stack_4_block11_MB_pw_bn (Batc (None, 14, 14, 256) 1024 ['stack_4_block11_MB_pw_conv[0] Y \n",
- " hNormalization) [0]'] \n",
- " \n",
- " add_43 (Add) (None, 14, 14, 256) 0 ['add_42[0][0]', Y \n",
- " 'stack_4_block11_MB_pw_bn[0][0 \n",
- " ]'] \n",
- " \n",
- " stack_4_block12_sortcut_conv ( (None, 14, 14, 1536 393216 ['add_43[0][0]'] Y \n",
- " Conv2D) ) \n",
- " \n",
- " stack_4_block12_sortcut_bn (Ba (None, 14, 14, 1536 6144 ['stack_4_block12_sortcut_conv[ Y \n",
- " tchNormalization) ) 0][0]'] \n",
- " \n",
- " stack_4_block12_sortcut_swish (None, 14, 14, 1536 0 ['stack_4_block12_sortcut_bn[0] Y \n",
- " (Activation) ) [0]'] \n",
- " \n",
- " stack_4_block12_MB_dw_ (Depthw (None, 14, 14, 1536 13824 ['stack_4_block12_sortcut_swish Y \n",
- " iseConv2D) ) [0][0]'] \n",
- " \n",
- " stack_4_block12_MB_dw_bn (Batc (None, 14, 14, 1536 6144 ['stack_4_block12_MB_dw_[0][0]' Y \n",
- " hNormalization) ) ] \n",
- " \n",
- " stack_4_block12_MB_dw_swish (A (None, 14, 14, 1536 0 ['stack_4_block12_MB_dw_bn[0][0 Y \n",
- " ctivation) ) ]'] \n",
- " \n",
- " tf.math.reduce_mean_28 (TFOpLa (None, 1, 1, 1536) 0 ['stack_4_block12_MB_dw_swish[0 Y \n",
- " mbda) ][0]'] \n",
- " \n",
- " stack_4_block12_se_1_conv (Con (None, 1, 1, 64) 98368 ['tf.math.reduce_mean_28[0][0]' Y \n",
- " v2D) ] \n",
- " \n",
- " activation_56 (Activation) (None, 1, 1, 64) 0 ['stack_4_block12_se_1_conv[0][ Y \n",
- " 0]'] \n",
- " \n",
- " stack_4_block12_se_2_conv (Con (None, 1, 1, 1536) 99840 ['activation_56[0][0]'] Y \n",
- " v2D) \n",
- " \n",
- " activation_57 (Activation) (None, 1, 1, 1536) 0 ['stack_4_block12_se_2_conv[0][ Y \n",
- " 0]'] \n",
- " \n",
- " multiply_28 (Multiply) (None, 14, 14, 1536 0 ['stack_4_block12_MB_dw_swish[0 Y \n",
- " ) ][0]', \n",
- " 'activation_57[0][0]'] \n",
- " \n",
- " stack_4_block12_MB_pw_conv (Co (None, 14, 14, 256) 393216 ['multiply_28[0][0]'] Y \n",
- " nv2D) \n",
- " \n",
- " stack_4_block12_MB_pw_bn (Batc (None, 14, 14, 256) 1024 ['stack_4_block12_MB_pw_conv[0] Y \n",
- " hNormalization) [0]'] \n",
- " \n",
- " add_44 (Add) (None, 14, 14, 256) 0 ['add_43[0][0]', Y \n",
- " 'stack_4_block12_MB_pw_bn[0][0 \n",
- " ]'] \n",
- " \n",
- " stack_4_block13_sortcut_conv ( (None, 14, 14, 1536 393216 ['add_44[0][0]'] Y \n",
- " Conv2D) ) \n",
- " \n",
- " stack_4_block13_sortcut_bn (Ba (None, 14, 14, 1536 6144 ['stack_4_block13_sortcut_conv[ Y \n",
- " tchNormalization) ) 0][0]'] \n",
- " \n",
- " stack_4_block13_sortcut_swish (None, 14, 14, 1536 0 ['stack_4_block13_sortcut_bn[0] Y \n",
- " (Activation) ) [0]'] \n",
- " \n",
- " stack_4_block13_MB_dw_ (Depthw (None, 14, 14, 1536 13824 ['stack_4_block13_sortcut_swish Y \n",
- " iseConv2D) ) [0][0]'] \n",
- " \n",
- " stack_4_block13_MB_dw_bn (Batc (None, 14, 14, 1536 6144 ['stack_4_block13_MB_dw_[0][0]' Y \n",
- " hNormalization) ) ] \n",
- " \n",
- " stack_4_block13_MB_dw_swish (A (None, 14, 14, 1536 0 ['stack_4_block13_MB_dw_bn[0][0 Y \n",
- " ctivation) ) ]'] \n",
- " \n",
- " tf.math.reduce_mean_29 (TFOpLa (None, 1, 1, 1536) 0 ['stack_4_block13_MB_dw_swish[0 Y \n",
- " mbda) ][0]'] \n",
- " \n",
- " stack_4_block13_se_1_conv (Con (None, 1, 1, 64) 98368 ['tf.math.reduce_mean_29[0][0]' Y \n",
- " v2D) ] \n",
- " \n",
- " activation_58 (Activation) (None, 1, 1, 64) 0 ['stack_4_block13_se_1_conv[0][ Y \n",
- " 0]'] \n",
- " \n",
- " stack_4_block13_se_2_conv (Con (None, 1, 1, 1536) 99840 ['activation_58[0][0]'] Y \n",
- " v2D) \n",
- " \n",
- " activation_59 (Activation) (None, 1, 1, 1536) 0 ['stack_4_block13_se_2_conv[0][ Y \n",
- " 0]'] \n",
- " \n",
- " multiply_29 (Multiply) (None, 14, 14, 1536 0 ['stack_4_block13_MB_dw_swish[0 Y \n",
- " ) ][0]', \n",
- " 'activation_59[0][0]'] \n",
- " \n",
- " stack_4_block13_MB_pw_conv (Co (None, 14, 14, 256) 393216 ['multiply_29[0][0]'] Y \n",
- " nv2D) \n",
- " \n",
- " stack_4_block13_MB_pw_bn (Batc (None, 14, 14, 256) 1024 ['stack_4_block13_MB_pw_conv[0] Y \n",
- " hNormalization) [0]'] \n",
- " \n",
- " add_45 (Add) (None, 14, 14, 256) 0 ['add_44[0][0]', Y \n",
- " 'stack_4_block13_MB_pw_bn[0][0 \n",
- " ]'] \n",
- " \n",
- " stack_4_block14_sortcut_conv ( (None, 14, 14, 1536 393216 ['add_45[0][0]'] Y \n",
- " Conv2D) ) \n",
- " \n",
- " stack_4_block14_sortcut_bn (Ba (None, 14, 14, 1536 6144 ['stack_4_block14_sortcut_conv[ Y \n",
- " tchNormalization) ) 0][0]'] \n",
- " \n",
- " stack_4_block14_sortcut_swish (None, 14, 14, 1536 0 ['stack_4_block14_sortcut_bn[0] Y \n",
- " (Activation) ) [0]'] \n",
- " \n",
- " stack_4_block14_MB_dw_ (Depthw (None, 14, 14, 1536 13824 ['stack_4_block14_sortcut_swish Y \n",
- " iseConv2D) ) [0][0]'] \n",
- " \n",
- " stack_4_block14_MB_dw_bn (Batc (None, 14, 14, 1536 6144 ['stack_4_block14_MB_dw_[0][0]' Y \n",
- " hNormalization) ) ] \n",
- " \n",
- " stack_4_block14_MB_dw_swish (A (None, 14, 14, 1536 0 ['stack_4_block14_MB_dw_bn[0][0 Y \n",
- " ctivation) ) ]'] \n",
- " \n",
- " tf.math.reduce_mean_30 (TFOpLa (None, 1, 1, 1536) 0 ['stack_4_block14_MB_dw_swish[0 Y \n",
- " mbda) ][0]'] \n",
- " \n",
- " stack_4_block14_se_1_conv (Con (None, 1, 1, 64) 98368 ['tf.math.reduce_mean_30[0][0]' Y \n",
- " v2D) ] \n",
- " \n",
- " activation_60 (Activation) (None, 1, 1, 64) 0 ['stack_4_block14_se_1_conv[0][ Y \n",
- " 0]'] \n",
- " \n",
- " stack_4_block14_se_2_conv (Con (None, 1, 1, 1536) 99840 ['activation_60[0][0]'] Y \n",
- " v2D) \n",
- " \n",
- " activation_61 (Activation) (None, 1, 1, 1536) 0 ['stack_4_block14_se_2_conv[0][ Y \n",
- " 0]'] \n",
- " \n",
- " multiply_30 (Multiply) (None, 14, 14, 1536 0 ['stack_4_block14_MB_dw_swish[0 Y \n",
- " ) ][0]', \n",
- " 'activation_61[0][0]'] \n",
- " \n",
- " stack_4_block14_MB_pw_conv (Co (None, 14, 14, 256) 393216 ['multiply_30[0][0]'] Y \n",
- " nv2D) \n",
- " \n",
- " stack_4_block14_MB_pw_bn (Batc (None, 14, 14, 256) 1024 ['stack_4_block14_MB_pw_conv[0] Y \n",
- " hNormalization) [0]'] \n",
- " \n",
- " add_46 (Add) (None, 14, 14, 256) 0 ['add_45[0][0]', Y \n",
- " 'stack_4_block14_MB_pw_bn[0][0 \n",
- " ]'] \n",
- " \n",
- " stack_4_block15_sortcut_conv ( (None, 14, 14, 1536 393216 ['add_46[0][0]'] Y \n",
- " Conv2D) ) \n",
- " \n",
- " stack_4_block15_sortcut_bn (Ba (None, 14, 14, 1536 6144 ['stack_4_block15_sortcut_conv[ Y \n",
- " tchNormalization) ) 0][0]'] \n",
- " \n",
- " stack_4_block15_sortcut_swish (None, 14, 14, 1536 0 ['stack_4_block15_sortcut_bn[0] Y \n",
- " (Activation) ) [0]'] \n",
- " \n",
- " stack_4_block15_MB_dw_ (Depthw (None, 14, 14, 1536 13824 ['stack_4_block15_sortcut_swish Y \n",
- " iseConv2D) ) [0][0]'] \n",
- " \n",
- " stack_4_block15_MB_dw_bn (Batc (None, 14, 14, 1536 6144 ['stack_4_block15_MB_dw_[0][0]' Y \n",
- " hNormalization) ) ] \n",
- " \n",
- " stack_4_block15_MB_dw_swish (A (None, 14, 14, 1536 0 ['stack_4_block15_MB_dw_bn[0][0 Y \n",
- " ctivation) ) ]'] \n",
- " \n",
- " tf.math.reduce_mean_31 (TFOpLa (None, 1, 1, 1536) 0 ['stack_4_block15_MB_dw_swish[0 Y \n",
- " mbda) ][0]'] \n",
- " \n",
- " stack_4_block15_se_1_conv (Con (None, 1, 1, 64) 98368 ['tf.math.reduce_mean_31[0][0]' Y \n",
- " v2D) ] \n",
- " \n",
- " activation_62 (Activation) (None, 1, 1, 64) 0 ['stack_4_block15_se_1_conv[0][ Y \n",
- " 0]'] \n",
- " \n",
- " stack_4_block15_se_2_conv (Con (None, 1, 1, 1536) 99840 ['activation_62[0][0]'] Y \n",
- " v2D) \n",
- " \n",
- " activation_63 (Activation) (None, 1, 1, 1536) 0 ['stack_4_block15_se_2_conv[0][ Y \n",
- " 0]'] \n",
- " \n",
- " multiply_31 (Multiply) (None, 14, 14, 1536 0 ['stack_4_block15_MB_dw_swish[0 Y \n",
- " ) ][0]', \n",
- " 'activation_63[0][0]'] \n",
- " \n",
- " stack_4_block15_MB_pw_conv (Co (None, 14, 14, 256) 393216 ['multiply_31[0][0]'] Y \n",
- " nv2D) \n",
- " \n",
- " stack_4_block15_MB_pw_bn (Batc (None, 14, 14, 256) 1024 ['stack_4_block15_MB_pw_conv[0] Y \n",
- " hNormalization) [0]'] \n",
- " \n",
- " add_47 (Add) (None, 14, 14, 256) 0 ['add_46[0][0]', Y \n",
- " 'stack_4_block15_MB_pw_bn[0][0 \n",
- " ]'] \n",
- " \n",
- " stack_4_block16_sortcut_conv ( (None, 14, 14, 1536 393216 ['add_47[0][0]'] Y \n",
- " Conv2D) ) \n",
- " \n",
- " stack_4_block16_sortcut_bn (Ba (None, 14, 14, 1536 6144 ['stack_4_block16_sortcut_conv[ Y \n",
- " tchNormalization) ) 0][0]'] \n",
- " \n",
- " stack_4_block16_sortcut_swish (None, 14, 14, 1536 0 ['stack_4_block16_sortcut_bn[0] Y \n",
- " (Activation) ) [0]'] \n",
- " \n",
- " stack_4_block16_MB_dw_ (Depthw (None, 14, 14, 1536 13824 ['stack_4_block16_sortcut_swish Y \n",
- " iseConv2D) ) [0][0]'] \n",
- " \n",
- " stack_4_block16_MB_dw_bn (Batc (None, 14, 14, 1536 6144 ['stack_4_block16_MB_dw_[0][0]' Y \n",
- " hNormalization) ) ] \n",
- " \n",
- " stack_4_block16_MB_dw_swish (A (None, 14, 14, 1536 0 ['stack_4_block16_MB_dw_bn[0][0 Y \n",
- " ctivation) ) ]'] \n",
- " \n",
- " tf.math.reduce_mean_32 (TFOpLa (None, 1, 1, 1536) 0 ['stack_4_block16_MB_dw_swish[0 Y \n",
- " mbda) ][0]'] \n",
- " \n",
- " stack_4_block16_se_1_conv (Con (None, 1, 1, 64) 98368 ['tf.math.reduce_mean_32[0][0]' Y \n",
- " v2D) ] \n",
- " \n",
- " activation_64 (Activation) (None, 1, 1, 64) 0 ['stack_4_block16_se_1_conv[0][ Y \n",
- " 0]'] \n",
- " \n",
- " stack_4_block16_se_2_conv (Con (None, 1, 1, 1536) 99840 ['activation_64[0][0]'] Y \n",
- " v2D) \n",
- " \n",
- " activation_65 (Activation) (None, 1, 1, 1536) 0 ['stack_4_block16_se_2_conv[0][ Y \n",
- " 0]'] \n",
- " \n",
- " multiply_32 (Multiply) (None, 14, 14, 1536 0 ['stack_4_block16_MB_dw_swish[0 Y \n",
- " ) ][0]', \n",
- " 'activation_65[0][0]'] \n",
- " \n",
- " stack_4_block16_MB_pw_conv (Co (None, 14, 14, 256) 393216 ['multiply_32[0][0]'] Y \n",
- " nv2D) \n",
- " \n",
- " stack_4_block16_MB_pw_bn (Batc (None, 14, 14, 256) 1024 ['stack_4_block16_MB_pw_conv[0] Y \n",
- " hNormalization) [0]'] \n",
- " \n",
- " add_48 (Add) (None, 14, 14, 256) 0 ['add_47[0][0]', Y \n",
- " 'stack_4_block16_MB_pw_bn[0][0 \n",
- " ]'] \n",
- " \n",
- " stack_4_block17_sortcut_conv ( (None, 14, 14, 1536 393216 ['add_48[0][0]'] Y \n",
- " Conv2D) ) \n",
- " \n",
- " stack_4_block17_sortcut_bn (Ba (None, 14, 14, 1536 6144 ['stack_4_block17_sortcut_conv[ Y \n",
- " tchNormalization) ) 0][0]'] \n",
- " \n",
- " stack_4_block17_sortcut_swish (None, 14, 14, 1536 0 ['stack_4_block17_sortcut_bn[0] Y \n",
- " (Activation) ) [0]'] \n",
- " \n",
- " stack_4_block17_MB_dw_ (Depthw (None, 14, 14, 1536 13824 ['stack_4_block17_sortcut_swish Y \n",
- " iseConv2D) ) [0][0]'] \n",
- " \n",
- " stack_4_block17_MB_dw_bn (Batc (None, 14, 14, 1536 6144 ['stack_4_block17_MB_dw_[0][0]' Y \n",
- " hNormalization) ) ] \n",
- " \n",
- " stack_4_block17_MB_dw_swish (A (None, 14, 14, 1536 0 ['stack_4_block17_MB_dw_bn[0][0 Y \n",
- " ctivation) ) ]'] \n",
- " \n",
- " tf.math.reduce_mean_33 (TFOpLa (None, 1, 1, 1536) 0 ['stack_4_block17_MB_dw_swish[0 Y \n",
- " mbda) ][0]'] \n",
- " \n",
- " stack_4_block17_se_1_conv (Con (None, 1, 1, 64) 98368 ['tf.math.reduce_mean_33[0][0]' Y \n",
- " v2D) ] \n",
- " \n",
- " activation_66 (Activation) (None, 1, 1, 64) 0 ['stack_4_block17_se_1_conv[0][ Y \n",
- " 0]'] \n",
- " \n",
- " stack_4_block17_se_2_conv (Con (None, 1, 1, 1536) 99840 ['activation_66[0][0]'] Y \n",
- " v2D) \n",
- " \n",
- " activation_67 (Activation) (None, 1, 1, 1536) 0 ['stack_4_block17_se_2_conv[0][ Y \n",
- " 0]'] \n",
- " \n",
- " multiply_33 (Multiply) (None, 14, 14, 1536 0 ['stack_4_block17_MB_dw_swish[0 Y \n",
- " ) ][0]', \n",
- " 'activation_67[0][0]'] \n",
- " \n",
- " stack_4_block17_MB_pw_conv (Co (None, 14, 14, 256) 393216 ['multiply_33[0][0]'] Y \n",
- " nv2D) \n",
- " \n",
- " stack_4_block17_MB_pw_bn (Batc (None, 14, 14, 256) 1024 ['stack_4_block17_MB_pw_conv[0] Y \n",
- " hNormalization) [0]'] \n",
- " \n",
- " add_49 (Add) (None, 14, 14, 256) 0 ['add_48[0][0]', Y \n",
- " 'stack_4_block17_MB_pw_bn[0][0 \n",
- " ]'] \n",
- " \n",
- " stack_4_block18_sortcut_conv ( (None, 14, 14, 1536 393216 ['add_49[0][0]'] Y \n",
- " Conv2D) ) \n",
- " \n",
- " stack_4_block18_sortcut_bn (Ba (None, 14, 14, 1536 6144 ['stack_4_block18_sortcut_conv[ Y \n",
- " tchNormalization) ) 0][0]'] \n",
- " \n",
- " stack_4_block18_sortcut_swish (None, 14, 14, 1536 0 ['stack_4_block18_sortcut_bn[0] Y \n",
- " (Activation) ) [0]'] \n",
- " \n",
- " stack_4_block18_MB_dw_ (Depthw (None, 14, 14, 1536 13824 ['stack_4_block18_sortcut_swish Y \n",
- " iseConv2D) ) [0][0]'] \n",
- " \n",
- " stack_4_block18_MB_dw_bn (Batc (None, 14, 14, 1536 6144 ['stack_4_block18_MB_dw_[0][0]' Y \n",
- " hNormalization) ) ] \n",
- " \n",
- " stack_4_block18_MB_dw_swish (A (None, 14, 14, 1536 0 ['stack_4_block18_MB_dw_bn[0][0 Y \n",
- " ctivation) ) ]'] \n",
- " \n",
- " tf.math.reduce_mean_34 (TFOpLa (None, 1, 1, 1536) 0 ['stack_4_block18_MB_dw_swish[0 Y \n",
- " mbda) ][0]'] \n",
- " \n",
- " stack_4_block18_se_1_conv (Con (None, 1, 1, 64) 98368 ['tf.math.reduce_mean_34[0][0]' Y \n",
- " v2D) ] \n",
- " \n",
- " activation_68 (Activation) (None, 1, 1, 64) 0 ['stack_4_block18_se_1_conv[0][ Y \n",
- " 0]'] \n",
- " \n",
- " stack_4_block18_se_2_conv (Con (None, 1, 1, 1536) 99840 ['activation_68[0][0]'] Y \n",
- " v2D) \n",
- " \n",
- " activation_69 (Activation) (None, 1, 1, 1536) 0 ['stack_4_block18_se_2_conv[0][ Y \n",
- " 0]'] \n",
- " \n",
- " multiply_34 (Multiply) (None, 14, 14, 1536 0 ['stack_4_block18_MB_dw_swish[0 Y \n",
- " ) ][0]', \n",
- " 'activation_69[0][0]'] \n",
- " \n",
- " stack_4_block18_MB_pw_conv (Co (None, 14, 14, 256) 393216 ['multiply_34[0][0]'] Y \n",
- " nv2D) \n",
- " \n",
- " stack_4_block18_MB_pw_bn (Batc (None, 14, 14, 256) 1024 ['stack_4_block18_MB_pw_conv[0] Y \n",
- " hNormalization) [0]'] \n",
- " \n",
- " add_50 (Add) (None, 14, 14, 256) 0 ['add_49[0][0]', Y \n",
- " 'stack_4_block18_MB_pw_bn[0][0 \n",
- " ]'] \n",
- " \n",
- " stack_4_block19_sortcut_conv ( (None, 14, 14, 1536 393216 ['add_50[0][0]'] Y \n",
- " Conv2D) ) \n",
- " \n",
- " stack_4_block19_sortcut_bn (Ba (None, 14, 14, 1536 6144 ['stack_4_block19_sortcut_conv[ Y \n",
- " tchNormalization) ) 0][0]'] \n",
- " \n",
- " stack_4_block19_sortcut_swish (None, 14, 14, 1536 0 ['stack_4_block19_sortcut_bn[0] Y \n",
- " (Activation) ) [0]'] \n",
- " \n",
- " stack_4_block19_MB_dw_ (Depthw (None, 14, 14, 1536 13824 ['stack_4_block19_sortcut_swish Y \n",
- " iseConv2D) ) [0][0]'] \n",
- " \n",
- " stack_4_block19_MB_dw_bn (Batc (None, 14, 14, 1536 6144 ['stack_4_block19_MB_dw_[0][0]' Y \n",
- " hNormalization) ) ] \n",
- " \n",
- " stack_4_block19_MB_dw_swish (A (None, 14, 14, 1536 0 ['stack_4_block19_MB_dw_bn[0][0 Y \n",
- " ctivation) ) ]'] \n",
- " \n",
- " tf.math.reduce_mean_35 (TFOpLa (None, 1, 1, 1536) 0 ['stack_4_block19_MB_dw_swish[0 Y \n",
- " mbda) ][0]'] \n",
- " \n",
- " stack_4_block19_se_1_conv (Con (None, 1, 1, 64) 98368 ['tf.math.reduce_mean_35[0][0]' Y \n",
- " v2D) ] \n",
- " \n",
- " activation_70 (Activation) (None, 1, 1, 64) 0 ['stack_4_block19_se_1_conv[0][ Y \n",
- " 0]'] \n",
- " \n",
- " stack_4_block19_se_2_conv (Con (None, 1, 1, 1536) 99840 ['activation_70[0][0]'] Y \n",
- " v2D) \n",
- " \n",
- " activation_71 (Activation) (None, 1, 1, 1536) 0 ['stack_4_block19_se_2_conv[0][ Y \n",
- " 0]'] \n",
- " \n",
- " multiply_35 (Multiply) (None, 14, 14, 1536 0 ['stack_4_block19_MB_dw_swish[0 Y \n",
- " ) ][0]', \n",
- " 'activation_71[0][0]'] \n",
- " \n",
- " stack_4_block19_MB_pw_conv (Co (None, 14, 14, 256) 393216 ['multiply_35[0][0]'] Y \n",
- " nv2D) \n",
- " \n",
- " stack_4_block19_MB_pw_bn (Batc (None, 14, 14, 256) 1024 ['stack_4_block19_MB_pw_conv[0] Y \n",
- " hNormalization) [0]'] \n",
- " \n",
- " add_51 (Add) (None, 14, 14, 256) 0 ['add_50[0][0]', Y \n",
- " 'stack_4_block19_MB_pw_bn[0][0 \n",
- " ]'] \n",
- " \n",
- " stack_4_block20_sortcut_conv ( (None, 14, 14, 1536 393216 ['add_51[0][0]'] Y \n",
- " Conv2D) ) \n",
- " \n",
- " stack_4_block20_sortcut_bn (Ba (None, 14, 14, 1536 6144 ['stack_4_block20_sortcut_conv[ Y \n",
- " tchNormalization) ) 0][0]'] \n",
- " \n",
- " stack_4_block20_sortcut_swish (None, 14, 14, 1536 0 ['stack_4_block20_sortcut_bn[0] Y \n",
- " (Activation) ) [0]'] \n",
- " \n",
- " stack_4_block20_MB_dw_ (Depthw (None, 14, 14, 1536 13824 ['stack_4_block20_sortcut_swish Y \n",
- " iseConv2D) ) [0][0]'] \n",
- " \n",
- " stack_4_block20_MB_dw_bn (Batc (None, 14, 14, 1536 6144 ['stack_4_block20_MB_dw_[0][0]' Y \n",
- " hNormalization) ) ] \n",
- " \n",
- " stack_4_block20_MB_dw_swish (A (None, 14, 14, 1536 0 ['stack_4_block20_MB_dw_bn[0][0 Y \n",
- " ctivation) ) ]'] \n",
- " \n",
- " tf.math.reduce_mean_36 (TFOpLa (None, 1, 1, 1536) 0 ['stack_4_block20_MB_dw_swish[0 Y \n",
- " mbda) ][0]'] \n",
- " \n",
- " stack_4_block20_se_1_conv (Con (None, 1, 1, 64) 98368 ['tf.math.reduce_mean_36[0][0]' Y \n",
- " v2D) ] \n",
- " \n",
- " activation_72 (Activation) (None, 1, 1, 64) 0 ['stack_4_block20_se_1_conv[0][ Y \n",
- " 0]'] \n",
- " \n",
- " stack_4_block20_se_2_conv (Con (None, 1, 1, 1536) 99840 ['activation_72[0][0]'] Y \n",
- " v2D) \n",
- " \n",
- " activation_73 (Activation) (None, 1, 1, 1536) 0 ['stack_4_block20_se_2_conv[0][ Y \n",
- " 0]'] \n",
- " \n",
- " multiply_36 (Multiply) (None, 14, 14, 1536 0 ['stack_4_block20_MB_dw_swish[0 Y \n",
- " ) ][0]', \n",
- " 'activation_73[0][0]'] \n",
- " \n",
- " stack_4_block20_MB_pw_conv (Co (None, 14, 14, 256) 393216 ['multiply_36[0][0]'] Y \n",
- " nv2D) \n",
- " \n",
- " stack_4_block20_MB_pw_bn (Batc (None, 14, 14, 256) 1024 ['stack_4_block20_MB_pw_conv[0] Y \n",
- " hNormalization) [0]'] \n",
- " \n",
- " add_52 (Add) (None, 14, 14, 256) 0 ['add_51[0][0]', Y \n",
- " 'stack_4_block20_MB_pw_bn[0][0 \n",
- " ]'] \n",
- " \n",
- " stack_4_block21_sortcut_conv ( (None, 14, 14, 1536 393216 ['add_52[0][0]'] Y \n",
- " Conv2D) ) \n",
- " \n",
- " stack_4_block21_sortcut_bn (Ba (None, 14, 14, 1536 6144 ['stack_4_block21_sortcut_conv[ Y \n",
- " tchNormalization) ) 0][0]'] \n",
- " \n",
- " stack_4_block21_sortcut_swish (None, 14, 14, 1536 0 ['stack_4_block21_sortcut_bn[0] Y \n",
- " (Activation) ) [0]'] \n",
- " \n",
- " stack_4_block21_MB_dw_ (Depthw (None, 14, 14, 1536 13824 ['stack_4_block21_sortcut_swish Y \n",
- " iseConv2D) ) [0][0]'] \n",
- " \n",
- " stack_4_block21_MB_dw_bn (Batc (None, 14, 14, 1536 6144 ['stack_4_block21_MB_dw_[0][0]' Y \n",
- " hNormalization) ) ] \n",
- " \n",
- " stack_4_block21_MB_dw_swish (A (None, 14, 14, 1536 0 ['stack_4_block21_MB_dw_bn[0][0 Y \n",
- " ctivation) ) ]'] \n",
- " \n",
- " tf.math.reduce_mean_37 (TFOpLa (None, 1, 1, 1536) 0 ['stack_4_block21_MB_dw_swish[0 Y \n",
- " mbda) ][0]'] \n",
- " \n",
- " stack_4_block21_se_1_conv (Con (None, 1, 1, 64) 98368 ['tf.math.reduce_mean_37[0][0]' Y \n",
- " v2D) ] \n",
- " \n",
- " activation_74 (Activation) (None, 1, 1, 64) 0 ['stack_4_block21_se_1_conv[0][ Y \n",
- " 0]'] \n",
- " \n",
- " stack_4_block21_se_2_conv (Con (None, 1, 1, 1536) 99840 ['activation_74[0][0]'] Y \n",
- " v2D) \n",
- " \n",
- " activation_75 (Activation) (None, 1, 1, 1536) 0 ['stack_4_block21_se_2_conv[0][ Y \n",
- " 0]'] \n",
- " \n",
- " multiply_37 (Multiply) (None, 14, 14, 1536 0 ['stack_4_block21_MB_dw_swish[0 Y \n",
- " ) ][0]', \n",
- " 'activation_75[0][0]'] \n",
- " \n",
- " stack_4_block21_MB_pw_conv (Co (None, 14, 14, 256) 393216 ['multiply_37[0][0]'] Y \n",
- " nv2D) \n",
- " \n",
- " stack_4_block21_MB_pw_bn (Batc (None, 14, 14, 256) 1024 ['stack_4_block21_MB_pw_conv[0] Y \n",
- " hNormalization) [0]'] \n",
- " \n",
- " add_53 (Add) (None, 14, 14, 256) 0 ['add_52[0][0]', Y \n",
- " 'stack_4_block21_MB_pw_bn[0][0 \n",
- " ]'] \n",
- " \n",
- " stack_4_block22_sortcut_conv ( (None, 14, 14, 1536 393216 ['add_53[0][0]'] Y \n",
- " Conv2D) ) \n",
- " \n",
- " stack_4_block22_sortcut_bn (Ba (None, 14, 14, 1536 6144 ['stack_4_block22_sortcut_conv[ Y \n",
- " tchNormalization) ) 0][0]'] \n",
- " \n",
- " stack_4_block22_sortcut_swish (None, 14, 14, 1536 0 ['stack_4_block22_sortcut_bn[0] Y \n",
- " (Activation) ) [0]'] \n",
- " \n",
- " stack_4_block22_MB_dw_ (Depthw (None, 14, 14, 1536 13824 ['stack_4_block22_sortcut_swish Y \n",
- " iseConv2D) ) [0][0]'] \n",
- " \n",
- " stack_4_block22_MB_dw_bn (Batc (None, 14, 14, 1536 6144 ['stack_4_block22_MB_dw_[0][0]' Y \n",
- " hNormalization) ) ] \n",
- " \n",
- " stack_4_block22_MB_dw_swish (A (None, 14, 14, 1536 0 ['stack_4_block22_MB_dw_bn[0][0 Y \n",
- " ctivation) ) ]'] \n",
- " \n",
- " tf.math.reduce_mean_38 (TFOpLa (None, 1, 1, 1536) 0 ['stack_4_block22_MB_dw_swish[0 Y \n",
- " mbda) ][0]'] \n",
- " \n",
- " stack_4_block22_se_1_conv (Con (None, 1, 1, 64) 98368 ['tf.math.reduce_mean_38[0][0]' Y \n",
- " v2D) ] \n",
- " \n",
- " activation_76 (Activation) (None, 1, 1, 64) 0 ['stack_4_block22_se_1_conv[0][ Y \n",
- " 0]'] \n",
- " \n",
- " stack_4_block22_se_2_conv (Con (None, 1, 1, 1536) 99840 ['activation_76[0][0]'] Y \n",
- " v2D) \n",
- " \n",
- " activation_77 (Activation) (None, 1, 1, 1536) 0 ['stack_4_block22_se_2_conv[0][ Y \n",
- " 0]'] \n",
- " \n",
- " multiply_38 (Multiply) (None, 14, 14, 1536 0 ['stack_4_block22_MB_dw_swish[0 Y \n",
- " ) ][0]', \n",
- " 'activation_77[0][0]'] \n",
- " \n",
- " stack_4_block22_MB_pw_conv (Co (None, 14, 14, 256) 393216 ['multiply_38[0][0]'] Y \n",
- " nv2D) \n",
- " \n",
- " stack_4_block22_MB_pw_bn (Batc (None, 14, 14, 256) 1024 ['stack_4_block22_MB_pw_conv[0] Y \n",
- " hNormalization) [0]'] \n",
- " \n",
- " add_54 (Add) (None, 14, 14, 256) 0 ['add_53[0][0]', Y \n",
- " 'stack_4_block22_MB_pw_bn[0][0 \n",
- " ]'] \n",
- " \n",
- " stack_4_block23_sortcut_conv ( (None, 14, 14, 1536 393216 ['add_54[0][0]'] Y \n",
- " Conv2D) ) \n",
- " \n",
- " stack_4_block23_sortcut_bn (Ba (None, 14, 14, 1536 6144 ['stack_4_block23_sortcut_conv[ Y \n",
- " tchNormalization) ) 0][0]'] \n",
- " \n",
- " stack_4_block23_sortcut_swish (None, 14, 14, 1536 0 ['stack_4_block23_sortcut_bn[0] Y \n",
- " (Activation) ) [0]'] \n",
- " \n",
- " stack_4_block23_MB_dw_ (Depthw (None, 14, 14, 1536 13824 ['stack_4_block23_sortcut_swish Y \n",
- " iseConv2D) ) [0][0]'] \n",
- " \n",
- " stack_4_block23_MB_dw_bn (Batc (None, 14, 14, 1536 6144 ['stack_4_block23_MB_dw_[0][0]' Y \n",
- " hNormalization) ) ] \n",
- " \n",
- " stack_4_block23_MB_dw_swish (A (None, 14, 14, 1536 0 ['stack_4_block23_MB_dw_bn[0][0 Y \n",
- " ctivation) ) ]'] \n",
- " \n",
- " tf.math.reduce_mean_39 (TFOpLa (None, 1, 1, 1536) 0 ['stack_4_block23_MB_dw_swish[0 Y \n",
- " mbda) ][0]'] \n",
- " \n",
- " stack_4_block23_se_1_conv (Con (None, 1, 1, 64) 98368 ['tf.math.reduce_mean_39[0][0]' Y \n",
- " v2D) ] \n",
- " \n",
- " activation_78 (Activation) (None, 1, 1, 64) 0 ['stack_4_block23_se_1_conv[0][ Y \n",
- " 0]'] \n",
- " \n",
- " stack_4_block23_se_2_conv (Con (None, 1, 1, 1536) 99840 ['activation_78[0][0]'] Y \n",
- " v2D) \n",
- " \n",
- " activation_79 (Activation) (None, 1, 1, 1536) 0 ['stack_4_block23_se_2_conv[0][ Y \n",
- " 0]'] \n",
- " \n",
- " multiply_39 (Multiply) (None, 14, 14, 1536 0 ['stack_4_block23_MB_dw_swish[0 Y \n",
- " ) ][0]', \n",
- " 'activation_79[0][0]'] \n",
- " \n",
- " stack_4_block23_MB_pw_conv (Co (None, 14, 14, 256) 393216 ['multiply_39[0][0]'] Y \n",
- " nv2D) \n",
- " \n",
- " stack_4_block23_MB_pw_bn (Batc (None, 14, 14, 256) 1024 ['stack_4_block23_MB_pw_conv[0] Y \n",
- " hNormalization) [0]'] \n",
- " \n",
- " add_55 (Add) (None, 14, 14, 256) 0 ['add_54[0][0]', Y \n",
- " 'stack_4_block23_MB_pw_bn[0][0 \n",
- " ]'] \n",
- " \n",
- " stack_5_block0_sortcut_conv (C (None, 14, 14, 1536 393216 ['add_55[0][0]'] Y \n",
- " onv2D) ) \n",
- " \n",
- " stack_5_block0_sortcut_bn (Bat (None, 14, 14, 1536 6144 ['stack_5_block0_sortcut_conv[0 Y \n",
- " chNormalization) ) ][0]'] \n",
- " \n",
- " stack_5_block0_sortcut_swish ( (None, 14, 14, 1536 0 ['stack_5_block0_sortcut_bn[0][ Y \n",
- " Activation) ) 0]'] \n",
- " \n",
- " stack_5_block0_MB_dw_ (Depthwi (None, 7, 7, 1536) 13824 ['stack_5_block0_sortcut_swish[ Y \n",
- " seConv2D) 0][0]'] \n",
- " \n",
- " stack_5_block0_MB_dw_bn (Batch (None, 7, 7, 1536) 6144 ['stack_5_block0_MB_dw_[0][0]'] Y \n",
- " Normalization) \n",
- " \n",
- " stack_5_block0_MB_dw_swish (Ac (None, 7, 7, 1536) 0 ['stack_5_block0_MB_dw_bn[0][0] Y \n",
- " tivation) '] \n",
- " \n",
- " tf.math.reduce_mean_40 (TFOpLa (None, 1, 1, 1536) 0 ['stack_5_block0_MB_dw_swish[0] Y \n",
- " mbda) [0]'] \n",
- " \n",
- " stack_5_block0_se_1_conv (Conv (None, 1, 1, 64) 98368 ['tf.math.reduce_mean_40[0][0]' Y \n",
- " 2D) ] \n",
- " \n",
- " activation_80 (Activation) (None, 1, 1, 64) 0 ['stack_5_block0_se_1_conv[0][0 Y \n",
- " ]'] \n",
- " \n",
- " stack_5_block0_se_2_conv (Conv (None, 1, 1, 1536) 99840 ['activation_80[0][0]'] Y \n",
- " 2D) \n",
- " \n",
- " activation_81 (Activation) (None, 1, 1, 1536) 0 ['stack_5_block0_se_2_conv[0][0 Y \n",
- " ]'] \n",
- " \n",
- " multiply_40 (Multiply) (None, 7, 7, 1536) 0 ['stack_5_block0_MB_dw_swish[0] Y \n",
- " [0]', \n",
- " 'activation_81[0][0]'] \n",
- " \n",
- " stack_5_block0_MB_pw_conv (Con (None, 7, 7, 512) 786432 ['multiply_40[0][0]'] Y \n",
- " v2D) \n",
- " \n",
- " stack_5_block0_MB_pw_bn (Batch (None, 7, 7, 512) 2048 ['stack_5_block0_MB_pw_conv[0][ Y \n",
- " Normalization) 0]'] \n",
- " \n",
- " stack_5_block1_sortcut_conv (C (None, 7, 7, 3072) 1572864 ['stack_5_block0_MB_pw_bn[0][0] Y \n",
- " onv2D) '] \n",
- " \n",
- " stack_5_block1_sortcut_bn (Bat (None, 7, 7, 3072) 12288 ['stack_5_block1_sortcut_conv[0 Y \n",
- " chNormalization) ][0]'] \n",
- " \n",
- " stack_5_block1_sortcut_swish ( (None, 7, 7, 3072) 0 ['stack_5_block1_sortcut_bn[0][ Y \n",
- " Activation) 0]'] \n",
- " \n",
- " stack_5_block1_MB_dw_ (Depthwi (None, 7, 7, 3072) 27648 ['stack_5_block1_sortcut_swish[ Y \n",
- " seConv2D) 0][0]'] \n",
- " \n",
- " stack_5_block1_MB_dw_bn (Batch (None, 7, 7, 3072) 12288 ['stack_5_block1_MB_dw_[0][0]'] Y \n",
- " Normalization) \n",
- " \n",
- " stack_5_block1_MB_dw_swish (Ac (None, 7, 7, 3072) 0 ['stack_5_block1_MB_dw_bn[0][0] Y \n",
- " tivation) '] \n",
- " \n",
- " tf.math.reduce_mean_41 (TFOpLa (None, 1, 1, 3072) 0 ['stack_5_block1_MB_dw_swish[0] Y \n",
- " mbda) [0]'] \n",
- " \n",
- " stack_5_block1_se_1_conv (Conv (None, 1, 1, 128) 393344 ['tf.math.reduce_mean_41[0][0]' Y \n",
- " 2D) ] \n",
- " \n",
- " activation_82 (Activation) (None, 1, 1, 128) 0 ['stack_5_block1_se_1_conv[0][0 Y \n",
- " ]'] \n",
- " \n",
- " stack_5_block1_se_2_conv (Conv (None, 1, 1, 3072) 396288 ['activation_82[0][0]'] Y \n",
- " 2D) \n",
- " \n",
- " activation_83 (Activation) (None, 1, 1, 3072) 0 ['stack_5_block1_se_2_conv[0][0 Y \n",
- " ]'] \n",
- " \n",
- " multiply_41 (Multiply) (None, 7, 7, 3072) 0 ['stack_5_block1_MB_dw_swish[0] Y \n",
- " [0]', \n",
- " 'activation_83[0][0]'] \n",
- " \n",
- " stack_5_block1_MB_pw_conv (Con (None, 7, 7, 512) 1572864 ['multiply_41[0][0]'] Y \n",
- " v2D) \n",
- " \n",
- " stack_5_block1_MB_pw_bn (Batch (None, 7, 7, 512) 2048 ['stack_5_block1_MB_pw_conv[0][ Y \n",
- " Normalization) 0]'] \n",
- " \n",
- " add_56 (Add) (None, 7, 7, 512) 0 ['stack_5_block0_MB_pw_bn[0][0] Y \n",
- " ', \n",
- " 'stack_5_block1_MB_pw_bn[0][0] \n",
- " '] \n",
- " \n",
- " stack_5_block2_sortcut_conv (C (None, 7, 7, 3072) 1572864 ['add_56[0][0]'] Y \n",
- " onv2D) \n",
- " \n",
- " stack_5_block2_sortcut_bn (Bat (None, 7, 7, 3072) 12288 ['stack_5_block2_sortcut_conv[0 Y \n",
- " chNormalization) ][0]'] \n",
- " \n",
- " stack_5_block2_sortcut_swish ( (None, 7, 7, 3072) 0 ['stack_5_block2_sortcut_bn[0][ Y \n",
- " Activation) 0]'] \n",
- " \n",
- " stack_5_block2_MB_dw_ (Depthwi (None, 7, 7, 3072) 27648 ['stack_5_block2_sortcut_swish[ Y \n",
- " seConv2D) 0][0]'] \n",
- " \n",
- " stack_5_block2_MB_dw_bn (Batch (None, 7, 7, 3072) 12288 ['stack_5_block2_MB_dw_[0][0]'] Y \n",
- " Normalization) \n",
- " \n",
- " stack_5_block2_MB_dw_swish (Ac (None, 7, 7, 3072) 0 ['stack_5_block2_MB_dw_bn[0][0] Y \n",
- " tivation) '] \n",
- " \n",
- " tf.math.reduce_mean_42 (TFOpLa (None, 1, 1, 3072) 0 ['stack_5_block2_MB_dw_swish[0] Y \n",
- " mbda) [0]'] \n",
- " \n",
- " stack_5_block2_se_1_conv (Conv (None, 1, 1, 128) 393344 ['tf.math.reduce_mean_42[0][0]' Y \n",
- " 2D) ] \n",
- " \n",
- " activation_84 (Activation) (None, 1, 1, 128) 0 ['stack_5_block2_se_1_conv[0][0 Y \n",
- " ]'] \n",
- " \n",
- " stack_5_block2_se_2_conv (Conv (None, 1, 1, 3072) 396288 ['activation_84[0][0]'] Y \n",
- " 2D) \n",
- " \n",
- " activation_85 (Activation) (None, 1, 1, 3072) 0 ['stack_5_block2_se_2_conv[0][0 Y \n",
- " ]'] \n",
- " \n",
- " multiply_42 (Multiply) (None, 7, 7, 3072) 0 ['stack_5_block2_MB_dw_swish[0] Y \n",
- " [0]', \n",
- " 'activation_85[0][0]'] \n",
- " \n",
- " stack_5_block2_MB_pw_conv (Con (None, 7, 7, 512) 1572864 ['multiply_42[0][0]'] Y \n",
- " v2D) \n",
- " \n",
- " stack_5_block2_MB_pw_bn (Batch (None, 7, 7, 512) 2048 ['stack_5_block2_MB_pw_conv[0][ Y \n",
- " Normalization) 0]'] \n",
- " \n",
- " add_57 (Add) (None, 7, 7, 512) 0 ['add_56[0][0]', Y \n",
- " 'stack_5_block2_MB_pw_bn[0][0] \n",
- " '] \n",
- " \n",
- " stack_5_block3_sortcut_conv (C (None, 7, 7, 3072) 1572864 ['add_57[0][0]'] Y \n",
- " onv2D) \n",
- " \n",
- " stack_5_block3_sortcut_bn (Bat (None, 7, 7, 3072) 12288 ['stack_5_block3_sortcut_conv[0 Y \n",
- " chNormalization) ][0]'] \n",
- " \n",
- " stack_5_block3_sortcut_swish ( (None, 7, 7, 3072) 0 ['stack_5_block3_sortcut_bn[0][ Y \n",
- " Activation) 0]'] \n",
- " \n",
- " stack_5_block3_MB_dw_ (Depthwi (None, 7, 7, 3072) 27648 ['stack_5_block3_sortcut_swish[ Y \n",
- " seConv2D) 0][0]'] \n",
- " \n",
- " stack_5_block3_MB_dw_bn (Batch (None, 7, 7, 3072) 12288 ['stack_5_block3_MB_dw_[0][0]'] Y \n",
- " Normalization) \n",
- " \n",
- " stack_5_block3_MB_dw_swish (Ac (None, 7, 7, 3072) 0 ['stack_5_block3_MB_dw_bn[0][0] Y \n",
- " tivation) '] \n",
- " \n",
- " tf.math.reduce_mean_43 (TFOpLa (None, 1, 1, 3072) 0 ['stack_5_block3_MB_dw_swish[0] Y \n",
- " mbda) [0]'] \n",
- " \n",
- " stack_5_block3_se_1_conv (Conv (None, 1, 1, 128) 393344 ['tf.math.reduce_mean_43[0][0]' Y \n",
- " 2D) ] \n",
- " \n",
- " activation_86 (Activation) (None, 1, 1, 128) 0 ['stack_5_block3_se_1_conv[0][0 Y \n",
- " ]'] \n",
- " \n",
- " stack_5_block3_se_2_conv (Conv (None, 1, 1, 3072) 396288 ['activation_86[0][0]'] Y \n",
- " 2D) \n",
- " \n",
- " activation_87 (Activation) (None, 1, 1, 3072) 0 ['stack_5_block3_se_2_conv[0][0 Y \n",
- " ]'] \n",
- " \n",
- " multiply_43 (Multiply) (None, 7, 7, 3072) 0 ['stack_5_block3_MB_dw_swish[0] Y \n",
- " [0]', \n",
- " 'activation_87[0][0]'] \n",
- " \n",
- " stack_5_block3_MB_pw_conv (Con (None, 7, 7, 512) 1572864 ['multiply_43[0][0]'] Y \n",
- " v2D) \n",
- " \n",
- " stack_5_block3_MB_pw_bn (Batch (None, 7, 7, 512) 2048 ['stack_5_block3_MB_pw_conv[0][ Y \n",
- " Normalization) 0]'] \n",
- " \n",
- " add_58 (Add) (None, 7, 7, 512) 0 ['add_57[0][0]', Y \n",
- " 'stack_5_block3_MB_pw_bn[0][0] \n",
- " '] \n",
- " \n",
- " stack_5_block4_sortcut_conv (C (None, 7, 7, 3072) 1572864 ['add_58[0][0]'] Y \n",
- " onv2D) \n",
- " \n",
- " stack_5_block4_sortcut_bn (Bat (None, 7, 7, 3072) 12288 ['stack_5_block4_sortcut_conv[0 Y \n",
- " chNormalization) ][0]'] \n",
- " \n",
- " stack_5_block4_sortcut_swish ( (None, 7, 7, 3072) 0 ['stack_5_block4_sortcut_bn[0][ Y \n",
- " Activation) 0]'] \n",
- " \n",
- " stack_5_block4_MB_dw_ (Depthwi (None, 7, 7, 3072) 27648 ['stack_5_block4_sortcut_swish[ Y \n",
- " seConv2D) 0][0]'] \n",
- " \n",
- " stack_5_block4_MB_dw_bn (Batch (None, 7, 7, 3072) 12288 ['stack_5_block4_MB_dw_[0][0]'] Y \n",
- " Normalization) \n",
- " \n",
- " stack_5_block4_MB_dw_swish (Ac (None, 7, 7, 3072) 0 ['stack_5_block4_MB_dw_bn[0][0] Y \n",
- " tivation) '] \n",
- " \n",
- " tf.math.reduce_mean_44 (TFOpLa (None, 1, 1, 3072) 0 ['stack_5_block4_MB_dw_swish[0] Y \n",
- " mbda) [0]'] \n",
- " \n",
- " stack_5_block4_se_1_conv (Conv (None, 1, 1, 128) 393344 ['tf.math.reduce_mean_44[0][0]' Y \n",
- " 2D) ] \n",
- " \n",
- " activation_88 (Activation) (None, 1, 1, 128) 0 ['stack_5_block4_se_1_conv[0][0 Y \n",
- " ]'] \n",
- " \n",
- " stack_5_block4_se_2_conv (Conv (None, 1, 1, 3072) 396288 ['activation_88[0][0]'] Y \n",
- " 2D) \n",
- " \n",
- " activation_89 (Activation) (None, 1, 1, 3072) 0 ['stack_5_block4_se_2_conv[0][0 Y \n",
- " ]'] \n",
- " \n",
- " multiply_44 (Multiply) (None, 7, 7, 3072) 0 ['stack_5_block4_MB_dw_swish[0] Y \n",
- " [0]', \n",
- " 'activation_89[0][0]'] \n",
- " \n",
- " stack_5_block4_MB_pw_conv (Con (None, 7, 7, 512) 1572864 ['multiply_44[0][0]'] Y \n",
- " v2D) \n",
- " \n",
- " stack_5_block4_MB_pw_bn (Batch (None, 7, 7, 512) 2048 ['stack_5_block4_MB_pw_conv[0][ Y \n",
- " Normalization) 0]'] \n",
- " \n",
- " add_59 (Add) (None, 7, 7, 512) 0 ['add_58[0][0]', Y \n",
- " 'stack_5_block4_MB_pw_bn[0][0] \n",
- " '] \n",
- " \n",
- " stack_5_block5_sortcut_conv (C (None, 7, 7, 3072) 1572864 ['add_59[0][0]'] Y \n",
- " onv2D) \n",
- " \n",
- " stack_5_block5_sortcut_bn (Bat (None, 7, 7, 3072) 12288 ['stack_5_block5_sortcut_conv[0 Y \n",
- " chNormalization) ][0]'] \n",
- " \n",
- " stack_5_block5_sortcut_swish ( (None, 7, 7, 3072) 0 ['stack_5_block5_sortcut_bn[0][ Y \n",
- " Activation) 0]'] \n",
- " \n",
- " stack_5_block5_MB_dw_ (Depthwi (None, 7, 7, 3072) 27648 ['stack_5_block5_sortcut_swish[ Y \n",
- " seConv2D) 0][0]'] \n",
- " \n",
- " stack_5_block5_MB_dw_bn (Batch (None, 7, 7, 3072) 12288 ['stack_5_block5_MB_dw_[0][0]'] Y \n",
- " Normalization) \n",
- " \n",
- " stack_5_block5_MB_dw_swish (Ac (None, 7, 7, 3072) 0 ['stack_5_block5_MB_dw_bn[0][0] Y \n",
- " tivation) '] \n",
- " \n",
- " tf.math.reduce_mean_45 (TFOpLa (None, 1, 1, 3072) 0 ['stack_5_block5_MB_dw_swish[0] Y \n",
- " mbda) [0]'] \n",
- " \n",
- " stack_5_block5_se_1_conv (Conv (None, 1, 1, 128) 393344 ['tf.math.reduce_mean_45[0][0]' Y \n",
- " 2D) ] \n",
- " \n",
- " activation_90 (Activation) (None, 1, 1, 128) 0 ['stack_5_block5_se_1_conv[0][0 Y \n",
- " ]'] \n",
- " \n",
- " stack_5_block5_se_2_conv (Conv (None, 1, 1, 3072) 396288 ['activation_90[0][0]'] Y \n",
- " 2D) \n",
- " \n",
- " activation_91 (Activation) (None, 1, 1, 3072) 0 ['stack_5_block5_se_2_conv[0][0 Y \n",
- " ]'] \n",
- " \n",
- " multiply_45 (Multiply) (None, 7, 7, 3072) 0 ['stack_5_block5_MB_dw_swish[0] Y \n",
- " [0]', \n",
- " 'activation_91[0][0]'] \n",
- " \n",
- " stack_5_block5_MB_pw_conv (Con (None, 7, 7, 512) 1572864 ['multiply_45[0][0]'] Y \n",
- " v2D) \n",
- " \n",
- " stack_5_block5_MB_pw_bn (Batch (None, 7, 7, 512) 2048 ['stack_5_block5_MB_pw_conv[0][ Y \n",
- " Normalization) 0]'] \n",
- " \n",
- " add_60 (Add) (None, 7, 7, 512) 0 ['add_59[0][0]', Y \n",
- " 'stack_5_block5_MB_pw_bn[0][0] \n",
- " '] \n",
- " \n",
- " stack_5_block6_sortcut_conv (C (None, 7, 7, 3072) 1572864 ['add_60[0][0]'] Y \n",
- " onv2D) \n",
- " \n",
- " stack_5_block6_sortcut_bn (Bat (None, 7, 7, 3072) 12288 ['stack_5_block6_sortcut_conv[0 Y \n",
- " chNormalization) ][0]'] \n",
- " \n",
- " stack_5_block6_sortcut_swish ( (None, 7, 7, 3072) 0 ['stack_5_block6_sortcut_bn[0][ Y \n",
- " Activation) 0]'] \n",
- " \n",
- " stack_5_block6_MB_dw_ (Depthwi (None, 7, 7, 3072) 27648 ['stack_5_block6_sortcut_swish[ Y \n",
- " seConv2D) 0][0]'] \n",
- " \n",
- " stack_5_block6_MB_dw_bn (Batch (None, 7, 7, 3072) 12288 ['stack_5_block6_MB_dw_[0][0]'] Y \n",
- " Normalization) \n",
- " \n",
- " stack_5_block6_MB_dw_swish (Ac (None, 7, 7, 3072) 0 ['stack_5_block6_MB_dw_bn[0][0] Y \n",
- " tivation) '] \n",
- " \n",
- " tf.math.reduce_mean_46 (TFOpLa (None, 1, 1, 3072) 0 ['stack_5_block6_MB_dw_swish[0] Y \n",
- " mbda) [0]'] \n",
- " \n",
- " stack_5_block6_se_1_conv (Conv (None, 1, 1, 128) 393344 ['tf.math.reduce_mean_46[0][0]' Y \n",
- " 2D) ] \n",
- " \n",
- " activation_92 (Activation) (None, 1, 1, 128) 0 ['stack_5_block6_se_1_conv[0][0 Y \n",
- " ]'] \n",
- " \n",
- " stack_5_block6_se_2_conv (Conv (None, 1, 1, 3072) 396288 ['activation_92[0][0]'] Y \n",
- " 2D) \n",
- " \n",
- " activation_93 (Activation) (None, 1, 1, 3072) 0 ['stack_5_block6_se_2_conv[0][0 Y \n",
- " ]'] \n",
- " \n",
- " multiply_46 (Multiply) (None, 7, 7, 3072) 0 ['stack_5_block6_MB_dw_swish[0] Y \n",
- " [0]', \n",
- " 'activation_93[0][0]'] \n",
- " \n",
- " stack_5_block6_MB_pw_conv (Con (None, 7, 7, 512) 1572864 ['multiply_46[0][0]'] Y \n",
- " v2D) \n",
- " \n",
- " stack_5_block6_MB_pw_bn (Batch (None, 7, 7, 512) 2048 ['stack_5_block6_MB_pw_conv[0][ Y \n",
- " Normalization) 0]'] \n",
- " \n",
- " add_61 (Add) (None, 7, 7, 512) 0 ['add_60[0][0]', Y \n",
- " 'stack_5_block6_MB_pw_bn[0][0] \n",
- " '] \n",
- " \n",
- " stack_5_block7_sortcut_conv (C (None, 7, 7, 3072) 1572864 ['add_61[0][0]'] Y \n",
- " onv2D) \n",
- " \n",
- " stack_5_block7_sortcut_bn (Bat (None, 7, 7, 3072) 12288 ['stack_5_block7_sortcut_conv[0 Y \n",
- " chNormalization) ][0]'] \n",
- " \n",
- " stack_5_block7_sortcut_swish ( (None, 7, 7, 3072) 0 ['stack_5_block7_sortcut_bn[0][ Y \n",
- " Activation) 0]'] \n",
- " \n",
- " stack_5_block7_MB_dw_ (Depthwi (None, 7, 7, 3072) 27648 ['stack_5_block7_sortcut_swish[ Y \n",
- " seConv2D) 0][0]'] \n",
- " \n",
- " stack_5_block7_MB_dw_bn (Batch (None, 7, 7, 3072) 12288 ['stack_5_block7_MB_dw_[0][0]'] Y \n",
- " Normalization) \n",
- " \n",
- " stack_5_block7_MB_dw_swish (Ac (None, 7, 7, 3072) 0 ['stack_5_block7_MB_dw_bn[0][0] Y \n",
- " tivation) '] \n",
- " \n",
- " tf.math.reduce_mean_47 (TFOpLa (None, 1, 1, 3072) 0 ['stack_5_block7_MB_dw_swish[0] Y \n",
- " mbda) [0]'] \n",
- " \n",
- " stack_5_block7_se_1_conv (Conv (None, 1, 1, 128) 393344 ['tf.math.reduce_mean_47[0][0]' Y \n",
- " 2D) ] \n",
- " \n",
- " activation_94 (Activation) (None, 1, 1, 128) 0 ['stack_5_block7_se_1_conv[0][0 Y \n",
- " ]'] \n",
- " \n",
- " stack_5_block7_se_2_conv (Conv (None, 1, 1, 3072) 396288 ['activation_94[0][0]'] Y \n",
- " 2D) \n",
- " \n",
- " activation_95 (Activation) (None, 1, 1, 3072) 0 ['stack_5_block7_se_2_conv[0][0 Y \n",
- " ]'] \n",
- " \n",
- " multiply_47 (Multiply) (None, 7, 7, 3072) 0 ['stack_5_block7_MB_dw_swish[0] Y \n",
- " [0]', \n",
- " 'activation_95[0][0]'] \n",
- " \n",
- " stack_5_block7_MB_pw_conv (Con (None, 7, 7, 512) 1572864 ['multiply_47[0][0]'] Y \n",
- " v2D) \n",
- " \n",
- " stack_5_block7_MB_pw_bn (Batch (None, 7, 7, 512) 2048 ['stack_5_block7_MB_pw_conv[0][ Y \n",
- " Normalization) 0]'] \n",
- " \n",
- " add_62 (Add) (None, 7, 7, 512) 0 ['add_61[0][0]', Y \n",
- " 'stack_5_block7_MB_pw_bn[0][0] \n",
- " '] \n",
- " \n",
- " stack_5_block8_sortcut_conv (C (None, 7, 7, 3072) 1572864 ['add_62[0][0]'] Y \n",
- " onv2D) \n",
- " \n",
- " stack_5_block8_sortcut_bn (Bat (None, 7, 7, 3072) 12288 ['stack_5_block8_sortcut_conv[0 Y \n",
- " chNormalization) ][0]'] \n",
- " \n",
- " stack_5_block8_sortcut_swish ( (None, 7, 7, 3072) 0 ['stack_5_block8_sortcut_bn[0][ Y \n",
- " Activation) 0]'] \n",
- " \n",
- " stack_5_block8_MB_dw_ (Depthwi (None, 7, 7, 3072) 27648 ['stack_5_block8_sortcut_swish[ Y \n",
- " seConv2D) 0][0]'] \n",
- " \n",
- " stack_5_block8_MB_dw_bn (Batch (None, 7, 7, 3072) 12288 ['stack_5_block8_MB_dw_[0][0]'] Y \n",
- " Normalization) \n",
- " \n",
- " stack_5_block8_MB_dw_swish (Ac (None, 7, 7, 3072) 0 ['stack_5_block8_MB_dw_bn[0][0] Y \n",
- " tivation) '] \n",
- " \n",
- " tf.math.reduce_mean_48 (TFOpLa (None, 1, 1, 3072) 0 ['stack_5_block8_MB_dw_swish[0] Y \n",
- " mbda) [0]'] \n",
- " \n",
- " stack_5_block8_se_1_conv (Conv (None, 1, 1, 128) 393344 ['tf.math.reduce_mean_48[0][0]' Y \n",
- " 2D) ] \n",
- " \n",
- " activation_96 (Activation) (None, 1, 1, 128) 0 ['stack_5_block8_se_1_conv[0][0 Y \n",
- " ]'] \n",
- " \n",
- " stack_5_block8_se_2_conv (Conv (None, 1, 1, 3072) 396288 ['activation_96[0][0]'] Y \n",
- " 2D) \n",
- " \n",
- " activation_97 (Activation) (None, 1, 1, 3072) 0 ['stack_5_block8_se_2_conv[0][0 Y \n",
- " ]'] \n",
- " \n",
- " multiply_48 (Multiply) (None, 7, 7, 3072) 0 ['stack_5_block8_MB_dw_swish[0] Y \n",
- " [0]', \n",
- " 'activation_97[0][0]'] \n",
- " \n",
- " stack_5_block8_MB_pw_conv (Con (None, 7, 7, 512) 1572864 ['multiply_48[0][0]'] Y \n",
- " v2D) \n",
- " \n",
- " stack_5_block8_MB_pw_bn (Batch (None, 7, 7, 512) 2048 ['stack_5_block8_MB_pw_conv[0][ Y \n",
- " Normalization) 0]'] \n",
- " \n",
- " add_63 (Add) (None, 7, 7, 512) 0 ['add_62[0][0]', Y \n",
- " 'stack_5_block8_MB_pw_bn[0][0] \n",
- " '] \n",
- " \n",
- " stack_5_block9_sortcut_conv (C (None, 7, 7, 3072) 1572864 ['add_63[0][0]'] Y \n",
- " onv2D) \n",
- " \n",
- " stack_5_block9_sortcut_bn (Bat (None, 7, 7, 3072) 12288 ['stack_5_block9_sortcut_conv[0 Y \n",
- " chNormalization) ][0]'] \n",
- " \n",
- " stack_5_block9_sortcut_swish ( (None, 7, 7, 3072) 0 ['stack_5_block9_sortcut_bn[0][ Y \n",
- " Activation) 0]'] \n",
- " \n",
- " stack_5_block9_MB_dw_ (Depthwi (None, 7, 7, 3072) 27648 ['stack_5_block9_sortcut_swish[ Y \n",
- " seConv2D) 0][0]'] \n",
- " \n",
- " stack_5_block9_MB_dw_bn (Batch (None, 7, 7, 3072) 12288 ['stack_5_block9_MB_dw_[0][0]'] Y \n",
- " Normalization) \n",
- " \n",
- " stack_5_block9_MB_dw_swish (Ac (None, 7, 7, 3072) 0 ['stack_5_block9_MB_dw_bn[0][0] Y \n",
- " tivation) '] \n",
- " \n",
- " tf.math.reduce_mean_49 (TFOpLa (None, 1, 1, 3072) 0 ['stack_5_block9_MB_dw_swish[0] Y \n",
- " mbda) [0]'] \n",
- " \n",
- " stack_5_block9_se_1_conv (Conv (None, 1, 1, 128) 393344 ['tf.math.reduce_mean_49[0][0]' Y \n",
- " 2D) ] \n",
- " \n",
- " activation_98 (Activation) (None, 1, 1, 128) 0 ['stack_5_block9_se_1_conv[0][0 Y \n",
- " ]'] \n",
- " \n",
- " stack_5_block9_se_2_conv (Conv (None, 1, 1, 3072) 396288 ['activation_98[0][0]'] Y \n",
- " 2D) \n",
- " \n",
- " activation_99 (Activation) (None, 1, 1, 3072) 0 ['stack_5_block9_se_2_conv[0][0 Y \n",
- " ]'] \n",
- " \n",
- " multiply_49 (Multiply) (None, 7, 7, 3072) 0 ['stack_5_block9_MB_dw_swish[0] Y \n",
- " [0]', \n",
- " 'activation_99[0][0]'] \n",
- " \n",
- " stack_5_block9_MB_pw_conv (Con (None, 7, 7, 512) 1572864 ['multiply_49[0][0]'] Y \n",
- " v2D) \n",
- " \n",
- " stack_5_block9_MB_pw_bn (Batch (None, 7, 7, 512) 2048 ['stack_5_block9_MB_pw_conv[0][ Y \n",
- " Normalization) 0]'] \n",
- " \n",
- " add_64 (Add) (None, 7, 7, 512) 0 ['add_63[0][0]', Y \n",
- " 'stack_5_block9_MB_pw_bn[0][0] \n",
- " '] \n",
- " \n",
- " stack_5_block10_sortcut_conv ( (None, 7, 7, 3072) 1572864 ['add_64[0][0]'] Y \n",
- " Conv2D) \n",
- " \n",
- " stack_5_block10_sortcut_bn (Ba (None, 7, 7, 3072) 12288 ['stack_5_block10_sortcut_conv[ Y \n",
- " tchNormalization) 0][0]'] \n",
- " \n",
- " stack_5_block10_sortcut_swish (None, 7, 7, 3072) 0 ['stack_5_block10_sortcut_bn[0] Y \n",
- " (Activation) [0]'] \n",
- " \n",
- " stack_5_block10_MB_dw_ (Depthw (None, 7, 7, 3072) 27648 ['stack_5_block10_sortcut_swish Y \n",
- " iseConv2D) [0][0]'] \n",
- " \n",
- " stack_5_block10_MB_dw_bn (Batc (None, 7, 7, 3072) 12288 ['stack_5_block10_MB_dw_[0][0]' Y \n",
- " hNormalization) ] \n",
- " \n",
- " stack_5_block10_MB_dw_swish (A (None, 7, 7, 3072) 0 ['stack_5_block10_MB_dw_bn[0][0 Y \n",
- " ctivation) ]'] \n",
- " \n",
- " tf.math.reduce_mean_50 (TFOpLa (None, 1, 1, 3072) 0 ['stack_5_block10_MB_dw_swish[0 Y \n",
- " mbda) ][0]'] \n",
- " \n",
- " stack_5_block10_se_1_conv (Con (None, 1, 1, 128) 393344 ['tf.math.reduce_mean_50[0][0]' Y \n",
- " v2D) ] \n",
- " \n",
- " activation_100 (Activation) (None, 1, 1, 128) 0 ['stack_5_block10_se_1_conv[0][ Y \n",
- " 0]'] \n",
- " \n",
- " stack_5_block10_se_2_conv (Con (None, 1, 1, 3072) 396288 ['activation_100[0][0]'] Y \n",
- " v2D) \n",
- " \n",
- " activation_101 (Activation) (None, 1, 1, 3072) 0 ['stack_5_block10_se_2_conv[0][ Y \n",
- " 0]'] \n",
- " \n",
- " multiply_50 (Multiply) (None, 7, 7, 3072) 0 ['stack_5_block10_MB_dw_swish[0 Y \n",
- " ][0]', \n",
- " 'activation_101[0][0]'] \n",
- " \n",
- " stack_5_block10_MB_pw_conv (Co (None, 7, 7, 512) 1572864 ['multiply_50[0][0]'] Y \n",
- " nv2D) \n",
- " \n",
- " stack_5_block10_MB_pw_bn (Batc (None, 7, 7, 512) 2048 ['stack_5_block10_MB_pw_conv[0] Y \n",
- " hNormalization) [0]'] \n",
- " \n",
- " add_65 (Add) (None, 7, 7, 512) 0 ['add_64[0][0]', Y \n",
- " 'stack_5_block10_MB_pw_bn[0][0 \n",
- " ]'] \n",
- " \n",
- " stack_5_block11_sortcut_conv ( (None, 7, 7, 3072) 1572864 ['add_65[0][0]'] Y \n",
- " Conv2D) \n",
- " \n",
- " stack_5_block11_sortcut_bn (Ba (None, 7, 7, 3072) 12288 ['stack_5_block11_sortcut_conv[ Y \n",
- " tchNormalization) 0][0]'] \n",
- " \n",
- " stack_5_block11_sortcut_swish (None, 7, 7, 3072) 0 ['stack_5_block11_sortcut_bn[0] Y \n",
- " (Activation) [0]'] \n",
- " \n",
- " stack_5_block11_MB_dw_ (Depthw (None, 7, 7, 3072) 27648 ['stack_5_block11_sortcut_swish Y \n",
- " iseConv2D) [0][0]'] \n",
- " \n",
- " stack_5_block11_MB_dw_bn (Batc (None, 7, 7, 3072) 12288 ['stack_5_block11_MB_dw_[0][0]' Y \n",
- " hNormalization) ] \n",
- " \n",
- " stack_5_block11_MB_dw_swish (A (None, 7, 7, 3072) 0 ['stack_5_block11_MB_dw_bn[0][0 Y \n",
- " ctivation) ]'] \n",
- " \n",
- " tf.math.reduce_mean_51 (TFOpLa (None, 1, 1, 3072) 0 ['stack_5_block11_MB_dw_swish[0 Y \n",
- " mbda) ][0]'] \n",
- " \n",
- " stack_5_block11_se_1_conv (Con (None, 1, 1, 128) 393344 ['tf.math.reduce_mean_51[0][0]' Y \n",
- " v2D) ] \n",
- " \n",
- " activation_102 (Activation) (None, 1, 1, 128) 0 ['stack_5_block11_se_1_conv[0][ Y \n",
- " 0]'] \n",
- " \n",
- " stack_5_block11_se_2_conv (Con (None, 1, 1, 3072) 396288 ['activation_102[0][0]'] Y \n",
- " v2D) \n",
- " \n",
- " activation_103 (Activation) (None, 1, 1, 3072) 0 ['stack_5_block11_se_2_conv[0][ Y \n",
- " 0]'] \n",
- " \n",
- " multiply_51 (Multiply) (None, 7, 7, 3072) 0 ['stack_5_block11_MB_dw_swish[0 Y \n",
- " ][0]', \n",
- " 'activation_103[0][0]'] \n",
- " \n",
- " stack_5_block11_MB_pw_conv (Co (None, 7, 7, 512) 1572864 ['multiply_51[0][0]'] Y \n",
- " nv2D) \n",
- " \n",
- " stack_5_block11_MB_pw_bn (Batc (None, 7, 7, 512) 2048 ['stack_5_block11_MB_pw_conv[0] Y \n",
- " hNormalization) [0]'] \n",
- " \n",
- " add_66 (Add) (None, 7, 7, 512) 0 ['add_65[0][0]', Y \n",
- " 'stack_5_block11_MB_pw_bn[0][0 \n",
- " ]'] \n",
- " \n",
- " stack_5_block12_sortcut_conv ( (None, 7, 7, 3072) 1572864 ['add_66[0][0]'] Y \n",
- " Conv2D) \n",
- " \n",
- " stack_5_block12_sortcut_bn (Ba (None, 7, 7, 3072) 12288 ['stack_5_block12_sortcut_conv[ Y \n",
- " tchNormalization) 0][0]'] \n",
- " \n",
- " stack_5_block12_sortcut_swish (None, 7, 7, 3072) 0 ['stack_5_block12_sortcut_bn[0] Y \n",
- " (Activation) [0]'] \n",
- " \n",
- " stack_5_block12_MB_dw_ (Depthw (None, 7, 7, 3072) 27648 ['stack_5_block12_sortcut_swish Y \n",
- " iseConv2D) [0][0]'] \n",
- " \n",
- " stack_5_block12_MB_dw_bn (Batc (None, 7, 7, 3072) 12288 ['stack_5_block12_MB_dw_[0][0]' Y \n",
- " hNormalization) ] \n",
- " \n",
- " stack_5_block12_MB_dw_swish (A (None, 7, 7, 3072) 0 ['stack_5_block12_MB_dw_bn[0][0 Y \n",
- " ctivation) ]'] \n",
- " \n",
- " tf.math.reduce_mean_52 (TFOpLa (None, 1, 1, 3072) 0 ['stack_5_block12_MB_dw_swish[0 Y \n",
- " mbda) ][0]'] \n",
- " \n",
- " stack_5_block12_se_1_conv (Con (None, 1, 1, 128) 393344 ['tf.math.reduce_mean_52[0][0]' Y \n",
- " v2D) ] \n",
- " \n",
- " activation_104 (Activation) (None, 1, 1, 128) 0 ['stack_5_block12_se_1_conv[0][ Y \n",
- " 0]'] \n",
- " \n",
- " stack_5_block12_se_2_conv (Con (None, 1, 1, 3072) 396288 ['activation_104[0][0]'] Y \n",
- " v2D) \n",
- " \n",
- " activation_105 (Activation) (None, 1, 1, 3072) 0 ['stack_5_block12_se_2_conv[0][ Y \n",
- " 0]'] \n",
- " \n",
- " multiply_52 (Multiply) (None, 7, 7, 3072) 0 ['stack_5_block12_MB_dw_swish[0 Y \n",
- " ][0]', \n",
- " 'activation_105[0][0]'] \n",
- " \n",
- " stack_5_block12_MB_pw_conv (Co (None, 7, 7, 512) 1572864 ['multiply_52[0][0]'] Y \n",
- " nv2D) \n",
- " \n",
- " stack_5_block12_MB_pw_bn (Batc (None, 7, 7, 512) 2048 ['stack_5_block12_MB_pw_conv[0] Y \n",
- " hNormalization) [0]'] \n",
- " \n",
- " add_67 (Add) (None, 7, 7, 512) 0 ['add_66[0][0]', Y \n",
- " 'stack_5_block12_MB_pw_bn[0][0 \n",
- " ]'] \n",
- " \n",
- " stack_5_block13_sortcut_conv ( (None, 7, 7, 3072) 1572864 ['add_67[0][0]'] Y \n",
- " Conv2D) \n",
- " \n",
- " stack_5_block13_sortcut_bn (Ba (None, 7, 7, 3072) 12288 ['stack_5_block13_sortcut_conv[ Y \n",
- " tchNormalization) 0][0]'] \n",
- " \n",
- " stack_5_block13_sortcut_swish (None, 7, 7, 3072) 0 ['stack_5_block13_sortcut_bn[0] Y \n",
- " (Activation) [0]'] \n",
- " \n",
- " stack_5_block13_MB_dw_ (Depthw (None, 7, 7, 3072) 27648 ['stack_5_block13_sortcut_swish Y \n",
- " iseConv2D) [0][0]'] \n",
- " \n",
- " stack_5_block13_MB_dw_bn (Batc (None, 7, 7, 3072) 12288 ['stack_5_block13_MB_dw_[0][0]' Y \n",
- " hNormalization) ] \n",
- " \n",
- " stack_5_block13_MB_dw_swish (A (None, 7, 7, 3072) 0 ['stack_5_block13_MB_dw_bn[0][0 Y \n",
- " ctivation) ]'] \n",
- " \n",
- " tf.math.reduce_mean_53 (TFOpLa (None, 1, 1, 3072) 0 ['stack_5_block13_MB_dw_swish[0 Y \n",
- " mbda) ][0]'] \n",
- " \n",
- " stack_5_block13_se_1_conv (Con (None, 1, 1, 128) 393344 ['tf.math.reduce_mean_53[0][0]' Y \n",
- " v2D) ] \n",
- " \n",
- " activation_106 (Activation) (None, 1, 1, 128) 0 ['stack_5_block13_se_1_conv[0][ Y \n",
- " 0]'] \n",
- " \n",
- " stack_5_block13_se_2_conv (Con (None, 1, 1, 3072) 396288 ['activation_106[0][0]'] Y \n",
- " v2D) \n",
- " \n",
- " activation_107 (Activation) (None, 1, 1, 3072) 0 ['stack_5_block13_se_2_conv[0][ Y \n",
- " 0]'] \n",
- " \n",
- " multiply_53 (Multiply) (None, 7, 7, 3072) 0 ['stack_5_block13_MB_dw_swish[0 Y \n",
- " ][0]', \n",
- " 'activation_107[0][0]'] \n",
- " \n",
- " stack_5_block13_MB_pw_conv (Co (None, 7, 7, 512) 1572864 ['multiply_53[0][0]'] Y \n",
- " nv2D) \n",
- " \n",
- " stack_5_block13_MB_pw_bn (Batc (None, 7, 7, 512) 2048 ['stack_5_block13_MB_pw_conv[0] Y \n",
- " hNormalization) [0]'] \n",
- " \n",
- " add_68 (Add) (None, 7, 7, 512) 0 ['add_67[0][0]', Y \n",
- " 'stack_5_block13_MB_pw_bn[0][0 \n",
- " ]'] \n",
- " \n",
- " stack_5_block14_sortcut_conv ( (None, 7, 7, 3072) 1572864 ['add_68[0][0]'] Y \n",
- " Conv2D) \n",
- " \n",
- " stack_5_block14_sortcut_bn (Ba (None, 7, 7, 3072) 12288 ['stack_5_block14_sortcut_conv[ Y \n",
- " tchNormalization) 0][0]'] \n",
- " \n",
- " stack_5_block14_sortcut_swish (None, 7, 7, 3072) 0 ['stack_5_block14_sortcut_bn[0] Y \n",
- " (Activation) [0]'] \n",
- " \n",
- " stack_5_block14_MB_dw_ (Depthw (None, 7, 7, 3072) 27648 ['stack_5_block14_sortcut_swish Y \n",
- " iseConv2D) [0][0]'] \n",
- " \n",
- " stack_5_block14_MB_dw_bn (Batc (None, 7, 7, 3072) 12288 ['stack_5_block14_MB_dw_[0][0]' Y \n",
- " hNormalization) ] \n",
- " \n",
- " stack_5_block14_MB_dw_swish (A (None, 7, 7, 3072) 0 ['stack_5_block14_MB_dw_bn[0][0 Y \n",
- " ctivation) ]'] \n",
- " \n",
- " tf.math.reduce_mean_54 (TFOpLa (None, 1, 1, 3072) 0 ['stack_5_block14_MB_dw_swish[0 Y \n",
- " mbda) ][0]'] \n",
- " \n",
- " stack_5_block14_se_1_conv (Con (None, 1, 1, 128) 393344 ['tf.math.reduce_mean_54[0][0]' Y \n",
- " v2D) ] \n",
- " \n",
- " activation_108 (Activation) (None, 1, 1, 128) 0 ['stack_5_block14_se_1_conv[0][ Y \n",
- " 0]'] \n",
- " \n",
- " stack_5_block14_se_2_conv (Con (None, 1, 1, 3072) 396288 ['activation_108[0][0]'] Y \n",
- " v2D) \n",
- " \n",
- " activation_109 (Activation) (None, 1, 1, 3072) 0 ['stack_5_block14_se_2_conv[0][ Y \n",
- " 0]'] \n",
- " \n",
- " multiply_54 (Multiply) (None, 7, 7, 3072) 0 ['stack_5_block14_MB_dw_swish[0 Y \n",
- " ][0]', \n",
- " 'activation_109[0][0]'] \n",
- " \n",
- " stack_5_block14_MB_pw_conv (Co (None, 7, 7, 512) 1572864 ['multiply_54[0][0]'] Y \n",
- " nv2D) \n",
- " \n",
- " stack_5_block14_MB_pw_bn (Batc (None, 7, 7, 512) 2048 ['stack_5_block14_MB_pw_conv[0] Y \n",
- " hNormalization) [0]'] \n",
- " \n",
- " add_69 (Add) (None, 7, 7, 512) 0 ['add_68[0][0]', Y \n",
- " 'stack_5_block14_MB_pw_bn[0][0 \n",
- " ]'] \n",
- " \n",
- " stack_5_block15_sortcut_conv ( (None, 7, 7, 3072) 1572864 ['add_69[0][0]'] Y \n",
- " Conv2D) \n",
- " \n",
- " stack_5_block15_sortcut_bn (Ba (None, 7, 7, 3072) 12288 ['stack_5_block15_sortcut_conv[ Y \n",
- " tchNormalization) 0][0]'] \n",
- " \n",
- " stack_5_block15_sortcut_swish (None, 7, 7, 3072) 0 ['stack_5_block15_sortcut_bn[0] Y \n",
- " (Activation) [0]'] \n",
- " \n",
- " stack_5_block15_MB_dw_ (Depthw (None, 7, 7, 3072) 27648 ['stack_5_block15_sortcut_swish Y \n",
- " iseConv2D) [0][0]'] \n",
- " \n",
- " stack_5_block15_MB_dw_bn (Batc (None, 7, 7, 3072) 12288 ['stack_5_block15_MB_dw_[0][0]' Y \n",
- " hNormalization) ] \n",
- " \n",
- " stack_5_block15_MB_dw_swish (A (None, 7, 7, 3072) 0 ['stack_5_block15_MB_dw_bn[0][0 Y \n",
- " ctivation) ]'] \n",
- " \n",
- " tf.math.reduce_mean_55 (TFOpLa (None, 1, 1, 3072) 0 ['stack_5_block15_MB_dw_swish[0 Y \n",
- " mbda) ][0]'] \n",
- " \n",
- " stack_5_block15_se_1_conv (Con (None, 1, 1, 128) 393344 ['tf.math.reduce_mean_55[0][0]' Y \n",
- " v2D) ] \n",
- " \n",
- " activation_110 (Activation) (None, 1, 1, 128) 0 ['stack_5_block15_se_1_conv[0][ Y \n",
- " 0]'] \n",
- " \n",
- " stack_5_block15_se_2_conv (Con (None, 1, 1, 3072) 396288 ['activation_110[0][0]'] Y \n",
- " v2D) \n",
- " \n",
- " activation_111 (Activation) (None, 1, 1, 3072) 0 ['stack_5_block15_se_2_conv[0][ Y \n",
- " 0]'] \n",
- " \n",
- " multiply_55 (Multiply) (None, 7, 7, 3072) 0 ['stack_5_block15_MB_dw_swish[0 Y \n",
- " ][0]', \n",
- " 'activation_111[0][0]'] \n",
- " \n",
- " stack_5_block15_MB_pw_conv (Co (None, 7, 7, 512) 1572864 ['multiply_55[0][0]'] Y \n",
- " nv2D) \n",
- " \n",
- " stack_5_block15_MB_pw_bn (Batc (None, 7, 7, 512) 2048 ['stack_5_block15_MB_pw_conv[0] Y \n",
- " hNormalization) [0]'] \n",
- " \n",
- " add_70 (Add) (None, 7, 7, 512) 0 ['add_69[0][0]', Y \n",
- " 'stack_5_block15_MB_pw_bn[0][0 \n",
- " ]'] \n",
- " \n",
- " stack_5_block16_sortcut_conv ( (None, 7, 7, 3072) 1572864 ['add_70[0][0]'] Y \n",
- " Conv2D) \n",
- " \n",
- " stack_5_block16_sortcut_bn (Ba (None, 7, 7, 3072) 12288 ['stack_5_block16_sortcut_conv[ Y \n",
- " tchNormalization) 0][0]'] \n",
- " \n",
- " stack_5_block16_sortcut_swish (None, 7, 7, 3072) 0 ['stack_5_block16_sortcut_bn[0] Y \n",
- " (Activation) [0]'] \n",
- " \n",
- " stack_5_block16_MB_dw_ (Depthw (None, 7, 7, 3072) 27648 ['stack_5_block16_sortcut_swish Y \n",
- " iseConv2D) [0][0]'] \n",
- " \n",
- " stack_5_block16_MB_dw_bn (Batc (None, 7, 7, 3072) 12288 ['stack_5_block16_MB_dw_[0][0]' Y \n",
- " hNormalization) ] \n",
- " \n",
- " stack_5_block16_MB_dw_swish (A (None, 7, 7, 3072) 0 ['stack_5_block16_MB_dw_bn[0][0 Y \n",
- " ctivation) ]'] \n",
- " \n",
- " tf.math.reduce_mean_56 (TFOpLa (None, 1, 1, 3072) 0 ['stack_5_block16_MB_dw_swish[0 Y \n",
- " mbda) ][0]'] \n",
- " \n",
- " stack_5_block16_se_1_conv (Con (None, 1, 1, 128) 393344 ['tf.math.reduce_mean_56[0][0]' Y \n",
- " v2D) ] \n",
- " \n",
- " activation_112 (Activation) (None, 1, 1, 128) 0 ['stack_5_block16_se_1_conv[0][ Y \n",
- " 0]'] \n",
- " \n",
- " stack_5_block16_se_2_conv (Con (None, 1, 1, 3072) 396288 ['activation_112[0][0]'] Y \n",
- " v2D) \n",
- " \n",
- " activation_113 (Activation) (None, 1, 1, 3072) 0 ['stack_5_block16_se_2_conv[0][ Y \n",
- " 0]'] \n",
- " \n",
- " multiply_56 (Multiply) (None, 7, 7, 3072) 0 ['stack_5_block16_MB_dw_swish[0 Y \n",
- " ][0]', \n",
- " 'activation_113[0][0]'] \n",
- " \n",
- " stack_5_block16_MB_pw_conv (Co (None, 7, 7, 512) 1572864 ['multiply_56[0][0]'] Y \n",
- " nv2D) \n",
- " \n",
- " stack_5_block16_MB_pw_bn (Batc (None, 7, 7, 512) 2048 ['stack_5_block16_MB_pw_conv[0] Y \n",
- " hNormalization) [0]'] \n",
- " \n",
- " add_71 (Add) (None, 7, 7, 512) 0 ['add_70[0][0]', Y \n",
- " 'stack_5_block16_MB_pw_bn[0][0 \n",
- " ]'] \n",
- " \n",
- " stack_5_block17_sortcut_conv ( (None, 7, 7, 3072) 1572864 ['add_71[0][0]'] Y \n",
- " Conv2D) \n",
- " \n",
- " stack_5_block17_sortcut_bn (Ba (None, 7, 7, 3072) 12288 ['stack_5_block17_sortcut_conv[ Y \n",
- " tchNormalization) 0][0]'] \n",
- " \n",
- " stack_5_block17_sortcut_swish (None, 7, 7, 3072) 0 ['stack_5_block17_sortcut_bn[0] Y \n",
- " (Activation) [0]'] \n",
- " \n",
- " stack_5_block17_MB_dw_ (Depthw (None, 7, 7, 3072) 27648 ['stack_5_block17_sortcut_swish Y \n",
- " iseConv2D) [0][0]'] \n",
- " \n",
- " stack_5_block17_MB_dw_bn (Batc (None, 7, 7, 3072) 12288 ['stack_5_block17_MB_dw_[0][0]' Y \n",
- " hNormalization) ] \n",
- " \n",
- " stack_5_block17_MB_dw_swish (A (None, 7, 7, 3072) 0 ['stack_5_block17_MB_dw_bn[0][0 Y \n",
- " ctivation) ]'] \n",
- " \n",
- " tf.math.reduce_mean_57 (TFOpLa (None, 1, 1, 3072) 0 ['stack_5_block17_MB_dw_swish[0 Y \n",
- " mbda) ][0]'] \n",
- " \n",
- " stack_5_block17_se_1_conv (Con (None, 1, 1, 128) 393344 ['tf.math.reduce_mean_57[0][0]' Y \n",
- " v2D) ] \n",
- " \n",
- " activation_114 (Activation) (None, 1, 1, 128) 0 ['stack_5_block17_se_1_conv[0][ Y \n",
- " 0]'] \n",
- " \n",
- " stack_5_block17_se_2_conv (Con (None, 1, 1, 3072) 396288 ['activation_114[0][0]'] Y \n",
- " v2D) \n",
- " \n",
- " activation_115 (Activation) (None, 1, 1, 3072) 0 ['stack_5_block17_se_2_conv[0][ Y \n",
- " 0]'] \n",
- " \n",
- " multiply_57 (Multiply) (None, 7, 7, 3072) 0 ['stack_5_block17_MB_dw_swish[0 Y \n",
- " ][0]', \n",
- " 'activation_115[0][0]'] \n",
- " \n",
- " stack_5_block17_MB_pw_conv (Co (None, 7, 7, 512) 1572864 ['multiply_57[0][0]'] Y \n",
- " nv2D) \n",
- " \n",
- " stack_5_block17_MB_pw_bn (Batc (None, 7, 7, 512) 2048 ['stack_5_block17_MB_pw_conv[0] Y \n",
- " hNormalization) [0]'] \n",
- " \n",
- " add_72 (Add) (None, 7, 7, 512) 0 ['add_71[0][0]', Y \n",
- " 'stack_5_block17_MB_pw_bn[0][0 \n",
- " ]'] \n",
- " \n",
- " stack_5_block18_sortcut_conv ( (None, 7, 7, 3072) 1572864 ['add_72[0][0]'] Y \n",
- " Conv2D) \n",
- " \n",
- " stack_5_block18_sortcut_bn (Ba (None, 7, 7, 3072) 12288 ['stack_5_block18_sortcut_conv[ Y \n",
- " tchNormalization) 0][0]'] \n",
- " \n",
- " stack_5_block18_sortcut_swish (None, 7, 7, 3072) 0 ['stack_5_block18_sortcut_bn[0] Y \n",
- " (Activation) [0]'] \n",
- " \n",
- " stack_5_block18_MB_dw_ (Depthw (None, 7, 7, 3072) 27648 ['stack_5_block18_sortcut_swish Y \n",
- " iseConv2D) [0][0]'] \n",
- " \n",
- " stack_5_block18_MB_dw_bn (Batc (None, 7, 7, 3072) 12288 ['stack_5_block18_MB_dw_[0][0]' Y \n",
- " hNormalization) ] \n",
- " \n",
- " stack_5_block18_MB_dw_swish (A (None, 7, 7, 3072) 0 ['stack_5_block18_MB_dw_bn[0][0 Y \n",
- " ctivation) ]'] \n",
- " \n",
- " tf.math.reduce_mean_58 (TFOpLa (None, 1, 1, 3072) 0 ['stack_5_block18_MB_dw_swish[0 Y \n",
- " mbda) ][0]'] \n",
- " \n",
- " stack_5_block18_se_1_conv (Con (None, 1, 1, 128) 393344 ['tf.math.reduce_mean_58[0][0]' Y \n",
- " v2D) ] \n",
- " \n",
- " activation_116 (Activation) (None, 1, 1, 128) 0 ['stack_5_block18_se_1_conv[0][ Y \n",
- " 0]'] \n",
- " \n",
- " stack_5_block18_se_2_conv (Con (None, 1, 1, 3072) 396288 ['activation_116[0][0]'] Y \n",
- " v2D) \n",
- " \n",
- " activation_117 (Activation) (None, 1, 1, 3072) 0 ['stack_5_block18_se_2_conv[0][ Y \n",
- " 0]'] \n",
- " \n",
- " multiply_58 (Multiply) (None, 7, 7, 3072) 0 ['stack_5_block18_MB_dw_swish[0 Y \n",
- " ][0]', \n",
- " 'activation_117[0][0]'] \n",
- " \n",
- " stack_5_block18_MB_pw_conv (Co (None, 7, 7, 512) 1572864 ['multiply_58[0][0]'] Y \n",
- " nv2D) \n",
- " \n",
- " stack_5_block18_MB_pw_bn (Batc (None, 7, 7, 512) 2048 ['stack_5_block18_MB_pw_conv[0] Y \n",
- " hNormalization) [0]'] \n",
- " \n",
- " add_73 (Add) (None, 7, 7, 512) 0 ['add_72[0][0]', Y \n",
- " 'stack_5_block18_MB_pw_bn[0][0 \n",
- " ]'] \n",
- " \n",
- " stack_5_block19_sortcut_conv ( (None, 7, 7, 3072) 1572864 ['add_73[0][0]'] Y \n",
- " Conv2D) \n",
- " \n",
- " stack_5_block19_sortcut_bn (Ba (None, 7, 7, 3072) 12288 ['stack_5_block19_sortcut_conv[ Y \n",
- " tchNormalization) 0][0]'] \n",
- " \n",
- " stack_5_block19_sortcut_swish (None, 7, 7, 3072) 0 ['stack_5_block19_sortcut_bn[0] Y \n",
- " (Activation) [0]'] \n",
- " \n",
- " stack_5_block19_MB_dw_ (Depthw (None, 7, 7, 3072) 27648 ['stack_5_block19_sortcut_swish Y \n",
- " iseConv2D) [0][0]'] \n",
- " \n",
- " stack_5_block19_MB_dw_bn (Batc (None, 7, 7, 3072) 12288 ['stack_5_block19_MB_dw_[0][0]' Y \n",
- " hNormalization) ] \n",
- " \n",
- " stack_5_block19_MB_dw_swish (A (None, 7, 7, 3072) 0 ['stack_5_block19_MB_dw_bn[0][0 Y \n",
- " ctivation) ]'] \n",
- " \n",
- " tf.math.reduce_mean_59 (TFOpLa (None, 1, 1, 3072) 0 ['stack_5_block19_MB_dw_swish[0 Y \n",
- " mbda) ][0]'] \n",
- " \n",
- " stack_5_block19_se_1_conv (Con (None, 1, 1, 128) 393344 ['tf.math.reduce_mean_59[0][0]' Y \n",
- " v2D) ] \n",
- " \n",
- " activation_118 (Activation) (None, 1, 1, 128) 0 ['stack_5_block19_se_1_conv[0][ Y \n",
- " 0]'] \n",
- " \n",
- " stack_5_block19_se_2_conv (Con (None, 1, 1, 3072) 396288 ['activation_118[0][0]'] Y \n",
- " v2D) \n",
- " \n",
- " activation_119 (Activation) (None, 1, 1, 3072) 0 ['stack_5_block19_se_2_conv[0][ Y \n",
- " 0]'] \n",
- " \n",
- " multiply_59 (Multiply) (None, 7, 7, 3072) 0 ['stack_5_block19_MB_dw_swish[0 Y \n",
- " ][0]', \n",
- " 'activation_119[0][0]'] \n",
- " \n",
- " stack_5_block19_MB_pw_conv (Co (None, 7, 7, 512) 1572864 ['multiply_59[0][0]'] Y \n",
- " nv2D) \n",
- " \n",
- " stack_5_block19_MB_pw_bn (Batc (None, 7, 7, 512) 2048 ['stack_5_block19_MB_pw_conv[0] Y \n",
- " hNormalization) [0]'] \n",
- " \n",
- " add_74 (Add) (None, 7, 7, 512) 0 ['add_73[0][0]', Y \n",
- " 'stack_5_block19_MB_pw_bn[0][0 \n",
- " ]'] \n",
- " \n",
- " stack_5_block20_sortcut_conv ( (None, 7, 7, 3072) 1572864 ['add_74[0][0]'] Y \n",
- " Conv2D) \n",
- " \n",
- " stack_5_block20_sortcut_bn (Ba (None, 7, 7, 3072) 12288 ['stack_5_block20_sortcut_conv[ Y \n",
- " tchNormalization) 0][0]'] \n",
- " \n",
- " stack_5_block20_sortcut_swish (None, 7, 7, 3072) 0 ['stack_5_block20_sortcut_bn[0] Y \n",
- " (Activation) [0]'] \n",
- " \n",
- " stack_5_block20_MB_dw_ (Depthw (None, 7, 7, 3072) 27648 ['stack_5_block20_sortcut_swish Y \n",
- " iseConv2D) [0][0]'] \n",
- " \n",
- " stack_5_block20_MB_dw_bn (Batc (None, 7, 7, 3072) 12288 ['stack_5_block20_MB_dw_[0][0]' Y \n",
- " hNormalization) ] \n",
- " \n",
- " stack_5_block20_MB_dw_swish (A (None, 7, 7, 3072) 0 ['stack_5_block20_MB_dw_bn[0][0 Y \n",
- " ctivation) ]'] \n",
- " \n",
- " tf.math.reduce_mean_60 (TFOpLa (None, 1, 1, 3072) 0 ['stack_5_block20_MB_dw_swish[0 Y \n",
- " mbda) ][0]'] \n",
- " \n",
- " stack_5_block20_se_1_conv (Con (None, 1, 1, 128) 393344 ['tf.math.reduce_mean_60[0][0]' Y \n",
- " v2D) ] \n",
- " \n",
- " activation_120 (Activation) (None, 1, 1, 128) 0 ['stack_5_block20_se_1_conv[0][ Y \n",
- " 0]'] \n",
- " \n",
- " stack_5_block20_se_2_conv (Con (None, 1, 1, 3072) 396288 ['activation_120[0][0]'] Y \n",
- " v2D) \n",
- " \n",
- " activation_121 (Activation) (None, 1, 1, 3072) 0 ['stack_5_block20_se_2_conv[0][ Y \n",
- " 0]'] \n",
- " \n",
- " multiply_60 (Multiply) (None, 7, 7, 3072) 0 ['stack_5_block20_MB_dw_swish[0 Y \n",
- " ][0]', \n",
- " 'activation_121[0][0]'] \n",
- " \n",
- " stack_5_block20_MB_pw_conv (Co (None, 7, 7, 512) 1572864 ['multiply_60[0][0]'] Y \n",
- " nv2D) \n",
- " \n",
- " stack_5_block20_MB_pw_bn (Batc (None, 7, 7, 512) 2048 ['stack_5_block20_MB_pw_conv[0] Y \n",
- " hNormalization) [0]'] \n",
- " \n",
- " add_75 (Add) (None, 7, 7, 512) 0 ['add_74[0][0]', Y \n",
- " 'stack_5_block20_MB_pw_bn[0][0 \n",
- " ]'] \n",
- " \n",
- " stack_5_block21_sortcut_conv ( (None, 7, 7, 3072) 1572864 ['add_75[0][0]'] Y \n",
- " Conv2D) \n",
- " \n",
- " stack_5_block21_sortcut_bn (Ba (None, 7, 7, 3072) 12288 ['stack_5_block21_sortcut_conv[ Y \n",
- " tchNormalization) 0][0]'] \n",
- " \n",
- " stack_5_block21_sortcut_swish (None, 7, 7, 3072) 0 ['stack_5_block21_sortcut_bn[0] Y \n",
- " (Activation) [0]'] \n",
- " \n",
- " stack_5_block21_MB_dw_ (Depthw (None, 7, 7, 3072) 27648 ['stack_5_block21_sortcut_swish Y \n",
- " iseConv2D) [0][0]'] \n",
- " \n",
- " stack_5_block21_MB_dw_bn (Batc (None, 7, 7, 3072) 12288 ['stack_5_block21_MB_dw_[0][0]' Y \n",
- " hNormalization) ] \n",
- " \n",
- " stack_5_block21_MB_dw_swish (A (None, 7, 7, 3072) 0 ['stack_5_block21_MB_dw_bn[0][0 Y \n",
- " ctivation) ]'] \n",
- " \n",
- " tf.math.reduce_mean_61 (TFOpLa (None, 1, 1, 3072) 0 ['stack_5_block21_MB_dw_swish[0 Y \n",
- " mbda) ][0]'] \n",
- " \n",
- " stack_5_block21_se_1_conv (Con (None, 1, 1, 128) 393344 ['tf.math.reduce_mean_61[0][0]' Y \n",
- " v2D) ] \n",
- " \n",
- " activation_122 (Activation) (None, 1, 1, 128) 0 ['stack_5_block21_se_1_conv[0][ Y \n",
- " 0]'] \n",
- " \n",
- " stack_5_block21_se_2_conv (Con (None, 1, 1, 3072) 396288 ['activation_122[0][0]'] Y \n",
- " v2D) \n",
- " \n",
- " activation_123 (Activation) (None, 1, 1, 3072) 0 ['stack_5_block21_se_2_conv[0][ Y \n",
- " 0]'] \n",
- " \n",
- " multiply_61 (Multiply) (None, 7, 7, 3072) 0 ['stack_5_block21_MB_dw_swish[0 Y \n",
- " ][0]', \n",
- " 'activation_123[0][0]'] \n",
- " \n",
- " stack_5_block21_MB_pw_conv (Co (None, 7, 7, 512) 1572864 ['multiply_61[0][0]'] Y \n",
- " nv2D) \n",
- " \n",
- " stack_5_block21_MB_pw_bn (Batc (None, 7, 7, 512) 2048 ['stack_5_block21_MB_pw_conv[0] Y \n",
- " hNormalization) [0]'] \n",
- " \n",
- " add_76 (Add) (None, 7, 7, 512) 0 ['add_75[0][0]', Y \n",
- " 'stack_5_block21_MB_pw_bn[0][0 \n",
- " ]'] \n",
- " \n",
- " stack_5_block22_sortcut_conv ( (None, 7, 7, 3072) 1572864 ['add_76[0][0]'] Y \n",
- " Conv2D) \n",
- " \n",
- " stack_5_block22_sortcut_bn (Ba (None, 7, 7, 3072) 12288 ['stack_5_block22_sortcut_conv[ Y \n",
- " tchNormalization) 0][0]'] \n",
- " \n",
- " stack_5_block22_sortcut_swish (None, 7, 7, 3072) 0 ['stack_5_block22_sortcut_bn[0] Y \n",
- " (Activation) [0]'] \n",
- " \n",
- " stack_5_block22_MB_dw_ (Depthw (None, 7, 7, 3072) 27648 ['stack_5_block22_sortcut_swish Y \n",
- " iseConv2D) [0][0]'] \n",
- " \n",
- " stack_5_block22_MB_dw_bn (Batc (None, 7, 7, 3072) 12288 ['stack_5_block22_MB_dw_[0][0]' Y \n",
- " hNormalization) ] \n",
- " \n",
- " stack_5_block22_MB_dw_swish (A (None, 7, 7, 3072) 0 ['stack_5_block22_MB_dw_bn[0][0 Y \n",
- " ctivation) ]'] \n",
- " \n",
- " tf.math.reduce_mean_62 (TFOpLa (None, 1, 1, 3072) 0 ['stack_5_block22_MB_dw_swish[0 Y \n",
- " mbda) ][0]'] \n",
- " \n",
- " stack_5_block22_se_1_conv (Con (None, 1, 1, 128) 393344 ['tf.math.reduce_mean_62[0][0]' Y \n",
- " v2D) ] \n",
- " \n",
- " activation_124 (Activation) (None, 1, 1, 128) 0 ['stack_5_block22_se_1_conv[0][ Y \n",
- " 0]'] \n",
- " \n",
- " stack_5_block22_se_2_conv (Con (None, 1, 1, 3072) 396288 ['activation_124[0][0]'] Y \n",
- " v2D) \n",
- " \n",
- " activation_125 (Activation) (None, 1, 1, 3072) 0 ['stack_5_block22_se_2_conv[0][ Y \n",
- " 0]'] \n",
- " \n",
- " multiply_62 (Multiply) (None, 7, 7, 3072) 0 ['stack_5_block22_MB_dw_swish[0 Y \n",
- " ][0]', \n",
- " 'activation_125[0][0]'] \n",
- " \n",
- " stack_5_block22_MB_pw_conv (Co (None, 7, 7, 512) 1572864 ['multiply_62[0][0]'] Y \n",
- " nv2D) \n",
- " \n",
- " stack_5_block22_MB_pw_bn (Batc (None, 7, 7, 512) 2048 ['stack_5_block22_MB_pw_conv[0] Y \n",
- " hNormalization) [0]'] \n",
- " \n",
- " add_77 (Add) (None, 7, 7, 512) 0 ['add_76[0][0]', Y \n",
- " 'stack_5_block22_MB_pw_bn[0][0 \n",
- " ]'] \n",
- " \n",
- " stack_5_block23_sortcut_conv ( (None, 7, 7, 3072) 1572864 ['add_77[0][0]'] Y \n",
- " Conv2D) \n",
- " \n",
- " stack_5_block23_sortcut_bn (Ba (None, 7, 7, 3072) 12288 ['stack_5_block23_sortcut_conv[ Y \n",
- " tchNormalization) 0][0]'] \n",
- " \n",
- " stack_5_block23_sortcut_swish (None, 7, 7, 3072) 0 ['stack_5_block23_sortcut_bn[0] Y \n",
- " (Activation) [0]'] \n",
- " \n",
- " stack_5_block23_MB_dw_ (Depthw (None, 7, 7, 3072) 27648 ['stack_5_block23_sortcut_swish Y \n",
- " iseConv2D) [0][0]'] \n",
- " \n",
- " stack_5_block23_MB_dw_bn (Batc (None, 7, 7, 3072) 12288 ['stack_5_block23_MB_dw_[0][0]' Y \n",
- " hNormalization) ] \n",
- " \n",
- " stack_5_block23_MB_dw_swish (A (None, 7, 7, 3072) 0 ['stack_5_block23_MB_dw_bn[0][0 Y \n",
- " ctivation) ]'] \n",
- " \n",
- " tf.math.reduce_mean_63 (TFOpLa (None, 1, 1, 3072) 0 ['stack_5_block23_MB_dw_swish[0 Y \n",
- " mbda) ][0]'] \n",
- " \n",
- " stack_5_block23_se_1_conv (Con (None, 1, 1, 128) 393344 ['tf.math.reduce_mean_63[0][0]' Y \n",
- " v2D) ] \n",
- " \n",
- " activation_126 (Activation) (None, 1, 1, 128) 0 ['stack_5_block23_se_1_conv[0][ Y \n",
- " 0]'] \n",
- " \n",
- " stack_5_block23_se_2_conv (Con (None, 1, 1, 3072) 396288 ['activation_126[0][0]'] Y \n",
- " v2D) \n",
- " \n",
- " activation_127 (Activation) (None, 1, 1, 3072) 0 ['stack_5_block23_se_2_conv[0][ Y \n",
- " 0]'] \n",
- " \n",
- " multiply_63 (Multiply) (None, 7, 7, 3072) 0 ['stack_5_block23_MB_dw_swish[0 Y \n",
- " ][0]', \n",
- " 'activation_127[0][0]'] \n",
- " \n",
- " stack_5_block23_MB_pw_conv (Co (None, 7, 7, 512) 1572864 ['multiply_63[0][0]'] Y \n",
- " nv2D) \n",
- " \n",
- " stack_5_block23_MB_pw_bn (Batc (None, 7, 7, 512) 2048 ['stack_5_block23_MB_pw_conv[0] Y \n",
- " hNormalization) [0]'] \n",
- " \n",
- " add_78 (Add) (None, 7, 7, 512) 0 ['add_77[0][0]', Y \n",
- " 'stack_5_block23_MB_pw_bn[0][0 \n",
- " ]'] \n",
- " \n",
- " stack_5_block24_sortcut_conv ( (None, 7, 7, 3072) 1572864 ['add_78[0][0]'] Y \n",
- " Conv2D) \n",
- " \n",
- " stack_5_block24_sortcut_bn (Ba (None, 7, 7, 3072) 12288 ['stack_5_block24_sortcut_conv[ Y \n",
- " tchNormalization) 0][0]'] \n",
- " \n",
- " stack_5_block24_sortcut_swish (None, 7, 7, 3072) 0 ['stack_5_block24_sortcut_bn[0] Y \n",
- " (Activation) [0]'] \n",
- " \n",
- " stack_5_block24_MB_dw_ (Depthw (None, 7, 7, 3072) 27648 ['stack_5_block24_sortcut_swish Y \n",
- " iseConv2D) [0][0]'] \n",
- " \n",
- " stack_5_block24_MB_dw_bn (Batc (None, 7, 7, 3072) 12288 ['stack_5_block24_MB_dw_[0][0]' Y \n",
- " hNormalization) ] \n",
- " \n",
- " stack_5_block24_MB_dw_swish (A (None, 7, 7, 3072) 0 ['stack_5_block24_MB_dw_bn[0][0 Y \n",
- " ctivation) ]'] \n",
- " \n",
- " tf.math.reduce_mean_64 (TFOpLa (None, 1, 1, 3072) 0 ['stack_5_block24_MB_dw_swish[0 Y \n",
- " mbda) ][0]'] \n",
- " \n",
- " stack_5_block24_se_1_conv (Con (None, 1, 1, 128) 393344 ['tf.math.reduce_mean_64[0][0]' Y \n",
- " v2D) ] \n",
- " \n",
- " activation_128 (Activation) (None, 1, 1, 128) 0 ['stack_5_block24_se_1_conv[0][ Y \n",
- " 0]'] \n",
- " \n",
- " stack_5_block24_se_2_conv (Con (None, 1, 1, 3072) 396288 ['activation_128[0][0]'] Y \n",
- " v2D) \n",
- " \n",
- " activation_129 (Activation) (None, 1, 1, 3072) 0 ['stack_5_block24_se_2_conv[0][ Y \n",
- " 0]'] \n",
- " \n",
- " multiply_64 (Multiply) (None, 7, 7, 3072) 0 ['stack_5_block24_MB_dw_swish[0 Y \n",
- " ][0]', \n",
- " 'activation_129[0][0]'] \n",
- " \n",
- " stack_5_block24_MB_pw_conv (Co (None, 7, 7, 512) 1572864 ['multiply_64[0][0]'] Y \n",
- " nv2D) \n",
- " \n",
- " stack_5_block24_MB_pw_bn (Batc (None, 7, 7, 512) 2048 ['stack_5_block24_MB_pw_conv[0] Y \n",
- " hNormalization) [0]'] \n",
- " \n",
- " add_79 (Add) (None, 7, 7, 512) 0 ['add_78[0][0]', Y \n",
- " 'stack_5_block24_MB_pw_bn[0][0 \n",
- " ]'] \n",
- " \n",
- " stack_5_block25_sortcut_conv ( (None, 7, 7, 3072) 1572864 ['add_79[0][0]'] Y \n",
- " Conv2D) \n",
- " \n",
- " stack_5_block25_sortcut_bn (Ba (None, 7, 7, 3072) 12288 ['stack_5_block25_sortcut_conv[ Y \n",
- " tchNormalization) 0][0]'] \n",
- " \n",
- " stack_5_block25_sortcut_swish (None, 7, 7, 3072) 0 ['stack_5_block25_sortcut_bn[0] Y \n",
- " (Activation) [0]'] \n",
- " \n",
- " stack_5_block25_MB_dw_ (Depthw (None, 7, 7, 3072) 27648 ['stack_5_block25_sortcut_swish Y \n",
- " iseConv2D) [0][0]'] \n",
- " \n",
- " stack_5_block25_MB_dw_bn (Batc (None, 7, 7, 3072) 12288 ['stack_5_block25_MB_dw_[0][0]' Y \n",
- " hNormalization) ] \n",
- " \n",
- " stack_5_block25_MB_dw_swish (A (None, 7, 7, 3072) 0 ['stack_5_block25_MB_dw_bn[0][0 Y \n",
- " ctivation) ]'] \n",
- " \n",
- " tf.math.reduce_mean_65 (TFOpLa (None, 1, 1, 3072) 0 ['stack_5_block25_MB_dw_swish[0 Y \n",
- " mbda) ][0]'] \n",
- " \n",
- " stack_5_block25_se_1_conv (Con (None, 1, 1, 128) 393344 ['tf.math.reduce_mean_65[0][0]' Y \n",
- " v2D) ] \n",
- " \n",
- " activation_130 (Activation) (None, 1, 1, 128) 0 ['stack_5_block25_se_1_conv[0][ Y \n",
- " 0]'] \n",
- " \n",
- " stack_5_block25_se_2_conv (Con (None, 1, 1, 3072) 396288 ['activation_130[0][0]'] Y \n",
- " v2D) \n",
- " \n",
- " activation_131 (Activation) (None, 1, 1, 3072) 0 ['stack_5_block25_se_2_conv[0][ Y \n",
- " 0]'] \n",
- " \n",
- " multiply_65 (Multiply) (None, 7, 7, 3072) 0 ['stack_5_block25_MB_dw_swish[0 Y \n",
- " ][0]', \n",
- " 'activation_131[0][0]'] \n",
- " \n",
- " stack_5_block25_MB_pw_conv (Co (None, 7, 7, 512) 1572864 ['multiply_65[0][0]'] Y \n",
- " nv2D) \n",
- " \n",
- " stack_5_block25_MB_pw_bn (Batc (None, 7, 7, 512) 2048 ['stack_5_block25_MB_pw_conv[0] Y \n",
- " hNormalization) [0]'] \n",
- " \n",
- " add_80 (Add) (None, 7, 7, 512) 0 ['add_79[0][0]', Y \n",
- " 'stack_5_block25_MB_pw_bn[0][0 \n",
- " ]'] \n",
- " \n",
- " stack_5_block26_sortcut_conv ( (None, 7, 7, 3072) 1572864 ['add_80[0][0]'] Y \n",
- " Conv2D) \n",
- " \n",
- " stack_5_block26_sortcut_bn (Ba (None, 7, 7, 3072) 12288 ['stack_5_block26_sortcut_conv[ Y \n",
- " tchNormalization) 0][0]'] \n",
- " \n",
- " stack_5_block26_sortcut_swish (None, 7, 7, 3072) 0 ['stack_5_block26_sortcut_bn[0] Y \n",
- " (Activation) [0]'] \n",
- " \n",
- " stack_5_block26_MB_dw_ (Depthw (None, 7, 7, 3072) 27648 ['stack_5_block26_sortcut_swish Y \n",
- " iseConv2D) [0][0]'] \n",
- " \n",
- " stack_5_block26_MB_dw_bn (Batc (None, 7, 7, 3072) 12288 ['stack_5_block26_MB_dw_[0][0]' Y \n",
- " hNormalization) ] \n",
- " \n",
- " stack_5_block26_MB_dw_swish (A (None, 7, 7, 3072) 0 ['stack_5_block26_MB_dw_bn[0][0 Y \n",
- " ctivation) ]'] \n",
- " \n",
- " tf.math.reduce_mean_66 (TFOpLa (None, 1, 1, 3072) 0 ['stack_5_block26_MB_dw_swish[0 Y \n",
- " mbda) ][0]'] \n",
- " \n",
- " stack_5_block26_se_1_conv (Con (None, 1, 1, 128) 393344 ['tf.math.reduce_mean_66[0][0]' Y \n",
- " v2D) ] \n",
- " \n",
- " activation_132 (Activation) (None, 1, 1, 128) 0 ['stack_5_block26_se_1_conv[0][ Y \n",
- " 0]'] \n",
- " \n",
- " stack_5_block26_se_2_conv (Con (None, 1, 1, 3072) 396288 ['activation_132[0][0]'] Y \n",
- " v2D) \n",
- " \n",
- " activation_133 (Activation) (None, 1, 1, 3072) 0 ['stack_5_block26_se_2_conv[0][ Y \n",
- " 0]'] \n",
- " \n",
- " multiply_66 (Multiply) (None, 7, 7, 3072) 0 ['stack_5_block26_MB_dw_swish[0 Y \n",
- " ][0]', \n",
- " 'activation_133[0][0]'] \n",
- " \n",
- " stack_5_block26_MB_pw_conv (Co (None, 7, 7, 512) 1572864 ['multiply_66[0][0]'] Y \n",
- " nv2D) \n",
- " \n",
- " stack_5_block26_MB_pw_bn (Batc (None, 7, 7, 512) 2048 ['stack_5_block26_MB_pw_conv[0] Y \n",
- " hNormalization) [0]'] \n",
- " \n",
- " add_81 (Add) (None, 7, 7, 512) 0 ['add_80[0][0]', Y \n",
- " 'stack_5_block26_MB_pw_bn[0][0 \n",
- " ]'] \n",
- " \n",
- " stack_5_block27_sortcut_conv ( (None, 7, 7, 3072) 1572864 ['add_81[0][0]'] Y \n",
- " Conv2D) \n",
- " \n",
- " stack_5_block27_sortcut_bn (Ba (None, 7, 7, 3072) 12288 ['stack_5_block27_sortcut_conv[ Y \n",
- " tchNormalization) 0][0]'] \n",
- " \n",
- " stack_5_block27_sortcut_swish (None, 7, 7, 3072) 0 ['stack_5_block27_sortcut_bn[0] Y \n",
- " (Activation) [0]'] \n",
- " \n",
- " stack_5_block27_MB_dw_ (Depthw (None, 7, 7, 3072) 27648 ['stack_5_block27_sortcut_swish Y \n",
- " iseConv2D) [0][0]'] \n",
- " \n",
- " stack_5_block27_MB_dw_bn (Batc (None, 7, 7, 3072) 12288 ['stack_5_block27_MB_dw_[0][0]' Y \n",
- " hNormalization) ] \n",
- " \n",
- " stack_5_block27_MB_dw_swish (A (None, 7, 7, 3072) 0 ['stack_5_block27_MB_dw_bn[0][0 Y \n",
- " ctivation) ]'] \n",
- " \n",
- " tf.math.reduce_mean_67 (TFOpLa (None, 1, 1, 3072) 0 ['stack_5_block27_MB_dw_swish[0 Y \n",
- " mbda) ][0]'] \n",
- " \n",
- " stack_5_block27_se_1_conv (Con (None, 1, 1, 128) 393344 ['tf.math.reduce_mean_67[0][0]' Y \n",
- " v2D) ] \n",
- " \n",
- " activation_134 (Activation) (None, 1, 1, 128) 0 ['stack_5_block27_se_1_conv[0][ Y \n",
- " 0]'] \n",
- " \n",
- " stack_5_block27_se_2_conv (Con (None, 1, 1, 3072) 396288 ['activation_134[0][0]'] Y \n",
- " v2D) \n",
- " \n",
- " activation_135 (Activation) (None, 1, 1, 3072) 0 ['stack_5_block27_se_2_conv[0][ Y \n",
- " 0]'] \n",
- " \n",
- " multiply_67 (Multiply) (None, 7, 7, 3072) 0 ['stack_5_block27_MB_dw_swish[0 Y \n",
- " ][0]', \n",
- " 'activation_135[0][0]'] \n",
- " \n",
- " stack_5_block27_MB_pw_conv (Co (None, 7, 7, 512) 1572864 ['multiply_67[0][0]'] Y \n",
- " nv2D) \n",
- " \n",
- " stack_5_block27_MB_pw_bn (Batc (None, 7, 7, 512) 2048 ['stack_5_block27_MB_pw_conv[0] Y \n",
- " hNormalization) [0]'] \n",
- " \n",
- " add_82 (Add) (None, 7, 7, 512) 0 ['add_81[0][0]', Y \n",
- " 'stack_5_block27_MB_pw_bn[0][0 \n",
- " ]'] \n",
- " \n",
- " stack_5_block28_sortcut_conv ( (None, 7, 7, 3072) 1572864 ['add_82[0][0]'] Y \n",
- " Conv2D) \n",
- " \n",
- " stack_5_block28_sortcut_bn (Ba (None, 7, 7, 3072) 12288 ['stack_5_block28_sortcut_conv[ Y \n",
- " tchNormalization) 0][0]'] \n",
- " \n",
- " stack_5_block28_sortcut_swish (None, 7, 7, 3072) 0 ['stack_5_block28_sortcut_bn[0] Y \n",
- " (Activation) [0]'] \n",
- " \n",
- " stack_5_block28_MB_dw_ (Depthw (None, 7, 7, 3072) 27648 ['stack_5_block28_sortcut_swish Y \n",
- " iseConv2D) [0][0]'] \n",
- " \n",
- " stack_5_block28_MB_dw_bn (Batc (None, 7, 7, 3072) 12288 ['stack_5_block28_MB_dw_[0][0]' Y \n",
- " hNormalization) ] \n",
- " \n",
- " stack_5_block28_MB_dw_swish (A (None, 7, 7, 3072) 0 ['stack_5_block28_MB_dw_bn[0][0 Y \n",
- " ctivation) ]'] \n",
- " \n",
- " tf.math.reduce_mean_68 (TFOpLa (None, 1, 1, 3072) 0 ['stack_5_block28_MB_dw_swish[0 Y \n",
- " mbda) ][0]'] \n",
- " \n",
- " stack_5_block28_se_1_conv (Con (None, 1, 1, 128) 393344 ['tf.math.reduce_mean_68[0][0]' Y \n",
- " v2D) ] \n",
- " \n",
- " activation_136 (Activation) (None, 1, 1, 128) 0 ['stack_5_block28_se_1_conv[0][ Y \n",
- " 0]'] \n",
- " \n",
- " stack_5_block28_se_2_conv (Con (None, 1, 1, 3072) 396288 ['activation_136[0][0]'] Y \n",
- " v2D) \n",
- " \n",
- " activation_137 (Activation) (None, 1, 1, 3072) 0 ['stack_5_block28_se_2_conv[0][ Y \n",
- " 0]'] \n",
- " \n",
- " multiply_68 (Multiply) (None, 7, 7, 3072) 0 ['stack_5_block28_MB_dw_swish[0 Y \n",
- " ][0]', \n",
- " 'activation_137[0][0]'] \n",
- " \n",
- " stack_5_block28_MB_pw_conv (Co (None, 7, 7, 512) 1572864 ['multiply_68[0][0]'] Y \n",
- " nv2D) \n",
- " \n",
- " stack_5_block28_MB_pw_bn (Batc (None, 7, 7, 512) 2048 ['stack_5_block28_MB_pw_conv[0] Y \n",
- " hNormalization) [0]'] \n",
- " \n",
- " add_83 (Add) (None, 7, 7, 512) 0 ['add_82[0][0]', Y \n",
- " 'stack_5_block28_MB_pw_bn[0][0 \n",
- " ]'] \n",
- " \n",
- " stack_5_block29_sortcut_conv ( (None, 7, 7, 3072) 1572864 ['add_83[0][0]'] Y \n",
- " Conv2D) \n",
- " \n",
- " stack_5_block29_sortcut_bn (Ba (None, 7, 7, 3072) 12288 ['stack_5_block29_sortcut_conv[ Y \n",
- " tchNormalization) 0][0]'] \n",
- " \n",
- " stack_5_block29_sortcut_swish (None, 7, 7, 3072) 0 ['stack_5_block29_sortcut_bn[0] Y \n",
- " (Activation) [0]'] \n",
- " \n",
- " stack_5_block29_MB_dw_ (Depthw (None, 7, 7, 3072) 27648 ['stack_5_block29_sortcut_swish Y \n",
- " iseConv2D) [0][0]'] \n",
- " \n",
- " stack_5_block29_MB_dw_bn (Batc (None, 7, 7, 3072) 12288 ['stack_5_block29_MB_dw_[0][0]' Y \n",
- " hNormalization) ] \n",
- " \n",
- " stack_5_block29_MB_dw_swish (A (None, 7, 7, 3072) 0 ['stack_5_block29_MB_dw_bn[0][0 Y \n",
- " ctivation) ]'] \n",
- " \n",
- " tf.math.reduce_mean_69 (TFOpLa (None, 1, 1, 3072) 0 ['stack_5_block29_MB_dw_swish[0 Y \n",
- " mbda) ][0]'] \n",
- " \n",
- " stack_5_block29_se_1_conv (Con (None, 1, 1, 128) 393344 ['tf.math.reduce_mean_69[0][0]' Y \n",
- " v2D) ] \n",
- " \n",
- " activation_138 (Activation) (None, 1, 1, 128) 0 ['stack_5_block29_se_1_conv[0][ Y \n",
- " 0]'] \n",
- " \n",
- " stack_5_block29_se_2_conv (Con (None, 1, 1, 3072) 396288 ['activation_138[0][0]'] Y \n",
- " v2D) \n",
- " \n",
- " activation_139 (Activation) (None, 1, 1, 3072) 0 ['stack_5_block29_se_2_conv[0][ Y \n",
- " 0]'] \n",
- " \n",
- " multiply_69 (Multiply) (None, 7, 7, 3072) 0 ['stack_5_block29_MB_dw_swish[0 Y \n",
- " ][0]', \n",
- " 'activation_139[0][0]'] \n",
- " \n",
- " stack_5_block29_MB_pw_conv (Co (None, 7, 7, 512) 1572864 ['multiply_69[0][0]'] Y \n",
- " nv2D) \n",
- " \n",
- " stack_5_block29_MB_pw_bn (Batc (None, 7, 7, 512) 2048 ['stack_5_block29_MB_pw_conv[0] Y \n",
- " hNormalization) [0]'] \n",
- " \n",
- " add_84 (Add) (None, 7, 7, 512) 0 ['add_83[0][0]', Y \n",
- " 'stack_5_block29_MB_pw_bn[0][0 \n",
- " ]'] \n",
- " \n",
- " stack_5_block30_sortcut_conv ( (None, 7, 7, 3072) 1572864 ['add_84[0][0]'] Y \n",
- " Conv2D) \n",
- " \n",
- " stack_5_block30_sortcut_bn (Ba (None, 7, 7, 3072) 12288 ['stack_5_block30_sortcut_conv[ Y \n",
- " tchNormalization) 0][0]'] \n",
- " \n",
- " stack_5_block30_sortcut_swish (None, 7, 7, 3072) 0 ['stack_5_block30_sortcut_bn[0] Y \n",
- " (Activation) [0]'] \n",
- " \n",
- " stack_5_block30_MB_dw_ (Depthw (None, 7, 7, 3072) 27648 ['stack_5_block30_sortcut_swish Y \n",
- " iseConv2D) [0][0]'] \n",
- " \n",
- " stack_5_block30_MB_dw_bn (Batc (None, 7, 7, 3072) 12288 ['stack_5_block30_MB_dw_[0][0]' Y \n",
- " hNormalization) ] \n",
- " \n",
- " stack_5_block30_MB_dw_swish (A (None, 7, 7, 3072) 0 ['stack_5_block30_MB_dw_bn[0][0 Y \n",
- " ctivation) ]'] \n",
- " \n",
- " tf.math.reduce_mean_70 (TFOpLa (None, 1, 1, 3072) 0 ['stack_5_block30_MB_dw_swish[0 Y \n",
- " mbda) ][0]'] \n",
- " \n",
- " stack_5_block30_se_1_conv (Con (None, 1, 1, 128) 393344 ['tf.math.reduce_mean_70[0][0]' Y \n",
- " v2D) ] \n",
- " \n",
- " activation_140 (Activation) (None, 1, 1, 128) 0 ['stack_5_block30_se_1_conv[0][ Y \n",
- " 0]'] \n",
- " \n",
- " stack_5_block30_se_2_conv (Con (None, 1, 1, 3072) 396288 ['activation_140[0][0]'] Y \n",
- " v2D) \n",
- " \n",
- " activation_141 (Activation) (None, 1, 1, 3072) 0 ['stack_5_block30_se_2_conv[0][ Y \n",
- " 0]'] \n",
- " \n",
- " multiply_70 (Multiply) (None, 7, 7, 3072) 0 ['stack_5_block30_MB_dw_swish[0 Y \n",
- " ][0]', \n",
- " 'activation_141[0][0]'] \n",
- " \n",
- " stack_5_block30_MB_pw_conv (Co (None, 7, 7, 512) 1572864 ['multiply_70[0][0]'] Y \n",
- " nv2D) \n",
- " \n",
- " stack_5_block30_MB_pw_bn (Batc (None, 7, 7, 512) 2048 ['stack_5_block30_MB_pw_conv[0] Y \n",
- " hNormalization) [0]'] \n",
- " \n",
- " add_85 (Add) (None, 7, 7, 512) 0 ['add_84[0][0]', Y \n",
- " 'stack_5_block30_MB_pw_bn[0][0 \n",
- " ]'] \n",
- " \n",
- " stack_5_block31_sortcut_conv ( (None, 7, 7, 3072) 1572864 ['add_85[0][0]'] Y \n",
- " Conv2D) \n",
- " \n",
- " stack_5_block31_sortcut_bn (Ba (None, 7, 7, 3072) 12288 ['stack_5_block31_sortcut_conv[ Y \n",
- " tchNormalization) 0][0]'] \n",
- " \n",
- " stack_5_block31_sortcut_swish (None, 7, 7, 3072) 0 ['stack_5_block31_sortcut_bn[0] Y \n",
- " (Activation) [0]'] \n",
- " \n",
- " stack_5_block31_MB_dw_ (Depthw (None, 7, 7, 3072) 27648 ['stack_5_block31_sortcut_swish Y \n",
- " iseConv2D) [0][0]'] \n",
- " \n",
- " stack_5_block31_MB_dw_bn (Batc (None, 7, 7, 3072) 12288 ['stack_5_block31_MB_dw_[0][0]' Y \n",
- " hNormalization) ] \n",
- " \n",
- " stack_5_block31_MB_dw_swish (A (None, 7, 7, 3072) 0 ['stack_5_block31_MB_dw_bn[0][0 Y \n",
- " ctivation) ]'] \n",
- " \n",
- " tf.math.reduce_mean_71 (TFOpLa (None, 1, 1, 3072) 0 ['stack_5_block31_MB_dw_swish[0 Y \n",
- " mbda) ][0]'] \n",
- " \n",
- " stack_5_block31_se_1_conv (Con (None, 1, 1, 128) 393344 ['tf.math.reduce_mean_71[0][0]' Y \n",
- " v2D) ] \n",
- " \n",
- " activation_142 (Activation) (None, 1, 1, 128) 0 ['stack_5_block31_se_1_conv[0][ Y \n",
- " 0]'] \n",
- " \n",
- " stack_5_block31_se_2_conv (Con (None, 1, 1, 3072) 396288 ['activation_142[0][0]'] Y \n",
- " v2D) \n",
- " \n",
- " activation_143 (Activation) (None, 1, 1, 3072) 0 ['stack_5_block31_se_2_conv[0][ Y \n",
- " 0]'] \n",
- " \n",
- " multiply_71 (Multiply) (None, 7, 7, 3072) 0 ['stack_5_block31_MB_dw_swish[0 Y \n",
- " ][0]', \n",
- " 'activation_143[0][0]'] \n",
- " \n",
- " stack_5_block31_MB_pw_conv (Co (None, 7, 7, 512) 1572864 ['multiply_71[0][0]'] Y \n",
- " nv2D) \n",
- " \n",
- " stack_5_block31_MB_pw_bn (Batc (None, 7, 7, 512) 2048 ['stack_5_block31_MB_pw_conv[0] Y \n",
- " hNormalization) [0]'] \n",
- " \n",
- " add_86 (Add) (None, 7, 7, 512) 0 ['add_85[0][0]', Y \n",
- " 'stack_5_block31_MB_pw_bn[0][0 \n",
- " ]'] \n",
- " \n",
- " stack_6_block0_sortcut_conv (C (None, 7, 7, 3072) 1572864 ['add_86[0][0]'] Y \n",
- " onv2D) \n",
- " \n",
- " stack_6_block0_sortcut_bn (Bat (None, 7, 7, 3072) 12288 ['stack_6_block0_sortcut_conv[0 Y \n",
- " chNormalization) ][0]'] \n",
- " \n",
- " stack_6_block0_sortcut_swish ( (None, 7, 7, 3072) 0 ['stack_6_block0_sortcut_bn[0][ Y \n",
- " Activation) 0]'] \n",
- " \n",
- " stack_6_block0_MB_dw_ (Depthwi (None, 7, 7, 3072) 27648 ['stack_6_block0_sortcut_swish[ Y \n",
- " seConv2D) 0][0]'] \n",
- " \n",
- " stack_6_block0_MB_dw_bn (Batch (None, 7, 7, 3072) 12288 ['stack_6_block0_MB_dw_[0][0]'] Y \n",
- " Normalization) \n",
- " \n",
- " stack_6_block0_MB_dw_swish (Ac (None, 7, 7, 3072) 0 ['stack_6_block0_MB_dw_bn[0][0] Y \n",
- " tivation) '] \n",
- " \n",
- " tf.math.reduce_mean_72 (TFOpLa (None, 1, 1, 3072) 0 ['stack_6_block0_MB_dw_swish[0] Y \n",
- " mbda) [0]'] \n",
- " \n",
- " stack_6_block0_se_1_conv (Conv (None, 1, 1, 128) 393344 ['tf.math.reduce_mean_72[0][0]' Y \n",
- " 2D) ] \n",
- " \n",
- " activation_144 (Activation) (None, 1, 1, 128) 0 ['stack_6_block0_se_1_conv[0][0 Y \n",
- " ]'] \n",
- " \n",
- " stack_6_block0_se_2_conv (Conv (None, 1, 1, 3072) 396288 ['activation_144[0][0]'] Y \n",
- " 2D) \n",
- " \n",
- " activation_145 (Activation) (None, 1, 1, 3072) 0 ['stack_6_block0_se_2_conv[0][0 Y \n",
- " ]'] \n",
- " \n",
- " multiply_72 (Multiply) (None, 7, 7, 3072) 0 ['stack_6_block0_MB_dw_swish[0] Y \n",
- " [0]', \n",
- " 'activation_145[0][0]'] \n",
- " \n",
- " stack_6_block0_MB_pw_conv (Con (None, 7, 7, 640) 1966080 ['multiply_72[0][0]'] Y \n",
- " v2D) \n",
- " \n",
- " stack_6_block0_MB_pw_bn (Batch (None, 7, 7, 640) 2560 ['stack_6_block0_MB_pw_conv[0][ Y \n",
- " Normalization) 0]'] \n",
- " \n",
- " stack_6_block1_sortcut_conv (C (None, 7, 7, 3840) 2457600 ['stack_6_block0_MB_pw_bn[0][0] Y \n",
- " onv2D) '] \n",
- " \n",
- " stack_6_block1_sortcut_bn (Bat (None, 7, 7, 3840) 15360 ['stack_6_block1_sortcut_conv[0 Y \n",
- " chNormalization) ][0]'] \n",
- " \n",
- " stack_6_block1_sortcut_swish ( (None, 7, 7, 3840) 0 ['stack_6_block1_sortcut_bn[0][ Y \n",
- " Activation) 0]'] \n",
- " \n",
- " stack_6_block1_MB_dw_ (Depthwi (None, 7, 7, 3840) 34560 ['stack_6_block1_sortcut_swish[ Y \n",
- " seConv2D) 0][0]'] \n",
- " \n",
- " stack_6_block1_MB_dw_bn (Batch (None, 7, 7, 3840) 15360 ['stack_6_block1_MB_dw_[0][0]'] Y \n",
- " Normalization) \n",
- " \n",
- " stack_6_block1_MB_dw_swish (Ac (None, 7, 7, 3840) 0 ['stack_6_block1_MB_dw_bn[0][0] Y \n",
- " tivation) '] \n",
- " \n",
- " tf.math.reduce_mean_73 (TFOpLa (None, 1, 1, 3840) 0 ['stack_6_block1_MB_dw_swish[0] Y \n",
- " mbda) [0]'] \n",
- " \n",
- " stack_6_block1_se_1_conv (Conv (None, 1, 1, 160) 614560 ['tf.math.reduce_mean_73[0][0]' Y \n",
- " 2D) ] \n",
- " \n",
- " activation_146 (Activation) (None, 1, 1, 160) 0 ['stack_6_block1_se_1_conv[0][0 Y \n",
- " ]'] \n",
- " \n",
- " stack_6_block1_se_2_conv (Conv (None, 1, 1, 3840) 618240 ['activation_146[0][0]'] Y \n",
- " 2D) \n",
- " \n",
- " activation_147 (Activation) (None, 1, 1, 3840) 0 ['stack_6_block1_se_2_conv[0][0 Y \n",
- " ]'] \n",
- " \n",
- " multiply_73 (Multiply) (None, 7, 7, 3840) 0 ['stack_6_block1_MB_dw_swish[0] Y \n",
- " [0]', \n",
- " 'activation_147[0][0]'] \n",
- " \n",
- " stack_6_block1_MB_pw_conv (Con (None, 7, 7, 640) 2457600 ['multiply_73[0][0]'] Y \n",
- " v2D) \n",
- " \n",
- " stack_6_block1_MB_pw_bn (Batch (None, 7, 7, 640) 2560 ['stack_6_block1_MB_pw_conv[0][ Y \n",
- " Normalization) 0]'] \n",
- " \n",
- " add_87 (Add) (None, 7, 7, 640) 0 ['stack_6_block0_MB_pw_bn[0][0] Y \n",
- " ', \n",
- " 'stack_6_block1_MB_pw_bn[0][0] \n",
- " '] \n",
- " \n",
- " stack_6_block2_sortcut_conv (C (None, 7, 7, 3840) 2457600 ['add_87[0][0]'] Y \n",
- " onv2D) \n",
- " \n",
- " stack_6_block2_sortcut_bn (Bat (None, 7, 7, 3840) 15360 ['stack_6_block2_sortcut_conv[0 Y \n",
- " chNormalization) ][0]'] \n",
- " \n",
- " stack_6_block2_sortcut_swish ( (None, 7, 7, 3840) 0 ['stack_6_block2_sortcut_bn[0][ Y \n",
- " Activation) 0]'] \n",
- " \n",
- " stack_6_block2_MB_dw_ (Depthwi (None, 7, 7, 3840) 34560 ['stack_6_block2_sortcut_swish[ Y \n",
- " seConv2D) 0][0]'] \n",
- " \n",
- " stack_6_block2_MB_dw_bn (Batch (None, 7, 7, 3840) 15360 ['stack_6_block2_MB_dw_[0][0]'] Y \n",
- " Normalization) \n",
- " \n",
- " stack_6_block2_MB_dw_swish (Ac (None, 7, 7, 3840) 0 ['stack_6_block2_MB_dw_bn[0][0] Y \n",
- " tivation) '] \n",
- " \n",
- " tf.math.reduce_mean_74 (TFOpLa (None, 1, 1, 3840) 0 ['stack_6_block2_MB_dw_swish[0] Y \n",
- " mbda) [0]'] \n",
- " \n",
- " stack_6_block2_se_1_conv (Conv (None, 1, 1, 160) 614560 ['tf.math.reduce_mean_74[0][0]' Y \n",
- " 2D) ] \n",
- " \n",
- " activation_148 (Activation) (None, 1, 1, 160) 0 ['stack_6_block2_se_1_conv[0][0 Y \n",
- " ]'] \n",
- " \n",
- " stack_6_block2_se_2_conv (Conv (None, 1, 1, 3840) 618240 ['activation_148[0][0]'] Y \n",
- " 2D) \n",
- " \n",
- " activation_149 (Activation) (None, 1, 1, 3840) 0 ['stack_6_block2_se_2_conv[0][0 Y \n",
- " ]'] \n",
- " \n",
- " multiply_74 (Multiply) (None, 7, 7, 3840) 0 ['stack_6_block2_MB_dw_swish[0] Y \n",
- " [0]', \n",
- " 'activation_149[0][0]'] \n",
- " \n",
- " stack_6_block2_MB_pw_conv (Con (None, 7, 7, 640) 2457600 ['multiply_74[0][0]'] Y \n",
- " v2D) \n",
- " \n",
- " stack_6_block2_MB_pw_bn (Batch (None, 7, 7, 640) 2560 ['stack_6_block2_MB_pw_conv[0][ Y \n",
- " Normalization) 0]'] \n",
- " \n",
- " add_88 (Add) (None, 7, 7, 640) 0 ['add_87[0][0]', Y \n",
- " 'stack_6_block2_MB_pw_bn[0][0] \n",
- " '] \n",
- " \n",
- " stack_6_block3_sortcut_conv (C (None, 7, 7, 3840) 2457600 ['add_88[0][0]'] Y \n",
- " onv2D) \n",
- " \n",
- " stack_6_block3_sortcut_bn (Bat (None, 7, 7, 3840) 15360 ['stack_6_block3_sortcut_conv[0 Y \n",
- " chNormalization) ][0]'] \n",
- " \n",
- " stack_6_block3_sortcut_swish ( (None, 7, 7, 3840) 0 ['stack_6_block3_sortcut_bn[0][ Y \n",
- " Activation) 0]'] \n",
- " \n",
- " stack_6_block3_MB_dw_ (Depthwi (None, 7, 7, 3840) 34560 ['stack_6_block3_sortcut_swish[ Y \n",
- " seConv2D) 0][0]'] \n",
- " \n",
- " stack_6_block3_MB_dw_bn (Batch (None, 7, 7, 3840) 15360 ['stack_6_block3_MB_dw_[0][0]'] Y \n",
- " Normalization) \n",
- " \n",
- " stack_6_block3_MB_dw_swish (Ac (None, 7, 7, 3840) 0 ['stack_6_block3_MB_dw_bn[0][0] Y \n",
- " tivation) '] \n",
- " \n",
- " tf.math.reduce_mean_75 (TFOpLa (None, 1, 1, 3840) 0 ['stack_6_block3_MB_dw_swish[0] Y \n",
- " mbda) [0]'] \n",
- " \n",
- " stack_6_block3_se_1_conv (Conv (None, 1, 1, 160) 614560 ['tf.math.reduce_mean_75[0][0]' Y \n",
- " 2D) ] \n",
- " \n",
- " activation_150 (Activation) (None, 1, 1, 160) 0 ['stack_6_block3_se_1_conv[0][0 Y \n",
- " ]'] \n",
- " \n",
- " stack_6_block3_se_2_conv (Conv (None, 1, 1, 3840) 618240 ['activation_150[0][0]'] Y \n",
- " 2D) \n",
- " \n",
- " activation_151 (Activation) (None, 1, 1, 3840) 0 ['stack_6_block3_se_2_conv[0][0 Y \n",
- " ]'] \n",
- " \n",
- " multiply_75 (Multiply) (None, 7, 7, 3840) 0 ['stack_6_block3_MB_dw_swish[0] Y \n",
- " [0]', \n",
- " 'activation_151[0][0]'] \n",
- " \n",
- " stack_6_block3_MB_pw_conv (Con (None, 7, 7, 640) 2457600 ['multiply_75[0][0]'] Y \n",
- " v2D) \n",
- " \n",
- " stack_6_block3_MB_pw_bn (Batch (None, 7, 7, 640) 2560 ['stack_6_block3_MB_pw_conv[0][ Y \n",
- " Normalization) 0]'] \n",
- " \n",
- " add_89 (Add) (None, 7, 7, 640) 0 ['add_88[0][0]', Y \n",
- " 'stack_6_block3_MB_pw_bn[0][0] \n",
- " '] \n",
- " \n",
- " stack_6_block4_sortcut_conv (C (None, 7, 7, 3840) 2457600 ['add_89[0][0]'] Y \n",
- " onv2D) \n",
- " \n",
- " stack_6_block4_sortcut_bn (Bat (None, 7, 7, 3840) 15360 ['stack_6_block4_sortcut_conv[0 Y \n",
- " chNormalization) ][0]'] \n",
- " \n",
- " stack_6_block4_sortcut_swish ( (None, 7, 7, 3840) 0 ['stack_6_block4_sortcut_bn[0][ Y \n",
- " Activation) 0]'] \n",
- " \n",
- " stack_6_block4_MB_dw_ (Depthwi (None, 7, 7, 3840) 34560 ['stack_6_block4_sortcut_swish[ Y \n",
- " seConv2D) 0][0]'] \n",
- " \n",
- " stack_6_block4_MB_dw_bn (Batch (None, 7, 7, 3840) 15360 ['stack_6_block4_MB_dw_[0][0]'] Y \n",
- " Normalization) \n",
- " \n",
- " stack_6_block4_MB_dw_swish (Ac (None, 7, 7, 3840) 0 ['stack_6_block4_MB_dw_bn[0][0] Y \n",
- " tivation) '] \n",
- " \n",
- " tf.math.reduce_mean_76 (TFOpLa (None, 1, 1, 3840) 0 ['stack_6_block4_MB_dw_swish[0] Y \n",
- " mbda) [0]'] \n",
- " \n",
- " stack_6_block4_se_1_conv (Conv (None, 1, 1, 160) 614560 ['tf.math.reduce_mean_76[0][0]' Y \n",
- " 2D) ] \n",
- " \n",
- " activation_152 (Activation) (None, 1, 1, 160) 0 ['stack_6_block4_se_1_conv[0][0 Y \n",
- " ]'] \n",
- " \n",
- " stack_6_block4_se_2_conv (Conv (None, 1, 1, 3840) 618240 ['activation_152[0][0]'] Y \n",
- " 2D) \n",
- " \n",
- " activation_153 (Activation) (None, 1, 1, 3840) 0 ['stack_6_block4_se_2_conv[0][0 Y \n",
- " ]'] \n",
- " \n",
- " multiply_76 (Multiply) (None, 7, 7, 3840) 0 ['stack_6_block4_MB_dw_swish[0] Y \n",
- " [0]', \n",
- " 'activation_153[0][0]'] \n",
- " \n",
- " stack_6_block4_MB_pw_conv (Con (None, 7, 7, 640) 2457600 ['multiply_76[0][0]'] Y \n",
- " v2D) \n",
- " \n",
- " stack_6_block4_MB_pw_bn (Batch (None, 7, 7, 640) 2560 ['stack_6_block4_MB_pw_conv[0][ Y \n",
- " Normalization) 0]'] \n",
- " \n",
- " add_90 (Add) (None, 7, 7, 640) 0 ['add_89[0][0]', Y \n",
- " 'stack_6_block4_MB_pw_bn[0][0] \n",
- " '] \n",
- " \n",
- " stack_6_block5_sortcut_conv (C (None, 7, 7, 3840) 2457600 ['add_90[0][0]'] Y \n",
- " onv2D) \n",
- " \n",
- " stack_6_block5_sortcut_bn (Bat (None, 7, 7, 3840) 15360 ['stack_6_block5_sortcut_conv[0 Y \n",
- " chNormalization) ][0]'] \n",
- " \n",
- " stack_6_block5_sortcut_swish ( (None, 7, 7, 3840) 0 ['stack_6_block5_sortcut_bn[0][ Y \n",
- " Activation) 0]'] \n",
- " \n",
- " stack_6_block5_MB_dw_ (Depthwi (None, 7, 7, 3840) 34560 ['stack_6_block5_sortcut_swish[ Y \n",
- " seConv2D) 0][0]'] \n",
- " \n",
- " stack_6_block5_MB_dw_bn (Batch (None, 7, 7, 3840) 15360 ['stack_6_block5_MB_dw_[0][0]'] Y \n",
- " Normalization) \n",
- " \n",
- " stack_6_block5_MB_dw_swish (Ac (None, 7, 7, 3840) 0 ['stack_6_block5_MB_dw_bn[0][0] Y \n",
- " tivation) '] \n",
- " \n",
- " tf.math.reduce_mean_77 (TFOpLa (None, 1, 1, 3840) 0 ['stack_6_block5_MB_dw_swish[0] Y \n",
- " mbda) [0]'] \n",
- " \n",
- " stack_6_block5_se_1_conv (Conv (None, 1, 1, 160) 614560 ['tf.math.reduce_mean_77[0][0]' Y \n",
- " 2D) ] \n",
- " \n",
- " activation_154 (Activation) (None, 1, 1, 160) 0 ['stack_6_block5_se_1_conv[0][0 Y \n",
- " ]'] \n",
- " \n",
- " stack_6_block5_se_2_conv (Conv (None, 1, 1, 3840) 618240 ['activation_154[0][0]'] Y \n",
- " 2D) \n",
- " \n",
- " activation_155 (Activation) (None, 1, 1, 3840) 0 ['stack_6_block5_se_2_conv[0][0 Y \n",
- " ]'] \n",
- " \n",
- " multiply_77 (Multiply) (None, 7, 7, 3840) 0 ['stack_6_block5_MB_dw_swish[0] Y \n",
- " [0]', \n",
- " 'activation_155[0][0]'] \n",
- " \n",
- " stack_6_block5_MB_pw_conv (Con (None, 7, 7, 640) 2457600 ['multiply_77[0][0]'] Y \n",
- " v2D) \n",
- " \n",
- " stack_6_block5_MB_pw_bn (Batch (None, 7, 7, 640) 2560 ['stack_6_block5_MB_pw_conv[0][ Y \n",
- " Normalization) 0]'] \n",
- " \n",
- " add_91 (Add) (None, 7, 7, 640) 0 ['add_90[0][0]', Y \n",
- " 'stack_6_block5_MB_pw_bn[0][0] \n",
- " '] \n",
- " \n",
- " stack_6_block6_sortcut_conv (C (None, 7, 7, 3840) 2457600 ['add_91[0][0]'] Y \n",
- " onv2D) \n",
- " \n",
- " stack_6_block6_sortcut_bn (Bat (None, 7, 7, 3840) 15360 ['stack_6_block6_sortcut_conv[0 Y \n",
- " chNormalization) ][0]'] \n",
- " \n",
- " stack_6_block6_sortcut_swish ( (None, 7, 7, 3840) 0 ['stack_6_block6_sortcut_bn[0][ Y \n",
- " Activation) 0]'] \n",
- " \n",
- " stack_6_block6_MB_dw_ (Depthwi (None, 7, 7, 3840) 34560 ['stack_6_block6_sortcut_swish[ Y \n",
- " seConv2D) 0][0]'] \n",
- " \n",
- " stack_6_block6_MB_dw_bn (Batch (None, 7, 7, 3840) 15360 ['stack_6_block6_MB_dw_[0][0]'] Y \n",
- " Normalization) \n",
- " \n",
- " stack_6_block6_MB_dw_swish (Ac (None, 7, 7, 3840) 0 ['stack_6_block6_MB_dw_bn[0][0] Y \n",
- " tivation) '] \n",
- " \n",
- " tf.math.reduce_mean_78 (TFOpLa (None, 1, 1, 3840) 0 ['stack_6_block6_MB_dw_swish[0] Y \n",
- " mbda) [0]'] \n",
- " \n",
- " stack_6_block6_se_1_conv (Conv (None, 1, 1, 160) 614560 ['tf.math.reduce_mean_78[0][0]' Y \n",
- " 2D) ] \n",
- " \n",
- " activation_156 (Activation) (None, 1, 1, 160) 0 ['stack_6_block6_se_1_conv[0][0 Y \n",
- " ]'] \n",
- " \n",
- " stack_6_block6_se_2_conv (Conv (None, 1, 1, 3840) 618240 ['activation_156[0][0]'] Y \n",
- " 2D) \n",
- " \n",
- " activation_157 (Activation) (None, 1, 1, 3840) 0 ['stack_6_block6_se_2_conv[0][0 Y \n",
- " ]'] \n",
- " \n",
- " multiply_78 (Multiply) (None, 7, 7, 3840) 0 ['stack_6_block6_MB_dw_swish[0] Y \n",
- " [0]', \n",
- " 'activation_157[0][0]'] \n",
- " \n",
- " stack_6_block6_MB_pw_conv (Con (None, 7, 7, 640) 2457600 ['multiply_78[0][0]'] Y \n",
- " v2D) \n",
- " \n",
- " stack_6_block6_MB_pw_bn (Batch (None, 7, 7, 640) 2560 ['stack_6_block6_MB_pw_conv[0][ Y \n",
- " Normalization) 0]'] \n",
- " \n",
- " add_92 (Add) (None, 7, 7, 640) 0 ['add_91[0][0]', Y \n",
- " 'stack_6_block6_MB_pw_bn[0][0] \n",
- " '] \n",
- " \n",
- " stack_6_block7_sortcut_conv (C (None, 7, 7, 3840) 2457600 ['add_92[0][0]'] Y \n",
- " onv2D) \n",
- " \n",
- " stack_6_block7_sortcut_bn (Bat (None, 7, 7, 3840) 15360 ['stack_6_block7_sortcut_conv[0 Y \n",
- " chNormalization) ][0]'] \n",
- " \n",
- " stack_6_block7_sortcut_swish ( (None, 7, 7, 3840) 0 ['stack_6_block7_sortcut_bn[0][ Y \n",
- " Activation) 0]'] \n",
- " \n",
- " stack_6_block7_MB_dw_ (Depthwi (None, 7, 7, 3840) 34560 ['stack_6_block7_sortcut_swish[ Y \n",
- " seConv2D) 0][0]'] \n",
- " \n",
- " stack_6_block7_MB_dw_bn (Batch (None, 7, 7, 3840) 15360 ['stack_6_block7_MB_dw_[0][0]'] Y \n",
- " Normalization) \n",
- " \n",
- " stack_6_block7_MB_dw_swish (Ac (None, 7, 7, 3840) 0 ['stack_6_block7_MB_dw_bn[0][0] Y \n",
- " tivation) '] \n",
- " \n",
- " tf.math.reduce_mean_79 (TFOpLa (None, 1, 1, 3840) 0 ['stack_6_block7_MB_dw_swish[0] Y \n",
- " mbda) [0]'] \n",
- " \n",
- " stack_6_block7_se_1_conv (Conv (None, 1, 1, 160) 614560 ['tf.math.reduce_mean_79[0][0]' Y \n",
- " 2D) ] \n",
- " \n",
- " activation_158 (Activation) (None, 1, 1, 160) 0 ['stack_6_block7_se_1_conv[0][0 Y \n",
- " ]'] \n",
- " \n",
- " stack_6_block7_se_2_conv (Conv (None, 1, 1, 3840) 618240 ['activation_158[0][0]'] Y \n",
- " 2D) \n",
- " \n",
- " activation_159 (Activation) (None, 1, 1, 3840) 0 ['stack_6_block7_se_2_conv[0][0 Y \n",
- " ]'] \n",
- " \n",
- " multiply_79 (Multiply) (None, 7, 7, 3840) 0 ['stack_6_block7_MB_dw_swish[0] Y \n",
- " [0]', \n",
- " 'activation_159[0][0]'] \n",
- " \n",
- " stack_6_block7_MB_pw_conv (Con (None, 7, 7, 640) 2457600 ['multiply_79[0][0]'] Y \n",
- " v2D) \n",
- " \n",
- " stack_6_block7_MB_pw_bn (Batch (None, 7, 7, 640) 2560 ['stack_6_block7_MB_pw_conv[0][ Y \n",
- " Normalization) 0]'] \n",
- " \n",
- " add_93 (Add) (None, 7, 7, 640) 0 ['add_92[0][0]', Y \n",
- " 'stack_6_block7_MB_pw_bn[0][0] \n",
- " '] \n",
- " \n",
- " post_conv (Conv2D) (None, 7, 7, 1280) 819200 ['add_93[0][0]'] Y \n",
- " \n",
- " post_bn (BatchNormalization) (None, 7, 7, 1280) 5120 ['post_conv[0][0]'] Y \n",
- " \n",
- " post_swish (Activation) (None, 7, 7, 1280) 0 ['post_bn[0][0]'] Y \n",
- " \n",
- " avg_pool (GlobalAveragePooling (None, 1280) 0 ['post_swish[0][0]'] Y \n",
- " 2D) \n",
- " \n",
- " dropout (Dropout) (None, 1280) 0 ['avg_pool[0][0]'] Y \n",
- " \n",
- " predictions (Dense) (None, 2) 2562 ['dropout[0][0]'] Y \n",
- " \n",
- "=============================================================================================================\n",
- "Total params: 207,618,394\n",
- "Trainable params: 206,841,370\n",
- "Non-trainable params: 777,024\n",
- "_____________________________________________________________________________________________________________\n",
- "done.\n"
- ]
- }
- ],
- "source": [
- "from keras_efficientnet_v2 import EfficientNetV2XL\n",
- "\n",
- "EfficientNet_M = EfficientNetV2XL(\n",
- " input_shape=(img_res[0], img_res[1], img_res[2]),\n",
- " pretrained=\"imagenet21k-ft1k\",\n",
- " num_classes=2,\n",
- " dropout=0.4,\n",
- ")\n",
- "# define new model\n",
- "model = Model(inputs=EfficientNet_M.inputs, outputs=EfficientNet_M.outputs)\n",
- "\n",
- "# compile model\n",
- "opt = SGD(momentum=0.9) # noqa: F405\n",
- "# opt = AdaBeliefOptimizer(epsilon=1e-7, rectify=False, weight_decay=1e-2, print_change_log=False, total_steps=0, amsgrad=False)\n",
- "# opt = AdaBeliefOptimizer(epsilon=1e-7, rectify=False, weight_decay=1e-3)\n",
- "# opt = Adam()\n",
- "model.compile(optimizer=opt, loss=\"categorical_crossentropy\", metrics=[\"accuracy\"])\n",
- "\n",
- "freeze_layers = 0\n",
- "model.summary(show_trainable=True, expand_nested=True)\n",
- "print(\"done.\")"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### Rev1.5.2 (The best one)\n",
- "```\n",
- "recommended: ✅\n",
- "statuses: Ready\n",
- "Working: ✅\n",
- "Max fine tuned acc: 95.54\n",
- "Max fine tuned acc TLRev2: 97.12\n",
- "type: transfer learning>>>(EfficientNetB4::CCL)\n",
- "```"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 16,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Creating the model...\n",
- "Total layers in the base model: 467\n",
- "Freezing 0 layers in the base model...\n",
- "Percentage of the base model that is frozen: 0.00%\n",
- "Total model layers: 475\n",
- "Model: \"model\"\n",
- "_____________________________________________________________________________________________________________\n",
- " Layer (type) Output Shape Param # Connected to Trainable \n",
- "=============================================================================================================\n",
- " input_2 (InputLayer) [(None, 224, 224, 3 0 [] Y \n",
- " )] \n",
- " \n",
- " stem_conv (Conv2D) (None, 112, 112, 48 1296 ['input_2[0][0]'] Y \n",
- " ) \n",
- " \n",
- " stem_bn (BatchNormalization) (None, 112, 112, 48 192 ['stem_conv[0][0]'] Y \n",
- " ) \n",
- " \n",
- " stem_activation (Activation) (None, 112, 112, 48 0 ['stem_bn[0][0]'] Y \n",
- " ) \n",
- " \n",
- " block1a_dwconv (DepthwiseConv2 (None, 112, 112, 48 432 ['stem_activation[0][0]'] Y \n",
- " D) ) \n",
- " \n",
- " block1a_bn (BatchNormalization (None, 112, 112, 48 192 ['block1a_dwconv[0][0]'] Y \n",
- " ) ) \n",
- " \n",
- " block1a_activation (Activation (None, 112, 112, 48 0 ['block1a_bn[0][0]'] Y \n",
- " ) ) \n",
- " \n",
- " block1a_se_squeeze (GlobalAver (None, 48) 0 ['block1a_activation[0][0]'] Y \n",
- " agePooling2D) \n",
- " \n",
- " block1a_se_reshape (Reshape) (None, 1, 1, 48) 0 ['block1a_se_squeeze[0][0]'] Y \n",
- " \n",
- " block1a_se_reduce (Conv2D) (None, 1, 1, 12) 588 ['block1a_se_reshape[0][0]'] Y \n",
- " \n",
- " block1a_se_expand (Conv2D) (None, 1, 1, 48) 624 ['block1a_se_reduce[0][0]'] Y \n",
- " \n",
- " block1a_se_excite (Multiply) (None, 112, 112, 48 0 ['block1a_activation[0][0]', Y \n",
- " ) 'block1a_se_expand[0][0]'] \n",
- " \n",
- " block1a_project_conv (Conv2D) (None, 112, 112, 24 1152 ['block1a_se_excite[0][0]'] Y \n",
- " ) \n",
- " \n",
- " block1a_project_bn (BatchNorma (None, 112, 112, 24 96 ['block1a_project_conv[0][0]'] Y \n",
- " lization) ) \n",
- " \n",
- " block1b_dwconv (DepthwiseConv2 (None, 112, 112, 24 216 ['block1a_project_bn[0][0]'] Y \n",
- " D) ) \n",
- " \n",
- " block1b_bn (BatchNormalization (None, 112, 112, 24 96 ['block1b_dwconv[0][0]'] Y \n",
- " ) ) \n",
- " \n",
- " block1b_activation (Activation (None, 112, 112, 24 0 ['block1b_bn[0][0]'] Y \n",
- " ) ) \n",
- " \n",
- " block1b_se_squeeze (GlobalAver (None, 24) 0 ['block1b_activation[0][0]'] Y \n",
- " agePooling2D) \n",
- " \n",
- " block1b_se_reshape (Reshape) (None, 1, 1, 24) 0 ['block1b_se_squeeze[0][0]'] Y \n",
- " \n",
- " block1b_se_reduce (Conv2D) (None, 1, 1, 6) 150 ['block1b_se_reshape[0][0]'] Y \n",
- " \n",
- " block1b_se_expand (Conv2D) (None, 1, 1, 24) 168 ['block1b_se_reduce[0][0]'] Y \n",
- " \n",
- " block1b_se_excite (Multiply) (None, 112, 112, 24 0 ['block1b_activation[0][0]', Y \n",
- " ) 'block1b_se_expand[0][0]'] \n",
- " \n",
- " block1b_project_conv (Conv2D) (None, 112, 112, 24 576 ['block1b_se_excite[0][0]'] Y \n",
- " ) \n",
- " \n",
- " block1b_project_bn (BatchNorma (None, 112, 112, 24 96 ['block1b_project_conv[0][0]'] Y \n",
- " lization) ) \n",
- " \n",
- " block1b_drop (FixedDropout) (None, 112, 112, 24 0 ['block1b_project_bn[0][0]'] Y \n",
- " ) \n",
- " \n",
- " block1b_add (Add) (None, 112, 112, 24 0 ['block1b_drop[0][0]', Y \n",
- " ) 'block1a_project_bn[0][0]'] \n",
- " \n",
- " block2a_expand_conv (Conv2D) (None, 112, 112, 14 3456 ['block1b_add[0][0]'] Y \n",
- " 4) \n",
- " \n",
- " block2a_expand_bn (BatchNormal (None, 112, 112, 14 576 ['block2a_expand_conv[0][0]'] Y \n",
- " ization) 4) \n",
- " \n",
- " block2a_expand_activation (Act (None, 112, 112, 14 0 ['block2a_expand_bn[0][0]'] Y \n",
- " ivation) 4) \n",
- " \n",
- " block2a_dwconv (DepthwiseConv2 (None, 56, 56, 144) 1296 ['block2a_expand_activation[0][ Y \n",
- " D) 0]'] \n",
- " \n",
- " block2a_bn (BatchNormalization (None, 56, 56, 144) 576 ['block2a_dwconv[0][0]'] Y \n",
- " ) \n",
- " \n",
- " block2a_activation (Activation (None, 56, 56, 144) 0 ['block2a_bn[0][0]'] Y \n",
- " ) \n",
- " \n",
- " block2a_se_squeeze (GlobalAver (None, 144) 0 ['block2a_activation[0][0]'] Y \n",
- " agePooling2D) \n",
- " \n",
- " block2a_se_reshape (Reshape) (None, 1, 1, 144) 0 ['block2a_se_squeeze[0][0]'] Y \n",
- " \n",
- " block2a_se_reduce (Conv2D) (None, 1, 1, 6) 870 ['block2a_se_reshape[0][0]'] Y \n",
- " \n",
- " block2a_se_expand (Conv2D) (None, 1, 1, 144) 1008 ['block2a_se_reduce[0][0]'] Y \n",
- " \n",
- " block2a_se_excite (Multiply) (None, 56, 56, 144) 0 ['block2a_activation[0][0]', Y \n",
- " 'block2a_se_expand[0][0]'] \n",
- " \n",
- " block2a_project_conv (Conv2D) (None, 56, 56, 32) 4608 ['block2a_se_excite[0][0]'] Y \n",
- " \n",
- " block2a_project_bn (BatchNorma (None, 56, 56, 32) 128 ['block2a_project_conv[0][0]'] Y \n",
- " lization) \n",
- " \n",
- " block2b_expand_conv (Conv2D) (None, 56, 56, 192) 6144 ['block2a_project_bn[0][0]'] Y \n",
- " \n",
- " block2b_expand_bn (BatchNormal (None, 56, 56, 192) 768 ['block2b_expand_conv[0][0]'] Y \n",
- " ization) \n",
- " \n",
- " block2b_expand_activation (Act (None, 56, 56, 192) 0 ['block2b_expand_bn[0][0]'] Y \n",
- " ivation) \n",
- " \n",
- " block2b_dwconv (DepthwiseConv2 (None, 56, 56, 192) 1728 ['block2b_expand_activation[0][ Y \n",
- " D) 0]'] \n",
- " \n",
- " block2b_bn (BatchNormalization (None, 56, 56, 192) 768 ['block2b_dwconv[0][0]'] Y \n",
- " ) \n",
- " \n",
- " block2b_activation (Activation (None, 56, 56, 192) 0 ['block2b_bn[0][0]'] Y \n",
- " ) \n",
- " \n",
- " block2b_se_squeeze (GlobalAver (None, 192) 0 ['block2b_activation[0][0]'] Y \n",
- " agePooling2D) \n",
- " \n",
- " block2b_se_reshape (Reshape) (None, 1, 1, 192) 0 ['block2b_se_squeeze[0][0]'] Y \n",
- " \n",
- " block2b_se_reduce (Conv2D) (None, 1, 1, 8) 1544 ['block2b_se_reshape[0][0]'] Y \n",
- " \n",
- " block2b_se_expand (Conv2D) (None, 1, 1, 192) 1728 ['block2b_se_reduce[0][0]'] Y \n",
- " \n",
- " block2b_se_excite (Multiply) (None, 56, 56, 192) 0 ['block2b_activation[0][0]', Y \n",
- " 'block2b_se_expand[0][0]'] \n",
- " \n",
- " block2b_project_conv (Conv2D) (None, 56, 56, 32) 6144 ['block2b_se_excite[0][0]'] Y \n",
- " \n",
- " block2b_project_bn (BatchNorma (None, 56, 56, 32) 128 ['block2b_project_conv[0][0]'] Y \n",
- " lization) \n",
- " \n",
- " block2b_drop (FixedDropout) (None, 56, 56, 32) 0 ['block2b_project_bn[0][0]'] Y \n",
- " \n",
- " block2b_add (Add) (None, 56, 56, 32) 0 ['block2b_drop[0][0]', Y \n",
- " 'block2a_project_bn[0][0]'] \n",
- " \n",
- " block2c_expand_conv (Conv2D) (None, 56, 56, 192) 6144 ['block2b_add[0][0]'] Y \n",
- " \n",
- " block2c_expand_bn (BatchNormal (None, 56, 56, 192) 768 ['block2c_expand_conv[0][0]'] Y \n",
- " ization) \n",
- " \n",
- " block2c_expand_activation (Act (None, 56, 56, 192) 0 ['block2c_expand_bn[0][0]'] Y \n",
- " ivation) \n",
- " \n",
- " block2c_dwconv (DepthwiseConv2 (None, 56, 56, 192) 1728 ['block2c_expand_activation[0][ Y \n",
- " D) 0]'] \n",
- " \n",
- " block2c_bn (BatchNormalization (None, 56, 56, 192) 768 ['block2c_dwconv[0][0]'] Y \n",
- " ) \n",
- " \n",
- " block2c_activation (Activation (None, 56, 56, 192) 0 ['block2c_bn[0][0]'] Y \n",
- " ) \n",
- " \n",
- " block2c_se_squeeze (GlobalAver (None, 192) 0 ['block2c_activation[0][0]'] Y \n",
- " agePooling2D) \n",
- " \n",
- " block2c_se_reshape (Reshape) (None, 1, 1, 192) 0 ['block2c_se_squeeze[0][0]'] Y \n",
- " \n",
- " block2c_se_reduce (Conv2D) (None, 1, 1, 8) 1544 ['block2c_se_reshape[0][0]'] Y \n",
- " \n",
- " block2c_se_expand (Conv2D) (None, 1, 1, 192) 1728 ['block2c_se_reduce[0][0]'] Y \n",
- " \n",
- " block2c_se_excite (Multiply) (None, 56, 56, 192) 0 ['block2c_activation[0][0]', Y \n",
- " 'block2c_se_expand[0][0]'] \n",
- " \n",
- " block2c_project_conv (Conv2D) (None, 56, 56, 32) 6144 ['block2c_se_excite[0][0]'] Y \n",
- " \n",
- " block2c_project_bn (BatchNorma (None, 56, 56, 32) 128 ['block2c_project_conv[0][0]'] Y \n",
- " lization) \n",
- " \n",
- " block2c_drop (FixedDropout) (None, 56, 56, 32) 0 ['block2c_project_bn[0][0]'] Y \n",
- " \n",
- " block2c_add (Add) (None, 56, 56, 32) 0 ['block2c_drop[0][0]', Y \n",
- " 'block2b_add[0][0]'] \n",
- " \n",
- " block2d_expand_conv (Conv2D) (None, 56, 56, 192) 6144 ['block2c_add[0][0]'] Y \n",
- " \n",
- " block2d_expand_bn (BatchNormal (None, 56, 56, 192) 768 ['block2d_expand_conv[0][0]'] Y \n",
- " ization) \n",
- " \n",
- " block2d_expand_activation (Act (None, 56, 56, 192) 0 ['block2d_expand_bn[0][0]'] Y \n",
- " ivation) \n",
- " \n",
- " block2d_dwconv (DepthwiseConv2 (None, 56, 56, 192) 1728 ['block2d_expand_activation[0][ Y \n",
- " D) 0]'] \n",
- " \n",
- " block2d_bn (BatchNormalization (None, 56, 56, 192) 768 ['block2d_dwconv[0][0]'] Y \n",
- " ) \n",
- " \n",
- " block2d_activation (Activation (None, 56, 56, 192) 0 ['block2d_bn[0][0]'] Y \n",
- " ) \n",
- " \n",
- " block2d_se_squeeze (GlobalAver (None, 192) 0 ['block2d_activation[0][0]'] Y \n",
- " agePooling2D) \n",
- " \n",
- " block2d_se_reshape (Reshape) (None, 1, 1, 192) 0 ['block2d_se_squeeze[0][0]'] Y \n",
- " \n",
- " block2d_se_reduce (Conv2D) (None, 1, 1, 8) 1544 ['block2d_se_reshape[0][0]'] Y \n",
- " \n",
- " block2d_se_expand (Conv2D) (None, 1, 1, 192) 1728 ['block2d_se_reduce[0][0]'] Y \n",
- " \n",
- " block2d_se_excite (Multiply) (None, 56, 56, 192) 0 ['block2d_activation[0][0]', Y \n",
- " 'block2d_se_expand[0][0]'] \n",
- " \n",
- " block2d_project_conv (Conv2D) (None, 56, 56, 32) 6144 ['block2d_se_excite[0][0]'] Y \n",
- " \n",
- " block2d_project_bn (BatchNorma (None, 56, 56, 32) 128 ['block2d_project_conv[0][0]'] Y \n",
- " lization) \n",
- " \n",
- " block2d_drop (FixedDropout) (None, 56, 56, 32) 0 ['block2d_project_bn[0][0]'] Y \n",
- " \n",
- " block2d_add (Add) (None, 56, 56, 32) 0 ['block2d_drop[0][0]', Y \n",
- " 'block2c_add[0][0]'] \n",
- " \n",
- " block3a_expand_conv (Conv2D) (None, 56, 56, 192) 6144 ['block2d_add[0][0]'] Y \n",
- " \n",
- " block3a_expand_bn (BatchNormal (None, 56, 56, 192) 768 ['block3a_expand_conv[0][0]'] Y \n",
- " ization) \n",
- " \n",
- " block3a_expand_activation (Act (None, 56, 56, 192) 0 ['block3a_expand_bn[0][0]'] Y \n",
- " ivation) \n",
- " \n",
- " block3a_dwconv (DepthwiseConv2 (None, 28, 28, 192) 4800 ['block3a_expand_activation[0][ Y \n",
- " D) 0]'] \n",
- " \n",
- " block3a_bn (BatchNormalization (None, 28, 28, 192) 768 ['block3a_dwconv[0][0]'] Y \n",
- " ) \n",
- " \n",
- " block3a_activation (Activation (None, 28, 28, 192) 0 ['block3a_bn[0][0]'] Y \n",
- " ) \n",
- " \n",
- " block3a_se_squeeze (GlobalAver (None, 192) 0 ['block3a_activation[0][0]'] Y \n",
- " agePooling2D) \n",
- " \n",
- " block3a_se_reshape (Reshape) (None, 1, 1, 192) 0 ['block3a_se_squeeze[0][0]'] Y \n",
- " \n",
- " block3a_se_reduce (Conv2D) (None, 1, 1, 8) 1544 ['block3a_se_reshape[0][0]'] Y \n",
- " \n",
- " block3a_se_expand (Conv2D) (None, 1, 1, 192) 1728 ['block3a_se_reduce[0][0]'] Y \n",
- " \n",
- " block3a_se_excite (Multiply) (None, 28, 28, 192) 0 ['block3a_activation[0][0]', Y \n",
- " 'block3a_se_expand[0][0]'] \n",
- " \n",
- " block3a_project_conv (Conv2D) (None, 28, 28, 56) 10752 ['block3a_se_excite[0][0]'] Y \n",
- " \n",
- " block3a_project_bn (BatchNorma (None, 28, 28, 56) 224 ['block3a_project_conv[0][0]'] Y \n",
- " lization) \n",
- " \n",
- " block3b_expand_conv (Conv2D) (None, 28, 28, 336) 18816 ['block3a_project_bn[0][0]'] Y \n",
- " \n",
- " block3b_expand_bn (BatchNormal (None, 28, 28, 336) 1344 ['block3b_expand_conv[0][0]'] Y \n",
- " ization) \n",
- " \n",
- " block3b_expand_activation (Act (None, 28, 28, 336) 0 ['block3b_expand_bn[0][0]'] Y \n",
- " ivation) \n",
- " \n",
- " block3b_dwconv (DepthwiseConv2 (None, 28, 28, 336) 8400 ['block3b_expand_activation[0][ Y \n",
- " D) 0]'] \n",
- " \n",
- " block3b_bn (BatchNormalization (None, 28, 28, 336) 1344 ['block3b_dwconv[0][0]'] Y \n",
- " ) \n",
- " \n",
- " block3b_activation (Activation (None, 28, 28, 336) 0 ['block3b_bn[0][0]'] Y \n",
- " ) \n",
- " \n",
- " block3b_se_squeeze (GlobalAver (None, 336) 0 ['block3b_activation[0][0]'] Y \n",
- " agePooling2D) \n",
- " \n",
- " block3b_se_reshape (Reshape) (None, 1, 1, 336) 0 ['block3b_se_squeeze[0][0]'] Y \n",
- " \n",
- " block3b_se_reduce (Conv2D) (None, 1, 1, 14) 4718 ['block3b_se_reshape[0][0]'] Y \n",
- " \n",
- " block3b_se_expand (Conv2D) (None, 1, 1, 336) 5040 ['block3b_se_reduce[0][0]'] Y \n",
- " \n",
- " block3b_se_excite (Multiply) (None, 28, 28, 336) 0 ['block3b_activation[0][0]', Y \n",
- " 'block3b_se_expand[0][0]'] \n",
- " \n",
- " block3b_project_conv (Conv2D) (None, 28, 28, 56) 18816 ['block3b_se_excite[0][0]'] Y \n",
- " \n",
- " block3b_project_bn (BatchNorma (None, 28, 28, 56) 224 ['block3b_project_conv[0][0]'] Y \n",
- " lization) \n",
- " \n",
- " block3b_drop (FixedDropout) (None, 28, 28, 56) 0 ['block3b_project_bn[0][0]'] Y \n",
- " \n",
- " block3b_add (Add) (None, 28, 28, 56) 0 ['block3b_drop[0][0]', Y \n",
- " 'block3a_project_bn[0][0]'] \n",
- " \n",
- " block3c_expand_conv (Conv2D) (None, 28, 28, 336) 18816 ['block3b_add[0][0]'] Y \n",
- " \n",
- " block3c_expand_bn (BatchNormal (None, 28, 28, 336) 1344 ['block3c_expand_conv[0][0]'] Y \n",
- " ization) \n",
- " \n",
- " block3c_expand_activation (Act (None, 28, 28, 336) 0 ['block3c_expand_bn[0][0]'] Y \n",
- " ivation) \n",
- " \n",
- " block3c_dwconv (DepthwiseConv2 (None, 28, 28, 336) 8400 ['block3c_expand_activation[0][ Y \n",
- " D) 0]'] \n",
- " \n",
- " block3c_bn (BatchNormalization (None, 28, 28, 336) 1344 ['block3c_dwconv[0][0]'] Y \n",
- " ) \n",
- " \n",
- " block3c_activation (Activation (None, 28, 28, 336) 0 ['block3c_bn[0][0]'] Y \n",
- " ) \n",
- " \n",
- " block3c_se_squeeze (GlobalAver (None, 336) 0 ['block3c_activation[0][0]'] Y \n",
- " agePooling2D) \n",
- " \n",
- " block3c_se_reshape (Reshape) (None, 1, 1, 336) 0 ['block3c_se_squeeze[0][0]'] Y \n",
- " \n",
- " block3c_se_reduce (Conv2D) (None, 1, 1, 14) 4718 ['block3c_se_reshape[0][0]'] Y \n",
- " \n",
- " block3c_se_expand (Conv2D) (None, 1, 1, 336) 5040 ['block3c_se_reduce[0][0]'] Y \n",
- " \n",
- " block3c_se_excite (Multiply) (None, 28, 28, 336) 0 ['block3c_activation[0][0]', Y \n",
- " 'block3c_se_expand[0][0]'] \n",
- " \n",
- " block3c_project_conv (Conv2D) (None, 28, 28, 56) 18816 ['block3c_se_excite[0][0]'] Y \n",
- " \n",
- " block3c_project_bn (BatchNorma (None, 28, 28, 56) 224 ['block3c_project_conv[0][0]'] Y \n",
- " lization) \n",
- " \n",
- " block3c_drop (FixedDropout) (None, 28, 28, 56) 0 ['block3c_project_bn[0][0]'] Y \n",
- " \n",
- " block3c_add (Add) (None, 28, 28, 56) 0 ['block3c_drop[0][0]', Y \n",
- " 'block3b_add[0][0]'] \n",
- " \n",
- " block3d_expand_conv (Conv2D) (None, 28, 28, 336) 18816 ['block3c_add[0][0]'] Y \n",
- " \n",
- " block3d_expand_bn (BatchNormal (None, 28, 28, 336) 1344 ['block3d_expand_conv[0][0]'] Y \n",
- " ization) \n",
- " \n",
- " block3d_expand_activation (Act (None, 28, 28, 336) 0 ['block3d_expand_bn[0][0]'] Y \n",
- " ivation) \n",
- " \n",
- " block3d_dwconv (DepthwiseConv2 (None, 28, 28, 336) 8400 ['block3d_expand_activation[0][ Y \n",
- " D) 0]'] \n",
- " \n",
- " block3d_bn (BatchNormalization (None, 28, 28, 336) 1344 ['block3d_dwconv[0][0]'] Y \n",
- " ) \n",
- " \n",
- " block3d_activation (Activation (None, 28, 28, 336) 0 ['block3d_bn[0][0]'] Y \n",
- " ) \n",
- " \n",
- " block3d_se_squeeze (GlobalAver (None, 336) 0 ['block3d_activation[0][0]'] Y \n",
- " agePooling2D) \n",
- " \n",
- " block3d_se_reshape (Reshape) (None, 1, 1, 336) 0 ['block3d_se_squeeze[0][0]'] Y \n",
- " \n",
- " block3d_se_reduce (Conv2D) (None, 1, 1, 14) 4718 ['block3d_se_reshape[0][0]'] Y \n",
- " \n",
- " block3d_se_expand (Conv2D) (None, 1, 1, 336) 5040 ['block3d_se_reduce[0][0]'] Y \n",
- " \n",
- " block3d_se_excite (Multiply) (None, 28, 28, 336) 0 ['block3d_activation[0][0]', Y \n",
- " 'block3d_se_expand[0][0]'] \n",
- " \n",
- " block3d_project_conv (Conv2D) (None, 28, 28, 56) 18816 ['block3d_se_excite[0][0]'] Y \n",
- " \n",
- " block3d_project_bn (BatchNorma (None, 28, 28, 56) 224 ['block3d_project_conv[0][0]'] Y \n",
- " lization) \n",
- " \n",
- " block3d_drop (FixedDropout) (None, 28, 28, 56) 0 ['block3d_project_bn[0][0]'] Y \n",
- " \n",
- " block3d_add (Add) (None, 28, 28, 56) 0 ['block3d_drop[0][0]', Y \n",
- " 'block3c_add[0][0]'] \n",
- " \n",
- " block4a_expand_conv (Conv2D) (None, 28, 28, 336) 18816 ['block3d_add[0][0]'] Y \n",
- " \n",
- " block4a_expand_bn (BatchNormal (None, 28, 28, 336) 1344 ['block4a_expand_conv[0][0]'] Y \n",
- " ization) \n",
- " \n",
- " block4a_expand_activation (Act (None, 28, 28, 336) 0 ['block4a_expand_bn[0][0]'] Y \n",
- " ivation) \n",
- " \n",
- " block4a_dwconv (DepthwiseConv2 (None, 14, 14, 336) 3024 ['block4a_expand_activation[0][ Y \n",
- " D) 0]'] \n",
- " \n",
- " block4a_bn (BatchNormalization (None, 14, 14, 336) 1344 ['block4a_dwconv[0][0]'] Y \n",
- " ) \n",
- " \n",
- " block4a_activation (Activation (None, 14, 14, 336) 0 ['block4a_bn[0][0]'] Y \n",
- " ) \n",
- " \n",
- " block4a_se_squeeze (GlobalAver (None, 336) 0 ['block4a_activation[0][0]'] Y \n",
- " agePooling2D) \n",
- " \n",
- " block4a_se_reshape (Reshape) (None, 1, 1, 336) 0 ['block4a_se_squeeze[0][0]'] Y \n",
- " \n",
- " block4a_se_reduce (Conv2D) (None, 1, 1, 14) 4718 ['block4a_se_reshape[0][0]'] Y \n",
- " \n",
- " block4a_se_expand (Conv2D) (None, 1, 1, 336) 5040 ['block4a_se_reduce[0][0]'] Y \n",
- " \n",
- " block4a_se_excite (Multiply) (None, 14, 14, 336) 0 ['block4a_activation[0][0]', Y \n",
- " 'block4a_se_expand[0][0]'] \n",
- " \n",
- " block4a_project_conv (Conv2D) (None, 14, 14, 112) 37632 ['block4a_se_excite[0][0]'] Y \n",
- " \n",
- " block4a_project_bn (BatchNorma (None, 14, 14, 112) 448 ['block4a_project_conv[0][0]'] Y \n",
- " lization) \n",
- " \n",
- " block4b_expand_conv (Conv2D) (None, 14, 14, 672) 75264 ['block4a_project_bn[0][0]'] Y \n",
- " \n",
- " block4b_expand_bn (BatchNormal (None, 14, 14, 672) 2688 ['block4b_expand_conv[0][0]'] Y \n",
- " ization) \n",
- " \n",
- " block4b_expand_activation (Act (None, 14, 14, 672) 0 ['block4b_expand_bn[0][0]'] Y \n",
- " ivation) \n",
- " \n",
- " block4b_dwconv (DepthwiseConv2 (None, 14, 14, 672) 6048 ['block4b_expand_activation[0][ Y \n",
- " D) 0]'] \n",
- " \n",
- " block4b_bn (BatchNormalization (None, 14, 14, 672) 2688 ['block4b_dwconv[0][0]'] Y \n",
- " ) \n",
- " \n",
- " block4b_activation (Activation (None, 14, 14, 672) 0 ['block4b_bn[0][0]'] Y \n",
- " ) \n",
- " \n",
- " block4b_se_squeeze (GlobalAver (None, 672) 0 ['block4b_activation[0][0]'] Y \n",
- " agePooling2D) \n",
- " \n",
- " block4b_se_reshape (Reshape) (None, 1, 1, 672) 0 ['block4b_se_squeeze[0][0]'] Y \n",
- " \n",
- " block4b_se_reduce (Conv2D) (None, 1, 1, 28) 18844 ['block4b_se_reshape[0][0]'] Y \n",
- " \n",
- " block4b_se_expand (Conv2D) (None, 1, 1, 672) 19488 ['block4b_se_reduce[0][0]'] Y \n",
- " \n",
- " block4b_se_excite (Multiply) (None, 14, 14, 672) 0 ['block4b_activation[0][0]', Y \n",
- " 'block4b_se_expand[0][0]'] \n",
- " \n",
- " block4b_project_conv (Conv2D) (None, 14, 14, 112) 75264 ['block4b_se_excite[0][0]'] Y \n",
- " \n",
- " block4b_project_bn (BatchNorma (None, 14, 14, 112) 448 ['block4b_project_conv[0][0]'] Y \n",
- " lization) \n",
- " \n",
- " block4b_drop (FixedDropout) (None, 14, 14, 112) 0 ['block4b_project_bn[0][0]'] Y \n",
- " \n",
- " block4b_add (Add) (None, 14, 14, 112) 0 ['block4b_drop[0][0]', Y \n",
- " 'block4a_project_bn[0][0]'] \n",
- " \n",
- " block4c_expand_conv (Conv2D) (None, 14, 14, 672) 75264 ['block4b_add[0][0]'] Y \n",
- " \n",
- " block4c_expand_bn (BatchNormal (None, 14, 14, 672) 2688 ['block4c_expand_conv[0][0]'] Y \n",
- " ization) \n",
- " \n",
- " block4c_expand_activation (Act (None, 14, 14, 672) 0 ['block4c_expand_bn[0][0]'] Y \n",
- " ivation) \n",
- " \n",
- " block4c_dwconv (DepthwiseConv2 (None, 14, 14, 672) 6048 ['block4c_expand_activation[0][ Y \n",
- " D) 0]'] \n",
- " \n",
- " block4c_bn (BatchNormalization (None, 14, 14, 672) 2688 ['block4c_dwconv[0][0]'] Y \n",
- " ) \n",
- " \n",
- " block4c_activation (Activation (None, 14, 14, 672) 0 ['block4c_bn[0][0]'] Y \n",
- " ) \n",
- " \n",
- " block4c_se_squeeze (GlobalAver (None, 672) 0 ['block4c_activation[0][0]'] Y \n",
- " agePooling2D) \n",
- " \n",
- " block4c_se_reshape (Reshape) (None, 1, 1, 672) 0 ['block4c_se_squeeze[0][0]'] Y \n",
- " \n",
- " block4c_se_reduce (Conv2D) (None, 1, 1, 28) 18844 ['block4c_se_reshape[0][0]'] Y \n",
- " \n",
- " block4c_se_expand (Conv2D) (None, 1, 1, 672) 19488 ['block4c_se_reduce[0][0]'] Y \n",
- " \n",
- " block4c_se_excite (Multiply) (None, 14, 14, 672) 0 ['block4c_activation[0][0]', Y \n",
- " 'block4c_se_expand[0][0]'] \n",
- " \n",
- " block4c_project_conv (Conv2D) (None, 14, 14, 112) 75264 ['block4c_se_excite[0][0]'] Y \n",
- " \n",
- " block4c_project_bn (BatchNorma (None, 14, 14, 112) 448 ['block4c_project_conv[0][0]'] Y \n",
- " lization) \n",
- " \n",
- " block4c_drop (FixedDropout) (None, 14, 14, 112) 0 ['block4c_project_bn[0][0]'] Y \n",
- " \n",
- " block4c_add (Add) (None, 14, 14, 112) 0 ['block4c_drop[0][0]', Y \n",
- " 'block4b_add[0][0]'] \n",
- " \n",
- " block4d_expand_conv (Conv2D) (None, 14, 14, 672) 75264 ['block4c_add[0][0]'] Y \n",
- " \n",
- " block4d_expand_bn (BatchNormal (None, 14, 14, 672) 2688 ['block4d_expand_conv[0][0]'] Y \n",
- " ization) \n",
- " \n",
- " block4d_expand_activation (Act (None, 14, 14, 672) 0 ['block4d_expand_bn[0][0]'] Y \n",
- " ivation) \n",
- " \n",
- " block4d_dwconv (DepthwiseConv2 (None, 14, 14, 672) 6048 ['block4d_expand_activation[0][ Y \n",
- " D) 0]'] \n",
- " \n",
- " block4d_bn (BatchNormalization (None, 14, 14, 672) 2688 ['block4d_dwconv[0][0]'] Y \n",
- " ) \n",
- " \n",
- " block4d_activation (Activation (None, 14, 14, 672) 0 ['block4d_bn[0][0]'] Y \n",
- " ) \n",
- " \n",
- " block4d_se_squeeze (GlobalAver (None, 672) 0 ['block4d_activation[0][0]'] Y \n",
- " agePooling2D) \n",
- " \n",
- " block4d_se_reshape (Reshape) (None, 1, 1, 672) 0 ['block4d_se_squeeze[0][0]'] Y \n",
- " \n",
- " block4d_se_reduce (Conv2D) (None, 1, 1, 28) 18844 ['block4d_se_reshape[0][0]'] Y \n",
- " \n",
- " block4d_se_expand (Conv2D) (None, 1, 1, 672) 19488 ['block4d_se_reduce[0][0]'] Y \n",
- " \n",
- " block4d_se_excite (Multiply) (None, 14, 14, 672) 0 ['block4d_activation[0][0]', Y \n",
- " 'block4d_se_expand[0][0]'] \n",
- " \n",
- " block4d_project_conv (Conv2D) (None, 14, 14, 112) 75264 ['block4d_se_excite[0][0]'] Y \n",
- " \n",
- " block4d_project_bn (BatchNorma (None, 14, 14, 112) 448 ['block4d_project_conv[0][0]'] Y \n",
- " lization) \n",
- " \n",
- " block4d_drop (FixedDropout) (None, 14, 14, 112) 0 ['block4d_project_bn[0][0]'] Y \n",
- " \n",
- " block4d_add (Add) (None, 14, 14, 112) 0 ['block4d_drop[0][0]', Y \n",
- " 'block4c_add[0][0]'] \n",
- " \n",
- " block4e_expand_conv (Conv2D) (None, 14, 14, 672) 75264 ['block4d_add[0][0]'] Y \n",
- " \n",
- " block4e_expand_bn (BatchNormal (None, 14, 14, 672) 2688 ['block4e_expand_conv[0][0]'] Y \n",
- " ization) \n",
- " \n",
- " block4e_expand_activation (Act (None, 14, 14, 672) 0 ['block4e_expand_bn[0][0]'] Y \n",
- " ivation) \n",
- " \n",
- " block4e_dwconv (DepthwiseConv2 (None, 14, 14, 672) 6048 ['block4e_expand_activation[0][ Y \n",
- " D) 0]'] \n",
- " \n",
- " block4e_bn (BatchNormalization (None, 14, 14, 672) 2688 ['block4e_dwconv[0][0]'] Y \n",
- " ) \n",
- " \n",
- " block4e_activation (Activation (None, 14, 14, 672) 0 ['block4e_bn[0][0]'] Y \n",
- " ) \n",
- " \n",
- " block4e_se_squeeze (GlobalAver (None, 672) 0 ['block4e_activation[0][0]'] Y \n",
- " agePooling2D) \n",
- " \n",
- " block4e_se_reshape (Reshape) (None, 1, 1, 672) 0 ['block4e_se_squeeze[0][0]'] Y \n",
- " \n",
- " block4e_se_reduce (Conv2D) (None, 1, 1, 28) 18844 ['block4e_se_reshape[0][0]'] Y \n",
- " \n",
- " block4e_se_expand (Conv2D) (None, 1, 1, 672) 19488 ['block4e_se_reduce[0][0]'] Y \n",
- " \n",
- " block4e_se_excite (Multiply) (None, 14, 14, 672) 0 ['block4e_activation[0][0]', Y \n",
- " 'block4e_se_expand[0][0]'] \n",
- " \n",
- " block4e_project_conv (Conv2D) (None, 14, 14, 112) 75264 ['block4e_se_excite[0][0]'] Y \n",
- " \n",
- " block4e_project_bn (BatchNorma (None, 14, 14, 112) 448 ['block4e_project_conv[0][0]'] Y \n",
- " lization) \n",
- " \n",
- " block4e_drop (FixedDropout) (None, 14, 14, 112) 0 ['block4e_project_bn[0][0]'] Y \n",
- " \n",
- " block4e_add (Add) (None, 14, 14, 112) 0 ['block4e_drop[0][0]', Y \n",
- " 'block4d_add[0][0]'] \n",
- " \n",
- " block4f_expand_conv (Conv2D) (None, 14, 14, 672) 75264 ['block4e_add[0][0]'] Y \n",
- " \n",
- " block4f_expand_bn (BatchNormal (None, 14, 14, 672) 2688 ['block4f_expand_conv[0][0]'] Y \n",
- " ization) \n",
- " \n",
- " block4f_expand_activation (Act (None, 14, 14, 672) 0 ['block4f_expand_bn[0][0]'] Y \n",
- " ivation) \n",
- " \n",
- " block4f_dwconv (DepthwiseConv2 (None, 14, 14, 672) 6048 ['block4f_expand_activation[0][ Y \n",
- " D) 0]'] \n",
- " \n",
- " block4f_bn (BatchNormalization (None, 14, 14, 672) 2688 ['block4f_dwconv[0][0]'] Y \n",
- " ) \n",
- " \n",
- " block4f_activation (Activation (None, 14, 14, 672) 0 ['block4f_bn[0][0]'] Y \n",
- " ) \n",
- " \n",
- " block4f_se_squeeze (GlobalAver (None, 672) 0 ['block4f_activation[0][0]'] Y \n",
- " agePooling2D) \n",
- " \n",
- " block4f_se_reshape (Reshape) (None, 1, 1, 672) 0 ['block4f_se_squeeze[0][0]'] Y \n",
- " \n",
- " block4f_se_reduce (Conv2D) (None, 1, 1, 28) 18844 ['block4f_se_reshape[0][0]'] Y \n",
- " \n",
- " block4f_se_expand (Conv2D) (None, 1, 1, 672) 19488 ['block4f_se_reduce[0][0]'] Y \n",
- " \n",
- " block4f_se_excite (Multiply) (None, 14, 14, 672) 0 ['block4f_activation[0][0]', Y \n",
- " 'block4f_se_expand[0][0]'] \n",
- " \n",
- " block4f_project_conv (Conv2D) (None, 14, 14, 112) 75264 ['block4f_se_excite[0][0]'] Y \n",
- " \n",
- " block4f_project_bn (BatchNorma (None, 14, 14, 112) 448 ['block4f_project_conv[0][0]'] Y \n",
- " lization) \n",
- " \n",
- " block4f_drop (FixedDropout) (None, 14, 14, 112) 0 ['block4f_project_bn[0][0]'] Y \n",
- " \n",
- " block4f_add (Add) (None, 14, 14, 112) 0 ['block4f_drop[0][0]', Y \n",
- " 'block4e_add[0][0]'] \n",
- " \n",
- " block5a_expand_conv (Conv2D) (None, 14, 14, 672) 75264 ['block4f_add[0][0]'] Y \n",
- " \n",
- " block5a_expand_bn (BatchNormal (None, 14, 14, 672) 2688 ['block5a_expand_conv[0][0]'] Y \n",
- " ization) \n",
- " \n",
- " block5a_expand_activation (Act (None, 14, 14, 672) 0 ['block5a_expand_bn[0][0]'] Y \n",
- " ivation) \n",
- " \n",
- " block5a_dwconv (DepthwiseConv2 (None, 14, 14, 672) 16800 ['block5a_expand_activation[0][ Y \n",
- " D) 0]'] \n",
- " \n",
- " block5a_bn (BatchNormalization (None, 14, 14, 672) 2688 ['block5a_dwconv[0][0]'] Y \n",
- " ) \n",
- " \n",
- " block5a_activation (Activation (None, 14, 14, 672) 0 ['block5a_bn[0][0]'] Y \n",
- " ) \n",
- " \n",
- " block5a_se_squeeze (GlobalAver (None, 672) 0 ['block5a_activation[0][0]'] Y \n",
- " agePooling2D) \n",
- " \n",
- " block5a_se_reshape (Reshape) (None, 1, 1, 672) 0 ['block5a_se_squeeze[0][0]'] Y \n",
- " \n",
- " block5a_se_reduce (Conv2D) (None, 1, 1, 28) 18844 ['block5a_se_reshape[0][0]'] Y \n",
- " \n",
- " block5a_se_expand (Conv2D) (None, 1, 1, 672) 19488 ['block5a_se_reduce[0][0]'] Y \n",
- " \n",
- " block5a_se_excite (Multiply) (None, 14, 14, 672) 0 ['block5a_activation[0][0]', Y \n",
- " 'block5a_se_expand[0][0]'] \n",
- " \n",
- " block5a_project_conv (Conv2D) (None, 14, 14, 160) 107520 ['block5a_se_excite[0][0]'] Y \n",
- " \n",
- " block5a_project_bn (BatchNorma (None, 14, 14, 160) 640 ['block5a_project_conv[0][0]'] Y \n",
- " lization) \n",
- " \n",
- " block5b_expand_conv (Conv2D) (None, 14, 14, 960) 153600 ['block5a_project_bn[0][0]'] Y \n",
- " \n",
- " block5b_expand_bn (BatchNormal (None, 14, 14, 960) 3840 ['block5b_expand_conv[0][0]'] Y \n",
- " ization) \n",
- " \n",
- " block5b_expand_activation (Act (None, 14, 14, 960) 0 ['block5b_expand_bn[0][0]'] Y \n",
- " ivation) \n",
- " \n",
- " block5b_dwconv (DepthwiseConv2 (None, 14, 14, 960) 24000 ['block5b_expand_activation[0][ Y \n",
- " D) 0]'] \n",
- " \n",
- " block5b_bn (BatchNormalization (None, 14, 14, 960) 3840 ['block5b_dwconv[0][0]'] Y \n",
- " ) \n",
- " \n",
- " block5b_activation (Activation (None, 14, 14, 960) 0 ['block5b_bn[0][0]'] Y \n",
- " ) \n",
- " \n",
- " block5b_se_squeeze (GlobalAver (None, 960) 0 ['block5b_activation[0][0]'] Y \n",
- " agePooling2D) \n",
- " \n",
- " block5b_se_reshape (Reshape) (None, 1, 1, 960) 0 ['block5b_se_squeeze[0][0]'] Y \n",
- " \n",
- " block5b_se_reduce (Conv2D) (None, 1, 1, 40) 38440 ['block5b_se_reshape[0][0]'] Y \n",
- " \n",
- " block5b_se_expand (Conv2D) (None, 1, 1, 960) 39360 ['block5b_se_reduce[0][0]'] Y \n",
- " \n",
- " block5b_se_excite (Multiply) (None, 14, 14, 960) 0 ['block5b_activation[0][0]', Y \n",
- " 'block5b_se_expand[0][0]'] \n",
- " \n",
- " block5b_project_conv (Conv2D) (None, 14, 14, 160) 153600 ['block5b_se_excite[0][0]'] Y \n",
- " \n",
- " block5b_project_bn (BatchNorma (None, 14, 14, 160) 640 ['block5b_project_conv[0][0]'] Y \n",
- " lization) \n",
- " \n",
- " block5b_drop (FixedDropout) (None, 14, 14, 160) 0 ['block5b_project_bn[0][0]'] Y \n",
- " \n",
- " block5b_add (Add) (None, 14, 14, 160) 0 ['block5b_drop[0][0]', Y \n",
- " 'block5a_project_bn[0][0]'] \n",
- " \n",
- " block5c_expand_conv (Conv2D) (None, 14, 14, 960) 153600 ['block5b_add[0][0]'] Y \n",
- " \n",
- " block5c_expand_bn (BatchNormal (None, 14, 14, 960) 3840 ['block5c_expand_conv[0][0]'] Y \n",
- " ization) \n",
- " \n",
- " block5c_expand_activation (Act (None, 14, 14, 960) 0 ['block5c_expand_bn[0][0]'] Y \n",
- " ivation) \n",
- " \n",
- " block5c_dwconv (DepthwiseConv2 (None, 14, 14, 960) 24000 ['block5c_expand_activation[0][ Y \n",
- " D) 0]'] \n",
- " \n",
- " block5c_bn (BatchNormalization (None, 14, 14, 960) 3840 ['block5c_dwconv[0][0]'] Y \n",
- " ) \n",
- " \n",
- " block5c_activation (Activation (None, 14, 14, 960) 0 ['block5c_bn[0][0]'] Y \n",
- " ) \n",
- " \n",
- " block5c_se_squeeze (GlobalAver (None, 960) 0 ['block5c_activation[0][0]'] Y \n",
- " agePooling2D) \n",
- " \n",
- " block5c_se_reshape (Reshape) (None, 1, 1, 960) 0 ['block5c_se_squeeze[0][0]'] Y \n",
- " \n",
- " block5c_se_reduce (Conv2D) (None, 1, 1, 40) 38440 ['block5c_se_reshape[0][0]'] Y \n",
- " \n",
- " block5c_se_expand (Conv2D) (None, 1, 1, 960) 39360 ['block5c_se_reduce[0][0]'] Y \n",
- " \n",
- " block5c_se_excite (Multiply) (None, 14, 14, 960) 0 ['block5c_activation[0][0]', Y \n",
- " 'block5c_se_expand[0][0]'] \n",
- " \n",
- " block5c_project_conv (Conv2D) (None, 14, 14, 160) 153600 ['block5c_se_excite[0][0]'] Y \n",
- " \n",
- " block5c_project_bn (BatchNorma (None, 14, 14, 160) 640 ['block5c_project_conv[0][0]'] Y \n",
- " lization) \n",
- " \n",
- " block5c_drop (FixedDropout) (None, 14, 14, 160) 0 ['block5c_project_bn[0][0]'] Y \n",
- " \n",
- " block5c_add (Add) (None, 14, 14, 160) 0 ['block5c_drop[0][0]', Y \n",
- " 'block5b_add[0][0]'] \n",
- " \n",
- " block5d_expand_conv (Conv2D) (None, 14, 14, 960) 153600 ['block5c_add[0][0]'] Y \n",
- " \n",
- " block5d_expand_bn (BatchNormal (None, 14, 14, 960) 3840 ['block5d_expand_conv[0][0]'] Y \n",
- " ization) \n",
- " \n",
- " block5d_expand_activation (Act (None, 14, 14, 960) 0 ['block5d_expand_bn[0][0]'] Y \n",
- " ivation) \n",
- " \n",
- " block5d_dwconv (DepthwiseConv2 (None, 14, 14, 960) 24000 ['block5d_expand_activation[0][ Y \n",
- " D) 0]'] \n",
- " \n",
- " block5d_bn (BatchNormalization (None, 14, 14, 960) 3840 ['block5d_dwconv[0][0]'] Y \n",
- " ) \n",
- " \n",
- " block5d_activation (Activation (None, 14, 14, 960) 0 ['block5d_bn[0][0]'] Y \n",
- " ) \n",
- " \n",
- " block5d_se_squeeze (GlobalAver (None, 960) 0 ['block5d_activation[0][0]'] Y \n",
- " agePooling2D) \n",
- " \n",
- " block5d_se_reshape (Reshape) (None, 1, 1, 960) 0 ['block5d_se_squeeze[0][0]'] Y \n",
- " \n",
- " block5d_se_reduce (Conv2D) (None, 1, 1, 40) 38440 ['block5d_se_reshape[0][0]'] Y \n",
- " \n",
- " block5d_se_expand (Conv2D) (None, 1, 1, 960) 39360 ['block5d_se_reduce[0][0]'] Y \n",
- " \n",
- " block5d_se_excite (Multiply) (None, 14, 14, 960) 0 ['block5d_activation[0][0]', Y \n",
- " 'block5d_se_expand[0][0]'] \n",
- " \n",
- " block5d_project_conv (Conv2D) (None, 14, 14, 160) 153600 ['block5d_se_excite[0][0]'] Y \n",
- " \n",
- " block5d_project_bn (BatchNorma (None, 14, 14, 160) 640 ['block5d_project_conv[0][0]'] Y \n",
- " lization) \n",
- " \n",
- " block5d_drop (FixedDropout) (None, 14, 14, 160) 0 ['block5d_project_bn[0][0]'] Y \n",
- " \n",
- " block5d_add (Add) (None, 14, 14, 160) 0 ['block5d_drop[0][0]', Y \n",
- " 'block5c_add[0][0]'] \n",
- " \n",
- " block5e_expand_conv (Conv2D) (None, 14, 14, 960) 153600 ['block5d_add[0][0]'] Y \n",
- " \n",
- " block5e_expand_bn (BatchNormal (None, 14, 14, 960) 3840 ['block5e_expand_conv[0][0]'] Y \n",
- " ization) \n",
- " \n",
- " block5e_expand_activation (Act (None, 14, 14, 960) 0 ['block5e_expand_bn[0][0]'] Y \n",
- " ivation) \n",
- " \n",
- " block5e_dwconv (DepthwiseConv2 (None, 14, 14, 960) 24000 ['block5e_expand_activation[0][ Y \n",
- " D) 0]'] \n",
- " \n",
- " block5e_bn (BatchNormalization (None, 14, 14, 960) 3840 ['block5e_dwconv[0][0]'] Y \n",
- " ) \n",
- " \n",
- " block5e_activation (Activation (None, 14, 14, 960) 0 ['block5e_bn[0][0]'] Y \n",
- " ) \n",
- " \n",
- " block5e_se_squeeze (GlobalAver (None, 960) 0 ['block5e_activation[0][0]'] Y \n",
- " agePooling2D) \n",
- " \n",
- " block5e_se_reshape (Reshape) (None, 1, 1, 960) 0 ['block5e_se_squeeze[0][0]'] Y \n",
- " \n",
- " block5e_se_reduce (Conv2D) (None, 1, 1, 40) 38440 ['block5e_se_reshape[0][0]'] Y \n",
- " \n",
- " block5e_se_expand (Conv2D) (None, 1, 1, 960) 39360 ['block5e_se_reduce[0][0]'] Y \n",
- " \n",
- " block5e_se_excite (Multiply) (None, 14, 14, 960) 0 ['block5e_activation[0][0]', Y \n",
- " 'block5e_se_expand[0][0]'] \n",
- " \n",
- " block5e_project_conv (Conv2D) (None, 14, 14, 160) 153600 ['block5e_se_excite[0][0]'] Y \n",
- " \n",
- " block5e_project_bn (BatchNorma (None, 14, 14, 160) 640 ['block5e_project_conv[0][0]'] Y \n",
- " lization) \n",
- " \n",
- " block5e_drop (FixedDropout) (None, 14, 14, 160) 0 ['block5e_project_bn[0][0]'] Y \n",
- " \n",
- " block5e_add (Add) (None, 14, 14, 160) 0 ['block5e_drop[0][0]', Y \n",
- " 'block5d_add[0][0]'] \n",
- " \n",
- " block5f_expand_conv (Conv2D) (None, 14, 14, 960) 153600 ['block5e_add[0][0]'] Y \n",
- " \n",
- " block5f_expand_bn (BatchNormal (None, 14, 14, 960) 3840 ['block5f_expand_conv[0][0]'] Y \n",
- " ization) \n",
- " \n",
- " block5f_expand_activation (Act (None, 14, 14, 960) 0 ['block5f_expand_bn[0][0]'] Y \n",
- " ivation) \n",
- " \n",
- " block5f_dwconv (DepthwiseConv2 (None, 14, 14, 960) 24000 ['block5f_expand_activation[0][ Y \n",
- " D) 0]'] \n",
- " \n",
- " block5f_bn (BatchNormalization (None, 14, 14, 960) 3840 ['block5f_dwconv[0][0]'] Y \n",
- " ) \n",
- " \n",
- " block5f_activation (Activation (None, 14, 14, 960) 0 ['block5f_bn[0][0]'] Y \n",
- " ) \n",
- " \n",
- " block5f_se_squeeze (GlobalAver (None, 960) 0 ['block5f_activation[0][0]'] Y \n",
- " agePooling2D) \n",
- " \n",
- " block5f_se_reshape (Reshape) (None, 1, 1, 960) 0 ['block5f_se_squeeze[0][0]'] Y \n",
- " \n",
- " block5f_se_reduce (Conv2D) (None, 1, 1, 40) 38440 ['block5f_se_reshape[0][0]'] Y \n",
- " \n",
- " block5f_se_expand (Conv2D) (None, 1, 1, 960) 39360 ['block5f_se_reduce[0][0]'] Y \n",
- " \n",
- " block5f_se_excite (Multiply) (None, 14, 14, 960) 0 ['block5f_activation[0][0]', Y \n",
- " 'block5f_se_expand[0][0]'] \n",
- " \n",
- " block5f_project_conv (Conv2D) (None, 14, 14, 160) 153600 ['block5f_se_excite[0][0]'] Y \n",
- " \n",
- " block5f_project_bn (BatchNorma (None, 14, 14, 160) 640 ['block5f_project_conv[0][0]'] Y \n",
- " lization) \n",
- " \n",
- " block5f_drop (FixedDropout) (None, 14, 14, 160) 0 ['block5f_project_bn[0][0]'] Y \n",
- " \n",
- " block5f_add (Add) (None, 14, 14, 160) 0 ['block5f_drop[0][0]', Y \n",
- " 'block5e_add[0][0]'] \n",
- " \n",
- " block6a_expand_conv (Conv2D) (None, 14, 14, 960) 153600 ['block5f_add[0][0]'] Y \n",
- " \n",
- " block6a_expand_bn (BatchNormal (None, 14, 14, 960) 3840 ['block6a_expand_conv[0][0]'] Y \n",
- " ization) \n",
- " \n",
- " block6a_expand_activation (Act (None, 14, 14, 960) 0 ['block6a_expand_bn[0][0]'] Y \n",
- " ivation) \n",
- " \n",
- " block6a_dwconv (DepthwiseConv2 (None, 7, 7, 960) 24000 ['block6a_expand_activation[0][ Y \n",
- " D) 0]'] \n",
- " \n",
- " block6a_bn (BatchNormalization (None, 7, 7, 960) 3840 ['block6a_dwconv[0][0]'] Y \n",
- " ) \n",
- " \n",
- " block6a_activation (Activation (None, 7, 7, 960) 0 ['block6a_bn[0][0]'] Y \n",
- " ) \n",
- " \n",
- " block6a_se_squeeze (GlobalAver (None, 960) 0 ['block6a_activation[0][0]'] Y \n",
- " agePooling2D) \n",
- " \n",
- " block6a_se_reshape (Reshape) (None, 1, 1, 960) 0 ['block6a_se_squeeze[0][0]'] Y \n",
- " \n",
- " block6a_se_reduce (Conv2D) (None, 1, 1, 40) 38440 ['block6a_se_reshape[0][0]'] Y \n",
- " \n",
- " block6a_se_expand (Conv2D) (None, 1, 1, 960) 39360 ['block6a_se_reduce[0][0]'] Y \n",
- " \n",
- " block6a_se_excite (Multiply) (None, 7, 7, 960) 0 ['block6a_activation[0][0]', Y \n",
- " 'block6a_se_expand[0][0]'] \n",
- " \n",
- " block6a_project_conv (Conv2D) (None, 7, 7, 272) 261120 ['block6a_se_excite[0][0]'] Y \n",
- " \n",
- " block6a_project_bn (BatchNorma (None, 7, 7, 272) 1088 ['block6a_project_conv[0][0]'] Y \n",
- " lization) \n",
- " \n",
- " block6b_expand_conv (Conv2D) (None, 7, 7, 1632) 443904 ['block6a_project_bn[0][0]'] Y \n",
- " \n",
- " block6b_expand_bn (BatchNormal (None, 7, 7, 1632) 6528 ['block6b_expand_conv[0][0]'] Y \n",
- " ization) \n",
- " \n",
- " block6b_expand_activation (Act (None, 7, 7, 1632) 0 ['block6b_expand_bn[0][0]'] Y \n",
- " ivation) \n",
- " \n",
- " block6b_dwconv (DepthwiseConv2 (None, 7, 7, 1632) 40800 ['block6b_expand_activation[0][ Y \n",
- " D) 0]'] \n",
- " \n",
- " block6b_bn (BatchNormalization (None, 7, 7, 1632) 6528 ['block6b_dwconv[0][0]'] Y \n",
- " ) \n",
- " \n",
- " block6b_activation (Activation (None, 7, 7, 1632) 0 ['block6b_bn[0][0]'] Y \n",
- " ) \n",
- " \n",
- " block6b_se_squeeze (GlobalAver (None, 1632) 0 ['block6b_activation[0][0]'] Y \n",
- " agePooling2D) \n",
- " \n",
- " block6b_se_reshape (Reshape) (None, 1, 1, 1632) 0 ['block6b_se_squeeze[0][0]'] Y \n",
- " \n",
- " block6b_se_reduce (Conv2D) (None, 1, 1, 68) 111044 ['block6b_se_reshape[0][0]'] Y \n",
- " \n",
- " block6b_se_expand (Conv2D) (None, 1, 1, 1632) 112608 ['block6b_se_reduce[0][0]'] Y \n",
- " \n",
- " block6b_se_excite (Multiply) (None, 7, 7, 1632) 0 ['block6b_activation[0][0]', Y \n",
- " 'block6b_se_expand[0][0]'] \n",
- " \n",
- " block6b_project_conv (Conv2D) (None, 7, 7, 272) 443904 ['block6b_se_excite[0][0]'] Y \n",
- " \n",
- " block6b_project_bn (BatchNorma (None, 7, 7, 272) 1088 ['block6b_project_conv[0][0]'] Y \n",
- " lization) \n",
- " \n",
- " block6b_drop (FixedDropout) (None, 7, 7, 272) 0 ['block6b_project_bn[0][0]'] Y \n",
- " \n",
- " block6b_add (Add) (None, 7, 7, 272) 0 ['block6b_drop[0][0]', Y \n",
- " 'block6a_project_bn[0][0]'] \n",
- " \n",
- " block6c_expand_conv (Conv2D) (None, 7, 7, 1632) 443904 ['block6b_add[0][0]'] Y \n",
- " \n",
- " block6c_expand_bn (BatchNormal (None, 7, 7, 1632) 6528 ['block6c_expand_conv[0][0]'] Y \n",
- " ization) \n",
- " \n",
- " block6c_expand_activation (Act (None, 7, 7, 1632) 0 ['block6c_expand_bn[0][0]'] Y \n",
- " ivation) \n",
- " \n",
- " block6c_dwconv (DepthwiseConv2 (None, 7, 7, 1632) 40800 ['block6c_expand_activation[0][ Y \n",
- " D) 0]'] \n",
- " \n",
- " block6c_bn (BatchNormalization (None, 7, 7, 1632) 6528 ['block6c_dwconv[0][0]'] Y \n",
- " ) \n",
- " \n",
- " block6c_activation (Activation (None, 7, 7, 1632) 0 ['block6c_bn[0][0]'] Y \n",
- " ) \n",
- " \n",
- " block6c_se_squeeze (GlobalAver (None, 1632) 0 ['block6c_activation[0][0]'] Y \n",
- " agePooling2D) \n",
- " \n",
- " block6c_se_reshape (Reshape) (None, 1, 1, 1632) 0 ['block6c_se_squeeze[0][0]'] Y \n",
- " \n",
- " block6c_se_reduce (Conv2D) (None, 1, 1, 68) 111044 ['block6c_se_reshape[0][0]'] Y \n",
- " \n",
- " block6c_se_expand (Conv2D) (None, 1, 1, 1632) 112608 ['block6c_se_reduce[0][0]'] Y \n",
- " \n",
- " block6c_se_excite (Multiply) (None, 7, 7, 1632) 0 ['block6c_activation[0][0]', Y \n",
- " 'block6c_se_expand[0][0]'] \n",
- " \n",
- " block6c_project_conv (Conv2D) (None, 7, 7, 272) 443904 ['block6c_se_excite[0][0]'] Y \n",
- " \n",
- " block6c_project_bn (BatchNorma (None, 7, 7, 272) 1088 ['block6c_project_conv[0][0]'] Y \n",
- " lization) \n",
- " \n",
- " block6c_drop (FixedDropout) (None, 7, 7, 272) 0 ['block6c_project_bn[0][0]'] Y \n",
- " \n",
- " block6c_add (Add) (None, 7, 7, 272) 0 ['block6c_drop[0][0]', Y \n",
- " 'block6b_add[0][0]'] \n",
- " \n",
- " block6d_expand_conv (Conv2D) (None, 7, 7, 1632) 443904 ['block6c_add[0][0]'] Y \n",
- " \n",
- " block6d_expand_bn (BatchNormal (None, 7, 7, 1632) 6528 ['block6d_expand_conv[0][0]'] Y \n",
- " ization) \n",
- " \n",
- " block6d_expand_activation (Act (None, 7, 7, 1632) 0 ['block6d_expand_bn[0][0]'] Y \n",
- " ivation) \n",
- " \n",
- " block6d_dwconv (DepthwiseConv2 (None, 7, 7, 1632) 40800 ['block6d_expand_activation[0][ Y \n",
- " D) 0]'] \n",
- " \n",
- " block6d_bn (BatchNormalization (None, 7, 7, 1632) 6528 ['block6d_dwconv[0][0]'] Y \n",
- " ) \n",
- " \n",
- " block6d_activation (Activation (None, 7, 7, 1632) 0 ['block6d_bn[0][0]'] Y \n",
- " ) \n",
- " \n",
- " block6d_se_squeeze (GlobalAver (None, 1632) 0 ['block6d_activation[0][0]'] Y \n",
- " agePooling2D) \n",
- " \n",
- " block6d_se_reshape (Reshape) (None, 1, 1, 1632) 0 ['block6d_se_squeeze[0][0]'] Y \n",
- " \n",
- " block6d_se_reduce (Conv2D) (None, 1, 1, 68) 111044 ['block6d_se_reshape[0][0]'] Y \n",
- " \n",
- " block6d_se_expand (Conv2D) (None, 1, 1, 1632) 112608 ['block6d_se_reduce[0][0]'] Y \n",
- " \n",
- " block6d_se_excite (Multiply) (None, 7, 7, 1632) 0 ['block6d_activation[0][0]', Y \n",
- " 'block6d_se_expand[0][0]'] \n",
- " \n",
- " block6d_project_conv (Conv2D) (None, 7, 7, 272) 443904 ['block6d_se_excite[0][0]'] Y \n",
- " \n",
- " block6d_project_bn (BatchNorma (None, 7, 7, 272) 1088 ['block6d_project_conv[0][0]'] Y \n",
- " lization) \n",
- " \n",
- " block6d_drop (FixedDropout) (None, 7, 7, 272) 0 ['block6d_project_bn[0][0]'] Y \n",
- " \n",
- " block6d_add (Add) (None, 7, 7, 272) 0 ['block6d_drop[0][0]', Y \n",
- " 'block6c_add[0][0]'] \n",
- " \n",
- " block6e_expand_conv (Conv2D) (None, 7, 7, 1632) 443904 ['block6d_add[0][0]'] Y \n",
- " \n",
- " block6e_expand_bn (BatchNormal (None, 7, 7, 1632) 6528 ['block6e_expand_conv[0][0]'] Y \n",
- " ization) \n",
- " \n",
- " block6e_expand_activation (Act (None, 7, 7, 1632) 0 ['block6e_expand_bn[0][0]'] Y \n",
- " ivation) \n",
- " \n",
- " block6e_dwconv (DepthwiseConv2 (None, 7, 7, 1632) 40800 ['block6e_expand_activation[0][ Y \n",
- " D) 0]'] \n",
- " \n",
- " block6e_bn (BatchNormalization (None, 7, 7, 1632) 6528 ['block6e_dwconv[0][0]'] Y \n",
- " ) \n",
- " \n",
- " block6e_activation (Activation (None, 7, 7, 1632) 0 ['block6e_bn[0][0]'] Y \n",
- " ) \n",
- " \n",
- " block6e_se_squeeze (GlobalAver (None, 1632) 0 ['block6e_activation[0][0]'] Y \n",
- " agePooling2D) \n",
- " \n",
- " block6e_se_reshape (Reshape) (None, 1, 1, 1632) 0 ['block6e_se_squeeze[0][0]'] Y \n",
- " \n",
- " block6e_se_reduce (Conv2D) (None, 1, 1, 68) 111044 ['block6e_se_reshape[0][0]'] Y \n",
- " \n",
- " block6e_se_expand (Conv2D) (None, 1, 1, 1632) 112608 ['block6e_se_reduce[0][0]'] Y \n",
- " \n",
- " block6e_se_excite (Multiply) (None, 7, 7, 1632) 0 ['block6e_activation[0][0]', Y \n",
- " 'block6e_se_expand[0][0]'] \n",
- " \n",
- " block6e_project_conv (Conv2D) (None, 7, 7, 272) 443904 ['block6e_se_excite[0][0]'] Y \n",
- " \n",
- " block6e_project_bn (BatchNorma (None, 7, 7, 272) 1088 ['block6e_project_conv[0][0]'] Y \n",
- " lization) \n",
- " \n",
- " block6e_drop (FixedDropout) (None, 7, 7, 272) 0 ['block6e_project_bn[0][0]'] Y \n",
- " \n",
- " block6e_add (Add) (None, 7, 7, 272) 0 ['block6e_drop[0][0]', Y \n",
- " 'block6d_add[0][0]'] \n",
- " \n",
- " block6f_expand_conv (Conv2D) (None, 7, 7, 1632) 443904 ['block6e_add[0][0]'] Y \n",
- " \n",
- " block6f_expand_bn (BatchNormal (None, 7, 7, 1632) 6528 ['block6f_expand_conv[0][0]'] Y \n",
- " ization) \n",
- " \n",
- " block6f_expand_activation (Act (None, 7, 7, 1632) 0 ['block6f_expand_bn[0][0]'] Y \n",
- " ivation) \n",
- " \n",
- " block6f_dwconv (DepthwiseConv2 (None, 7, 7, 1632) 40800 ['block6f_expand_activation[0][ Y \n",
- " D) 0]'] \n",
- " \n",
- " block6f_bn (BatchNormalization (None, 7, 7, 1632) 6528 ['block6f_dwconv[0][0]'] Y \n",
- " ) \n",
- " \n",
- " block6f_activation (Activation (None, 7, 7, 1632) 0 ['block6f_bn[0][0]'] Y \n",
- " ) \n",
- " \n",
- " block6f_se_squeeze (GlobalAver (None, 1632) 0 ['block6f_activation[0][0]'] Y \n",
- " agePooling2D) \n",
- " \n",
- " block6f_se_reshape (Reshape) (None, 1, 1, 1632) 0 ['block6f_se_squeeze[0][0]'] Y \n",
- " \n",
- " block6f_se_reduce (Conv2D) (None, 1, 1, 68) 111044 ['block6f_se_reshape[0][0]'] Y \n",
- " \n",
- " block6f_se_expand (Conv2D) (None, 1, 1, 1632) 112608 ['block6f_se_reduce[0][0]'] Y \n",
- " \n",
- " block6f_se_excite (Multiply) (None, 7, 7, 1632) 0 ['block6f_activation[0][0]', Y \n",
- " 'block6f_se_expand[0][0]'] \n",
- " \n",
- " block6f_project_conv (Conv2D) (None, 7, 7, 272) 443904 ['block6f_se_excite[0][0]'] Y \n",
- " \n",
- " block6f_project_bn (BatchNorma (None, 7, 7, 272) 1088 ['block6f_project_conv[0][0]'] Y \n",
- " lization) \n",
- " \n",
- " block6f_drop (FixedDropout) (None, 7, 7, 272) 0 ['block6f_project_bn[0][0]'] Y \n",
- " \n",
- " block6f_add (Add) (None, 7, 7, 272) 0 ['block6f_drop[0][0]', Y \n",
- " 'block6e_add[0][0]'] \n",
- " \n",
- " block6g_expand_conv (Conv2D) (None, 7, 7, 1632) 443904 ['block6f_add[0][0]'] Y \n",
- " \n",
- " block6g_expand_bn (BatchNormal (None, 7, 7, 1632) 6528 ['block6g_expand_conv[0][0]'] Y \n",
- " ization) \n",
- " \n",
- " block6g_expand_activation (Act (None, 7, 7, 1632) 0 ['block6g_expand_bn[0][0]'] Y \n",
- " ivation) \n",
- " \n",
- " block6g_dwconv (DepthwiseConv2 (None, 7, 7, 1632) 40800 ['block6g_expand_activation[0][ Y \n",
- " D) 0]'] \n",
- " \n",
- " block6g_bn (BatchNormalization (None, 7, 7, 1632) 6528 ['block6g_dwconv[0][0]'] Y \n",
- " ) \n",
- " \n",
- " block6g_activation (Activation (None, 7, 7, 1632) 0 ['block6g_bn[0][0]'] Y \n",
- " ) \n",
- " \n",
- " block6g_se_squeeze (GlobalAver (None, 1632) 0 ['block6g_activation[0][0]'] Y \n",
- " agePooling2D) \n",
- " \n",
- " block6g_se_reshape (Reshape) (None, 1, 1, 1632) 0 ['block6g_se_squeeze[0][0]'] Y \n",
- " \n",
- " block6g_se_reduce (Conv2D) (None, 1, 1, 68) 111044 ['block6g_se_reshape[0][0]'] Y \n",
- " \n",
- " block6g_se_expand (Conv2D) (None, 1, 1, 1632) 112608 ['block6g_se_reduce[0][0]'] Y \n",
- " \n",
- " block6g_se_excite (Multiply) (None, 7, 7, 1632) 0 ['block6g_activation[0][0]', Y \n",
- " 'block6g_se_expand[0][0]'] \n",
- " \n",
- " block6g_project_conv (Conv2D) (None, 7, 7, 272) 443904 ['block6g_se_excite[0][0]'] Y \n",
- " \n",
- " block6g_project_bn (BatchNorma (None, 7, 7, 272) 1088 ['block6g_project_conv[0][0]'] Y \n",
- " lization) \n",
- " \n",
- " block6g_drop (FixedDropout) (None, 7, 7, 272) 0 ['block6g_project_bn[0][0]'] Y \n",
- " \n",
- " block6g_add (Add) (None, 7, 7, 272) 0 ['block6g_drop[0][0]', Y \n",
- " 'block6f_add[0][0]'] \n",
- " \n",
- " block6h_expand_conv (Conv2D) (None, 7, 7, 1632) 443904 ['block6g_add[0][0]'] Y \n",
- " \n",
- " block6h_expand_bn (BatchNormal (None, 7, 7, 1632) 6528 ['block6h_expand_conv[0][0]'] Y \n",
- " ization) \n",
- " \n",
- " block6h_expand_activation (Act (None, 7, 7, 1632) 0 ['block6h_expand_bn[0][0]'] Y \n",
- " ivation) \n",
- " \n",
- " block6h_dwconv (DepthwiseConv2 (None, 7, 7, 1632) 40800 ['block6h_expand_activation[0][ Y \n",
- " D) 0]'] \n",
- " \n",
- " block6h_bn (BatchNormalization (None, 7, 7, 1632) 6528 ['block6h_dwconv[0][0]'] Y \n",
- " ) \n",
- " \n",
- " block6h_activation (Activation (None, 7, 7, 1632) 0 ['block6h_bn[0][0]'] Y \n",
- " ) \n",
- " \n",
- " block6h_se_squeeze (GlobalAver (None, 1632) 0 ['block6h_activation[0][0]'] Y \n",
- " agePooling2D) \n",
- " \n",
- " block6h_se_reshape (Reshape) (None, 1, 1, 1632) 0 ['block6h_se_squeeze[0][0]'] Y \n",
- " \n",
- " block6h_se_reduce (Conv2D) (None, 1, 1, 68) 111044 ['block6h_se_reshape[0][0]'] Y \n",
- " \n",
- " block6h_se_expand (Conv2D) (None, 1, 1, 1632) 112608 ['block6h_se_reduce[0][0]'] Y \n",
- " \n",
- " block6h_se_excite (Multiply) (None, 7, 7, 1632) 0 ['block6h_activation[0][0]', Y \n",
- " 'block6h_se_expand[0][0]'] \n",
- " \n",
- " block6h_project_conv (Conv2D) (None, 7, 7, 272) 443904 ['block6h_se_excite[0][0]'] Y \n",
- " \n",
- " block6h_project_bn (BatchNorma (None, 7, 7, 272) 1088 ['block6h_project_conv[0][0]'] Y \n",
- " lization) \n",
- " \n",
- " block6h_drop (FixedDropout) (None, 7, 7, 272) 0 ['block6h_project_bn[0][0]'] Y \n",
- " \n",
- " block6h_add (Add) (None, 7, 7, 272) 0 ['block6h_drop[0][0]', Y \n",
- " 'block6g_add[0][0]'] \n",
- " \n",
- " block7a_expand_conv (Conv2D) (None, 7, 7, 1632) 443904 ['block6h_add[0][0]'] Y \n",
- " \n",
- " block7a_expand_bn (BatchNormal (None, 7, 7, 1632) 6528 ['block7a_expand_conv[0][0]'] Y \n",
- " ization) \n",
- " \n",
- " block7a_expand_activation (Act (None, 7, 7, 1632) 0 ['block7a_expand_bn[0][0]'] Y \n",
- " ivation) \n",
- " \n",
- " block7a_dwconv (DepthwiseConv2 (None, 7, 7, 1632) 14688 ['block7a_expand_activation[0][ Y \n",
- " D) 0]'] \n",
- " \n",
- " block7a_bn (BatchNormalization (None, 7, 7, 1632) 6528 ['block7a_dwconv[0][0]'] Y \n",
- " ) \n",
- " \n",
- " block7a_activation (Activation (None, 7, 7, 1632) 0 ['block7a_bn[0][0]'] Y \n",
- " ) \n",
- " \n",
- " block7a_se_squeeze (GlobalAver (None, 1632) 0 ['block7a_activation[0][0]'] Y \n",
- " agePooling2D) \n",
- " \n",
- " block7a_se_reshape (Reshape) (None, 1, 1, 1632) 0 ['block7a_se_squeeze[0][0]'] Y \n",
- " \n",
- " block7a_se_reduce (Conv2D) (None, 1, 1, 68) 111044 ['block7a_se_reshape[0][0]'] Y \n",
- " \n",
- " block7a_se_expand (Conv2D) (None, 1, 1, 1632) 112608 ['block7a_se_reduce[0][0]'] Y \n",
- " \n",
- " block7a_se_excite (Multiply) (None, 7, 7, 1632) 0 ['block7a_activation[0][0]', Y \n",
- " 'block7a_se_expand[0][0]'] \n",
- " \n",
- " block7a_project_conv (Conv2D) (None, 7, 7, 448) 731136 ['block7a_se_excite[0][0]'] Y \n",
- " \n",
- " block7a_project_bn (BatchNorma (None, 7, 7, 448) 1792 ['block7a_project_conv[0][0]'] Y \n",
- " lization) \n",
- " \n",
- " block7b_expand_conv (Conv2D) (None, 7, 7, 2688) 1204224 ['block7a_project_bn[0][0]'] Y \n",
- " \n",
- " block7b_expand_bn (BatchNormal (None, 7, 7, 2688) 10752 ['block7b_expand_conv[0][0]'] Y \n",
- " ization) \n",
- " \n",
- " block7b_expand_activation (Act (None, 7, 7, 2688) 0 ['block7b_expand_bn[0][0]'] Y \n",
- " ivation) \n",
- " \n",
- " block7b_dwconv (DepthwiseConv2 (None, 7, 7, 2688) 24192 ['block7b_expand_activation[0][ Y \n",
- " D) 0]'] \n",
- " \n",
- " block7b_bn (BatchNormalization (None, 7, 7, 2688) 10752 ['block7b_dwconv[0][0]'] Y \n",
- " ) \n",
- " \n",
- " block7b_activation (Activation (None, 7, 7, 2688) 0 ['block7b_bn[0][0]'] Y \n",
- " ) \n",
- " \n",
- " block7b_se_squeeze (GlobalAver (None, 2688) 0 ['block7b_activation[0][0]'] Y \n",
- " agePooling2D) \n",
- " \n",
- " block7b_se_reshape (Reshape) (None, 1, 1, 2688) 0 ['block7b_se_squeeze[0][0]'] Y \n",
- " \n",
- " block7b_se_reduce (Conv2D) (None, 1, 1, 112) 301168 ['block7b_se_reshape[0][0]'] Y \n",
- " \n",
- " block7b_se_expand (Conv2D) (None, 1, 1, 2688) 303744 ['block7b_se_reduce[0][0]'] Y \n",
- " \n",
- " block7b_se_excite (Multiply) (None, 7, 7, 2688) 0 ['block7b_activation[0][0]', Y \n",
- " 'block7b_se_expand[0][0]'] \n",
- " \n",
- " block7b_project_conv (Conv2D) (None, 7, 7, 448) 1204224 ['block7b_se_excite[0][0]'] Y \n",
- " \n",
- " block7b_project_bn (BatchNorma (None, 7, 7, 448) 1792 ['block7b_project_conv[0][0]'] Y \n",
- " lization) \n",
- " \n",
- " block7b_drop (FixedDropout) (None, 7, 7, 448) 0 ['block7b_project_bn[0][0]'] Y \n",
- " \n",
- " block7b_add (Add) (None, 7, 7, 448) 0 ['block7b_drop[0][0]', Y \n",
- " 'block7a_project_bn[0][0]'] \n",
- " \n",
- " top_conv (Conv2D) (None, 7, 7, 1792) 802816 ['block7b_add[0][0]'] Y \n",
- " \n",
- " top_bn (BatchNormalization) (None, 7, 7, 1792) 7168 ['top_conv[0][0]'] Y \n",
- " \n",
- " top_activation (Activation) (None, 7, 7, 1792) 0 ['top_bn[0][0]'] Y \n",
- " \n",
- " FC_INPUT_Avg-Pooling (GlobalAv (None, 1792) 0 ['top_activation[0][0]'] Y \n",
- " eragePooling2D) \n",
- " \n",
- " FC_C_Dense-L1-512 (Dense) (None, 512) 918016 ['FC_INPUT_Avg-Pooling[0][0]'] Y \n",
- " \n",
- " FC_C_Dropout-L1-0.1 (Dropout) (None, 512) 0 ['FC_C_Dense-L1-512[0][0]'] Y \n",
- " \n",
- " FC_C_Avg-BatchNormalization-L1 (None, 512) 2048 ['FC_C_Dropout-L1-0.1[0][0]'] Y \n",
- " (BatchNormalization) \n",
- " \n",
- " FC_C_Dense-L2-512 (Dense) (None, 512) 262656 ['FC_C_Avg-BatchNormalization-L Y \n",
- " 1[0][0]'] \n",
- " \n",
- " FC_C_Avg-BatchNormalization-L2 (None, 512) 2048 ['FC_C_Dense-L2-512[0][0]'] Y \n",
- " (BatchNormalization) \n",
- " \n",
- " FC_C_Dense-L3-128 (Dense) (None, 128) 65664 ['FC_C_Avg-BatchNormalization-L Y \n",
- " 2[0][0]'] \n",
- " \n",
- " FC_OUTPUT_Dense-2 (Dense) (None, 2) 258 ['FC_C_Dense-L3-128[0][0]'] Y \n",
- " \n",
- "=============================================================================================================\n",
- "Total params: 18,924,506\n",
- "Trainable params: 18,797,258\n",
- "Non-trainable params: 127,248\n",
- "_____________________________________________________________________________________________________________\n",
- "done.\n"
- ]
- }
- ],
+ "outputs": [],
+ "source": [
+ "from keras_efficientnet_v2 import EfficientNetV2XL\n",
+ "\n",
+ "EfficientNet_M = EfficientNetV2XL(\n",
+ " input_shape=(img_res[0], img_res[1], img_res[2]),\n",
+ " pretrained=\"imagenet21k-ft1k\",\n",
+ " num_classes=2,\n",
+ " dropout=0.4,\n",
+ ")\n",
+ "# define new model\n",
+ "model = Model(inputs=EfficientNet_M.inputs, outputs=EfficientNet_M.outputs)\n",
+ "\n",
+ "# compile model\n",
+ "opt = SGD(momentum=0.9) # noqa: F405\n",
+ "# opt = AdaBeliefOptimizer(epsilon=1e-7, rectify=False, weight_decay=1e-2, print_change_log=False, total_steps=0, amsgrad=False)\n",
+ "# opt = AdaBeliefOptimizer(epsilon=1e-7, rectify=False, weight_decay=1e-3)\n",
+ "# opt = Adam()\n",
+ "model.compile(optimizer=opt, loss=\"categorical_crossentropy\", metrics=[\"accuracy\"])\n",
+ "\n",
+ "freeze_layers = 0\n",
+ "model.summary(show_trainable=True, expand_nested=True)\n",
+ "print(\"done.\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Rev1.5.2 (The best one)\n",
+ "```\n",
+ "recommended: ✅\n",
+ "statuses: Ready\n",
+ "Working: ✅\n",
+ "Max fine tuned acc: 95.54\n",
+ "Max fine tuned acc TLRev2: 97.12\n",
+ "type: transfer learning>>>(EfficientNetB4::CCL)\n",
+ "```"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
"source": [
"from efficientnet.keras import EfficientNetB4 as KENB4\n",
"\n",
@@ -8986,9 +3593,1297 @@
" # BatchNormalization\n",
" BatchNorm_L2 = BatchNormalization(name=\"FC_C_Avg-Pooling-L1\")(Dropout_L1)\n",
" # Dense\n",
- " Dense_L2 = Dense(512, activation=\"relu\", kernel_regularizer=l2(0.01), name=\"FC_C_Dense-L2-512\")(BatchNorm_L2)\n",
+ " Dense_L2 = Dense(512, activation=\"relu\", kernel_regularizer=l2(0.01), name=\"FC_C_Dense-L2-512\")(BatchNorm_L2)\n",
+ " # BatchNormalization\n",
+ " BatchNorm_L3 = BatchNormalization(name=\"FC_C_Avg-Pooling-L2\")(Dense_L2)\n",
+ " # Dense\n",
+ " Dense_L3 = Dense(128, activation=\"relu\", name=\"FC_C_Dense-L3-128\")(BatchNorm_L3)\n",
+ " # Dense\n",
+ " # predictions = Dense(2, activation='softmax')(Dense_L3) / predictions = Dense(1, activation='sigmoid')(Dense_L3)\n",
+ " predictions = Dense(2, activation=\"softmax\", name=\"FC_OUTPUT_Dense-2\")(Dense_L3)\n",
+ " # CDL<<<\n",
+ " model_EfficientNetB7_NS = Model(inputs=base_model.input, outputs=predictions)\n",
+ " print(\"Total model layers: \", len(model_EfficientNetB7_NS.layers))\n",
+ " # OPT/compile\n",
+ " opt = SGD(momentum=0.9, nesterov=False) # noqa: F405\n",
+ " # opt = Nadam()\n",
+ " # opt = Adamax()\n",
+ " # opt = RMSprop(momentum=0.9)\n",
+ " # opt = Adagrad()\n",
+ " # opt = AdaBeliefOptimizer(epsilon=1e-7, rectify=False, weight_decay=5e-4, print_change_log=False, total_steps=0, amsgrad=False)\n",
+ " # opt = Yogi()\n",
+ " model_EfficientNetB7_NS.compile(\n",
+ " optimizer=opt, loss=\"categorical_crossentropy\", metrics=[\"accuracy\"]\n",
+ " ) # categorical_crossentropy / binary_crossentropy\n",
+ "\n",
+ " return model_EfficientNetB7_NS\n",
+ "\n",
+ "\n",
+ "print(\"Creating the model...\")\n",
+ "# Main\n",
+ "freeze_layers = 0\n",
+ "model = Eff_B7_NS(freeze_layers)\n",
+ "model.summary(show_trainable=True, expand_nested=True)\n",
+ "print(\"done.\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### V(T) Beta3"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 13,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Creating the model...\n",
+ "Total model layers: 10\n",
+ "Model: \"model\"\n",
+ "____________________________________________________________________________\n",
+ " Layer (type) Output Shape Param # Trainable \n",
+ "============================================================================\n",
+ " input_3 (InputLayer) [(None, 224, 224, 3)] 0 Y \n",
+ " \n",
+ " convnext_xlarge (Functional (None, None, None, 2048) 34814796 Y \n",
+ " ) 8 \n",
+ "|¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯|\n",
+ "| input_2 (InputLayer) [(None, None, None, 3)] 0 Y |\n",
+ "| |\n",
+ "| convnext_xlarge_prestem_nor (None, None, None, 3) 0 Y |\n",
+ "| malization (Normalization) |\n",
+ "| |\n",
+ "| convnext_xlarge_stem (Seque (None, None, None, 256) 13056 Y |\n",
+ "| ntial) |\n",
+ "||¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯||\n",
+ "|| convnext_xlarge_stem_conv ( (None, None, None, 256) 12544 Y ||\n",
+ "|| Conv2D) ||\n",
+ "|| ||\n",
+ "|| convnext_xlarge_stem_layern (None, None, None, 256) 512 Y ||\n",
+ "|| orm (LayerNormalization) ||\n",
+ "|¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯|\n",
+ "| convnext_xlarge_stage_0_blo (None, None, None, 256) 12800 Y |\n",
+ "| ck_0_depthwise_conv (Conv2D |\n",
+ "| ) |\n",
+ "| |\n",
+ "| convnext_xlarge_stage_0_blo (None, None, None, 256) 512 Y |\n",
+ "| ck_0_layernorm (LayerNormal |\n",
+ "| ization) |\n",
+ "| |\n",
+ "| convnext_xlarge_stage_0_blo (None, None, None, 1024) 263168 Y |\n",
+ "| ck_0_pointwise_conv_1 (Dens |\n",
+ "| e) |\n",
+ "| |\n",
+ "| convnext_xlarge_stage_0_blo (None, None, None, 1024) 0 Y |\n",
+ "| ck_0_gelu (Activation) |\n",
+ "| |\n",
+ "| convnext_xlarge_stage_0_blo (None, None, None, 256) 262400 Y |\n",
+ "| ck_0_pointwise_conv_2 (Dens |\n",
+ "| e) |\n",
+ "| |\n",
+ "| convnext_xlarge_stage_0_blo (None, None, None, 256) 256 Y |\n",
+ "| ck_0_layer_scale (LayerScal |\n",
+ "| e) |\n",
+ "| |\n",
+ "| convnext_xlarge_stage_0_blo (None, None, None, 256) 0 Y |\n",
+ "| ck_0_identity (Activation) |\n",
+ "| |\n",
+ "| tf.__operators__.add (TFOpL (None, None, None, 256) 0 Y |\n",
+ "| ambda) |\n",
+ "| |\n",
+ "| convnext_xlarge_stage_0_blo (None, None, None, 256) 12800 Y |\n",
+ "| ck_1_depthwise_conv (Conv2D |\n",
+ "| ) |\n",
+ "| |\n",
+ "| convnext_xlarge_stage_0_blo (None, None, None, 256) 512 Y |\n",
+ "| ck_1_layernorm (LayerNormal |\n",
+ "| ization) |\n",
+ "| |\n",
+ "| convnext_xlarge_stage_0_blo (None, None, None, 1024) 263168 Y |\n",
+ "| ck_1_pointwise_conv_1 (Dens |\n",
+ "| e) |\n",
+ "| |\n",
+ "| convnext_xlarge_stage_0_blo (None, None, None, 1024) 0 Y |\n",
+ "| ck_1_gelu (Activation) |\n",
+ "| |\n",
+ "| convnext_xlarge_stage_0_blo (None, None, None, 256) 262400 Y |\n",
+ "| ck_1_pointwise_conv_2 (Dens |\n",
+ "| e) |\n",
+ "| |\n",
+ "| convnext_xlarge_stage_0_blo (None, None, None, 256) 256 Y |\n",
+ "| ck_1_layer_scale (LayerScal |\n",
+ "| e) |\n",
+ "| |\n",
+ "| convnext_xlarge_stage_0_blo (None, None, None, 256) 0 Y |\n",
+ "| ck_1_identity (Activation) |\n",
+ "| |\n",
+ "| tf.__operators__.add_1 (TFO (None, None, None, 256) 0 Y |\n",
+ "| pLambda) |\n",
+ "| |\n",
+ "| convnext_xlarge_stage_0_blo (None, None, None, 256) 12800 Y |\n",
+ "| ck_2_depthwise_conv (Conv2D |\n",
+ "| ) |\n",
+ "| |\n",
+ "| convnext_xlarge_stage_0_blo (None, None, None, 256) 512 Y |\n",
+ "| ck_2_layernorm (LayerNormal |\n",
+ "| ization) |\n",
+ "| |\n",
+ "| convnext_xlarge_stage_0_blo (None, None, None, 1024) 263168 Y |\n",
+ "| ck_2_pointwise_conv_1 (Dens |\n",
+ "| e) |\n",
+ "| |\n",
+ "| convnext_xlarge_stage_0_blo (None, None, None, 1024) 0 Y |\n",
+ "| ck_2_gelu (Activation) |\n",
+ "| |\n",
+ "| convnext_xlarge_stage_0_blo (None, None, None, 256) 262400 Y |\n",
+ "| ck_2_pointwise_conv_2 (Dens |\n",
+ "| e) |\n",
+ "| |\n",
+ "| convnext_xlarge_stage_0_blo (None, None, None, 256) 256 Y |\n",
+ "| ck_2_layer_scale (LayerScal |\n",
+ "| e) |\n",
+ "| |\n",
+ "| convnext_xlarge_stage_0_blo (None, None, None, 256) 0 Y |\n",
+ "| ck_2_identity (Activation) |\n",
+ "| |\n",
+ "| tf.__operators__.add_2 (TFO (None, None, None, 256) 0 Y |\n",
+ "| pLambda) |\n",
+ "| |\n",
+ "| convnext_xlarge_downsamplin (None, None, None, 512) 525312 Y |\n",
+ "| g_block_0 (Sequential) |\n",
+ "||¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯||\n",
+ "|| convnext_xlarge_downsamplin (None, None, None, 256) 512 Y ||\n",
+ "|| g_layernorm_0 (LayerNormali ||\n",
+ "|| zation) ||\n",
+ "|| ||\n",
+ "|| convnext_xlarge_downsamplin (None, None, None, 512) 524800 Y ||\n",
+ "|| g_conv_0 (Conv2D) ||\n",
+ "|¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯|\n",
+ "| convnext_xlarge_stage_1_blo (None, None, None, 512) 25600 Y |\n",
+ "| ck_0_depthwise_conv (Conv2D |\n",
+ "| ) |\n",
+ "| |\n",
+ "| convnext_xlarge_stage_1_blo (None, None, None, 512) 1024 Y |\n",
+ "| ck_0_layernorm (LayerNormal |\n",
+ "| ization) |\n",
+ "| |\n",
+ "| convnext_xlarge_stage_1_blo (None, None, None, 2048) 1050624 Y |\n",
+ "| ck_0_pointwise_conv_1 (Dens |\n",
+ "| e) |\n",
+ "| |\n",
+ "| convnext_xlarge_stage_1_blo (None, None, None, 2048) 0 Y |\n",
+ "| ck_0_gelu (Activation) |\n",
+ "| |\n",
+ "| convnext_xlarge_stage_1_blo (None, None, None, 512) 1049088 Y |\n",
+ "| ck_0_pointwise_conv_2 (Dens |\n",
+ "| e) |\n",
+ "| |\n",
+ "| convnext_xlarge_stage_1_blo (None, None, None, 512) 512 Y |\n",
+ "| ck_0_layer_scale (LayerScal |\n",
+ "| e) |\n",
+ "| |\n",
+ "| convnext_xlarge_stage_1_blo (None, None, None, 512) 0 Y |\n",
+ "| ck_0_identity (Activation) |\n",
+ "| |\n",
+ "| tf.__operators__.add_3 (TFO (None, None, None, 512) 0 Y |\n",
+ "| pLambda) |\n",
+ "| |\n",
+ "| convnext_xlarge_stage_1_blo (None, None, None, 512) 25600 Y |\n",
+ "| ck_1_depthwise_conv (Conv2D |\n",
+ "| ) |\n",
+ "| |\n",
+ "| convnext_xlarge_stage_1_blo (None, None, None, 512) 1024 Y |\n",
+ "| ck_1_layernorm (LayerNormal |\n",
+ "| ization) |\n",
+ "| |\n",
+ "| convnext_xlarge_stage_1_blo (None, None, None, 2048) 1050624 Y |\n",
+ "| ck_1_pointwise_conv_1 (Dens |\n",
+ "| e) |\n",
+ "| |\n",
+ "| convnext_xlarge_stage_1_blo (None, None, None, 2048) 0 Y |\n",
+ "| ck_1_gelu (Activation) |\n",
+ "| |\n",
+ "| convnext_xlarge_stage_1_blo (None, None, None, 512) 1049088 Y |\n",
+ "| ck_1_pointwise_conv_2 (Dens |\n",
+ "| e) |\n",
+ "| |\n",
+ "| convnext_xlarge_stage_1_blo (None, None, None, 512) 512 Y |\n",
+ "| ck_1_layer_scale (LayerScal |\n",
+ "| e) |\n",
+ "| |\n",
+ "| convnext_xlarge_stage_1_blo (None, None, None, 512) 0 Y |\n",
+ "| ck_1_identity (Activation) |\n",
+ "| |\n",
+ "| tf.__operators__.add_4 (TFO (None, None, None, 512) 0 Y |\n",
+ "| pLambda) |\n",
+ "| |\n",
+ "| convnext_xlarge_stage_1_blo (None, None, None, 512) 25600 Y |\n",
+ "| ck_2_depthwise_conv (Conv2D |\n",
+ "| ) |\n",
+ "| |\n",
+ "| convnext_xlarge_stage_1_blo (None, None, None, 512) 1024 Y |\n",
+ "| ck_2_layernorm (LayerNormal |\n",
+ "| ization) |\n",
+ "| |\n",
+ "| convnext_xlarge_stage_1_blo (None, None, None, 2048) 1050624 Y |\n",
+ "| ck_2_pointwise_conv_1 (Dens |\n",
+ "| e) |\n",
+ "| |\n",
+ "| convnext_xlarge_stage_1_blo (None, None, None, 2048) 0 Y |\n",
+ "| ck_2_gelu (Activation) |\n",
+ "| |\n",
+ "| convnext_xlarge_stage_1_blo (None, None, None, 512) 1049088 Y |\n",
+ "| ck_2_pointwise_conv_2 (Dens |\n",
+ "| e) |\n",
+ "| |\n",
+ "| convnext_xlarge_stage_1_blo (None, None, None, 512) 512 Y |\n",
+ "| ck_2_layer_scale (LayerScal |\n",
+ "| e) |\n",
+ "| |\n",
+ "| convnext_xlarge_stage_1_blo (None, None, None, 512) 0 Y |\n",
+ "| ck_2_identity (Activation) |\n",
+ "| |\n",
+ "| tf.__operators__.add_5 (TFO (None, None, None, 512) 0 Y |\n",
+ "| pLambda) |\n",
+ "| |\n",
+ "| convnext_xlarge_downsamplin (None, None, None, 1024) 2099200 Y |\n",
+ "| g_block_1 (Sequential) |\n",
+ "||¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯||\n",
+ "|| convnext_xlarge_downsamplin (None, None, None, 512) 1024 Y ||\n",
+ "|| g_layernorm_1 (LayerNormali ||\n",
+ "|| zation) ||\n",
+ "|| ||\n",
+ "|| convnext_xlarge_downsamplin (None, None, None, 1024) 2098176 Y ||\n",
+ "|| g_conv_1 (Conv2D) ||\n",
+ "|¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯|\n",
+ "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 51200 Y |\n",
+ "| ck_0_depthwise_conv (Conv2D |\n",
+ "| ) |\n",
+ "| |\n",
+ "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 2048 Y |\n",
+ "| ck_0_layernorm (LayerNormal |\n",
+ "| ization) |\n",
+ "| |\n",
+ "| convnext_xlarge_stage_2_blo (None, None, None, 4096) 4198400 Y |\n",
+ "| ck_0_pointwise_conv_1 (Dens |\n",
+ "| e) |\n",
+ "| |\n",
+ "| convnext_xlarge_stage_2_blo (None, None, None, 4096) 0 Y |\n",
+ "| ck_0_gelu (Activation) |\n",
+ "| |\n",
+ "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 4195328 Y |\n",
+ "| ck_0_pointwise_conv_2 (Dens |\n",
+ "| e) |\n",
+ "| |\n",
+ "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 1024 Y |\n",
+ "| ck_0_layer_scale (LayerScal |\n",
+ "| e) |\n",
+ "| |\n",
+ "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 0 Y |\n",
+ "| ck_0_identity (Activation) |\n",
+ "| |\n",
+ "| tf.__operators__.add_6 (TFO (None, None, None, 1024) 0 Y |\n",
+ "| pLambda) |\n",
+ "| |\n",
+ "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 51200 Y |\n",
+ "| ck_1_depthwise_conv (Conv2D |\n",
+ "| ) |\n",
+ "| |\n",
+ "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 2048 Y |\n",
+ "| ck_1_layernorm (LayerNormal |\n",
+ "| ization) |\n",
+ "| |\n",
+ "| convnext_xlarge_stage_2_blo (None, None, None, 4096) 4198400 Y |\n",
+ "| ck_1_pointwise_conv_1 (Dens |\n",
+ "| e) |\n",
+ "| |\n",
+ "| convnext_xlarge_stage_2_blo (None, None, None, 4096) 0 Y |\n",
+ "| ck_1_gelu (Activation) |\n",
+ "| |\n",
+ "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 4195328 Y |\n",
+ "| ck_1_pointwise_conv_2 (Dens |\n",
+ "| e) |\n",
+ "| |\n",
+ "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 1024 Y |\n",
+ "| ck_1_layer_scale (LayerScal |\n",
+ "| e) |\n",
+ "| |\n",
+ "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 0 Y |\n",
+ "| ck_1_identity (Activation) |\n",
+ "| |\n",
+ "| tf.__operators__.add_7 (TFO (None, None, None, 1024) 0 Y |\n",
+ "| pLambda) |\n",
+ "| |\n",
+ "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 51200 Y |\n",
+ "| ck_2_depthwise_conv (Conv2D |\n",
+ "| ) |\n",
+ "| |\n",
+ "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 2048 Y |\n",
+ "| ck_2_layernorm (LayerNormal |\n",
+ "| ization) |\n",
+ "| |\n",
+ "| convnext_xlarge_stage_2_blo (None, None, None, 4096) 4198400 Y |\n",
+ "| ck_2_pointwise_conv_1 (Dens |\n",
+ "| e) |\n",
+ "| |\n",
+ "| convnext_xlarge_stage_2_blo (None, None, None, 4096) 0 Y |\n",
+ "| ck_2_gelu (Activation) |\n",
+ "| |\n",
+ "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 4195328 Y |\n",
+ "| ck_2_pointwise_conv_2 (Dens |\n",
+ "| e) |\n",
+ "| |\n",
+ "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 1024 Y |\n",
+ "| ck_2_layer_scale (LayerScal |\n",
+ "| e) |\n",
+ "| |\n",
+ "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 0 Y |\n",
+ "| ck_2_identity (Activation) |\n",
+ "| |\n",
+ "| tf.__operators__.add_8 (TFO (None, None, None, 1024) 0 Y |\n",
+ "| pLambda) |\n",
+ "| |\n",
+ "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 51200 Y |\n",
+ "| ck_3_depthwise_conv (Conv2D |\n",
+ "| ) |\n",
+ "| |\n",
+ "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 2048 Y |\n",
+ "| ck_3_layernorm (LayerNormal |\n",
+ "| ization) |\n",
+ "| |\n",
+ "| convnext_xlarge_stage_2_blo (None, None, None, 4096) 4198400 Y |\n",
+ "| ck_3_pointwise_conv_1 (Dens |\n",
+ "| e) |\n",
+ "| |\n",
+ "| convnext_xlarge_stage_2_blo (None, None, None, 4096) 0 Y |\n",
+ "| ck_3_gelu (Activation) |\n",
+ "| |\n",
+ "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 4195328 Y |\n",
+ "| ck_3_pointwise_conv_2 (Dens |\n",
+ "| e) |\n",
+ "| |\n",
+ "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 1024 Y |\n",
+ "| ck_3_layer_scale (LayerScal |\n",
+ "| e) |\n",
+ "| |\n",
+ "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 0 Y |\n",
+ "| ck_3_identity (Activation) |\n",
+ "| |\n",
+ "| tf.__operators__.add_9 (TFO (None, None, None, 1024) 0 Y |\n",
+ "| pLambda) |\n",
+ "| |\n",
+ "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 51200 Y |\n",
+ "| ck_4_depthwise_conv (Conv2D |\n",
+ "| ) |\n",
+ "| |\n",
+ "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 2048 Y |\n",
+ "| ck_4_layernorm (LayerNormal |\n",
+ "| ization) |\n",
+ "| |\n",
+ "| convnext_xlarge_stage_2_blo (None, None, None, 4096) 4198400 Y |\n",
+ "| ck_4_pointwise_conv_1 (Dens |\n",
+ "| e) |\n",
+ "| |\n",
+ "| convnext_xlarge_stage_2_blo (None, None, None, 4096) 0 Y |\n",
+ "| ck_4_gelu (Activation) |\n",
+ "| |\n",
+ "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 4195328 Y |\n",
+ "| ck_4_pointwise_conv_2 (Dens |\n",
+ "| e) |\n",
+ "| |\n",
+ "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 1024 Y |\n",
+ "| ck_4_layer_scale (LayerScal |\n",
+ "| e) |\n",
+ "| |\n",
+ "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 0 Y |\n",
+ "| ck_4_identity (Activation) |\n",
+ "| |\n",
+ "| tf.__operators__.add_10 (TF (None, None, None, 1024) 0 Y |\n",
+ "| OpLambda) |\n",
+ "| |\n",
+ "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 51200 Y |\n",
+ "| ck_5_depthwise_conv (Conv2D |\n",
+ "| ) |\n",
+ "| |\n",
+ "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 2048 Y |\n",
+ "| ck_5_layernorm (LayerNormal |\n",
+ "| ization) |\n",
+ "| |\n",
+ "| convnext_xlarge_stage_2_blo (None, None, None, 4096) 4198400 Y |\n",
+ "| ck_5_pointwise_conv_1 (Dens |\n",
+ "| e) |\n",
+ "| |\n",
+ "| convnext_xlarge_stage_2_blo (None, None, None, 4096) 0 Y |\n",
+ "| ck_5_gelu (Activation) |\n",
+ "| |\n",
+ "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 4195328 Y |\n",
+ "| ck_5_pointwise_conv_2 (Dens |\n",
+ "| e) |\n",
+ "| |\n",
+ "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 1024 Y |\n",
+ "| ck_5_layer_scale (LayerScal |\n",
+ "| e) |\n",
+ "| |\n",
+ "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 0 Y |\n",
+ "| ck_5_identity (Activation) |\n",
+ "| |\n",
+ "| tf.__operators__.add_11 (TF (None, None, None, 1024) 0 Y |\n",
+ "| OpLambda) |\n",
+ "| |\n",
+ "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 51200 Y |\n",
+ "| ck_6_depthwise_conv (Conv2D |\n",
+ "| ) |\n",
+ "| |\n",
+ "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 2048 Y |\n",
+ "| ck_6_layernorm (LayerNormal |\n",
+ "| ization) |\n",
+ "| |\n",
+ "| convnext_xlarge_stage_2_blo (None, None, None, 4096) 4198400 Y |\n",
+ "| ck_6_pointwise_conv_1 (Dens |\n",
+ "| e) |\n",
+ "| |\n",
+ "| convnext_xlarge_stage_2_blo (None, None, None, 4096) 0 Y |\n",
+ "| ck_6_gelu (Activation) |\n",
+ "| |\n",
+ "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 4195328 Y |\n",
+ "| ck_6_pointwise_conv_2 (Dens |\n",
+ "| e) |\n",
+ "| |\n",
+ "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 1024 Y |\n",
+ "| ck_6_layer_scale (LayerScal |\n",
+ "| e) |\n",
+ "| |\n",
+ "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 0 Y |\n",
+ "| ck_6_identity (Activation) |\n",
+ "| |\n",
+ "| tf.__operators__.add_12 (TF (None, None, None, 1024) 0 Y |\n",
+ "| OpLambda) |\n",
+ "| |\n",
+ "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 51200 Y |\n",
+ "| ck_7_depthwise_conv (Conv2D |\n",
+ "| ) |\n",
+ "| |\n",
+ "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 2048 Y |\n",
+ "| ck_7_layernorm (LayerNormal |\n",
+ "| ization) |\n",
+ "| |\n",
+ "| convnext_xlarge_stage_2_blo (None, None, None, 4096) 4198400 Y |\n",
+ "| ck_7_pointwise_conv_1 (Dens |\n",
+ "| e) |\n",
+ "| |\n",
+ "| convnext_xlarge_stage_2_blo (None, None, None, 4096) 0 Y |\n",
+ "| ck_7_gelu (Activation) |\n",
+ "| |\n",
+ "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 4195328 Y |\n",
+ "| ck_7_pointwise_conv_2 (Dens |\n",
+ "| e) |\n",
+ "| |\n",
+ "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 1024 Y |\n",
+ "| ck_7_layer_scale (LayerScal |\n",
+ "| e) |\n",
+ "| |\n",
+ "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 0 Y |\n",
+ "| ck_7_identity (Activation) |\n",
+ "| |\n",
+ "| tf.__operators__.add_13 (TF (None, None, None, 1024) 0 Y |\n",
+ "| OpLambda) |\n",
+ "| |\n",
+ "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 51200 Y |\n",
+ "| ck_8_depthwise_conv (Conv2D |\n",
+ "| ) |\n",
+ "| |\n",
+ "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 2048 Y |\n",
+ "| ck_8_layernorm (LayerNormal |\n",
+ "| ization) |\n",
+ "| |\n",
+ "| convnext_xlarge_stage_2_blo (None, None, None, 4096) 4198400 Y |\n",
+ "| ck_8_pointwise_conv_1 (Dens |\n",
+ "| e) |\n",
+ "| |\n",
+ "| convnext_xlarge_stage_2_blo (None, None, None, 4096) 0 Y |\n",
+ "| ck_8_gelu (Activation) |\n",
+ "| |\n",
+ "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 4195328 Y |\n",
+ "| ck_8_pointwise_conv_2 (Dens |\n",
+ "| e) |\n",
+ "| |\n",
+ "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 1024 Y |\n",
+ "| ck_8_layer_scale (LayerScal |\n",
+ "| e) |\n",
+ "| |\n",
+ "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 0 Y |\n",
+ "| ck_8_identity (Activation) |\n",
+ "| |\n",
+ "| tf.__operators__.add_14 (TF (None, None, None, 1024) 0 Y |\n",
+ "| OpLambda) |\n",
+ "| |\n",
+ "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 51200 Y |\n",
+ "| ck_9_depthwise_conv (Conv2D |\n",
+ "| ) |\n",
+ "| |\n",
+ "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 2048 Y |\n",
+ "| ck_9_layernorm (LayerNormal |\n",
+ "| ization) |\n",
+ "| |\n",
+ "| convnext_xlarge_stage_2_blo (None, None, None, 4096) 4198400 Y |\n",
+ "| ck_9_pointwise_conv_1 (Dens |\n",
+ "| e) |\n",
+ "| |\n",
+ "| convnext_xlarge_stage_2_blo (None, None, None, 4096) 0 Y |\n",
+ "| ck_9_gelu (Activation) |\n",
+ "| |\n",
+ "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 4195328 Y |\n",
+ "| ck_9_pointwise_conv_2 (Dens |\n",
+ "| e) |\n",
+ "| |\n",
+ "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 1024 Y |\n",
+ "| ck_9_layer_scale (LayerScal |\n",
+ "| e) |\n",
+ "| |\n",
+ "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 0 Y |\n",
+ "| ck_9_identity (Activation) |\n",
+ "| |\n",
+ "| tf.__operators__.add_15 (TF (None, None, None, 1024) 0 Y |\n",
+ "| OpLambda) |\n",
+ "| |\n",
+ "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 51200 Y |\n",
+ "| ck_10_depthwise_conv (Conv2 |\n",
+ "| D) |\n",
+ "| |\n",
+ "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 2048 Y |\n",
+ "| ck_10_layernorm (LayerNorma |\n",
+ "| lization) |\n",
+ "| |\n",
+ "| convnext_xlarge_stage_2_blo (None, None, None, 4096) 4198400 Y |\n",
+ "| ck_10_pointwise_conv_1 (Den |\n",
+ "| se) |\n",
+ "| |\n",
+ "| convnext_xlarge_stage_2_blo (None, None, None, 4096) 0 Y |\n",
+ "| ck_10_gelu (Activation) |\n",
+ "| |\n",
+ "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 4195328 Y |\n",
+ "| ck_10_pointwise_conv_2 (Den |\n",
+ "| se) |\n",
+ "| |\n",
+ "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 1024 Y |\n",
+ "| ck_10_layer_scale (LayerSca |\n",
+ "| le) |\n",
+ "| |\n",
+ "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 0 Y |\n",
+ "| ck_10_identity (Activation) |\n",
+ "| |\n",
+ "| tf.__operators__.add_16 (TF (None, None, None, 1024) 0 Y |\n",
+ "| OpLambda) |\n",
+ "| |\n",
+ "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 51200 Y |\n",
+ "| ck_11_depthwise_conv (Conv2 |\n",
+ "| D) |\n",
+ "| |\n",
+ "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 2048 Y |\n",
+ "| ck_11_layernorm (LayerNorma |\n",
+ "| lization) |\n",
+ "| |\n",
+ "| convnext_xlarge_stage_2_blo (None, None, None, 4096) 4198400 Y |\n",
+ "| ck_11_pointwise_conv_1 (Den |\n",
+ "| se) |\n",
+ "| |\n",
+ "| convnext_xlarge_stage_2_blo (None, None, None, 4096) 0 Y |\n",
+ "| ck_11_gelu (Activation) |\n",
+ "| |\n",
+ "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 4195328 Y |\n",
+ "| ck_11_pointwise_conv_2 (Den |\n",
+ "| se) |\n",
+ "| |\n",
+ "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 1024 Y |\n",
+ "| ck_11_layer_scale (LayerSca |\n",
+ "| le) |\n",
+ "| |\n",
+ "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 0 Y |\n",
+ "| ck_11_identity (Activation) |\n",
+ "| |\n",
+ "| tf.__operators__.add_17 (TF (None, None, None, 1024) 0 Y |\n",
+ "| OpLambda) |\n",
+ "| |\n",
+ "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 51200 Y |\n",
+ "| ck_12_depthwise_conv (Conv2 |\n",
+ "| D) |\n",
+ "| |\n",
+ "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 2048 Y |\n",
+ "| ck_12_layernorm (LayerNorma |\n",
+ "| lization) |\n",
+ "| |\n",
+ "| convnext_xlarge_stage_2_blo (None, None, None, 4096) 4198400 Y |\n",
+ "| ck_12_pointwise_conv_1 (Den |\n",
+ "| se) |\n",
+ "| |\n",
+ "| convnext_xlarge_stage_2_blo (None, None, None, 4096) 0 Y |\n",
+ "| ck_12_gelu (Activation) |\n",
+ "| |\n",
+ "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 4195328 Y |\n",
+ "| ck_12_pointwise_conv_2 (Den |\n",
+ "| se) |\n",
+ "| |\n",
+ "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 1024 Y |\n",
+ "| ck_12_layer_scale (LayerSca |\n",
+ "| le) |\n",
+ "| |\n",
+ "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 0 Y |\n",
+ "| ck_12_identity (Activation) |\n",
+ "| |\n",
+ "| tf.__operators__.add_18 (TF (None, None, None, 1024) 0 Y |\n",
+ "| OpLambda) |\n",
+ "| |\n",
+ "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 51200 Y |\n",
+ "| ck_13_depthwise_conv (Conv2 |\n",
+ "| D) |\n",
+ "| |\n",
+ "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 2048 Y |\n",
+ "| ck_13_layernorm (LayerNorma |\n",
+ "| lization) |\n",
+ "| |\n",
+ "| convnext_xlarge_stage_2_blo (None, None, None, 4096) 4198400 Y |\n",
+ "| ck_13_pointwise_conv_1 (Den |\n",
+ "| se) |\n",
+ "| |\n",
+ "| convnext_xlarge_stage_2_blo (None, None, None, 4096) 0 Y |\n",
+ "| ck_13_gelu (Activation) |\n",
+ "| |\n",
+ "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 4195328 Y |\n",
+ "| ck_13_pointwise_conv_2 (Den |\n",
+ "| se) |\n",
+ "| |\n",
+ "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 1024 Y |\n",
+ "| ck_13_layer_scale (LayerSca |\n",
+ "| le) |\n",
+ "| |\n",
+ "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 0 Y |\n",
+ "| ck_13_identity (Activation) |\n",
+ "| |\n",
+ "| tf.__operators__.add_19 (TF (None, None, None, 1024) 0 Y |\n",
+ "| OpLambda) |\n",
+ "| |\n",
+ "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 51200 Y |\n",
+ "| ck_14_depthwise_conv (Conv2 |\n",
+ "| D) |\n",
+ "| |\n",
+ "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 2048 Y |\n",
+ "| ck_14_layernorm (LayerNorma |\n",
+ "| lization) |\n",
+ "| |\n",
+ "| convnext_xlarge_stage_2_blo (None, None, None, 4096) 4198400 Y |\n",
+ "| ck_14_pointwise_conv_1 (Den |\n",
+ "| se) |\n",
+ "| |\n",
+ "| convnext_xlarge_stage_2_blo (None, None, None, 4096) 0 Y |\n",
+ "| ck_14_gelu (Activation) |\n",
+ "| |\n",
+ "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 4195328 Y |\n",
+ "| ck_14_pointwise_conv_2 (Den |\n",
+ "| se) |\n",
+ "| |\n",
+ "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 1024 Y |\n",
+ "| ck_14_layer_scale (LayerSca |\n",
+ "| le) |\n",
+ "| |\n",
+ "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 0 Y |\n",
+ "| ck_14_identity (Activation) |\n",
+ "| |\n",
+ "| tf.__operators__.add_20 (TF (None, None, None, 1024) 0 Y |\n",
+ "| OpLambda) |\n",
+ "| |\n",
+ "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 51200 Y |\n",
+ "| ck_15_depthwise_conv (Conv2 |\n",
+ "| D) |\n",
+ "| |\n",
+ "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 2048 Y |\n",
+ "| ck_15_layernorm (LayerNorma |\n",
+ "| lization) |\n",
+ "| |\n",
+ "| convnext_xlarge_stage_2_blo (None, None, None, 4096) 4198400 Y |\n",
+ "| ck_15_pointwise_conv_1 (Den |\n",
+ "| se) |\n",
+ "| |\n",
+ "| convnext_xlarge_stage_2_blo (None, None, None, 4096) 0 Y |\n",
+ "| ck_15_gelu (Activation) |\n",
+ "| |\n",
+ "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 4195328 Y |\n",
+ "| ck_15_pointwise_conv_2 (Den |\n",
+ "| se) |\n",
+ "| |\n",
+ "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 1024 Y |\n",
+ "| ck_15_layer_scale (LayerSca |\n",
+ "| le) |\n",
+ "| |\n",
+ "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 0 Y |\n",
+ "| ck_15_identity (Activation) |\n",
+ "| |\n",
+ "| tf.__operators__.add_21 (TF (None, None, None, 1024) 0 Y |\n",
+ "| OpLambda) |\n",
+ "| |\n",
+ "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 51200 Y |\n",
+ "| ck_16_depthwise_conv (Conv2 |\n",
+ "| D) |\n",
+ "| |\n",
+ "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 2048 Y |\n",
+ "| ck_16_layernorm (LayerNorma |\n",
+ "| lization) |\n",
+ "| |\n",
+ "| convnext_xlarge_stage_2_blo (None, None, None, 4096) 4198400 Y |\n",
+ "| ck_16_pointwise_conv_1 (Den |\n",
+ "| se) |\n",
+ "| |\n",
+ "| convnext_xlarge_stage_2_blo (None, None, None, 4096) 0 Y |\n",
+ "| ck_16_gelu (Activation) |\n",
+ "| |\n",
+ "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 4195328 Y |\n",
+ "| ck_16_pointwise_conv_2 (Den |\n",
+ "| se) |\n",
+ "| |\n",
+ "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 1024 Y |\n",
+ "| ck_16_layer_scale (LayerSca |\n",
+ "| le) |\n",
+ "| |\n",
+ "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 0 Y |\n",
+ "| ck_16_identity (Activation) |\n",
+ "| |\n",
+ "| tf.__operators__.add_22 (TF (None, None, None, 1024) 0 Y |\n",
+ "| OpLambda) |\n",
+ "| |\n",
+ "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 51200 Y |\n",
+ "| ck_17_depthwise_conv (Conv2 |\n",
+ "| D) |\n",
+ "| |\n",
+ "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 2048 Y |\n",
+ "| ck_17_layernorm (LayerNorma |\n",
+ "| lization) |\n",
+ "| |\n",
+ "| convnext_xlarge_stage_2_blo (None, None, None, 4096) 4198400 Y |\n",
+ "| ck_17_pointwise_conv_1 (Den |\n",
+ "| se) |\n",
+ "| |\n",
+ "| convnext_xlarge_stage_2_blo (None, None, None, 4096) 0 Y |\n",
+ "| ck_17_gelu (Activation) |\n",
+ "| |\n",
+ "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 4195328 Y |\n",
+ "| ck_17_pointwise_conv_2 (Den |\n",
+ "| se) |\n",
+ "| |\n",
+ "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 1024 Y |\n",
+ "| ck_17_layer_scale (LayerSca |\n",
+ "| le) |\n",
+ "| |\n",
+ "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 0 Y |\n",
+ "| ck_17_identity (Activation) |\n",
+ "| |\n",
+ "| tf.__operators__.add_23 (TF (None, None, None, 1024) 0 Y |\n",
+ "| OpLambda) |\n",
+ "| |\n",
+ "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 51200 Y |\n",
+ "| ck_18_depthwise_conv (Conv2 |\n",
+ "| D) |\n",
+ "| |\n",
+ "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 2048 Y |\n",
+ "| ck_18_layernorm (LayerNorma |\n",
+ "| lization) |\n",
+ "| |\n",
+ "| convnext_xlarge_stage_2_blo (None, None, None, 4096) 4198400 Y |\n",
+ "| ck_18_pointwise_conv_1 (Den |\n",
+ "| se) |\n",
+ "| |\n",
+ "| convnext_xlarge_stage_2_blo (None, None, None, 4096) 0 Y |\n",
+ "| ck_18_gelu (Activation) |\n",
+ "| |\n",
+ "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 4195328 Y |\n",
+ "| ck_18_pointwise_conv_2 (Den |\n",
+ "| se) |\n",
+ "| |\n",
+ "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 1024 Y |\n",
+ "| ck_18_layer_scale (LayerSca |\n",
+ "| le) |\n",
+ "| |\n",
+ "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 0 Y |\n",
+ "| ck_18_identity (Activation) |\n",
+ "| |\n",
+ "| tf.__operators__.add_24 (TF (None, None, None, 1024) 0 Y |\n",
+ "| OpLambda) |\n",
+ "| |\n",
+ "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 51200 Y |\n",
+ "| ck_19_depthwise_conv (Conv2 |\n",
+ "| D) |\n",
+ "| |\n",
+ "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 2048 Y |\n",
+ "| ck_19_layernorm (LayerNorma |\n",
+ "| lization) |\n",
+ "| |\n",
+ "| convnext_xlarge_stage_2_blo (None, None, None, 4096) 4198400 Y |\n",
+ "| ck_19_pointwise_conv_1 (Den |\n",
+ "| se) |\n",
+ "| |\n",
+ "| convnext_xlarge_stage_2_blo (None, None, None, 4096) 0 Y |\n",
+ "| ck_19_gelu (Activation) |\n",
+ "| |\n",
+ "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 4195328 Y |\n",
+ "| ck_19_pointwise_conv_2 (Den |\n",
+ "| se) |\n",
+ "| |\n",
+ "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 1024 Y |\n",
+ "| ck_19_layer_scale (LayerSca |\n",
+ "| le) |\n",
+ "| |\n",
+ "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 0 Y |\n",
+ "| ck_19_identity (Activation) |\n",
+ "| |\n",
+ "| tf.__operators__.add_25 (TF (None, None, None, 1024) 0 Y |\n",
+ "| OpLambda) |\n",
+ "| |\n",
+ "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 51200 Y |\n",
+ "| ck_20_depthwise_conv (Conv2 |\n",
+ "| D) |\n",
+ "| |\n",
+ "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 2048 Y |\n",
+ "| ck_20_layernorm (LayerNorma |\n",
+ "| lization) |\n",
+ "| |\n",
+ "| convnext_xlarge_stage_2_blo (None, None, None, 4096) 4198400 Y |\n",
+ "| ck_20_pointwise_conv_1 (Den |\n",
+ "| se) |\n",
+ "| |\n",
+ "| convnext_xlarge_stage_2_blo (None, None, None, 4096) 0 Y |\n",
+ "| ck_20_gelu (Activation) |\n",
+ "| |\n",
+ "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 4195328 Y |\n",
+ "| ck_20_pointwise_conv_2 (Den |\n",
+ "| se) |\n",
+ "| |\n",
+ "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 1024 Y |\n",
+ "| ck_20_layer_scale (LayerSca |\n",
+ "| le) |\n",
+ "| |\n",
+ "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 0 Y |\n",
+ "| ck_20_identity (Activation) |\n",
+ "| |\n",
+ "| tf.__operators__.add_26 (TF (None, None, None, 1024) 0 Y |\n",
+ "| OpLambda) |\n",
+ "| |\n",
+ "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 51200 Y |\n",
+ "| ck_21_depthwise_conv (Conv2 |\n",
+ "| D) |\n",
+ "| |\n",
+ "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 2048 Y |\n",
+ "| ck_21_layernorm (LayerNorma |\n",
+ "| lization) |\n",
+ "| |\n",
+ "| convnext_xlarge_stage_2_blo (None, None, None, 4096) 4198400 Y |\n",
+ "| ck_21_pointwise_conv_1 (Den |\n",
+ "| se) |\n",
+ "| |\n",
+ "| convnext_xlarge_stage_2_blo (None, None, None, 4096) 0 Y |\n",
+ "| ck_21_gelu (Activation) |\n",
+ "| |\n",
+ "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 4195328 Y |\n",
+ "| ck_21_pointwise_conv_2 (Den |\n",
+ "| se) |\n",
+ "| |\n",
+ "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 1024 Y |\n",
+ "| ck_21_layer_scale (LayerSca |\n",
+ "| le) |\n",
+ "| |\n",
+ "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 0 Y |\n",
+ "| ck_21_identity (Activation) |\n",
+ "| |\n",
+ "| tf.__operators__.add_27 (TF (None, None, None, 1024) 0 Y |\n",
+ "| OpLambda) |\n",
+ "| |\n",
+ "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 51200 Y |\n",
+ "| ck_22_depthwise_conv (Conv2 |\n",
+ "| D) |\n",
+ "| |\n",
+ "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 2048 Y |\n",
+ "| ck_22_layernorm (LayerNorma |\n",
+ "| lization) |\n",
+ "| |\n",
+ "| convnext_xlarge_stage_2_blo (None, None, None, 4096) 4198400 Y |\n",
+ "| ck_22_pointwise_conv_1 (Den |\n",
+ "| se) |\n",
+ "| |\n",
+ "| convnext_xlarge_stage_2_blo (None, None, None, 4096) 0 Y |\n",
+ "| ck_22_gelu (Activation) |\n",
+ "| |\n",
+ "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 4195328 Y |\n",
+ "| ck_22_pointwise_conv_2 (Den |\n",
+ "| se) |\n",
+ "| |\n",
+ "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 1024 Y |\n",
+ "| ck_22_layer_scale (LayerSca |\n",
+ "| le) |\n",
+ "| |\n",
+ "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 0 Y |\n",
+ "| ck_22_identity (Activation) |\n",
+ "| |\n",
+ "| tf.__operators__.add_28 (TF (None, None, None, 1024) 0 Y |\n",
+ "| OpLambda) |\n",
+ "| |\n",
+ "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 51200 Y |\n",
+ "| ck_23_depthwise_conv (Conv2 |\n",
+ "| D) |\n",
+ "| |\n",
+ "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 2048 Y |\n",
+ "| ck_23_layernorm (LayerNorma |\n",
+ "| lization) |\n",
+ "| |\n",
+ "| convnext_xlarge_stage_2_blo (None, None, None, 4096) 4198400 Y |\n",
+ "| ck_23_pointwise_conv_1 (Den |\n",
+ "| se) |\n",
+ "| |\n",
+ "| convnext_xlarge_stage_2_blo (None, None, None, 4096) 0 Y |\n",
+ "| ck_23_gelu (Activation) |\n",
+ "| |\n",
+ "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 4195328 Y |\n",
+ "| ck_23_pointwise_conv_2 (Den |\n",
+ "| se) |\n",
+ "| |\n",
+ "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 1024 Y |\n",
+ "| ck_23_layer_scale (LayerSca |\n",
+ "| le) |\n",
+ "| |\n",
+ "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 0 Y |\n",
+ "| ck_23_identity (Activation) |\n",
+ "| |\n",
+ "| tf.__operators__.add_29 (TF (None, None, None, 1024) 0 Y |\n",
+ "| OpLambda) |\n",
+ "| |\n",
+ "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 51200 Y |\n",
+ "| ck_24_depthwise_conv (Conv2 |\n",
+ "| D) |\n",
+ "| |\n",
+ "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 2048 Y |\n",
+ "| ck_24_layernorm (LayerNorma |\n",
+ "| lization) |\n",
+ "| |\n",
+ "| convnext_xlarge_stage_2_blo (None, None, None, 4096) 4198400 Y |\n",
+ "| ck_24_pointwise_conv_1 (Den |\n",
+ "| se) |\n",
+ "| |\n",
+ "| convnext_xlarge_stage_2_blo (None, None, None, 4096) 0 Y |\n",
+ "| ck_24_gelu (Activation) |\n",
+ "| |\n",
+ "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 4195328 Y |\n",
+ "| ck_24_pointwise_conv_2 (Den |\n",
+ "| se) |\n",
+ "| |\n",
+ "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 1024 Y |\n",
+ "| ck_24_layer_scale (LayerSca |\n",
+ "| le) |\n",
+ "| |\n",
+ "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 0 Y |\n",
+ "| ck_24_identity (Activation) |\n",
+ "| |\n",
+ "| tf.__operators__.add_30 (TF (None, None, None, 1024) 0 Y |\n",
+ "| OpLambda) |\n",
+ "| |\n",
+ "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 51200 Y |\n",
+ "| ck_25_depthwise_conv (Conv2 |\n",
+ "| D) |\n",
+ "| |\n",
+ "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 2048 Y |\n",
+ "| ck_25_layernorm (LayerNorma |\n",
+ "| lization) |\n",
+ "| |\n",
+ "| convnext_xlarge_stage_2_blo (None, None, None, 4096) 4198400 Y |\n",
+ "| ck_25_pointwise_conv_1 (Den |\n",
+ "| se) |\n",
+ "| |\n",
+ "| convnext_xlarge_stage_2_blo (None, None, None, 4096) 0 Y |\n",
+ "| ck_25_gelu (Activation) |\n",
+ "| |\n",
+ "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 4195328 Y |\n",
+ "| ck_25_pointwise_conv_2 (Den |\n",
+ "| se) |\n",
+ "| |\n",
+ "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 1024 Y |\n",
+ "| ck_25_layer_scale (LayerSca |\n",
+ "| le) |\n",
+ "| |\n",
+ "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 0 Y |\n",
+ "| ck_25_identity (Activation) |\n",
+ "| |\n",
+ "| tf.__operators__.add_31 (TF (None, None, None, 1024) 0 Y |\n",
+ "| OpLambda) |\n",
+ "| |\n",
+ "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 51200 Y |\n",
+ "| ck_26_depthwise_conv (Conv2 |\n",
+ "| D) |\n",
+ "| |\n",
+ "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 2048 Y |\n",
+ "| ck_26_layernorm (LayerNorma |\n",
+ "| lization) |\n",
+ "| |\n",
+ "| convnext_xlarge_stage_2_blo (None, None, None, 4096) 4198400 Y |\n",
+ "| ck_26_pointwise_conv_1 (Den |\n",
+ "| se) |\n",
+ "| |\n",
+ "| convnext_xlarge_stage_2_blo (None, None, None, 4096) 0 Y |\n",
+ "| ck_26_gelu (Activation) |\n",
+ "| |\n",
+ "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 4195328 Y |\n",
+ "| ck_26_pointwise_conv_2 (Den |\n",
+ "| se) |\n",
+ "| |\n",
+ "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 1024 Y |\n",
+ "| ck_26_layer_scale (LayerSca |\n",
+ "| le) |\n",
+ "| |\n",
+ "| convnext_xlarge_stage_2_blo (None, None, None, 1024) 0 Y |\n",
+ "| ck_26_identity (Activation) |\n",
+ "| |\n",
+ "| tf.__operators__.add_32 (TF (None, None, None, 1024) 0 Y |\n",
+ "| OpLambda) |\n",
+ "| |\n",
+ "| convnext_xlarge_downsamplin (None, None, None, 2048) 8392704 Y |\n",
+ "| g_block_2 (Sequential) |\n",
+ "||¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯||\n",
+ "|| convnext_xlarge_downsamplin (None, None, None, 1024) 2048 Y ||\n",
+ "|| g_layernorm_2 (LayerNormali ||\n",
+ "|| zation) ||\n",
+ "|| ||\n",
+ "|| convnext_xlarge_downsamplin (None, None, None, 2048) 8390656 Y ||\n",
+ "|| g_conv_2 (Conv2D) ||\n",
+ "|¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯|\n",
+ "| convnext_xlarge_stage_3_blo (None, None, None, 2048) 102400 Y |\n",
+ "| ck_0_depthwise_conv (Conv2D |\n",
+ "| ) |\n",
+ "| |\n",
+ "| convnext_xlarge_stage_3_blo (None, None, None, 2048) 4096 Y |\n",
+ "| ck_0_layernorm (LayerNormal |\n",
+ "| ization) |\n",
+ "| |\n",
+ "| convnext_xlarge_stage_3_blo (None, None, None, 8192) 16785408 Y |\n",
+ "| ck_0_pointwise_conv_1 (Dens |\n",
+ "| e) |\n",
+ "| |\n",
+ "| convnext_xlarge_stage_3_blo (None, None, None, 8192) 0 Y |\n",
+ "| ck_0_gelu (Activation) |\n",
+ "| |\n",
+ "| convnext_xlarge_stage_3_blo (None, None, None, 2048) 16779264 Y |\n",
+ "| ck_0_pointwise_conv_2 (Dens |\n",
+ "| e) |\n",
+ "| |\n",
+ "| convnext_xlarge_stage_3_blo (None, None, None, 2048) 2048 Y |\n",
+ "| ck_0_layer_scale (LayerScal |\n",
+ "| e) |\n",
+ "| |\n",
+ "| convnext_xlarge_stage_3_blo (None, None, None, 2048) 0 Y |\n",
+ "| ck_0_identity (Activation) |\n",
+ "| |\n",
+ "| tf.__operators__.add_33 (TF (None, None, None, 2048) 0 Y |\n",
+ "| OpLambda) |\n",
+ "| |\n",
+ "| convnext_xlarge_stage_3_blo (None, None, None, 2048) 102400 Y |\n",
+ "| ck_1_depthwise_conv (Conv2D |\n",
+ "| ) |\n",
+ "| |\n",
+ "| convnext_xlarge_stage_3_blo (None, None, None, 2048) 4096 Y |\n",
+ "| ck_1_layernorm (LayerNormal |\n",
+ "| ization) |\n",
+ "| |\n",
+ "| convnext_xlarge_stage_3_blo (None, None, None, 8192) 16785408 Y |\n",
+ "| ck_1_pointwise_conv_1 (Dens |\n",
+ "| e) |\n",
+ "| |\n",
+ "| convnext_xlarge_stage_3_blo (None, None, None, 8192) 0 Y |\n",
+ "| ck_1_gelu (Activation) |\n",
+ "| |\n",
+ "| convnext_xlarge_stage_3_blo (None, None, None, 2048) 16779264 Y |\n",
+ "| ck_1_pointwise_conv_2 (Dens |\n",
+ "| e) |\n",
+ "| |\n",
+ "| convnext_xlarge_stage_3_blo (None, None, None, 2048) 2048 Y |\n",
+ "| ck_1_layer_scale (LayerScal |\n",
+ "| e) |\n",
+ "| |\n",
+ "| convnext_xlarge_stage_3_blo (None, None, None, 2048) 0 Y |\n",
+ "| ck_1_identity (Activation) |\n",
+ "| |\n",
+ "| tf.__operators__.add_34 (TF (None, None, None, 2048) 0 Y |\n",
+ "| OpLambda) |\n",
+ "| |\n",
+ "| convnext_xlarge_stage_3_blo (None, None, None, 2048) 102400 Y |\n",
+ "| ck_2_depthwise_conv (Conv2D |\n",
+ "| ) |\n",
+ "| |\n",
+ "| convnext_xlarge_stage_3_blo (None, None, None, 2048) 4096 Y |\n",
+ "| ck_2_layernorm (LayerNormal |\n",
+ "| ization) |\n",
+ "| |\n",
+ "| convnext_xlarge_stage_3_blo (None, None, None, 8192) 16785408 Y |\n",
+ "| ck_2_pointwise_conv_1 (Dens |\n",
+ "| e) |\n",
+ "| |\n",
+ "| convnext_xlarge_stage_3_blo (None, None, None, 8192) 0 Y |\n",
+ "| ck_2_gelu (Activation) |\n",
+ "| |\n",
+ "| convnext_xlarge_stage_3_blo (None, None, None, 2048) 16779264 Y |\n",
+ "| ck_2_pointwise_conv_2 (Dens |\n",
+ "| e) |\n",
+ "| |\n",
+ "| convnext_xlarge_stage_3_blo (None, None, None, 2048) 2048 Y |\n",
+ "| ck_2_layer_scale (LayerScal |\n",
+ "| e) |\n",
+ "| |\n",
+ "| convnext_xlarge_stage_3_blo (None, None, None, 2048) 0 Y |\n",
+ "| ck_2_identity (Activation) |\n",
+ "| |\n",
+ "| tf.__operators__.add_35 (TF (None, None, None, 2048) 0 Y |\n",
+ "| OpLambda) |\n",
+ "| |\n",
+ "| layer_normalization (LayerN (None, None, None, 2048) 4096 Y |\n",
+ "| ormalization) |\n",
+ "¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯\n",
+ " global_average_pooling2d (G (None, 2048) 0 Y \n",
+ " lobalAveragePooling2D) \n",
+ " \n",
+ " dense (Dense) (None, 512) 1049088 Y \n",
+ " \n",
+ " dropout (Dropout) (None, 512) 0 Y \n",
+ " \n",
+ " batch_normalization (BatchN (None, 512) 2048 Y \n",
+ " ormalization) \n",
+ " \n",
+ " dense_1 (Dense) (None, 512) 262656 Y \n",
+ " \n",
+ " batch_normalization_1 (Batc (None, 512) 2048 Y \n",
+ " hNormalization) \n",
+ " \n",
+ " dense_2 (Dense) (None, 128) 65664 Y \n",
+ " \n",
+ " dense_3 (Dense) (None, 2) 258 Y \n",
+ " \n",
+ "============================================================================\n",
+ "Total params: 349,529,730\n",
+ "Trainable params: 349,527,682\n",
+ "Non-trainable params: 2,048\n",
+ "____________________________________________________________________________\n",
+ "done.\n"
+ ]
+ }
+ ],
+ "source": [
+ "from keras.applications import ConvNeXtXLarge\n",
+ "from keras.layers import Lambda\n",
+ "\n",
+ "\n",
+ "# FUNC\n",
+ "def Eff_B7_NS():\n",
+ " # Add a Lambda layer at the beginning to scale the input\n",
+ " input = Input(shape=(img_res[0], img_res[1], img_res[2]))\n",
+ " x = Lambda(lambda image: image * 255)(input)\n",
+ "\n",
+ " base_model = ConvNeXtXLarge(\n",
+ " include_top=False,\n",
+ " weights=\"imagenet\",\n",
+ " classes=2,\n",
+ " classifier_activation=\"softmax\",\n",
+ " include_preprocessing=True,\n",
+ " )(x)\n",
+ " # adding CDL\n",
+ " base_model_FT = GlobalAveragePooling2D()(base_model)\n",
+ " Dense_L1 = Dense(512, activation=\"relu\", kernel_regularizer=l2(0.02))(base_model_FT)\n",
+ " Dropout_L1 = Dropout(0.1)(Dense_L1)\n",
+ " BatchNorm_L2 = BatchNormalization()(Dropout_L1)\n",
+ " Dense_L2 = Dense(512, activation=\"relu\", kernel_regularizer=l2(0.01))(BatchNorm_L2)\n",
+ " BatchNorm_L3 = BatchNormalization()(Dense_L2)\n",
+ " Dense_L3 = Dense(128, activation=\"relu\")(BatchNorm_L3)\n",
+ " predictions = Dense(2, activation=\"softmax\")(Dense_L3)\n",
+ "\n",
+ " model_EfficientNetB7_NS = Model(inputs=x, outputs=predictions)\n",
+ " print(\"Total model layers: \", len(model_EfficientNetB7_NS.layers))\n",
+ " # OPT/compile\n",
+ " opt = SGD(momentum=0.9) # noqa: F405\n",
+ " # opt = Yogi()\n",
+ " model_EfficientNetB7_NS.compile(optimizer=opt, loss=\"categorical_crossentropy\", metrics=[\"accuracy\"])\n",
+ "\n",
+ " return model_EfficientNetB7_NS\n",
+ "\n",
+ "\n",
+ "print(\"Creating the model...\")\n",
+ "# Main\n",
+ "model = Eff_B7_NS()\n",
+ "model.summary(show_trainable=True, expand_nested=True)\n",
+ "print(\"done.\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### V(T) Beta4"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 9,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Creating the model...\n"
+ ]
+ },
+ {
+ "ename": "NameError",
+ "evalue": "name 'KENB7' is not defined",
+ "output_type": "error",
+ "traceback": [
+ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+ "\u001b[1;31mNameError\u001b[0m Traceback (most recent call last)",
+ "Cell \u001b[1;32mIn[9], line 61\u001b[0m\n\u001b[0;32m 59\u001b[0m \u001b[38;5;66;03m# Main\u001b[39;00m\n\u001b[0;32m 60\u001b[0m freeze_layers \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m0\u001b[39m\n\u001b[1;32m---> 61\u001b[0m model \u001b[38;5;241m=\u001b[39m \u001b[43mEff_B7_NS\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfreeze_layers\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 62\u001b[0m model\u001b[38;5;241m.\u001b[39msummary(show_trainable\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m, expand_nested\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m)\n\u001b[0;32m 63\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mdone.\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n",
+ "Cell \u001b[1;32mIn[9], line 3\u001b[0m, in \u001b[0;36mEff_B7_NS\u001b[1;34m(freeze_layers)\u001b[0m\n\u001b[0;32m 2\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mEff_B7_NS\u001b[39m(freeze_layers):\n\u001b[1;32m----> 3\u001b[0m base_model \u001b[38;5;241m=\u001b[39m \u001b[43mKENB7\u001b[49m(\n\u001b[0;32m 4\u001b[0m input_shape\u001b[38;5;241m=\u001b[39m(img_res[\u001b[38;5;241m0\u001b[39m], img_res[\u001b[38;5;241m1\u001b[39m], img_res[\u001b[38;5;241m2\u001b[39m]),\n\u001b[0;32m 5\u001b[0m weights\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mnoisy-student\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[0;32m 6\u001b[0m include_top\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m,\n\u001b[0;32m 7\u001b[0m )\n\u001b[0;32m 8\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mTotal layers in the base model: \u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;28mlen\u001b[39m(base_model\u001b[38;5;241m.\u001b[39mlayers))\n\u001b[0;32m 9\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mFreezing \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mfreeze_layers\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m layers in the base model...\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n",
+ "\u001b[1;31mNameError\u001b[0m: name 'KENB7' is not defined"
+ ]
+ }
+ ],
+ "source": [
+ "# FUNC\n",
+ "def Eff_B7_NS(freeze_layers):\n",
+ " base_model = KENB7(\n",
+ " input_shape=(img_res[0], img_res[1], img_res[2]),\n",
+ " weights=\"noisy-student\",\n",
+ " include_top=False,\n",
+ " )\n",
+ " print(\"Total layers in the base model: \", len(base_model.layers))\n",
+ " print(f\"Freezing {freeze_layers} layers in the base model...\")\n",
+ " # Freeze the specified number of layers\n",
+ " for layer in base_model.layers[:freeze_layers]:\n",
+ " layer.trainable = False\n",
+ "\n",
+ " # Unfreeze the rest\n",
+ " for layer in base_model.layers[freeze_layers:]:\n",
+ " layer.trainable = True\n",
+ "\n",
+ " # Calculate the percentage of the model that is frozen\n",
+ " frozen_percentage = ((freeze_layers + 1e-10) / len(base_model.layers)) * 100\n",
+ " print(f\"Percentage of the base model that is frozen: {frozen_percentage:.2f}%\")\n",
+ " # adding CDL>>>\n",
+ " # GlobalAveragePooling2D\n",
+ " base_model_FT = GlobalAveragePooling2D(name=\"FC_INPUT_Avg-Pooling\")(base_model.output)\n",
+ " # Dense\n",
+ " Dense_L1 = Dense(512, activation=\"relu\", kernel_regularizer=l2(0.0026), name=\"FC_C_Dense-L1-512\")(base_model_FT)\n",
+ " # Dropout\n",
+ " Dropout_L1 = Dropout(0.125, name=\"FC_C_Dropout-L1-0.1\")(Dense_L1)\n",
+ " # BatchNormalization\n",
+ " BatchNorm_L2 = BatchNormalization(name=\"FC_C_Avg-BatchNormalization-L1\")(Dropout_L1)\n",
+ " # Dense\n",
+ " Dense_L2 = Dense(256, activation=\"relu\", kernel_regularizer=l2(0.0015), name=\"FC_C_Dense-L2-512\")(BatchNorm_L2)\n",
" # BatchNormalization\n",
- " BatchNorm_L3 = BatchNormalization(name=\"FC_C_Avg-Pooling-L2\")(Dense_L2)\n",
+ " BatchNorm_L3 = BatchNormalization(name=\"FC_C_Avg-BatchNormalization-L2\")(Dense_L2)\n",
" # Dense\n",
" Dense_L3 = Dense(128, activation=\"relu\", name=\"FC_C_Dense-L3-128\")(BatchNorm_L3)\n",
" # Dense\n",
@@ -8998,13 +4893,14 @@
" model_EfficientNetB7_NS = Model(inputs=base_model.input, outputs=predictions)\n",
" print(\"Total model layers: \", len(model_EfficientNetB7_NS.layers))\n",
" # OPT/compile\n",
- " opt = SGD(momentum=0.9, nesterov=False) # noqa: F405\n",
- " # opt = Nadam()\n",
- " # opt = Adamax()\n",
- " # opt = RMSprop(momentum=0.9)\n",
- " # opt = Adagrad()\n",
- " # opt = AdaBeliefOptimizer(epsilon=1e-7, rectify=False, weight_decay=5e-4, print_change_log=False, total_steps=0, amsgrad=False)\n",
- " # opt = Yogi()\n",
+ " opt = SGD(momentum=0.86, learning_rate=0.01, nesterov=False) # noqa: F405\n",
+ " # opt = Nadam() # noqa: F405\n",
+ " # opt = Adamax() # noqa: F405\n",
+ " # opt = Adam(amsgrad=True) # noqa: F405\n",
+ " # opt = RMSprop(momentum=0.9) # noqa: F405\n",
+ " # opt = Adagrad() # noqa: F405\n",
+ " # opt = AdaBeliefOptimizer(epsilon=1e-7, rectify=False, weight_decay=5e-4, print_change_log=False, amsgrad=True) # noqa: F405\n",
+ " # opt = Yogi() # noqa: F405\n",
" model_EfficientNetB7_NS.compile(\n",
" optimizer=opt, loss=\"categorical_crossentropy\", metrics=[\"accuracy\"]\n",
" ) # categorical_crossentropy / binary_crossentropy\n",
@@ -9024,80 +4920,118 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "### V(T) Beta3"
+ "### LR FINDER"
]
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 15,
"metadata": {},
- "outputs": [],
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Finding best initial lr over 256 steps\n"
+ ]
+ },
+ {
+ "data": {
+ "application/vnd.jupyter.widget-view+json": {
+ "model_id": "64af37c2d330409898076c8cab9c0be6",
+ "version_major": 2,
+ "version_minor": 0
+ },
+ "text/plain": [
+ " 0%| | 0/256 [00:00"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
"source": [
- "from keras.applications import ConvNeXtXLarge\n",
- "from keras.layers import Lambda\n",
- "\n",
- "\n",
- "# FUNC\n",
- "def Eff_B7_NS():\n",
- " # Add a Lambda layer at the beginning to scale the input\n",
- " input = Input(shape=(img_res[0], img_res[1], img_res[2]))\n",
- " x = Lambda(lambda image: image * 255)(input)\n",
- "\n",
- " base_model = ConvNeXtXLarge(\n",
- " include_top=False,\n",
- " weights=\"imagenet\",\n",
- " classes=2,\n",
- " classifier_activation=\"softmax\",\n",
- " include_preprocessing=True,\n",
- " )(x)\n",
- " # adding CDL\n",
- " base_model_FT = GlobalAveragePooling2D()(base_model)\n",
- " Dense_L1 = Dense(512, activation=\"relu\", kernel_regularizer=l2(0.02))(base_model_FT)\n",
- " Dropout_L1 = Dropout(0.1)(Dense_L1)\n",
- " BatchNorm_L2 = BatchNormalization()(Dropout_L1)\n",
- " Dense_L2 = Dense(512, activation=\"relu\", kernel_regularizer=l2(0.01))(BatchNorm_L2)\n",
- " BatchNorm_L3 = BatchNormalization()(Dense_L2)\n",
- " Dense_L3 = Dense(128, activation=\"relu\")(BatchNorm_L3)\n",
- " predictions = Dense(2, activation=\"softmax\")(Dense_L3)\n",
- "\n",
- " model_EfficientNetB7_NS = Model(inputs=input, outputs=predictions)\n",
- " print(\"Total model layers: \", len(model_EfficientNetB7_NS.layers))\n",
- " # OPT/compile\n",
- " opt = SGD(momentum=0.9) # noqa: F405\n",
- " # opt = Yogi()\n",
- " model_EfficientNetB7_NS.compile(optimizer=opt, loss=\"categorical_crossentropy\", metrics=[\"accuracy\"])\n",
- "\n",
- " return model_EfficientNetB7_NS\n",
+ "import gc\n",
"\n",
+ "# Garbage Collection (memory)\n",
+ "gc.collect()\n",
+ "tf.keras.backend.clear_session()\n",
+ "# CONF/Other\n",
+ "LRF_OPT = SGD(momentum=0.92) # noqa: F405\n",
+ "LFR_batch_size = 16 # or any other batch size that fits in your memory\n",
+ "# Data prep\n",
+ "num_samples = x_train.shape[0]\n",
+ "SITD = np.random.choice(num_samples, size=4096, replace=False)\n",
+ "LRF_dataset = tf.data.Dataset.from_tensor_slices((x_train[SITD], y_train[SITD])).batch(LFR_batch_size)\n",
+ "# Instantiate LrFinder\n",
+ "lr_find = LrFinder(model, LRF_OPT, tf.keras.losses.categorical_crossentropy)\n",
"\n",
- "print(\"Creating the model...\")\n",
- "# Main\n",
- "model = Eff_B7_NS()\n",
- "model.summary(show_trainable=True, expand_nested=True)\n",
- "print(\"done.\")"
+ "# Start range_test\n",
+ "lr_find.range_test(LRF_dataset, end_lr=0.9, num_iter=256, beta=0.8)\n",
+ "lr_find.plot_lrs(skip_end=0, suggestion=True, show_grid=True)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
- "### V(T) Beta4"
+ "### Model vis"
]
},
{
"cell_type": "code",
- "execution_count": 8,
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "dot_img_file = \"model_1.png\"\n",
+ "keras.utils.plot_model(model, to_file=dot_img_file, show_shapes=True)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Loading the model"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Loading the full model"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 9,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
- "Creating the model...\n",
- "Total layers in the base model: 230\n",
- "Freezing 0 layers in the base model...\n",
- "Percentage of the base model that is frozen: 0.00%\n",
- "Total model layers: 238\n",
+ "\u001b[92mLoading model done.\n",
+ "Compiling the AI model...\u001b[0m\n",
"Model: \"model\"\n",
"_____________________________________________________________________________________________________________\n",
" Layer (type) Output Shape Param # Connected to Trainable \n",
@@ -9105,765 +5039,2147 @@
" input_1 (InputLayer) [(None, 224, 224, 3 0 [] Y \n",
" )] \n",
" \n",
- " stem_conv (Conv2D) (None, 112, 112, 32 864 ['input_1[0][0]'] Y \n",
- " ) \n",
+ " stem_conv (Conv2D) (None, 112, 112, 64 1728 ['input_1[0][0]'] Y \n",
+ " ) \n",
+ " \n",
+ " stem_bn (BatchNormalization) (None, 112, 112, 64 256 ['stem_conv[0][0]'] Y \n",
+ " ) \n",
+ " \n",
+ " stem_activation (Activation) (None, 112, 112, 64 0 ['stem_bn[0][0]'] Y \n",
+ " ) \n",
+ " \n",
+ " block1a_dwconv (DepthwiseConv2 (None, 112, 112, 64 576 ['stem_activation[0][0]'] Y \n",
+ " D) ) \n",
+ " \n",
+ " block1a_bn (BatchNormalization (None, 112, 112, 64 256 ['block1a_dwconv[0][0]'] Y \n",
+ " ) ) \n",
+ " \n",
+ " block1a_activation (Activation (None, 112, 112, 64 0 ['block1a_bn[0][0]'] Y \n",
+ " ) ) \n",
+ " \n",
+ " block1a_se_squeeze (GlobalAver (None, 64) 0 ['block1a_activation[0][0]'] Y \n",
+ " agePooling2D) \n",
+ " \n",
+ " block1a_se_reshape (Reshape) (None, 1, 1, 64) 0 ['block1a_se_squeeze[0][0]'] Y \n",
+ " \n",
+ " block1a_se_reduce (Conv2D) (None, 1, 1, 16) 1040 ['block1a_se_reshape[0][0]'] Y \n",
+ " \n",
+ " block1a_se_expand (Conv2D) (None, 1, 1, 64) 1088 ['block1a_se_reduce[0][0]'] Y \n",
+ " \n",
+ " block1a_se_excite (Multiply) (None, 112, 112, 64 0 ['block1a_activation[0][0]', Y \n",
+ " ) 'block1a_se_expand[0][0]'] \n",
+ " \n",
+ " block1a_project_conv (Conv2D) (None, 112, 112, 32 2048 ['block1a_se_excite[0][0]'] Y \n",
+ " ) \n",
+ " \n",
+ " block1a_project_bn (BatchNorma (None, 112, 112, 32 128 ['block1a_project_conv[0][0]'] Y \n",
+ " lization) ) \n",
+ " \n",
+ " block1b_dwconv (DepthwiseConv2 (None, 112, 112, 32 288 ['block1a_project_bn[0][0]'] Y \n",
+ " D) ) \n",
+ " \n",
+ " block1b_bn (BatchNormalization (None, 112, 112, 32 128 ['block1b_dwconv[0][0]'] Y \n",
+ " ) ) \n",
+ " \n",
+ " block1b_activation (Activation (None, 112, 112, 32 0 ['block1b_bn[0][0]'] Y \n",
+ " ) ) \n",
+ " \n",
+ " block1b_se_squeeze (GlobalAver (None, 32) 0 ['block1b_activation[0][0]'] Y \n",
+ " agePooling2D) \n",
+ " \n",
+ " block1b_se_reshape (Reshape) (None, 1, 1, 32) 0 ['block1b_se_squeeze[0][0]'] Y \n",
+ " \n",
+ " block1b_se_reduce (Conv2D) (None, 1, 1, 8) 264 ['block1b_se_reshape[0][0]'] Y \n",
+ " \n",
+ " block1b_se_expand (Conv2D) (None, 1, 1, 32) 288 ['block1b_se_reduce[0][0]'] Y \n",
+ " \n",
+ " block1b_se_excite (Multiply) (None, 112, 112, 32 0 ['block1b_activation[0][0]', Y \n",
+ " ) 'block1b_se_expand[0][0]'] \n",
+ " \n",
+ " block1b_project_conv (Conv2D) (None, 112, 112, 32 1024 ['block1b_se_excite[0][0]'] Y \n",
+ " ) \n",
+ " \n",
+ " block1b_project_bn (BatchNorma (None, 112, 112, 32 128 ['block1b_project_conv[0][0]'] Y \n",
+ " lization) ) \n",
+ " \n",
+ " block1b_drop (FixedDropout) (None, 112, 112, 32 0 ['block1b_project_bn[0][0]'] Y \n",
+ " ) \n",
+ " \n",
+ " block1b_add (Add) (None, 112, 112, 32 0 ['block1b_drop[0][0]', Y \n",
+ " ) 'block1a_project_bn[0][0]'] \n",
+ " \n",
+ " block1c_dwconv (DepthwiseConv2 (None, 112, 112, 32 288 ['block1b_add[0][0]'] Y \n",
+ " D) ) \n",
+ " \n",
+ " block1c_bn (BatchNormalization (None, 112, 112, 32 128 ['block1c_dwconv[0][0]'] Y \n",
+ " ) ) \n",
+ " \n",
+ " block1c_activation (Activation (None, 112, 112, 32 0 ['block1c_bn[0][0]'] Y \n",
+ " ) ) \n",
+ " \n",
+ " block1c_se_squeeze (GlobalAver (None, 32) 0 ['block1c_activation[0][0]'] Y \n",
+ " agePooling2D) \n",
+ " \n",
+ " block1c_se_reshape (Reshape) (None, 1, 1, 32) 0 ['block1c_se_squeeze[0][0]'] Y \n",
+ " \n",
+ " block1c_se_reduce (Conv2D) (None, 1, 1, 8) 264 ['block1c_se_reshape[0][0]'] Y \n",
+ " \n",
+ " block1c_se_expand (Conv2D) (None, 1, 1, 32) 288 ['block1c_se_reduce[0][0]'] Y \n",
+ " \n",
+ " block1c_se_excite (Multiply) (None, 112, 112, 32 0 ['block1c_activation[0][0]', Y \n",
+ " ) 'block1c_se_expand[0][0]'] \n",
+ " \n",
+ " block1c_project_conv (Conv2D) (None, 112, 112, 32 1024 ['block1c_se_excite[0][0]'] Y \n",
+ " ) \n",
+ " \n",
+ " block1c_project_bn (BatchNorma (None, 112, 112, 32 128 ['block1c_project_conv[0][0]'] Y \n",
+ " lization) ) \n",
+ " \n",
+ " block1c_drop (FixedDropout) (None, 112, 112, 32 0 ['block1c_project_bn[0][0]'] Y \n",
+ " ) \n",
+ " \n",
+ " block1c_add (Add) (None, 112, 112, 32 0 ['block1c_drop[0][0]', Y \n",
+ " ) 'block1b_add[0][0]'] \n",
+ " \n",
+ " block1d_dwconv (DepthwiseConv2 (None, 112, 112, 32 288 ['block1c_add[0][0]'] Y \n",
+ " D) ) \n",
+ " \n",
+ " block1d_bn (BatchNormalization (None, 112, 112, 32 128 ['block1d_dwconv[0][0]'] Y \n",
+ " ) ) \n",
+ " \n",
+ " block1d_activation (Activation (None, 112, 112, 32 0 ['block1d_bn[0][0]'] Y \n",
+ " ) ) \n",
+ " \n",
+ " block1d_se_squeeze (GlobalAver (None, 32) 0 ['block1d_activation[0][0]'] Y \n",
+ " agePooling2D) \n",
+ " \n",
+ " block1d_se_reshape (Reshape) (None, 1, 1, 32) 0 ['block1d_se_squeeze[0][0]'] Y \n",
+ " \n",
+ " block1d_se_reduce (Conv2D) (None, 1, 1, 8) 264 ['block1d_se_reshape[0][0]'] Y \n",
+ " \n",
+ " block1d_se_expand (Conv2D) (None, 1, 1, 32) 288 ['block1d_se_reduce[0][0]'] Y \n",
+ " \n",
+ " block1d_se_excite (Multiply) (None, 112, 112, 32 0 ['block1d_activation[0][0]', Y \n",
+ " ) 'block1d_se_expand[0][0]'] \n",
+ " \n",
+ " block1d_project_conv (Conv2D) (None, 112, 112, 32 1024 ['block1d_se_excite[0][0]'] Y \n",
+ " ) \n",
+ " \n",
+ " block1d_project_bn (BatchNorma (None, 112, 112, 32 128 ['block1d_project_conv[0][0]'] Y \n",
+ " lization) ) \n",
+ " \n",
+ " block1d_drop (FixedDropout) (None, 112, 112, 32 0 ['block1d_project_bn[0][0]'] Y \n",
+ " ) \n",
+ " \n",
+ " block1d_add (Add) (None, 112, 112, 32 0 ['block1d_drop[0][0]', Y \n",
+ " ) 'block1c_add[0][0]'] \n",
+ " \n",
+ " block2a_expand_conv (Conv2D) (None, 112, 112, 19 6144 ['block1d_add[0][0]'] Y \n",
+ " 2) \n",
+ " \n",
+ " block2a_expand_bn (BatchNormal (None, 112, 112, 19 768 ['block2a_expand_conv[0][0]'] Y \n",
+ " ization) 2) \n",
+ " \n",
+ " block2a_expand_activation (Act (None, 112, 112, 19 0 ['block2a_expand_bn[0][0]'] Y \n",
+ " ivation) 2) \n",
+ " \n",
+ " block2a_dwconv (DepthwiseConv2 (None, 56, 56, 192) 1728 ['block2a_expand_activation[0][ Y \n",
+ " D) 0]'] \n",
+ " \n",
+ " block2a_bn (BatchNormalization (None, 56, 56, 192) 768 ['block2a_dwconv[0][0]'] Y \n",
+ " ) \n",
+ " \n",
+ " block2a_activation (Activation (None, 56, 56, 192) 0 ['block2a_bn[0][0]'] Y \n",
+ " ) \n",
+ " \n",
+ " block2a_se_squeeze (GlobalAver (None, 192) 0 ['block2a_activation[0][0]'] Y \n",
+ " agePooling2D) \n",
+ " \n",
+ " block2a_se_reshape (Reshape) (None, 1, 1, 192) 0 ['block2a_se_squeeze[0][0]'] Y \n",
+ " \n",
+ " block2a_se_reduce (Conv2D) (None, 1, 1, 8) 1544 ['block2a_se_reshape[0][0]'] Y \n",
+ " \n",
+ " block2a_se_expand (Conv2D) (None, 1, 1, 192) 1728 ['block2a_se_reduce[0][0]'] Y \n",
+ " \n",
+ " block2a_se_excite (Multiply) (None, 56, 56, 192) 0 ['block2a_activation[0][0]', Y \n",
+ " 'block2a_se_expand[0][0]'] \n",
+ " \n",
+ " block2a_project_conv (Conv2D) (None, 56, 56, 48) 9216 ['block2a_se_excite[0][0]'] Y \n",
+ " \n",
+ " block2a_project_bn (BatchNorma (None, 56, 56, 48) 192 ['block2a_project_conv[0][0]'] Y \n",
+ " lization) \n",
+ " \n",
+ " block2b_expand_conv (Conv2D) (None, 56, 56, 288) 13824 ['block2a_project_bn[0][0]'] Y \n",
+ " \n",
+ " block2b_expand_bn (BatchNormal (None, 56, 56, 288) 1152 ['block2b_expand_conv[0][0]'] Y \n",
+ " ization) \n",
+ " \n",
+ " block2b_expand_activation (Act (None, 56, 56, 288) 0 ['block2b_expand_bn[0][0]'] Y \n",
+ " ivation) \n",
+ " \n",
+ " block2b_dwconv (DepthwiseConv2 (None, 56, 56, 288) 2592 ['block2b_expand_activation[0][ Y \n",
+ " D) 0]'] \n",
+ " \n",
+ " block2b_bn (BatchNormalization (None, 56, 56, 288) 1152 ['block2b_dwconv[0][0]'] Y \n",
+ " ) \n",
+ " \n",
+ " block2b_activation (Activation (None, 56, 56, 288) 0 ['block2b_bn[0][0]'] Y \n",
+ " ) \n",
+ " \n",
+ " block2b_se_squeeze (GlobalAver (None, 288) 0 ['block2b_activation[0][0]'] Y \n",
+ " agePooling2D) \n",
+ " \n",
+ " block2b_se_reshape (Reshape) (None, 1, 1, 288) 0 ['block2b_se_squeeze[0][0]'] Y \n",
+ " \n",
+ " block2b_se_reduce (Conv2D) (None, 1, 1, 12) 3468 ['block2b_se_reshape[0][0]'] Y \n",
+ " \n",
+ " block2b_se_expand (Conv2D) (None, 1, 1, 288) 3744 ['block2b_se_reduce[0][0]'] Y \n",
+ " \n",
+ " block2b_se_excite (Multiply) (None, 56, 56, 288) 0 ['block2b_activation[0][0]', Y \n",
+ " 'block2b_se_expand[0][0]'] \n",
+ " \n",
+ " block2b_project_conv (Conv2D) (None, 56, 56, 48) 13824 ['block2b_se_excite[0][0]'] Y \n",
+ " \n",
+ " block2b_project_bn (BatchNorma (None, 56, 56, 48) 192 ['block2b_project_conv[0][0]'] Y \n",
+ " lization) \n",
+ " \n",
+ " block2b_drop (FixedDropout) (None, 56, 56, 48) 0 ['block2b_project_bn[0][0]'] Y \n",
+ " \n",
+ " block2b_add (Add) (None, 56, 56, 48) 0 ['block2b_drop[0][0]', Y \n",
+ " 'block2a_project_bn[0][0]'] \n",
+ " \n",
+ " block2c_expand_conv (Conv2D) (None, 56, 56, 288) 13824 ['block2b_add[0][0]'] Y \n",
+ " \n",
+ " block2c_expand_bn (BatchNormal (None, 56, 56, 288) 1152 ['block2c_expand_conv[0][0]'] Y \n",
+ " ization) \n",
+ " \n",
+ " block2c_expand_activation (Act (None, 56, 56, 288) 0 ['block2c_expand_bn[0][0]'] Y \n",
+ " ivation) \n",
+ " \n",
+ " block2c_dwconv (DepthwiseConv2 (None, 56, 56, 288) 2592 ['block2c_expand_activation[0][ Y \n",
+ " D) 0]'] \n",
+ " \n",
+ " block2c_bn (BatchNormalization (None, 56, 56, 288) 1152 ['block2c_dwconv[0][0]'] Y \n",
+ " ) \n",
+ " \n",
+ " block2c_activation (Activation (None, 56, 56, 288) 0 ['block2c_bn[0][0]'] Y \n",
+ " ) \n",
+ " \n",
+ " block2c_se_squeeze (GlobalAver (None, 288) 0 ['block2c_activation[0][0]'] Y \n",
+ " agePooling2D) \n",
+ " \n",
+ " block2c_se_reshape (Reshape) (None, 1, 1, 288) 0 ['block2c_se_squeeze[0][0]'] Y \n",
+ " \n",
+ " block2c_se_reduce (Conv2D) (None, 1, 1, 12) 3468 ['block2c_se_reshape[0][0]'] Y \n",
+ " \n",
+ " block2c_se_expand (Conv2D) (None, 1, 1, 288) 3744 ['block2c_se_reduce[0][0]'] Y \n",
+ " \n",
+ " block2c_se_excite (Multiply) (None, 56, 56, 288) 0 ['block2c_activation[0][0]', Y \n",
+ " 'block2c_se_expand[0][0]'] \n",
+ " \n",
+ " block2c_project_conv (Conv2D) (None, 56, 56, 48) 13824 ['block2c_se_excite[0][0]'] Y \n",
+ " \n",
+ " block2c_project_bn (BatchNorma (None, 56, 56, 48) 192 ['block2c_project_conv[0][0]'] Y \n",
+ " lization) \n",
+ " \n",
+ " block2c_drop (FixedDropout) (None, 56, 56, 48) 0 ['block2c_project_bn[0][0]'] Y \n",
+ " \n",
+ " block2c_add (Add) (None, 56, 56, 48) 0 ['block2c_drop[0][0]', Y \n",
+ " 'block2b_add[0][0]'] \n",
+ " \n",
+ " block2d_expand_conv (Conv2D) (None, 56, 56, 288) 13824 ['block2c_add[0][0]'] Y \n",
+ " \n",
+ " block2d_expand_bn (BatchNormal (None, 56, 56, 288) 1152 ['block2d_expand_conv[0][0]'] Y \n",
+ " ization) \n",
+ " \n",
+ " block2d_expand_activation (Act (None, 56, 56, 288) 0 ['block2d_expand_bn[0][0]'] Y \n",
+ " ivation) \n",
+ " \n",
+ " block2d_dwconv (DepthwiseConv2 (None, 56, 56, 288) 2592 ['block2d_expand_activation[0][ Y \n",
+ " D) 0]'] \n",
+ " \n",
+ " block2d_bn (BatchNormalization (None, 56, 56, 288) 1152 ['block2d_dwconv[0][0]'] Y \n",
+ " ) \n",
+ " \n",
+ " block2d_activation (Activation (None, 56, 56, 288) 0 ['block2d_bn[0][0]'] Y \n",
+ " ) \n",
+ " \n",
+ " block2d_se_squeeze (GlobalAver (None, 288) 0 ['block2d_activation[0][0]'] Y \n",
+ " agePooling2D) \n",
+ " \n",
+ " block2d_se_reshape (Reshape) (None, 1, 1, 288) 0 ['block2d_se_squeeze[0][0]'] Y \n",
+ " \n",
+ " block2d_se_reduce (Conv2D) (None, 1, 1, 12) 3468 ['block2d_se_reshape[0][0]'] Y \n",
+ " \n",
+ " block2d_se_expand (Conv2D) (None, 1, 1, 288) 3744 ['block2d_se_reduce[0][0]'] Y \n",
+ " \n",
+ " block2d_se_excite (Multiply) (None, 56, 56, 288) 0 ['block2d_activation[0][0]', Y \n",
+ " 'block2d_se_expand[0][0]'] \n",
+ " \n",
+ " block2d_project_conv (Conv2D) (None, 56, 56, 48) 13824 ['block2d_se_excite[0][0]'] Y \n",
+ " \n",
+ " block2d_project_bn (BatchNorma (None, 56, 56, 48) 192 ['block2d_project_conv[0][0]'] Y \n",
+ " lization) \n",
+ " \n",
+ " block2d_drop (FixedDropout) (None, 56, 56, 48) 0 ['block2d_project_bn[0][0]'] Y \n",
+ " \n",
+ " block2d_add (Add) (None, 56, 56, 48) 0 ['block2d_drop[0][0]', Y \n",
+ " 'block2c_add[0][0]'] \n",
+ " \n",
+ " block2e_expand_conv (Conv2D) (None, 56, 56, 288) 13824 ['block2d_add[0][0]'] Y \n",
+ " \n",
+ " block2e_expand_bn (BatchNormal (None, 56, 56, 288) 1152 ['block2e_expand_conv[0][0]'] Y \n",
+ " ization) \n",
+ " \n",
+ " block2e_expand_activation (Act (None, 56, 56, 288) 0 ['block2e_expand_bn[0][0]'] Y \n",
+ " ivation) \n",
+ " \n",
+ " block2e_dwconv (DepthwiseConv2 (None, 56, 56, 288) 2592 ['block2e_expand_activation[0][ Y \n",
+ " D) 0]'] \n",
+ " \n",
+ " block2e_bn (BatchNormalization (None, 56, 56, 288) 1152 ['block2e_dwconv[0][0]'] Y \n",
+ " ) \n",
+ " \n",
+ " block2e_activation (Activation (None, 56, 56, 288) 0 ['block2e_bn[0][0]'] Y \n",
+ " ) \n",
+ " \n",
+ " block2e_se_squeeze (GlobalAver (None, 288) 0 ['block2e_activation[0][0]'] Y \n",
+ " agePooling2D) \n",
+ " \n",
+ " block2e_se_reshape (Reshape) (None, 1, 1, 288) 0 ['block2e_se_squeeze[0][0]'] Y \n",
+ " \n",
+ " block2e_se_reduce (Conv2D) (None, 1, 1, 12) 3468 ['block2e_se_reshape[0][0]'] Y \n",
+ " \n",
+ " block2e_se_expand (Conv2D) (None, 1, 1, 288) 3744 ['block2e_se_reduce[0][0]'] Y \n",
+ " \n",
+ " block2e_se_excite (Multiply) (None, 56, 56, 288) 0 ['block2e_activation[0][0]', Y \n",
+ " 'block2e_se_expand[0][0]'] \n",
+ " \n",
+ " block2e_project_conv (Conv2D) (None, 56, 56, 48) 13824 ['block2e_se_excite[0][0]'] Y \n",
+ " \n",
+ " block2e_project_bn (BatchNorma (None, 56, 56, 48) 192 ['block2e_project_conv[0][0]'] Y \n",
+ " lization) \n",
+ " \n",
+ " block2e_drop (FixedDropout) (None, 56, 56, 48) 0 ['block2e_project_bn[0][0]'] Y \n",
+ " \n",
+ " block2e_add (Add) (None, 56, 56, 48) 0 ['block2e_drop[0][0]', Y \n",
+ " 'block2d_add[0][0]'] \n",
+ " \n",
+ " block2f_expand_conv (Conv2D) (None, 56, 56, 288) 13824 ['block2e_add[0][0]'] Y \n",
+ " \n",
+ " block2f_expand_bn (BatchNormal (None, 56, 56, 288) 1152 ['block2f_expand_conv[0][0]'] Y \n",
+ " ization) \n",
+ " \n",
+ " block2f_expand_activation (Act (None, 56, 56, 288) 0 ['block2f_expand_bn[0][0]'] Y \n",
+ " ivation) \n",
+ " \n",
+ " block2f_dwconv (DepthwiseConv2 (None, 56, 56, 288) 2592 ['block2f_expand_activation[0][ Y \n",
+ " D) 0]'] \n",
+ " \n",
+ " block2f_bn (BatchNormalization (None, 56, 56, 288) 1152 ['block2f_dwconv[0][0]'] Y \n",
+ " ) \n",
+ " \n",
+ " block2f_activation (Activation (None, 56, 56, 288) 0 ['block2f_bn[0][0]'] Y \n",
+ " ) \n",
+ " \n",
+ " block2f_se_squeeze (GlobalAver (None, 288) 0 ['block2f_activation[0][0]'] Y \n",
+ " agePooling2D) \n",
+ " \n",
+ " block2f_se_reshape (Reshape) (None, 1, 1, 288) 0 ['block2f_se_squeeze[0][0]'] Y \n",
+ " \n",
+ " block2f_se_reduce (Conv2D) (None, 1, 1, 12) 3468 ['block2f_se_reshape[0][0]'] Y \n",
+ " \n",
+ " block2f_se_expand (Conv2D) (None, 1, 1, 288) 3744 ['block2f_se_reduce[0][0]'] Y \n",
+ " \n",
+ " block2f_se_excite (Multiply) (None, 56, 56, 288) 0 ['block2f_activation[0][0]', Y \n",
+ " 'block2f_se_expand[0][0]'] \n",
+ " \n",
+ " block2f_project_conv (Conv2D) (None, 56, 56, 48) 13824 ['block2f_se_excite[0][0]'] Y \n",
+ " \n",
+ " block2f_project_bn (BatchNorma (None, 56, 56, 48) 192 ['block2f_project_conv[0][0]'] Y \n",
+ " lization) \n",
+ " \n",
+ " block2f_drop (FixedDropout) (None, 56, 56, 48) 0 ['block2f_project_bn[0][0]'] Y \n",
+ " \n",
+ " block2f_add (Add) (None, 56, 56, 48) 0 ['block2f_drop[0][0]', Y \n",
+ " 'block2e_add[0][0]'] \n",
+ " \n",
+ " block2g_expand_conv (Conv2D) (None, 56, 56, 288) 13824 ['block2f_add[0][0]'] Y \n",
+ " \n",
+ " block2g_expand_bn (BatchNormal (None, 56, 56, 288) 1152 ['block2g_expand_conv[0][0]'] Y \n",
+ " ization) \n",
+ " \n",
+ " block2g_expand_activation (Act (None, 56, 56, 288) 0 ['block2g_expand_bn[0][0]'] Y \n",
+ " ivation) \n",
+ " \n",
+ " block2g_dwconv (DepthwiseConv2 (None, 56, 56, 288) 2592 ['block2g_expand_activation[0][ Y \n",
+ " D) 0]'] \n",
+ " \n",
+ " block2g_bn (BatchNormalization (None, 56, 56, 288) 1152 ['block2g_dwconv[0][0]'] Y \n",
+ " ) \n",
+ " \n",
+ " block2g_activation (Activation (None, 56, 56, 288) 0 ['block2g_bn[0][0]'] Y \n",
+ " ) \n",
+ " \n",
+ " block2g_se_squeeze (GlobalAver (None, 288) 0 ['block2g_activation[0][0]'] Y \n",
+ " agePooling2D) \n",
+ " \n",
+ " block2g_se_reshape (Reshape) (None, 1, 1, 288) 0 ['block2g_se_squeeze[0][0]'] Y \n",
+ " \n",
+ " block2g_se_reduce (Conv2D) (None, 1, 1, 12) 3468 ['block2g_se_reshape[0][0]'] Y \n",
+ " \n",
+ " block2g_se_expand (Conv2D) (None, 1, 1, 288) 3744 ['block2g_se_reduce[0][0]'] Y \n",
+ " \n",
+ " block2g_se_excite (Multiply) (None, 56, 56, 288) 0 ['block2g_activation[0][0]', Y \n",
+ " 'block2g_se_expand[0][0]'] \n",
+ " \n",
+ " block2g_project_conv (Conv2D) (None, 56, 56, 48) 13824 ['block2g_se_excite[0][0]'] Y \n",
+ " \n",
+ " block2g_project_bn (BatchNorma (None, 56, 56, 48) 192 ['block2g_project_conv[0][0]'] Y \n",
+ " lization) \n",
+ " \n",
+ " block2g_drop (FixedDropout) (None, 56, 56, 48) 0 ['block2g_project_bn[0][0]'] Y \n",
+ " \n",
+ " block2g_add (Add) (None, 56, 56, 48) 0 ['block2g_drop[0][0]', Y \n",
+ " 'block2f_add[0][0]'] \n",
+ " \n",
+ " block3a_expand_conv (Conv2D) (None, 56, 56, 288) 13824 ['block2g_add[0][0]'] Y \n",
+ " \n",
+ " block3a_expand_bn (BatchNormal (None, 56, 56, 288) 1152 ['block3a_expand_conv[0][0]'] Y \n",
+ " ization) \n",
+ " \n",
+ " block3a_expand_activation (Act (None, 56, 56, 288) 0 ['block3a_expand_bn[0][0]'] Y \n",
+ " ivation) \n",
+ " \n",
+ " block3a_dwconv (DepthwiseConv2 (None, 28, 28, 288) 7200 ['block3a_expand_activation[0][ Y \n",
+ " D) 0]'] \n",
+ " \n",
+ " block3a_bn (BatchNormalization (None, 28, 28, 288) 1152 ['block3a_dwconv[0][0]'] Y \n",
+ " ) \n",
+ " \n",
+ " block3a_activation (Activation (None, 28, 28, 288) 0 ['block3a_bn[0][0]'] Y \n",
+ " ) \n",
+ " \n",
+ " block3a_se_squeeze (GlobalAver (None, 288) 0 ['block3a_activation[0][0]'] Y \n",
+ " agePooling2D) \n",
+ " \n",
+ " block3a_se_reshape (Reshape) (None, 1, 1, 288) 0 ['block3a_se_squeeze[0][0]'] Y \n",
+ " \n",
+ " block3a_se_reduce (Conv2D) (None, 1, 1, 12) 3468 ['block3a_se_reshape[0][0]'] Y \n",
+ " \n",
+ " block3a_se_expand (Conv2D) (None, 1, 1, 288) 3744 ['block3a_se_reduce[0][0]'] Y \n",
+ " \n",
+ " block3a_se_excite (Multiply) (None, 28, 28, 288) 0 ['block3a_activation[0][0]', Y \n",
+ " 'block3a_se_expand[0][0]'] \n",
+ " \n",
+ " block3a_project_conv (Conv2D) (None, 28, 28, 80) 23040 ['block3a_se_excite[0][0]'] Y \n",
+ " \n",
+ " block3a_project_bn (BatchNorma (None, 28, 28, 80) 320 ['block3a_project_conv[0][0]'] Y \n",
+ " lization) \n",
+ " \n",
+ " block3b_expand_conv (Conv2D) (None, 28, 28, 480) 38400 ['block3a_project_bn[0][0]'] Y \n",
+ " \n",
+ " block3b_expand_bn (BatchNormal (None, 28, 28, 480) 1920 ['block3b_expand_conv[0][0]'] Y \n",
+ " ization) \n",
+ " \n",
+ " block3b_expand_activation (Act (None, 28, 28, 480) 0 ['block3b_expand_bn[0][0]'] Y \n",
+ " ivation) \n",
+ " \n",
+ " block3b_dwconv (DepthwiseConv2 (None, 28, 28, 480) 12000 ['block3b_expand_activation[0][ Y \n",
+ " D) 0]'] \n",
+ " \n",
+ " block3b_bn (BatchNormalization (None, 28, 28, 480) 1920 ['block3b_dwconv[0][0]'] Y \n",
+ " ) \n",
+ " \n",
+ " block3b_activation (Activation (None, 28, 28, 480) 0 ['block3b_bn[0][0]'] Y \n",
+ " ) \n",
+ " \n",
+ " block3b_se_squeeze (GlobalAver (None, 480) 0 ['block3b_activation[0][0]'] Y \n",
+ " agePooling2D) \n",
+ " \n",
+ " block3b_se_reshape (Reshape) (None, 1, 1, 480) 0 ['block3b_se_squeeze[0][0]'] Y \n",
+ " \n",
+ " block3b_se_reduce (Conv2D) (None, 1, 1, 20) 9620 ['block3b_se_reshape[0][0]'] Y \n",
+ " \n",
+ " block3b_se_expand (Conv2D) (None, 1, 1, 480) 10080 ['block3b_se_reduce[0][0]'] Y \n",
+ " \n",
+ " block3b_se_excite (Multiply) (None, 28, 28, 480) 0 ['block3b_activation[0][0]', Y \n",
+ " 'block3b_se_expand[0][0]'] \n",
+ " \n",
+ " block3b_project_conv (Conv2D) (None, 28, 28, 80) 38400 ['block3b_se_excite[0][0]'] Y \n",
+ " \n",
+ " block3b_project_bn (BatchNorma (None, 28, 28, 80) 320 ['block3b_project_conv[0][0]'] Y \n",
+ " lization) \n",
+ " \n",
+ " block3b_drop (FixedDropout) (None, 28, 28, 80) 0 ['block3b_project_bn[0][0]'] Y \n",
+ " \n",
+ " block3b_add (Add) (None, 28, 28, 80) 0 ['block3b_drop[0][0]', Y \n",
+ " 'block3a_project_bn[0][0]'] \n",
+ " \n",
+ " block3c_expand_conv (Conv2D) (None, 28, 28, 480) 38400 ['block3b_add[0][0]'] Y \n",
+ " \n",
+ " block3c_expand_bn (BatchNormal (None, 28, 28, 480) 1920 ['block3c_expand_conv[0][0]'] Y \n",
+ " ization) \n",
+ " \n",
+ " block3c_expand_activation (Act (None, 28, 28, 480) 0 ['block3c_expand_bn[0][0]'] Y \n",
+ " ivation) \n",
+ " \n",
+ " block3c_dwconv (DepthwiseConv2 (None, 28, 28, 480) 12000 ['block3c_expand_activation[0][ Y \n",
+ " D) 0]'] \n",
+ " \n",
+ " block3c_bn (BatchNormalization (None, 28, 28, 480) 1920 ['block3c_dwconv[0][0]'] Y \n",
+ " ) \n",
+ " \n",
+ " block3c_activation (Activation (None, 28, 28, 480) 0 ['block3c_bn[0][0]'] Y \n",
+ " ) \n",
+ " \n",
+ " block3c_se_squeeze (GlobalAver (None, 480) 0 ['block3c_activation[0][0]'] Y \n",
+ " agePooling2D) \n",
+ " \n",
+ " block3c_se_reshape (Reshape) (None, 1, 1, 480) 0 ['block3c_se_squeeze[0][0]'] Y \n",
+ " \n",
+ " block3c_se_reduce (Conv2D) (None, 1, 1, 20) 9620 ['block3c_se_reshape[0][0]'] Y \n",
+ " \n",
+ " block3c_se_expand (Conv2D) (None, 1, 1, 480) 10080 ['block3c_se_reduce[0][0]'] Y \n",
+ " \n",
+ " block3c_se_excite (Multiply) (None, 28, 28, 480) 0 ['block3c_activation[0][0]', Y \n",
+ " 'block3c_se_expand[0][0]'] \n",
+ " \n",
+ " block3c_project_conv (Conv2D) (None, 28, 28, 80) 38400 ['block3c_se_excite[0][0]'] Y \n",
+ " \n",
+ " block3c_project_bn (BatchNorma (None, 28, 28, 80) 320 ['block3c_project_conv[0][0]'] Y \n",
+ " lization) \n",
+ " \n",
+ " block3c_drop (FixedDropout) (None, 28, 28, 80) 0 ['block3c_project_bn[0][0]'] Y \n",
+ " \n",
+ " block3c_add (Add) (None, 28, 28, 80) 0 ['block3c_drop[0][0]', Y \n",
+ " 'block3b_add[0][0]'] \n",
+ " \n",
+ " block3d_expand_conv (Conv2D) (None, 28, 28, 480) 38400 ['block3c_add[0][0]'] Y \n",
+ " \n",
+ " block3d_expand_bn (BatchNormal (None, 28, 28, 480) 1920 ['block3d_expand_conv[0][0]'] Y \n",
+ " ization) \n",
+ " \n",
+ " block3d_expand_activation (Act (None, 28, 28, 480) 0 ['block3d_expand_bn[0][0]'] Y \n",
+ " ivation) \n",
+ " \n",
+ " block3d_dwconv (DepthwiseConv2 (None, 28, 28, 480) 12000 ['block3d_expand_activation[0][ Y \n",
+ " D) 0]'] \n",
+ " \n",
+ " block3d_bn (BatchNormalization (None, 28, 28, 480) 1920 ['block3d_dwconv[0][0]'] Y \n",
+ " ) \n",
+ " \n",
+ " block3d_activation (Activation (None, 28, 28, 480) 0 ['block3d_bn[0][0]'] Y \n",
+ " ) \n",
+ " \n",
+ " block3d_se_squeeze (GlobalAver (None, 480) 0 ['block3d_activation[0][0]'] Y \n",
+ " agePooling2D) \n",
+ " \n",
+ " block3d_se_reshape (Reshape) (None, 1, 1, 480) 0 ['block3d_se_squeeze[0][0]'] Y \n",
+ " \n",
+ " block3d_se_reduce (Conv2D) (None, 1, 1, 20) 9620 ['block3d_se_reshape[0][0]'] Y \n",
+ " \n",
+ " block3d_se_expand (Conv2D) (None, 1, 1, 480) 10080 ['block3d_se_reduce[0][0]'] Y \n",
+ " \n",
+ " block3d_se_excite (Multiply) (None, 28, 28, 480) 0 ['block3d_activation[0][0]', Y \n",
+ " 'block3d_se_expand[0][0]'] \n",
+ " \n",
+ " block3d_project_conv (Conv2D) (None, 28, 28, 80) 38400 ['block3d_se_excite[0][0]'] Y \n",
+ " \n",
+ " block3d_project_bn (BatchNorma (None, 28, 28, 80) 320 ['block3d_project_conv[0][0]'] Y \n",
+ " lization) \n",
+ " \n",
+ " block3d_drop (FixedDropout) (None, 28, 28, 80) 0 ['block3d_project_bn[0][0]'] Y \n",
+ " \n",
+ " block3d_add (Add) (None, 28, 28, 80) 0 ['block3d_drop[0][0]', Y \n",
+ " 'block3c_add[0][0]'] \n",
+ " \n",
+ " block3e_expand_conv (Conv2D) (None, 28, 28, 480) 38400 ['block3d_add[0][0]'] Y \n",
+ " \n",
+ " block3e_expand_bn (BatchNormal (None, 28, 28, 480) 1920 ['block3e_expand_conv[0][0]'] Y \n",
+ " ization) \n",
+ " \n",
+ " block3e_expand_activation (Act (None, 28, 28, 480) 0 ['block3e_expand_bn[0][0]'] Y \n",
+ " ivation) \n",
+ " \n",
+ " block3e_dwconv (DepthwiseConv2 (None, 28, 28, 480) 12000 ['block3e_expand_activation[0][ Y \n",
+ " D) 0]'] \n",
+ " \n",
+ " block3e_bn (BatchNormalization (None, 28, 28, 480) 1920 ['block3e_dwconv[0][0]'] Y \n",
+ " ) \n",
+ " \n",
+ " block3e_activation (Activation (None, 28, 28, 480) 0 ['block3e_bn[0][0]'] Y \n",
+ " ) \n",
+ " \n",
+ " block3e_se_squeeze (GlobalAver (None, 480) 0 ['block3e_activation[0][0]'] Y \n",
+ " agePooling2D) \n",
+ " \n",
+ " block3e_se_reshape (Reshape) (None, 1, 1, 480) 0 ['block3e_se_squeeze[0][0]'] Y \n",
+ " \n",
+ " block3e_se_reduce (Conv2D) (None, 1, 1, 20) 9620 ['block3e_se_reshape[0][0]'] Y \n",
+ " \n",
+ " block3e_se_expand (Conv2D) (None, 1, 1, 480) 10080 ['block3e_se_reduce[0][0]'] Y \n",
+ " \n",
+ " block3e_se_excite (Multiply) (None, 28, 28, 480) 0 ['block3e_activation[0][0]', Y \n",
+ " 'block3e_se_expand[0][0]'] \n",
+ " \n",
+ " block3e_project_conv (Conv2D) (None, 28, 28, 80) 38400 ['block3e_se_excite[0][0]'] Y \n",
+ " \n",
+ " block3e_project_bn (BatchNorma (None, 28, 28, 80) 320 ['block3e_project_conv[0][0]'] Y \n",
+ " lization) \n",
+ " \n",
+ " block3e_drop (FixedDropout) (None, 28, 28, 80) 0 ['block3e_project_bn[0][0]'] Y \n",
+ " \n",
+ " block3e_add (Add) (None, 28, 28, 80) 0 ['block3e_drop[0][0]', Y \n",
+ " 'block3d_add[0][0]'] \n",
+ " \n",
+ " block3f_expand_conv (Conv2D) (None, 28, 28, 480) 38400 ['block3e_add[0][0]'] Y \n",
+ " \n",
+ " block3f_expand_bn (BatchNormal (None, 28, 28, 480) 1920 ['block3f_expand_conv[0][0]'] Y \n",
+ " ization) \n",
+ " \n",
+ " block3f_expand_activation (Act (None, 28, 28, 480) 0 ['block3f_expand_bn[0][0]'] Y \n",
+ " ivation) \n",
+ " \n",
+ " block3f_dwconv (DepthwiseConv2 (None, 28, 28, 480) 12000 ['block3f_expand_activation[0][ Y \n",
+ " D) 0]'] \n",
+ " \n",
+ " block3f_bn (BatchNormalization (None, 28, 28, 480) 1920 ['block3f_dwconv[0][0]'] Y \n",
+ " ) \n",
+ " \n",
+ " block3f_activation (Activation (None, 28, 28, 480) 0 ['block3f_bn[0][0]'] Y \n",
+ " ) \n",
+ " \n",
+ " block3f_se_squeeze (GlobalAver (None, 480) 0 ['block3f_activation[0][0]'] Y \n",
+ " agePooling2D) \n",
+ " \n",
+ " block3f_se_reshape (Reshape) (None, 1, 1, 480) 0 ['block3f_se_squeeze[0][0]'] Y \n",
+ " \n",
+ " block3f_se_reduce (Conv2D) (None, 1, 1, 20) 9620 ['block3f_se_reshape[0][0]'] Y \n",
+ " \n",
+ " block3f_se_expand (Conv2D) (None, 1, 1, 480) 10080 ['block3f_se_reduce[0][0]'] Y \n",
+ " \n",
+ " block3f_se_excite (Multiply) (None, 28, 28, 480) 0 ['block3f_activation[0][0]', Y \n",
+ " 'block3f_se_expand[0][0]'] \n",
+ " \n",
+ " block3f_project_conv (Conv2D) (None, 28, 28, 80) 38400 ['block3f_se_excite[0][0]'] Y \n",
+ " \n",
+ " block3f_project_bn (BatchNorma (None, 28, 28, 80) 320 ['block3f_project_conv[0][0]'] Y \n",
+ " lization) \n",
+ " \n",
+ " block3f_drop (FixedDropout) (None, 28, 28, 80) 0 ['block3f_project_bn[0][0]'] Y \n",
+ " \n",
+ " block3f_add (Add) (None, 28, 28, 80) 0 ['block3f_drop[0][0]', Y \n",
+ " 'block3e_add[0][0]'] \n",
+ " \n",
+ " block3g_expand_conv (Conv2D) (None, 28, 28, 480) 38400 ['block3f_add[0][0]'] Y \n",
+ " \n",
+ " block3g_expand_bn (BatchNormal (None, 28, 28, 480) 1920 ['block3g_expand_conv[0][0]'] Y \n",
+ " ization) \n",
+ " \n",
+ " block3g_expand_activation (Act (None, 28, 28, 480) 0 ['block3g_expand_bn[0][0]'] Y \n",
+ " ivation) \n",
+ " \n",
+ " block3g_dwconv (DepthwiseConv2 (None, 28, 28, 480) 12000 ['block3g_expand_activation[0][ Y \n",
+ " D) 0]'] \n",
+ " \n",
+ " block3g_bn (BatchNormalization (None, 28, 28, 480) 1920 ['block3g_dwconv[0][0]'] Y \n",
+ " ) \n",
+ " \n",
+ " block3g_activation (Activation (None, 28, 28, 480) 0 ['block3g_bn[0][0]'] Y \n",
+ " ) \n",
+ " \n",
+ " block3g_se_squeeze (GlobalAver (None, 480) 0 ['block3g_activation[0][0]'] Y \n",
+ " agePooling2D) \n",
+ " \n",
+ " block3g_se_reshape (Reshape) (None, 1, 1, 480) 0 ['block3g_se_squeeze[0][0]'] Y \n",
+ " \n",
+ " block3g_se_reduce (Conv2D) (None, 1, 1, 20) 9620 ['block3g_se_reshape[0][0]'] Y \n",
+ " \n",
+ " block3g_se_expand (Conv2D) (None, 1, 1, 480) 10080 ['block3g_se_reduce[0][0]'] Y \n",
+ " \n",
+ " block3g_se_excite (Multiply) (None, 28, 28, 480) 0 ['block3g_activation[0][0]', Y \n",
+ " 'block3g_se_expand[0][0]'] \n",
+ " \n",
+ " block3g_project_conv (Conv2D) (None, 28, 28, 80) 38400 ['block3g_se_excite[0][0]'] Y \n",
+ " \n",
+ " block3g_project_bn (BatchNorma (None, 28, 28, 80) 320 ['block3g_project_conv[0][0]'] Y \n",
+ " lization) \n",
+ " \n",
+ " block3g_drop (FixedDropout) (None, 28, 28, 80) 0 ['block3g_project_bn[0][0]'] Y \n",
+ " \n",
+ " block3g_add (Add) (None, 28, 28, 80) 0 ['block3g_drop[0][0]', Y \n",
+ " 'block3f_add[0][0]'] \n",
+ " \n",
+ " block4a_expand_conv (Conv2D) (None, 28, 28, 480) 38400 ['block3g_add[0][0]'] Y \n",
+ " \n",
+ " block4a_expand_bn (BatchNormal (None, 28, 28, 480) 1920 ['block4a_expand_conv[0][0]'] Y \n",
+ " ization) \n",
+ " \n",
+ " block4a_expand_activation (Act (None, 28, 28, 480) 0 ['block4a_expand_bn[0][0]'] Y \n",
+ " ivation) \n",
+ " \n",
+ " block4a_dwconv (DepthwiseConv2 (None, 14, 14, 480) 4320 ['block4a_expand_activation[0][ Y \n",
+ " D) 0]'] \n",
+ " \n",
+ " block4a_bn (BatchNormalization (None, 14, 14, 480) 1920 ['block4a_dwconv[0][0]'] Y \n",
+ " ) \n",
+ " \n",
+ " block4a_activation (Activation (None, 14, 14, 480) 0 ['block4a_bn[0][0]'] Y \n",
+ " ) \n",
+ " \n",
+ " block4a_se_squeeze (GlobalAver (None, 480) 0 ['block4a_activation[0][0]'] Y \n",
+ " agePooling2D) \n",
+ " \n",
+ " block4a_se_reshape (Reshape) (None, 1, 1, 480) 0 ['block4a_se_squeeze[0][0]'] Y \n",
+ " \n",
+ " block4a_se_reduce (Conv2D) (None, 1, 1, 20) 9620 ['block4a_se_reshape[0][0]'] Y \n",
+ " \n",
+ " block4a_se_expand (Conv2D) (None, 1, 1, 480) 10080 ['block4a_se_reduce[0][0]'] Y \n",
+ " \n",
+ " block4a_se_excite (Multiply) (None, 14, 14, 480) 0 ['block4a_activation[0][0]', Y \n",
+ " 'block4a_se_expand[0][0]'] \n",
+ " \n",
+ " block4a_project_conv (Conv2D) (None, 14, 14, 160) 76800 ['block4a_se_excite[0][0]'] Y \n",
+ " \n",
+ " block4a_project_bn (BatchNorma (None, 14, 14, 160) 640 ['block4a_project_conv[0][0]'] Y \n",
+ " lization) \n",
+ " \n",
+ " block4b_expand_conv (Conv2D) (None, 14, 14, 960) 153600 ['block4a_project_bn[0][0]'] Y \n",
+ " \n",
+ " block4b_expand_bn (BatchNormal (None, 14, 14, 960) 3840 ['block4b_expand_conv[0][0]'] Y \n",
+ " ization) \n",
+ " \n",
+ " block4b_expand_activation (Act (None, 14, 14, 960) 0 ['block4b_expand_bn[0][0]'] Y \n",
+ " ivation) \n",
+ " \n",
+ " block4b_dwconv (DepthwiseConv2 (None, 14, 14, 960) 8640 ['block4b_expand_activation[0][ Y \n",
+ " D) 0]'] \n",
+ " \n",
+ " block4b_bn (BatchNormalization (None, 14, 14, 960) 3840 ['block4b_dwconv[0][0]'] Y \n",
+ " ) \n",
+ " \n",
+ " block4b_activation (Activation (None, 14, 14, 960) 0 ['block4b_bn[0][0]'] Y \n",
+ " ) \n",
+ " \n",
+ " block4b_se_squeeze (GlobalAver (None, 960) 0 ['block4b_activation[0][0]'] Y \n",
+ " agePooling2D) \n",
+ " \n",
+ " block4b_se_reshape (Reshape) (None, 1, 1, 960) 0 ['block4b_se_squeeze[0][0]'] Y \n",
+ " \n",
+ " block4b_se_reduce (Conv2D) (None, 1, 1, 40) 38440 ['block4b_se_reshape[0][0]'] Y \n",
+ " \n",
+ " block4b_se_expand (Conv2D) (None, 1, 1, 960) 39360 ['block4b_se_reduce[0][0]'] Y \n",
+ " \n",
+ " block4b_se_excite (Multiply) (None, 14, 14, 960) 0 ['block4b_activation[0][0]', Y \n",
+ " 'block4b_se_expand[0][0]'] \n",
+ " \n",
+ " block4b_project_conv (Conv2D) (None, 14, 14, 160) 153600 ['block4b_se_excite[0][0]'] Y \n",
+ " \n",
+ " block4b_project_bn (BatchNorma (None, 14, 14, 160) 640 ['block4b_project_conv[0][0]'] Y \n",
+ " lization) \n",
+ " \n",
+ " block4b_drop (FixedDropout) (None, 14, 14, 160) 0 ['block4b_project_bn[0][0]'] Y \n",
+ " \n",
+ " block4b_add (Add) (None, 14, 14, 160) 0 ['block4b_drop[0][0]', Y \n",
+ " 'block4a_project_bn[0][0]'] \n",
+ " \n",
+ " block4c_expand_conv (Conv2D) (None, 14, 14, 960) 153600 ['block4b_add[0][0]'] Y \n",
+ " \n",
+ " block4c_expand_bn (BatchNormal (None, 14, 14, 960) 3840 ['block4c_expand_conv[0][0]'] Y \n",
+ " ization) \n",
+ " \n",
+ " block4c_expand_activation (Act (None, 14, 14, 960) 0 ['block4c_expand_bn[0][0]'] Y \n",
+ " ivation) \n",
+ " \n",
+ " block4c_dwconv (DepthwiseConv2 (None, 14, 14, 960) 8640 ['block4c_expand_activation[0][ Y \n",
+ " D) 0]'] \n",
+ " \n",
+ " block4c_bn (BatchNormalization (None, 14, 14, 960) 3840 ['block4c_dwconv[0][0]'] Y \n",
+ " ) \n",
+ " \n",
+ " block4c_activation (Activation (None, 14, 14, 960) 0 ['block4c_bn[0][0]'] Y \n",
+ " ) \n",
+ " \n",
+ " block4c_se_squeeze (GlobalAver (None, 960) 0 ['block4c_activation[0][0]'] Y \n",
+ " agePooling2D) \n",
+ " \n",
+ " block4c_se_reshape (Reshape) (None, 1, 1, 960) 0 ['block4c_se_squeeze[0][0]'] Y \n",
+ " \n",
+ " block4c_se_reduce (Conv2D) (None, 1, 1, 40) 38440 ['block4c_se_reshape[0][0]'] Y \n",
+ " \n",
+ " block4c_se_expand (Conv2D) (None, 1, 1, 960) 39360 ['block4c_se_reduce[0][0]'] Y \n",
+ " \n",
+ " block4c_se_excite (Multiply) (None, 14, 14, 960) 0 ['block4c_activation[0][0]', Y \n",
+ " 'block4c_se_expand[0][0]'] \n",
+ " \n",
+ " block4c_project_conv (Conv2D) (None, 14, 14, 160) 153600 ['block4c_se_excite[0][0]'] Y \n",
+ " \n",
+ " block4c_project_bn (BatchNorma (None, 14, 14, 160) 640 ['block4c_project_conv[0][0]'] Y \n",
+ " lization) \n",
+ " \n",
+ " block4c_drop (FixedDropout) (None, 14, 14, 160) 0 ['block4c_project_bn[0][0]'] Y \n",
+ " \n",
+ " block4c_add (Add) (None, 14, 14, 160) 0 ['block4c_drop[0][0]', Y \n",
+ " 'block4b_add[0][0]'] \n",
+ " \n",
+ " block4d_expand_conv (Conv2D) (None, 14, 14, 960) 153600 ['block4c_add[0][0]'] Y \n",
+ " \n",
+ " block4d_expand_bn (BatchNormal (None, 14, 14, 960) 3840 ['block4d_expand_conv[0][0]'] Y \n",
+ " ization) \n",
+ " \n",
+ " block4d_expand_activation (Act (None, 14, 14, 960) 0 ['block4d_expand_bn[0][0]'] Y \n",
+ " ivation) \n",
+ " \n",
+ " block4d_dwconv (DepthwiseConv2 (None, 14, 14, 960) 8640 ['block4d_expand_activation[0][ Y \n",
+ " D) 0]'] \n",
+ " \n",
+ " block4d_bn (BatchNormalization (None, 14, 14, 960) 3840 ['block4d_dwconv[0][0]'] Y \n",
+ " ) \n",
+ " \n",
+ " block4d_activation (Activation (None, 14, 14, 960) 0 ['block4d_bn[0][0]'] Y \n",
+ " ) \n",
+ " \n",
+ " block4d_se_squeeze (GlobalAver (None, 960) 0 ['block4d_activation[0][0]'] Y \n",
+ " agePooling2D) \n",
+ " \n",
+ " block4d_se_reshape (Reshape) (None, 1, 1, 960) 0 ['block4d_se_squeeze[0][0]'] Y \n",
+ " \n",
+ " block4d_se_reduce (Conv2D) (None, 1, 1, 40) 38440 ['block4d_se_reshape[0][0]'] Y \n",
+ " \n",
+ " block4d_se_expand (Conv2D) (None, 1, 1, 960) 39360 ['block4d_se_reduce[0][0]'] Y \n",
+ " \n",
+ " block4d_se_excite (Multiply) (None, 14, 14, 960) 0 ['block4d_activation[0][0]', Y \n",
+ " 'block4d_se_expand[0][0]'] \n",
+ " \n",
+ " block4d_project_conv (Conv2D) (None, 14, 14, 160) 153600 ['block4d_se_excite[0][0]'] Y \n",
+ " \n",
+ " block4d_project_bn (BatchNorma (None, 14, 14, 160) 640 ['block4d_project_conv[0][0]'] Y \n",
+ " lization) \n",
+ " \n",
+ " block4d_drop (FixedDropout) (None, 14, 14, 160) 0 ['block4d_project_bn[0][0]'] Y \n",
+ " \n",
+ " block4d_add (Add) (None, 14, 14, 160) 0 ['block4d_drop[0][0]', Y \n",
+ " 'block4c_add[0][0]'] \n",
+ " \n",
+ " block4e_expand_conv (Conv2D) (None, 14, 14, 960) 153600 ['block4d_add[0][0]'] Y \n",
+ " \n",
+ " block4e_expand_bn (BatchNormal (None, 14, 14, 960) 3840 ['block4e_expand_conv[0][0]'] Y \n",
+ " ization) \n",
+ " \n",
+ " block4e_expand_activation (Act (None, 14, 14, 960) 0 ['block4e_expand_bn[0][0]'] Y \n",
+ " ivation) \n",
+ " \n",
+ " block4e_dwconv (DepthwiseConv2 (None, 14, 14, 960) 8640 ['block4e_expand_activation[0][ Y \n",
+ " D) 0]'] \n",
+ " \n",
+ " block4e_bn (BatchNormalization (None, 14, 14, 960) 3840 ['block4e_dwconv[0][0]'] Y \n",
+ " ) \n",
+ " \n",
+ " block4e_activation (Activation (None, 14, 14, 960) 0 ['block4e_bn[0][0]'] Y \n",
+ " ) \n",
+ " \n",
+ " block4e_se_squeeze (GlobalAver (None, 960) 0 ['block4e_activation[0][0]'] Y \n",
+ " agePooling2D) \n",
+ " \n",
+ " block4e_se_reshape (Reshape) (None, 1, 1, 960) 0 ['block4e_se_squeeze[0][0]'] Y \n",
+ " \n",
+ " block4e_se_reduce (Conv2D) (None, 1, 1, 40) 38440 ['block4e_se_reshape[0][0]'] Y \n",
+ " \n",
+ " block4e_se_expand (Conv2D) (None, 1, 1, 960) 39360 ['block4e_se_reduce[0][0]'] Y \n",
+ " \n",
+ " block4e_se_excite (Multiply) (None, 14, 14, 960) 0 ['block4e_activation[0][0]', Y \n",
+ " 'block4e_se_expand[0][0]'] \n",
+ " \n",
+ " block4e_project_conv (Conv2D) (None, 14, 14, 160) 153600 ['block4e_se_excite[0][0]'] Y \n",
+ " \n",
+ " block4e_project_bn (BatchNorma (None, 14, 14, 160) 640 ['block4e_project_conv[0][0]'] Y \n",
+ " lization) \n",
+ " \n",
+ " block4e_drop (FixedDropout) (None, 14, 14, 160) 0 ['block4e_project_bn[0][0]'] Y \n",
+ " \n",
+ " block4e_add (Add) (None, 14, 14, 160) 0 ['block4e_drop[0][0]', Y \n",
+ " 'block4d_add[0][0]'] \n",
+ " \n",
+ " block4f_expand_conv (Conv2D) (None, 14, 14, 960) 153600 ['block4e_add[0][0]'] Y \n",
+ " \n",
+ " block4f_expand_bn (BatchNormal (None, 14, 14, 960) 3840 ['block4f_expand_conv[0][0]'] Y \n",
+ " ization) \n",
+ " \n",
+ " block4f_expand_activation (Act (None, 14, 14, 960) 0 ['block4f_expand_bn[0][0]'] Y \n",
+ " ivation) \n",
+ " \n",
+ " block4f_dwconv (DepthwiseConv2 (None, 14, 14, 960) 8640 ['block4f_expand_activation[0][ Y \n",
+ " D) 0]'] \n",
+ " \n",
+ " block4f_bn (BatchNormalization (None, 14, 14, 960) 3840 ['block4f_dwconv[0][0]'] Y \n",
+ " ) \n",
+ " \n",
+ " block4f_activation (Activation (None, 14, 14, 960) 0 ['block4f_bn[0][0]'] Y \n",
+ " ) \n",
+ " \n",
+ " block4f_se_squeeze (GlobalAver (None, 960) 0 ['block4f_activation[0][0]'] Y \n",
+ " agePooling2D) \n",
+ " \n",
+ " block4f_se_reshape (Reshape) (None, 1, 1, 960) 0 ['block4f_se_squeeze[0][0]'] Y \n",
+ " \n",
+ " block4f_se_reduce (Conv2D) (None, 1, 1, 40) 38440 ['block4f_se_reshape[0][0]'] Y \n",
+ " \n",
+ " block4f_se_expand (Conv2D) (None, 1, 1, 960) 39360 ['block4f_se_reduce[0][0]'] Y \n",
+ " \n",
+ " block4f_se_excite (Multiply) (None, 14, 14, 960) 0 ['block4f_activation[0][0]', Y \n",
+ " 'block4f_se_expand[0][0]'] \n",
+ " \n",
+ " block4f_project_conv (Conv2D) (None, 14, 14, 160) 153600 ['block4f_se_excite[0][0]'] Y \n",
+ " \n",
+ " block4f_project_bn (BatchNorma (None, 14, 14, 160) 640 ['block4f_project_conv[0][0]'] Y \n",
+ " lization) \n",
+ " \n",
+ " block4f_drop (FixedDropout) (None, 14, 14, 160) 0 ['block4f_project_bn[0][0]'] Y \n",
+ " \n",
+ " block4f_add (Add) (None, 14, 14, 160) 0 ['block4f_drop[0][0]', Y \n",
+ " 'block4e_add[0][0]'] \n",
+ " \n",
+ " block4g_expand_conv (Conv2D) (None, 14, 14, 960) 153600 ['block4f_add[0][0]'] Y \n",
+ " \n",
+ " block4g_expand_bn (BatchNormal (None, 14, 14, 960) 3840 ['block4g_expand_conv[0][0]'] Y \n",
+ " ization) \n",
+ " \n",
+ " block4g_expand_activation (Act (None, 14, 14, 960) 0 ['block4g_expand_bn[0][0]'] Y \n",
+ " ivation) \n",
+ " \n",
+ " block4g_dwconv (DepthwiseConv2 (None, 14, 14, 960) 8640 ['block4g_expand_activation[0][ Y \n",
+ " D) 0]'] \n",
+ " \n",
+ " block4g_bn (BatchNormalization (None, 14, 14, 960) 3840 ['block4g_dwconv[0][0]'] Y \n",
+ " ) \n",
+ " \n",
+ " block4g_activation (Activation (None, 14, 14, 960) 0 ['block4g_bn[0][0]'] Y \n",
+ " ) \n",
+ " \n",
+ " block4g_se_squeeze (GlobalAver (None, 960) 0 ['block4g_activation[0][0]'] Y \n",
+ " agePooling2D) \n",
+ " \n",
+ " block4g_se_reshape (Reshape) (None, 1, 1, 960) 0 ['block4g_se_squeeze[0][0]'] Y \n",
+ " \n",
+ " block4g_se_reduce (Conv2D) (None, 1, 1, 40) 38440 ['block4g_se_reshape[0][0]'] Y \n",
+ " \n",
+ " block4g_se_expand (Conv2D) (None, 1, 1, 960) 39360 ['block4g_se_reduce[0][0]'] Y \n",
+ " \n",
+ " block4g_se_excite (Multiply) (None, 14, 14, 960) 0 ['block4g_activation[0][0]', Y \n",
+ " 'block4g_se_expand[0][0]'] \n",
+ " \n",
+ " block4g_project_conv (Conv2D) (None, 14, 14, 160) 153600 ['block4g_se_excite[0][0]'] Y \n",
+ " \n",
+ " block4g_project_bn (BatchNorma (None, 14, 14, 160) 640 ['block4g_project_conv[0][0]'] Y \n",
+ " lization) \n",
+ " \n",
+ " block4g_drop (FixedDropout) (None, 14, 14, 160) 0 ['block4g_project_bn[0][0]'] Y \n",
+ " \n",
+ " block4g_add (Add) (None, 14, 14, 160) 0 ['block4g_drop[0][0]', Y \n",
+ " 'block4f_add[0][0]'] \n",
+ " \n",
+ " block4h_expand_conv (Conv2D) (None, 14, 14, 960) 153600 ['block4g_add[0][0]'] Y \n",
+ " \n",
+ " block4h_expand_bn (BatchNormal (None, 14, 14, 960) 3840 ['block4h_expand_conv[0][0]'] Y \n",
+ " ization) \n",
+ " \n",
+ " block4h_expand_activation (Act (None, 14, 14, 960) 0 ['block4h_expand_bn[0][0]'] Y \n",
+ " ivation) \n",
+ " \n",
+ " block4h_dwconv (DepthwiseConv2 (None, 14, 14, 960) 8640 ['block4h_expand_activation[0][ Y \n",
+ " D) 0]'] \n",
+ " \n",
+ " block4h_bn (BatchNormalization (None, 14, 14, 960) 3840 ['block4h_dwconv[0][0]'] Y \n",
+ " ) \n",
+ " \n",
+ " block4h_activation (Activation (None, 14, 14, 960) 0 ['block4h_bn[0][0]'] Y \n",
+ " ) \n",
+ " \n",
+ " block4h_se_squeeze (GlobalAver (None, 960) 0 ['block4h_activation[0][0]'] Y \n",
+ " agePooling2D) \n",
+ " \n",
+ " block4h_se_reshape (Reshape) (None, 1, 1, 960) 0 ['block4h_se_squeeze[0][0]'] Y \n",
+ " \n",
+ " block4h_se_reduce (Conv2D) (None, 1, 1, 40) 38440 ['block4h_se_reshape[0][0]'] Y \n",
+ " \n",
+ " block4h_se_expand (Conv2D) (None, 1, 1, 960) 39360 ['block4h_se_reduce[0][0]'] Y \n",
+ " \n",
+ " block4h_se_excite (Multiply) (None, 14, 14, 960) 0 ['block4h_activation[0][0]', Y \n",
+ " 'block4h_se_expand[0][0]'] \n",
+ " \n",
+ " block4h_project_conv (Conv2D) (None, 14, 14, 160) 153600 ['block4h_se_excite[0][0]'] Y \n",
+ " \n",
+ " block4h_project_bn (BatchNorma (None, 14, 14, 160) 640 ['block4h_project_conv[0][0]'] Y \n",
+ " lization) \n",
+ " \n",
+ " block4h_drop (FixedDropout) (None, 14, 14, 160) 0 ['block4h_project_bn[0][0]'] Y \n",
+ " \n",
+ " block4h_add (Add) (None, 14, 14, 160) 0 ['block4h_drop[0][0]', Y \n",
+ " 'block4g_add[0][0]'] \n",
+ " \n",
+ " block4i_expand_conv (Conv2D) (None, 14, 14, 960) 153600 ['block4h_add[0][0]'] Y \n",
+ " \n",
+ " block4i_expand_bn (BatchNormal (None, 14, 14, 960) 3840 ['block4i_expand_conv[0][0]'] Y \n",
+ " ization) \n",
+ " \n",
+ " block4i_expand_activation (Act (None, 14, 14, 960) 0 ['block4i_expand_bn[0][0]'] Y \n",
+ " ivation) \n",
+ " \n",
+ " block4i_dwconv (DepthwiseConv2 (None, 14, 14, 960) 8640 ['block4i_expand_activation[0][ Y \n",
+ " D) 0]'] \n",
+ " \n",
+ " block4i_bn (BatchNormalization (None, 14, 14, 960) 3840 ['block4i_dwconv[0][0]'] Y \n",
+ " ) \n",
+ " \n",
+ " block4i_activation (Activation (None, 14, 14, 960) 0 ['block4i_bn[0][0]'] Y \n",
+ " ) \n",
+ " \n",
+ " block4i_se_squeeze (GlobalAver (None, 960) 0 ['block4i_activation[0][0]'] Y \n",
+ " agePooling2D) \n",
+ " \n",
+ " block4i_se_reshape (Reshape) (None, 1, 1, 960) 0 ['block4i_se_squeeze[0][0]'] Y \n",
+ " \n",
+ " block4i_se_reduce (Conv2D) (None, 1, 1, 40) 38440 ['block4i_se_reshape[0][0]'] Y \n",
+ " \n",
+ " block4i_se_expand (Conv2D) (None, 1, 1, 960) 39360 ['block4i_se_reduce[0][0]'] Y \n",
+ " \n",
+ " block4i_se_excite (Multiply) (None, 14, 14, 960) 0 ['block4i_activation[0][0]', Y \n",
+ " 'block4i_se_expand[0][0]'] \n",
+ " \n",
+ " block4i_project_conv (Conv2D) (None, 14, 14, 160) 153600 ['block4i_se_excite[0][0]'] Y \n",
+ " \n",
+ " block4i_project_bn (BatchNorma (None, 14, 14, 160) 640 ['block4i_project_conv[0][0]'] Y \n",
+ " lization) \n",
+ " \n",
+ " block4i_drop (FixedDropout) (None, 14, 14, 160) 0 ['block4i_project_bn[0][0]'] Y \n",
+ " \n",
+ " block4i_add (Add) (None, 14, 14, 160) 0 ['block4i_drop[0][0]', Y \n",
+ " 'block4h_add[0][0]'] \n",
+ " \n",
+ " block4j_expand_conv (Conv2D) (None, 14, 14, 960) 153600 ['block4i_add[0][0]'] Y \n",
+ " \n",
+ " block4j_expand_bn (BatchNormal (None, 14, 14, 960) 3840 ['block4j_expand_conv[0][0]'] Y \n",
+ " ization) \n",
+ " \n",
+ " block4j_expand_activation (Act (None, 14, 14, 960) 0 ['block4j_expand_bn[0][0]'] Y \n",
+ " ivation) \n",
+ " \n",
+ " block4j_dwconv (DepthwiseConv2 (None, 14, 14, 960) 8640 ['block4j_expand_activation[0][ Y \n",
+ " D) 0]'] \n",
+ " \n",
+ " block4j_bn (BatchNormalization (None, 14, 14, 960) 3840 ['block4j_dwconv[0][0]'] Y \n",
+ " ) \n",
+ " \n",
+ " block4j_activation (Activation (None, 14, 14, 960) 0 ['block4j_bn[0][0]'] Y \n",
+ " ) \n",
+ " \n",
+ " block4j_se_squeeze (GlobalAver (None, 960) 0 ['block4j_activation[0][0]'] Y \n",
+ " agePooling2D) \n",
+ " \n",
+ " block4j_se_reshape (Reshape) (None, 1, 1, 960) 0 ['block4j_se_squeeze[0][0]'] Y \n",
+ " \n",
+ " block4j_se_reduce (Conv2D) (None, 1, 1, 40) 38440 ['block4j_se_reshape[0][0]'] Y \n",
+ " \n",
+ " block4j_se_expand (Conv2D) (None, 1, 1, 960) 39360 ['block4j_se_reduce[0][0]'] Y \n",
+ " \n",
+ " block4j_se_excite (Multiply) (None, 14, 14, 960) 0 ['block4j_activation[0][0]', Y \n",
+ " 'block4j_se_expand[0][0]'] \n",
+ " \n",
+ " block4j_project_conv (Conv2D) (None, 14, 14, 160) 153600 ['block4j_se_excite[0][0]'] Y \n",
+ " \n",
+ " block4j_project_bn (BatchNorma (None, 14, 14, 160) 640 ['block4j_project_conv[0][0]'] Y \n",
+ " lization) \n",
+ " \n",
+ " block4j_drop (FixedDropout) (None, 14, 14, 160) 0 ['block4j_project_bn[0][0]'] Y \n",
+ " \n",
+ " block4j_add (Add) (None, 14, 14, 160) 0 ['block4j_drop[0][0]', Y \n",
+ " 'block4i_add[0][0]'] \n",
+ " \n",
+ " block5a_expand_conv (Conv2D) (None, 14, 14, 960) 153600 ['block4j_add[0][0]'] Y \n",
+ " \n",
+ " block5a_expand_bn (BatchNormal (None, 14, 14, 960) 3840 ['block5a_expand_conv[0][0]'] Y \n",
+ " ization) \n",
+ " \n",
+ " block5a_expand_activation (Act (None, 14, 14, 960) 0 ['block5a_expand_bn[0][0]'] Y \n",
+ " ivation) \n",
+ " \n",
+ " block5a_dwconv (DepthwiseConv2 (None, 14, 14, 960) 24000 ['block5a_expand_activation[0][ Y \n",
+ " D) 0]'] \n",
+ " \n",
+ " block5a_bn (BatchNormalization (None, 14, 14, 960) 3840 ['block5a_dwconv[0][0]'] Y \n",
+ " ) \n",
+ " \n",
+ " block5a_activation (Activation (None, 14, 14, 960) 0 ['block5a_bn[0][0]'] Y \n",
+ " ) \n",
+ " \n",
+ " block5a_se_squeeze (GlobalAver (None, 960) 0 ['block5a_activation[0][0]'] Y \n",
+ " agePooling2D) \n",
+ " \n",
+ " block5a_se_reshape (Reshape) (None, 1, 1, 960) 0 ['block5a_se_squeeze[0][0]'] Y \n",
+ " \n",
+ " block5a_se_reduce (Conv2D) (None, 1, 1, 40) 38440 ['block5a_se_reshape[0][0]'] Y \n",
+ " \n",
+ " block5a_se_expand (Conv2D) (None, 1, 1, 960) 39360 ['block5a_se_reduce[0][0]'] Y \n",
+ " \n",
+ " block5a_se_excite (Multiply) (None, 14, 14, 960) 0 ['block5a_activation[0][0]', Y \n",
+ " 'block5a_se_expand[0][0]'] \n",
+ " \n",
+ " block5a_project_conv (Conv2D) (None, 14, 14, 224) 215040 ['block5a_se_excite[0][0]'] Y \n",
+ " \n",
+ " block5a_project_bn (BatchNorma (None, 14, 14, 224) 896 ['block5a_project_conv[0][0]'] Y \n",
+ " lization) \n",
+ " \n",
+ " block5b_expand_conv (Conv2D) (None, 14, 14, 1344 301056 ['block5a_project_bn[0][0]'] Y \n",
+ " ) \n",
+ " \n",
+ " block5b_expand_bn (BatchNormal (None, 14, 14, 1344 5376 ['block5b_expand_conv[0][0]'] Y \n",
+ " ization) ) \n",
+ " \n",
+ " block5b_expand_activation (Act (None, 14, 14, 1344 0 ['block5b_expand_bn[0][0]'] Y \n",
+ " ivation) ) \n",
+ " \n",
+ " block5b_dwconv (DepthwiseConv2 (None, 14, 14, 1344 33600 ['block5b_expand_activation[0][ Y \n",
+ " D) ) 0]'] \n",
+ " \n",
+ " block5b_bn (BatchNormalization (None, 14, 14, 1344 5376 ['block5b_dwconv[0][0]'] Y \n",
+ " ) ) \n",
+ " \n",
+ " block5b_activation (Activation (None, 14, 14, 1344 0 ['block5b_bn[0][0]'] Y \n",
+ " ) ) \n",
+ " \n",
+ " block5b_se_squeeze (GlobalAver (None, 1344) 0 ['block5b_activation[0][0]'] Y \n",
+ " agePooling2D) \n",
+ " \n",
+ " block5b_se_reshape (Reshape) (None, 1, 1, 1344) 0 ['block5b_se_squeeze[0][0]'] Y \n",
+ " \n",
+ " block5b_se_reduce (Conv2D) (None, 1, 1, 56) 75320 ['block5b_se_reshape[0][0]'] Y \n",
+ " \n",
+ " block5b_se_expand (Conv2D) (None, 1, 1, 1344) 76608 ['block5b_se_reduce[0][0]'] Y \n",
+ " \n",
+ " block5b_se_excite (Multiply) (None, 14, 14, 1344 0 ['block5b_activation[0][0]', Y \n",
+ " ) 'block5b_se_expand[0][0]'] \n",
+ " \n",
+ " block5b_project_conv (Conv2D) (None, 14, 14, 224) 301056 ['block5b_se_excite[0][0]'] Y \n",
+ " \n",
+ " block5b_project_bn (BatchNorma (None, 14, 14, 224) 896 ['block5b_project_conv[0][0]'] Y \n",
+ " lization) \n",
+ " \n",
+ " block5b_drop (FixedDropout) (None, 14, 14, 224) 0 ['block5b_project_bn[0][0]'] Y \n",
+ " \n",
+ " block5b_add (Add) (None, 14, 14, 224) 0 ['block5b_drop[0][0]', Y \n",
+ " 'block5a_project_bn[0][0]'] \n",
+ " \n",
+ " block5c_expand_conv (Conv2D) (None, 14, 14, 1344 301056 ['block5b_add[0][0]'] Y \n",
+ " ) \n",
+ " \n",
+ " block5c_expand_bn (BatchNormal (None, 14, 14, 1344 5376 ['block5c_expand_conv[0][0]'] Y \n",
+ " ization) ) \n",
+ " \n",
+ " block5c_expand_activation (Act (None, 14, 14, 1344 0 ['block5c_expand_bn[0][0]'] Y \n",
+ " ivation) ) \n",
+ " \n",
+ " block5c_dwconv (DepthwiseConv2 (None, 14, 14, 1344 33600 ['block5c_expand_activation[0][ Y \n",
+ " D) ) 0]'] \n",
+ " \n",
+ " block5c_bn (BatchNormalization (None, 14, 14, 1344 5376 ['block5c_dwconv[0][0]'] Y \n",
+ " ) ) \n",
+ " \n",
+ " block5c_activation (Activation (None, 14, 14, 1344 0 ['block5c_bn[0][0]'] Y \n",
+ " ) ) \n",
+ " \n",
+ " block5c_se_squeeze (GlobalAver (None, 1344) 0 ['block5c_activation[0][0]'] Y \n",
+ " agePooling2D) \n",
+ " \n",
+ " block5c_se_reshape (Reshape) (None, 1, 1, 1344) 0 ['block5c_se_squeeze[0][0]'] Y \n",
+ " \n",
+ " block5c_se_reduce (Conv2D) (None, 1, 1, 56) 75320 ['block5c_se_reshape[0][0]'] Y \n",
+ " \n",
+ " block5c_se_expand (Conv2D) (None, 1, 1, 1344) 76608 ['block5c_se_reduce[0][0]'] Y \n",
+ " \n",
+ " block5c_se_excite (Multiply) (None, 14, 14, 1344 0 ['block5c_activation[0][0]', Y \n",
+ " ) 'block5c_se_expand[0][0]'] \n",
+ " \n",
+ " block5c_project_conv (Conv2D) (None, 14, 14, 224) 301056 ['block5c_se_excite[0][0]'] Y \n",
+ " \n",
+ " block5c_project_bn (BatchNorma (None, 14, 14, 224) 896 ['block5c_project_conv[0][0]'] Y \n",
+ " lization) \n",
+ " \n",
+ " block5c_drop (FixedDropout) (None, 14, 14, 224) 0 ['block5c_project_bn[0][0]'] Y \n",
+ " \n",
+ " block5c_add (Add) (None, 14, 14, 224) 0 ['block5c_drop[0][0]', Y \n",
+ " 'block5b_add[0][0]'] \n",
+ " \n",
+ " block5d_expand_conv (Conv2D) (None, 14, 14, 1344 301056 ['block5c_add[0][0]'] Y \n",
+ " ) \n",
+ " \n",
+ " block5d_expand_bn (BatchNormal (None, 14, 14, 1344 5376 ['block5d_expand_conv[0][0]'] Y \n",
+ " ization) ) \n",
+ " \n",
+ " block5d_expand_activation (Act (None, 14, 14, 1344 0 ['block5d_expand_bn[0][0]'] Y \n",
+ " ivation) ) \n",
+ " \n",
+ " block5d_dwconv (DepthwiseConv2 (None, 14, 14, 1344 33600 ['block5d_expand_activation[0][ Y \n",
+ " D) ) 0]'] \n",
+ " \n",
+ " block5d_bn (BatchNormalization (None, 14, 14, 1344 5376 ['block5d_dwconv[0][0]'] Y \n",
+ " ) ) \n",
+ " \n",
+ " block5d_activation (Activation (None, 14, 14, 1344 0 ['block5d_bn[0][0]'] Y \n",
+ " ) ) \n",
+ " \n",
+ " block5d_se_squeeze (GlobalAver (None, 1344) 0 ['block5d_activation[0][0]'] Y \n",
+ " agePooling2D) \n",
+ " \n",
+ " block5d_se_reshape (Reshape) (None, 1, 1, 1344) 0 ['block5d_se_squeeze[0][0]'] Y \n",
+ " \n",
+ " block5d_se_reduce (Conv2D) (None, 1, 1, 56) 75320 ['block5d_se_reshape[0][0]'] Y \n",
+ " \n",
+ " block5d_se_expand (Conv2D) (None, 1, 1, 1344) 76608 ['block5d_se_reduce[0][0]'] Y \n",
+ " \n",
+ " block5d_se_excite (Multiply) (None, 14, 14, 1344 0 ['block5d_activation[0][0]', Y \n",
+ " ) 'block5d_se_expand[0][0]'] \n",
+ " \n",
+ " block5d_project_conv (Conv2D) (None, 14, 14, 224) 301056 ['block5d_se_excite[0][0]'] Y \n",
+ " \n",
+ " block5d_project_bn (BatchNorma (None, 14, 14, 224) 896 ['block5d_project_conv[0][0]'] Y \n",
+ " lization) \n",
+ " \n",
+ " block5d_drop (FixedDropout) (None, 14, 14, 224) 0 ['block5d_project_bn[0][0]'] Y \n",
+ " \n",
+ " block5d_add (Add) (None, 14, 14, 224) 0 ['block5d_drop[0][0]', Y \n",
+ " 'block5c_add[0][0]'] \n",
+ " \n",
+ " block5e_expand_conv (Conv2D) (None, 14, 14, 1344 301056 ['block5d_add[0][0]'] Y \n",
+ " ) \n",
+ " \n",
+ " block5e_expand_bn (BatchNormal (None, 14, 14, 1344 5376 ['block5e_expand_conv[0][0]'] Y \n",
+ " ization) ) \n",
+ " \n",
+ " block5e_expand_activation (Act (None, 14, 14, 1344 0 ['block5e_expand_bn[0][0]'] Y \n",
+ " ivation) ) \n",
+ " \n",
+ " block5e_dwconv (DepthwiseConv2 (None, 14, 14, 1344 33600 ['block5e_expand_activation[0][ Y \n",
+ " D) ) 0]'] \n",
+ " \n",
+ " block5e_bn (BatchNormalization (None, 14, 14, 1344 5376 ['block5e_dwconv[0][0]'] Y \n",
+ " ) ) \n",
+ " \n",
+ " block5e_activation (Activation (None, 14, 14, 1344 0 ['block5e_bn[0][0]'] Y \n",
+ " ) ) \n",
+ " \n",
+ " block5e_se_squeeze (GlobalAver (None, 1344) 0 ['block5e_activation[0][0]'] Y \n",
+ " agePooling2D) \n",
+ " \n",
+ " block5e_se_reshape (Reshape) (None, 1, 1, 1344) 0 ['block5e_se_squeeze[0][0]'] Y \n",
+ " \n",
+ " block5e_se_reduce (Conv2D) (None, 1, 1, 56) 75320 ['block5e_se_reshape[0][0]'] Y \n",
+ " \n",
+ " block5e_se_expand (Conv2D) (None, 1, 1, 1344) 76608 ['block5e_se_reduce[0][0]'] Y \n",
+ " \n",
+ " block5e_se_excite (Multiply) (None, 14, 14, 1344 0 ['block5e_activation[0][0]', Y \n",
+ " ) 'block5e_se_expand[0][0]'] \n",
+ " \n",
+ " block5e_project_conv (Conv2D) (None, 14, 14, 224) 301056 ['block5e_se_excite[0][0]'] Y \n",
+ " \n",
+ " block5e_project_bn (BatchNorma (None, 14, 14, 224) 896 ['block5e_project_conv[0][0]'] Y \n",
+ " lization) \n",
+ " \n",
+ " block5e_drop (FixedDropout) (None, 14, 14, 224) 0 ['block5e_project_bn[0][0]'] Y \n",
+ " \n",
+ " block5e_add (Add) (None, 14, 14, 224) 0 ['block5e_drop[0][0]', Y \n",
+ " 'block5d_add[0][0]'] \n",
+ " \n",
+ " block5f_expand_conv (Conv2D) (None, 14, 14, 1344 301056 ['block5e_add[0][0]'] Y \n",
+ " ) \n",
+ " \n",
+ " block5f_expand_bn (BatchNormal (None, 14, 14, 1344 5376 ['block5f_expand_conv[0][0]'] Y \n",
+ " ization) ) \n",
+ " \n",
+ " block5f_expand_activation (Act (None, 14, 14, 1344 0 ['block5f_expand_bn[0][0]'] Y \n",
+ " ivation) ) \n",
+ " \n",
+ " block5f_dwconv (DepthwiseConv2 (None, 14, 14, 1344 33600 ['block5f_expand_activation[0][ Y \n",
+ " D) ) 0]'] \n",
+ " \n",
+ " block5f_bn (BatchNormalization (None, 14, 14, 1344 5376 ['block5f_dwconv[0][0]'] Y \n",
+ " ) ) \n",
+ " \n",
+ " block5f_activation (Activation (None, 14, 14, 1344 0 ['block5f_bn[0][0]'] Y \n",
+ " ) ) \n",
+ " \n",
+ " block5f_se_squeeze (GlobalAver (None, 1344) 0 ['block5f_activation[0][0]'] Y \n",
+ " agePooling2D) \n",
+ " \n",
+ " block5f_se_reshape (Reshape) (None, 1, 1, 1344) 0 ['block5f_se_squeeze[0][0]'] Y \n",
+ " \n",
+ " block5f_se_reduce (Conv2D) (None, 1, 1, 56) 75320 ['block5f_se_reshape[0][0]'] Y \n",
+ " \n",
+ " block5f_se_expand (Conv2D) (None, 1, 1, 1344) 76608 ['block5f_se_reduce[0][0]'] Y \n",
+ " \n",
+ " block5f_se_excite (Multiply) (None, 14, 14, 1344 0 ['block5f_activation[0][0]', Y \n",
+ " ) 'block5f_se_expand[0][0]'] \n",
+ " \n",
+ " block5f_project_conv (Conv2D) (None, 14, 14, 224) 301056 ['block5f_se_excite[0][0]'] Y \n",
+ " \n",
+ " block5f_project_bn (BatchNorma (None, 14, 14, 224) 896 ['block5f_project_conv[0][0]'] Y \n",
+ " lization) \n",
+ " \n",
+ " block5f_drop (FixedDropout) (None, 14, 14, 224) 0 ['block5f_project_bn[0][0]'] Y \n",
+ " \n",
+ " block5f_add (Add) (None, 14, 14, 224) 0 ['block5f_drop[0][0]', Y \n",
+ " 'block5e_add[0][0]'] \n",
+ " \n",
+ " block5g_expand_conv (Conv2D) (None, 14, 14, 1344 301056 ['block5f_add[0][0]'] Y \n",
+ " ) \n",
+ " \n",
+ " block5g_expand_bn (BatchNormal (None, 14, 14, 1344 5376 ['block5g_expand_conv[0][0]'] Y \n",
+ " ization) ) \n",
+ " \n",
+ " block5g_expand_activation (Act (None, 14, 14, 1344 0 ['block5g_expand_bn[0][0]'] Y \n",
+ " ivation) ) \n",
+ " \n",
+ " block5g_dwconv (DepthwiseConv2 (None, 14, 14, 1344 33600 ['block5g_expand_activation[0][ Y \n",
+ " D) ) 0]'] \n",
+ " \n",
+ " block5g_bn (BatchNormalization (None, 14, 14, 1344 5376 ['block5g_dwconv[0][0]'] Y \n",
+ " ) ) \n",
+ " \n",
+ " block5g_activation (Activation (None, 14, 14, 1344 0 ['block5g_bn[0][0]'] Y \n",
+ " ) ) \n",
+ " \n",
+ " block5g_se_squeeze (GlobalAver (None, 1344) 0 ['block5g_activation[0][0]'] Y \n",
+ " agePooling2D) \n",
+ " \n",
+ " block5g_se_reshape (Reshape) (None, 1, 1, 1344) 0 ['block5g_se_squeeze[0][0]'] Y \n",
+ " \n",
+ " block5g_se_reduce (Conv2D) (None, 1, 1, 56) 75320 ['block5g_se_reshape[0][0]'] Y \n",
+ " \n",
+ " block5g_se_expand (Conv2D) (None, 1, 1, 1344) 76608 ['block5g_se_reduce[0][0]'] Y \n",
+ " \n",
+ " block5g_se_excite (Multiply) (None, 14, 14, 1344 0 ['block5g_activation[0][0]', Y \n",
+ " ) 'block5g_se_expand[0][0]'] \n",
+ " \n",
+ " block5g_project_conv (Conv2D) (None, 14, 14, 224) 301056 ['block5g_se_excite[0][0]'] Y \n",
+ " \n",
+ " block5g_project_bn (BatchNorma (None, 14, 14, 224) 896 ['block5g_project_conv[0][0]'] Y \n",
+ " lization) \n",
+ " \n",
+ " block5g_drop (FixedDropout) (None, 14, 14, 224) 0 ['block5g_project_bn[0][0]'] Y \n",
+ " \n",
+ " block5g_add (Add) (None, 14, 14, 224) 0 ['block5g_drop[0][0]', Y \n",
+ " 'block5f_add[0][0]'] \n",
+ " \n",
+ " block5h_expand_conv (Conv2D) (None, 14, 14, 1344 301056 ['block5g_add[0][0]'] Y \n",
+ " ) \n",
+ " \n",
+ " block5h_expand_bn (BatchNormal (None, 14, 14, 1344 5376 ['block5h_expand_conv[0][0]'] Y \n",
+ " ization) ) \n",
+ " \n",
+ " block5h_expand_activation (Act (None, 14, 14, 1344 0 ['block5h_expand_bn[0][0]'] Y \n",
+ " ivation) ) \n",
+ " \n",
+ " block5h_dwconv (DepthwiseConv2 (None, 14, 14, 1344 33600 ['block5h_expand_activation[0][ Y \n",
+ " D) ) 0]'] \n",
+ " \n",
+ " block5h_bn (BatchNormalization (None, 14, 14, 1344 5376 ['block5h_dwconv[0][0]'] Y \n",
+ " ) ) \n",
+ " \n",
+ " block5h_activation (Activation (None, 14, 14, 1344 0 ['block5h_bn[0][0]'] Y \n",
+ " ) ) \n",
+ " \n",
+ " block5h_se_squeeze (GlobalAver (None, 1344) 0 ['block5h_activation[0][0]'] Y \n",
+ " agePooling2D) \n",
+ " \n",
+ " block5h_se_reshape (Reshape) (None, 1, 1, 1344) 0 ['block5h_se_squeeze[0][0]'] Y \n",
+ " \n",
+ " block5h_se_reduce (Conv2D) (None, 1, 1, 56) 75320 ['block5h_se_reshape[0][0]'] Y \n",
+ " \n",
+ " block5h_se_expand (Conv2D) (None, 1, 1, 1344) 76608 ['block5h_se_reduce[0][0]'] Y \n",
+ " \n",
+ " block5h_se_excite (Multiply) (None, 14, 14, 1344 0 ['block5h_activation[0][0]', Y \n",
+ " ) 'block5h_se_expand[0][0]'] \n",
+ " \n",
+ " block5h_project_conv (Conv2D) (None, 14, 14, 224) 301056 ['block5h_se_excite[0][0]'] Y \n",
+ " \n",
+ " block5h_project_bn (BatchNorma (None, 14, 14, 224) 896 ['block5h_project_conv[0][0]'] Y \n",
+ " lization) \n",
+ " \n",
+ " block5h_drop (FixedDropout) (None, 14, 14, 224) 0 ['block5h_project_bn[0][0]'] Y \n",
+ " \n",
+ " block5h_add (Add) (None, 14, 14, 224) 0 ['block5h_drop[0][0]', Y \n",
+ " 'block5g_add[0][0]'] \n",
+ " \n",
+ " block5i_expand_conv (Conv2D) (None, 14, 14, 1344 301056 ['block5h_add[0][0]'] Y \n",
+ " ) \n",
+ " \n",
+ " block5i_expand_bn (BatchNormal (None, 14, 14, 1344 5376 ['block5i_expand_conv[0][0]'] Y \n",
+ " ization) ) \n",
+ " \n",
+ " block5i_expand_activation (Act (None, 14, 14, 1344 0 ['block5i_expand_bn[0][0]'] Y \n",
+ " ivation) ) \n",
+ " \n",
+ " block5i_dwconv (DepthwiseConv2 (None, 14, 14, 1344 33600 ['block5i_expand_activation[0][ Y \n",
+ " D) ) 0]'] \n",
+ " \n",
+ " block5i_bn (BatchNormalization (None, 14, 14, 1344 5376 ['block5i_dwconv[0][0]'] Y \n",
+ " ) ) \n",
+ " \n",
+ " block5i_activation (Activation (None, 14, 14, 1344 0 ['block5i_bn[0][0]'] Y \n",
+ " ) ) \n",
+ " \n",
+ " block5i_se_squeeze (GlobalAver (None, 1344) 0 ['block5i_activation[0][0]'] Y \n",
+ " agePooling2D) \n",
+ " \n",
+ " block5i_se_reshape (Reshape) (None, 1, 1, 1344) 0 ['block5i_se_squeeze[0][0]'] Y \n",
+ " \n",
+ " block5i_se_reduce (Conv2D) (None, 1, 1, 56) 75320 ['block5i_se_reshape[0][0]'] Y \n",
+ " \n",
+ " block5i_se_expand (Conv2D) (None, 1, 1, 1344) 76608 ['block5i_se_reduce[0][0]'] Y \n",
+ " \n",
+ " block5i_se_excite (Multiply) (None, 14, 14, 1344 0 ['block5i_activation[0][0]', Y \n",
+ " ) 'block5i_se_expand[0][0]'] \n",
+ " \n",
+ " block5i_project_conv (Conv2D) (None, 14, 14, 224) 301056 ['block5i_se_excite[0][0]'] Y \n",
+ " \n",
+ " block5i_project_bn (BatchNorma (None, 14, 14, 224) 896 ['block5i_project_conv[0][0]'] Y \n",
+ " lization) \n",
+ " \n",
+ " block5i_drop (FixedDropout) (None, 14, 14, 224) 0 ['block5i_project_bn[0][0]'] Y \n",
" \n",
- " stem_bn (BatchNormalization) (None, 112, 112, 32 128 ['stem_conv[0][0]'] Y \n",
- " ) \n",
+ " block5i_add (Add) (None, 14, 14, 224) 0 ['block5i_drop[0][0]', Y \n",
+ " 'block5h_add[0][0]'] \n",
" \n",
- " stem_activation (Activation) (None, 112, 112, 32 0 ['stem_bn[0][0]'] Y \n",
+ " block5j_expand_conv (Conv2D) (None, 14, 14, 1344 301056 ['block5i_add[0][0]'] Y \n",
" ) \n",
" \n",
- " block1a_dwconv (DepthwiseConv2 (None, 112, 112, 32 288 ['stem_activation[0][0]'] Y \n",
- " D) ) \n",
+ " block5j_expand_bn (BatchNormal (None, 14, 14, 1344 5376 ['block5j_expand_conv[0][0]'] Y \n",
+ " ization) ) \n",
+ " \n",
+ " block5j_expand_activation (Act (None, 14, 14, 1344 0 ['block5j_expand_bn[0][0]'] Y \n",
+ " ivation) ) \n",
+ " \n",
+ " block5j_dwconv (DepthwiseConv2 (None, 14, 14, 1344 33600 ['block5j_expand_activation[0][ Y \n",
+ " D) ) 0]'] \n",
" \n",
- " block1a_bn (BatchNormalization (None, 112, 112, 32 128 ['block1a_dwconv[0][0]'] Y \n",
+ " block5j_bn (BatchNormalization (None, 14, 14, 1344 5376 ['block5j_dwconv[0][0]'] Y \n",
" ) ) \n",
" \n",
- " block1a_activation (Activation (None, 112, 112, 32 0 ['block1a_bn[0][0]'] Y \n",
+ " block5j_activation (Activation (None, 14, 14, 1344 0 ['block5j_bn[0][0]'] Y \n",
" ) ) \n",
" \n",
- " block1a_se_squeeze (GlobalAver (None, 32) 0 ['block1a_activation[0][0]'] Y \n",
+ " block5j_se_squeeze (GlobalAver (None, 1344) 0 ['block5j_activation[0][0]'] Y \n",
" agePooling2D) \n",
" \n",
- " block1a_se_reshape (Reshape) (None, 1, 1, 32) 0 ['block1a_se_squeeze[0][0]'] Y \n",
+ " block5j_se_reshape (Reshape) (None, 1, 1, 1344) 0 ['block5j_se_squeeze[0][0]'] Y \n",
" \n",
- " block1a_se_reduce (Conv2D) (None, 1, 1, 8) 264 ['block1a_se_reshape[0][0]'] Y \n",
+ " block5j_se_reduce (Conv2D) (None, 1, 1, 56) 75320 ['block5j_se_reshape[0][0]'] Y \n",
" \n",
- " block1a_se_expand (Conv2D) (None, 1, 1, 32) 288 ['block1a_se_reduce[0][0]'] Y \n",
+ " block5j_se_expand (Conv2D) (None, 1, 1, 1344) 76608 ['block5j_se_reduce[0][0]'] Y \n",
" \n",
- " block1a_se_excite (Multiply) (None, 112, 112, 32 0 ['block1a_activation[0][0]', Y \n",
- " ) 'block1a_se_expand[0][0]'] \n",
+ " block5j_se_excite (Multiply) (None, 14, 14, 1344 0 ['block5j_activation[0][0]', Y \n",
+ " ) 'block5j_se_expand[0][0]'] \n",
" \n",
- " block1a_project_conv (Conv2D) (None, 112, 112, 16 512 ['block1a_se_excite[0][0]'] Y \n",
- " ) \n",
+ " block5j_project_conv (Conv2D) (None, 14, 14, 224) 301056 ['block5j_se_excite[0][0]'] Y \n",
" \n",
- " block1a_project_bn (BatchNorma (None, 112, 112, 16 64 ['block1a_project_conv[0][0]'] Y \n",
- " lization) ) \n",
+ " block5j_project_bn (BatchNorma (None, 14, 14, 224) 896 ['block5j_project_conv[0][0]'] Y \n",
+ " lization) \n",
+ " \n",
+ " block5j_drop (FixedDropout) (None, 14, 14, 224) 0 ['block5j_project_bn[0][0]'] Y \n",
" \n",
- " block2a_expand_conv (Conv2D) (None, 112, 112, 96 1536 ['block1a_project_bn[0][0]'] Y \n",
+ " block5j_add (Add) (None, 14, 14, 224) 0 ['block5j_drop[0][0]', Y \n",
+ " 'block5i_add[0][0]'] \n",
+ " \n",
+ " block6a_expand_conv (Conv2D) (None, 14, 14, 1344 301056 ['block5j_add[0][0]'] Y \n",
" ) \n",
" \n",
- " block2a_expand_bn (BatchNormal (None, 112, 112, 96 384 ['block2a_expand_conv[0][0]'] Y \n",
+ " block6a_expand_bn (BatchNormal (None, 14, 14, 1344 5376 ['block6a_expand_conv[0][0]'] Y \n",
" ization) ) \n",
" \n",
- " block2a_expand_activation (Act (None, 112, 112, 96 0 ['block2a_expand_bn[0][0]'] Y \n",
+ " block6a_expand_activation (Act (None, 14, 14, 1344 0 ['block6a_expand_bn[0][0]'] Y \n",
" ivation) ) \n",
" \n",
- " block2a_dwconv (DepthwiseConv2 (None, 56, 56, 96) 864 ['block2a_expand_activation[0][ Y \n",
+ " block6a_dwconv (DepthwiseConv2 (None, 7, 7, 1344) 33600 ['block6a_expand_activation[0][ Y \n",
" D) 0]'] \n",
" \n",
- " block2a_bn (BatchNormalization (None, 56, 56, 96) 384 ['block2a_dwconv[0][0]'] Y \n",
+ " block6a_bn (BatchNormalization (None, 7, 7, 1344) 5376 ['block6a_dwconv[0][0]'] Y \n",
" ) \n",
" \n",
- " block2a_activation (Activation (None, 56, 56, 96) 0 ['block2a_bn[0][0]'] Y \n",
+ " block6a_activation (Activation (None, 7, 7, 1344) 0 ['block6a_bn[0][0]'] Y \n",
" ) \n",
" \n",
- " block2a_se_squeeze (GlobalAver (None, 96) 0 ['block2a_activation[0][0]'] Y \n",
+ " block6a_se_squeeze (GlobalAver (None, 1344) 0 ['block6a_activation[0][0]'] Y \n",
" agePooling2D) \n",
" \n",
- " block2a_se_reshape (Reshape) (None, 1, 1, 96) 0 ['block2a_se_squeeze[0][0]'] Y \n",
+ " block6a_se_reshape (Reshape) (None, 1, 1, 1344) 0 ['block6a_se_squeeze[0][0]'] Y \n",
" \n",
- " block2a_se_reduce (Conv2D) (None, 1, 1, 4) 388 ['block2a_se_reshape[0][0]'] Y \n",
+ " block6a_se_reduce (Conv2D) (None, 1, 1, 56) 75320 ['block6a_se_reshape[0][0]'] Y \n",
" \n",
- " block2a_se_expand (Conv2D) (None, 1, 1, 96) 480 ['block2a_se_reduce[0][0]'] Y \n",
+ " block6a_se_expand (Conv2D) (None, 1, 1, 1344) 76608 ['block6a_se_reduce[0][0]'] Y \n",
" \n",
- " block2a_se_excite (Multiply) (None, 56, 56, 96) 0 ['block2a_activation[0][0]', Y \n",
- " 'block2a_se_expand[0][0]'] \n",
+ " block6a_se_excite (Multiply) (None, 7, 7, 1344) 0 ['block6a_activation[0][0]', Y \n",
+ " 'block6a_se_expand[0][0]'] \n",
" \n",
- " block2a_project_conv (Conv2D) (None, 56, 56, 24) 2304 ['block2a_se_excite[0][0]'] Y \n",
+ " block6a_project_conv (Conv2D) (None, 7, 7, 384) 516096 ['block6a_se_excite[0][0]'] Y \n",
" \n",
- " block2a_project_bn (BatchNorma (None, 56, 56, 24) 96 ['block2a_project_conv[0][0]'] Y \n",
+ " block6a_project_bn (BatchNorma (None, 7, 7, 384) 1536 ['block6a_project_conv[0][0]'] Y \n",
" lization) \n",
" \n",
- " block2b_expand_conv (Conv2D) (None, 56, 56, 144) 3456 ['block2a_project_bn[0][0]'] Y \n",
+ " block6b_expand_conv (Conv2D) (None, 7, 7, 2304) 884736 ['block6a_project_bn[0][0]'] Y \n",
" \n",
- " block2b_expand_bn (BatchNormal (None, 56, 56, 144) 576 ['block2b_expand_conv[0][0]'] Y \n",
+ " block6b_expand_bn (BatchNormal (None, 7, 7, 2304) 9216 ['block6b_expand_conv[0][0]'] Y \n",
" ization) \n",
" \n",
- " block2b_expand_activation (Act (None, 56, 56, 144) 0 ['block2b_expand_bn[0][0]'] Y \n",
+ " block6b_expand_activation (Act (None, 7, 7, 2304) 0 ['block6b_expand_bn[0][0]'] Y \n",
" ivation) \n",
" \n",
- " block2b_dwconv (DepthwiseConv2 (None, 56, 56, 144) 1296 ['block2b_expand_activation[0][ Y \n",
+ " block6b_dwconv (DepthwiseConv2 (None, 7, 7, 2304) 57600 ['block6b_expand_activation[0][ Y \n",
" D) 0]'] \n",
" \n",
- " block2b_bn (BatchNormalization (None, 56, 56, 144) 576 ['block2b_dwconv[0][0]'] Y \n",
+ " block6b_bn (BatchNormalization (None, 7, 7, 2304) 9216 ['block6b_dwconv[0][0]'] Y \n",
" ) \n",
" \n",
- " block2b_activation (Activation (None, 56, 56, 144) 0 ['block2b_bn[0][0]'] Y \n",
+ " block6b_activation (Activation (None, 7, 7, 2304) 0 ['block6b_bn[0][0]'] Y \n",
" ) \n",
" \n",
- " block2b_se_squeeze (GlobalAver (None, 144) 0 ['block2b_activation[0][0]'] Y \n",
+ " block6b_se_squeeze (GlobalAver (None, 2304) 0 ['block6b_activation[0][0]'] Y \n",
" agePooling2D) \n",
" \n",
- " block2b_se_reshape (Reshape) (None, 1, 1, 144) 0 ['block2b_se_squeeze[0][0]'] Y \n",
+ " block6b_se_reshape (Reshape) (None, 1, 1, 2304) 0 ['block6b_se_squeeze[0][0]'] Y \n",
" \n",
- " block2b_se_reduce (Conv2D) (None, 1, 1, 6) 870 ['block2b_se_reshape[0][0]'] Y \n",
+ " block6b_se_reduce (Conv2D) (None, 1, 1, 96) 221280 ['block6b_se_reshape[0][0]'] Y \n",
" \n",
- " block2b_se_expand (Conv2D) (None, 1, 1, 144) 1008 ['block2b_se_reduce[0][0]'] Y \n",
+ " block6b_se_expand (Conv2D) (None, 1, 1, 2304) 223488 ['block6b_se_reduce[0][0]'] Y \n",
" \n",
- " block2b_se_excite (Multiply) (None, 56, 56, 144) 0 ['block2b_activation[0][0]', Y \n",
- " 'block2b_se_expand[0][0]'] \n",
+ " block6b_se_excite (Multiply) (None, 7, 7, 2304) 0 ['block6b_activation[0][0]', Y \n",
+ " 'block6b_se_expand[0][0]'] \n",
" \n",
- " block2b_project_conv (Conv2D) (None, 56, 56, 24) 3456 ['block2b_se_excite[0][0]'] Y \n",
+ " block6b_project_conv (Conv2D) (None, 7, 7, 384) 884736 ['block6b_se_excite[0][0]'] Y \n",
" \n",
- " block2b_project_bn (BatchNorma (None, 56, 56, 24) 96 ['block2b_project_conv[0][0]'] Y \n",
+ " block6b_project_bn (BatchNorma (None, 7, 7, 384) 1536 ['block6b_project_conv[0][0]'] Y \n",
" lization) \n",
" \n",
- " block2b_drop (FixedDropout) (None, 56, 56, 24) 0 ['block2b_project_bn[0][0]'] Y \n",
+ " block6b_drop (FixedDropout) (None, 7, 7, 384) 0 ['block6b_project_bn[0][0]'] Y \n",
" \n",
- " block2b_add (Add) (None, 56, 56, 24) 0 ['block2b_drop[0][0]', Y \n",
- " 'block2a_project_bn[0][0]'] \n",
+ " block6b_add (Add) (None, 7, 7, 384) 0 ['block6b_drop[0][0]', Y \n",
+ " 'block6a_project_bn[0][0]'] \n",
" \n",
- " block3a_expand_conv (Conv2D) (None, 56, 56, 144) 3456 ['block2b_add[0][0]'] Y \n",
+ " block6c_expand_conv (Conv2D) (None, 7, 7, 2304) 884736 ['block6b_add[0][0]'] Y \n",
" \n",
- " block3a_expand_bn (BatchNormal (None, 56, 56, 144) 576 ['block3a_expand_conv[0][0]'] Y \n",
+ " block6c_expand_bn (BatchNormal (None, 7, 7, 2304) 9216 ['block6c_expand_conv[0][0]'] Y \n",
" ization) \n",
" \n",
- " block3a_expand_activation (Act (None, 56, 56, 144) 0 ['block3a_expand_bn[0][0]'] Y \n",
+ " block6c_expand_activation (Act (None, 7, 7, 2304) 0 ['block6c_expand_bn[0][0]'] Y \n",
" ivation) \n",
" \n",
- " block3a_dwconv (DepthwiseConv2 (None, 28, 28, 144) 3600 ['block3a_expand_activation[0][ Y \n",
+ " block6c_dwconv (DepthwiseConv2 (None, 7, 7, 2304) 57600 ['block6c_expand_activation[0][ Y \n",
" D) 0]'] \n",
" \n",
- " block3a_bn (BatchNormalization (None, 28, 28, 144) 576 ['block3a_dwconv[0][0]'] Y \n",
+ " block6c_bn (BatchNormalization (None, 7, 7, 2304) 9216 ['block6c_dwconv[0][0]'] Y \n",
" ) \n",
" \n",
- " block3a_activation (Activation (None, 28, 28, 144) 0 ['block3a_bn[0][0]'] Y \n",
+ " block6c_activation (Activation (None, 7, 7, 2304) 0 ['block6c_bn[0][0]'] Y \n",
" ) \n",
" \n",
- " block3a_se_squeeze (GlobalAver (None, 144) 0 ['block3a_activation[0][0]'] Y \n",
+ " block6c_se_squeeze (GlobalAver (None, 2304) 0 ['block6c_activation[0][0]'] Y \n",
" agePooling2D) \n",
" \n",
- " block3a_se_reshape (Reshape) (None, 1, 1, 144) 0 ['block3a_se_squeeze[0][0]'] Y \n",
+ " block6c_se_reshape (Reshape) (None, 1, 1, 2304) 0 ['block6c_se_squeeze[0][0]'] Y \n",
" \n",
- " block3a_se_reduce (Conv2D) (None, 1, 1, 6) 870 ['block3a_se_reshape[0][0]'] Y \n",
+ " block6c_se_reduce (Conv2D) (None, 1, 1, 96) 221280 ['block6c_se_reshape[0][0]'] Y \n",
" \n",
- " block3a_se_expand (Conv2D) (None, 1, 1, 144) 1008 ['block3a_se_reduce[0][0]'] Y \n",
+ " block6c_se_expand (Conv2D) (None, 1, 1, 2304) 223488 ['block6c_se_reduce[0][0]'] Y \n",
" \n",
- " block3a_se_excite (Multiply) (None, 28, 28, 144) 0 ['block3a_activation[0][0]', Y \n",
- " 'block3a_se_expand[0][0]'] \n",
+ " block6c_se_excite (Multiply) (None, 7, 7, 2304) 0 ['block6c_activation[0][0]', Y \n",
+ " 'block6c_se_expand[0][0]'] \n",
" \n",
- " block3a_project_conv (Conv2D) (None, 28, 28, 40) 5760 ['block3a_se_excite[0][0]'] Y \n",
+ " block6c_project_conv (Conv2D) (None, 7, 7, 384) 884736 ['block6c_se_excite[0][0]'] Y \n",
" \n",
- " block3a_project_bn (BatchNorma (None, 28, 28, 40) 160 ['block3a_project_conv[0][0]'] Y \n",
+ " block6c_project_bn (BatchNorma (None, 7, 7, 384) 1536 ['block6c_project_conv[0][0]'] Y \n",
" lization) \n",
" \n",
- " block3b_expand_conv (Conv2D) (None, 28, 28, 240) 9600 ['block3a_project_bn[0][0]'] Y \n",
+ " block6c_drop (FixedDropout) (None, 7, 7, 384) 0 ['block6c_project_bn[0][0]'] Y \n",
+ " \n",
+ " block6c_add (Add) (None, 7, 7, 384) 0 ['block6c_drop[0][0]', Y \n",
+ " 'block6b_add[0][0]'] \n",
+ " \n",
+ " block6d_expand_conv (Conv2D) (None, 7, 7, 2304) 884736 ['block6c_add[0][0]'] Y \n",
" \n",
- " block3b_expand_bn (BatchNormal (None, 28, 28, 240) 960 ['block3b_expand_conv[0][0]'] Y \n",
+ " block6d_expand_bn (BatchNormal (None, 7, 7, 2304) 9216 ['block6d_expand_conv[0][0]'] Y \n",
" ization) \n",
" \n",
- " block3b_expand_activation (Act (None, 28, 28, 240) 0 ['block3b_expand_bn[0][0]'] Y \n",
+ " block6d_expand_activation (Act (None, 7, 7, 2304) 0 ['block6d_expand_bn[0][0]'] Y \n",
" ivation) \n",
" \n",
- " block3b_dwconv (DepthwiseConv2 (None, 28, 28, 240) 6000 ['block3b_expand_activation[0][ Y \n",
+ " block6d_dwconv (DepthwiseConv2 (None, 7, 7, 2304) 57600 ['block6d_expand_activation[0][ Y \n",
" D) 0]'] \n",
" \n",
- " block3b_bn (BatchNormalization (None, 28, 28, 240) 960 ['block3b_dwconv[0][0]'] Y \n",
+ " block6d_bn (BatchNormalization (None, 7, 7, 2304) 9216 ['block6d_dwconv[0][0]'] Y \n",
" ) \n",
" \n",
- " block3b_activation (Activation (None, 28, 28, 240) 0 ['block3b_bn[0][0]'] Y \n",
+ " block6d_activation (Activation (None, 7, 7, 2304) 0 ['block6d_bn[0][0]'] Y \n",
" ) \n",
" \n",
- " block3b_se_squeeze (GlobalAver (None, 240) 0 ['block3b_activation[0][0]'] Y \n",
+ " block6d_se_squeeze (GlobalAver (None, 2304) 0 ['block6d_activation[0][0]'] Y \n",
" agePooling2D) \n",
" \n",
- " block3b_se_reshape (Reshape) (None, 1, 1, 240) 0 ['block3b_se_squeeze[0][0]'] Y \n",
+ " block6d_se_reshape (Reshape) (None, 1, 1, 2304) 0 ['block6d_se_squeeze[0][0]'] Y \n",
" \n",
- " block3b_se_reduce (Conv2D) (None, 1, 1, 10) 2410 ['block3b_se_reshape[0][0]'] Y \n",
+ " block6d_se_reduce (Conv2D) (None, 1, 1, 96) 221280 ['block6d_se_reshape[0][0]'] Y \n",
" \n",
- " block3b_se_expand (Conv2D) (None, 1, 1, 240) 2640 ['block3b_se_reduce[0][0]'] Y \n",
+ " block6d_se_expand (Conv2D) (None, 1, 1, 2304) 223488 ['block6d_se_reduce[0][0]'] Y \n",
" \n",
- " block3b_se_excite (Multiply) (None, 28, 28, 240) 0 ['block3b_activation[0][0]', Y \n",
- " 'block3b_se_expand[0][0]'] \n",
+ " block6d_se_excite (Multiply) (None, 7, 7, 2304) 0 ['block6d_activation[0][0]', Y \n",
+ " 'block6d_se_expand[0][0]'] \n",
" \n",
- " block3b_project_conv (Conv2D) (None, 28, 28, 40) 9600 ['block3b_se_excite[0][0]'] Y \n",
+ " block6d_project_conv (Conv2D) (None, 7, 7, 384) 884736 ['block6d_se_excite[0][0]'] Y \n",
" \n",
- " block3b_project_bn (BatchNorma (None, 28, 28, 40) 160 ['block3b_project_conv[0][0]'] Y \n",
+ " block6d_project_bn (BatchNorma (None, 7, 7, 384) 1536 ['block6d_project_conv[0][0]'] Y \n",
" lization) \n",
" \n",
- " block3b_drop (FixedDropout) (None, 28, 28, 40) 0 ['block3b_project_bn[0][0]'] Y \n",
+ " block6d_drop (FixedDropout) (None, 7, 7, 384) 0 ['block6d_project_bn[0][0]'] Y \n",
" \n",
- " block3b_add (Add) (None, 28, 28, 40) 0 ['block3b_drop[0][0]', Y \n",
- " 'block3a_project_bn[0][0]'] \n",
+ " block6d_add (Add) (None, 7, 7, 384) 0 ['block6d_drop[0][0]', Y \n",
+ " 'block6c_add[0][0]'] \n",
" \n",
- " block4a_expand_conv (Conv2D) (None, 28, 28, 240) 9600 ['block3b_add[0][0]'] Y \n",
+ " block6e_expand_conv (Conv2D) (None, 7, 7, 2304) 884736 ['block6d_add[0][0]'] Y \n",
" \n",
- " block4a_expand_bn (BatchNormal (None, 28, 28, 240) 960 ['block4a_expand_conv[0][0]'] Y \n",
+ " block6e_expand_bn (BatchNormal (None, 7, 7, 2304) 9216 ['block6e_expand_conv[0][0]'] Y \n",
" ization) \n",
" \n",
- " block4a_expand_activation (Act (None, 28, 28, 240) 0 ['block4a_expand_bn[0][0]'] Y \n",
+ " block6e_expand_activation (Act (None, 7, 7, 2304) 0 ['block6e_expand_bn[0][0]'] Y \n",
" ivation) \n",
" \n",
- " block4a_dwconv (DepthwiseConv2 (None, 14, 14, 240) 2160 ['block4a_expand_activation[0][ Y \n",
+ " block6e_dwconv (DepthwiseConv2 (None, 7, 7, 2304) 57600 ['block6e_expand_activation[0][ Y \n",
" D) 0]'] \n",
" \n",
- " block4a_bn (BatchNormalization (None, 14, 14, 240) 960 ['block4a_dwconv[0][0]'] Y \n",
+ " block6e_bn (BatchNormalization (None, 7, 7, 2304) 9216 ['block6e_dwconv[0][0]'] Y \n",
" ) \n",
" \n",
- " block4a_activation (Activation (None, 14, 14, 240) 0 ['block4a_bn[0][0]'] Y \n",
+ " block6e_activation (Activation (None, 7, 7, 2304) 0 ['block6e_bn[0][0]'] Y \n",
" ) \n",
" \n",
- " block4a_se_squeeze (GlobalAver (None, 240) 0 ['block4a_activation[0][0]'] Y \n",
+ " block6e_se_squeeze (GlobalAver (None, 2304) 0 ['block6e_activation[0][0]'] Y \n",
" agePooling2D) \n",
" \n",
- " block4a_se_reshape (Reshape) (None, 1, 1, 240) 0 ['block4a_se_squeeze[0][0]'] Y \n",
+ " block6e_se_reshape (Reshape) (None, 1, 1, 2304) 0 ['block6e_se_squeeze[0][0]'] Y \n",
" \n",
- " block4a_se_reduce (Conv2D) (None, 1, 1, 10) 2410 ['block4a_se_reshape[0][0]'] Y \n",
+ " block6e_se_reduce (Conv2D) (None, 1, 1, 96) 221280 ['block6e_se_reshape[0][0]'] Y \n",
" \n",
- " block4a_se_expand (Conv2D) (None, 1, 1, 240) 2640 ['block4a_se_reduce[0][0]'] Y \n",
+ " block6e_se_expand (Conv2D) (None, 1, 1, 2304) 223488 ['block6e_se_reduce[0][0]'] Y \n",
" \n",
- " block4a_se_excite (Multiply) (None, 14, 14, 240) 0 ['block4a_activation[0][0]', Y \n",
- " 'block4a_se_expand[0][0]'] \n",
+ " block6e_se_excite (Multiply) (None, 7, 7, 2304) 0 ['block6e_activation[0][0]', Y \n",
+ " 'block6e_se_expand[0][0]'] \n",
+ " \n",
+ " block6e_project_conv (Conv2D) (None, 7, 7, 384) 884736 ['block6e_se_excite[0][0]'] Y \n",
+ " \n",
+ " block6e_project_bn (BatchNorma (None, 7, 7, 384) 1536 ['block6e_project_conv[0][0]'] Y \n",
+ " lization) \n",
+ " \n",
+ " block6e_drop (FixedDropout) (None, 7, 7, 384) 0 ['block6e_project_bn[0][0]'] Y \n",
" \n",
- " block4a_project_conv (Conv2D) (None, 14, 14, 80) 19200 ['block4a_se_excite[0][0]'] Y \n",
+ " block6e_add (Add) (None, 7, 7, 384) 0 ['block6e_drop[0][0]', Y \n",
+ " 'block6d_add[0][0]'] \n",
+ " \n",
+ " block6f_expand_conv (Conv2D) (None, 7, 7, 2304) 884736 ['block6e_add[0][0]'] Y \n",
+ " \n",
+ " block6f_expand_bn (BatchNormal (None, 7, 7, 2304) 9216 ['block6f_expand_conv[0][0]'] Y \n",
+ " ization) \n",
+ " \n",
+ " block6f_expand_activation (Act (None, 7, 7, 2304) 0 ['block6f_expand_bn[0][0]'] Y \n",
+ " ivation) \n",
+ " \n",
+ " block6f_dwconv (DepthwiseConv2 (None, 7, 7, 2304) 57600 ['block6f_expand_activation[0][ Y \n",
+ " D) 0]'] \n",
+ " \n",
+ " block6f_bn (BatchNormalization (None, 7, 7, 2304) 9216 ['block6f_dwconv[0][0]'] Y \n",
+ " ) \n",
+ " \n",
+ " block6f_activation (Activation (None, 7, 7, 2304) 0 ['block6f_bn[0][0]'] Y \n",
+ " ) \n",
+ " \n",
+ " block6f_se_squeeze (GlobalAver (None, 2304) 0 ['block6f_activation[0][0]'] Y \n",
+ " agePooling2D) \n",
+ " \n",
+ " block6f_se_reshape (Reshape) (None, 1, 1, 2304) 0 ['block6f_se_squeeze[0][0]'] Y \n",
+ " \n",
+ " block6f_se_reduce (Conv2D) (None, 1, 1, 96) 221280 ['block6f_se_reshape[0][0]'] Y \n",
+ " \n",
+ " block6f_se_expand (Conv2D) (None, 1, 1, 2304) 223488 ['block6f_se_reduce[0][0]'] Y \n",
+ " \n",
+ " block6f_se_excite (Multiply) (None, 7, 7, 2304) 0 ['block6f_activation[0][0]', Y \n",
+ " 'block6f_se_expand[0][0]'] \n",
+ " \n",
+ " block6f_project_conv (Conv2D) (None, 7, 7, 384) 884736 ['block6f_se_excite[0][0]'] Y \n",
" \n",
- " block4a_project_bn (BatchNorma (None, 14, 14, 80) 320 ['block4a_project_conv[0][0]'] Y \n",
+ " block6f_project_bn (BatchNorma (None, 7, 7, 384) 1536 ['block6f_project_conv[0][0]'] Y \n",
" lization) \n",
" \n",
- " block4b_expand_conv (Conv2D) (None, 14, 14, 480) 38400 ['block4a_project_bn[0][0]'] Y \n",
+ " block6f_drop (FixedDropout) (None, 7, 7, 384) 0 ['block6f_project_bn[0][0]'] Y \n",
+ " \n",
+ " block6f_add (Add) (None, 7, 7, 384) 0 ['block6f_drop[0][0]', Y \n",
+ " 'block6e_add[0][0]'] \n",
+ " \n",
+ " block6g_expand_conv (Conv2D) (None, 7, 7, 2304) 884736 ['block6f_add[0][0]'] Y \n",
" \n",
- " block4b_expand_bn (BatchNormal (None, 14, 14, 480) 1920 ['block4b_expand_conv[0][0]'] Y \n",
+ " block6g_expand_bn (BatchNormal (None, 7, 7, 2304) 9216 ['block6g_expand_conv[0][0]'] Y \n",
" ization) \n",
" \n",
- " block4b_expand_activation (Act (None, 14, 14, 480) 0 ['block4b_expand_bn[0][0]'] Y \n",
+ " block6g_expand_activation (Act (None, 7, 7, 2304) 0 ['block6g_expand_bn[0][0]'] Y \n",
" ivation) \n",
" \n",
- " block4b_dwconv (DepthwiseConv2 (None, 14, 14, 480) 4320 ['block4b_expand_activation[0][ Y \n",
+ " block6g_dwconv (DepthwiseConv2 (None, 7, 7, 2304) 57600 ['block6g_expand_activation[0][ Y \n",
" D) 0]'] \n",
" \n",
- " block4b_bn (BatchNormalization (None, 14, 14, 480) 1920 ['block4b_dwconv[0][0]'] Y \n",
+ " block6g_bn (BatchNormalization (None, 7, 7, 2304) 9216 ['block6g_dwconv[0][0]'] Y \n",
" ) \n",
" \n",
- " block4b_activation (Activation (None, 14, 14, 480) 0 ['block4b_bn[0][0]'] Y \n",
+ " block6g_activation (Activation (None, 7, 7, 2304) 0 ['block6g_bn[0][0]'] Y \n",
" ) \n",
" \n",
- " block4b_se_squeeze (GlobalAver (None, 480) 0 ['block4b_activation[0][0]'] Y \n",
+ " block6g_se_squeeze (GlobalAver (None, 2304) 0 ['block6g_activation[0][0]'] Y \n",
" agePooling2D) \n",
" \n",
- " block4b_se_reshape (Reshape) (None, 1, 1, 480) 0 ['block4b_se_squeeze[0][0]'] Y \n",
+ " block6g_se_reshape (Reshape) (None, 1, 1, 2304) 0 ['block6g_se_squeeze[0][0]'] Y \n",
" \n",
- " block4b_se_reduce (Conv2D) (None, 1, 1, 20) 9620 ['block4b_se_reshape[0][0]'] Y \n",
+ " block6g_se_reduce (Conv2D) (None, 1, 1, 96) 221280 ['block6g_se_reshape[0][0]'] Y \n",
" \n",
- " block4b_se_expand (Conv2D) (None, 1, 1, 480) 10080 ['block4b_se_reduce[0][0]'] Y \n",
+ " block6g_se_expand (Conv2D) (None, 1, 1, 2304) 223488 ['block6g_se_reduce[0][0]'] Y \n",
" \n",
- " block4b_se_excite (Multiply) (None, 14, 14, 480) 0 ['block4b_activation[0][0]', Y \n",
- " 'block4b_se_expand[0][0]'] \n",
+ " block6g_se_excite (Multiply) (None, 7, 7, 2304) 0 ['block6g_activation[0][0]', Y \n",
+ " 'block6g_se_expand[0][0]'] \n",
" \n",
- " block4b_project_conv (Conv2D) (None, 14, 14, 80) 38400 ['block4b_se_excite[0][0]'] Y \n",
+ " block6g_project_conv (Conv2D) (None, 7, 7, 384) 884736 ['block6g_se_excite[0][0]'] Y \n",
" \n",
- " block4b_project_bn (BatchNorma (None, 14, 14, 80) 320 ['block4b_project_conv[0][0]'] Y \n",
+ " block6g_project_bn (BatchNorma (None, 7, 7, 384) 1536 ['block6g_project_conv[0][0]'] Y \n",
" lization) \n",
" \n",
- " block4b_drop (FixedDropout) (None, 14, 14, 80) 0 ['block4b_project_bn[0][0]'] Y \n",
+ " block6g_drop (FixedDropout) (None, 7, 7, 384) 0 ['block6g_project_bn[0][0]'] Y \n",
" \n",
- " block4b_add (Add) (None, 14, 14, 80) 0 ['block4b_drop[0][0]', Y \n",
- " 'block4a_project_bn[0][0]'] \n",
+ " block6g_add (Add) (None, 7, 7, 384) 0 ['block6g_drop[0][0]', Y \n",
+ " 'block6f_add[0][0]'] \n",
" \n",
- " block4c_expand_conv (Conv2D) (None, 14, 14, 480) 38400 ['block4b_add[0][0]'] Y \n",
+ " block6h_expand_conv (Conv2D) (None, 7, 7, 2304) 884736 ['block6g_add[0][0]'] Y \n",
" \n",
- " block4c_expand_bn (BatchNormal (None, 14, 14, 480) 1920 ['block4c_expand_conv[0][0]'] Y \n",
+ " block6h_expand_bn (BatchNormal (None, 7, 7, 2304) 9216 ['block6h_expand_conv[0][0]'] Y \n",
" ization) \n",
" \n",
- " block4c_expand_activation (Act (None, 14, 14, 480) 0 ['block4c_expand_bn[0][0]'] Y \n",
+ " block6h_expand_activation (Act (None, 7, 7, 2304) 0 ['block6h_expand_bn[0][0]'] Y \n",
" ivation) \n",
" \n",
- " block4c_dwconv (DepthwiseConv2 (None, 14, 14, 480) 4320 ['block4c_expand_activation[0][ Y \n",
+ " block6h_dwconv (DepthwiseConv2 (None, 7, 7, 2304) 57600 ['block6h_expand_activation[0][ Y \n",
" D) 0]'] \n",
" \n",
- " block4c_bn (BatchNormalization (None, 14, 14, 480) 1920 ['block4c_dwconv[0][0]'] Y \n",
+ " block6h_bn (BatchNormalization (None, 7, 7, 2304) 9216 ['block6h_dwconv[0][0]'] Y \n",
" ) \n",
" \n",
- " block4c_activation (Activation (None, 14, 14, 480) 0 ['block4c_bn[0][0]'] Y \n",
+ " block6h_activation (Activation (None, 7, 7, 2304) 0 ['block6h_bn[0][0]'] Y \n",
" ) \n",
" \n",
- " block4c_se_squeeze (GlobalAver (None, 480) 0 ['block4c_activation[0][0]'] Y \n",
+ " block6h_se_squeeze (GlobalAver (None, 2304) 0 ['block6h_activation[0][0]'] Y \n",
" agePooling2D) \n",
" \n",
- " block4c_se_reshape (Reshape) (None, 1, 1, 480) 0 ['block4c_se_squeeze[0][0]'] Y \n",
+ " block6h_se_reshape (Reshape) (None, 1, 1, 2304) 0 ['block6h_se_squeeze[0][0]'] Y \n",
" \n",
- " block4c_se_reduce (Conv2D) (None, 1, 1, 20) 9620 ['block4c_se_reshape[0][0]'] Y \n",
+ " block6h_se_reduce (Conv2D) (None, 1, 1, 96) 221280 ['block6h_se_reshape[0][0]'] Y \n",
" \n",
- " block4c_se_expand (Conv2D) (None, 1, 1, 480) 10080 ['block4c_se_reduce[0][0]'] Y \n",
+ " block6h_se_expand (Conv2D) (None, 1, 1, 2304) 223488 ['block6h_se_reduce[0][0]'] Y \n",
" \n",
- " block4c_se_excite (Multiply) (None, 14, 14, 480) 0 ['block4c_activation[0][0]', Y \n",
- " 'block4c_se_expand[0][0]'] \n",
+ " block6h_se_excite (Multiply) (None, 7, 7, 2304) 0 ['block6h_activation[0][0]', Y \n",
+ " 'block6h_se_expand[0][0]'] \n",
" \n",
- " block4c_project_conv (Conv2D) (None, 14, 14, 80) 38400 ['block4c_se_excite[0][0]'] Y \n",
+ " block6h_project_conv (Conv2D) (None, 7, 7, 384) 884736 ['block6h_se_excite[0][0]'] Y \n",
" \n",
- " block4c_project_bn (BatchNorma (None, 14, 14, 80) 320 ['block4c_project_conv[0][0]'] Y \n",
+ " block6h_project_bn (BatchNorma (None, 7, 7, 384) 1536 ['block6h_project_conv[0][0]'] Y \n",
" lization) \n",
" \n",
- " block4c_drop (FixedDropout) (None, 14, 14, 80) 0 ['block4c_project_bn[0][0]'] Y \n",
+ " block6h_drop (FixedDropout) (None, 7, 7, 384) 0 ['block6h_project_bn[0][0]'] Y \n",
" \n",
- " block4c_add (Add) (None, 14, 14, 80) 0 ['block4c_drop[0][0]', Y \n",
- " 'block4b_add[0][0]'] \n",
+ " block6h_add (Add) (None, 7, 7, 384) 0 ['block6h_drop[0][0]', Y \n",
+ " 'block6g_add[0][0]'] \n",
" \n",
- " block5a_expand_conv (Conv2D) (None, 14, 14, 480) 38400 ['block4c_add[0][0]'] Y \n",
+ " block6i_expand_conv (Conv2D) (None, 7, 7, 2304) 884736 ['block6h_add[0][0]'] Y \n",
" \n",
- " block5a_expand_bn (BatchNormal (None, 14, 14, 480) 1920 ['block5a_expand_conv[0][0]'] Y \n",
+ " block6i_expand_bn (BatchNormal (None, 7, 7, 2304) 9216 ['block6i_expand_conv[0][0]'] Y \n",
" ization) \n",
" \n",
- " block5a_expand_activation (Act (None, 14, 14, 480) 0 ['block5a_expand_bn[0][0]'] Y \n",
+ " block6i_expand_activation (Act (None, 7, 7, 2304) 0 ['block6i_expand_bn[0][0]'] Y \n",
" ivation) \n",
" \n",
- " block5a_dwconv (DepthwiseConv2 (None, 14, 14, 480) 12000 ['block5a_expand_activation[0][ Y \n",
+ " block6i_dwconv (DepthwiseConv2 (None, 7, 7, 2304) 57600 ['block6i_expand_activation[0][ Y \n",
" D) 0]'] \n",
" \n",
- " block5a_bn (BatchNormalization (None, 14, 14, 480) 1920 ['block5a_dwconv[0][0]'] Y \n",
+ " block6i_bn (BatchNormalization (None, 7, 7, 2304) 9216 ['block6i_dwconv[0][0]'] Y \n",
" ) \n",
" \n",
- " block5a_activation (Activation (None, 14, 14, 480) 0 ['block5a_bn[0][0]'] Y \n",
+ " block6i_activation (Activation (None, 7, 7, 2304) 0 ['block6i_bn[0][0]'] Y \n",
" ) \n",
" \n",
- " block5a_se_squeeze (GlobalAver (None, 480) 0 ['block5a_activation[0][0]'] Y \n",
+ " block6i_se_squeeze (GlobalAver (None, 2304) 0 ['block6i_activation[0][0]'] Y \n",
" agePooling2D) \n",
" \n",
- " block5a_se_reshape (Reshape) (None, 1, 1, 480) 0 ['block5a_se_squeeze[0][0]'] Y \n",
+ " block6i_se_reshape (Reshape) (None, 1, 1, 2304) 0 ['block6i_se_squeeze[0][0]'] Y \n",
" \n",
- " block5a_se_reduce (Conv2D) (None, 1, 1, 20) 9620 ['block5a_se_reshape[0][0]'] Y \n",
+ " block6i_se_reduce (Conv2D) (None, 1, 1, 96) 221280 ['block6i_se_reshape[0][0]'] Y \n",
" \n",
- " block5a_se_expand (Conv2D) (None, 1, 1, 480) 10080 ['block5a_se_reduce[0][0]'] Y \n",
+ " block6i_se_expand (Conv2D) (None, 1, 1, 2304) 223488 ['block6i_se_reduce[0][0]'] Y \n",
" \n",
- " block5a_se_excite (Multiply) (None, 14, 14, 480) 0 ['block5a_activation[0][0]', Y \n",
- " 'block5a_se_expand[0][0]'] \n",
+ " block6i_se_excite (Multiply) (None, 7, 7, 2304) 0 ['block6i_activation[0][0]', Y \n",
+ " 'block6i_se_expand[0][0]'] \n",
" \n",
- " block5a_project_conv (Conv2D) (None, 14, 14, 112) 53760 ['block5a_se_excite[0][0]'] Y \n",
+ " block6i_project_conv (Conv2D) (None, 7, 7, 384) 884736 ['block6i_se_excite[0][0]'] Y \n",
" \n",
- " block5a_project_bn (BatchNorma (None, 14, 14, 112) 448 ['block5a_project_conv[0][0]'] Y \n",
+ " block6i_project_bn (BatchNorma (None, 7, 7, 384) 1536 ['block6i_project_conv[0][0]'] Y \n",
" lization) \n",
" \n",
- " block5b_expand_conv (Conv2D) (None, 14, 14, 672) 75264 ['block5a_project_bn[0][0]'] Y \n",
+ " block6i_drop (FixedDropout) (None, 7, 7, 384) 0 ['block6i_project_bn[0][0]'] Y \n",
+ " \n",
+ " block6i_add (Add) (None, 7, 7, 384) 0 ['block6i_drop[0][0]', Y \n",
+ " 'block6h_add[0][0]'] \n",
+ " \n",
+ " block6j_expand_conv (Conv2D) (None, 7, 7, 2304) 884736 ['block6i_add[0][0]'] Y \n",
" \n",
- " block5b_expand_bn (BatchNormal (None, 14, 14, 672) 2688 ['block5b_expand_conv[0][0]'] Y \n",
+ " block6j_expand_bn (BatchNormal (None, 7, 7, 2304) 9216 ['block6j_expand_conv[0][0]'] Y \n",
" ization) \n",
" \n",
- " block5b_expand_activation (Act (None, 14, 14, 672) 0 ['block5b_expand_bn[0][0]'] Y \n",
+ " block6j_expand_activation (Act (None, 7, 7, 2304) 0 ['block6j_expand_bn[0][0]'] Y \n",
" ivation) \n",
" \n",
- " block5b_dwconv (DepthwiseConv2 (None, 14, 14, 672) 16800 ['block5b_expand_activation[0][ Y \n",
+ " block6j_dwconv (DepthwiseConv2 (None, 7, 7, 2304) 57600 ['block6j_expand_activation[0][ Y \n",
" D) 0]'] \n",
" \n",
- " block5b_bn (BatchNormalization (None, 14, 14, 672) 2688 ['block5b_dwconv[0][0]'] Y \n",
+ " block6j_bn (BatchNormalization (None, 7, 7, 2304) 9216 ['block6j_dwconv[0][0]'] Y \n",
" ) \n",
" \n",
- " block5b_activation (Activation (None, 14, 14, 672) 0 ['block5b_bn[0][0]'] Y \n",
+ " block6j_activation (Activation (None, 7, 7, 2304) 0 ['block6j_bn[0][0]'] Y \n",
" ) \n",
" \n",
- " block5b_se_squeeze (GlobalAver (None, 672) 0 ['block5b_activation[0][0]'] Y \n",
+ " block6j_se_squeeze (GlobalAver (None, 2304) 0 ['block6j_activation[0][0]'] Y \n",
" agePooling2D) \n",
" \n",
- " block5b_se_reshape (Reshape) (None, 1, 1, 672) 0 ['block5b_se_squeeze[0][0]'] Y \n",
+ " block6j_se_reshape (Reshape) (None, 1, 1, 2304) 0 ['block6j_se_squeeze[0][0]'] Y \n",
" \n",
- " block5b_se_reduce (Conv2D) (None, 1, 1, 28) 18844 ['block5b_se_reshape[0][0]'] Y \n",
+ " block6j_se_reduce (Conv2D) (None, 1, 1, 96) 221280 ['block6j_se_reshape[0][0]'] Y \n",
" \n",
- " block5b_se_expand (Conv2D) (None, 1, 1, 672) 19488 ['block5b_se_reduce[0][0]'] Y \n",
+ " block6j_se_expand (Conv2D) (None, 1, 1, 2304) 223488 ['block6j_se_reduce[0][0]'] Y \n",
" \n",
- " block5b_se_excite (Multiply) (None, 14, 14, 672) 0 ['block5b_activation[0][0]', Y \n",
- " 'block5b_se_expand[0][0]'] \n",
+ " block6j_se_excite (Multiply) (None, 7, 7, 2304) 0 ['block6j_activation[0][0]', Y \n",
+ " 'block6j_se_expand[0][0]'] \n",
" \n",
- " block5b_project_conv (Conv2D) (None, 14, 14, 112) 75264 ['block5b_se_excite[0][0]'] Y \n",
+ " block6j_project_conv (Conv2D) (None, 7, 7, 384) 884736 ['block6j_se_excite[0][0]'] Y \n",
" \n",
- " block5b_project_bn (BatchNorma (None, 14, 14, 112) 448 ['block5b_project_conv[0][0]'] Y \n",
+ " block6j_project_bn (BatchNorma (None, 7, 7, 384) 1536 ['block6j_project_conv[0][0]'] Y \n",
" lization) \n",
" \n",
- " block5b_drop (FixedDropout) (None, 14, 14, 112) 0 ['block5b_project_bn[0][0]'] Y \n",
+ " block6j_drop (FixedDropout) (None, 7, 7, 384) 0 ['block6j_project_bn[0][0]'] Y \n",
" \n",
- " block5b_add (Add) (None, 14, 14, 112) 0 ['block5b_drop[0][0]', Y \n",
- " 'block5a_project_bn[0][0]'] \n",
+ " block6j_add (Add) (None, 7, 7, 384) 0 ['block6j_drop[0][0]', Y \n",
+ " 'block6i_add[0][0]'] \n",
" \n",
- " block5c_expand_conv (Conv2D) (None, 14, 14, 672) 75264 ['block5b_add[0][0]'] Y \n",
+ " block6k_expand_conv (Conv2D) (None, 7, 7, 2304) 884736 ['block6j_add[0][0]'] Y \n",
" \n",
- " block5c_expand_bn (BatchNormal (None, 14, 14, 672) 2688 ['block5c_expand_conv[0][0]'] Y \n",
+ " block6k_expand_bn (BatchNormal (None, 7, 7, 2304) 9216 ['block6k_expand_conv[0][0]'] Y \n",
" ization) \n",
" \n",
- " block5c_expand_activation (Act (None, 14, 14, 672) 0 ['block5c_expand_bn[0][0]'] Y \n",
+ " block6k_expand_activation (Act (None, 7, 7, 2304) 0 ['block6k_expand_bn[0][0]'] Y \n",
" ivation) \n",
" \n",
- " block5c_dwconv (DepthwiseConv2 (None, 14, 14, 672) 16800 ['block5c_expand_activation[0][ Y \n",
+ " block6k_dwconv (DepthwiseConv2 (None, 7, 7, 2304) 57600 ['block6k_expand_activation[0][ Y \n",
" D) 0]'] \n",
" \n",
- " block5c_bn (BatchNormalization (None, 14, 14, 672) 2688 ['block5c_dwconv[0][0]'] Y \n",
+ " block6k_bn (BatchNormalization (None, 7, 7, 2304) 9216 ['block6k_dwconv[0][0]'] Y \n",
" ) \n",
" \n",
- " block5c_activation (Activation (None, 14, 14, 672) 0 ['block5c_bn[0][0]'] Y \n",
+ " block6k_activation (Activation (None, 7, 7, 2304) 0 ['block6k_bn[0][0]'] Y \n",
" ) \n",
" \n",
- " block5c_se_squeeze (GlobalAver (None, 672) 0 ['block5c_activation[0][0]'] Y \n",
+ " block6k_se_squeeze (GlobalAver (None, 2304) 0 ['block6k_activation[0][0]'] Y \n",
" agePooling2D) \n",
" \n",
- " block5c_se_reshape (Reshape) (None, 1, 1, 672) 0 ['block5c_se_squeeze[0][0]'] Y \n",
+ " block6k_se_reshape (Reshape) (None, 1, 1, 2304) 0 ['block6k_se_squeeze[0][0]'] Y \n",
" \n",
- " block5c_se_reduce (Conv2D) (None, 1, 1, 28) 18844 ['block5c_se_reshape[0][0]'] Y \n",
+ " block6k_se_reduce (Conv2D) (None, 1, 1, 96) 221280 ['block6k_se_reshape[0][0]'] Y \n",
" \n",
- " block5c_se_expand (Conv2D) (None, 1, 1, 672) 19488 ['block5c_se_reduce[0][0]'] Y \n",
+ " block6k_se_expand (Conv2D) (None, 1, 1, 2304) 223488 ['block6k_se_reduce[0][0]'] Y \n",
" \n",
- " block5c_se_excite (Multiply) (None, 14, 14, 672) 0 ['block5c_activation[0][0]', Y \n",
- " 'block5c_se_expand[0][0]'] \n",
+ " block6k_se_excite (Multiply) (None, 7, 7, 2304) 0 ['block6k_activation[0][0]', Y \n",
+ " 'block6k_se_expand[0][0]'] \n",
" \n",
- " block5c_project_conv (Conv2D) (None, 14, 14, 112) 75264 ['block5c_se_excite[0][0]'] Y \n",
+ " block6k_project_conv (Conv2D) (None, 7, 7, 384) 884736 ['block6k_se_excite[0][0]'] Y \n",
" \n",
- " block5c_project_bn (BatchNorma (None, 14, 14, 112) 448 ['block5c_project_conv[0][0]'] Y \n",
+ " block6k_project_bn (BatchNorma (None, 7, 7, 384) 1536 ['block6k_project_conv[0][0]'] Y \n",
" lization) \n",
" \n",
- " block5c_drop (FixedDropout) (None, 14, 14, 112) 0 ['block5c_project_bn[0][0]'] Y \n",
+ " block6k_drop (FixedDropout) (None, 7, 7, 384) 0 ['block6k_project_bn[0][0]'] Y \n",
" \n",
- " block5c_add (Add) (None, 14, 14, 112) 0 ['block5c_drop[0][0]', Y \n",
- " 'block5b_add[0][0]'] \n",
+ " block6k_add (Add) (None, 7, 7, 384) 0 ['block6k_drop[0][0]', Y \n",
+ " 'block6j_add[0][0]'] \n",
" \n",
- " block6a_expand_conv (Conv2D) (None, 14, 14, 672) 75264 ['block5c_add[0][0]'] Y \n",
+ " block6l_expand_conv (Conv2D) (None, 7, 7, 2304) 884736 ['block6k_add[0][0]'] Y \n",
" \n",
- " block6a_expand_bn (BatchNormal (None, 14, 14, 672) 2688 ['block6a_expand_conv[0][0]'] Y \n",
+ " block6l_expand_bn (BatchNormal (None, 7, 7, 2304) 9216 ['block6l_expand_conv[0][0]'] Y \n",
" ization) \n",
" \n",
- " block6a_expand_activation (Act (None, 14, 14, 672) 0 ['block6a_expand_bn[0][0]'] Y \n",
+ " block6l_expand_activation (Act (None, 7, 7, 2304) 0 ['block6l_expand_bn[0][0]'] Y \n",
" ivation) \n",
" \n",
- " block6a_dwconv (DepthwiseConv2 (None, 7, 7, 672) 16800 ['block6a_expand_activation[0][ Y \n",
+ " block6l_dwconv (DepthwiseConv2 (None, 7, 7, 2304) 57600 ['block6l_expand_activation[0][ Y \n",
" D) 0]'] \n",
" \n",
- " block6a_bn (BatchNormalization (None, 7, 7, 672) 2688 ['block6a_dwconv[0][0]'] Y \n",
+ " block6l_bn (BatchNormalization (None, 7, 7, 2304) 9216 ['block6l_dwconv[0][0]'] Y \n",
" ) \n",
" \n",
- " block6a_activation (Activation (None, 7, 7, 672) 0 ['block6a_bn[0][0]'] Y \n",
+ " block6l_activation (Activation (None, 7, 7, 2304) 0 ['block6l_bn[0][0]'] Y \n",
" ) \n",
" \n",
- " block6a_se_squeeze (GlobalAver (None, 672) 0 ['block6a_activation[0][0]'] Y \n",
+ " block6l_se_squeeze (GlobalAver (None, 2304) 0 ['block6l_activation[0][0]'] Y \n",
" agePooling2D) \n",
" \n",
- " block6a_se_reshape (Reshape) (None, 1, 1, 672) 0 ['block6a_se_squeeze[0][0]'] Y \n",
+ " block6l_se_reshape (Reshape) (None, 1, 1, 2304) 0 ['block6l_se_squeeze[0][0]'] Y \n",
" \n",
- " block6a_se_reduce (Conv2D) (None, 1, 1, 28) 18844 ['block6a_se_reshape[0][0]'] Y \n",
+ " block6l_se_reduce (Conv2D) (None, 1, 1, 96) 221280 ['block6l_se_reshape[0][0]'] Y \n",
" \n",
- " block6a_se_expand (Conv2D) (None, 1, 1, 672) 19488 ['block6a_se_reduce[0][0]'] Y \n",
+ " block6l_se_expand (Conv2D) (None, 1, 1, 2304) 223488 ['block6l_se_reduce[0][0]'] Y \n",
" \n",
- " block6a_se_excite (Multiply) (None, 7, 7, 672) 0 ['block6a_activation[0][0]', Y \n",
- " 'block6a_se_expand[0][0]'] \n",
+ " block6l_se_excite (Multiply) (None, 7, 7, 2304) 0 ['block6l_activation[0][0]', Y \n",
+ " 'block6l_se_expand[0][0]'] \n",
" \n",
- " block6a_project_conv (Conv2D) (None, 7, 7, 192) 129024 ['block6a_se_excite[0][0]'] Y \n",
+ " block6l_project_conv (Conv2D) (None, 7, 7, 384) 884736 ['block6l_se_excite[0][0]'] Y \n",
" \n",
- " block6a_project_bn (BatchNorma (None, 7, 7, 192) 768 ['block6a_project_conv[0][0]'] Y \n",
+ " block6l_project_bn (BatchNorma (None, 7, 7, 384) 1536 ['block6l_project_conv[0][0]'] Y \n",
" lization) \n",
" \n",
- " block6b_expand_conv (Conv2D) (None, 7, 7, 1152) 221184 ['block6a_project_bn[0][0]'] Y \n",
+ " block6l_drop (FixedDropout) (None, 7, 7, 384) 0 ['block6l_project_bn[0][0]'] Y \n",
+ " \n",
+ " block6l_add (Add) (None, 7, 7, 384) 0 ['block6l_drop[0][0]', Y \n",
+ " 'block6k_add[0][0]'] \n",
+ " \n",
+ " block6m_expand_conv (Conv2D) (None, 7, 7, 2304) 884736 ['block6l_add[0][0]'] Y \n",
" \n",
- " block6b_expand_bn (BatchNormal (None, 7, 7, 1152) 4608 ['block6b_expand_conv[0][0]'] Y \n",
+ " block6m_expand_bn (BatchNormal (None, 7, 7, 2304) 9216 ['block6m_expand_conv[0][0]'] Y \n",
" ization) \n",
" \n",
- " block6b_expand_activation (Act (None, 7, 7, 1152) 0 ['block6b_expand_bn[0][0]'] Y \n",
+ " block6m_expand_activation (Act (None, 7, 7, 2304) 0 ['block6m_expand_bn[0][0]'] Y \n",
" ivation) \n",
" \n",
- " block6b_dwconv (DepthwiseConv2 (None, 7, 7, 1152) 28800 ['block6b_expand_activation[0][ Y \n",
+ " block6m_dwconv (DepthwiseConv2 (None, 7, 7, 2304) 57600 ['block6m_expand_activation[0][ Y \n",
" D) 0]'] \n",
" \n",
- " block6b_bn (BatchNormalization (None, 7, 7, 1152) 4608 ['block6b_dwconv[0][0]'] Y \n",
+ " block6m_bn (BatchNormalization (None, 7, 7, 2304) 9216 ['block6m_dwconv[0][0]'] Y \n",
" ) \n",
" \n",
- " block6b_activation (Activation (None, 7, 7, 1152) 0 ['block6b_bn[0][0]'] Y \n",
+ " block6m_activation (Activation (None, 7, 7, 2304) 0 ['block6m_bn[0][0]'] Y \n",
" ) \n",
" \n",
- " block6b_se_squeeze (GlobalAver (None, 1152) 0 ['block6b_activation[0][0]'] Y \n",
+ " block6m_se_squeeze (GlobalAver (None, 2304) 0 ['block6m_activation[0][0]'] Y \n",
" agePooling2D) \n",
" \n",
- " block6b_se_reshape (Reshape) (None, 1, 1, 1152) 0 ['block6b_se_squeeze[0][0]'] Y \n",
+ " block6m_se_reshape (Reshape) (None, 1, 1, 2304) 0 ['block6m_se_squeeze[0][0]'] Y \n",
" \n",
- " block6b_se_reduce (Conv2D) (None, 1, 1, 48) 55344 ['block6b_se_reshape[0][0]'] Y \n",
+ " block6m_se_reduce (Conv2D) (None, 1, 1, 96) 221280 ['block6m_se_reshape[0][0]'] Y \n",
" \n",
- " block6b_se_expand (Conv2D) (None, 1, 1, 1152) 56448 ['block6b_se_reduce[0][0]'] Y \n",
+ " block6m_se_expand (Conv2D) (None, 1, 1, 2304) 223488 ['block6m_se_reduce[0][0]'] Y \n",
" \n",
- " block6b_se_excite (Multiply) (None, 7, 7, 1152) 0 ['block6b_activation[0][0]', Y \n",
- " 'block6b_se_expand[0][0]'] \n",
+ " block6m_se_excite (Multiply) (None, 7, 7, 2304) 0 ['block6m_activation[0][0]', Y \n",
+ " 'block6m_se_expand[0][0]'] \n",
" \n",
- " block6b_project_conv (Conv2D) (None, 7, 7, 192) 221184 ['block6b_se_excite[0][0]'] Y \n",
+ " block6m_project_conv (Conv2D) (None, 7, 7, 384) 884736 ['block6m_se_excite[0][0]'] Y \n",
" \n",
- " block6b_project_bn (BatchNorma (None, 7, 7, 192) 768 ['block6b_project_conv[0][0]'] Y \n",
+ " block6m_project_bn (BatchNorma (None, 7, 7, 384) 1536 ['block6m_project_conv[0][0]'] Y \n",
" lization) \n",
" \n",
- " block6b_drop (FixedDropout) (None, 7, 7, 192) 0 ['block6b_project_bn[0][0]'] Y \n",
+ " block6m_drop (FixedDropout) (None, 7, 7, 384) 0 ['block6m_project_bn[0][0]'] Y \n",
" \n",
- " block6b_add (Add) (None, 7, 7, 192) 0 ['block6b_drop[0][0]', Y \n",
- " 'block6a_project_bn[0][0]'] \n",
+ " block6m_add (Add) (None, 7, 7, 384) 0 ['block6m_drop[0][0]', Y \n",
+ " 'block6l_add[0][0]'] \n",
" \n",
- " block6c_expand_conv (Conv2D) (None, 7, 7, 1152) 221184 ['block6b_add[0][0]'] Y \n",
+ " block7a_expand_conv (Conv2D) (None, 7, 7, 2304) 884736 ['block6m_add[0][0]'] Y \n",
" \n",
- " block6c_expand_bn (BatchNormal (None, 7, 7, 1152) 4608 ['block6c_expand_conv[0][0]'] Y \n",
+ " block7a_expand_bn (BatchNormal (None, 7, 7, 2304) 9216 ['block7a_expand_conv[0][0]'] Y \n",
" ization) \n",
" \n",
- " block6c_expand_activation (Act (None, 7, 7, 1152) 0 ['block6c_expand_bn[0][0]'] Y \n",
+ " block7a_expand_activation (Act (None, 7, 7, 2304) 0 ['block7a_expand_bn[0][0]'] Y \n",
" ivation) \n",
" \n",
- " block6c_dwconv (DepthwiseConv2 (None, 7, 7, 1152) 28800 ['block6c_expand_activation[0][ Y \n",
+ " block7a_dwconv (DepthwiseConv2 (None, 7, 7, 2304) 20736 ['block7a_expand_activation[0][ Y \n",
" D) 0]'] \n",
" \n",
- " block6c_bn (BatchNormalization (None, 7, 7, 1152) 4608 ['block6c_dwconv[0][0]'] Y \n",
+ " block7a_bn (BatchNormalization (None, 7, 7, 2304) 9216 ['block7a_dwconv[0][0]'] Y \n",
" ) \n",
" \n",
- " block6c_activation (Activation (None, 7, 7, 1152) 0 ['block6c_bn[0][0]'] Y \n",
+ " block7a_activation (Activation (None, 7, 7, 2304) 0 ['block7a_bn[0][0]'] Y \n",
" ) \n",
" \n",
- " block6c_se_squeeze (GlobalAver (None, 1152) 0 ['block6c_activation[0][0]'] Y \n",
+ " block7a_se_squeeze (GlobalAver (None, 2304) 0 ['block7a_activation[0][0]'] Y \n",
" agePooling2D) \n",
" \n",
- " block6c_se_reshape (Reshape) (None, 1, 1, 1152) 0 ['block6c_se_squeeze[0][0]'] Y \n",
+ " block7a_se_reshape (Reshape) (None, 1, 1, 2304) 0 ['block7a_se_squeeze[0][0]'] Y \n",
" \n",
- " block6c_se_reduce (Conv2D) (None, 1, 1, 48) 55344 ['block6c_se_reshape[0][0]'] Y \n",
+ " block7a_se_reduce (Conv2D) (None, 1, 1, 96) 221280 ['block7a_se_reshape[0][0]'] Y \n",
" \n",
- " block6c_se_expand (Conv2D) (None, 1, 1, 1152) 56448 ['block6c_se_reduce[0][0]'] Y \n",
+ " block7a_se_expand (Conv2D) (None, 1, 1, 2304) 223488 ['block7a_se_reduce[0][0]'] Y \n",
" \n",
- " block6c_se_excite (Multiply) (None, 7, 7, 1152) 0 ['block6c_activation[0][0]', Y \n",
- " 'block6c_se_expand[0][0]'] \n",
+ " block7a_se_excite (Multiply) (None, 7, 7, 2304) 0 ['block7a_activation[0][0]', Y \n",
+ " 'block7a_se_expand[0][0]'] \n",
" \n",
- " block6c_project_conv (Conv2D) (None, 7, 7, 192) 221184 ['block6c_se_excite[0][0]'] Y \n",
+ " block7a_project_conv (Conv2D) (None, 7, 7, 640) 1474560 ['block7a_se_excite[0][0]'] Y \n",
" \n",
- " block6c_project_bn (BatchNorma (None, 7, 7, 192) 768 ['block6c_project_conv[0][0]'] Y \n",
+ " block7a_project_bn (BatchNorma (None, 7, 7, 640) 2560 ['block7a_project_conv[0][0]'] Y \n",
" lization) \n",
" \n",
- " block6c_drop (FixedDropout) (None, 7, 7, 192) 0 ['block6c_project_bn[0][0]'] Y \n",
- " \n",
- " block6c_add (Add) (None, 7, 7, 192) 0 ['block6c_drop[0][0]', Y \n",
- " 'block6b_add[0][0]'] \n",
- " \n",
- " block6d_expand_conv (Conv2D) (None, 7, 7, 1152) 221184 ['block6c_add[0][0]'] Y \n",
+ " block7b_expand_conv (Conv2D) (None, 7, 7, 3840) 2457600 ['block7a_project_bn[0][0]'] Y \n",
" \n",
- " block6d_expand_bn (BatchNormal (None, 7, 7, 1152) 4608 ['block6d_expand_conv[0][0]'] Y \n",
+ " block7b_expand_bn (BatchNormal (None, 7, 7, 3840) 15360 ['block7b_expand_conv[0][0]'] Y \n",
" ization) \n",
" \n",
- " block6d_expand_activation (Act (None, 7, 7, 1152) 0 ['block6d_expand_bn[0][0]'] Y \n",
+ " block7b_expand_activation (Act (None, 7, 7, 3840) 0 ['block7b_expand_bn[0][0]'] Y \n",
" ivation) \n",
" \n",
- " block6d_dwconv (DepthwiseConv2 (None, 7, 7, 1152) 28800 ['block6d_expand_activation[0][ Y \n",
+ " block7b_dwconv (DepthwiseConv2 (None, 7, 7, 3840) 34560 ['block7b_expand_activation[0][ Y \n",
" D) 0]'] \n",
" \n",
- " block6d_bn (BatchNormalization (None, 7, 7, 1152) 4608 ['block6d_dwconv[0][0]'] Y \n",
+ " block7b_bn (BatchNormalization (None, 7, 7, 3840) 15360 ['block7b_dwconv[0][0]'] Y \n",
" ) \n",
" \n",
- " block6d_activation (Activation (None, 7, 7, 1152) 0 ['block6d_bn[0][0]'] Y \n",
+ " block7b_activation (Activation (None, 7, 7, 3840) 0 ['block7b_bn[0][0]'] Y \n",
" ) \n",
" \n",
- " block6d_se_squeeze (GlobalAver (None, 1152) 0 ['block6d_activation[0][0]'] Y \n",
+ " block7b_se_squeeze (GlobalAver (None, 3840) 0 ['block7b_activation[0][0]'] Y \n",
" agePooling2D) \n",
" \n",
- " block6d_se_reshape (Reshape) (None, 1, 1, 1152) 0 ['block6d_se_squeeze[0][0]'] Y \n",
+ " block7b_se_reshape (Reshape) (None, 1, 1, 3840) 0 ['block7b_se_squeeze[0][0]'] Y \n",
" \n",
- " block6d_se_reduce (Conv2D) (None, 1, 1, 48) 55344 ['block6d_se_reshape[0][0]'] Y \n",
+ " block7b_se_reduce (Conv2D) (None, 1, 1, 160) 614560 ['block7b_se_reshape[0][0]'] Y \n",
" \n",
- " block6d_se_expand (Conv2D) (None, 1, 1, 1152) 56448 ['block6d_se_reduce[0][0]'] Y \n",
+ " block7b_se_expand (Conv2D) (None, 1, 1, 3840) 618240 ['block7b_se_reduce[0][0]'] Y \n",
" \n",
- " block6d_se_excite (Multiply) (None, 7, 7, 1152) 0 ['block6d_activation[0][0]', Y \n",
- " 'block6d_se_expand[0][0]'] \n",
+ " block7b_se_excite (Multiply) (None, 7, 7, 3840) 0 ['block7b_activation[0][0]', Y \n",
+ " 'block7b_se_expand[0][0]'] \n",
" \n",
- " block6d_project_conv (Conv2D) (None, 7, 7, 192) 221184 ['block6d_se_excite[0][0]'] Y \n",
+ " block7b_project_conv (Conv2D) (None, 7, 7, 640) 2457600 ['block7b_se_excite[0][0]'] Y \n",
" \n",
- " block6d_project_bn (BatchNorma (None, 7, 7, 192) 768 ['block6d_project_conv[0][0]'] Y \n",
+ " block7b_project_bn (BatchNorma (None, 7, 7, 640) 2560 ['block7b_project_conv[0][0]'] Y \n",
" lization) \n",
" \n",
- " block6d_drop (FixedDropout) (None, 7, 7, 192) 0 ['block6d_project_bn[0][0]'] Y \n",
+ " block7b_drop (FixedDropout) (None, 7, 7, 640) 0 ['block7b_project_bn[0][0]'] Y \n",
" \n",
- " block6d_add (Add) (None, 7, 7, 192) 0 ['block6d_drop[0][0]', Y \n",
- " 'block6c_add[0][0]'] \n",
+ " block7b_add (Add) (None, 7, 7, 640) 0 ['block7b_drop[0][0]', Y \n",
+ " 'block7a_project_bn[0][0]'] \n",
" \n",
- " block7a_expand_conv (Conv2D) (None, 7, 7, 1152) 221184 ['block6d_add[0][0]'] Y \n",
+ " block7c_expand_conv (Conv2D) (None, 7, 7, 3840) 2457600 ['block7b_add[0][0]'] Y \n",
" \n",
- " block7a_expand_bn (BatchNormal (None, 7, 7, 1152) 4608 ['block7a_expand_conv[0][0]'] Y \n",
+ " block7c_expand_bn (BatchNormal (None, 7, 7, 3840) 15360 ['block7c_expand_conv[0][0]'] Y \n",
" ization) \n",
" \n",
- " block7a_expand_activation (Act (None, 7, 7, 1152) 0 ['block7a_expand_bn[0][0]'] Y \n",
+ " block7c_expand_activation (Act (None, 7, 7, 3840) 0 ['block7c_expand_bn[0][0]'] Y \n",
" ivation) \n",
" \n",
- " block7a_dwconv (DepthwiseConv2 (None, 7, 7, 1152) 10368 ['block7a_expand_activation[0][ Y \n",
+ " block7c_dwconv (DepthwiseConv2 (None, 7, 7, 3840) 34560 ['block7c_expand_activation[0][ Y \n",
" D) 0]'] \n",
" \n",
- " block7a_bn (BatchNormalization (None, 7, 7, 1152) 4608 ['block7a_dwconv[0][0]'] Y \n",
+ " block7c_bn (BatchNormalization (None, 7, 7, 3840) 15360 ['block7c_dwconv[0][0]'] Y \n",
" ) \n",
" \n",
- " block7a_activation (Activation (None, 7, 7, 1152) 0 ['block7a_bn[0][0]'] Y \n",
+ " block7c_activation (Activation (None, 7, 7, 3840) 0 ['block7c_bn[0][0]'] Y \n",
" ) \n",
" \n",
- " block7a_se_squeeze (GlobalAver (None, 1152) 0 ['block7a_activation[0][0]'] Y \n",
+ " block7c_se_squeeze (GlobalAver (None, 3840) 0 ['block7c_activation[0][0]'] Y \n",
" agePooling2D) \n",
" \n",
- " block7a_se_reshape (Reshape) (None, 1, 1, 1152) 0 ['block7a_se_squeeze[0][0]'] Y \n",
+ " block7c_se_reshape (Reshape) (None, 1, 1, 3840) 0 ['block7c_se_squeeze[0][0]'] Y \n",
" \n",
- " block7a_se_reduce (Conv2D) (None, 1, 1, 48) 55344 ['block7a_se_reshape[0][0]'] Y \n",
+ " block7c_se_reduce (Conv2D) (None, 1, 1, 160) 614560 ['block7c_se_reshape[0][0]'] Y \n",
" \n",
- " block7a_se_expand (Conv2D) (None, 1, 1, 1152) 56448 ['block7a_se_reduce[0][0]'] Y \n",
+ " block7c_se_expand (Conv2D) (None, 1, 1, 3840) 618240 ['block7c_se_reduce[0][0]'] Y \n",
" \n",
- " block7a_se_excite (Multiply) (None, 7, 7, 1152) 0 ['block7a_activation[0][0]', Y \n",
- " 'block7a_se_expand[0][0]'] \n",
+ " block7c_se_excite (Multiply) (None, 7, 7, 3840) 0 ['block7c_activation[0][0]', Y \n",
+ " 'block7c_se_expand[0][0]'] \n",
" \n",
- " block7a_project_conv (Conv2D) (None, 7, 7, 320) 368640 ['block7a_se_excite[0][0]'] Y \n",
+ " block7c_project_conv (Conv2D) (None, 7, 7, 640) 2457600 ['block7c_se_excite[0][0]'] Y \n",
" \n",
- " block7a_project_bn (BatchNorma (None, 7, 7, 320) 1280 ['block7a_project_conv[0][0]'] Y \n",
+ " block7c_project_bn (BatchNorma (None, 7, 7, 640) 2560 ['block7c_project_conv[0][0]'] Y \n",
" lization) \n",
" \n",
- " top_conv (Conv2D) (None, 7, 7, 1280) 409600 ['block7a_project_bn[0][0]'] Y \n",
- " \n",
- " top_bn (BatchNormalization) (None, 7, 7, 1280) 5120 ['top_conv[0][0]'] Y \n",
- " \n",
- " top_activation (Activation) (None, 7, 7, 1280) 0 ['top_bn[0][0]'] Y \n",
- " \n",
- " FC_INPUT_Avg-Pooling (GlobalAv (None, 1280) 0 ['top_activation[0][0]'] Y \n",
- " eragePooling2D) \n",
- " \n",
- " FC_C_Dense-L1-512 (Dense) (None, 512) 655872 ['FC_INPUT_Avg-Pooling[0][0]'] Y \n",
+ " block7c_drop (FixedDropout) (None, 7, 7, 640) 0 ['block7c_project_bn[0][0]'] Y \n",
" \n",
- " FC_C_Dropout-L1-0.1 (Dropout) (None, 512) 0 ['FC_C_Dense-L1-512[0][0]'] Y \n",
+ " block7c_add (Add) (None, 7, 7, 640) 0 ['block7c_drop[0][0]', Y \n",
+ " 'block7b_add[0][0]'] \n",
" \n",
- " FC_C_Avg-BatchNormalization-L1 (None, 512) 2048 ['FC_C_Dropout-L1-0.1[0][0]'] Y \n",
- " (BatchNormalization) \n",
+ " block7d_expand_conv (Conv2D) (None, 7, 7, 3840) 2457600 ['block7c_add[0][0]'] Y \n",
" \n",
- " FC_C_Dense-L2-512 (Dense) (None, 256) 131328 ['FC_C_Avg-BatchNormalization-L Y \n",
- " 1[0][0]'] \n",
+ " block7d_expand_bn (BatchNormal (None, 7, 7, 3840) 15360 ['block7d_expand_conv[0][0]'] Y \n",
+ " ization) \n",
" \n",
- " FC_C_Avg-BatchNormalization-L2 (None, 256) 1024 ['FC_C_Dense-L2-512[0][0]'] Y \n",
- " (BatchNormalization) \n",
+ " block7d_expand_activation (Act (None, 7, 7, 3840) 0 ['block7d_expand_bn[0][0]'] Y \n",
+ " ivation) \n",
" \n",
- " FC_C_Dense-L3-128 (Dense) (None, 128) 32896 ['FC_C_Avg-BatchNormalization-L Y \n",
- " 2[0][0]'] \n",
+ " block7d_dwconv (DepthwiseConv2 (None, 7, 7, 3840) 34560 ['block7d_expand_activation[0][ Y \n",
+ " D) 0]'] \n",
" \n",
- " FC_OUTPUT_Dense-2 (Dense) (None, 2) 258 ['FC_C_Dense-L3-128[0][0]'] Y \n",
+ " block7d_bn (BatchNormalization (None, 7, 7, 3840) 15360 ['block7d_dwconv[0][0]'] Y \n",
+ " ) \n",
" \n",
- "=============================================================================================================\n",
- "Total params: 4,872,990\n",
- "Trainable params: 4,829,438\n",
- "Non-trainable params: 43,552\n",
- "_____________________________________________________________________________________________________________\n",
- "done.\n"
- ]
- }
- ],
- "source": [
- "# FUNC\n",
- "def Eff_B0_NS(freeze_layers):\n",
- " base_model = KENB0(\n",
- " input_shape=(img_res[0], img_res[1], img_res[2]),\n",
- " weights=\"noisy-student\",\n",
- " include_top=False,\n",
- " )\n",
- " print(\"Total layers in the base model: \", len(base_model.layers))\n",
- " print(f\"Freezing {freeze_layers} layers in the base model...\")\n",
- " # Freeze the specified number of layers\n",
- " for layer in base_model.layers[:freeze_layers]:\n",
- " layer.trainable = False\n",
- "\n",
- " # Unfreeze the rest\n",
- " for layer in base_model.layers[freeze_layers:]:\n",
- " layer.trainable = True\n",
- "\n",
- " # Calculate the percentage of the model that is frozen\n",
- " frozen_percentage = ((freeze_layers + 1e-10) / len(base_model.layers)) * 100\n",
- " print(f\"Percentage of the base model that is frozen: {frozen_percentage:.2f}%\")\n",
- " # adding CDL>>>\n",
- " # GlobalAveragePooling2D\n",
- " base_model_FT = GlobalAveragePooling2D(name=\"FC_INPUT_Avg-Pooling\")(base_model.output)\n",
- " # Dense\n",
- " Dense_L1 = Dense(512, activation=\"relu\", kernel_regularizer=l2(0.02), name=\"FC_C_Dense-L1-512\")(base_model_FT)\n",
- " # Dropout\n",
- " Dropout_L1 = Dropout(0.1, name=\"FC_C_Dropout-L1-0.1\")(Dense_L1)\n",
- " # BatchNormalization\n",
- " BatchNorm_L2 = BatchNormalization(name=\"FC_C_Avg-BatchNormalization-L1\")(Dropout_L1)\n",
- " # Dense\n",
- " Dense_L2 = Dense(256, activation=\"relu\", kernel_regularizer=l2(0.01), name=\"FC_C_Dense-L2-512\")(BatchNorm_L2)\n",
- " # BatchNormalization\n",
- " BatchNorm_L3 = BatchNormalization(name=\"FC_C_Avg-BatchNormalization-L2\")(Dense_L2)\n",
- " # Dense\n",
- " Dense_L3 = Dense(128, activation=\"relu\", name=\"FC_C_Dense-L3-128\")(BatchNorm_L3)\n",
- " # Dense\n",
- " # predictions = Dense(2, activation='softmax')(Dense_L3) / predictions = Dense(1, activation='sigmoid')(Dense_L3)\n",
- " predictions = Dense(2, activation=\"softmax\", name=\"FC_OUTPUT_Dense-2\")(Dense_L3)\n",
- " # CDL<<<\n",
- " model_EfficientNetB4_NS = Model(inputs=base_model.input, outputs=predictions)\n",
- " print(\"Total model layers: \", len(model_EfficientNetB4_NS.layers))\n",
- " # OPT/compile\n",
- " opt = SGD(momentum=0.92, nesterov=False) # noqa: F405\n",
- " # opt = Nadam()\n",
- " # opt = Adamax()\n",
- " # opt = RMSprop(momentum=0.9)\n",
- " # opt = Adagrad()\n",
- " # opt = AdaBeliefOptimizer(epsilon=1e-7, rectify=False, weight_decay=5e-4, print_change_log=False, total_steps=0, amsgrad=False)\n",
- " # opt = Yogi()\n",
- " model_EfficientNetB4_NS.compile(\n",
- " optimizer=opt, loss=\"categorical_crossentropy\", metrics=[\"accuracy\"]\n",
- " ) # categorical_crossentropy / binary_crossentropy\n",
- "\n",
- " return model_EfficientNetB4_NS\n",
- "\n",
- "\n",
- "print(\"Creating the model...\")\n",
- "# Main\n",
- "freeze_layers = 0\n",
- "model = Eff_B0_NS(freeze_layers)\n",
- "model.summary(show_trainable=True, expand_nested=True)\n",
- "print(\"done.\")"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### LR FINDER"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "import gc\n",
- "\n",
- "# Garbage Collection (memory)\n",
- "gc.collect()\n",
- "tf.keras.backend.clear_session()\n",
- "# CONF/Other\n",
- "LRF_OPT = SGD(momentum=0.9) # noqa: F405\n",
- "LFR_batch_size = 1 # or any other batch size that fits in your memory\n",
- "LRF_dataset = tf.data.Dataset.from_tensor_slices((x_train, y_train)).batch(LFR_batch_size)\n",
- "# Instantiate LrFinder\n",
- "lr_find = LrFinder(model, LRF_OPT, tf.keras.losses.categorical_crossentropy)\n",
- "\n",
- "# Start range_test\n",
- "lr_find.range_test(LRF_dataset)\n",
- "lr_find.plot_lrs(skip_end=0, suggestion=True, show_grid=True)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### Model vis"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "dot_img_file = \"model_1.png\"\n",
- "keras.utils.plot_model(model, to_file=dot_img_file, show_shapes=True)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Loading the model"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### Loading the full model"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
+ " block7d_activation (Activation (None, 7, 7, 3840) 0 ['block7d_bn[0][0]'] Y \n",
+ " ) \n",
+ " \n",
+ " block7d_se_squeeze (GlobalAver (None, 3840) 0 ['block7d_activation[0][0]'] Y \n",
+ " agePooling2D) \n",
+ " \n",
+ " block7d_se_reshape (Reshape) (None, 1, 1, 3840) 0 ['block7d_se_squeeze[0][0]'] Y \n",
+ " \n",
+ " block7d_se_reduce (Conv2D) (None, 1, 1, 160) 614560 ['block7d_se_reshape[0][0]'] Y \n",
+ " \n",
+ " block7d_se_expand (Conv2D) (None, 1, 1, 3840) 618240 ['block7d_se_reduce[0][0]'] Y \n",
+ " \n",
+ " block7d_se_excite (Multiply) (None, 7, 7, 3840) 0 ['block7d_activation[0][0]', Y \n",
+ " 'block7d_se_expand[0][0]'] \n",
+ " \n",
+ " block7d_project_conv (Conv2D) (None, 7, 7, 640) 2457600 ['block7d_se_excite[0][0]'] Y \n",
+ " \n",
+ " block7d_project_bn (BatchNorma (None, 7, 7, 640) 2560 ['block7d_project_conv[0][0]'] Y \n",
+ " lization) \n",
+ " \n",
+ " block7d_drop (FixedDropout) (None, 7, 7, 640) 0 ['block7d_project_bn[0][0]'] Y \n",
+ " \n",
+ " block7d_add (Add) (None, 7, 7, 640) 0 ['block7d_drop[0][0]', Y \n",
+ " 'block7c_add[0][0]'] \n",
+ " \n",
+ " top_conv (Conv2D) (None, 7, 7, 2560) 1638400 ['block7d_add[0][0]'] Y \n",
+ " \n",
+ " top_bn (BatchNormalization) (None, 7, 7, 2560) 10240 ['top_conv[0][0]'] Y \n",
+ " \n",
+ " top_activation (Activation) (None, 7, 7, 2560) 0 ['top_bn[0][0]'] Y \n",
+ " \n",
+ " FC_INPUT_Avg-Pooling (GlobalAv (None, 2560) 0 ['top_activation[0][0]'] Y \n",
+ " eragePooling2D) \n",
+ " \n",
+ " FC_C_Dense-L1-512 (Dense) (None, 512) 1311232 ['FC_INPUT_Avg-Pooling[0][0]'] Y \n",
+ " \n",
+ " FC_C_Dropout-L1-0.1 (Dropout) (None, 512) 0 ['FC_C_Dense-L1-512[0][0]'] Y \n",
+ " \n",
+ " FC_C_Avg-BatchNormalization-L1 (None, 512) 2048 ['FC_C_Dropout-L1-0.1[0][0]'] Y \n",
+ " (BatchNormalization) \n",
+ " \n",
+ " FC_C_Dense-L2-512 (Dense) (None, 256) 131328 ['FC_C_Avg-BatchNormalization-L Y \n",
+ " 1[0][0]'] \n",
+ " \n",
+ " FC_C_Avg-BatchNormalization-L2 (None, 256) 1024 ['FC_C_Dense-L2-512[0][0]'] Y \n",
+ " (BatchNormalization) \n",
+ " \n",
+ " FC_C_Dense-L3-128 (Dense) (None, 128) 32896 ['FC_C_Avg-BatchNormalization-L Y \n",
+ " 2[0][0]'] \n",
+ " \n",
+ " FC_OUTPUT_Dense-2 (Dense) (None, 2) 258 ['FC_C_Dense-L3-128[0][0]'] Y \n",
+ " \n",
+ "=============================================================================================================\n",
+ "Total params: 65,576,466\n",
+ "Trainable params: 65,264,210\n",
+ "Non-trainable params: 312,256\n",
+ "_____________________________________________________________________________________________________________\n",
+ "done.\n"
+ ]
+ }
+ ],
"source": [
"# Configuration\n",
"PRMC = False\n",
@@ -9875,7 +7191,7 @@
"# CEC_opt = Adagrad()\n",
"# CEC_opt = Yogi()\n",
"# CEC_opt = AdaBeliefOptimizer(epsilon=1e-7, rectify=False, weight_decay=1e-3)\n",
- "CEC_opt = SGD(momentum=0.9, nesterov=False) # noqa: F405\n",
+ "CEC_opt = SGD(momentum=0.99, learning_rate=0.001, nesterov=False) # noqa: F405\n",
"# CEC_opt = Adam()\n",
"# Main\n",
"try:\n",
@@ -9960,6 +7276,22 @@
" )"
]
},
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Unfreeze all layers"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 11,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "model.trainable = True"
+ ]
+ },
{
"cell_type": "markdown",
"metadata": {},
@@ -9986,7 +7318,7 @@
},
{
"cell_type": "code",
- "execution_count": 15,
+ "execution_count": 9,
"metadata": {
"ExecuteTime": {
"end_time": "2023-12-28T07:04:23.573633300Z",
@@ -10001,18 +7333,3058 @@
"Training the model...\n",
"\u001b[0;33m\n",
"Setup Verbose:\u001b[0m\n",
- "\u001b[0m\u001b[0m\u001b[0;36mSetting TensorBoard Log dir to \u001b[0m\u001b[0;32m[logs/fit/y2024_m03_d19-h21_m23_s17]\u001b[0m\u001b[0;36m...\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;36mExperiment name: \u001b[0m\u001b[0;32m[_y2024_m03_d29-h20_m44_s53]\u001b[0m\u001b[0;36m...\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;36mSetting TensorBoard Log dir to \u001b[0m\u001b[0;32m[logs/fit/_y2024_m03_d29-h20_m44_s53]\u001b[0m\u001b[0;36m...\u001b[0m\n",
"\u001b[0m\u001b[0m\u001b[0;36mUse_extended_tensorboard \u001b[0m\u001b[0;32m[False]\u001b[0m\u001b[0;36m.\u001b[0m\n",
"\u001b[0m\u001b[0m\u001b[0;36mDebug_OUTPUT_DPS \u001b[0m\u001b[0;32m[True]\u001b[0m\u001b[0;36m.\u001b[0m\n",
"\u001b[0m\u001b[0m\u001b[0;36mUse_OneCycleLr \u001b[0m\u001b[0;32m[False]\u001b[0m\u001b[0;36m.\u001b[0m\n",
"\u001b[0m\u001b[0m\u001b[0;36mUsing the modified ReduceLROnPlateau.\u001b[0m\n",
"\u001b[0m\u001b[0m\u001b[0;36mOneCycleLr_UFTS \u001b[0m\u001b[0;32m[False]\u001b[0m\u001b[0;36m.\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;36mOptimizer: \u001b[0m\u001b[0;32mSGD\u001b[0m\n",
+ "\u001b[0;36m Parameters:\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;36m -- \u001b[0m\u001b[0;96mname: \u001b[0m\u001b[0;32mSGD\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;36m -- \u001b[0m\u001b[0;96mlearning_rate: \u001b[0m\u001b[0;32m0.009999999776482582\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;36m -- \u001b[0m\u001b[0;96mdecay: \u001b[0m\u001b[0;32m0.0\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;36m -- \u001b[0m\u001b[0;96mmomentum: \u001b[0m\u001b[0;32m0.8899999856948853\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;36m -- \u001b[0m\u001b[0;96mnesterov: \u001b[0m\u001b[0;32mFalse\u001b[0m\n",
"\u001b[0;33mSetup Verbose END.\u001b[0m\n",
"\u001b[0m\n",
- "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m1\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m489 (TSEC: 0)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Stage 1]\u001b[0m\n",
+ "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m1\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m489 (TSEC: 0)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Stage 1]\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+ "\u001b[0;33mPreparing train data...\u001b[0m\n",
+ "\u001b[0;33m- Loading fitted ImageDataGenerator...\u001b[0m\n",
+ "\u001b[0;33m- ImageDataGenerator fit done.\u001b[0m\n",
+ "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n",
+ "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;31m- Debug DP Sample dir: \u001b[0m\u001b[0;32mSamples/TSR_SUB_400_y2024_m03_d29-h20_m45_s50\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+ "\u001b[0;32mTraining on subset...\u001b[0m\n",
+ "Epoch 1/6\n",
+ "256/256 [==============================] - 94s 289ms/step - loss: 2.1378 - accuracy: 0.6372 - val_loss: 1.8907 - val_accuracy: 0.7372 - lr: 0.0100\n",
+ "Epoch 2/6\n",
+ "256/256 [==============================] - 70s 273ms/step - loss: 1.7242 - accuracy: 0.8125 - val_loss: 1.4988 - val_accuracy: 0.8494 - lr: 0.0100\n",
+ "Epoch 3/6\n",
+ "256/256 [==============================] - 71s 278ms/step - loss: 1.4643 - accuracy: 0.8684 - val_loss: 1.3688 - val_accuracy: 0.8638 - lr: 0.0100\n",
+ "Epoch 4/6\n",
+ "256/256 [==============================] - 72s 281ms/step - loss: 1.2522 - accuracy: 0.8992 - val_loss: 1.2234 - val_accuracy: 0.8381 - lr: 0.0100\n",
+ "Epoch 5/6\n",
+ "256/256 [==============================] - 73s 284ms/step - loss: 1.1065 - accuracy: 0.9102 - val_loss: 1.1080 - val_accuracy: 0.9038 - lr: 0.0100\n",
+ "Epoch 6/6\n",
+ "256/256 [==============================] - 72s 281ms/step - loss: 0.9423 - accuracy: 0.9287 - val_loss: 0.9623 - val_accuracy: 0.8750 - lr: 0.0100\n",
+ "\u001b[0;32mSubset training done.\u001b[0m\n",
+ "\u001b[0;33mLoading the best weights...\u001b[0m\n",
+ "\u001b[0;33mLoading weights from file cache\\model_SUB_checkpoint-005-0.9038.h5...\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9038\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m1.0990\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;32mImproved model accuracy from 0.000000 to 0.903846. \u001b[0m\u001b[0;96mSaving model.\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;36mSaving full model H5 format...\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;32mImproved model loss from inf to 1.09902048. \u001b[0m\u001b[0;96mSaving model.\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;36mSaving full model H5 format...\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;32m(GPU-MEM)\u001b[0m\u001b[0;36m----[free: 1.95GB, used: 22.05GB, total, 24.00GB]\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m531.25 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m453.88 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m77.36 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0;36m<---------------------------------------|Epoch [1] END|--------------------------------------->\u001b[0m\n",
+ "\u001b[0m\n",
+ "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m2\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m489 (TSEC: 6)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Stage 1]\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+ "\u001b[0;33mPreparing train data...\u001b[0m\n",
+ "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n",
+ "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+ "\u001b[0;32mTraining on subset...\u001b[0m\n",
+ "Epoch 7/12\n",
+ "256/256 [==============================] - 77s 282ms/step - loss: 1.0754 - accuracy: 0.8726 - val_loss: 0.9810 - val_accuracy: 0.8910 - lr: 0.0100\n",
+ "Epoch 8/12\n",
+ "256/256 [==============================] - 69s 270ms/step - loss: 0.9141 - accuracy: 0.9070 - val_loss: 1.0838 - val_accuracy: 0.7997 - lr: 0.0100\n",
+ "Epoch 9/12\n",
+ "256/256 [==============================] - 70s 271ms/step - loss: 0.8049 - accuracy: 0.9192 - val_loss: 0.8255 - val_accuracy: 0.8814 - lr: 0.0100\n",
+ "Epoch 10/12\n",
+ "256/256 [==============================] - 69s 269ms/step - loss: 0.6986 - accuracy: 0.9326 - val_loss: 0.7473 - val_accuracy: 0.8894 - lr: 0.0100\n",
+ "Epoch 11/12\n",
+ "256/256 [==============================] - 69s 270ms/step - loss: 0.6178 - accuracy: 0.9426 - val_loss: 0.9366 - val_accuracy: 0.8397 - lr: 0.0100\n",
+ "Epoch 12/12\n",
+ "256/256 [==============================] - 69s 269ms/step - loss: 0.5278 - accuracy: 0.9568 - val_loss: 1.1692 - val_accuracy: 0.8478 - lr: 0.0100\n",
+ "\u001b[0;32mSubset training done.\u001b[0m\n",
+ "\u001b[0;33mLoading the best weights...\u001b[0m\n",
+ "\u001b[0;33mLoading weights from file cache\\model_SUB_checkpoint-007-0.8910.h5...\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.8910\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.9810\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9038461447. Not saving model.\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;32mImproved model loss from 1.09902048 to 0.98102009. \u001b[0m\u001b[0;96mSaving model.\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;36mSaving full model H5 format...\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;32m(GPU-MEM)\u001b[0m\u001b[0;36m----[free: 5.14GB, used: 18.86GB, total, 24.00GB]\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m509.46 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m424.94 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m84.52 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0;36m<---------------------------------------|Epoch [2] END|--------------------------------------->\u001b[0m\n",
+ "\u001b[0m\n",
+ "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m3\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m489 (TSEC: 12)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Stage 1]\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+ "\u001b[0;33mPreparing train data...\u001b[0m\n",
+ "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n",
+ "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+ "\u001b[0;32mTraining on subset...\u001b[0m\n",
+ "Epoch 13/18\n",
+ "256/256 [==============================] - 74s 275ms/step - loss: 0.9580 - accuracy: 0.8875 - val_loss: 0.8333 - val_accuracy: 0.9199 - lr: 0.0100\n",
+ "Epoch 14/18\n",
+ "256/256 [==============================] - 69s 269ms/step - loss: 0.8153 - accuracy: 0.9146 - val_loss: 0.8155 - val_accuracy: 0.8878 - lr: 0.0100\n",
+ "Epoch 15/18\n",
+ "256/256 [==============================] - 69s 271ms/step - loss: 0.7287 - accuracy: 0.9175 - val_loss: 0.7287 - val_accuracy: 0.9167 - lr: 0.0100\n",
+ "Epoch 16/18\n",
+ "256/256 [==============================] - 69s 270ms/step - loss: 0.6085 - accuracy: 0.9424 - val_loss: 0.6945 - val_accuracy: 0.9054 - lr: 0.0100\n",
+ "Epoch 17/18\n",
+ "256/256 [==============================] - 69s 271ms/step - loss: 0.5522 - accuracy: 0.9500 - val_loss: 0.6496 - val_accuracy: 0.8830 - lr: 0.0100\n",
+ "Epoch 18/18\n",
+ "256/256 [==============================] - 69s 270ms/step - loss: 0.4957 - accuracy: 0.9553 - val_loss: 0.5994 - val_accuracy: 0.9199 - lr: 0.0100\n",
+ "\u001b[0;32mSubset training done.\u001b[0m\n",
+ "\u001b[0;33mLoading the best weights...\u001b[0m\n",
+ "\u001b[0;33mLoading weights from file cache\\model_SUB_checkpoint-013-0.9199.h5...\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9199\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.8333\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;32mImproved model accuracy from 0.903846 to 0.919872. \u001b[0m\u001b[0;96mSaving model.\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;36mSaving full model H5 format...\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;32mImproved model loss from 0.98102009 to 0.83334637. \u001b[0m\u001b[0;96mSaving model.\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;36mSaving full model H5 format...\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;32m(GPU-MEM)\u001b[0m\u001b[0;36m----[free: 5.13GB, used: 18.87GB, total, 24.00GB]\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m491.59 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m422.14 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m69.45 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0;36m<---------------------------------------|Epoch [3] END|--------------------------------------->\u001b[0m\n",
+ "\u001b[0m\n",
+ "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m4\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m489 (TSEC: 18)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Stage 1]\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+ "\u001b[0;33mPreparing train data...\u001b[0m\n",
+ "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n",
+ "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+ "\u001b[0;32mTraining on subset...\u001b[0m\n",
+ "Epoch 19/24\n",
+ "256/256 [==============================] - 74s 275ms/step - loss: 0.8811 - accuracy: 0.8813 - val_loss: 0.8378 - val_accuracy: 0.9103 - lr: 0.0100\n",
+ "Epoch 20/24\n",
+ "256/256 [==============================] - 69s 269ms/step - loss: 0.7593 - accuracy: 0.9089 - val_loss: 0.8316 - val_accuracy: 0.8526 - lr: 0.0100\n",
+ "Epoch 21/24\n",
+ "256/256 [==============================] - 70s 272ms/step - loss: 0.6531 - accuracy: 0.9248 - val_loss: 0.6264 - val_accuracy: 0.9327 - lr: 0.0100\n",
+ "Epoch 22/24\n",
+ "256/256 [==============================] - 70s 272ms/step - loss: 0.5809 - accuracy: 0.9392 - val_loss: 0.5873 - val_accuracy: 0.9375 - lr: 0.0100\n",
+ "Epoch 23/24\n",
+ "256/256 [==============================] - 69s 270ms/step - loss: 0.5031 - accuracy: 0.9480 - val_loss: 0.5634 - val_accuracy: 0.9343 - lr: 0.0100\n",
+ "Epoch 24/24\n",
+ "256/256 [==============================] - 69s 269ms/step - loss: 0.4328 - accuracy: 0.9570 - val_loss: 0.5751 - val_accuracy: 0.8846 - lr: 0.0100\n",
+ "\u001b[0;32mSubset training done.\u001b[0m\n",
+ "\u001b[0;33mLoading the best weights...\u001b[0m\n",
+ "\u001b[0;33mLoading weights from file cache\\model_SUB_checkpoint-022-0.9375.h5...\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9375\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.5873\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;32mImproved model accuracy from 0.919872 to 0.937500. \u001b[0m\u001b[0;96mSaving model.\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;36mSaving full model H5 format...\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;32mImproved model loss from 0.83334637 to 0.58728427. \u001b[0m\u001b[0;96mSaving model.\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;36mSaving full model H5 format...\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;32m(GPU-MEM)\u001b[0m\u001b[0;36m----[free: 5.13GB, used: 18.87GB, total, 24.00GB]\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m492.19 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m422.44 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m69.75 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0;36m<---------------------------------------|Epoch [4] END|--------------------------------------->\u001b[0m\n",
+ "\u001b[0m\n",
+ "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m5\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m489 (TSEC: 24)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Stage 1]\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+ "\u001b[0;33mPreparing train data...\u001b[0m\n",
+ "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n",
+ "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+ "\u001b[0;32mTraining on subset...\u001b[0m\n",
+ "Epoch 25/30\n",
+ "256/256 [==============================] - 75s 277ms/step - loss: 0.6212 - accuracy: 0.9043 - val_loss: 0.7914 - val_accuracy: 0.8670 - lr: 0.0100\n",
+ "Epoch 26/30\n",
+ "256/256 [==============================] - 70s 273ms/step - loss: 0.5334 - accuracy: 0.9290 - val_loss: 0.5001 - val_accuracy: 0.9311 - lr: 0.0100\n",
+ "Epoch 27/30\n",
+ "256/256 [==============================] - 70s 271ms/step - loss: 0.4541 - accuracy: 0.9397 - val_loss: 0.5195 - val_accuracy: 0.9135 - lr: 0.0100\n",
+ "Epoch 28/30\n",
+ "256/256 [==============================] - 69s 270ms/step - loss: 0.4093 - accuracy: 0.9451 - val_loss: 0.5215 - val_accuracy: 0.9087 - lr: 0.0100\n",
+ "Epoch 29/30\n",
+ "256/256 [==============================] - 70s 274ms/step - loss: 0.3499 - accuracy: 0.9597 - val_loss: 0.4166 - val_accuracy: 0.9375 - lr: 0.0100\n",
+ "Epoch 30/30\n",
+ "256/256 [==============================] - 69s 269ms/step - loss: 0.3113 - accuracy: 0.9634 - val_loss: 0.4444 - val_accuracy: 0.9151 - lr: 0.0100\n",
+ "\u001b[0;32mSubset training done.\u001b[0m\n",
+ "\u001b[0;33mLoading the best weights...\u001b[0m\n",
+ "\u001b[0;33mLoading weights from file cache\\model_SUB_checkpoint-029-0.9375.h5...\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9375\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.4166\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9375000000. Not saving model.\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;32mImproved model loss from 0.58728427 to 0.41660872. \u001b[0m\u001b[0;96mSaving model.\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;36mSaving full model H5 format...\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;32m(GPU-MEM)\u001b[0m\u001b[0;36m----[free: 5.13GB, used: 18.87GB, total, 24.00GB]\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m493.68 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m424.53 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m69.15 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0;36m<---------------------------------------|Epoch [5] END|--------------------------------------->\u001b[0m\n",
+ "\u001b[0m\n",
+ "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m6\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m489 (TSEC: 30)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Stage 1]\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+ "\u001b[0;33mPreparing train data...\u001b[0m\n",
+ "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n",
+ "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+ "\u001b[0;32mTraining on subset...\u001b[0m\n",
+ "Epoch 31/36\n",
+ "256/256 [==============================] - 75s 276ms/step - loss: 0.4474 - accuracy: 0.9150 - val_loss: 0.3971 - val_accuracy: 0.9375 - lr: 0.0100\n",
+ "Epoch 32/36\n",
+ "256/256 [==============================] - 69s 270ms/step - loss: 0.3720 - accuracy: 0.9348 - val_loss: 0.3943 - val_accuracy: 0.9295 - lr: 0.0100\n",
+ "Epoch 33/36\n",
+ "256/256 [==============================] - 70s 274ms/step - loss: 0.3061 - accuracy: 0.9551 - val_loss: 0.3369 - val_accuracy: 0.9487 - lr: 0.0100\n",
+ "Epoch 34/36\n",
+ "256/256 [==============================] - 70s 271ms/step - loss: 0.2716 - accuracy: 0.9651 - val_loss: 0.3636 - val_accuracy: 0.9375 - lr: 0.0100\n",
+ "Epoch 35/36\n",
+ "256/256 [==============================] - 70s 271ms/step - loss: 0.2348 - accuracy: 0.9690 - val_loss: 0.3595 - val_accuracy: 0.9407 - lr: 0.0100\n",
+ "Epoch 36/36\n",
+ "256/256 [==============================] - 70s 271ms/step - loss: 0.2161 - accuracy: 0.9714 - val_loss: 0.3430 - val_accuracy: 0.9359 - lr: 0.0100\n",
+ "\u001b[0;32mSubset training done.\u001b[0m\n",
+ "\u001b[0;33mLoading the best weights...\u001b[0m\n",
+ "\u001b[0;33mLoading weights from file cache\\model_SUB_checkpoint-033-0.9487.h5...\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9487\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.3369\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;32mImproved model accuracy from 0.937500 to 0.948718. \u001b[0m\u001b[0;96mSaving model.\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;36mSaving full model H5 format...\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;32mImproved model loss from 0.41660872 to 0.33694357. \u001b[0m\u001b[0;96mSaving model.\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;36mSaving full model H5 format...\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;32m(GPU-MEM)\u001b[0m\u001b[0;36m----[free: 5.13GB, used: 18.87GB, total, 24.00GB]\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m496.62 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m424.14 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m72.47 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0;36m<---------------------------------------|Epoch [6] END|--------------------------------------->\u001b[0m\n",
+ "\u001b[0m\n",
+ "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m7\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m489 (TSEC: 36)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Stage 1]\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+ "\u001b[0;33mPreparing train data...\u001b[0m\n",
+ "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n",
+ "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+ "\u001b[0;32mTraining on subset...\u001b[0m\n",
+ "Epoch 37/42\n",
+ "256/256 [==============================] - 75s 277ms/step - loss: 0.3834 - accuracy: 0.9062 - val_loss: 0.3117 - val_accuracy: 0.9471 - lr: 0.0100\n",
+ "Epoch 38/42\n",
+ "256/256 [==============================] - 70s 271ms/step - loss: 0.3038 - accuracy: 0.9417 - val_loss: 0.3520 - val_accuracy: 0.9423 - lr: 0.0100\n",
+ "Epoch 39/42\n",
+ "256/256 [==============================] - 70s 272ms/step - loss: 0.2586 - accuracy: 0.9529 - val_loss: 0.3165 - val_accuracy: 0.9295 - lr: 0.0100\n",
+ "Epoch 40/42\n",
+ "256/256 [==============================] - 70s 272ms/step - loss: 0.2164 - accuracy: 0.9651 - val_loss: 1.0490 - val_accuracy: 0.7901 - lr: 0.0100\n",
+ "Epoch 41/42\n",
+ "256/256 [==============================] - 70s 271ms/step - loss: 0.1917 - accuracy: 0.9736 - val_loss: 0.3480 - val_accuracy: 0.9135 - lr: 0.0100\n",
+ "Epoch 42/42\n",
+ "256/256 [==============================] - 70s 271ms/step - loss: 0.1689 - accuracy: 0.9761 - val_loss: 0.2705 - val_accuracy: 0.9375 - lr: 0.0100\n",
+ "\u001b[0;32mSubset training done.\u001b[0m\n",
+ "\u001b[0;33mLoading the best weights...\u001b[0m\n",
+ "\u001b[0;33mLoading weights from file cache\\model_SUB_checkpoint-037-0.9471.h5...\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9471\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.3118\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9487179518. Not saving model.\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;32mImproved model loss from 0.33694357 to 0.31175852. \u001b[0m\u001b[0;96mSaving model.\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;36mSaving full model H5 format...\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;32m(GPU-MEM)\u001b[0m\u001b[0;36m----[free: 5.13GB, used: 18.87GB, total, 24.00GB]\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m496.05 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m424.58 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m71.48 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0;36m<---------------------------------------|Epoch [7] END|--------------------------------------->\u001b[0m\n",
+ "\u001b[0m\n",
+ "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m8\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m489 (TSEC: 42)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Stage 1]\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+ "\u001b[0;33mPreparing train data...\u001b[0m\n",
+ "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n",
+ "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+ "\u001b[0;32mTraining on subset...\u001b[0m\n",
+ "Epoch 43/48\n",
+ "256/256 [==============================] - 75s 277ms/step - loss: 0.3506 - accuracy: 0.9248 - val_loss: 0.3114 - val_accuracy: 0.9439 - lr: 0.0100\n",
+ "Epoch 44/48\n",
+ "256/256 [==============================] - 70s 274ms/step - loss: 0.3039 - accuracy: 0.9392 - val_loss: 0.3033 - val_accuracy: 0.9503 - lr: 0.0100\n",
+ "Epoch 45/48\n",
+ "256/256 [==============================] - 70s 271ms/step - loss: 0.2514 - accuracy: 0.9565 - val_loss: 0.3239 - val_accuracy: 0.9167 - lr: 0.0100\n",
+ "Epoch 46/48\n",
+ "256/256 [==============================] - 69s 271ms/step - loss: 0.2171 - accuracy: 0.9617 - val_loss: 0.2641 - val_accuracy: 0.9423 - lr: 0.0100\n",
+ "Epoch 47/48\n",
+ "256/256 [==============================] - 70s 271ms/step - loss: 0.1880 - accuracy: 0.9717 - val_loss: 0.3846 - val_accuracy: 0.9054 - lr: 0.0100\n",
+ "Epoch 48/48\n",
+ "256/256 [==============================] - 70s 271ms/step - loss: 0.1528 - accuracy: 0.9807 - val_loss: 0.2730 - val_accuracy: 0.9375 - lr: 0.0100\n",
+ "\u001b[0;32mSubset training done.\u001b[0m\n",
+ "\u001b[0;33mLoading the best weights...\u001b[0m\n",
+ "\u001b[0;33mLoading weights from file cache\\model_SUB_checkpoint-044-0.9503.h5...\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9503\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.3033\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;32mImproved model accuracy from 0.948718 to 0.950321. \u001b[0m\u001b[0;96mSaving model.\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;36mSaving full model H5 format...\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;32mImproved model loss from 0.31175852 to 0.30326003. \u001b[0m\u001b[0;96mSaving model.\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;36mSaving full model H5 format...\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;32m(GPU-MEM)\u001b[0m\u001b[0;36m----[free: 5.13GB, used: 18.87GB, total, 24.00GB]\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m498.87 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m424.45 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m74.42 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0;36m<---------------------------------------|Epoch [8] END|--------------------------------------->\u001b[0m\n",
+ "\u001b[0m\n",
+ "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m9\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m489 (TSEC: 48)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Stage 1]\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+ "\u001b[0;33mPreparing train data...\u001b[0m\n",
+ "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n",
+ "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+ "\u001b[0;32mTraining on subset...\u001b[0m\n",
+ "Epoch 49/54\n",
+ "256/256 [==============================] - 75s 279ms/step - loss: 0.3169 - accuracy: 0.9275 - val_loss: 0.3260 - val_accuracy: 0.9215 - lr: 0.0100\n",
+ "Epoch 50/54\n",
+ "256/256 [==============================] - 70s 271ms/step - loss: 0.2677 - accuracy: 0.9438 - val_loss: 0.3534 - val_accuracy: 0.9006 - lr: 0.0100\n",
+ "Epoch 51/54\n",
+ "256/256 [==============================] - 71s 276ms/step - loss: 0.2187 - accuracy: 0.9622 - val_loss: 0.3277 - val_accuracy: 0.9295 - lr: 0.0100\n",
+ "Epoch 52/54\n",
+ "256/256 [==============================] - 70s 271ms/step - loss: 0.1777 - accuracy: 0.9702 - val_loss: 0.4656 - val_accuracy: 0.9119 - lr: 0.0100\n",
+ "Epoch 53/54\n",
+ "256/256 [==============================] - 71s 275ms/step - loss: 0.1573 - accuracy: 0.9761 - val_loss: 0.2278 - val_accuracy: 0.9487 - lr: 0.0100\n",
+ "Epoch 54/54\n",
+ "256/256 [==============================] - 70s 272ms/step - loss: 0.1485 - accuracy: 0.9785 - val_loss: 0.5326 - val_accuracy: 0.8317 - lr: 0.0100\n",
+ "\u001b[0;32mSubset training done.\u001b[0m\n",
+ "\u001b[0;33mLoading the best weights...\u001b[0m\n",
+ "\u001b[0;33mLoading weights from file cache\\model_SUB_checkpoint-053-0.9487.h5...\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9487\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.2279\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9503205419. Not saving model.\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;32mImproved model loss from 0.30326003 to 0.22785568. \u001b[0m\u001b[0;96mSaving model.\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;36mSaving full model H5 format...\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;32m(GPU-MEM)\u001b[0m\u001b[0;36m----[free: 5.13GB, used: 18.87GB, total, 24.00GB]\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m497.91 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m427.05 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m70.86 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0;36m<---------------------------------------|Epoch [9] END|--------------------------------------->\u001b[0m\n",
+ "\u001b[0m\n",
+ "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m10\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m489 (TSEC: 54)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Stage 1]\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+ "\u001b[0;33mPreparing train data...\u001b[0m\n",
+ "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n",
+ "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+ "\u001b[0;32mTraining on subset...\u001b[0m\n",
+ "Epoch 55/60\n",
+ "256/256 [==============================] - 75s 277ms/step - loss: 0.2935 - accuracy: 0.9275 - val_loss: 0.2426 - val_accuracy: 0.9535 - lr: 0.0100\n",
+ "Epoch 56/60\n",
+ "256/256 [==============================] - 70s 271ms/step - loss: 0.2239 - accuracy: 0.9478 - val_loss: 0.2476 - val_accuracy: 0.9455 - lr: 0.0100\n",
+ "Epoch 57/60\n",
+ "256/256 [==============================] - 70s 271ms/step - loss: 0.1874 - accuracy: 0.9607 - val_loss: 0.3168 - val_accuracy: 0.9183 - lr: 0.0100\n",
+ "Epoch 58/60\n",
+ "256/256 [==============================] - 70s 271ms/step - loss: 0.1435 - accuracy: 0.9746 - val_loss: 0.2209 - val_accuracy: 0.9407 - lr: 0.0100\n",
+ "Epoch 59/60\n",
+ "256/256 [==============================] - 70s 271ms/step - loss: 0.1247 - accuracy: 0.9778 - val_loss: 0.2745 - val_accuracy: 0.9487 - lr: 0.0100\n",
+ "Epoch 60/60\n",
+ "256/256 [==============================] - 70s 272ms/step - loss: 0.1104 - accuracy: 0.9810 - val_loss: 0.2360 - val_accuracy: 0.9487 - lr: 0.0100\n",
+ "\u001b[0;32mSubset training done.\u001b[0m\n",
+ "\u001b[0;33mLoading the best weights...\u001b[0m\n",
+ "\u001b[0;33mLoading weights from file cache\\model_SUB_checkpoint-055-0.9535.h5...\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9535\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.2426\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;32mImproved model accuracy from 0.950321 to 0.953526. \u001b[0m\u001b[0;96mSaving model.\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;36mSaving full model H5 format...\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.2278556824. Not saving model.\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;32m(GPU-MEM)\u001b[0m\u001b[0;36m----[free: 5.13GB, used: 18.87GB, total, 24.00GB]\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m496.06 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m424.46 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m71.60 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0;36m<---------------------------------------|Epoch [10] END|--------------------------------------->\u001b[0m\n",
+ "\u001b[0m\n",
+ "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m11\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m489 (TSEC: 60)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Stage 1]\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+ "\u001b[0;33mPreparing train data...\u001b[0m\n",
+ "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n",
+ "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+ "\u001b[0;32mTraining on subset...\u001b[0m\n",
+ "Epoch 61/66\n",
+ "256/256 [==============================] - 75s 277ms/step - loss: 0.2740 - accuracy: 0.9314 - val_loss: 0.2571 - val_accuracy: 0.9519 - lr: 0.0100\n",
+ "Epoch 62/66\n",
+ "256/256 [==============================] - 70s 274ms/step - loss: 0.2121 - accuracy: 0.9539 - val_loss: 0.2049 - val_accuracy: 0.9551 - lr: 0.0100\n",
+ "Epoch 63/66\n",
+ "256/256 [==============================] - 70s 275ms/step - loss: 0.1764 - accuracy: 0.9653 - val_loss: 0.1865 - val_accuracy: 0.9615 - lr: 0.0100\n",
+ "Epoch 64/66\n",
+ "256/256 [==============================] - 70s 271ms/step - loss: 0.1430 - accuracy: 0.9741 - val_loss: 0.2043 - val_accuracy: 0.9471 - lr: 0.0100\n",
+ "Epoch 65/66\n",
+ "256/256 [==============================] - 70s 272ms/step - loss: 0.1236 - accuracy: 0.9753 - val_loss: 0.2097 - val_accuracy: 0.9583 - lr: 0.0100\n",
+ "Epoch 66/66\n",
+ "256/256 [==============================] - 70s 272ms/step - loss: 0.1056 - accuracy: 0.9819 - val_loss: 0.2676 - val_accuracy: 0.9535 - lr: 0.0100\n",
+ "\u001b[0;32mSubset training done.\u001b[0m\n",
+ "\u001b[0;33mLoading the best weights...\u001b[0m\n",
+ "\u001b[0;33mLoading weights from file cache\\model_SUB_checkpoint-063-0.9615.h5...\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9615\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.1865\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;32mImproved model accuracy from 0.953526 to 0.961538. \u001b[0m\u001b[0;96mSaving model.\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;36mSaving full model H5 format...\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;32mImproved model loss from 0.22785568 to 0.18650842. \u001b[0m\u001b[0;96mSaving model.\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;36mSaving full model H5 format...\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;32m(GPU-MEM)\u001b[0m\u001b[0;36m----[free: 5.13GB, used: 18.87GB, total, 24.00GB]\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m501.19 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m426.43 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m74.76 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0;36m<---------------------------------------|Epoch [11] END|--------------------------------------->\u001b[0m\n",
+ "\u001b[0m\n",
+ "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m12\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m489 (TSEC: 66)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Stage 1]\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+ "\u001b[0;33mPreparing train data...\u001b[0m\n",
+ "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n",
+ "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+ "\u001b[0;32mTraining on subset...\u001b[0m\n",
+ "Epoch 67/72\n",
+ "256/256 [==============================] - 76s 279ms/step - loss: 0.2568 - accuracy: 0.9346 - val_loss: 0.2103 - val_accuracy: 0.9519 - lr: 0.0100\n",
+ "Epoch 68/72\n",
+ "256/256 [==============================] - 71s 277ms/step - loss: 0.2037 - accuracy: 0.9536 - val_loss: 0.2022 - val_accuracy: 0.9535 - lr: 0.0100\n",
+ "Epoch 69/72\n",
+ "256/256 [==============================] - 71s 275ms/step - loss: 0.1751 - accuracy: 0.9573 - val_loss: 0.1843 - val_accuracy: 0.9551 - lr: 0.0100\n",
+ "Epoch 70/72\n",
+ "256/256 [==============================] - 71s 275ms/step - loss: 0.1368 - accuracy: 0.9707 - val_loss: 0.1812 - val_accuracy: 0.9599 - lr: 0.0100\n",
+ "Epoch 71/72\n",
+ "256/256 [==============================] - 70s 272ms/step - loss: 0.1159 - accuracy: 0.9780 - val_loss: 0.2526 - val_accuracy: 0.9407 - lr: 0.0100\n",
+ "Epoch 72/72\n",
+ "256/256 [==============================] - 70s 274ms/step - loss: 0.1069 - accuracy: 0.9795 - val_loss: 0.2513 - val_accuracy: 0.9231 - lr: 0.0100\n",
+ "\u001b[0;32mSubset training done.\u001b[0m\n",
+ "\u001b[0;33mLoading the best weights...\u001b[0m\n",
+ "\u001b[0;33mLoading weights from file cache\\model_SUB_checkpoint-070-0.9599.h5...\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9599\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.1812\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9615384340. Not saving model.\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;32mImproved model loss from 0.18650842 to 0.18118645. \u001b[0m\u001b[0;96mSaving model.\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;36mSaving full model H5 format...\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;32m(GPU-MEM)\u001b[0m\u001b[0;36m----[free: 5.13GB, used: 18.87GB, total, 24.00GB]\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m503.89 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m429.46 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m74.43 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0;36m<---------------------------------------|Epoch [12] END|--------------------------------------->\u001b[0m\n",
+ "\u001b[0m\n",
+ "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m13\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m489 (TSEC: 72)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Stage 1]\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+ "\u001b[0;33mPreparing train data...\u001b[0m\n",
+ "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n",
+ "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+ "\u001b[0;32mTraining on subset...\u001b[0m\n",
+ "Epoch 73/78\n",
+ "256/256 [==============================] - 75s 279ms/step - loss: 0.2414 - accuracy: 0.9294 - val_loss: 0.1788 - val_accuracy: 0.9599 - lr: 0.0100\n",
+ "Epoch 74/78\n",
+ "256/256 [==============================] - 70s 273ms/step - loss: 0.1754 - accuracy: 0.9548 - val_loss: 0.2104 - val_accuracy: 0.9439 - lr: 0.0100\n",
+ "Epoch 75/78\n",
+ "256/256 [==============================] - 70s 273ms/step - loss: 0.1465 - accuracy: 0.9670 - val_loss: 0.1927 - val_accuracy: 0.9471 - lr: 0.0100\n",
+ "Epoch 76/78\n",
+ "256/256 [==============================] - 70s 273ms/step - loss: 0.1156 - accuracy: 0.9741 - val_loss: 0.1797 - val_accuracy: 0.9535 - lr: 0.0100\n",
+ "Epoch 77/78\n",
+ "256/256 [==============================] - 70s 273ms/step - loss: 0.0964 - accuracy: 0.9812 - val_loss: 0.3014 - val_accuracy: 0.9423 - lr: 0.0100\n",
+ "Epoch 78/78\n",
+ "256/256 [==============================] - 70s 273ms/step - loss: 0.0891 - accuracy: 0.9807 - val_loss: 0.1904 - val_accuracy: 0.9423 - lr: 0.0100\n",
+ "\u001b[0;32mSubset training done.\u001b[0m\n",
+ "\u001b[0;33mLoading the best weights...\u001b[0m\n",
+ "\u001b[0;33mLoading weights from file cache\\model_SUB_checkpoint-073-0.9599.h5...\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9599\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.1788\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9615384340. Not saving model.\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;32mImproved model loss from 0.18118645 to 0.17884569. \u001b[0m\u001b[0;96mSaving model.\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;36mSaving full model H5 format...\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;32m(GPU-MEM)\u001b[0m\u001b[0;36m----[free: 5.14GB, used: 18.86GB, total, 24.00GB]\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m502.74 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m426.94 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m75.80 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0;36m<---------------------------------------|Epoch [13] END|--------------------------------------->\u001b[0m\n",
+ "\u001b[0m\n",
+ "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m14\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m489 (TSEC: 78)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Stage 1]\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+ "\u001b[0;33mPreparing train data...\u001b[0m\n",
+ "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n",
+ "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+ "\u001b[0;32mTraining on subset...\u001b[0m\n",
+ "Epoch 79/84\n",
+ "256/256 [==============================] - 75s 277ms/step - loss: 0.2316 - accuracy: 0.9380 - val_loss: 0.2060 - val_accuracy: 0.9407 - lr: 0.0100\n",
+ "Epoch 80/84\n",
+ "256/256 [==============================] - 71s 275ms/step - loss: 0.1715 - accuracy: 0.9580 - val_loss: 0.1804 - val_accuracy: 0.9503 - lr: 0.0100\n",
+ "Epoch 81/84\n",
+ "256/256 [==============================] - 70s 271ms/step - loss: 0.1476 - accuracy: 0.9646 - val_loss: 0.3042 - val_accuracy: 0.9087 - lr: 0.0100\n",
+ "Epoch 82/84\n",
+ "256/256 [==============================] - 70s 272ms/step - loss: 0.1162 - accuracy: 0.9746 - val_loss: 0.2478 - val_accuracy: 0.9279 - lr: 0.0100\n",
+ "Epoch 83/84\n",
+ "256/256 [==============================] - 70s 274ms/step - loss: 0.0961 - accuracy: 0.9824 - val_loss: 0.1980 - val_accuracy: 0.9599 - lr: 0.0100\n",
+ "Epoch 84/84\n",
+ "256/256 [==============================] - 70s 272ms/step - loss: 0.0851 - accuracy: 0.9861 - val_loss: 0.2208 - val_accuracy: 0.9535 - lr: 0.0100\n",
+ "\u001b[0;32mSubset training done.\u001b[0m\n",
+ "\u001b[0;33mLoading the best weights...\u001b[0m\n",
+ "\u001b[0;33mLoading weights from file cache\\model_SUB_checkpoint-083-0.9599.h5...\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9599\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.1980\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9615384340. Not saving model.\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1788456887. Not saving model.\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;32m(GPU-MEM)\u001b[0m\u001b[0;36m----[free: 5.18GB, used: 18.82GB, total, 24.00GB]\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m500.63 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m426.28 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m74.35 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0;36m<---------------------------------------|Epoch [14] END|--------------------------------------->\u001b[0m\n",
+ "\u001b[0m\n",
+ "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m15\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m489 (TSEC: 84)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Stage 1]\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+ "\u001b[0;33mPreparing train data...\u001b[0m\n",
+ "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n",
+ "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+ "\u001b[0;32mTraining on subset...\u001b[0m\n",
+ "Epoch 85/90\n",
+ "256/256 [==============================] - 75s 277ms/step - loss: 0.2205 - accuracy: 0.9375 - val_loss: 0.1953 - val_accuracy: 0.9487 - lr: 0.0100\n",
+ "Epoch 86/90\n",
+ "256/256 [==============================] - 69s 271ms/step - loss: 0.1596 - accuracy: 0.9578 - val_loss: 0.2314 - val_accuracy: 0.9471 - lr: 0.0100\n",
+ "Epoch 87/90\n",
+ "256/256 [==============================] - 70s 271ms/step - loss: 0.1303 - accuracy: 0.9661 - val_loss: 0.2036 - val_accuracy: 0.9471 - lr: 0.0100\n",
+ "Epoch 88/90\n",
+ "256/256 [==============================] - 70s 271ms/step - loss: 0.0946 - accuracy: 0.9812 - val_loss: 0.2871 - val_accuracy: 0.9311 - lr: 0.0100\n",
+ "Epoch 89/90\n",
+ "256/256 [==============================] - 69s 270ms/step - loss: 0.0762 - accuracy: 0.9836 - val_loss: 0.5652 - val_accuracy: 0.8686 - lr: 0.0100\n",
+ "Epoch 90/90\n",
+ "256/256 [==============================] - 70s 270ms/step - loss: 0.0751 - accuracy: 0.9851 - val_loss: 0.4255 - val_accuracy: 0.9006 - lr: 0.0100\n",
+ "\u001b[0;32mSubset training done.\u001b[0m\n",
+ "\u001b[0;33mLoading the best weights...\u001b[0m\n",
+ "\u001b[0;33mLoading weights from file cache\\model_SUB_checkpoint-085-0.9487.h5...\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9487\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.1953\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9615384340. Not saving model.\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1788456887. Not saving model.\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;32m(GPU-MEM)\u001b[0m\u001b[0;36m----[free: 5.18GB, used: 18.82GB, total, 24.00GB]\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m499.26 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m423.69 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m75.57 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0;36m<---------------------------------------|Epoch [15] END|--------------------------------------->\u001b[0m\n",
+ "\u001b[0m\n",
+ "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m16\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m489 (TSEC: 90)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Stage 1]\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+ "\u001b[0;33mPreparing train data...\u001b[0m\n",
+ "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n",
+ "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+ "\u001b[0;32mTraining on subset...\u001b[0m\n",
+ "Epoch 91/96\n",
+ "256/256 [==============================] - 76s 280ms/step - loss: 0.2224 - accuracy: 0.9333 - val_loss: 0.2097 - val_accuracy: 0.9471 - lr: 0.0100\n",
+ "Epoch 92/96\n",
+ "256/256 [==============================] - 71s 276ms/step - loss: 0.1542 - accuracy: 0.9565 - val_loss: 0.1671 - val_accuracy: 0.9599 - lr: 0.0100\n",
+ "Epoch 93/96\n",
+ "256/256 [==============================] - 70s 273ms/step - loss: 0.1241 - accuracy: 0.9712 - val_loss: 0.2496 - val_accuracy: 0.9199 - lr: 0.0100\n",
+ "Epoch 94/96\n",
+ "256/256 [==============================] - 70s 273ms/step - loss: 0.0903 - accuracy: 0.9802 - val_loss: 0.1669 - val_accuracy: 0.9583 - lr: 0.0100\n",
+ "Epoch 95/96\n",
+ "256/256 [==============================] - 70s 274ms/step - loss: 0.0800 - accuracy: 0.9841 - val_loss: 0.1707 - val_accuracy: 0.9599 - lr: 0.0100\n",
+ "Epoch 96/96\n",
+ "256/256 [==============================] - 70s 273ms/step - loss: 0.0814 - accuracy: 0.9822 - val_loss: 0.2124 - val_accuracy: 0.9455 - lr: 0.0100\n",
+ "\u001b[0;32mSubset training done.\u001b[0m\n",
+ "\u001b[0;33mLoading the best weights...\u001b[0m\n",
+ "\u001b[0;33mLoading weights from file cache\\model_SUB_checkpoint-092-0.9599.h5...\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9599\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.1671\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9615384340. Not saving model.\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;32mImproved model loss from 0.17884569 to 0.16713950. \u001b[0m\u001b[0;96mSaving model.\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;36mSaving full model H5 format...\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;32m(GPU-MEM)\u001b[0m\u001b[0;36m----[free: 5.18GB, used: 18.82GB, total, 24.00GB]\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m506.35 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m428.29 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m78.06 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0;36m<---------------------------------------|Epoch [16] END|--------------------------------------->\u001b[0m\n",
+ "\u001b[0m\n",
+ "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m17\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m489 (TSEC: 96)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Stage 1]\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+ "\u001b[0;33mPreparing train data...\u001b[0m\n",
+ "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n",
+ "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+ "\u001b[0;32mTraining on subset...\u001b[0m\n",
+ "Epoch 97/102\n",
+ "256/256 [==============================] - 76s 281ms/step - loss: 0.2000 - accuracy: 0.9419 - val_loss: 0.1803 - val_accuracy: 0.9455 - lr: 0.0100\n",
+ "Epoch 98/102\n",
+ "256/256 [==============================] - 70s 272ms/step - loss: 0.1603 - accuracy: 0.9570 - val_loss: 0.1950 - val_accuracy: 0.9391 - lr: 0.0100\n",
+ "Epoch 99/102\n",
+ "256/256 [==============================] - 71s 278ms/step - loss: 0.1286 - accuracy: 0.9688 - val_loss: 0.1884 - val_accuracy: 0.9471 - lr: 0.0100\n",
+ "Epoch 100/102\n",
+ "256/256 [==============================] - 70s 272ms/step - loss: 0.1045 - accuracy: 0.9773 - val_loss: 0.2358 - val_accuracy: 0.9311 - lr: 0.0100\n",
+ "Epoch 101/102\n",
+ "256/256 [==============================] - 70s 274ms/step - loss: 0.0909 - accuracy: 0.9807 - val_loss: 0.2543 - val_accuracy: 0.9359 - lr: 0.0100\n",
+ "Epoch 102/102\n",
+ "256/256 [==============================] - 70s 273ms/step - loss: 0.0815 - accuracy: 0.9836 - val_loss: 0.2290 - val_accuracy: 0.9247 - lr: 0.0100\n",
+ "\u001b[0;32mSubset training done.\u001b[0m\n",
+ "\u001b[0;33mLoading the best weights...\u001b[0m\n",
+ "\u001b[0;33mLoading weights from file cache\\model_SUB_checkpoint-099-0.9471.h5...\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9471\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.1884\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9615384340. Not saving model.\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1671395004. Not saving model.\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;32m(GPU-MEM)\u001b[0m\u001b[0;36m----[free: 5.18GB, used: 18.82GB, total, 24.00GB]\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m506.73 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m428.74 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m78.00 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0;36m<---------------------------------------|Epoch [17] END|--------------------------------------->\u001b[0m\n",
+ "\u001b[0m\n",
+ "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m18\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m489 (TSEC: 102)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Stage 1]\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+ "\u001b[0;33mPreparing train data...\u001b[0m\n",
+ "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n",
+ "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+ "\u001b[0;32mTraining on subset...\u001b[0m\n",
+ "Epoch 103/108\n",
+ "256/256 [==============================] - 76s 280ms/step - loss: 0.2191 - accuracy: 0.9355 - val_loss: 0.2022 - val_accuracy: 0.9439 - lr: 0.0100\n",
+ "Epoch 104/108\n",
+ "256/256 [==============================] - 71s 276ms/step - loss: 0.1620 - accuracy: 0.9548 - val_loss: 0.1848 - val_accuracy: 0.9471 - lr: 0.0100\n",
+ "Epoch 105/108\n",
+ "256/256 [==============================] - 70s 274ms/step - loss: 0.1338 - accuracy: 0.9631 - val_loss: 0.2283 - val_accuracy: 0.9279 - lr: 0.0100\n",
+ "Epoch 106/108\n",
+ "256/256 [==============================] - 71s 276ms/step - loss: 0.1021 - accuracy: 0.9741 - val_loss: 0.1688 - val_accuracy: 0.9551 - lr: 0.0100\n",
+ "Epoch 107/108\n",
+ "256/256 [==============================] - 70s 275ms/step - loss: 0.0819 - accuracy: 0.9819 - val_loss: 0.1969 - val_accuracy: 0.9455 - lr: 0.0100\n",
+ "Epoch 108/108\n",
+ "256/256 [==============================] - 70s 273ms/step - loss: 0.0776 - accuracy: 0.9841 - val_loss: 0.2636 - val_accuracy: 0.8990 - lr: 0.0100\n",
+ "\u001b[0;32mSubset training done.\u001b[0m\n",
+ "\u001b[0;33mLoading the best weights...\u001b[0m\n",
+ "\u001b[0;33mLoading weights from file cache\\model_SUB_checkpoint-106-0.9551.h5...\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9535\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.1688\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9615384340. Not saving model.\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1671395004. Not saving model.\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;32m(GPU-MEM)\u001b[0m\u001b[0;36m----[free: 5.18GB, used: 18.82GB, total, 24.00GB]\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m508.41 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m429.53 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m78.89 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0;36m<---------------------------------------|Epoch [18] END|--------------------------------------->\u001b[0m\n",
+ "\u001b[0m\n",
+ "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m19\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m489 (TSEC: 108)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Stage 1]\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+ "\u001b[0;33mPreparing train data...\u001b[0m\n",
+ "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n",
+ "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+ "\u001b[0;32mTraining on subset...\u001b[0m\n",
+ "Epoch 109/114\n",
+ "256/256 [==============================] - 76s 281ms/step - loss: 0.1952 - accuracy: 0.9424 - val_loss: 0.1801 - val_accuracy: 0.9487 - lr: 0.0100\n",
+ "Epoch 110/114\n",
+ "256/256 [==============================] - 71s 278ms/step - loss: 0.1472 - accuracy: 0.9558 - val_loss: 0.1730 - val_accuracy: 0.9503 - lr: 0.0100\n",
+ "Epoch 111/114\n",
+ "256/256 [==============================] - 70s 274ms/step - loss: 0.1048 - accuracy: 0.9722 - val_loss: 0.4313 - val_accuracy: 0.8670 - lr: 0.0100\n",
+ "Epoch 112/114\n",
+ "256/256 [==============================] - 70s 274ms/step - loss: 0.0790 - accuracy: 0.9790 - val_loss: 0.2087 - val_accuracy: 0.9391 - lr: 0.0100\n",
+ "Epoch 113/114\n",
+ "256/256 [==============================] - 70s 274ms/step - loss: 0.0691 - accuracy: 0.9861 - val_loss: 0.1956 - val_accuracy: 0.9471 - lr: 0.0100\n",
+ "Epoch 114/114\n",
+ "256/256 [==============================] - 70s 274ms/step - loss: 0.0683 - accuracy: 0.9849 - val_loss: 0.1962 - val_accuracy: 0.9423 - lr: 0.0100\n",
+ "\u001b[0;32mSubset training done.\u001b[0m\n",
+ "\u001b[0;33mLoading the best weights...\u001b[0m\n",
+ "\u001b[0;33mLoading weights from file cache\\model_SUB_checkpoint-110-0.9503.h5...\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9503\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.1730\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9615384340. Not saving model.\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1671395004. Not saving model.\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;32m(GPU-MEM)\u001b[0m\u001b[0;36m----[free: 5.18GB, used: 18.82GB, total, 24.00GB]\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m510.54 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m430.22 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m80.32 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0;36m<---------------------------------------|Epoch [19] END|--------------------------------------->\u001b[0m\n",
+ "\u001b[0m\n",
+ "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m20\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m489 (TSEC: 114)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Stage 1]\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+ "\u001b[0;33mPreparing train data...\u001b[0m\n",
+ "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n",
+ "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+ "\u001b[0;32mTraining on subset...\u001b[0m\n",
+ "Epoch 115/120\n",
+ "256/256 [==============================] - 76s 279ms/step - loss: 0.1874 - accuracy: 0.9419 - val_loss: 0.1975 - val_accuracy: 0.9375 - lr: 0.0100\n",
+ "Epoch 116/120\n",
+ "256/256 [==============================] - 70s 274ms/step - loss: 0.1582 - accuracy: 0.9531 - val_loss: 0.2660 - val_accuracy: 0.9263 - lr: 0.0100\n",
+ "Epoch 117/120\n",
+ "256/256 [==============================] - 70s 273ms/step - loss: 0.1196 - accuracy: 0.9690 - val_loss: 0.2343 - val_accuracy: 0.9327 - lr: 0.0100\n",
+ "Epoch 118/120\n",
+ "256/256 [==============================] - 71s 276ms/step - loss: 0.0896 - accuracy: 0.9792 - val_loss: 0.1768 - val_accuracy: 0.9471 - lr: 0.0100\n",
+ "Epoch 119/120\n",
+ "256/256 [==============================] - 71s 276ms/step - loss: 0.0643 - accuracy: 0.9858 - val_loss: 0.1641 - val_accuracy: 0.9615 - lr: 0.0100\n",
+ "Epoch 120/120\n",
+ "256/256 [==============================] - 70s 273ms/step - loss: 0.0559 - accuracy: 0.9863 - val_loss: 0.1774 - val_accuracy: 0.9471 - lr: 0.0100\n",
+ "\u001b[0;32mSubset training done.\u001b[0m\n",
+ "\u001b[0;33mLoading the best weights...\u001b[0m\n",
+ "\u001b[0;33mLoading weights from file cache\\model_SUB_checkpoint-119-0.9615.h5...\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9615\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.1641\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9615384340. Not saving model.\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;32mImproved model loss from 0.16713950 to 0.16408551. \u001b[0m\u001b[0;96mSaving model.\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;36mSaving full model H5 format...\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;32m(GPU-MEM)\u001b[0m\u001b[0;36m----[free: 5.19GB, used: 18.81GB, total, 24.00GB]\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m513.32 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m429.12 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m84.20 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0;36m<---------------------------------------|Epoch [20] END|--------------------------------------->\u001b[0m\n",
+ "\u001b[0m\n",
+ "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m21\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m489 (TSEC: 120)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Stage 1]\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+ "\u001b[0;33mPreparing train data...\u001b[0m\n",
+ "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n",
+ "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+ "\u001b[0;32mTraining on subset...\u001b[0m\n",
+ "Epoch 121/126\n",
+ "256/256 [==============================] - 76s 280ms/step - loss: 0.1836 - accuracy: 0.9478 - val_loss: 0.1733 - val_accuracy: 0.9439 - lr: 0.0100\n",
+ "Epoch 122/126\n",
+ "256/256 [==============================] - 70s 274ms/step - loss: 0.1378 - accuracy: 0.9592 - val_loss: 0.1685 - val_accuracy: 0.9439 - lr: 0.0100\n",
+ "Epoch 123/126\n",
+ "256/256 [==============================] - ETA: 0s - loss: 0.0880 - accuracy: 0.9766\n",
+ "Epoch 123: ReduceLROnPlateau reducing learning rate to 0.009819999780505895.\n",
+ "256/256 [==============================] - 70s 274ms/step - loss: 0.0880 - accuracy: 0.9766 - val_loss: 0.3659 - val_accuracy: 0.9022 - lr: 0.0100\n",
+ "Epoch 124/126\n",
+ "256/256 [==============================] - 70s 274ms/step - loss: 0.0708 - accuracy: 0.9817 - val_loss: 0.1868 - val_accuracy: 0.9423 - lr: 0.0098\n",
+ "Epoch 125/126\n",
+ "256/256 [==============================] - 70s 274ms/step - loss: 0.0506 - accuracy: 0.9890 - val_loss: 0.2319 - val_accuracy: 0.9407 - lr: 0.0098\n",
+ "Epoch 126/126\n",
+ "256/256 [==============================] - 70s 274ms/step - loss: 0.0529 - accuracy: 0.9868 - val_loss: 0.2361 - val_accuracy: 0.9391 - lr: 0.0098\n",
+ "\u001b[0;32mSubset training done.\u001b[0m\n",
+ "\u001b[0;33mLoading the best weights...\u001b[0m\n",
+ "\u001b[0;33mLoading weights from file cache\\model_SUB_checkpoint-121-0.9439.h5...\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9439\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.1734\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9615384340. Not saving model.\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1640855074. Not saving model.\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;32m(GPU-MEM)\u001b[0m\u001b[0;36m----[free: 5.17GB, used: 18.83GB, total, 24.00GB]\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m511.89 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m428.85 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m83.04 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0;36m<---------------------------------------|Epoch [21] END|--------------------------------------->\u001b[0m\n",
+ "\u001b[0m\n",
+ "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m22\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m489 (TSEC: 126)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Stage 1]\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+ "\u001b[0;33mPreparing train data...\u001b[0m\n",
+ "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n",
+ "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+ "\u001b[0;32mTraining on subset...\u001b[0m\n",
+ "Epoch 127/132\n",
+ "256/256 [==============================] - 76s 280ms/step - loss: 0.1919 - accuracy: 0.9417 - val_loss: 0.2013 - val_accuracy: 0.9487 - lr: 0.0098\n",
+ "Epoch 128/132\n",
+ "256/256 [==============================] - 71s 278ms/step - loss: 0.1440 - accuracy: 0.9563 - val_loss: 0.1692 - val_accuracy: 0.9503 - lr: 0.0098\n",
+ "Epoch 129/132\n",
+ "256/256 [==============================] - 70s 273ms/step - loss: 0.1061 - accuracy: 0.9709 - val_loss: 0.1920 - val_accuracy: 0.9391 - lr: 0.0098\n",
+ "Epoch 130/132\n",
+ "256/256 [==============================] - 70s 274ms/step - loss: 0.0866 - accuracy: 0.9783 - val_loss: 0.2752 - val_accuracy: 0.9167 - lr: 0.0098\n",
+ "Epoch 131/132\n",
+ "256/256 [==============================] - 70s 273ms/step - loss: 0.0707 - accuracy: 0.9834 - val_loss: 0.4520 - val_accuracy: 0.9038 - lr: 0.0098\n",
+ "Epoch 132/132\n",
+ "256/256 [==============================] - 70s 274ms/step - loss: 0.0596 - accuracy: 0.9868 - val_loss: 0.2635 - val_accuracy: 0.9359 - lr: 0.0098\n",
+ "\u001b[0;32mSubset training done.\u001b[0m\n",
+ "\u001b[0;33mLoading the best weights...\u001b[0m\n",
+ "\u001b[0;33mLoading weights from file cache\\model_SUB_checkpoint-128-0.9503.h5...\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9503\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.1692\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9615384340. Not saving model.\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1640855074. Not saving model.\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;32m(GPU-MEM)\u001b[0m\u001b[0;36m----[free: 5.18GB, used: 18.82GB, total, 24.00GB]\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m513.73 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m429.30 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m84.43 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0;36m<---------------------------------------|Epoch [22] END|--------------------------------------->\u001b[0m\n",
+ "\u001b[0m\n",
+ "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m23\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m489 (TSEC: 132)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Stage 1]\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+ "\u001b[0;33mPreparing train data...\u001b[0m\n",
+ "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n",
+ "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+ "\u001b[0;32mTraining on subset...\u001b[0m\n",
+ "Epoch 133/138\n",
+ "256/256 [==============================] - 76s 281ms/step - loss: 0.1859 - accuracy: 0.9434 - val_loss: 0.1613 - val_accuracy: 0.9519 - lr: 0.0098\n",
+ "Epoch 134/138\n",
+ "256/256 [==============================] - 70s 275ms/step - loss: 0.1337 - accuracy: 0.9622 - val_loss: 0.1738 - val_accuracy: 0.9487 - lr: 0.0098\n",
+ "Epoch 135/138\n",
+ "256/256 [==============================] - 71s 274ms/step - loss: 0.1014 - accuracy: 0.9734 - val_loss: 0.1834 - val_accuracy: 0.9375 - lr: 0.0098\n",
+ "Epoch 136/138\n",
+ "256/256 [==============================] - 71s 275ms/step - loss: 0.0863 - accuracy: 0.9761 - val_loss: 0.2131 - val_accuracy: 0.9327 - lr: 0.0098\n",
+ "Epoch 137/138\n",
+ "256/256 [==============================] - 71s 274ms/step - loss: 0.0600 - accuracy: 0.9868 - val_loss: 0.1950 - val_accuracy: 0.9391 - lr: 0.0098\n",
+ "Epoch 138/138\n",
+ "256/256 [==============================] - 71s 275ms/step - loss: 0.0604 - accuracy: 0.9858 - val_loss: 0.2959 - val_accuracy: 0.9279 - lr: 0.0098\n",
+ "\u001b[0;32mSubset training done.\u001b[0m\n",
+ "\u001b[0;33mLoading the best weights...\u001b[0m\n",
+ "\u001b[0;33mLoading weights from file cache\\model_SUB_checkpoint-133-0.9519.h5...\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9519\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.1613\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9615384340. Not saving model.\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;32mImproved model loss from 0.16408551 to 0.16128203. \u001b[0m\u001b[0;96mSaving model.\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;36mSaving full model H5 format...\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;32m(GPU-MEM)\u001b[0m\u001b[0;36m----[free: 5.18GB, used: 18.82GB, total, 24.00GB]\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m516.63 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m430.13 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m86.50 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0;36m<---------------------------------------|Epoch [23] END|--------------------------------------->\u001b[0m\n",
+ "\u001b[0m\n",
+ "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m24\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m489 (TSEC: 138)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Stage 1]\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+ "\u001b[0;33mPreparing train data...\u001b[0m\n",
+ "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n",
+ "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+ "\u001b[0;32mTraining on subset...\u001b[0m\n",
+ "Epoch 139/144\n",
+ "256/256 [==============================] - 76s 282ms/step - loss: 0.1792 - accuracy: 0.9421 - val_loss: 0.2648 - val_accuracy: 0.9167 - lr: 0.0098\n",
+ "Epoch 140/144\n",
+ "256/256 [==============================] - 71s 279ms/step - loss: 0.1274 - accuracy: 0.9597 - val_loss: 0.1855 - val_accuracy: 0.9295 - lr: 0.0098\n",
+ "Epoch 141/144\n",
+ "256/256 [==============================] - 72s 279ms/step - loss: 0.0982 - accuracy: 0.9697 - val_loss: 0.1819 - val_accuracy: 0.9439 - lr: 0.0098\n",
+ "Epoch 142/144\n",
+ "256/256 [==============================] - 71s 276ms/step - loss: 0.0807 - accuracy: 0.9780 - val_loss: 0.2425 - val_accuracy: 0.9423 - lr: 0.0098\n",
+ "Epoch 143/144\n",
+ "256/256 [==============================] - 71s 275ms/step - loss: 0.0618 - accuracy: 0.9851 - val_loss: 0.3697 - val_accuracy: 0.8878 - lr: 0.0098\n",
+ "Epoch 144/144\n",
+ "256/256 [==============================] - 71s 276ms/step - loss: 0.0544 - accuracy: 0.9866 - val_loss: 0.2781 - val_accuracy: 0.9359 - lr: 0.0098\n",
+ "\u001b[0;32mSubset training done.\u001b[0m\n",
+ "\u001b[0;33mLoading the best weights...\u001b[0m\n",
+ "\u001b[0;33mLoading weights from file cache\\model_SUB_checkpoint-141-0.9439.h5...\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9439\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.1820\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9615384340. Not saving model.\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1612820327. Not saving model.\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;32m(GPU-MEM)\u001b[0m\u001b[0;36m----[free: 5.19GB, used: 18.81GB, total, 24.00GB]\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m518.02 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m432.96 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m85.06 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0;36m<---------------------------------------|Epoch [24] END|--------------------------------------->\u001b[0m\n",
+ "\u001b[0m\n",
+ "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m25\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m489 (TSEC: 144)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Stage 1]\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+ "\u001b[0;33mPreparing train data...\u001b[0m\n",
+ "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n",
+ "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+ "\u001b[0;32mTraining on subset...\u001b[0m\n",
+ "Epoch 145/150\n",
+ "256/256 [==============================] - 76s 280ms/step - loss: 0.1798 - accuracy: 0.9436 - val_loss: 0.1756 - val_accuracy: 0.9407 - lr: 0.0098\n",
+ "Epoch 146/150\n",
+ "256/256 [==============================] - 71s 277ms/step - loss: 0.1337 - accuracy: 0.9600 - val_loss: 0.1838 - val_accuracy: 0.9615 - lr: 0.0098\n",
+ "Epoch 147/150\n",
+ "256/256 [==============================] - 70s 274ms/step - loss: 0.0957 - accuracy: 0.9753 - val_loss: 0.1846 - val_accuracy: 0.9567 - lr: 0.0098\n",
+ "Epoch 148/150\n",
+ "256/256 [==============================] - 71s 274ms/step - loss: 0.0823 - accuracy: 0.9788 - val_loss: 0.2423 - val_accuracy: 0.9439 - lr: 0.0098\n",
+ "Epoch 149/150\n",
+ "256/256 [==============================] - 70s 274ms/step - loss: 0.0595 - accuracy: 0.9846 - val_loss: 0.2196 - val_accuracy: 0.9503 - lr: 0.0098\n",
+ "Epoch 150/150\n",
+ "256/256 [==============================] - 71s 275ms/step - loss: 0.0646 - accuracy: 0.9846 - val_loss: 0.1869 - val_accuracy: 0.9391 - lr: 0.0098\n",
+ "\u001b[0;32mSubset training done.\u001b[0m\n",
+ "\u001b[0;33mLoading the best weights...\u001b[0m\n",
+ "\u001b[0;33mLoading weights from file cache\\model_SUB_checkpoint-146-0.9615.h5...\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9615\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.1839\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9615384340. Not saving model.\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1612820327. Not saving model.\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;32m(GPU-MEM)\u001b[0m\u001b[0;36m----[free: 5.19GB, used: 18.81GB, total, 24.00GB]\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m516.77 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m430.03 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m86.75 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0;36m<---------------------------------------|Epoch [25] END|--------------------------------------->\u001b[0m\n",
+ "\u001b[0m\n",
+ "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m26\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m489 (TSEC: 150)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Stage 2]\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+ "\u001b[0;33mPreparing train data...\u001b[0m\n",
+ "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n",
+ "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+ "\u001b[0;32mTraining on subset...\u001b[0m\n",
+ "Epoch 151/156\n",
+ "256/256 [==============================] - 76s 282ms/step - loss: 0.1690 - accuracy: 0.9475 - val_loss: 0.1507 - val_accuracy: 0.9567 - lr: 0.0098\n",
+ "Epoch 152/156\n",
+ "256/256 [==============================] - 70s 274ms/step - loss: 0.1301 - accuracy: 0.9624 - val_loss: 0.2251 - val_accuracy: 0.9038 - lr: 0.0098\n",
+ "Epoch 153/156\n",
+ "256/256 [==============================] - 71s 276ms/step - loss: 0.1059 - accuracy: 0.9702 - val_loss: 0.2136 - val_accuracy: 0.9183 - lr: 0.0098\n",
+ "Epoch 154/156\n",
+ "256/256 [==============================] - 71s 275ms/step - loss: 0.0606 - accuracy: 0.9868 - val_loss: 0.3920 - val_accuracy: 0.8942 - lr: 0.0098\n",
+ "Epoch 155/156\n",
+ "256/256 [==============================] - 71s 276ms/step - loss: 0.0563 - accuracy: 0.9861 - val_loss: 0.1865 - val_accuracy: 0.9423 - lr: 0.0098\n",
+ "Epoch 156/156\n",
+ "256/256 [==============================] - 70s 274ms/step - loss: 0.0489 - accuracy: 0.9871 - val_loss: 0.2106 - val_accuracy: 0.9487 - lr: 0.0098\n",
+ "\u001b[0;32mSubset training done.\u001b[0m\n",
+ "\u001b[0;33mLoading the best weights...\u001b[0m\n",
+ "\u001b[0;33mLoading weights from file cache\\model_SUB_checkpoint-151-0.9567.h5...\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9567\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.1507\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9615384340. Not saving model.\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;32mImproved model loss from 0.16128203 to 0.15071724. \u001b[0m\u001b[0;96mSaving model.\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;36mSaving full model H5 format...\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;32m(GPU-MEM)\u001b[0m\u001b[0;36m----[free: 5.19GB, used: 18.81GB, total, 24.00GB]\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m524.27 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m430.46 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m93.81 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0;36m<---------------------------------------|Epoch [26] END|--------------------------------------->\u001b[0m\n",
+ "\u001b[0m\n",
+ "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m27\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m489 (TSEC: 156)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Stage 2]\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+ "\u001b[0;33mPreparing train data...\u001b[0m\n",
+ "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n",
+ "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+ "\u001b[0;32mTraining on subset...\u001b[0m\n",
+ "Epoch 157/162\n",
+ "256/256 [==============================] - 76s 283ms/step - loss: 0.1683 - accuracy: 0.9421 - val_loss: 0.1730 - val_accuracy: 0.9423 - lr: 0.0098\n",
+ "Epoch 158/162\n",
+ "256/256 [==============================] - 71s 277ms/step - loss: 0.1310 - accuracy: 0.9622 - val_loss: 0.2105 - val_accuracy: 0.9439 - lr: 0.0098\n",
+ "Epoch 159/162\n",
+ "256/256 [==============================] - 70s 274ms/step - loss: 0.0979 - accuracy: 0.9705 - val_loss: 0.2328 - val_accuracy: 0.9183 - lr: 0.0098\n",
+ "Epoch 160/162\n",
+ "256/256 [==============================] - 70s 274ms/step - loss: 0.0757 - accuracy: 0.9795 - val_loss: 0.2100 - val_accuracy: 0.9343 - lr: 0.0098\n",
+ "Epoch 161/162\n",
+ "256/256 [==============================] - 71s 275ms/step - loss: 0.0616 - accuracy: 0.9822 - val_loss: 0.2751 - val_accuracy: 0.9423 - lr: 0.0098\n",
+ "Epoch 162/162\n",
+ "256/256 [==============================] - 71s 278ms/step - loss: 0.0492 - accuracy: 0.9849 - val_loss: 0.2698 - val_accuracy: 0.9471 - lr: 0.0098\n",
+ "\u001b[0;32mSubset training done.\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{0.9471}, \u001b[0m\u001b[0;33mloss{0.1730}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9615}, loss{0.1507}]\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9471\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.2698\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9615384340. Not saving model.\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1507172436. Not saving model.\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;32m(GPU-MEM)\u001b[0m\u001b[0;36m----[free: 5.19GB, used: 18.81GB, total, 24.00GB]\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m521.31 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m431.35 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m89.96 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0;36m<---------------------------------------|Epoch [27] END|--------------------------------------->\u001b[0m\n",
+ "\u001b[0m\n",
+ "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m28\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m489 (TSEC: 162)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Stage 2]\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+ "\u001b[0;33mPreparing train data...\u001b[0m\n",
+ "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n",
+ "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+ "\u001b[0;32mTraining on subset...\u001b[0m\n",
+ "Epoch 163/168\n",
+ "256/256 [==============================] - 77s 283ms/step - loss: 0.1891 - accuracy: 0.9412 - val_loss: 0.2367 - val_accuracy: 0.9279 - lr: 0.0098\n",
+ "Epoch 164/168\n",
+ "256/256 [==============================] - 71s 275ms/step - loss: 0.1229 - accuracy: 0.9609 - val_loss: 0.2379 - val_accuracy: 0.8814 - lr: 0.0098\n",
+ "Epoch 165/168\n",
+ "256/256 [==============================] - 72s 280ms/step - loss: 0.0927 - accuracy: 0.9749 - val_loss: 0.1685 - val_accuracy: 0.9407 - lr: 0.0098\n",
+ "Epoch 166/168\n",
+ "256/256 [==============================] - 70s 274ms/step - loss: 0.0695 - accuracy: 0.9792 - val_loss: 0.2267 - val_accuracy: 0.9071 - lr: 0.0098\n",
+ "Epoch 167/168\n",
+ "256/256 [==============================] - 72s 279ms/step - loss: 0.0488 - accuracy: 0.9885 - val_loss: 0.1868 - val_accuracy: 0.9503 - lr: 0.0098\n",
+ "Epoch 168/168\n",
+ "256/256 [==============================] - 71s 275ms/step - loss: 0.0394 - accuracy: 0.9922 - val_loss: 0.2207 - val_accuracy: 0.9439 - lr: 0.0098\n",
+ "\u001b[0;32mSubset training done.\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{0.9503}, \u001b[0m\u001b[0;33mloss{0.1685}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9615}, loss{0.1507}]\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9439\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.2207\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9615384340. Not saving model.\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1507172436. Not saving model.\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;32m(GPU-MEM)\u001b[0m\u001b[0;36m----[free: 5.18GB, used: 18.82GB, total, 24.00GB]\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m521.12 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m433.13 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m87.99 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0;36m<---------------------------------------|Epoch [28] END|--------------------------------------->\u001b[0m\n",
+ "\u001b[0m\n",
+ "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m29\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m489 (TSEC: 168)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Stage 2]\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+ "\u001b[0;33mPreparing train data...\u001b[0m\n",
+ "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n",
+ "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+ "\u001b[0;32mTraining on subset...\u001b[0m\n",
+ "Epoch 169/174\n",
+ "256/256 [==============================] - 77s 283ms/step - loss: 0.1920 - accuracy: 0.9395 - val_loss: 0.1970 - val_accuracy: 0.9183 - lr: 0.0098\n",
+ "Epoch 170/174\n",
+ "256/256 [==============================] - 71s 278ms/step - loss: 0.1269 - accuracy: 0.9636 - val_loss: 0.1754 - val_accuracy: 0.9455 - lr: 0.0098\n",
+ "Epoch 171/174\n",
+ "256/256 [==============================] - 70s 275ms/step - loss: 0.0881 - accuracy: 0.9753 - val_loss: 0.2025 - val_accuracy: 0.9423 - lr: 0.0098\n",
+ "Epoch 172/174\n",
+ "256/256 [==============================] - 70s 274ms/step - loss: 0.0649 - accuracy: 0.9812 - val_loss: 0.2053 - val_accuracy: 0.9263 - lr: 0.0098\n",
+ "Epoch 173/174\n",
+ "256/256 [==============================] - 71s 275ms/step - loss: 0.0409 - accuracy: 0.9890 - val_loss: 0.2178 - val_accuracy: 0.9327 - lr: 0.0098\n",
+ "Epoch 174/174\n",
+ "256/256 [==============================] - 70s 274ms/step - loss: 0.0601 - accuracy: 0.9810 - val_loss: 0.2450 - val_accuracy: 0.9311 - lr: 0.0098\n",
+ "\u001b[0;32mSubset training done.\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{0.9455}, \u001b[0m\u001b[0;33mloss{0.1754}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9615}, loss{0.1507}]\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9311\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.2450\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9615384340. Not saving model.\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1507172436. Not saving model.\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;32m(GPU-MEM)\u001b[0m\u001b[0;36m----[free: 5.18GB, used: 18.82GB, total, 24.00GB]\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m519.28 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m431.00 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m88.28 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0;36m<---------------------------------------|Epoch [29] END|--------------------------------------->\u001b[0m\n",
+ "\u001b[0m\n",
+ "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m30\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m489 (TSEC: 174)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Stage 2]\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+ "\u001b[0;33mPreparing train data...\u001b[0m\n",
+ "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n",
+ "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+ "\u001b[0;32mTraining on subset...\u001b[0m\n",
+ "Epoch 175/180\n",
+ "256/256 [==============================] - 77s 284ms/step - loss: 0.1802 - accuracy: 0.9473 - val_loss: 0.1820 - val_accuracy: 0.9503 - lr: 0.0098\n",
+ "Epoch 176/180\n",
+ "256/256 [==============================] - 71s 275ms/step - loss: 0.1165 - accuracy: 0.9634 - val_loss: 0.2613 - val_accuracy: 0.9263 - lr: 0.0098\n",
+ "Epoch 177/180\n",
+ "256/256 [==============================] - 71s 276ms/step - loss: 0.0847 - accuracy: 0.9751 - val_loss: 0.2826 - val_accuracy: 0.9247 - lr: 0.0098\n",
+ "Epoch 178/180\n",
+ "256/256 [==============================] - 71s 275ms/step - loss: 0.0618 - accuracy: 0.9819 - val_loss: 0.2232 - val_accuracy: 0.9295 - lr: 0.0098\n",
+ "Epoch 179/180\n",
+ "256/256 [==============================] - 71s 277ms/step - loss: 0.0464 - accuracy: 0.9858 - val_loss: 0.6330 - val_accuracy: 0.8718 - lr: 0.0098\n",
+ "Epoch 180/180\n",
+ "256/256 [==============================] - 71s 276ms/step - loss: 0.0441 - accuracy: 0.9873 - val_loss: 0.3597 - val_accuracy: 0.9247 - lr: 0.0098\n",
+ "\u001b[0;32mSubset training done.\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{0.9503}, \u001b[0m\u001b[0;33mloss{0.1820}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9615}, loss{0.1507}]\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9247\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.3597\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9615384340. Not saving model.\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1507172436. Not saving model.\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;32m(GPU-MEM)\u001b[0m\u001b[0;36m----[free: 5.19GB, used: 18.81GB, total, 24.00GB]\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m524.83 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m432.33 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m92.50 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0;36m<---------------------------------------|Epoch [30] END|--------------------------------------->\u001b[0m\n",
+ "\u001b[0m\n",
+ "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m31\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m489 (TSEC: 180)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Stage 2]\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+ "\u001b[0;33mPreparing train data...\u001b[0m\n",
+ "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n",
+ "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+ "\u001b[0;32mTraining on subset...\u001b[0m\n",
+ "Epoch 181/186\n",
+ "256/256 [==============================] - 77s 283ms/step - loss: 0.1875 - accuracy: 0.9390 - val_loss: 0.3153 - val_accuracy: 0.8798 - lr: 0.0098\n",
+ "Epoch 182/186\n",
+ "256/256 [==============================] - 72s 279ms/step - loss: 0.1251 - accuracy: 0.9646 - val_loss: 0.2459 - val_accuracy: 0.9215 - lr: 0.0098\n",
+ "Epoch 183/186\n",
+ "256/256 [==============================] - 72s 279ms/step - loss: 0.0922 - accuracy: 0.9729 - val_loss: 0.1697 - val_accuracy: 0.9439 - lr: 0.0098\n",
+ "Epoch 184/186\n",
+ "256/256 [==============================] - 71s 275ms/step - loss: 0.0712 - accuracy: 0.9814 - val_loss: 0.4005 - val_accuracy: 0.8926 - lr: 0.0098\n",
+ "Epoch 185/186\n",
+ "256/256 [==============================] - 71s 276ms/step - loss: 0.0475 - accuracy: 0.9875 - val_loss: 0.2829 - val_accuracy: 0.9311 - lr: 0.0098\n",
+ "Epoch 186/186\n",
+ "256/256 [==============================] - 71s 276ms/step - loss: 0.0462 - accuracy: 0.9895 - val_loss: 0.2588 - val_accuracy: 0.9231 - lr: 0.0098\n",
+ "\u001b[0;32mSubset training done.\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{0.9439}, \u001b[0m\u001b[0;33mloss{0.1697}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9615}, loss{0.1507}]\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9231\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.2588\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9615384340. Not saving model.\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1507172436. Not saving model.\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;32m(GPU-MEM)\u001b[0m\u001b[0;36m----[free: 5.19GB, used: 18.81GB, total, 24.00GB]\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m527.27 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m433.39 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m93.88 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0;36m<---------------------------------------|Epoch [31] END|--------------------------------------->\u001b[0m\n",
+ "\u001b[0m\n",
+ "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m32\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m489 (TSEC: 186)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Stage 2]\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+ "\u001b[0;33mPreparing train data...\u001b[0m\n",
+ "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n",
+ "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+ "\u001b[0;32mTraining on subset...\u001b[0m\n",
+ "Epoch 187/192\n",
+ "256/256 [==============================] - 78s 286ms/step - loss: 0.1648 - accuracy: 0.9492 - val_loss: 0.2811 - val_accuracy: 0.9087 - lr: 0.0098\n",
+ "Epoch 188/192\n",
+ "256/256 [==============================] - ETA: 0s - loss: 0.1060 - accuracy: 0.9678\n",
+ "Epoch 188: ReduceLROnPlateau reducing learning rate to 0.009643239349126816.\n",
+ "256/256 [==============================] - 72s 279ms/step - loss: 0.1060 - accuracy: 0.9678 - val_loss: 0.2874 - val_accuracy: 0.9135 - lr: 0.0098\n",
+ "Epoch 189/192\n",
+ "256/256 [==============================] - 72s 279ms/step - loss: 0.0762 - accuracy: 0.9807 - val_loss: 0.1937 - val_accuracy: 0.9343 - lr: 0.0096\n",
+ "Epoch 190/192\n",
+ "256/256 [==============================] - 71s 276ms/step - loss: 0.0565 - accuracy: 0.9854 - val_loss: 0.3148 - val_accuracy: 0.9263 - lr: 0.0096\n",
+ "Epoch 191/192\n",
+ "256/256 [==============================] - 71s 276ms/step - loss: 0.0482 - accuracy: 0.9880 - val_loss: 0.2285 - val_accuracy: 0.9006 - lr: 0.0096\n",
+ "Epoch 192/192\n",
+ "256/256 [==============================] - 72s 279ms/step - loss: 0.0387 - accuracy: 0.9895 - val_loss: 0.2529 - val_accuracy: 0.9375 - lr: 0.0096\n",
+ "\u001b[0;32mSubset training done.\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{0.9375}, \u001b[0m\u001b[0;33mloss{0.1937}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9615}, loss{0.1507}]\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9375\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.2529\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9615384340. Not saving model.\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1507172436. Not saving model.\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;32m(GPU-MEM)\u001b[0m\u001b[0;36m----[free: 5.18GB, used: 18.82GB, total, 24.00GB]\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m528.81 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m435.34 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m93.47 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0;36m<---------------------------------------|Epoch [32] END|--------------------------------------->\u001b[0m\n",
+ "\u001b[0m\n",
+ "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m33\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m489 (TSEC: 192)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Stage 2]\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+ "\u001b[0;33mPreparing train data...\u001b[0m\n",
+ "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n",
+ "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+ "\u001b[0;32mTraining on subset...\u001b[0m\n",
+ "Epoch 193/198\n",
+ "256/256 [==============================] - 77s 284ms/step - loss: 0.1693 - accuracy: 0.9453 - val_loss: 0.1848 - val_accuracy: 0.9343 - lr: 0.0096\n",
+ "Epoch 194/198\n",
+ "256/256 [==============================] - 71s 275ms/step - loss: 0.1097 - accuracy: 0.9668 - val_loss: 0.1614 - val_accuracy: 0.9343 - lr: 0.0096\n",
+ "Epoch 195/198\n",
+ "256/256 [==============================] - 71s 277ms/step - loss: 0.0705 - accuracy: 0.9771 - val_loss: 0.2779 - val_accuracy: 0.9247 - lr: 0.0096\n",
+ "Epoch 196/198\n",
+ "256/256 [==============================] - 72s 280ms/step - loss: 0.0583 - accuracy: 0.9824 - val_loss: 0.2878 - val_accuracy: 0.9407 - lr: 0.0096\n",
+ "Epoch 197/198\n",
+ "256/256 [==============================] - 71s 277ms/step - loss: 0.0482 - accuracy: 0.9868 - val_loss: 0.3605 - val_accuracy: 0.9247 - lr: 0.0096\n",
+ "Epoch 198/198\n",
+ "256/256 [==============================] - 71s 276ms/step - loss: 0.0361 - accuracy: 0.9885 - val_loss: 0.2136 - val_accuracy: 0.9391 - lr: 0.0096\n",
+ "\u001b[0;32mSubset training done.\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{0.9407}, \u001b[0m\u001b[0;33mloss{0.1614}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9615}, loss{0.1507}]\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9391\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.2137\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9615384340. Not saving model.\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1507172436. Not saving model.\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;32m(GPU-MEM)\u001b[0m\u001b[0;36m----[free: 5.18GB, used: 18.82GB, total, 24.00GB]\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m528.40 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m433.79 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m94.62 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0;36m<---------------------------------------|Epoch [33] END|--------------------------------------->\u001b[0m\n",
+ "\u001b[0m\n",
+ "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m34\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m489 (TSEC: 198)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Stage 2]\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+ "\u001b[0;33mPreparing train data...\u001b[0m\n",
+ "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n",
+ "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+ "\u001b[0;32mTraining on subset...\u001b[0m\n",
+ "Epoch 199/204\n",
+ "256/256 [==============================] - 77s 285ms/step - loss: 0.1925 - accuracy: 0.9436 - val_loss: 0.1574 - val_accuracy: 0.9519 - lr: 0.0096\n",
+ "Epoch 200/204\n",
+ "256/256 [==============================] - 71s 276ms/step - loss: 0.1273 - accuracy: 0.9609 - val_loss: 0.2147 - val_accuracy: 0.9407 - lr: 0.0096\n",
+ "Epoch 201/204\n",
+ "256/256 [==============================] - 71s 276ms/step - loss: 0.0876 - accuracy: 0.9717 - val_loss: 0.2848 - val_accuracy: 0.9022 - lr: 0.0096\n",
+ "Epoch 202/204\n",
+ "256/256 [==============================] - 71s 276ms/step - loss: 0.0617 - accuracy: 0.9822 - val_loss: 0.2692 - val_accuracy: 0.9327 - lr: 0.0096\n",
+ "Epoch 203/204\n",
+ "256/256 [==============================] - 71s 276ms/step - loss: 0.0496 - accuracy: 0.9861 - val_loss: 0.1747 - val_accuracy: 0.9503 - lr: 0.0096\n",
+ "Epoch 204/204\n",
+ "256/256 [==============================] - 71s 276ms/step - loss: 0.0379 - accuracy: 0.9900 - val_loss: 0.2128 - val_accuracy: 0.9407 - lr: 0.0096\n",
+ "\u001b[0;32mSubset training done.\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{0.9519}, \u001b[0m\u001b[0;33mloss{0.1574}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9615}, loss{0.1507}]\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9407\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.2128\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9615384340. Not saving model.\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1507172436. Not saving model.\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;32m(GPU-MEM)\u001b[0m\u001b[0;36m----[free: 5.19GB, used: 18.81GB, total, 24.00GB]\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m527.84 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m432.60 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m95.24 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0;36m<---------------------------------------|Epoch [34] END|--------------------------------------->\u001b[0m\n",
+ "\u001b[0m\n",
+ "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m35\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m489 (TSEC: 204)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Stage 2]\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+ "\u001b[0;33mPreparing train data...\u001b[0m\n",
+ "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n",
+ "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+ "\u001b[0;32mTraining on subset...\u001b[0m\n",
+ "Epoch 205/210\n",
+ "256/256 [==============================] - 77s 285ms/step - loss: 0.1705 - accuracy: 0.9463 - val_loss: 0.3243 - val_accuracy: 0.9103 - lr: 0.0096\n",
+ "Epoch 206/210\n",
+ "256/256 [==============================] - 72s 279ms/step - loss: 0.1079 - accuracy: 0.9634 - val_loss: 0.2504 - val_accuracy: 0.9359 - lr: 0.0096\n",
+ "Epoch 207/210\n",
+ "256/256 [==============================] - 71s 276ms/step - loss: 0.0722 - accuracy: 0.9766 - val_loss: 0.3823 - val_accuracy: 0.9119 - lr: 0.0096\n",
+ "Epoch 208/210\n",
+ "256/256 [==============================] - 72s 280ms/step - loss: 0.0482 - accuracy: 0.9863 - val_loss: 0.2689 - val_accuracy: 0.9375 - lr: 0.0096\n",
+ "Epoch 209/210\n",
+ "256/256 [==============================] - 72s 281ms/step - loss: 0.0357 - accuracy: 0.9900 - val_loss: 0.2405 - val_accuracy: 0.9423 - lr: 0.0096\n",
+ "Epoch 210/210\n",
+ "256/256 [==============================] - 71s 276ms/step - loss: 0.0374 - accuracy: 0.9885 - val_loss: 0.3618 - val_accuracy: 0.9423 - lr: 0.0096\n",
+ "\u001b[0;32mSubset training done.\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{0.9423}, \u001b[0m\u001b[0;33mloss{0.2405}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9615}, loss{0.1507}]\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9423\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.3618\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9615384340. Not saving model.\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1507172436. Not saving model.\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;32m(GPU-MEM)\u001b[0m\u001b[0;36m----[free: 5.19GB, used: 18.81GB, total, 24.00GB]\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m530.09 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m435.61 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m94.48 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0;36m<---------------------------------------|Epoch [35] END|--------------------------------------->\u001b[0m\n",
+ "\u001b[0m\n",
+ "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m36\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m489 (TSEC: 210)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Stage 2]\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+ "\u001b[0;33mPreparing train data...\u001b[0m\n",
+ "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n",
+ "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+ "\u001b[0;32mTraining on subset...\u001b[0m\n",
+ "Epoch 211/216\n",
+ "256/256 [==============================] - 77s 285ms/step - loss: 0.1826 - accuracy: 0.9414 - val_loss: 0.2124 - val_accuracy: 0.9359 - lr: 0.0096\n",
+ "Epoch 212/216\n",
+ "256/256 [==============================] - 71s 276ms/step - loss: 0.1050 - accuracy: 0.9666 - val_loss: 0.2501 - val_accuracy: 0.9327 - lr: 0.0096\n",
+ "Epoch 213/216\n",
+ "256/256 [==============================] - 72s 282ms/step - loss: 0.0695 - accuracy: 0.9780 - val_loss: 0.2009 - val_accuracy: 0.9423 - lr: 0.0096\n",
+ "Epoch 214/216\n",
+ "256/256 [==============================] - 71s 277ms/step - loss: 0.0432 - accuracy: 0.9868 - val_loss: 0.1850 - val_accuracy: 0.9423 - lr: 0.0096\n",
+ "Epoch 215/216\n",
+ "256/256 [==============================] - 72s 280ms/step - loss: 0.0376 - accuracy: 0.9890 - val_loss: 0.1988 - val_accuracy: 0.9535 - lr: 0.0096\n",
+ "Epoch 216/216\n",
+ "256/256 [==============================] - 71s 276ms/step - loss: 0.0287 - accuracy: 0.9939 - val_loss: 0.2456 - val_accuracy: 0.9439 - lr: 0.0096\n",
+ "\u001b[0;32mSubset training done.\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{0.9535}, \u001b[0m\u001b[0;33mloss{0.1850}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9615}, loss{0.1507}]\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9439\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.2456\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9615384340. Not saving model.\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1507172436. Not saving model.\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;32m(GPU-MEM)\u001b[0m\u001b[0;36m----[free: 5.19GB, used: 18.81GB, total, 24.00GB]\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m533.03 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m435.18 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m97.85 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0;36m<---------------------------------------|Epoch [36] END|--------------------------------------->\u001b[0m\n",
+ "\u001b[0m\n",
+ "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m37\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m489 (TSEC: 216)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Stage 2]\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+ "\u001b[0;33mPreparing train data...\u001b[0m\n",
+ "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n",
+ "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+ "\u001b[0;32mTraining on subset...\u001b[0m\n",
+ "Epoch 217/222\n",
+ "256/256 [==============================] - 77s 284ms/step - loss: 0.1854 - accuracy: 0.9436 - val_loss: 0.1820 - val_accuracy: 0.9423 - lr: 0.0096\n",
+ "Epoch 218/222\n",
+ "256/256 [==============================] - 71s 277ms/step - loss: 0.1210 - accuracy: 0.9619 - val_loss: 0.1830 - val_accuracy: 0.9407 - lr: 0.0096\n",
+ "Epoch 219/222\n",
+ "256/256 [==============================] - 71s 277ms/step - loss: 0.0846 - accuracy: 0.9763 - val_loss: 0.5565 - val_accuracy: 0.8590 - lr: 0.0096\n",
+ "Epoch 220/222\n",
+ "256/256 [==============================] - 71s 277ms/step - loss: 0.0700 - accuracy: 0.9812 - val_loss: 0.2388 - val_accuracy: 0.9423 - lr: 0.0096\n",
+ "Epoch 221/222\n",
+ "256/256 [==============================] - 71s 276ms/step - loss: 0.0406 - accuracy: 0.9895 - val_loss: 0.2682 - val_accuracy: 0.9343 - lr: 0.0096\n",
+ "Epoch 222/222\n",
+ "256/256 [==============================] - 71s 276ms/step - loss: 0.0396 - accuracy: 0.9912 - val_loss: 0.2547 - val_accuracy: 0.9423 - lr: 0.0096\n",
+ "\u001b[0;32mSubset training done.\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{0.9423}, \u001b[0m\u001b[0;33mloss{0.1820}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9615}, loss{0.1507}]\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9423\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.2547\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9615384340. Not saving model.\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1507172436. Not saving model.\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;32m(GPU-MEM)\u001b[0m\u001b[0;36m----[free: 5.18GB, used: 18.82GB, total, 24.00GB]\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m530.61 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m433.35 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m97.26 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0;36m<---------------------------------------|Epoch [37] END|--------------------------------------->\u001b[0m\n",
+ "\u001b[0m\n",
+ "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m38\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m489 (TSEC: 222)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Stage 2]\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+ "\u001b[0;33mPreparing train data...\u001b[0m\n",
+ "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n",
+ "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+ "\u001b[0;32mTraining on subset...\u001b[0m\n",
+ "Epoch 223/228\n",
+ "256/256 [==============================] - 77s 285ms/step - loss: 0.1648 - accuracy: 0.9492 - val_loss: 0.1929 - val_accuracy: 0.9359 - lr: 0.0096\n",
+ "Epoch 224/228\n",
+ "256/256 [==============================] - 72s 281ms/step - loss: 0.1032 - accuracy: 0.9680 - val_loss: 0.2054 - val_accuracy: 0.9391 - lr: 0.0096\n",
+ "Epoch 225/228\n",
+ "256/256 [==============================] - 71s 276ms/step - loss: 0.0676 - accuracy: 0.9827 - val_loss: 0.2465 - val_accuracy: 0.9327 - lr: 0.0096\n",
+ "Epoch 226/228\n",
+ "256/256 [==============================] - 72s 279ms/step - loss: 0.0484 - accuracy: 0.9873 - val_loss: 0.2819 - val_accuracy: 0.9407 - lr: 0.0096\n",
+ "Epoch 227/228\n",
+ "256/256 [==============================] - 71s 277ms/step - loss: 0.0391 - accuracy: 0.9883 - val_loss: 0.3457 - val_accuracy: 0.9343 - lr: 0.0096\n",
+ "Epoch 228/228\n",
+ "256/256 [==============================] - 71s 278ms/step - loss: 0.0236 - accuracy: 0.9941 - val_loss: 0.3316 - val_accuracy: 0.9263 - lr: 0.0096\n",
+ "\u001b[0;32mSubset training done.\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{0.9407}, \u001b[0m\u001b[0;33mloss{0.1929}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9615}, loss{0.1507}]\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9263\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.3316\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9615384340. Not saving model.\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1507172436. Not saving model.\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;32m(GPU-MEM)\u001b[0m\u001b[0;36m----[free: 5.19GB, used: 18.81GB, total, 24.00GB]\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m534.80 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m435.83 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m98.97 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0;36m<---------------------------------------|Epoch [38] END|--------------------------------------->\u001b[0m\n",
+ "\u001b[0m\n",
+ "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m39\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m489 (TSEC: 228)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Stage 2]\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+ "\u001b[0;33mPreparing train data...\u001b[0m\n",
+ "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n",
+ "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+ "\u001b[0;32mTraining on subset...\u001b[0m\n",
+ "Epoch 229/234\n",
+ "256/256 [==============================] - 77s 285ms/step - loss: 0.1775 - accuracy: 0.9451 - val_loss: 0.1967 - val_accuracy: 0.9231 - lr: 0.0096\n",
+ "Epoch 230/234\n",
+ "256/256 [==============================] - 72s 280ms/step - loss: 0.1063 - accuracy: 0.9644 - val_loss: 0.2430 - val_accuracy: 0.9343 - lr: 0.0096\n",
+ "Epoch 231/234\n",
+ "256/256 [==============================] - 71s 277ms/step - loss: 0.0785 - accuracy: 0.9775 - val_loss: 0.1958 - val_accuracy: 0.9279 - lr: 0.0096\n",
+ "Epoch 232/234\n",
+ "256/256 [==============================] - 71s 276ms/step - loss: 0.0495 - accuracy: 0.9863 - val_loss: 0.3232 - val_accuracy: 0.9311 - lr: 0.0096\n",
+ "Epoch 233/234\n",
+ "256/256 [==============================] - 72s 281ms/step - loss: 0.0378 - accuracy: 0.9888 - val_loss: 0.2346 - val_accuracy: 0.9439 - lr: 0.0096\n",
+ "Epoch 234/234\n",
+ "256/256 [==============================] - 71s 277ms/step - loss: 0.0411 - accuracy: 0.9888 - val_loss: 0.4498 - val_accuracy: 0.8926 - lr: 0.0096\n",
+ "\u001b[0;32mSubset training done.\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{0.9439}, \u001b[0m\u001b[0;33mloss{0.1958}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9615}, loss{0.1507}]\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.8926\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.4497\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9615384340. Not saving model.\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1507172436. Not saving model.\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;32m(GPU-MEM)\u001b[0m\u001b[0;36m----[free: 5.17GB, used: 18.83GB, total, 24.00GB]\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m534.70 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m435.67 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m99.02 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0;36m<---------------------------------------|Epoch [39] END|--------------------------------------->\u001b[0m\n",
+ "\u001b[0m\n",
+ "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m40\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m489 (TSEC: 234)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Stage 2]\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+ "\u001b[0;33mPreparing train data...\u001b[0m\n",
+ "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n",
+ "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+ "\u001b[0;32mTraining on subset...\u001b[0m\n",
+ "Epoch 235/240\n",
+ "256/256 [==============================] - 77s 285ms/step - loss: 0.1779 - accuracy: 0.9412 - val_loss: 0.2640 - val_accuracy: 0.9022 - lr: 0.0096\n",
+ "Epoch 236/240\n",
+ "256/256 [==============================] - 72s 280ms/step - loss: 0.1238 - accuracy: 0.9592 - val_loss: 0.2169 - val_accuracy: 0.9391 - lr: 0.0096\n",
+ "Epoch 237/240\n",
+ "256/256 [==============================] - 71s 278ms/step - loss: 0.0842 - accuracy: 0.9756 - val_loss: 0.4989 - val_accuracy: 0.9071 - lr: 0.0096\n",
+ "Epoch 238/240\n",
+ "256/256 [==============================] - 71s 278ms/step - loss: 0.0577 - accuracy: 0.9854 - val_loss: 0.4745 - val_accuracy: 0.8926 - lr: 0.0096\n",
+ "Epoch 239/240\n",
+ "256/256 [==============================] - 71s 277ms/step - loss: 0.0469 - accuracy: 0.9866 - val_loss: 0.2976 - val_accuracy: 0.9279 - lr: 0.0096\n",
+ "Epoch 240/240\n",
+ "256/256 [==============================] - 71s 277ms/step - loss: 0.0304 - accuracy: 0.9937 - val_loss: 0.4848 - val_accuracy: 0.9103 - lr: 0.0096\n",
+ "\u001b[0;32mSubset training done.\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{0.9391}, \u001b[0m\u001b[0;33mloss{0.2169}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9615}, loss{0.1507}]\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9103\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.4847\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9615384340. Not saving model.\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1507172436. Not saving model.\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;32m(GPU-MEM)\u001b[0m\u001b[0;36m----[free: 5.18GB, used: 18.82GB, total, 24.00GB]\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m536.43 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m435.41 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m101.02 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0;36m<---------------------------------------|Epoch [40] END|--------------------------------------->\u001b[0m\n",
+ "\u001b[0m\n",
+ "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m41\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m489 (TSEC: 240)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Stage 2]\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+ "\u001b[0;33mPreparing train data...\u001b[0m\n",
+ "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n",
+ "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+ "\u001b[0;32mTraining on subset...\u001b[0m\n",
+ "Epoch 241/246\n",
+ "256/256 [==============================] - 77s 286ms/step - loss: 0.1722 - accuracy: 0.9431 - val_loss: 0.3465 - val_accuracy: 0.9022 - lr: 0.0096\n",
+ "Epoch 242/246\n",
+ "256/256 [==============================] - 72s 279ms/step - loss: 0.1058 - accuracy: 0.9678 - val_loss: 0.2628 - val_accuracy: 0.9295 - lr: 0.0096\n",
+ "Epoch 243/246\n",
+ "256/256 [==============================] - 71s 275ms/step - loss: 0.0724 - accuracy: 0.9773 - val_loss: 0.2259 - val_accuracy: 0.9263 - lr: 0.0096\n",
+ "Epoch 244/246\n",
+ "256/256 [==============================] - 71s 276ms/step - loss: 0.0526 - accuracy: 0.9856 - val_loss: 0.4048 - val_accuracy: 0.9071 - lr: 0.0096\n",
+ "Epoch 245/246\n",
+ "256/256 [==============================] - 71s 275ms/step - loss: 0.0383 - accuracy: 0.9893 - val_loss: 0.3190 - val_accuracy: 0.9231 - lr: 0.0096\n",
+ "Epoch 246/246\n",
+ "256/256 [==============================] - 71s 276ms/step - loss: 0.0287 - accuracy: 0.9922 - val_loss: 0.5040 - val_accuracy: 0.9215 - lr: 0.0096\n",
+ "\u001b[0;32mSubset training done.\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{0.9295}, \u001b[0m\u001b[0;33mloss{0.2259}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9615}, loss{0.1507}]\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9215\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.5041\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9615384340. Not saving model.\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1507172436. Not saving model.\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;32m(GPU-MEM)\u001b[0m\u001b[0;36m----[free: 5.17GB, used: 18.82GB, total, 24.00GB]\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m532.98 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m432.80 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m100.18 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0;36m<---------------------------------------|Epoch [41] END|--------------------------------------->\u001b[0m\n",
+ "\u001b[0m\n",
+ "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m42\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m489 (TSEC: 246)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Stage 2]\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+ "\u001b[0;33m└───Shuffling data...\u001b[0m\n",
+ "\u001b[0;33mPreparing train data...\u001b[0m\n",
+ "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n",
+ "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;31m- Debug DP Sample dir: \u001b[0m\u001b[0;32mSamples/TSR_SUB_400_y2024_m03_d30-h02_m38_s03\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+ "\u001b[0;32mTraining on subset...\u001b[0m\n",
+ "Epoch 247/252\n",
+ "256/256 [==============================] - 77s 285ms/step - loss: 0.1686 - accuracy: 0.9458 - val_loss: 0.2969 - val_accuracy: 0.9295 - lr: 0.0096\n",
+ "Epoch 248/252\n",
+ "256/256 [==============================] - 72s 281ms/step - loss: 0.0966 - accuracy: 0.9727 - val_loss: 0.2599 - val_accuracy: 0.9423 - lr: 0.0096\n",
+ "Epoch 249/252\n",
+ "256/256 [==============================] - 71s 278ms/step - loss: 0.0879 - accuracy: 0.9775 - val_loss: 0.2880 - val_accuracy: 0.9199 - lr: 0.0096\n",
+ "Epoch 250/252\n",
+ "256/256 [==============================] - 71s 277ms/step - loss: 0.0486 - accuracy: 0.9863 - val_loss: 0.2426 - val_accuracy: 0.9391 - lr: 0.0096\n",
+ "Epoch 251/252\n",
+ "256/256 [==============================] - 71s 277ms/step - loss: 0.0358 - accuracy: 0.9917 - val_loss: 0.2913 - val_accuracy: 0.9263 - lr: 0.0096\n",
+ "Epoch 252/252\n",
+ "256/256 [==============================] - 71s 277ms/step - loss: 0.0311 - accuracy: 0.9915 - val_loss: 0.2226 - val_accuracy: 0.9407 - lr: 0.0096\n",
+ "\u001b[0;32mSubset training done.\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{0.9423}, \u001b[0m\u001b[0;33mloss{0.2226}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9615}, loss{0.1507}]\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9407\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.2226\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9615384340. Not saving model.\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1507172436. Not saving model.\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;32m(GPU-MEM)\u001b[0m\u001b[0;36m----[free: 5.18GB, used: 18.82GB, total, 24.00GB]\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m550.51 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m435.23 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m115.28 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0;36m<---------------------------------------|Epoch [42] END|--------------------------------------->\u001b[0m\n",
+ "\u001b[0m\n",
+ "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m43\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m489 (TSEC: 252)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Stage 2]\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+ "\u001b[0;33mPreparing train data...\u001b[0m\n",
+ "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n",
+ "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+ "\u001b[0;32mTraining on subset...\u001b[0m\n",
+ "Epoch 253/258\n",
+ "256/256 [==============================] - ETA: 0s - loss: 0.1562 - accuracy: 0.9495\n",
+ "Epoch 253: ReduceLROnPlateau reducing learning rate to 0.009469660667702556.\n",
+ "256/256 [==============================] - 78s 287ms/step - loss: 0.1562 - accuracy: 0.9495 - val_loss: 0.1962 - val_accuracy: 0.9279 - lr: 0.0096\n",
+ "Epoch 254/258\n",
+ "256/256 [==============================] - 71s 277ms/step - loss: 0.0941 - accuracy: 0.9719 - val_loss: 0.2699 - val_accuracy: 0.9103 - lr: 0.0095\n",
+ "Epoch 255/258\n",
+ "256/256 [==============================] - 71s 277ms/step - loss: 0.0692 - accuracy: 0.9788 - val_loss: 0.3234 - val_accuracy: 0.9103 - lr: 0.0095\n",
+ "Epoch 256/258\n",
+ "256/256 [==============================] - 71s 277ms/step - loss: 0.0420 - accuracy: 0.9895 - val_loss: 0.3635 - val_accuracy: 0.9151 - lr: 0.0095\n",
+ "Epoch 257/258\n",
+ "256/256 [==============================] - 72s 281ms/step - loss: 0.0337 - accuracy: 0.9910 - val_loss: 0.3884 - val_accuracy: 0.9295 - lr: 0.0095\n",
+ "Epoch 258/258\n",
+ "256/256 [==============================] - 71s 277ms/step - loss: 0.0294 - accuracy: 0.9919 - val_loss: 0.4633 - val_accuracy: 0.9167 - lr: 0.0095\n",
+ "\u001b[0;32mSubset training done.\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{0.9295}, \u001b[0m\u001b[0;33mloss{0.1962}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9615}, loss{0.1507}]\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9167\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.4633\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9615384340. Not saving model.\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1507172436. Not saving model.\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;32m(GPU-MEM)\u001b[0m\u001b[0;36m----[free: 5.17GB, used: 18.83GB, total, 24.00GB]\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m537.96 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m435.55 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m102.41 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0;36m<---------------------------------------|Epoch [43] END|--------------------------------------->\u001b[0m\n",
+ "\u001b[0m\n",
+ "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m44\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m489 (TSEC: 258)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Stage 2]\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+ "\u001b[0;33mPreparing train data...\u001b[0m\n",
+ "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n",
+ "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+ "\u001b[0;32mTraining on subset...\u001b[0m\n",
+ "Epoch 259/264\n",
+ "256/256 [==============================] - 78s 286ms/step - loss: 0.1649 - accuracy: 0.9478 - val_loss: 0.2313 - val_accuracy: 0.9343 - lr: 0.0095\n",
+ "Epoch 260/264\n",
+ "256/256 [==============================] - 72s 280ms/step - loss: 0.1053 - accuracy: 0.9668 - val_loss: 0.1970 - val_accuracy: 0.9391 - lr: 0.0095\n",
+ "Epoch 261/264\n",
+ "256/256 [==============================] - 71s 277ms/step - loss: 0.0735 - accuracy: 0.9790 - val_loss: 0.2894 - val_accuracy: 0.9103 - lr: 0.0095\n",
+ "Epoch 262/264\n",
+ "256/256 [==============================] - 71s 276ms/step - loss: 0.0540 - accuracy: 0.9861 - val_loss: 0.3597 - val_accuracy: 0.9215 - lr: 0.0095\n",
+ "Epoch 263/264\n",
+ "256/256 [==============================] - 71s 278ms/step - loss: 0.0364 - accuracy: 0.9922 - val_loss: 0.2845 - val_accuracy: 0.9343 - lr: 0.0095\n",
+ "Epoch 264/264\n",
+ "256/256 [==============================] - 71s 277ms/step - loss: 0.0347 - accuracy: 0.9912 - val_loss: 0.3006 - val_accuracy: 0.9327 - lr: 0.0095\n",
+ "\u001b[0;32mSubset training done.\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{0.9391}, \u001b[0m\u001b[0;33mloss{0.1970}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9615}, loss{0.1507}]\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9327\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.3006\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9615384340. Not saving model.\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1507172436. Not saving model.\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;32m(GPU-MEM)\u001b[0m\u001b[0;36m----[free: 5.18GB, used: 18.82GB, total, 24.00GB]\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m539.13 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m435.24 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m103.88 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0;36m<---------------------------------------|Epoch [44] END|--------------------------------------->\u001b[0m\n",
+ "\u001b[0m\n",
+ "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m45\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m489 (TSEC: 264)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Stage 2]\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+ "\u001b[0;33mPreparing train data...\u001b[0m\n",
+ "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n",
+ "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+ "\u001b[0;32mTraining on subset...\u001b[0m\n",
+ "Epoch 265/270\n",
+ "256/256 [==============================] - 78s 286ms/step - loss: 0.1549 - accuracy: 0.9541 - val_loss: 0.3054 - val_accuracy: 0.9295 - lr: 0.0095\n",
+ "Epoch 266/270\n",
+ "256/256 [==============================] - 72s 281ms/step - loss: 0.0943 - accuracy: 0.9709 - val_loss: 0.2693 - val_accuracy: 0.9327 - lr: 0.0095\n",
+ "Epoch 267/270\n",
+ "256/256 [==============================] - 72s 281ms/step - loss: 0.0682 - accuracy: 0.9802 - val_loss: 0.2379 - val_accuracy: 0.9343 - lr: 0.0095\n",
+ "Epoch 268/270\n",
+ "256/256 [==============================] - 72s 280ms/step - loss: 0.0489 - accuracy: 0.9868 - val_loss: 0.1810 - val_accuracy: 0.9423 - lr: 0.0095\n",
+ "Epoch 269/270\n",
+ "256/256 [==============================] - 71s 278ms/step - loss: 0.0295 - accuracy: 0.9917 - val_loss: 0.4588 - val_accuracy: 0.9006 - lr: 0.0095\n",
+ "Epoch 270/270\n",
+ "256/256 [==============================] - 71s 277ms/step - loss: 0.0214 - accuracy: 0.9951 - val_loss: 0.4001 - val_accuracy: 0.9119 - lr: 0.0095\n",
+ "\u001b[0;32mSubset training done.\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{0.9423}, \u001b[0m\u001b[0;33mloss{0.1810}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9615}, loss{0.1507}]\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9119\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.4001\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9615384340. Not saving model.\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1507172436. Not saving model.\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;32m(GPU-MEM)\u001b[0m\u001b[0;36m----[free: 5.18GB, used: 18.82GB, total, 24.00GB]\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m543.38 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m437.16 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m106.22 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0;36m<---------------------------------------|Epoch [45] END|--------------------------------------->\u001b[0m\n",
+ "\u001b[0m\n",
+ "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m46\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m489 (TSEC: 270)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Stage 2]\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+ "\u001b[0;33mPreparing train data...\u001b[0m\n",
+ "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n",
+ "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+ "\u001b[0;32mTraining on subset...\u001b[0m\n",
+ "Epoch 271/276\n",
+ "256/256 [==============================] - 78s 286ms/step - loss: 0.1875 - accuracy: 0.9353 - val_loss: 0.1975 - val_accuracy: 0.9423 - lr: 0.0095\n",
+ "Epoch 272/276\n",
+ "256/256 [==============================] - 71s 277ms/step - loss: 0.1151 - accuracy: 0.9612 - val_loss: 0.2110 - val_accuracy: 0.9327 - lr: 0.0095\n",
+ "Epoch 273/276\n",
+ "256/256 [==============================] - 71s 278ms/step - loss: 0.0753 - accuracy: 0.9773 - val_loss: 0.1985 - val_accuracy: 0.9423 - lr: 0.0095\n",
+ "Epoch 274/276\n",
+ "256/256 [==============================] - 71s 277ms/step - loss: 0.0634 - accuracy: 0.9802 - val_loss: 0.2084 - val_accuracy: 0.9343 - lr: 0.0095\n",
+ "Epoch 275/276\n",
+ "256/256 [==============================] - 71s 279ms/step - loss: 0.0407 - accuracy: 0.9868 - val_loss: 0.2229 - val_accuracy: 0.9407 - lr: 0.0095\n",
+ "Epoch 276/276\n",
+ "256/256 [==============================] - 72s 278ms/step - loss: 0.0294 - accuracy: 0.9922 - val_loss: 0.2511 - val_accuracy: 0.9423 - lr: 0.0095\n",
+ "\u001b[0;32mSubset training done.\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{0.9423}, \u001b[0m\u001b[0;33mloss{0.1975}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9615}, loss{0.1507}]\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9423\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.2511\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9615384340. Not saving model.\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1507172436. Not saving model.\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;32m(GPU-MEM)\u001b[0m\u001b[0;36m----[free: 5.18GB, used: 18.82GB, total, 24.00GB]\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m539.92 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m435.51 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m104.41 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0;36m<---------------------------------------|Epoch [46] END|--------------------------------------->\u001b[0m\n",
+ "\u001b[0m\n",
+ "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m47\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m489 (TSEC: 276)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Stage 2]\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+ "\u001b[0;33mPreparing train data...\u001b[0m\n",
+ "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n",
+ "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+ "\u001b[0;32mTraining on subset...\u001b[0m\n",
+ "Epoch 277/282\n",
+ "256/256 [==============================] - 77s 285ms/step - loss: 0.1688 - accuracy: 0.9482 - val_loss: 0.1570 - val_accuracy: 0.9471 - lr: 0.0095\n",
+ "Epoch 278/282\n",
+ "256/256 [==============================] - 71s 277ms/step - loss: 0.0959 - accuracy: 0.9692 - val_loss: 0.1613 - val_accuracy: 0.9375 - lr: 0.0095\n",
+ "Epoch 279/282\n",
+ "256/256 [==============================] - 71s 278ms/step - loss: 0.0664 - accuracy: 0.9802 - val_loss: 0.1662 - val_accuracy: 0.9359 - lr: 0.0095\n",
+ "Epoch 280/282\n",
+ "256/256 [==============================] - 71s 278ms/step - loss: 0.0480 - accuracy: 0.9856 - val_loss: 0.2022 - val_accuracy: 0.9343 - lr: 0.0095\n",
+ "Epoch 281/282\n",
+ "256/256 [==============================] - 71s 278ms/step - loss: 0.0376 - accuracy: 0.9893 - val_loss: 0.2449 - val_accuracy: 0.9327 - lr: 0.0095\n",
+ "Epoch 282/282\n",
+ "256/256 [==============================] - 71s 277ms/step - loss: 0.0293 - accuracy: 0.9924 - val_loss: 0.2447 - val_accuracy: 0.9391 - lr: 0.0095\n",
+ "\u001b[0;32mSubset training done.\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{0.9471}, \u001b[0m\u001b[0;33mloss{0.1570}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9615}, loss{0.1507}]\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9391\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.2447\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9615384340. Not saving model.\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1507172436. Not saving model.\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;32m(GPU-MEM)\u001b[0m\u001b[0;36m----[free: 5.16GB, used: 18.84GB, total, 24.00GB]\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m540.71 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m434.45 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m106.26 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0;36m<---------------------------------------|Epoch [47] END|--------------------------------------->\u001b[0m\n",
+ "\u001b[0m\n",
+ "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m48\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m489 (TSEC: 282)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Stage 2]\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+ "\u001b[0;33mPreparing train data...\u001b[0m\n",
+ "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n",
+ "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+ "\u001b[0;32mTraining on subset...\u001b[0m\n",
+ "Epoch 283/288\n",
+ "256/256 [==============================] - 77s 285ms/step - loss: 0.1552 - accuracy: 0.9521 - val_loss: 0.2704 - val_accuracy: 0.9295 - lr: 0.0095\n",
+ "Epoch 284/288\n",
+ "256/256 [==============================] - 71s 276ms/step - loss: 0.1001 - accuracy: 0.9688 - val_loss: 0.2616 - val_accuracy: 0.9295 - lr: 0.0095\n",
+ "Epoch 285/288\n",
+ "256/256 [==============================] - 71s 277ms/step - loss: 0.0536 - accuracy: 0.9817 - val_loss: 0.4002 - val_accuracy: 0.9135 - lr: 0.0095\n",
+ "Epoch 286/288\n",
+ "256/256 [==============================] - 71s 276ms/step - loss: 0.0378 - accuracy: 0.9888 - val_loss: 0.3390 - val_accuracy: 0.9215 - lr: 0.0095\n",
+ "Epoch 287/288\n",
+ "256/256 [==============================] - 71s 277ms/step - loss: 0.0190 - accuracy: 0.9954 - val_loss: 0.5053 - val_accuracy: 0.8862 - lr: 0.0095\n",
+ "Epoch 288/288\n",
+ "256/256 [==============================] - 71s 277ms/step - loss: 0.0199 - accuracy: 0.9939 - val_loss: 0.4386 - val_accuracy: 0.9103 - lr: 0.0095\n",
+ "\u001b[0;32mSubset training done.\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{0.9295}, \u001b[0m\u001b[0;33mloss{0.2616}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9615}, loss{0.1507}]\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9103\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.4385\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9615384340. Not saving model.\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1507172436. Not saving model.\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;32m(GPU-MEM)\u001b[0m\u001b[0;36m----[free: 5.17GB, used: 18.83GB, total, 24.00GB]\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m538.49 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m433.36 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m105.13 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0;36m<---------------------------------------|Epoch [48] END|--------------------------------------->\u001b[0m\n",
+ "\u001b[0m\n",
+ "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m49\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m489 (TSEC: 288)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Stage 2]\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+ "\u001b[0;33mPreparing train data...\u001b[0m\n",
+ "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n",
+ "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+ "\u001b[0;32mTraining on subset...\u001b[0m\n",
+ "Epoch 289/294\n",
+ "256/256 [==============================] - 78s 287ms/step - loss: 0.1457 - accuracy: 0.9519 - val_loss: 0.2238 - val_accuracy: 0.9279 - lr: 0.0095\n",
+ "Epoch 290/294\n",
+ "256/256 [==============================] - 72s 282ms/step - loss: 0.0863 - accuracy: 0.9753 - val_loss: 0.2242 - val_accuracy: 0.9343 - lr: 0.0095\n",
+ "Epoch 291/294\n",
+ "256/256 [==============================] - 72s 279ms/step - loss: 0.0620 - accuracy: 0.9812 - val_loss: 0.2938 - val_accuracy: 0.9231 - lr: 0.0095\n",
+ "Epoch 292/294\n",
+ "256/256 [==============================] - 71s 278ms/step - loss: 0.0400 - accuracy: 0.9880 - val_loss: 0.3755 - val_accuracy: 0.9054 - lr: 0.0095\n",
+ "Epoch 293/294\n",
+ "256/256 [==============================] - 72s 279ms/step - loss: 0.0354 - accuracy: 0.9900 - val_loss: 0.4175 - val_accuracy: 0.9103 - lr: 0.0095\n",
+ "Epoch 294/294\n",
+ "256/256 [==============================] - 71s 278ms/step - loss: 0.0219 - accuracy: 0.9941 - val_loss: 0.4969 - val_accuracy: 0.9054 - lr: 0.0095\n",
+ "\u001b[0;32mSubset training done.\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{0.9343}, \u001b[0m\u001b[0;33mloss{0.2238}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9615}, loss{0.1507}]\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9054\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.4968\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9615384340. Not saving model.\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1507172436. Not saving model.\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;32m(GPU-MEM)\u001b[0m\u001b[0;36m----[free: 5.17GB, used: 18.83GB, total, 24.00GB]\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m546.42 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m437.29 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m109.13 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0;36m<---------------------------------------|Epoch [49] END|--------------------------------------->\u001b[0m\n",
+ "\u001b[0m\n",
+ "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m50\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m489 (TSEC: 294)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Stage 2]\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+ "\u001b[0;33mPreparing train data...\u001b[0m\n",
+ "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n",
+ "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+ "\u001b[0;32mTraining on subset...\u001b[0m\n",
+ "Epoch 295/300\n",
+ "256/256 [==============================] - 78s 286ms/step - loss: 0.1733 - accuracy: 0.9436 - val_loss: 0.2319 - val_accuracy: 0.9295 - lr: 0.0095\n",
+ "Epoch 296/300\n",
+ "256/256 [==============================] - 72s 281ms/step - loss: 0.1085 - accuracy: 0.9646 - val_loss: 0.2159 - val_accuracy: 0.9311 - lr: 0.0095\n",
+ "Epoch 297/300\n",
+ "256/256 [==============================] - 72s 282ms/step - loss: 0.0659 - accuracy: 0.9795 - val_loss: 0.2045 - val_accuracy: 0.9343 - lr: 0.0095\n",
+ "Epoch 298/300\n",
+ "256/256 [==============================] - 72s 281ms/step - loss: 0.0421 - accuracy: 0.9885 - val_loss: 0.1744 - val_accuracy: 0.9407 - lr: 0.0095\n",
+ "Epoch 299/300\n",
+ "256/256 [==============================] - 73s 283ms/step - loss: 0.0349 - accuracy: 0.9902 - val_loss: 0.3020 - val_accuracy: 0.9439 - lr: 0.0095\n",
+ "Epoch 300/300\n",
+ "256/256 [==============================] - 72s 282ms/step - loss: 0.0290 - accuracy: 0.9924 - val_loss: 0.3009 - val_accuracy: 0.9455 - lr: 0.0095\n",
+ "\u001b[0;32mSubset training done.\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{0.9455}, \u001b[0m\u001b[0;33mloss{0.1744}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9615}, loss{0.1507}]\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9455\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.3008\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9615384340. Not saving model.\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1507172436. Not saving model.\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;32m(GPU-MEM)\u001b[0m\u001b[0;36m----[free: 5.17GB, used: 18.83GB, total, 24.00GB]\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m546.17 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m440.89 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m105.28 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0;36m<---------------------------------------|Epoch [50] END|--------------------------------------->\u001b[0m\n",
+ "\u001b[0m\n",
+ "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m51\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m489 (TSEC: 300)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Stage 2]\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+ "\u001b[0;33mPreparing train data...\u001b[0m\n",
+ "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n",
+ "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+ "\u001b[0;32mTraining on subset...\u001b[0m\n",
+ "Epoch 301/306\n",
+ "256/256 [==============================] - 78s 288ms/step - loss: 0.1551 - accuracy: 0.9495 - val_loss: 0.1855 - val_accuracy: 0.9487 - lr: 0.0095\n",
+ "Epoch 302/306\n",
+ "256/256 [==============================] - 72s 279ms/step - loss: 0.0895 - accuracy: 0.9719 - val_loss: 0.2617 - val_accuracy: 0.9359 - lr: 0.0095\n",
+ "Epoch 303/306\n",
+ "256/256 [==============================] - 71s 278ms/step - loss: 0.0615 - accuracy: 0.9844 - val_loss: 0.2679 - val_accuracy: 0.9359 - lr: 0.0095\n",
+ "Epoch 304/306\n",
+ "256/256 [==============================] - 71s 278ms/step - loss: 0.0486 - accuracy: 0.9863 - val_loss: 0.2134 - val_accuracy: 0.9359 - lr: 0.0095\n",
+ "Epoch 305/306\n",
+ "256/256 [==============================] - 72s 279ms/step - loss: 0.0334 - accuracy: 0.9912 - val_loss: 0.2021 - val_accuracy: 0.9295 - lr: 0.0095\n",
+ "Epoch 306/306\n",
+ "256/256 [==============================] - 72s 279ms/step - loss: 0.0224 - accuracy: 0.9944 - val_loss: 0.2594 - val_accuracy: 0.9343 - lr: 0.0095\n",
+ "\u001b[0;32mSubset training done.\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{0.9487}, \u001b[0m\u001b[0;33mloss{0.1855}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9615}, loss{0.1507}]\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9343\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.2593\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9615384340. Not saving model.\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1507172436. Not saving model.\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;32m(GPU-MEM)\u001b[0m\u001b[0;36m----[free: 5.17GB, used: 18.83GB, total, 24.00GB]\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m545.63 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m436.74 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m108.89 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0;36m<---------------------------------------|Epoch [51] END|--------------------------------------->\u001b[0m\n",
+ "\u001b[0m\n",
+ "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m52\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m489 (TSEC: 306)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Stage 2]\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+ "\u001b[0;33mPreparing train data...\u001b[0m\n",
+ "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n",
+ "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+ "\u001b[0;32mTraining on subset...\u001b[0m\n",
+ "Epoch 307/312\n",
+ "256/256 [==============================] - 78s 287ms/step - loss: 0.1859 - accuracy: 0.9370 - val_loss: 0.2198 - val_accuracy: 0.9327 - lr: 0.0095\n",
+ "Epoch 308/312\n",
+ "256/256 [==============================] - 71s 278ms/step - loss: 0.1066 - accuracy: 0.9648 - val_loss: 0.2533 - val_accuracy: 0.9247 - lr: 0.0095\n",
+ "Epoch 309/312\n",
+ "256/256 [==============================] - 72s 279ms/step - loss: 0.0696 - accuracy: 0.9780 - val_loss: 0.3515 - val_accuracy: 0.9263 - lr: 0.0095\n",
+ "Epoch 310/312\n",
+ "256/256 [==============================] - 71s 278ms/step - loss: 0.0519 - accuracy: 0.9836 - val_loss: 0.2943 - val_accuracy: 0.9311 - lr: 0.0095\n",
+ "Epoch 311/312\n",
+ "256/256 [==============================] - 72s 279ms/step - loss: 0.0390 - accuracy: 0.9900 - val_loss: 0.3001 - val_accuracy: 0.9311 - lr: 0.0095\n",
+ "Epoch 312/312\n",
+ "256/256 [==============================] - 71s 278ms/step - loss: 0.0265 - accuracy: 0.9937 - val_loss: 0.4250 - val_accuracy: 0.9295 - lr: 0.0095\n",
+ "\u001b[0;32mSubset training done.\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{0.9327}, \u001b[0m\u001b[0;33mloss{0.2198}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9615}, loss{0.1507}]\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9295\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.4250\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9615384340. Not saving model.\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1507172436. Not saving model.\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;32m(GPU-MEM)\u001b[0m\u001b[0;36m----[free: 5.17GB, used: 18.83GB, total, 24.00GB]\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m547.27 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m436.31 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m110.95 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0;36m<---------------------------------------|Epoch [52] END|--------------------------------------->\u001b[0m\n",
+ "\u001b[0m\n",
+ "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m53\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m489 (TSEC: 312)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Stage 2]\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+ "\u001b[0;33mPreparing train data...\u001b[0m\n",
+ "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n",
+ "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+ "\u001b[0;32mTraining on subset...\u001b[0m\n",
+ "Epoch 313/318\n",
+ "256/256 [==============================] - 78s 287ms/step - loss: 0.1569 - accuracy: 0.9460 - val_loss: 0.2276 - val_accuracy: 0.9375 - lr: 0.0095\n",
+ "Epoch 314/318\n",
+ "256/256 [==============================] - 72s 282ms/step - loss: 0.0949 - accuracy: 0.9717 - val_loss: 0.2151 - val_accuracy: 0.9407 - lr: 0.0095\n",
+ "Epoch 315/318\n",
+ "256/256 [==============================] - 72s 278ms/step - loss: 0.0694 - accuracy: 0.9800 - val_loss: 0.2282 - val_accuracy: 0.9391 - lr: 0.0095\n",
+ "Epoch 316/318\n",
+ "256/256 [==============================] - 71s 278ms/step - loss: 0.0497 - accuracy: 0.9868 - val_loss: 0.2215 - val_accuracy: 0.9375 - lr: 0.0095\n",
+ "Epoch 317/318\n",
+ "256/256 [==============================] - 72s 280ms/step - loss: 0.0354 - accuracy: 0.9900 - val_loss: 0.2642 - val_accuracy: 0.9311 - lr: 0.0095\n",
+ "Epoch 318/318\n",
+ "256/256 [==============================] - ETA: 0s - loss: 0.0274 - accuracy: 0.9937\n",
+ "Epoch 318: ReduceLROnPlateau reducing learning rate to 0.009299207033589482.\n",
+ "256/256 [==============================] - 71s 278ms/step - loss: 0.0274 - accuracy: 0.9937 - val_loss: 0.2980 - val_accuracy: 0.9263 - lr: 0.0095\n",
+ "\u001b[0;32mSubset training done.\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{0.9407}, \u001b[0m\u001b[0;33mloss{0.2151}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9615}, loss{0.1507}]\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9263\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.2981\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9615384340. Not saving model.\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1507172436. Not saving model.\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;32m(GPU-MEM)\u001b[0m\u001b[0;36m----[free: 5.18GB, used: 18.82GB, total, 24.00GB]\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m550.42 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m437.37 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m113.05 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0;36m<---------------------------------------|Epoch [53] END|--------------------------------------->\u001b[0m\n",
+ "\u001b[0m\n",
+ "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m54\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m489 (TSEC: 318)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Stage 2]\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+ "\u001b[0;33mPreparing train data...\u001b[0m\n",
+ "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n",
+ "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+ "\u001b[0;32mTraining on subset...\u001b[0m\n",
+ "Epoch 319/324\n",
+ "256/256 [==============================] - 78s 287ms/step - loss: 0.1510 - accuracy: 0.9531 - val_loss: 0.1710 - val_accuracy: 0.9407 - lr: 0.0093\n",
+ "Epoch 320/324\n",
+ "256/256 [==============================] - 72s 281ms/step - loss: 0.0903 - accuracy: 0.9714 - val_loss: 0.1643 - val_accuracy: 0.9423 - lr: 0.0093\n",
+ "Epoch 321/324\n",
+ "256/256 [==============================] - 72s 279ms/step - loss: 0.0601 - accuracy: 0.9824 - val_loss: 0.2904 - val_accuracy: 0.9327 - lr: 0.0093\n",
+ "Epoch 322/324\n",
+ "256/256 [==============================] - 72s 278ms/step - loss: 0.0391 - accuracy: 0.9893 - val_loss: 0.2208 - val_accuracy: 0.9391 - lr: 0.0093\n",
+ "Epoch 323/324\n",
+ "256/256 [==============================] - 72s 282ms/step - loss: 0.0324 - accuracy: 0.9915 - val_loss: 0.2139 - val_accuracy: 0.9439 - lr: 0.0093\n",
+ "Epoch 324/324\n",
+ "256/256 [==============================] - 71s 278ms/step - loss: 0.0207 - accuracy: 0.9939 - val_loss: 0.2766 - val_accuracy: 0.9407 - lr: 0.0093\n",
+ "\u001b[0;32mSubset training done.\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{0.9439}, \u001b[0m\u001b[0;33mloss{0.1643}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9615}, loss{0.1507}]\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9407\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.2766\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9615384340. Not saving model.\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1507172436. Not saving model.\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;32m(GPU-MEM)\u001b[0m\u001b[0;36m----[free: 5.18GB, used: 18.82GB, total, 24.00GB]\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m550.43 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m438.08 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m112.35 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0;36m<---------------------------------------|Epoch [54] END|--------------------------------------->\u001b[0m\n",
+ "\u001b[0m\n",
+ "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m55\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m489 (TSEC: 324)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Stage 2]\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+ "\u001b[0;33mPreparing train data...\u001b[0m\n",
+ "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n",
+ "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+ "\u001b[0;32mTraining on subset...\u001b[0m\n",
+ "Epoch 325/330\n",
+ "256/256 [==============================] - 78s 287ms/step - loss: 0.1381 - accuracy: 0.9590 - val_loss: 0.1834 - val_accuracy: 0.9391 - lr: 0.0093\n",
+ "Epoch 326/330\n",
+ "256/256 [==============================] - 72s 282ms/step - loss: 0.0842 - accuracy: 0.9736 - val_loss: 0.2042 - val_accuracy: 0.9407 - lr: 0.0093\n",
+ "Epoch 327/330\n",
+ "256/256 [==============================] - 72s 279ms/step - loss: 0.0535 - accuracy: 0.9854 - val_loss: 0.3113 - val_accuracy: 0.9199 - lr: 0.0093\n",
+ "Epoch 328/330\n",
+ "256/256 [==============================] - 72s 281ms/step - loss: 0.0324 - accuracy: 0.9924 - val_loss: 0.2379 - val_accuracy: 0.9439 - lr: 0.0093\n",
+ "Epoch 329/330\n",
+ "256/256 [==============================] - 72s 283ms/step - loss: 0.0262 - accuracy: 0.9937 - val_loss: 0.1793 - val_accuracy: 0.9487 - lr: 0.0093\n",
+ "Epoch 330/330\n",
+ "256/256 [==============================] - 71s 278ms/step - loss: 0.0233 - accuracy: 0.9941 - val_loss: 0.2066 - val_accuracy: 0.9455 - lr: 0.0093\n",
+ "\u001b[0;32mSubset training done.\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{0.9487}, \u001b[0m\u001b[0;33mloss{0.1793}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9615}, loss{0.1507}]\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9455\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.2066\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9615384340. Not saving model.\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1507172436. Not saving model.\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;32m(GPU-MEM)\u001b[0m\u001b[0;36m----[free: 5.18GB, used: 18.82GB, total, 24.00GB]\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m549.02 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m439.02 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m110.00 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0;36m<---------------------------------------|Epoch [55] END|--------------------------------------->\u001b[0m\n",
+ "\u001b[0m\n",
+ "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m56\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m489 (TSEC: 330)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Stage 2]\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+ "\u001b[0;33mPreparing train data...\u001b[0m\n",
+ "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n",
+ "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+ "\u001b[0;32mTraining on subset...\u001b[0m\n",
+ "Epoch 331/336\n",
+ "256/256 [==============================] - 78s 286ms/step - loss: 0.1498 - accuracy: 0.9478 - val_loss: 0.1643 - val_accuracy: 0.9455 - lr: 0.0093\n",
+ "Epoch 332/336\n",
+ "256/256 [==============================] - 72s 281ms/step - loss: 0.0890 - accuracy: 0.9705 - val_loss: 0.1541 - val_accuracy: 0.9471 - lr: 0.0093\n",
+ "Epoch 333/336\n",
+ "256/256 [==============================] - 72s 279ms/step - loss: 0.0583 - accuracy: 0.9802 - val_loss: 0.2413 - val_accuracy: 0.9359 - lr: 0.0093\n",
+ "Epoch 334/336\n",
+ "256/256 [==============================] - 72s 279ms/step - loss: 0.0374 - accuracy: 0.9866 - val_loss: 0.3158 - val_accuracy: 0.9375 - lr: 0.0093\n",
+ "Epoch 335/336\n",
+ "256/256 [==============================] - 72s 279ms/step - loss: 0.0314 - accuracy: 0.9912 - val_loss: 0.2443 - val_accuracy: 0.9407 - lr: 0.0093\n",
+ "Epoch 336/336\n",
+ "256/256 [==============================] - 72s 279ms/step - loss: 0.0226 - accuracy: 0.9934 - val_loss: 0.2873 - val_accuracy: 0.9295 - lr: 0.0093\n",
+ "\u001b[0;32mSubset training done.\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{0.9471}, \u001b[0m\u001b[0;33mloss{0.1541}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9615}, loss{0.1507}]\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9295\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.2872\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9615384340. Not saving model.\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1507172436. Not saving model.\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;32m(GPU-MEM)\u001b[0m\u001b[0;36m----[free: 5.16GB, used: 18.84GB, total, 24.00GB]\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m551.47 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m437.88 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m113.59 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0;36m<---------------------------------------|Epoch [56] END|--------------------------------------->\u001b[0m\n",
+ "\u001b[0m\n",
+ "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m57\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m489 (TSEC: 336)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Stage 2]\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+ "\u001b[0;33mPreparing train data...\u001b[0m\n",
+ "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n",
+ "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+ "\u001b[0;32mTraining on subset...\u001b[0m\n",
+ "Epoch 337/342\n",
+ "256/256 [==============================] - 78s 288ms/step - loss: 0.1644 - accuracy: 0.9434 - val_loss: 0.1689 - val_accuracy: 0.9471 - lr: 0.0093\n",
+ "Epoch 338/342\n",
+ "256/256 [==============================] - 72s 279ms/step - loss: 0.0994 - accuracy: 0.9683 - val_loss: 0.1996 - val_accuracy: 0.9391 - lr: 0.0093\n",
+ "Epoch 339/342\n",
+ "256/256 [==============================] - 71s 278ms/step - loss: 0.0547 - accuracy: 0.9817 - val_loss: 0.2180 - val_accuracy: 0.9439 - lr: 0.0093\n",
+ "Epoch 340/342\n",
+ "256/256 [==============================] - 71s 277ms/step - loss: 0.0324 - accuracy: 0.9888 - val_loss: 0.3856 - val_accuracy: 0.9135 - lr: 0.0093\n",
+ "Epoch 341/342\n",
+ "256/256 [==============================] - 71s 278ms/step - loss: 0.0305 - accuracy: 0.9907 - val_loss: 0.3454 - val_accuracy: 0.9391 - lr: 0.0093\n",
+ "Epoch 342/342\n",
+ "256/256 [==============================] - 71s 278ms/step - loss: 0.0255 - accuracy: 0.9939 - val_loss: 0.4204 - val_accuracy: 0.9215 - lr: 0.0093\n",
+ "\u001b[0;32mSubset training done.\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{0.9471}, \u001b[0m\u001b[0;33mloss{0.1689}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9615}, loss{0.1507}]\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9215\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.4204\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9615384340. Not saving model.\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1507172436. Not saving model.\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;32m(GPU-MEM)\u001b[0m\u001b[0;36m----[free: 5.18GB, used: 18.82GB, total, 24.00GB]\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m550.62 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m436.75 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m113.87 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0;36m<---------------------------------------|Epoch [57] END|--------------------------------------->\u001b[0m\n",
+ "\u001b[0m\n",
+ "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m58\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m489 (TSEC: 342)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Stage 2]\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+ "\u001b[0;33mPreparing train data...\u001b[0m\n",
+ "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n",
+ "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+ "\u001b[0;32mTraining on subset...\u001b[0m\n",
+ "Epoch 343/348\n",
+ "256/256 [==============================] - 78s 287ms/step - loss: 0.1762 - accuracy: 0.9375 - val_loss: 0.2056 - val_accuracy: 0.9183 - lr: 0.0093\n",
+ "Epoch 344/348\n",
+ "256/256 [==============================] - 72s 282ms/step - loss: 0.1109 - accuracy: 0.9626 - val_loss: 0.1948 - val_accuracy: 0.9311 - lr: 0.0093\n",
+ "Epoch 345/348\n",
+ "256/256 [==============================] - 72s 282ms/step - loss: 0.0762 - accuracy: 0.9780 - val_loss: 0.2274 - val_accuracy: 0.9343 - lr: 0.0093\n",
+ "Epoch 346/348\n",
+ "256/256 [==============================] - 72s 279ms/step - loss: 0.0495 - accuracy: 0.9851 - val_loss: 0.2694 - val_accuracy: 0.9311 - lr: 0.0093\n",
+ "Epoch 347/348\n",
+ "256/256 [==============================] - 72s 282ms/step - loss: 0.0362 - accuracy: 0.9895 - val_loss: 0.2763 - val_accuracy: 0.9423 - lr: 0.0093\n",
+ "Epoch 348/348\n",
+ "256/256 [==============================] - 72s 279ms/step - loss: 0.0209 - accuracy: 0.9946 - val_loss: 0.2977 - val_accuracy: 0.9391 - lr: 0.0093\n",
+ "\u001b[0;32mSubset training done.\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{0.9423}, \u001b[0m\u001b[0;33mloss{0.1948}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9615}, loss{0.1507}]\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9391\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.2976\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9615384340. Not saving model.\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1507172436. Not saving model.\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;32m(GPU-MEM)\u001b[0m\u001b[0;36m----[free: 5.17GB, used: 18.82GB, total, 24.00GB]\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m552.41 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m439.31 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m113.10 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0;36m<---------------------------------------|Epoch [58] END|--------------------------------------->\u001b[0m\n",
+ "\u001b[0m\n",
+ "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m59\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m489 (TSEC: 348)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Stage 2]\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+ "\u001b[0;33mPreparing train data...\u001b[0m\n",
+ "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n",
+ "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+ "\u001b[0;32mTraining on subset...\u001b[0m\n",
+ "Epoch 349/354\n",
+ "256/256 [==============================] - 78s 288ms/step - loss: 0.1428 - accuracy: 0.9558 - val_loss: 0.2571 - val_accuracy: 0.9151 - lr: 0.0093\n",
+ "Epoch 350/354\n",
+ "256/256 [==============================] - 72s 282ms/step - loss: 0.0798 - accuracy: 0.9736 - val_loss: 0.2159 - val_accuracy: 0.9535 - lr: 0.0093\n",
+ "Epoch 351/354\n",
+ "256/256 [==============================] - 72s 279ms/step - loss: 0.0534 - accuracy: 0.9836 - val_loss: 0.2903 - val_accuracy: 0.9263 - lr: 0.0093\n",
+ "Epoch 352/354\n",
+ "256/256 [==============================] - 71s 278ms/step - loss: 0.0337 - accuracy: 0.9927 - val_loss: 0.2496 - val_accuracy: 0.9423 - lr: 0.0093\n",
+ "Epoch 353/354\n",
+ "256/256 [==============================] - 72s 278ms/step - loss: 0.0310 - accuracy: 0.9924 - val_loss: 0.2418 - val_accuracy: 0.9407 - lr: 0.0093\n",
+ "Epoch 354/354\n",
+ "256/256 [==============================] - 71s 278ms/step - loss: 0.0266 - accuracy: 0.9922 - val_loss: 0.3734 - val_accuracy: 0.9311 - lr: 0.0093\n",
+ "\u001b[0;32mSubset training done.\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{0.9535}, \u001b[0m\u001b[0;33mloss{0.2159}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9615}, loss{0.1507}]\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9311\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.3735\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9615384340. Not saving model.\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1507172436. Not saving model.\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;32m(GPU-MEM)\u001b[0m\u001b[0;36m----[free: 5.18GB, used: 18.82GB, total, 24.00GB]\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m550.96 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m437.79 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m113.17 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0;36m<---------------------------------------|Epoch [59] END|--------------------------------------->\u001b[0m\n",
+ "\u001b[0m\n",
+ "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m60\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m489 (TSEC: 354)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Stage 2]\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+ "\u001b[0;33mPreparing train data...\u001b[0m\n",
+ "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n",
+ "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+ "\u001b[0;32mTraining on subset...\u001b[0m\n",
+ "Epoch 355/360\n",
+ "256/256 [==============================] - 78s 286ms/step - loss: 0.1507 - accuracy: 0.9507 - val_loss: 0.1681 - val_accuracy: 0.9359 - lr: 0.0093\n",
+ "Epoch 356/360\n",
+ "256/256 [==============================] - 72s 282ms/step - loss: 0.0962 - accuracy: 0.9705 - val_loss: 0.2051 - val_accuracy: 0.9391 - lr: 0.0093\n",
+ "Epoch 357/360\n",
+ "256/256 [==============================] - 72s 282ms/step - loss: 0.0647 - accuracy: 0.9788 - val_loss: 0.2295 - val_accuracy: 0.9423 - lr: 0.0093\n",
+ "Epoch 358/360\n",
+ "256/256 [==============================] - 72s 282ms/step - loss: 0.0327 - accuracy: 0.9905 - val_loss: 0.2502 - val_accuracy: 0.9455 - lr: 0.0093\n",
+ "Epoch 359/360\n",
+ "256/256 [==============================] - 72s 279ms/step - loss: 0.0246 - accuracy: 0.9937 - val_loss: 0.2248 - val_accuracy: 0.9439 - lr: 0.0093\n",
+ "Epoch 360/360\n",
+ "256/256 [==============================] - 72s 279ms/step - loss: 0.0268 - accuracy: 0.9944 - val_loss: 0.2514 - val_accuracy: 0.9391 - lr: 0.0093\n",
+ "\u001b[0;32mSubset training done.\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{0.9455}, \u001b[0m\u001b[0;33mloss{0.1681}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9615}, loss{0.1507}]\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9391\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.2514\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9615384340. Not saving model.\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1507172436. Not saving model.\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;32m(GPU-MEM)\u001b[0m\u001b[0;36m----[free: 5.18GB, used: 18.82GB, total, 24.00GB]\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m555.71 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m439.11 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m116.60 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0;36m<---------------------------------------|Epoch [60] END|--------------------------------------->\u001b[0m\n",
+ "\u001b[0m\n",
+ "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m61\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m489 (TSEC: 360)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Stage 2]\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+ "\u001b[0;33mPreparing train data...\u001b[0m\n",
+ "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n",
+ "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+ "\u001b[0;32mTraining on subset...\u001b[0m\n",
+ "Epoch 361/366\n",
+ "256/256 [==============================] - 78s 287ms/step - loss: 0.1526 - accuracy: 0.9478 - val_loss: 0.1712 - val_accuracy: 0.9375 - lr: 0.0093\n",
+ "Epoch 362/366\n",
+ "256/256 [==============================] - 73s 283ms/step - loss: 0.0930 - accuracy: 0.9712 - val_loss: 0.1701 - val_accuracy: 0.9487 - lr: 0.0093\n",
+ "Epoch 363/366\n",
+ "256/256 [==============================] - 72s 279ms/step - loss: 0.0547 - accuracy: 0.9846 - val_loss: 0.3175 - val_accuracy: 0.9295 - lr: 0.0093\n",
+ "Epoch 364/366\n",
+ "256/256 [==============================] - 71s 278ms/step - loss: 0.0312 - accuracy: 0.9895 - val_loss: 0.2525 - val_accuracy: 0.9407 - lr: 0.0093\n",
+ "Epoch 365/366\n",
+ "256/256 [==============================] - 72s 279ms/step - loss: 0.0218 - accuracy: 0.9941 - val_loss: 0.3255 - val_accuracy: 0.9327 - lr: 0.0093\n",
+ "Epoch 366/366\n",
+ "256/256 [==============================] - 72s 279ms/step - loss: 0.0187 - accuracy: 0.9954 - val_loss: 0.2214 - val_accuracy: 0.9439 - lr: 0.0093\n",
+ "\u001b[0;32mSubset training done.\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{0.9487}, \u001b[0m\u001b[0;33mloss{0.1701}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9615}, loss{0.1507}]\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9439\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.2214\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9615384340. Not saving model.\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1507172436. Not saving model.\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;32m(GPU-MEM)\u001b[0m\u001b[0;36m----[free: 5.18GB, used: 18.82GB, total, 24.00GB]\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m552.81 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m437.90 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m114.90 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0;36m<---------------------------------------|Epoch [61] END|--------------------------------------->\u001b[0m\n",
+ "\u001b[0m\n",
+ "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m62\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m489 (TSEC: 366)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Stage 2]\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+ "\u001b[0;33mPreparing train data...\u001b[0m\n",
+ "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n",
+ "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+ "\u001b[0;32mTraining on subset...\u001b[0m\n",
+ "Epoch 367/372\n",
+ "256/256 [==============================] - 78s 287ms/step - loss: 0.1570 - accuracy: 0.9517 - val_loss: 0.2226 - val_accuracy: 0.9263 - lr: 0.0093\n",
+ "Epoch 368/372\n",
+ "256/256 [==============================] - 73s 283ms/step - loss: 0.1003 - accuracy: 0.9690 - val_loss: 0.2266 - val_accuracy: 0.9327 - lr: 0.0093\n",
+ "Epoch 369/372\n",
+ "256/256 [==============================] - 72s 280ms/step - loss: 0.0663 - accuracy: 0.9817 - val_loss: 0.3376 - val_accuracy: 0.9167 - lr: 0.0093\n",
+ "Epoch 370/372\n",
+ "256/256 [==============================] - 71s 279ms/step - loss: 0.0470 - accuracy: 0.9844 - val_loss: 0.2824 - val_accuracy: 0.9151 - lr: 0.0093\n",
+ "Epoch 371/372\n",
+ "256/256 [==============================] - 72s 280ms/step - loss: 0.0282 - accuracy: 0.9912 - val_loss: 0.3004 - val_accuracy: 0.9167 - lr: 0.0093\n",
+ "Epoch 372/372\n",
+ "256/256 [==============================] - 72s 279ms/step - loss: 0.0243 - accuracy: 0.9944 - val_loss: 0.2254 - val_accuracy: 0.9327 - lr: 0.0093\n",
+ "\u001b[0;32mSubset training done.\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{0.9327}, \u001b[0m\u001b[0;33mloss{0.2226}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9615}, loss{0.1507}]\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9327\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.2254\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9615384340. Not saving model.\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1507172436. Not saving model.\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;32m(GPU-MEM)\u001b[0m\u001b[0;36m----[free: 5.18GB, used: 18.82GB, total, 24.00GB]\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m551.46 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m438.57 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m112.89 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0;36m<---------------------------------------|Epoch [62] END|--------------------------------------->\u001b[0m\n",
+ "\u001b[0m\n",
+ "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m63\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m489 (TSEC: 372)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Stage 2]\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+ "\u001b[0;33mPreparing train data...\u001b[0m\n",
+ "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n",
+ "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+ "\u001b[0;32mTraining on subset...\u001b[0m\n",
+ "Epoch 373/378\n",
+ "256/256 [==============================] - 78s 288ms/step - loss: 0.1464 - accuracy: 0.9521 - val_loss: 0.3296 - val_accuracy: 0.9135 - lr: 0.0093\n",
+ "Epoch 374/378\n",
+ "256/256 [==============================] - 73s 283ms/step - loss: 0.0846 - accuracy: 0.9758 - val_loss: 0.3144 - val_accuracy: 0.9167 - lr: 0.0093\n",
+ "Epoch 375/378\n",
+ "256/256 [==============================] - 72s 279ms/step - loss: 0.0528 - accuracy: 0.9841 - val_loss: 0.2769 - val_accuracy: 0.9151 - lr: 0.0093\n",
+ "Epoch 376/378\n",
+ "256/256 [==============================] - 72s 279ms/step - loss: 0.0304 - accuracy: 0.9897 - val_loss: 0.4158 - val_accuracy: 0.9071 - lr: 0.0093\n",
+ "Epoch 377/378\n",
+ "256/256 [==============================] - 73s 284ms/step - loss: 0.0290 - accuracy: 0.9937 - val_loss: 0.2375 - val_accuracy: 0.9215 - lr: 0.0093\n",
+ "Epoch 378/378\n",
+ "256/256 [==============================] - 73s 283ms/step - loss: 0.0267 - accuracy: 0.9927 - val_loss: 0.1940 - val_accuracy: 0.9407 - lr: 0.0093\n",
+ "\u001b[0;32mSubset training done.\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{0.9407}, \u001b[0m\u001b[0;33mloss{0.1940}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9615}, loss{0.1507}]\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9407\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.1940\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9615384340. Not saving model.\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1507172436. Not saving model.\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;32m(GPU-MEM)\u001b[0m\u001b[0;36m----[free: 5.18GB, used: 18.82GB, total, 24.00GB]\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m555.86 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m440.61 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m115.25 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0;36m<---------------------------------------|Epoch [63] END|--------------------------------------->\u001b[0m\n",
+ "\u001b[0m\n",
+ "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m64\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m489 (TSEC: 378)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Stage 2]\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+ "\u001b[0;33mPreparing train data...\u001b[0m\n",
+ "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n",
+ "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+ "\u001b[0;32mTraining on subset...\u001b[0m\n",
+ "Epoch 379/384\n",
+ "256/256 [==============================] - 79s 289ms/step - loss: 0.1658 - accuracy: 0.9478 - val_loss: 0.1683 - val_accuracy: 0.9311 - lr: 0.0093\n",
+ "Epoch 380/384\n",
+ "256/256 [==============================] - 73s 283ms/step - loss: 0.1027 - accuracy: 0.9697 - val_loss: 0.1709 - val_accuracy: 0.9327 - lr: 0.0093\n",
+ "Epoch 381/384\n",
+ "256/256 [==============================] - 73s 283ms/step - loss: 0.0663 - accuracy: 0.9802 - val_loss: 0.1952 - val_accuracy: 0.9423 - lr: 0.0093\n",
+ "Epoch 382/384\n",
+ "256/256 [==============================] - 72s 279ms/step - loss: 0.0364 - accuracy: 0.9895 - val_loss: 0.2132 - val_accuracy: 0.9279 - lr: 0.0093\n",
+ "Epoch 383/384\n",
+ "256/256 [==============================] - ETA: 0s - loss: 0.0272 - accuracy: 0.9941\n",
+ "Epoch 383: ReduceLROnPlateau reducing learning rate to 0.009131821744143963.\n",
+ "256/256 [==============================] - 72s 280ms/step - loss: 0.0272 - accuracy: 0.9941 - val_loss: 0.2627 - val_accuracy: 0.9151 - lr: 0.0093\n",
+ "Epoch 384/384\n",
+ "256/256 [==============================] - 72s 279ms/step - loss: 0.0305 - accuracy: 0.9937 - val_loss: 0.2253 - val_accuracy: 0.9263 - lr: 0.0091\n",
+ "\u001b[0;32mSubset training done.\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{0.9423}, \u001b[0m\u001b[0;33mloss{0.1683}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9615}, loss{0.1507}]\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9263\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.2253\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9615384340. Not saving model.\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1507172436. Not saving model.\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;32m(GPU-MEM)\u001b[0m\u001b[0;36m----[free: 5.17GB, used: 18.82GB, total, 24.00GB]\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m557.97 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m440.50 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m117.47 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0;36m<---------------------------------------|Epoch [64] END|--------------------------------------->\u001b[0m\n",
+ "\u001b[0m\n",
+ "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m65\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m489 (TSEC: 384)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Stage 2]\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+ "\u001b[0;33mPreparing train data...\u001b[0m\n",
+ "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n",
+ "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+ "\u001b[0;32mTraining on subset...\u001b[0m\n",
+ "Epoch 385/390\n",
+ "256/256 [==============================] - 78s 287ms/step - loss: 0.1475 - accuracy: 0.9512 - val_loss: 0.1970 - val_accuracy: 0.9247 - lr: 0.0091\n",
+ "Epoch 386/390\n",
+ "256/256 [==============================] - 72s 282ms/step - loss: 0.0880 - accuracy: 0.9719 - val_loss: 0.2173 - val_accuracy: 0.9375 - lr: 0.0091\n",
+ "Epoch 387/390\n",
+ "256/256 [==============================] - 72s 280ms/step - loss: 0.0646 - accuracy: 0.9817 - val_loss: 0.1910 - val_accuracy: 0.9375 - lr: 0.0091\n",
+ "Epoch 388/390\n",
+ "256/256 [==============================] - 72s 280ms/step - loss: 0.0267 - accuracy: 0.9937 - val_loss: 0.3254 - val_accuracy: 0.9311 - lr: 0.0091\n",
+ "Epoch 389/390\n",
+ "256/256 [==============================] - 72s 280ms/step - loss: 0.0243 - accuracy: 0.9934 - val_loss: 0.2513 - val_accuracy: 0.9359 - lr: 0.0091\n",
+ "Epoch 390/390\n",
+ "256/256 [==============================] - 72s 280ms/step - loss: 0.0185 - accuracy: 0.9941 - val_loss: 0.2746 - val_accuracy: 0.9295 - lr: 0.0091\n",
+ "\u001b[0;32mSubset training done.\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{0.9375}, \u001b[0m\u001b[0;33mloss{0.1910}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9615}, loss{0.1507}]\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9295\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.2746\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9615384340. Not saving model.\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1507172436. Not saving model.\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;32m(GPU-MEM)\u001b[0m\u001b[0;36m----[free: 5.18GB, used: 18.82GB, total, 24.00GB]\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m555.02 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m438.76 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m116.27 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0;36m<---------------------------------------|Epoch [65] END|--------------------------------------->\u001b[0m\n",
+ "\u001b[0m\n",
+ "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m66\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m489 (TSEC: 390)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Stage 2]\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+ "\u001b[0;33mPreparing train data...\u001b[0m\n",
+ "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n",
+ "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+ "\u001b[0;32mTraining on subset...\u001b[0m\n",
+ "Epoch 391/396\n",
+ "256/256 [==============================] - 78s 288ms/step - loss: 0.1466 - accuracy: 0.9553 - val_loss: 0.1735 - val_accuracy: 0.9311 - lr: 0.0091\n",
+ "Epoch 392/396\n",
+ "256/256 [==============================] - 72s 279ms/step - loss: 0.0834 - accuracy: 0.9744 - val_loss: 0.2050 - val_accuracy: 0.9311 - lr: 0.0091\n",
+ "Epoch 393/396\n",
+ "256/256 [==============================] - 72s 280ms/step - loss: 0.0528 - accuracy: 0.9849 - val_loss: 0.2451 - val_accuracy: 0.9247 - lr: 0.0091\n",
+ "Epoch 394/396\n",
+ "256/256 [==============================] - 72s 282ms/step - loss: 0.0319 - accuracy: 0.9907 - val_loss: 0.2601 - val_accuracy: 0.9407 - lr: 0.0091\n",
+ "Epoch 395/396\n",
+ "256/256 [==============================] - 72s 280ms/step - loss: 0.0238 - accuracy: 0.9944 - val_loss: 0.3207 - val_accuracy: 0.9311 - lr: 0.0091\n",
+ "Epoch 396/396\n",
+ "256/256 [==============================] - 73s 283ms/step - loss: 0.0176 - accuracy: 0.9961 - val_loss: 0.2425 - val_accuracy: 0.9455 - lr: 0.0091\n",
+ "\u001b[0;32mSubset training done.\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{0.9455}, \u001b[0m\u001b[0;33mloss{0.1735}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9615}, loss{0.1507}]\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9439\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.2558\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9615384340. Not saving model.\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1507172436. Not saving model.\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;32m(GPU-MEM)\u001b[0m\u001b[0;36m----[free: 5.17GB, used: 18.82GB, total, 24.00GB]\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m561.06 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m439.89 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m121.16 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0;36m<---------------------------------------|Epoch [66] END|--------------------------------------->\u001b[0m\n",
+ "\u001b[0m\n",
+ "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m67\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m489 (TSEC: 396)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Stage 2]\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+ "\u001b[0;33mPreparing train data...\u001b[0m\n",
+ "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n",
+ "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+ "\u001b[0;32mTraining on subset...\u001b[0m\n",
+ "Epoch 397/402\n",
+ "256/256 [==============================] - 78s 288ms/step - loss: 0.1413 - accuracy: 0.9556 - val_loss: 0.2070 - val_accuracy: 0.9215 - lr: 0.0091\n",
+ "Epoch 398/402\n",
+ "256/256 [==============================] - 72s 282ms/step - loss: 0.0911 - accuracy: 0.9719 - val_loss: 0.1711 - val_accuracy: 0.9407 - lr: 0.0091\n",
+ "Epoch 399/402\n",
+ "256/256 [==============================] - 72s 281ms/step - loss: 0.0450 - accuracy: 0.9868 - val_loss: 0.2181 - val_accuracy: 0.9263 - lr: 0.0091\n",
+ "Epoch 400/402\n",
+ "256/256 [==============================] - 72s 279ms/step - loss: 0.0310 - accuracy: 0.9922 - val_loss: 0.1772 - val_accuracy: 0.9375 - lr: 0.0091\n",
+ "Epoch 401/402\n",
+ "256/256 [==============================] - 73s 283ms/step - loss: 0.0230 - accuracy: 0.9941 - val_loss: 0.1968 - val_accuracy: 0.9471 - lr: 0.0091\n",
+ "Epoch 402/402\n",
+ "256/256 [==============================] - 72s 279ms/step - loss: 0.0228 - accuracy: 0.9944 - val_loss: 0.2389 - val_accuracy: 0.9263 - lr: 0.0091\n",
+ "\u001b[0;32mSubset training done.\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{0.9471}, \u001b[0m\u001b[0;33mloss{0.1711}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9615}, loss{0.1507}]\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9263\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.2390\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9615384340. Not saving model.\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1507172436. Not saving model.\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;32m(GPU-MEM)\u001b[0m\u001b[0;36m----[free: 5.18GB, used: 18.82GB, total, 24.00GB]\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m558.20 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m439.77 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m118.43 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0;36m<---------------------------------------|Epoch [67] END|--------------------------------------->\u001b[0m\n",
+ "\u001b[0m\n",
+ "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m68\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m489 (TSEC: 402)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Stage 2]\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+ "\u001b[0;33mPreparing train data...\u001b[0m\n",
+ "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n",
+ "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+ "\u001b[0;32mTraining on subset...\u001b[0m\n",
+ "Epoch 403/408\n",
+ "256/256 [==============================] - 78s 288ms/step - loss: 0.1415 - accuracy: 0.9543 - val_loss: 0.1678 - val_accuracy: 0.9423 - lr: 0.0091\n",
+ "Epoch 404/408\n",
+ "256/256 [==============================] - 72s 280ms/step - loss: 0.0974 - accuracy: 0.9717 - val_loss: 0.2233 - val_accuracy: 0.9167 - lr: 0.0091\n",
+ "Epoch 405/408\n",
+ "256/256 [==============================] - 72s 280ms/step - loss: 0.0535 - accuracy: 0.9839 - val_loss: 0.1927 - val_accuracy: 0.9279 - lr: 0.0091\n",
+ "Epoch 406/408\n",
+ "256/256 [==============================] - 72s 279ms/step - loss: 0.0391 - accuracy: 0.9883 - val_loss: 0.2091 - val_accuracy: 0.9279 - lr: 0.0091\n",
+ "Epoch 407/408\n",
+ "256/256 [==============================] - 72s 281ms/step - loss: 0.0260 - accuracy: 0.9924 - val_loss: 0.2332 - val_accuracy: 0.9295 - lr: 0.0091\n",
+ "Epoch 408/408\n",
+ "256/256 [==============================] - 72s 279ms/step - loss: 0.0215 - accuracy: 0.9949 - val_loss: 0.2343 - val_accuracy: 0.9279 - lr: 0.0091\n",
+ "\u001b[0;32mSubset training done.\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{0.9423}, \u001b[0m\u001b[0;33mloss{0.1678}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9615}, loss{0.1507}]\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9279\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.2343\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9615384340. Not saving model.\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1507172436. Not saving model.\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;32m(GPU-MEM)\u001b[0m\u001b[0;36m----[free: 5.18GB, used: 18.82GB, total, 24.00GB]\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m558.04 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m438.78 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m119.26 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0;36m<---------------------------------------|Epoch [68] END|--------------------------------------->\u001b[0m\n",
+ "\u001b[0m\n",
+ "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m69\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m489 (TSEC: 408)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Stage 2]\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+ "\u001b[0;33mPreparing train data...\u001b[0m\n",
+ "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n",
+ "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+ "\u001b[0;32mTraining on subset...\u001b[0m\n",
+ "Epoch 409/414\n",
+ "256/256 [==============================] - 79s 289ms/step - loss: 0.1623 - accuracy: 0.9475 - val_loss: 0.1975 - val_accuracy: 0.9391 - lr: 0.0091\n",
+ "Epoch 410/414\n",
+ "256/256 [==============================] - 73s 283ms/step - loss: 0.1023 - accuracy: 0.9653 - val_loss: 0.1627 - val_accuracy: 0.9471 - lr: 0.0091\n",
+ "Epoch 411/414\n",
+ "256/256 [==============================] - 72s 281ms/step - loss: 0.0608 - accuracy: 0.9824 - val_loss: 0.2578 - val_accuracy: 0.9199 - lr: 0.0091\n",
+ "Epoch 412/414\n",
+ "256/256 [==============================] - 72s 278ms/step - loss: 0.0401 - accuracy: 0.9888 - val_loss: 0.2376 - val_accuracy: 0.9375 - lr: 0.0091\n",
+ "Epoch 413/414\n",
+ "256/256 [==============================] - 72s 280ms/step - loss: 0.0334 - accuracy: 0.9902 - val_loss: 0.2274 - val_accuracy: 0.9359 - lr: 0.0091\n",
+ "Epoch 414/414\n",
+ "256/256 [==============================] - 72s 279ms/step - loss: 0.0218 - accuracy: 0.9941 - val_loss: 0.2506 - val_accuracy: 0.9311 - lr: 0.0091\n",
+ "\u001b[0;32mSubset training done.\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{0.9471}, \u001b[0m\u001b[0;33mloss{0.1627}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9615}, loss{0.1507}]\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9311\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.2506\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9615384340. Not saving model.\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1507172436. Not saving model.\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;32m(GPU-MEM)\u001b[0m\u001b[0;36m----[free: 5.18GB, used: 18.82GB, total, 24.00GB]\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m559.65 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m439.56 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m120.09 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0;36m<---------------------------------------|Epoch [69] END|--------------------------------------->\u001b[0m\n",
+ "\u001b[0m\n",
+ "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m70\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m489 (TSEC: 414)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Stage 2]\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+ "\u001b[0;33mPreparing train data...\u001b[0m\n",
+ "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n",
+ "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+ "\u001b[0;32mTraining on subset...\u001b[0m\n",
+ "Epoch 415/420\n",
+ "256/256 [==============================] - 78s 289ms/step - loss: 0.1488 - accuracy: 0.9524 - val_loss: 0.1494 - val_accuracy: 0.9423 - lr: 0.0091\n",
+ "Epoch 416/420\n",
+ "256/256 [==============================] - 73s 283ms/step - loss: 0.0901 - accuracy: 0.9719 - val_loss: 0.1522 - val_accuracy: 0.9439 - lr: 0.0091\n",
+ "Epoch 417/420\n",
+ "256/256 [==============================] - 73s 284ms/step - loss: 0.0483 - accuracy: 0.9863 - val_loss: 0.1585 - val_accuracy: 0.9455 - lr: 0.0091\n",
+ "Epoch 418/420\n",
+ "256/256 [==============================] - 72s 280ms/step - loss: 0.0380 - accuracy: 0.9880 - val_loss: 0.2341 - val_accuracy: 0.9263 - lr: 0.0091\n",
+ "Epoch 419/420\n",
+ "256/256 [==============================] - 73s 284ms/step - loss: 0.0260 - accuracy: 0.9922 - val_loss: 0.1966 - val_accuracy: 0.9487 - lr: 0.0091\n",
+ "Epoch 420/420\n",
+ "256/256 [==============================] - 72s 279ms/step - loss: 0.0216 - accuracy: 0.9937 - val_loss: 0.3120 - val_accuracy: 0.9135 - lr: 0.0091\n",
+ "\u001b[0;32mSubset training done.\u001b[0m\n",
+ "\u001b[0;33mLoading the best weights...\u001b[0m\n",
+ "\u001b[0;33mLoading weights from file cache\\model_SUB_checkpoint-419-0.9487.h5...\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9487\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.1967\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9615384340. Not saving model.\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1507172436. Not saving model.\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;32m(GPU-MEM)\u001b[0m\u001b[0;36m----[free: 5.18GB, used: 18.82GB, total, 24.00GB]\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m564.62 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m441.50 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m123.11 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0;36m<---------------------------------------|Epoch [70] END|--------------------------------------->\u001b[0m\n",
+ "\u001b[0m\n",
+ "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m71\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m489 (TSEC: 420)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Stage 2]\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+ "\u001b[0;33mPreparing train data...\u001b[0m\n",
+ "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n",
+ "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+ "\u001b[0;32mTraining on subset...\u001b[0m\n",
+ "Epoch 421/426\n",
+ "256/256 [==============================] - 78s 288ms/step - loss: 0.1333 - accuracy: 0.9587 - val_loss: 0.1808 - val_accuracy: 0.9407 - lr: 0.0091\n",
+ "Epoch 422/426\n",
+ "256/256 [==============================] - 72s 279ms/step - loss: 0.0725 - accuracy: 0.9785 - val_loss: 0.2205 - val_accuracy: 0.9263 - lr: 0.0091\n",
+ "Epoch 423/426\n",
+ "256/256 [==============================] - 72s 280ms/step - loss: 0.0480 - accuracy: 0.9873 - val_loss: 0.2139 - val_accuracy: 0.9311 - lr: 0.0091\n",
+ "Epoch 424/426\n",
+ "256/256 [==============================] - 72s 280ms/step - loss: 0.0302 - accuracy: 0.9927 - val_loss: 0.2470 - val_accuracy: 0.9311 - lr: 0.0091\n",
+ "Epoch 425/426\n",
+ "256/256 [==============================] - 72s 279ms/step - loss: 0.0242 - accuracy: 0.9939 - val_loss: 0.3428 - val_accuracy: 0.8958 - lr: 0.0091\n",
+ "Epoch 426/426\n",
+ "256/256 [==============================] - 72s 280ms/step - loss: 0.0226 - accuracy: 0.9937 - val_loss: 0.2293 - val_accuracy: 0.9359 - lr: 0.0091\n",
+ "\u001b[0;32mSubset training done.\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{0.9407}, \u001b[0m\u001b[0;33mloss{0.1808}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9615}, loss{0.1507}]\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9359\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.2293\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9615384340. Not saving model.\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1507172436. Not saving model.\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;32m(GPU-MEM)\u001b[0m\u001b[0;36m----[free: 5.18GB, used: 18.82GB, total, 24.00GB]\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m561.24 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m438.60 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m122.64 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0;36m<---------------------------------------|Epoch [71] END|--------------------------------------->\u001b[0m\n",
+ "\u001b[0m\n",
+ "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m72\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m489 (TSEC: 426)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Stage 2]\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+ "\u001b[0;33mPreparing train data...\u001b[0m\n",
+ "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n",
+ "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+ "\u001b[0;32mTraining on subset...\u001b[0m\n",
+ "Epoch 427/432\n",
+ "256/256 [==============================] - 78s 288ms/step - loss: 0.1330 - accuracy: 0.9551 - val_loss: 0.1782 - val_accuracy: 0.9375 - lr: 0.0091\n",
+ "Epoch 428/432\n",
+ "256/256 [==============================] - 72s 280ms/step - loss: 0.0870 - accuracy: 0.9707 - val_loss: 0.2001 - val_accuracy: 0.9231 - lr: 0.0091\n",
+ "Epoch 429/432\n",
+ "256/256 [==============================] - 72s 280ms/step - loss: 0.0539 - accuracy: 0.9814 - val_loss: 0.2954 - val_accuracy: 0.9215 - lr: 0.0091\n",
+ "Epoch 430/432\n",
+ "256/256 [==============================] - 72s 280ms/step - loss: 0.0305 - accuracy: 0.9929 - val_loss: 0.3820 - val_accuracy: 0.9151 - lr: 0.0091\n",
+ "Epoch 431/432\n",
+ "256/256 [==============================] - 72s 280ms/step - loss: 0.0232 - accuracy: 0.9956 - val_loss: 0.2771 - val_accuracy: 0.9279 - lr: 0.0091\n",
+ "Epoch 432/432\n",
+ "256/256 [==============================] - 72s 280ms/step - loss: 0.0206 - accuracy: 0.9941 - val_loss: 0.2467 - val_accuracy: 0.9199 - lr: 0.0091\n",
+ "\u001b[0;32mSubset training done.\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{0.9375}, \u001b[0m\u001b[0;33mloss{0.1782}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9615}, loss{0.1507}]\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9199\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.2466\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9615384340. Not saving model.\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1507172436. Not saving model.\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;32m(GPU-MEM)\u001b[0m\u001b[0;36m----[free: 5.16GB, used: 18.84GB, total, 24.00GB]\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m562.22 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m438.66 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m123.56 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0;36m<---------------------------------------|Epoch [72] END|--------------------------------------->\u001b[0m\n",
+ "\u001b[0m\n",
+ "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m73\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m489 (TSEC: 432)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Stage 2]\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+ "\u001b[0;33mPreparing train data...\u001b[0m\n",
+ "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n",
+ "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+ "\u001b[0;32mTraining on subset...\u001b[0m\n",
+ "Epoch 433/438\n",
+ "256/256 [==============================] - 79s 289ms/step - loss: 0.1536 - accuracy: 0.9478 - val_loss: 0.2038 - val_accuracy: 0.9311 - lr: 0.0091\n",
+ "Epoch 434/438\n",
+ "256/256 [==============================] - 72s 280ms/step - loss: 0.0833 - accuracy: 0.9751 - val_loss: 0.2294 - val_accuracy: 0.9087 - lr: 0.0091\n",
+ "Epoch 435/438\n",
+ "256/256 [==============================] - 72s 282ms/step - loss: 0.0488 - accuracy: 0.9866 - val_loss: 0.2332 - val_accuracy: 0.9151 - lr: 0.0091\n",
+ "Epoch 436/438\n",
+ "256/256 [==============================] - 72s 280ms/step - loss: 0.0327 - accuracy: 0.9897 - val_loss: 0.2499 - val_accuracy: 0.9167 - lr: 0.0091\n",
+ "Epoch 437/438\n",
+ "256/256 [==============================] - 72s 280ms/step - loss: 0.0235 - accuracy: 0.9927 - val_loss: 0.3192 - val_accuracy: 0.9119 - lr: 0.0091\n",
+ "Epoch 438/438\n",
+ "256/256 [==============================] - 72s 279ms/step - loss: 0.0188 - accuracy: 0.9944 - val_loss: 0.2932 - val_accuracy: 0.9167 - lr: 0.0091\n",
+ "\u001b[0;32mSubset training done.\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{0.9311}, \u001b[0m\u001b[0;33mloss{0.2038}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9615}, loss{0.1507}]\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9167\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.2931\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9615384340. Not saving model.\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1507172436. Not saving model.\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;32m(GPU-MEM)\u001b[0m\u001b[0;36m----[free: 5.18GB, used: 18.82GB, total, 24.00GB]\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m561.86 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m439.36 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m122.50 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0;36m<---------------------------------------|Epoch [73] END|--------------------------------------->\u001b[0m\n",
+ "\u001b[0m\n",
+ "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m74\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m489 (TSEC: 438)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Stage 2]\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+ "\u001b[0;33mPreparing train data...\u001b[0m\n",
+ "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n",
+ "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+ "\u001b[0;32mTraining on subset...\u001b[0m\n",
+ "Epoch 439/444\n",
+ "256/256 [==============================] - 78s 289ms/step - loss: 0.1456 - accuracy: 0.9500 - val_loss: 0.2129 - val_accuracy: 0.9022 - lr: 0.0091\n",
+ "Epoch 440/444\n",
+ "256/256 [==============================] - 73s 283ms/step - loss: 0.0840 - accuracy: 0.9722 - val_loss: 0.3310 - val_accuracy: 0.9071 - lr: 0.0091\n",
+ "Epoch 441/444\n",
+ "256/256 [==============================] - 73s 284ms/step - loss: 0.0483 - accuracy: 0.9849 - val_loss: 0.3311 - val_accuracy: 0.9151 - lr: 0.0091\n",
+ "Epoch 442/444\n",
+ "256/256 [==============================] - 73s 283ms/step - loss: 0.0315 - accuracy: 0.9915 - val_loss: 0.2708 - val_accuracy: 0.9231 - lr: 0.0091\n",
+ "Epoch 443/444\n",
+ "256/256 [==============================] - 72s 281ms/step - loss: 0.0227 - accuracy: 0.9951 - val_loss: 0.2968 - val_accuracy: 0.9231 - lr: 0.0091\n",
+ "Epoch 444/444\n",
+ "256/256 [==============================] - 72s 280ms/step - loss: 0.0191 - accuracy: 0.9961 - val_loss: 0.2900 - val_accuracy: 0.9183 - lr: 0.0091\n",
+ "\u001b[0;32mSubset training done.\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{0.9231}, \u001b[0m\u001b[0;33mloss{0.2129}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9615}, loss{0.1507}]\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9183\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.2900\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9615384340. Not saving model.\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1507172436. Not saving model.\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;32m(GPU-MEM)\u001b[0m\u001b[0;36m----[free: 5.17GB, used: 18.83GB, total, 24.00GB]\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m569.56 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m442.04 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m127.52 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0;36m<---------------------------------------|Epoch [74] END|--------------------------------------->\u001b[0m\n",
+ "\u001b[0m\n",
+ "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m75\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m489 (TSEC: 444)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Stage 2]\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+ "\u001b[0;33mPreparing train data...\u001b[0m\n",
+ "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n",
+ "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+ "\u001b[0;32mTraining on subset...\u001b[0m\n",
+ "Epoch 445/450\n",
+ "256/256 [==============================] - 79s 289ms/step - loss: 0.1509 - accuracy: 0.9526 - val_loss: 0.2378 - val_accuracy: 0.9006 - lr: 0.0091\n",
+ "Epoch 446/450\n",
+ "256/256 [==============================] - 73s 284ms/step - loss: 0.0915 - accuracy: 0.9707 - val_loss: 0.2024 - val_accuracy: 0.9199 - lr: 0.0091\n",
+ "Epoch 447/450\n",
+ "256/256 [==============================] - 72s 281ms/step - loss: 0.0512 - accuracy: 0.9819 - val_loss: 0.3725 - val_accuracy: 0.9135 - lr: 0.0091\n",
+ "Epoch 448/450\n",
+ "256/256 [==============================] - ETA: 0s - loss: 0.0394 - accuracy: 0.9893\n",
+ "Epoch 448: ReduceLROnPlateau reducing learning rate to 0.008967449011281133.\n",
+ "256/256 [==============================] - 72s 281ms/step - loss: 0.0394 - accuracy: 0.9893 - val_loss: 0.2490 - val_accuracy: 0.9119 - lr: 0.0091\n",
+ "Epoch 449/450\n",
+ "256/256 [==============================] - 73s 284ms/step - loss: 0.0200 - accuracy: 0.9951 - val_loss: 0.2879 - val_accuracy: 0.9231 - lr: 0.0090\n",
+ "Epoch 450/450\n",
+ "256/256 [==============================] - 72s 281ms/step - loss: 0.0201 - accuracy: 0.9954 - val_loss: 0.3820 - val_accuracy: 0.9119 - lr: 0.0090\n",
+ "\u001b[0;32mSubset training done.\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{0.9231}, \u001b[0m\u001b[0;33mloss{0.2024}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9615}, loss{0.1507}]\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9119\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.3820\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9615384340. Not saving model.\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1507172436. Not saving model.\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;32m(GPU-MEM)\u001b[0m\u001b[0;36m----[free: 5.17GB, used: 18.83GB, total, 24.00GB]\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m564.96 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m441.96 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m122.99 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0;36m<---------------------------------------|Epoch [75] END|--------------------------------------->\u001b[0m\n",
+ "\u001b[0m\n",
+ "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m76\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m489 (TSEC: 450)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Stage 2]\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+ "\u001b[0;33mPreparing train data...\u001b[0m\n",
+ "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n",
+ "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+ "\u001b[0;32mTraining on subset...\u001b[0m\n",
+ "Epoch 451/456\n",
+ "256/256 [==============================] - 78s 288ms/step - loss: 0.1432 - accuracy: 0.9556 - val_loss: 0.3285 - val_accuracy: 0.8990 - lr: 0.0090\n",
+ "Epoch 452/456\n",
+ "256/256 [==============================] - 73s 284ms/step - loss: 0.0862 - accuracy: 0.9749 - val_loss: 0.2752 - val_accuracy: 0.9119 - lr: 0.0090\n",
+ "Epoch 453/456\n",
+ "256/256 [==============================] - 73s 284ms/step - loss: 0.0580 - accuracy: 0.9849 - val_loss: 0.2599 - val_accuracy: 0.9215 - lr: 0.0090\n",
+ "Epoch 454/456\n",
+ "256/256 [==============================] - 72s 279ms/step - loss: 0.0491 - accuracy: 0.9873 - val_loss: 0.3314 - val_accuracy: 0.9199 - lr: 0.0090\n",
+ "Epoch 455/456\n",
+ "256/256 [==============================] - 72s 280ms/step - loss: 0.0426 - accuracy: 0.9885 - val_loss: 0.4193 - val_accuracy: 0.9103 - lr: 0.0090\n",
+ "Epoch 456/456\n",
+ "256/256 [==============================] - 72s 279ms/step - loss: 0.0298 - accuracy: 0.9927 - val_loss: 0.3315 - val_accuracy: 0.9183 - lr: 0.0090\n",
+ "\u001b[0;32mSubset training done.\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{0.9215}, \u001b[0m\u001b[0;33mloss{0.2599}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9615}, loss{0.1507}]\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9199\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.3315\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9615384340. Not saving model.\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1507172436. Not saving model.\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;32m(GPU-MEM)\u001b[0m\u001b[0;36m----[free: 5.17GB, used: 18.83GB, total, 24.00GB]\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m565.54 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m440.57 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m124.97 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0;36m<---------------------------------------|Epoch [76] END|--------------------------------------->\u001b[0m\n",
+ "\u001b[0m\n",
+ "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m77\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m489 (TSEC: 456)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Stage 2]\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+ "\u001b[0;33mPreparing train data...\u001b[0m\n",
+ "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n",
+ "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+ "\u001b[0;32mTraining on subset...\u001b[0m\n",
+ "Epoch 457/462\n",
+ "256/256 [==============================] - 78s 288ms/step - loss: 0.1317 - accuracy: 0.9607 - val_loss: 0.1910 - val_accuracy: 0.9359 - lr: 0.0090\n",
+ "Epoch 458/462\n",
+ "256/256 [==============================] - 72s 279ms/step - loss: 0.0786 - accuracy: 0.9778 - val_loss: 0.2189 - val_accuracy: 0.9231 - lr: 0.0090\n",
+ "Epoch 459/462\n",
+ "256/256 [==============================] - 72s 281ms/step - loss: 0.0537 - accuracy: 0.9846 - val_loss: 0.2701 - val_accuracy: 0.9071 - lr: 0.0090\n",
+ "Epoch 460/462\n",
+ "256/256 [==============================] - 72s 279ms/step - loss: 0.0304 - accuracy: 0.9919 - val_loss: 0.2740 - val_accuracy: 0.9279 - lr: 0.0090\n",
+ "Epoch 461/462\n",
+ "256/256 [==============================] - 72s 281ms/step - loss: 0.0240 - accuracy: 0.9937 - val_loss: 0.3064 - val_accuracy: 0.9263 - lr: 0.0090\n",
+ "Epoch 462/462\n",
+ "256/256 [==============================] - 72s 280ms/step - loss: 0.0166 - accuracy: 0.9951 - val_loss: 0.3085 - val_accuracy: 0.9311 - lr: 0.0090\n",
+ "\u001b[0;32mSubset training done.\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{0.9359}, \u001b[0m\u001b[0;33mloss{0.1910}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9615}, loss{0.1507}]\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9311\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.3085\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9615384340. Not saving model.\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1507172436. Not saving model.\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;32m(GPU-MEM)\u001b[0m\u001b[0;36m----[free: 5.18GB, used: 18.82GB, total, 24.00GB]\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m562.59 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m439.06 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m123.53 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0;36m<---------------------------------------|Epoch [77] END|--------------------------------------->\u001b[0m\n",
+ "\u001b[0m\n",
+ "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m78\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m489 (TSEC: 462)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Stage 2]\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+ "\u001b[0;33mPreparing train data...\u001b[0m\n",
+ "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n",
+ "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+ "\u001b[0;32mTraining on subset...\u001b[0m\n",
+ "Epoch 463/468\n",
+ "256/256 [==============================] - 79s 290ms/step - loss: 0.1341 - accuracy: 0.9551 - val_loss: 0.2453 - val_accuracy: 0.8894 - lr: 0.0090\n",
+ "Epoch 464/468\n",
+ "256/256 [==============================] - 73s 284ms/step - loss: 0.0742 - accuracy: 0.9746 - val_loss: 0.2062 - val_accuracy: 0.9199 - lr: 0.0090\n",
+ "Epoch 465/468\n",
+ "256/256 [==============================] - 73s 284ms/step - loss: 0.0496 - accuracy: 0.9841 - val_loss: 0.2408 - val_accuracy: 0.9295 - lr: 0.0090\n",
+ "Epoch 466/468\n",
+ "256/256 [==============================] - 73s 284ms/step - loss: 0.0233 - accuracy: 0.9927 - val_loss: 0.2731 - val_accuracy: 0.9311 - lr: 0.0090\n",
+ "Epoch 467/468\n",
+ "256/256 [==============================] - 73s 282ms/step - loss: 0.0219 - accuracy: 0.9949 - val_loss: 0.2976 - val_accuracy: 0.9295 - lr: 0.0090\n",
+ "Epoch 468/468\n",
+ "256/256 [==============================] - 72s 281ms/step - loss: 0.0160 - accuracy: 0.9954 - val_loss: 0.3426 - val_accuracy: 0.9263 - lr: 0.0090\n",
+ "\u001b[0;32mSubset training done.\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{0.9311}, \u001b[0m\u001b[0;33mloss{0.2062}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9615}, loss{0.1507}]\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9263\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.3425\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9615384340. Not saving model.\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1507172436. Not saving model.\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;32m(GPU-MEM)\u001b[0m\u001b[0;36m----[free: 5.18GB, used: 18.82GB, total, 24.00GB]\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m566.24 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m443.29 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m122.95 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0;36m<---------------------------------------|Epoch [78] END|--------------------------------------->\u001b[0m\n",
+ "\u001b[0m\n",
+ "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m79\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m489 (TSEC: 468)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Stage 2]\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+ "\u001b[0;33mPreparing train data...\u001b[0m\n",
+ "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n",
+ "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+ "\u001b[0;32mTraining on subset...\u001b[0m\n",
+ "Epoch 469/474\n",
+ "256/256 [==============================] - 78s 288ms/step - loss: 0.1446 - accuracy: 0.9548 - val_loss: 0.1887 - val_accuracy: 0.9311 - lr: 0.0090\n",
+ "Epoch 470/474\n",
+ "256/256 [==============================] - 73s 283ms/step - loss: 0.0811 - accuracy: 0.9758 - val_loss: 0.1896 - val_accuracy: 0.9407 - lr: 0.0090\n",
+ "Epoch 471/474\n",
+ "256/256 [==============================] - 72s 281ms/step - loss: 0.0573 - accuracy: 0.9822 - val_loss: 0.2068 - val_accuracy: 0.9359 - lr: 0.0090\n",
+ "Epoch 472/474\n",
+ "256/256 [==============================] - 72s 280ms/step - loss: 0.0319 - accuracy: 0.9912 - val_loss: 0.2018 - val_accuracy: 0.9343 - lr: 0.0090\n",
+ "Epoch 473/474\n",
+ "256/256 [==============================] - 73s 285ms/step - loss: 0.0297 - accuracy: 0.9915 - val_loss: 0.2370 - val_accuracy: 0.9423 - lr: 0.0090\n",
+ "Epoch 474/474\n",
+ "256/256 [==============================] - 72s 280ms/step - loss: 0.0218 - accuracy: 0.9958 - val_loss: 0.2261 - val_accuracy: 0.9423 - lr: 0.0090\n",
+ "\u001b[0;32mSubset training done.\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{0.9423}, \u001b[0m\u001b[0;33mloss{0.1887}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9615}, loss{0.1507}]\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9423\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.2261\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9615384340. Not saving model.\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1507172436. Not saving model.\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;32m(GPU-MEM)\u001b[0m\u001b[0;36m----[free: 5.18GB, used: 18.82GB, total, 24.00GB]\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m570.84 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m441.02 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m129.82 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0;36m<---------------------------------------|Epoch [79] END|--------------------------------------->\u001b[0m\n",
+ "\u001b[0m\n",
+ "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m80\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m489 (TSEC: 474)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Stage 2]\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+ "\u001b[0;33mPreparing train data...\u001b[0m\n",
+ "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n",
+ "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+ "\u001b[0;32mTraining on subset...\u001b[0m\n",
+ "Epoch 475/480\n",
+ "256/256 [==============================] - 79s 291ms/step - loss: 0.1372 - accuracy: 0.9600 - val_loss: 0.1696 - val_accuracy: 0.9407 - lr: 0.0090\n",
+ "Epoch 476/480\n",
+ "256/256 [==============================] - 73s 283ms/step - loss: 0.0866 - accuracy: 0.9739 - val_loss: 0.1879 - val_accuracy: 0.9487 - lr: 0.0090\n",
+ "Epoch 477/480\n",
+ "256/256 [==============================] - 72s 281ms/step - loss: 0.0508 - accuracy: 0.9839 - val_loss: 0.2168 - val_accuracy: 0.9439 - lr: 0.0090\n",
+ "Epoch 478/480\n",
+ "256/256 [==============================] - 72s 280ms/step - loss: 0.0376 - accuracy: 0.9883 - val_loss: 0.2498 - val_accuracy: 0.9439 - lr: 0.0090\n",
+ "Epoch 479/480\n",
+ "256/256 [==============================] - 72s 281ms/step - loss: 0.0249 - accuracy: 0.9941 - val_loss: 0.2011 - val_accuracy: 0.9343 - lr: 0.0090\n",
+ "Epoch 480/480\n",
+ "256/256 [==============================] - 72s 281ms/step - loss: 0.0199 - accuracy: 0.9963 - val_loss: 0.2483 - val_accuracy: 0.9295 - lr: 0.0090\n",
+ "\u001b[0;32mSubset training done.\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{0.9487}, \u001b[0m\u001b[0;33mloss{0.1696}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9615}, loss{0.1507}]\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9295\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.2483\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9615384340. Not saving model.\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1507172436. Not saving model.\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;32m(GPU-MEM)\u001b[0m\u001b[0;36m----[free: 5.17GB, used: 18.82GB, total, 24.00GB]\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m568.81 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m441.35 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m127.46 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0;36m<---------------------------------------|Epoch [80] END|--------------------------------------->\u001b[0m\n",
+ "\u001b[0m\n",
+ "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m81\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m489 (TSEC: 480)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Stage 2]\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+ "\u001b[0;33mPreparing train data...\u001b[0m\n",
+ "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n",
+ "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+ "\u001b[0;32mTraining on subset...\u001b[0m\n",
+ "Epoch 481/486\n",
+ "256/256 [==============================] - 78s 288ms/step - loss: 0.1453 - accuracy: 0.9512 - val_loss: 0.2448 - val_accuracy: 0.9231 - lr: 0.0090\n",
+ "Epoch 482/486\n",
+ "256/256 [==============================] - 72s 282ms/step - loss: 0.0865 - accuracy: 0.9734 - val_loss: 0.5154 - val_accuracy: 0.9119 - lr: 0.0090\n",
+ "Epoch 483/486\n",
+ "256/256 [==============================] - 73s 284ms/step - loss: 0.0515 - accuracy: 0.9846 - val_loss: 0.2043 - val_accuracy: 0.9375 - lr: 0.0090\n",
+ "Epoch 484/486\n",
+ "256/256 [==============================] - 73s 284ms/step - loss: 0.0302 - accuracy: 0.9917 - val_loss: 0.1861 - val_accuracy: 0.9423 - lr: 0.0090\n",
+ "Epoch 485/486\n",
+ "256/256 [==============================] - 72s 280ms/step - loss: 0.0303 - accuracy: 0.9910 - val_loss: 0.6762 - val_accuracy: 0.8990 - lr: 0.0090\n",
+ "Epoch 486/486\n",
+ "256/256 [==============================] - 72s 280ms/step - loss: 0.0413 - accuracy: 0.9907 - val_loss: 0.3812 - val_accuracy: 0.9054 - lr: 0.0090\n",
+ "\u001b[0;32mSubset training done.\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{0.9423}, \u001b[0m\u001b[0;33mloss{0.1861}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9615}, loss{0.1507}]\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9038\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.4067\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9615384340. Not saving model.\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1507172436. Not saving model.\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;32m(GPU-MEM)\u001b[0m\u001b[0;36m----[free: 5.18GB, used: 18.82GB, total, 24.00GB]\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m571.00 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m441.56 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m129.43 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0;36m<---------------------------------------|Epoch [81] END|--------------------------------------->\u001b[0m\n",
+ "\u001b[0m\n",
+ "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m82\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m489 (TSEC: 486)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Stage 2]\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+ "\u001b[0;33mPreparing train data...\u001b[0m\n",
+ "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n",
+ "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+ "\u001b[0;32mTraining on subset...\u001b[0m\n",
+ "Epoch 487/492\n",
+ "256/256 [==============================] - 79s 289ms/step - loss: 0.1381 - accuracy: 0.9534 - val_loss: 0.2291 - val_accuracy: 0.9183 - lr: 0.0090\n",
+ "Epoch 488/492\n",
+ "256/256 [==============================] - 72s 280ms/step - loss: 0.0866 - accuracy: 0.9744 - val_loss: 0.2882 - val_accuracy: 0.8894 - lr: 0.0090\n",
+ "Epoch 489/492\n",
+ "256/256 [==============================] - 72s 282ms/step - loss: 0.0542 - accuracy: 0.9829 - val_loss: 0.3209 - val_accuracy: 0.9006 - lr: 0.0090\n",
+ "Epoch 490/492\n",
+ "256/256 [==============================] - 72s 281ms/step - loss: 0.0381 - accuracy: 0.9883 - val_loss: 0.6577 - val_accuracy: 0.8782 - lr: 0.0090\n",
+ "Epoch 491/492\n",
+ "256/256 [==============================] - 72s 281ms/step - loss: 0.0260 - accuracy: 0.9924 - val_loss: 0.6497 - val_accuracy: 0.8926 - lr: 0.0090\n",
+ "Epoch 492/492\n",
+ "256/256 [==============================] - 72s 280ms/step - loss: 0.0223 - accuracy: 0.9939 - val_loss: 1.7101 - val_accuracy: 0.7997 - lr: 0.0090\n",
+ "\u001b[0;32mSubset training done.\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{0.9183}, \u001b[0m\u001b[0;33mloss{0.2291}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9615}, loss{0.1507}]\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.7965\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m1.7177\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9615384340. Not saving model.\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1507172436. Not saving model.\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;32m(GPU-MEM)\u001b[0m\u001b[0;36m----[free: 5.17GB, used: 18.82GB, total, 24.00GB]\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m568.85 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m440.24 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m128.62 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0;36m<---------------------------------------|Epoch [82] END|--------------------------------------->\u001b[0m\n",
+ "\u001b[0m\n",
+ "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m83\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m489 (TSEC: 492)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Stage 2]\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+ "\u001b[0;33mPreparing train data...\u001b[0m\n",
+ "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n",
+ "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+ "\u001b[0;32mTraining on subset...\u001b[0m\n",
+ "Epoch 493/498\n",
+ "256/256 [==============================] - 79s 289ms/step - loss: 0.1347 - accuracy: 0.9539 - val_loss: 1.3282 - val_accuracy: 0.8141 - lr: 0.0090\n",
+ "Epoch 494/498\n",
+ "256/256 [==============================] - 72s 280ms/step - loss: 0.0738 - accuracy: 0.9780 - val_loss: 1.7828 - val_accuracy: 0.8013 - lr: 0.0090\n",
+ "Epoch 495/498\n",
+ "256/256 [==============================] - 72s 282ms/step - loss: 0.0451 - accuracy: 0.9863 - val_loss: 2.3645 - val_accuracy: 0.8109 - lr: 0.0090\n",
+ "Epoch 496/498\n",
+ "256/256 [==============================] - 73s 285ms/step - loss: 0.0307 - accuracy: 0.9924 - val_loss: 0.8665 - val_accuracy: 0.8734 - lr: 0.0090\n",
+ "Epoch 497/498\n",
+ "256/256 [==============================] - 73s 285ms/step - loss: 0.0228 - accuracy: 0.9949 - val_loss: 0.3687 - val_accuracy: 0.9215 - lr: 0.0090\n",
+ "Epoch 498/498\n",
+ "256/256 [==============================] - 73s 285ms/step - loss: 0.0248 - accuracy: 0.9937 - val_loss: 0.3038 - val_accuracy: 0.9359 - lr: 0.0090\n",
+ "\u001b[0;32mSubset training done.\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{0.9359}, \u001b[0m\u001b[0;33mloss{0.3038}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9615}, loss{0.1507}]\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9359\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.3038\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9615384340. Not saving model.\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1507172436. Not saving model.\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;32m(GPU-MEM)\u001b[0m\u001b[0;36m----[free: 5.18GB, used: 18.82GB, total, 24.00GB]\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m569.57 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m443.66 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m125.92 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0;36m<---------------------------------------|Epoch [83] END|--------------------------------------->\u001b[0m\n",
+ "\u001b[0m\n",
+ "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m84\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m489 (TSEC: 498)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Stage 2]\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+ "\u001b[0;33m└───Shuffling data...\u001b[0m\n",
+ "\u001b[0;33mPreparing train data...\u001b[0m\n",
+ "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n",
+ "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;31m- Debug DP Sample dir: \u001b[0m\u001b[0;32mSamples/TSR_SUB_400_y2024_m03_d30-h09_m07_s13\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+ "\u001b[0;32mTraining on subset...\u001b[0m\n",
+ "Epoch 499/504\n",
+ "256/256 [==============================] - 79s 288ms/step - loss: 0.1438 - accuracy: 0.9543 - val_loss: 0.2068 - val_accuracy: 0.9295 - lr: 0.0090\n",
+ "Epoch 500/504\n",
+ "256/256 [==============================] - 72s 280ms/step - loss: 0.0814 - accuracy: 0.9758 - val_loss: 0.3795 - val_accuracy: 0.9054 - lr: 0.0090\n",
+ "Epoch 501/504\n",
+ "256/256 [==============================] - 72s 280ms/step - loss: 0.0593 - accuracy: 0.9827 - val_loss: 0.6725 - val_accuracy: 0.8942 - lr: 0.0090\n",
+ "Epoch 502/504\n",
+ "256/256 [==============================] - 72s 280ms/step - loss: 0.0305 - accuracy: 0.9927 - val_loss: 0.4958 - val_accuracy: 0.9151 - lr: 0.0090\n",
+ "Epoch 503/504\n",
+ "256/256 [==============================] - 72s 281ms/step - loss: 0.0261 - accuracy: 0.9939 - val_loss: 0.3201 - val_accuracy: 0.9279 - lr: 0.0090\n",
+ "Epoch 504/504\n",
+ "256/256 [==============================] - 72s 281ms/step - loss: 0.0251 - accuracy: 0.9941 - val_loss: 0.5018 - val_accuracy: 0.9151 - lr: 0.0090\n",
+ "\u001b[0;32mSubset training done.\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{0.9295}, \u001b[0m\u001b[0;33mloss{0.2068}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9615}, loss{0.1507}]\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9151\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.4984\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9615384340. Not saving model.\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1507172436. Not saving model.\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;32m(GPU-MEM)\u001b[0m\u001b[0;36m----[free: 5.18GB, used: 18.82GB, total, 24.00GB]\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m581.46 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m439.90 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m141.55 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0;36m<---------------------------------------|Epoch [84] END|--------------------------------------->\u001b[0m\n",
+ "\u001b[0m\n",
+ "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m85\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m489 (TSEC: 504)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Stage 2]\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+ "\u001b[0;33mPreparing train data...\u001b[0m\n",
+ "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n",
+ "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+ "\u001b[0;32mTraining on subset...\u001b[0m\n",
+ "Epoch 505/510\n",
+ "256/256 [==============================] - 78s 288ms/step - loss: 0.1284 - accuracy: 0.9575 - val_loss: 0.9978 - val_accuracy: 0.8830 - lr: 0.0090\n",
+ "Epoch 506/510\n",
+ "256/256 [==============================] - 73s 284ms/step - loss: 0.0736 - accuracy: 0.9758 - val_loss: 0.7478 - val_accuracy: 0.8910 - lr: 0.0090\n",
+ "Epoch 507/510\n",
+ "256/256 [==============================] - 73s 285ms/step - loss: 0.0506 - accuracy: 0.9856 - val_loss: 0.5992 - val_accuracy: 0.9054 - lr: 0.0090\n",
+ "Epoch 508/510\n",
+ "256/256 [==============================] - 72s 281ms/step - loss: 0.0228 - accuracy: 0.9939 - val_loss: 0.7798 - val_accuracy: 0.8782 - lr: 0.0090\n",
+ "Epoch 509/510\n",
+ "256/256 [==============================] - 72s 282ms/step - loss: 0.0185 - accuracy: 0.9958 - val_loss: 0.7670 - val_accuracy: 0.8750 - lr: 0.0090\n",
+ "Epoch 510/510\n",
+ "256/256 [==============================] - 72s 281ms/step - loss: 0.0102 - accuracy: 0.9976 - val_loss: 0.8993 - val_accuracy: 0.8670 - lr: 0.0090\n",
+ "\u001b[0;32mSubset training done.\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{0.9054}, \u001b[0m\u001b[0;33mloss{0.5992}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9615}, loss{0.1507}]\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.8670\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.8991\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9615384340. Not saving model.\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1507172436. Not saving model.\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;32m(GPU-MEM)\u001b[0m\u001b[0;36m----[free: 5.18GB, used: 18.82GB, total, 24.00GB]\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m568.18 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m442.62 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m125.56 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0;36m<---------------------------------------|Epoch [85] END|--------------------------------------->\u001b[0m\n",
+ "\u001b[0m\n",
+ "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m86\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m489 (TSEC: 510)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Stage 2]\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+ "\u001b[0;33mPreparing train data...\u001b[0m\n",
+ "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n",
+ "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+ "\u001b[0;32mTraining on subset...\u001b[0m\n",
+ "Epoch 511/516\n",
+ "256/256 [==============================] - 79s 289ms/step - loss: 0.1551 - accuracy: 0.9495 - val_loss: 0.3373 - val_accuracy: 0.8862 - lr: 0.0090\n",
+ "Epoch 512/516\n",
+ "256/256 [==============================] - 72s 281ms/step - loss: 0.0868 - accuracy: 0.9727 - val_loss: 0.8053 - val_accuracy: 0.8814 - lr: 0.0090\n",
+ "Epoch 513/516\n",
+ "256/256 [==============================] - ETA: 0s - loss: 0.0534 - accuracy: 0.9841\n",
+ "Epoch 513: ReduceLROnPlateau reducing learning rate to 0.008806034876033663.\n",
+ "256/256 [==============================] - 73s 286ms/step - loss: 0.0534 - accuracy: 0.9841 - val_loss: 0.2876 - val_accuracy: 0.9167 - lr: 0.0090\n",
+ "Epoch 514/516\n",
+ "256/256 [==============================] - 72s 282ms/step - loss: 0.0333 - accuracy: 0.9900 - val_loss: 0.6914 - val_accuracy: 0.8878 - lr: 0.0088\n",
+ "Epoch 515/516\n",
+ "256/256 [==============================] - 73s 283ms/step - loss: 0.0242 - accuracy: 0.9932 - val_loss: 0.5452 - val_accuracy: 0.8942 - lr: 0.0088\n",
+ "Epoch 516/516\n",
+ "256/256 [==============================] - 72s 281ms/step - loss: 0.0241 - accuracy: 0.9934 - val_loss: 0.7726 - val_accuracy: 0.8638 - lr: 0.0088\n",
+ "\u001b[0;32mSubset training done.\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{0.9167}, \u001b[0m\u001b[0;33mloss{0.2876}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9615}, loss{0.1507}]\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.8638\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.7727\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9615384340. Not saving model.\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1507172436. Not saving model.\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;32m(GPU-MEM)\u001b[0m\u001b[0;36m----[free: 5.18GB, used: 18.82GB, total, 24.00GB]\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m574.58 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m442.74 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m131.84 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0;36m<---------------------------------------|Epoch [86] END|--------------------------------------->\u001b[0m\n",
+ "\u001b[0m\n",
+ "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m87\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m489 (TSEC: 516)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Stage 2]\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+ "\u001b[0;33mPreparing train data...\u001b[0m\n",
+ "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n",
+ "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+ "\u001b[0;32mTraining on subset...\u001b[0m\n",
+ "Epoch 517/522\n",
+ "256/256 [==============================] - 79s 291ms/step - loss: 0.1453 - accuracy: 0.9541 - val_loss: 0.4466 - val_accuracy: 0.8974 - lr: 0.0088\n",
+ "Epoch 518/522\n",
+ "256/256 [==============================] - 72s 281ms/step - loss: 0.0795 - accuracy: 0.9761 - val_loss: 0.5019 - val_accuracy: 0.8926 - lr: 0.0088\n",
+ "Epoch 519/522\n",
+ "256/256 [==============================] - 72s 281ms/step - loss: 0.0578 - accuracy: 0.9841 - val_loss: 0.6239 - val_accuracy: 0.8622 - lr: 0.0088\n",
+ "Epoch 520/522\n",
+ "256/256 [==============================] - 73s 284ms/step - loss: 0.0289 - accuracy: 0.9915 - val_loss: 0.4784 - val_accuracy: 0.9006 - lr: 0.0088\n",
+ "Epoch 521/522\n",
+ "256/256 [==============================] - 72s 282ms/step - loss: 0.0198 - accuracy: 0.9927 - val_loss: 0.8253 - val_accuracy: 0.8846 - lr: 0.0088\n",
+ "Epoch 522/522\n",
+ "256/256 [==============================] - 72s 281ms/step - loss: 0.0190 - accuracy: 0.9954 - val_loss: 1.0015 - val_accuracy: 0.8702 - lr: 0.0088\n",
+ "\u001b[0;32mSubset training done.\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{0.9006}, \u001b[0m\u001b[0;33mloss{0.4466}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9615}, loss{0.1507}]\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.8702\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m1.0208\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9615384340. Not saving model.\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1507172436. Not saving model.\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;32m(GPU-MEM)\u001b[0m\u001b[0;36m----[free: 5.18GB, used: 18.82GB, total, 24.00GB]\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m573.36 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m441.95 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m131.40 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0;36m<---------------------------------------|Epoch [87] END|--------------------------------------->\u001b[0m\n",
+ "\u001b[0m\n",
+ "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m88\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m489 (TSEC: 522)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Stage 2]\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+ "\u001b[0;33mPreparing train data...\u001b[0m\n",
+ "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n",
+ "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+ "\u001b[0;32mTraining on subset...\u001b[0m\n",
+ "Epoch 523/528\n",
+ "256/256 [==============================] - 79s 289ms/step - loss: 0.1377 - accuracy: 0.9536 - val_loss: 0.3953 - val_accuracy: 0.9038 - lr: 0.0088\n",
+ "Epoch 524/528\n",
+ "256/256 [==============================] - 72s 281ms/step - loss: 0.0610 - accuracy: 0.9807 - val_loss: 0.4758 - val_accuracy: 0.9038 - lr: 0.0088\n",
+ "Epoch 525/528\n",
+ "256/256 [==============================] - 73s 284ms/step - loss: 0.0381 - accuracy: 0.9873 - val_loss: 0.5061 - val_accuracy: 0.9087 - lr: 0.0088\n",
+ "Epoch 526/528\n",
+ "256/256 [==============================] - 72s 280ms/step - loss: 0.0293 - accuracy: 0.9917 - val_loss: 0.8461 - val_accuracy: 0.8990 - lr: 0.0088\n",
+ "Epoch 527/528\n",
+ "256/256 [==============================] - 73s 284ms/step - loss: 0.0155 - accuracy: 0.9958 - val_loss: 0.4623 - val_accuracy: 0.9183 - lr: 0.0088\n",
+ "Epoch 528/528\n",
+ "256/256 [==============================] - 73s 284ms/step - loss: 0.0247 - accuracy: 0.9941 - val_loss: 0.2015 - val_accuracy: 0.9535 - lr: 0.0088\n",
+ "\u001b[0;32mSubset training done.\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{0.9535}, \u001b[0m\u001b[0;33mloss{0.2015}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9615}, loss{0.1507}]\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9535\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.2014\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9615384340. Not saving model.\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1507172436. Not saving model.\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;32m(GPU-MEM)\u001b[0m\u001b[0;36m----[free: 5.18GB, used: 18.82GB, total, 24.00GB]\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m572.14 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m442.48 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m129.67 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0;36m<---------------------------------------|Epoch [88] END|--------------------------------------->\u001b[0m\n",
+ "\u001b[0m\n",
+ "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m89\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m489 (TSEC: 528)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Stage 2]\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+ "\u001b[0;33mPreparing train data...\u001b[0m\n",
+ "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n",
+ "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+ "\u001b[0;32mTraining on subset...\u001b[0m\n",
+ "Epoch 529/534\n",
+ "256/256 [==============================] - 79s 290ms/step - loss: 0.1402 - accuracy: 0.9553 - val_loss: 0.3024 - val_accuracy: 0.9359 - lr: 0.0088\n",
+ "Epoch 530/534\n",
+ "256/256 [==============================] - 72s 281ms/step - loss: 0.0776 - accuracy: 0.9746 - val_loss: 0.7350 - val_accuracy: 0.9151 - lr: 0.0088\n",
+ "Epoch 531/534\n",
+ "256/256 [==============================] - 72s 281ms/step - loss: 0.0541 - accuracy: 0.9839 - val_loss: 1.0363 - val_accuracy: 0.8894 - lr: 0.0088\n",
+ "Epoch 532/534\n",
+ "256/256 [==============================] - 73s 283ms/step - loss: 0.0306 - accuracy: 0.9917 - val_loss: 2.3302 - val_accuracy: 0.8574 - lr: 0.0088\n",
+ "Epoch 533/534\n",
+ "256/256 [==============================] - 72s 282ms/step - loss: 0.0208 - accuracy: 0.9946 - val_loss: 2.3128 - val_accuracy: 0.8462 - lr: 0.0088\n",
+ "Epoch 534/534\n",
+ "256/256 [==============================] - 72s 281ms/step - loss: 0.0186 - accuracy: 0.9954 - val_loss: 1.8001 - val_accuracy: 0.8654 - lr: 0.0088\n",
+ "\u001b[0;32mSubset training done.\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{0.9359}, \u001b[0m\u001b[0;33mloss{0.3024}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9615}, loss{0.1507}]\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.8654\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m1.7998\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9615384340. Not saving model.\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1507172436. Not saving model.\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;32m(GPU-MEM)\u001b[0m\u001b[0;36m----[free: 5.18GB, used: 18.82GB, total, 24.00GB]\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m572.27 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m441.16 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m131.11 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0;36m<---------------------------------------|Epoch [89] END|--------------------------------------->\u001b[0m\n",
+ "\u001b[0m\n",
+ "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m90\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m489 (TSEC: 534)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Stage 2]\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+ "\u001b[0;33mPreparing train data...\u001b[0m\n",
+ "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n",
+ "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+ "\u001b[0;32mTraining on subset...\u001b[0m\n",
+ "Epoch 535/540\n",
+ "256/256 [==============================] - 79s 290ms/step - loss: 0.1489 - accuracy: 0.9570 - val_loss: 0.4613 - val_accuracy: 0.8846 - lr: 0.0088\n",
+ "Epoch 536/540\n",
+ "256/256 [==============================] - 73s 285ms/step - loss: 0.0844 - accuracy: 0.9763 - val_loss: 0.1915 - val_accuracy: 0.9375 - lr: 0.0088\n",
+ "Epoch 537/540\n",
+ "256/256 [==============================] - 72s 282ms/step - loss: 0.0488 - accuracy: 0.9880 - val_loss: 0.2034 - val_accuracy: 0.9295 - lr: 0.0088\n",
+ "Epoch 538/540\n",
+ "256/256 [==============================] - 72s 282ms/step - loss: 0.0293 - accuracy: 0.9932 - val_loss: 0.5313 - val_accuracy: 0.9054 - lr: 0.0088\n",
+ "Epoch 539/540\n",
+ "256/256 [==============================] - 73s 283ms/step - loss: 0.0214 - accuracy: 0.9951 - val_loss: 0.4062 - val_accuracy: 0.9183 - lr: 0.0088\n",
+ "Epoch 540/540\n",
+ "256/256 [==============================] - 72s 281ms/step - loss: 0.0269 - accuracy: 0.9934 - val_loss: 0.5009 - val_accuracy: 0.9183 - lr: 0.0088\n",
+ "\u001b[0;32mSubset training done.\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{0.9375}, \u001b[0m\u001b[0;33mloss{0.1915}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9615}, loss{0.1507}]\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9183\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.5005\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9615384340. Not saving model.\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1507172436. Not saving model.\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;32m(GPU-MEM)\u001b[0m\u001b[0;36m----[free: 5.18GB, used: 18.82GB, total, 24.00GB]\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m575.44 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m442.71 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m132.73 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0;36m<---------------------------------------|Epoch [90] END|--------------------------------------->\u001b[0m\n",
+ "\u001b[0m\n",
+ "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m91\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m489 (TSEC: 540)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Stage 2]\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+ "\u001b[0;33mPreparing train data...\u001b[0m\n",
+ "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n",
+ "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+ "\u001b[0;32mTraining on subset...\u001b[0m\n",
+ "Epoch 541/546\n",
+ "256/256 [==============================] - 79s 291ms/step - loss: 0.1381 - accuracy: 0.9539 - val_loss: 0.1409 - val_accuracy: 0.9503 - lr: 0.0088\n",
+ "Epoch 542/546\n",
+ "256/256 [==============================] - 72s 281ms/step - loss: 0.0859 - accuracy: 0.9731 - val_loss: 0.1610 - val_accuracy: 0.9455 - lr: 0.0088\n",
+ "Epoch 543/546\n",
+ "256/256 [==============================] - 73s 283ms/step - loss: 0.0549 - accuracy: 0.9832 - val_loss: 0.1565 - val_accuracy: 0.9471 - lr: 0.0088\n",
+ "Epoch 544/546\n",
+ "256/256 [==============================] - 72s 281ms/step - loss: 0.0320 - accuracy: 0.9912 - val_loss: 0.2411 - val_accuracy: 0.9199 - lr: 0.0088\n",
+ "Epoch 545/546\n",
+ "256/256 [==============================] - 73s 283ms/step - loss: 0.0251 - accuracy: 0.9944 - val_loss: 0.2242 - val_accuracy: 0.9279 - lr: 0.0088\n",
+ "Epoch 546/546\n",
+ "256/256 [==============================] - 72s 282ms/step - loss: 0.0158 - accuracy: 0.9961 - val_loss: 0.3142 - val_accuracy: 0.9247 - lr: 0.0088\n",
+ "\u001b[0;32mSubset training done.\u001b[0m\n",
+ "\u001b[0;33mLoading the best weights...\u001b[0m\n",
+ "\u001b[0;33mLoading weights from file cache\\model_SUB_checkpoint-541-0.9503.h5...\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9503\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.1409\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9615384340. Not saving model.\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;32mImproved model loss from 0.15071724 to 0.14092456. \u001b[0m\u001b[0;96mSaving model.\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;36mSaving full model H5 format...\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;32m(GPU-MEM)\u001b[0m\u001b[0;36m----[free: 5.17GB, used: 18.82GB, total, 24.00GB]\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m580.86 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m442.09 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m138.76 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0;36m<---------------------------------------|Epoch [91] END|--------------------------------------->\u001b[0m\n",
+ "\u001b[0m\n",
+ "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m92\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m489 (TSEC: 546)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Stage 2]\u001b[0m\n",
"\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n",
"\u001b[0;33mPreparing train data...\u001b[0m\n",
- "\u001b[0;33m- Fitting ImageDataGenerator...\u001b[0m\n"
+ "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n",
+ "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+ "\u001b[0;32mTraining on subset...\u001b[0m\n",
+ "Epoch 547/552\n",
+ "256/256 [==============================] - 79s 290ms/step - loss: 0.1398 - accuracy: 0.9526 - val_loss: 0.4507 - val_accuracy: 0.9071 - lr: 0.0088\n",
+ "Epoch 548/552\n",
+ "256/256 [==============================] - 73s 285ms/step - loss: 0.0904 - accuracy: 0.9714 - val_loss: 0.4890 - val_accuracy: 0.9183 - lr: 0.0088\n",
+ "Epoch 549/552\n",
+ "256/256 [==============================] - 73s 286ms/step - loss: 0.0518 - accuracy: 0.9824 - val_loss: 0.4768 - val_accuracy: 0.9199 - lr: 0.0088\n",
+ "Epoch 550/552\n",
+ "256/256 [==============================] - 73s 282ms/step - loss: 0.0311 - accuracy: 0.9912 - val_loss: 1.0640 - val_accuracy: 0.8862 - lr: 0.0088\n",
+ "Epoch 551/552\n",
+ "256/256 [==============================] - 73s 282ms/step - loss: 0.0231 - accuracy: 0.9932 - val_loss: 0.8470 - val_accuracy: 0.8974 - lr: 0.0088\n",
+ "Epoch 552/552\n",
+ "256/256 [==============================] - 73s 283ms/step - loss: 0.0222 - accuracy: 0.9951 - val_loss: 0.8176 - val_accuracy: 0.9135 - lr: 0.0088\n",
+ "\u001b[0;32mSubset training done.\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{0.9199}, \u001b[0m\u001b[0;33mloss{0.4507}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9615}, loss{0.1409}]\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9135\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.8165\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9615384340. Not saving model.\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1409245580. Not saving model.\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;32m(GPU-MEM)\u001b[0m\u001b[0;36m----[free: 5.18GB, used: 18.82GB, total, 24.00GB]\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m578.78 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m443.96 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m134.82 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0;36m<---------------------------------------|Epoch [92] END|--------------------------------------->\u001b[0m\n",
+ "\u001b[0m\n",
+ "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m93\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m489 (TSEC: 552)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Stage 2]\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+ "\u001b[0;33mPreparing train data...\u001b[0m\n",
+ "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n",
+ "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+ "\u001b[0;32mTraining on subset...\u001b[0m\n",
+ "Epoch 553/558\n",
+ "256/256 [==============================] - 79s 290ms/step - loss: 0.1370 - accuracy: 0.9578 - val_loss: 0.4271 - val_accuracy: 0.9199 - lr: 0.0088\n",
+ "Epoch 554/558\n",
+ "256/256 [==============================] - 73s 285ms/step - loss: 0.0709 - accuracy: 0.9800 - val_loss: 0.2865 - val_accuracy: 0.9247 - lr: 0.0088\n",
+ "Epoch 555/558\n",
+ "256/256 [==============================] - 72s 282ms/step - loss: 0.0485 - accuracy: 0.9873 - val_loss: 0.5766 - val_accuracy: 0.8990 - lr: 0.0088\n",
+ "Epoch 556/558\n",
+ "256/256 [==============================] - 72s 281ms/step - loss: 0.0308 - accuracy: 0.9934 - val_loss: 0.3557 - val_accuracy: 0.9199 - lr: 0.0088\n",
+ "Epoch 557/558\n",
+ "256/256 [==============================] - 73s 286ms/step - loss: 0.0189 - accuracy: 0.9946 - val_loss: 0.3058 - val_accuracy: 0.9343 - lr: 0.0088\n",
+ "Epoch 558/558\n",
+ "256/256 [==============================] - 72s 282ms/step - loss: 0.0183 - accuracy: 0.9968 - val_loss: 0.2928 - val_accuracy: 0.9327 - lr: 0.0088\n",
+ "\u001b[0;32mSubset training done.\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{0.9343}, \u001b[0m\u001b[0;33mloss{0.2865}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9615}, loss{0.1409}]\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9327\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.2928\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9615384340. Not saving model.\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1409245580. Not saving model.\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;32m(GPU-MEM)\u001b[0m\u001b[0;36m----[free: 5.18GB, used: 18.82GB, total, 24.00GB]\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m576.39 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m443.67 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m132.72 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0;36m<---------------------------------------|Epoch [93] END|--------------------------------------->\u001b[0m\n",
+ "\u001b[0m\n",
+ "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m94\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m489 (TSEC: 558)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Stage 2]\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+ "\u001b[0;33mPreparing train data...\u001b[0m\n",
+ "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n",
+ "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+ "\u001b[0;32mTraining on subset...\u001b[0m\n",
+ "Epoch 559/564\n",
+ "256/256 [==============================] - 79s 289ms/step - loss: 0.1468 - accuracy: 0.9463 - val_loss: 0.3421 - val_accuracy: 0.8990 - lr: 0.0088\n",
+ "Epoch 560/564\n",
+ "256/256 [==============================] - 73s 284ms/step - loss: 0.0839 - accuracy: 0.9719 - val_loss: 0.3228 - val_accuracy: 0.9247 - lr: 0.0088\n",
+ "Epoch 561/564\n",
+ "256/256 [==============================] - 72s 281ms/step - loss: 0.0465 - accuracy: 0.9854 - val_loss: 0.2343 - val_accuracy: 0.9231 - lr: 0.0088\n",
+ "Epoch 562/564\n",
+ "256/256 [==============================] - 72s 279ms/step - loss: 0.0293 - accuracy: 0.9902 - val_loss: 0.4216 - val_accuracy: 0.9151 - lr: 0.0088\n",
+ "Epoch 563/564\n",
+ "256/256 [==============================] - 73s 284ms/step - loss: 0.0215 - accuracy: 0.9939 - val_loss: 0.3030 - val_accuracy: 0.9263 - lr: 0.0088\n",
+ "Epoch 564/564\n",
+ "256/256 [==============================] - 73s 284ms/step - loss: 0.0165 - accuracy: 0.9956 - val_loss: 0.2694 - val_accuracy: 0.9327 - lr: 0.0088\n",
+ "\u001b[0;32mSubset training done.\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{0.9327}, \u001b[0m\u001b[0;33mloss{0.2343}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9615}, loss{0.1409}]\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9327\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.2693\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9615384340. Not saving model.\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1409245580. Not saving model.\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;32m(GPU-MEM)\u001b[0m\u001b[0;36m----[free: 5.18GB, used: 18.82GB, total, 24.00GB]\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m574.38 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m442.23 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m132.15 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0;36m<---------------------------------------|Epoch [94] END|--------------------------------------->\u001b[0m\n",
+ "\u001b[0m\n",
+ "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m95\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m489 (TSEC: 564)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Stage 2]\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+ "\u001b[0;33mPreparing train data...\u001b[0m\n",
+ "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n",
+ "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+ "\u001b[0;32mTraining on subset...\u001b[0m\n",
+ "Epoch 565/570\n",
+ "256/256 [==============================] - 79s 290ms/step - loss: 0.1450 - accuracy: 0.9534 - val_loss: 0.2132 - val_accuracy: 0.9311 - lr: 0.0088\n",
+ "Epoch 566/570\n",
+ "256/256 [==============================] - 73s 285ms/step - loss: 0.0841 - accuracy: 0.9724 - val_loss: 0.2178 - val_accuracy: 0.9343 - lr: 0.0088\n",
+ "Epoch 567/570\n",
+ "256/256 [==============================] - 72s 282ms/step - loss: 0.0584 - accuracy: 0.9822 - val_loss: 0.3858 - val_accuracy: 0.9167 - lr: 0.0088\n",
+ "Epoch 568/570\n",
+ "256/256 [==============================] - 72s 282ms/step - loss: 0.0284 - accuracy: 0.9924 - val_loss: 0.2594 - val_accuracy: 0.9343 - lr: 0.0088\n",
+ "Epoch 569/570\n",
+ "256/256 [==============================] - 72s 282ms/step - loss: 0.0303 - accuracy: 0.9917 - val_loss: 0.7569 - val_accuracy: 0.8894 - lr: 0.0088\n",
+ "Epoch 570/570\n",
+ "256/256 [==============================] - 72s 282ms/step - loss: 0.0161 - accuracy: 0.9968 - val_loss: 1.0834 - val_accuracy: 0.8910 - lr: 0.0088\n",
+ "\u001b[0;32mSubset training done.\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{0.9343}, \u001b[0m\u001b[0;33mloss{0.2132}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9615}, loss{0.1409}]\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.8910\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m1.0829\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9615384340. Not saving model.\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1409245580. Not saving model.\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;32m(GPU-MEM)\u001b[0m\u001b[0;36m----[free: 5.18GB, used: 18.82GB, total, 24.00GB]\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m576.87 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m442.54 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m134.34 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+ "\u001b[0;36m<---------------------------------------|Epoch [95] END|--------------------------------------->\u001b[0m\n",
+ "\u001b[0m\n",
+ "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m96\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m489 (TSEC: 570)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Stage 2]\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+ "\u001b[0;33mPreparing train data...\u001b[0m\n",
+ "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n",
+ "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n",
+ "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+ "\u001b[0;32mTraining on subset...\u001b[0m\n",
+ "Epoch 571/576\n",
+ "256/256 [==============================] - 79s 289ms/step - loss: 0.1359 - accuracy: 0.9592 - val_loss: 0.8639 - val_accuracy: 0.8333 - lr: 0.0088\n",
+ "Epoch 572/576\n",
+ "256/256 [==============================] - 73s 283ms/step - loss: 0.0774 - accuracy: 0.9768 - val_loss: 0.9179 - val_accuracy: 0.8526 - lr: 0.0088\n",
+ "Epoch 573/576\n",
+ "256/256 [==============================] - 72s 281ms/step - loss: 0.0396 - accuracy: 0.9885 - val_loss: 1.5042 - val_accuracy: 0.8381 - lr: 0.0088\n",
+ "Epoch 574/576\n",
+ "256/256 [==============================] - ETA: 0s - loss: 0.0300 - accuracy: 0.9919\n",
+ "KeyboardInterrupt. (Training stopped)\n",
+ "Training done.\n",
+ "\n"
]
}
],
@@ -10032,11 +10404,14 @@
"subset_size = 4096 # subset_size: Size of each training subset. Common values: 512, 1024, 2048, 3200, 4096, 5846, 8192.\n",
"Conf_batch_size_REV2 = 16 # Conf_batch_size_REV2: Batch size.\n",
"RES_Train = False # RES_Train: Resume training if True.\n",
- "MAX_LR = 0.001 # MAX_LR: Maximum learning rate.\n",
- "DEC_LR = 0.000005 # DEC_LR: Learning rate decay.\n",
- "MIN_LR = 0.00005 # MIN_LR: Minimum learning rate.\n",
- "RES_LR = 0.0006 # RES_LR: Resuming learning rate.\n",
- "ReduceLROnPlateau_factor = 0.9985 # ReduceLROnPlateau_factor: ReduceLROnPlateau factor. (Lr = Factor * Lr_prev)\n",
+ "STR_M = 0.89 # STR_M: Starting momentum.\n",
+ "STR_LR = 0.01 # STR_LR: Starting learning rate.\n",
+ "MAX_LR = 0.01 # MAX_LR: Maximum learning rate.\n",
+ "DEC_LR = 0.00005 # DEC_LR: Learning rate decay.\n",
+ "MIN_LR = 0.0005 # MIN_LR: Minimum learning rate.\n",
+ "RES_LR = 0.006 # RES_LR: Resuming learning rate.\n",
+ "ReduceLROnPlateau_factor = 0.982 # ReduceLROnPlateau_factor: ReduceLROnPlateau factor. (Lr = Factor * Lr_prev)\n",
+ "ReduceLROnPlateau_patience = 10 # ReduceLROnPlateau_patience: ReduceLROnPlateau patience. (pt = ReduceLROnPlateau_patience * subset_epochs)\n",
"Use_OneCycleLr = False # Use_OneCycleLr: Use OneCycleLr if True. if false, use ReduceLROnPlateau.\n",
"OneCycleLr_UFTS = False # OneCycleLr_UFTS: Set the OneCycleLr max epochs to the estimated full training SUB epochs. (DEC_LR and MIN_LR dont have any effect if True)\n",
"Debug_OUTPUT_DPS = True # Debug_OUTPUT_DPS: Output debug image samples if True.\n",
@@ -10057,16 +10432,19 @@
"Use_tensorboard_profiler = False # Use_tensorboard_profiler: Enable tensorboard profiler.\n",
"Use_extended_tensorboard = False # Use_extended_tensorboard: Enable extended tensorboard (Some funcs may not work).\n",
"Use_tensorBoard_img = False # Use_tensorBoard_img: Enable tensorboard image logging.\n",
- "Use_noise_func_TRLRev2 = True # Use_noise_func_TRLRev2: Use noise function for IDG if True.\n",
"Show_confusion_matrix_tensorBoard = False # Show_confusion_matrix_tensorBoard: Show confusion matrix on tensorboard.\n",
"BEST_RSN = \"PAI_model_T\" # Best model save name prefix. (Uses a lot of memory and storage).\n",
- "ALWAYS_REFIT_IDG = 1 # ALWAYS_REFIT_IDG: if 0/False - do not always refit IDG. if 1 - always refit IDG (In Start). if 2 - always refit IDG (After each epoch) (slow).\n",
+ "ALWAYS_REFIT_IDG = 0 # ALWAYS_REFIT_IDG: if 0/False - do not always refit IDG. if 1 - always refit IDG (In Start). if 2 - always refit IDG (After each epoch) (slow).\n",
"IDG_FitP_PATH = \"Data\\\\image_SUB_generator.pkl\"\n",
+ "Experiment_EXT = input(\"Experiment name: \") # Experiment_EXT: Experiment name extension.\n",
"# CONF END <---------------------------------------------------------------------->\n",
"# Prep\n",
"if RES_Train:\n",
" MAX_LR = RES_LR\n",
" Stage1_epoch = 1\n",
+ "EXPR_name = f\"{Experiment_EXT}_\" + datetime.datetime.now().strftime(\"y%Y_m%m_d%d-h%H_m%M_s%S\")\n",
+ "set_optimizer_attribute(model.optimizer, \"learning_rate\", STR_LR, verbose=True) # noqa: F405\n",
+ "set_optimizer_attribute(model.optimizer, \"momentum\", STR_M, verbose=True) # noqa: F405\n",
"# VAR\n",
"Total_SUB_epoch_C = 0 # TO FIX TensorBoard\n",
"CU_LR = MAX_LR\n",
@@ -10077,6 +10455,63 @@
"best_loss = float(\"inf\")\n",
"\n",
"\n",
+ "# apply_clahe_rgb_array\n",
+ "def apply_clahe_rgb_array(images, clip_limit=1.8, tile_grid_size=(8, 8)): # noqa: F811\n",
+ " # Create a CLAHE object\n",
+ " clahe = cv2.createCLAHE(clipLimit=clip_limit, tileGridSize=tile_grid_size)\n",
+ "\n",
+ " # Iterate over each image in the array\n",
+ " for i in range(len(images)):\n",
+ " # Split the image into color channels\n",
+ " b, g, r = cv2.split(images[i])\n",
+ "\n",
+ " # Convert the channels to the appropriate format\n",
+ " b = cv2.convertScaleAbs(b)\n",
+ " g = cv2.convertScaleAbs(g)\n",
+ " r = cv2.convertScaleAbs(r)\n",
+ "\n",
+ " # Apply adaptive histogram equalization to each channel\n",
+ " equalized_b = clahe.apply(b)\n",
+ " equalized_g = clahe.apply(g)\n",
+ " equalized_r = clahe.apply(r)\n",
+ "\n",
+ " # Merge the equalized channels back into an image\n",
+ " equalized_image = cv2.merge((equalized_b, equalized_g, equalized_r))\n",
+ "\n",
+ " # Replace the original image with the equalized image in the array\n",
+ " images[i] = equalized_image\n",
+ "\n",
+ " return images\n",
+ "\n",
+ "\n",
+ "# save_images_to_dir\n",
+ "def save_images_to_dir(images, labels, dir_path): # noqa: F811\n",
+ " # create the directory if it doesn't exist\n",
+ " if not os.path.exists(dir_path):\n",
+ " os.makedirs(dir_path)\n",
+ " # iterate over the images and labels\n",
+ " for i, (image, label) in enumerate(zip(images, labels)):\n",
+ " # get the class label\n",
+ " class_label = np.argmax(label)\n",
+ " # create the file path\n",
+ " file_path = os.path.join(dir_path, f\"image_{i}_class_{class_label}.png\")\n",
+ " # save the image to the file path\n",
+ " plt.imsave(file_path, image.squeeze())\n",
+ " # compress the directory\n",
+ " shutil.make_archive(dir_path, \"gztar\", dir_path)\n",
+ " # remove the original directory\n",
+ " shutil.rmtree(dir_path)\n",
+ "\n",
+ "\n",
+ "# shuffle_data\n",
+ "def shuffle_data(x, y): # noqa: F811\n",
+ " indices = np.arange(x.shape[0])\n",
+ " np.random.shuffle(indices)\n",
+ " x = x[indices]\n",
+ " y = y[indices]\n",
+ " return x, y\n",
+ "\n",
+ "\n",
"# Funcs\n",
"def normalize_TO_RANGE(arr, min_val, max_val): # noqa: F811\n",
" arr = arr.astype(\"float32\")\n",
@@ -10138,7 +10573,7 @@
" new_image[i : i + block_size_L1, j : j + block_size_L1] = block\n",
"\n",
" if add_img_grain:\n",
- " intensity = random.uniform(0, 0.068) # Random intensity\n",
+ " intensity = random.uniform(0, 0.07) # Random intensity\n",
" new_image = add_image_grain_TRLRev2(new_image, intensity=intensity)\n",
" return new_image\n",
"\n",
@@ -10151,14 +10586,14 @@
" zoom_range=0.16,\n",
" shear_range=0.16,\n",
" width_shift_range=0.16,\n",
- " brightness_range=(0.805, 1.195),\n",
+ " brightness_range=(0.802, 1.198),\n",
" height_shift_range=0.16,\n",
" featurewise_center=True,\n",
" featurewise_std_normalization=True,\n",
" zca_whitening=False,\n",
" interpolation_order=2,\n",
" fill_mode=\"nearest\",\n",
- " preprocessing_function=noise_func_TRLRev2 if Use_noise_func_TRLRev2 else None,\n",
+ " preprocessing_function=noise_func_TRLRev2,\n",
")\n",
"\n",
"\n",
@@ -10256,7 +10691,7 @@
"# confusion_matrix_callback\n",
"confusion_matrix_callback = LambdaCallback(on_epoch_end=plot_confusion_matrix_TensorBoard)\n",
"# TensorBoard\n",
- "log_dir = \"logs/fit/\" + datetime.datetime.now().strftime(\"y%Y_m%m_d%d-h%H_m%M_s%S\")\n",
+ "log_dir = f\"logs/fit/{EXPR_name}\"\n",
"file_writer = tf.summary.create_file_writer(log_dir + \"\\\\Data\")\n",
"if Use_extended_tensorboard:\n",
" tensorboard_callback = ExtendedTensorBoard(\n",
@@ -10289,11 +10724,13 @@
" monitor=\"val_accuracy\",\n",
" factor=ReduceLROnPlateau_factor,\n",
" cooldown=subset_epoch,\n",
- " patience=subset_epoch * 8,\n",
+ " patience=subset_epoch * ReduceLROnPlateau_patience,\n",
" min_lr=MIN_LR,\n",
" verbose=1,\n",
" )\n",
" learning_rate_schedule_SUB.on_train_begin = DummyFunc # Remove on_train_begin to make it work with subset training.\n",
+ "# TerminateOnNaN\n",
+ "TerminateOnNaN_callback = TerminateOnNaN()\n",
"# PRES\n",
"callbacks_active = {\n",
" \"learning_rate_schedule_SUB\": True,\n",
@@ -10302,12 +10739,18 @@
" \"early_stopping\": Use_ES_ONSUBT,\n",
" \"tensorboard_callback\": True,\n",
" \"confusion_matrix_callback\": Show_confusion_matrix_tensorBoard,\n",
+ " \"TerminateOnNaN_callback\": True,\n",
"}\n",
"# MAIN\n",
"print(\"Training the model...\")\n",
"# INFOp\n",
"print_Color(\"\\nSetup Verbose:\", [\"yellow\"])\n",
"print_Color(\n",
+ " f\"~*Experiment name: ~*[{EXPR_name}]~*...\",\n",
+ " [\"cyan\", \"green\", \"cyan\"],\n",
+ " advanced_mode=True,\n",
+ ")\n",
+ "print_Color(\n",
" f\"~*Setting TensorBoard Log dir to ~*[{log_dir}]~*...\",\n",
" [\"cyan\", \"green\", \"cyan\"],\n",
" advanced_mode=True,\n",
@@ -10334,6 +10777,7 @@
" [\"cyan\", \"green\", \"cyan\"],\n",
" advanced_mode=True,\n",
")\n",
+ "print_optimizer_info(model) # noqa: F405\n",
"# warnings\n",
"P_warning(\"[RES_Train -> True].\") if RES_Train else None # noqa: F405\n",
"P_warning(\"[TerminateOnHighTemp_M -> False] GPU temperature protection is OFF\") if not TerminateOnHighTemp_M else None # noqa: F405\n",
@@ -10411,7 +10855,7 @@
" else:\n",
" print_Color(\"- Fitting ImageDataGenerator...\", [\"yellow\"])\n",
" IDG_FIT_rc = 3 if ALWAYS_REFIT_IDG == 2 else 12\n",
- " train_SUB_datagen.fit(x_SUB_train * 255, augment=True, rounds=6)\n",
+ " train_SUB_datagen.fit(x_SUB_train * 255, augment=True, rounds=2)\n",
" pickle.dump(train_SUB_datagen, open(IDG_FitP_PATH, \"wb\"))\n",
" print_Color(\"- ImageDataGenerator fit done.\", [\"yellow\"])\n",
"\n",
@@ -10481,6 +10925,7 @@
" \"early_stopping\": early_stopping,\n",
" \"tensorboard_callback\": tensorboard_callback,\n",
" \"confusion_matrix_callback\": confusion_matrix_callback,\n",
+ " \"TerminateOnNaN_callback\": TerminateOnNaN_callback,\n",
" }\n",
" Active_callbacks = [callbacks_dict[cb] for cb, active in callbacks_active.items() if active]\n",
" start_SUBO_time = time.time()\n",
@@ -10626,7 +11071,9 @@
" history[key] = np.concatenate([h[key] for h in all_histories])\n",
"except Exception as Err:\n",
" print(f\"Failed to make model `history` var.\\nERROR: {Err}\")\n",
- "\n",
+ "else:\n",
+ " # Save history\n",
+ " save_list(history, f\"history\\\\Archive\\\\model_{EXPR_name}_history.pkl.gz\", compress=True) # noqa: F405\n",
"print(\"Training done.\\n\")\n",
"# del vars\n",
"try:\n",
@@ -10884,7 +11331,7 @@
},
{
"cell_type": "code",
- "execution_count": 9,
+ "execution_count": null,
"metadata": {},
"outputs": [],
"source": [
@@ -10904,20 +11351,9 @@
},
{
"cell_type": "code",
- "execution_count": 10,
+ "execution_count": null,
"metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\u001b[0m\u001b[0m\u001b[0;32m(GPU-MEM)\u001b[0m\u001b[0;36m----[free: 5.11GB, used: 18.89GB, total, 24.00GB]\u001b[0m\n",
- "Realising all memory...\n",
- "\u001b[0m\u001b[0m\u001b[0;32m(GPU-MEM)\u001b[0m\u001b[0;36m----[free: 22.77GB, used: 1.23GB, total, 24.00GB]\u001b[0m\n",
- "done.\n"
- ]
- }
- ],
+ "outputs": [],
"source": [
"from numba import cuda\n",
"\n",
@@ -10938,7 +11374,7 @@
},
{
"cell_type": "code",
- "execution_count": 10,
+ "execution_count": null,
"metadata": {},
"outputs": [],
"source": [
@@ -10958,7 +11394,7 @@
},
{
"cell_type": "code",
- "execution_count": 11,
+ "execution_count": 10,
"metadata": {
"ExecuteTime": {
"end_time": "2023-12-28T07:04:52.565658900Z",
@@ -10968,7 +11404,7 @@
"outputs": [
{
"data": {
- "image/png": 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OAJ7fl4xcknV1ddLPL730UuzatQs/+MEPsHfvXnz3u98N3Lcg+P0eDOh9d+t3kP35+b6aD/we686dOx3b2sc20/dpQkh2MMSbENJucQuxFpkyZQpOOeUU/OxnP8P69esxatQozJ07Fy+88AKuv/56M+fk4YcfjosuuggPPvggdu/ejWOPPRbz58/HmjVrHG3+6le/wsKFCzFmzBhcffXVGDFiBHbu3IklS5bgtddek37x8cMzzzxj/sprP86f/vSn+Ne//oUzzjgD1113HXr27InHHnsM69atwzPPPINIRP89asKECejfvz+OO+449OvXDytWrMCf/vQnTJ48GV27dkVdXR0GDx6MCy64AKNGjUKXLl3w2muv4YMPPsD//u//ZtVvQgghhJCwefnll7Fy5UokEgnU1tZiwYIFmDdvHoYOHYoXX3wRlZWV5roPPPAAjj/+eBx22GG4+uqrse+++6K2thaLFi3C5s2bsWzZskD7fuyxx/Dggw/i3HPPxX777Yc9e/bgb3/7G7p164ZJkya5bnfkkUcCAH72s5/hwgsvRFlZGaZMmWIKl0cccQQOPfRQPPXUUzj44IPxzW9+M4sz4x+/34ONvr/22mu47777MHDgQAwfPhxjxowJtD+/31ez5cMPP8QvfvELx/KTTz7Z97HeddddePPNNzF58mQMHToUW7duxYMPPojBgwebxX0yfZ8mhGRJgaqHE0JIqDzyyCMaAO2DDz7wXG/o0KHa5MmTLcv27Nmj3XDDDdrAgQO1srIy7YADDtDuvfdeTVVVy3pNTU3addddp/Xq1Uvr3LmzNmXKFG3Tpk0aAO3222+3rFtbW6tNnTpVGzJkiFZWVqb1799fO+2007S//vWv5jrr1q3TAGiPPPKIZ58XLlyoAXD976233tI0TdPWrl2rXXDBBVp1dbVWWVmpjR49Wps9e7alrb/85S/aiSeeqPXq1UurqKjQ9ttvP+2mm27Sdu/erWmaprW0tGg33XSTNmrUKK1r165a586dtVGjRmkPPvigZx8JIYQQQtoC4zuf8V95ebnWv39/bfz48drvf/97rb6+Xrrd2rVrtcsuu0zr37+/VlZWpg0aNEg788wztaefftrRtv37pPFdbOHChZqmadqSJUu0iy66SNtnn320iooKrW/fvtqZZ56pffjhh5btZN8R7777bm3QoEFaJBLRAGjr1q2zfP6b3/xGA6D98pe/9H1OTjrpJO2QQw7JuN7ll1+uDR061LLM7/fglStXaieeeKLWqVMnDYB2+eWXe+4LgDZ16lTHcj/fV43z/dRTT2U8JnF/bv/dfffdvo91/vz52tlnn60NHDhQKy8v1wYOHKhddNFF2urVq811Mn2fJoRkh6JpNm86IYQQQgghhBBC2pzf//73uOGGG7B+/XpHNXFCCGnPUKAkhBBCCCGEEEIKjKZpGDVqFHr16mUpyEIIIR0B5qAkhBBCCCGEEEIKRENDA1588UUsXLgQn376KV544YVCd4kQQtocOigJIYQQQgghhJACsX79egwfPhzV1dX40Y9+hHvuuafQXSKEkDaHAiUhhBBCCCGEEEIIIaRgRArdAUIIIYQQQgghhBBCSMeFAiUhhBBCCCGEEEIIIaRgsEiOBFVVsWXLFnTt2hWKohS6O4QQQgghgdE0DXv27MHAgQMRifA36VKD30cJIYQQUuoE+T5KgVLCli1bMGTIkEJ3gxBCCCEkZzZt2oTBgwcXuhskIPw+SgghhJD2gp/voxQoJXTt2hWAfgK7deuWt/00Pj0cVepO3Ie/Y/q3L8jbfog38Xgcc+fOxYQJE1BWVlbo7nRYchqH/3TX/574LND/tPA718HgPVEccByKg1Ieh/r6egwZMsT8XkNKC2Pc1q1bh549exa4NyRsSvnZQrzh2LZfOLbtF45t/gjyfZQCpQQjjKZbt255FSijVVF0VoFKVOV1P8SbeDyOqip9DPgwKhw5jUNV6m/XzgDvpZzhPVEccByKg/YwDgwPLk2McevatSu/J7ZD2sOzhcjh2LZfOLbtF45t/vHzfZQJiYoABVqhu0AIIYQQQgghhBBCSEGgQFlQlNT/KVASQgghhBBCCCGEkI4JBUpCCCGEEEIIIYQQQkjBoEBZFNBBSQghhBBCCCGEEEI6JhQoC4gGJq0nhBBCCCGEEEIIIR0bCpRFAGVKQgghhBBCCCGEENJRoUBZSFJl1jWoBe4IIYQQQgghhBBCCCGFgQJlQaF3khBCCCGEEEIIIYR0bChQFgEKi+QQQgghhBBCCCGEkA4KBcoCwiI5hBBCCCGEEEIIIaSjQ4GyoCip/9NBSUjWaLx/CCGEEEIIIYSQUoYCJSGkxKFASQghhBBCCCGElDIUKAsIQ7wJCQE6KAkhhBBCCCGEkJImVugOEIZ4E5IbvH8IIYQQGXfcUegeeFPs/SOEEEJI20EHZVFAgYWQ7OH9QwghhBBCCCGElDIUKAtKKsSb+goh2cMQb0IIIYQQQgghpKShQFkUUGAhJHt4/xBCCCGEEEIIIaUMBcoCwiI5hIQBBUpCCCGEEEIIIaSUoUBZBLBIDiE5wBBvQgghhBBCCCGkpKFAWVDooCQkdyhQEkIIIYQQQgghpQwFygLCEG9CwoACJSGEEEIIIYQQUspQoCwCGOJNSA4wxJsQQgghhBBCCClpKFAWEoUOSkJyhwIlIYQQQgghhBBSylCgLCiGQEmBhZCs0dRC94AQQgghhBBCCCE5QIGyKKBASUj28P4hhBBCCCGEEEJKGQqUBYSyCiFhwDuJEEIIIYQQQggpZShQFgEskkNIDliK5DCvKyGEEEIIIYQQUmpQoCwoFFMIyR0K/IQQQgghhBBCSClDgbKgUKAkJGc0CpSEEEIIIYQQQkgpQ4GyCGCINyG5wPuHEEIIIYQQQggpZShQFhCNDkpCQkBzeU0IIYQQQgghhJBSgAJlEUAHJSE5IIZ4M9ybEEIIIYQQQggpOShQFhTDQUlRhZDsoYOSEEIIIYQQQggpZShQFhIlJVBSUyEkByhQEkIIIYQQQgghpQwFyiKAId6E5ABDvAkhhBBCCCGEkJKGAmUBYZEcQsKADkpCCCGEEEIIIaSUoUBJCClxKFASQgghhBBCCCGlDAXKIoAh3oTkAEO8CSGEEEIIIYSQkoYCZQFhiDchYUAHJSGEEEIIIYQQUspQoCwgivmXogoh2UOBkhBCCCGEEEIIKWUoUBYQOigJCQGGeBNCCCGEEEIIISUNBcqCYgiUFFUIyR46KAkhhITHzJkzcfTRR6Nr167o27cvzjnnHKxatcqyzsknnwxFUSz//fCHP7Sss3HjRkyePBlVVVXo27cvbrrpJiQSibY8FEIIIYSQkiFW6A4QhngTkhsUKAkhhITHG2+8galTp+Loo49GIpHALbfcggkTJuDzzz9H586dzfWuvvpq3HXXXeb7qqoq83UymcTkyZPRv39/vPvuu/j6669x2WWXoaysDL/85S/b9HgIIYQQQkoBCpQFxAjxpqRCSA4wxJsQQkiIvPLKK5b3jz76KPr27YuPPvoIJ554orm8qqoK/fv3l7Yxd+5cfP7553jttdfQr18/HH744bj77rtx880344477kB5eXlej4EQQgghpNSgQFlQlNT/KaoQkj10UBJCCMkfu3fvBgD07NnTsvyJJ57A448/jv79+2PKlCn4+c9/brooFy1ahMMOOwz9+vUz1584cSKuvfZaLF++HEcccYRjPy0tLWhpaTHf19fXAwDi8Tji8XhWfY8UeTKnLA+rXWCMabZjS4oXjm37hWPbfuHY5o8g55QCZVFAUYWQrNEoUBJCCMkPqqri+uuvx3HHHYdDDz3UXH7xxRdj6NChGDhwID755BPcfPPNWLVqFZ599lkAQE1NjUWcBGC+r6mpke5r5syZuPPOOx3LFy5caAkfD8KoUVlt1mbMmVPoHhSeefPmFboLJE9wbNsvHNv2C8c2fBobG32vS4GykCis4k1I7jDEmxBCSH6YOnUqPvvsM7z99tuW5ddcc435+rDDDsOAAQNw2mmnYe3atdhvv/2y2teMGTMwffp08319fT2GDBmCU045Bb169cqqzZkzs9qszZgxo9A9KBzxeBzz5s3D+PHjUVZWVujukBDh2LZfOLbtF45t/jAiQvxQEgLlAw88gHvvvRc1NTUYNWoU/vjHP2L06NGu699///3485//jI0bN6J379644IILMHPmTFRWVrZhr/3DEG9CcoEOSkIIIeEzbdo0zJ49G2+++SYGDx7sue6YMWMAAGvWrMF+++2H/v37Y/HixZZ1amtrAcA1b2VFRQUqKiocy8vKyrKeLKlqVpu1GZwD5ja+pLjh2LZfOLbtF45t+AQ5n0WemQZ48sknMX36dNx+++1YsmQJRo0ahYkTJ2Lr1q3S9WfNmoWf/vSnuP3227FixQr8/e9/x5NPPolbbrmljXvuBzooCckZFskhhBASIpqmYdq0aXjuueewYMECDB8+POM2S5cuBQAMGDAAADB27Fh8+umnlu+r8+bNQ7du3TBixIi89JsQQgghpJQpeoHyvvvuw9VXX40rr7wSI0aMwEMPPYSqqio8/PDD0vXfffddHHfccbj44osxbNgwTJgwARdddJHjV+xiQJO8IoQERbSH8F4ihBCSG1OnTsXjjz+OWbNmoWvXrqipqUFNTQ2ampoAAGvXrsXdd9+Njz76COvXr8eLL76Iyy67DCeeeCJGjhwJAJgwYQJGjBiBSy+9FMuWLcOrr76KW2+9FVOnTpW6JAkhhBBCOjpFHeLd2tqKjz76CDOEBDWRSATjxo3DokWLpNsce+yxePzxx7F48WKMHj0aX375JebMmYNLL73UdT/5qJroB9PspWmsFlVAWLGrOMh6HOKtMEzjiWQcGscxZ3hPFAcch+KglMehFPtcDPz5z38GAJx88smW5Y888giuuOIKlJeX47XXXsP999+PhoYGDBkyBOeffz5uvfVWc91oNIrZs2fj2muvxdixY9G5c2dcfvnluOuuu9ryUAghhBBCSoaiFii3b9+OZDIprYK4cuVK6TYXX3wxtm/fjuOPPx6apiGRSOCHP/yhZ4i3W9XEuXPnZl010Q+jmpqBGFBXtwtzWMaw4LBiV3EQdBy6qhtwaur1sqVLsfmz7uF3qoPCe6I44DgUB6U4DkGqJpI0WoZ0IUOGDMEbb7yRsZ2hQ4fy+x0hhBBCiE+KWqDMhtdffx2//OUv8eCDD2LMmDFYs2YNfvzjH+Puu+/Gz3/+c+k2blUTJ0yYgG7duuWtrzufvwOIA92rqzFp0qS87Yd4w4pdxUHW47D7U2Cu/nLUqJEYOZT3Uq7wnigOOA7FQSmPQ5CqiYQQQgghhBSSohYoe/fujWg0alY9NKitrXWtgPjzn/8cl156Kb7//e8DAA477DA0NDTgmmuuwc9+9jNEIs60m/momugLRS+SE4FScpOe9ggrdhUHgcchmn6MxaJRlgQNEd4TxQHHoTgoxXEotf4SQgghhJCOS1EXySkvL8eRRx6J+fPnm8tUVcX8+fMxduxY6TaNjY0OETIajQLIHLLT9hhVvIutX4SUEqziTQghhBBCCCGElDJF7aAEgOnTp+Pyyy/HUUcdhdGjR5sJya+88koAwGWXXYZBgwZh5syZAIApU6bgvvvuwxFHHGGGeP/85z/HlClTTKGy2FAoUBKSA5rLa0IIIYQQQgghhJQCRS9Qfuc738G2bdtw2223oaamBocffjheeeUVs3DOxo0bLY7JW2+9FYqi4NZbb8VXX32FPn36YMqUKbjnnnsKdQjuKErmdQgh3mgUKAkhhBBCCCGEkFKm6AVKAJg2bRqmTZsm/ez111+3vI/FYrj99ttx++23t0HPckNLhXhTpiQkFxjiTQghhBBCCCGElDJFnYOSEEIyQwclIYQQQgghhBBSylCgLCgskkNIzjDEmxBCCCGEEEIIKWkoUBYBLJJDSC4wxJsQQgghhBBCCCllKFAWAZRUCMkBOigJIYQQQgghhJCShgJlATGL5CgUVQjJHgqUhBBCCCGEEEJIKUOBsoCkq3dTVCEkexjiTQghhBBCCCGElDIUKAuIJkiUhJAsYYg3IYQQQgghhBBS0lCgLAIoUxKSCxQoCSGEEEIIIYSQUoYCZUGhNElI7jDEmxBCCCGEEEIIKWUoUBaQdIh3G4oqarLt9kVIW8AQb0IIIYQQQgghpKShQFkEKG0lqiy6Anh+ENBa1zb7I6RNoIOSEEIIIYQQQggpZShQFpQ2DvFe9xjQXAusf6Jt90tIXqGDkhBCCCGEEEIIKWUoUBYSJSVQ0vVFSPYwxJsQQgghhBBCCClpKFAWAW0vqbA4D2lPMMSbEEIIIYQQQggpZShQFhQ6KAnJHTooCSGEEEIIIYSQUoYCZVGgFroDhJQuYYV4J1uB974HrP9Xzl0ihBBCCCGEEEKIfyhQFpJUDso2N1AqDPEm7YmQQry/fET/792Lc+8SIYQQQgghhBBCfEOBsqAYQiHDUgnJGk10IOdwLzXX5twVQgghhBBCCCGEBIcCZQEx5UnmoCQkB8LKQcn7kBBCCCGEEEIIKQQUKAsJQ60JyR2NVbwJIQFprQM+vxdo2FTonhBCCCGEEEJAgbLAFKqKN4VR0p4IyUFJcZOQjsPia4Cl/w+Yd3yhe0IIIYQQQggBBcqigLIIIbkQVog3IaTDsOUV/W/jxsL2gxBCCCGEEAKAAmVhMUO8Vc/VCCEeMMSbEEIIIYQQQggpaShQFhAz0LrNNRWGeJP2BIvkEEKCwvudEEIIIYSQYoICZUHRhUKVri9CcoAh3oQQQgghhBBCSClDgbKQmCHeFFUIyZrQQrx5HxJCCCGEEEIIIYWAAmVB0QXKti/izRBv0p6gg5IQEhQ+KwghhBBCCCkmKFAWBZwoEZI9ITkomWqBkI4D73dCCCGEEEKKCgqUBURhiDchuaOxSA4hhBBCCCGEEFLKUKAsKEaId5vHeLfx/gjJJwzxJoQQQgghhBBCShkKlIWkLR2UDGcj7RYWySEhkmwGvnwMaKopdE9IXuH9TgghhBBCSDFBgbKAmD7GtpgnaWob7ISQAhBaiDchAD65DXjvCmDuMYXuCSGEEEIIIYR0GChQFpRUiHfbKJSO/RLSPghJoKTLmADA5uf1vw0bCtoNQgghhBBCCOlIUKAsJEaId1sII3RQknZLWCHehJCOA58VhBBCCCGEFBMUKIuAttFUKFCSdgpDvAkhQeGPGYQQQgghhBQVFCgLiMIiOYSEQFgCJe8RQgghhBBCCCGkEFCgLAbaRDwUHJQKc1CSdoTGEG9CCCGEEEIIIaSUoUBZQJS2LJLDHJSk3UIHJSEkKLzfCSGEEEIIKSYoUBaStgzx5mSMtFtYxZsQEhTe74QQQgghhBQTFCiLgDbRRSwOSoZ4k3YEQ7xJR2TJjcDK+wvdC0IIIYQQQggJhVihO9ChUQx9mCHehGQPQ7xJB2PXMmDl/+qvD7q+oF0hhBBCCCGEkDCgg7KAmD7GtrFQtsE+CCkEYQmUhKA0XLiJvYXuQelTCuNMCCGEEEJIB4ICZUExiuS0AXRQkvZKaCHeFCwI6TjwfieEEEIIIaSYoEBZSNqySI5FoOTEjLQnQrq26agihBACYObMmTj66KPRtWtX9O3bF+eccw5WrVplWae5uRlTp05Fr1690KVLF5x//vmora21rLNx40ZMnjwZVVVV6Nu3L2666SYkEom2PBRCCCGEkJKBAmUBMeXJNtFFBBGHQgxpT7BIDiGEkBB54403MHXqVLz33nuYN28e4vE4JkyYgIaGBnOdG264Af/973/x1FNP4Y033sCWLVtw3nnnmZ8nk0lMnjwZra2tePfdd/HYY4/h0UcfxW233VaIQyKEEEIIKXpYJKeQtKmDknn6SHuF1zbpwGia8G8J8Q+fFcSdV155xfL+0UcfRd++ffHRRx/hxBNPxO7du/H3v/8ds2bNwqmnngoAeOSRR3DwwQfjvffewzHHHIO5c+fi888/x2uvvYZ+/frh8MMPx913342bb74Zd9xxB8rLywtxaIQQQgghRQsFygKioA0FSouDkvkoSXsiDwIlRR9SMmgQSq4RQvLA7t27AQA9e/YEAHz00UeIx+MYN26cuc5BBx2EffbZB4sWLcIxxxyDRYsW4bDDDkO/fv3MdSZOnIhrr70Wy5cvxxFHHOHYT0tLC1paWsz39fX1AIB4PI54PJ5V3yNFHiuV5WG1C4wxzXZsSfHCsW2/cGzbLxzb/BHknFKgLCRtqU8yByVpr+SjSI6mAko0h7ZI6VJiz0dNoz5JSB5RVRXXX389jjvuOBx66KEAgJqaGpSXl6O6utqybr9+/VBTU2OuI4qTxufGZzJmzpyJO++807F84cKFqKqqyqr/o0ZltVmbMWdOoXtQeObNm1foLpA8wbFtv3Bs2y8c2/BpbGz0vS4FyoKi/6ytoS0cjQyDJe2VkK5tpkEgJYkKgGJ6YJivlvhk6tSp+Oyzz/D222/nfV8zZszA9OnTzff19fUYMmQITjnlFPTq1SurNmfODKt3+WHGjEL3oHDE43HMmzcP48ePR1lZWaG7Q0KEY9t+4di2Xzi2+cOICPEDBcoC0qamF40h3qS9kg9hkeIFKREotGUJzxvJzLRp0zB79my8+eabGDx4sLm8f//+aG1tRV1dncVFWVtbi/79+5vrLF682NKeUeXbWMdORUUFKioqHMvLysqyniypRf6Vj3PA3MaXFDcc2/YLx7b9wrENnyDns8gz07RzUjnutLaYYGqs4k3aKfkK8SakJOC1SkjYaJqGadOm4bnnnsOCBQswfPhwy+dHHnkkysrKMH/+fHPZqlWrsHHjRowdOxYAMHbsWHz66afYunWruc68efPQrVs3jBgxom0OhBBCCCGkhKCDsqAoqf+3SRJKl9eElDp0UJIODMV0QkJn6tSpmDVrFl544QV07drVzBnZvXt3dOrUCd27d8dVV12F6dOno2fPnujWrRv+53/+B2PHjsUxxxwDAJgwYQJGjBiBSy+9FL/5zW9QU1ODW2+9FVOnTpW6JAkhhBBCOjoUKAuIUSS4TQyNDPEm7ZawBEo6KAlQeuJ0qfWXkOLnz3/+MwDg5JNPtix/5JFHcMUVVwAAfve73yESieD8889HS0sLJk6ciAcffNBcNxqNYvbs2bj22msxduxYdO7cGZdffjnuuuuutjoMQgghhJCSggJlAVHasow3q3iT9kpYId6lUiRnxW+B3SuAMf+X/pWDdFwophMSOn5S71RWVuKBBx7AAw884LrO0KFDMYdlqgkhhBBCfEGBspC0qYWSDkrSXsmDsFjMeVo/vkn/O/xSoN/JBe0KKQaK+FolhBBCCCGEEJ+wSE4R0CbTy1JxhxESlNCubXHbEhDxE42F7gEpBviDEyGEEEIIIaQdQIGygChKG4Z4gyHepL0SVhVvsckSuEcY3k0ACpSEEEIIIYSQdgEFyoKiCwx+ch3lDIvkkHZLPtzBpXCPUKAkAH9wIoQQQgghhLQHKFAWEMNB2TYyA0O8STslHyHepeCgpEBJAP7gRAghhBBCCGkXUKAsKCkHZVu4tSwOylIQXwjxSz6ExRK4Rxji3XFhTmFCCCGEEEJIO4MCZSFpyxSUFpcNHTekPRGSWCOKPsXqSrMIUxQo80JJ/IBTAtcqIYQQQgghhASAAmUBUdpUYCi18FVCfJKXKt5Feo+IYhQdlB0X5hQmhBBCCCGEtDMoUBYBWluIIZzQknZLSOJ7SdwjYr8oUHZcSkBMJ4QQQgghhJAAlIRA+cADD2DYsGGorKzEmDFjsHjxYs/16+rqMHXqVAwYMAAVFRX4xje+gTlz5rRRb/1jFMlp+xBvTmhJeyIssaYE7hEtKbyhQNlhKQkxnRBCCCGEEEL8Eyt0BzLx5JNPYvr06XjooYcwZswY3H///Zg4cSJWrVqFvn37OtZvbW3F+PHj0bdvXzz99NMYNGgQNmzYgOrq6rbvfCYUXR9WFA2apqUFy7xQAuILIdmg5cNBWaT3iChQMsS7A0MHJSGEEEIIIaR9UfQC5X333Yerr74aV155JQDgoYcewksvvYSHH34YP/3pTx3rP/zww9i5cyfeffddlJWVAQCGDRvWll32jSgvqBoQzafeUAoFQAjJirCK5JRAISmNId75pwQEPzooCSGEEEIIIe2MohYoW1tb8dFHH2HGjBnmskgkgnHjxmHRokXSbV588UWMHTsWU6dOxQsvvIA+ffrg4osvxs0334xoNCrdpqWlBS0tLeb7+vp6AEA8Hkc8Hg/xiKxoKdFQgYbW1lbEovmLuFcSreZgJ5MJqHk8rlLDGON8jjXJTLbjEEkmYNzZqppEMstxjKoJM+dFPN4KFOP10NqMstTLRDIJLU997Mj3RExLS7+FPn63cRCf5/FEkV6rRU6Z8DrTOJfy/VCKfSaEEEIIIR2TohYot2/fjmQyiX79+lmW9+vXDytXrpRu8+WXX2LBggW45JJLMGfOHKxZswY/+tGPEI/Hcfvtt0u3mTlzJu68807H8rlz56Kqqir3A3HhkKavsT90gXLOy68glseMoL2Sn+L41Os1a77Ayo3Fl5Oz0MybN6/QXSAIPg4HtK7EiNTrr776CkuyzDd7RMsm7JN6vXDBfDRFnCkkCk2ZtgeTUq/fe+997Ijuzev+OuI9Ma6pEZ1Tr4sld7F9HPomlmBs6vXrCxegMTKg7TtV4pwtvPY7zqV4PzQ2Nha6C4QQQgghhPiiqAXKbFBVFX379sVf//pXRKNRHHnkkfjqq69w7733ugqUM2bMwPTp08339fX1GDJkCCZMmIBu3brlra+J954HNumvJ06ciIoyucMzDJStVcAb+uv999sX+x42yXuDDkQ8Hse8efMwfvx4My0AaXuyHYfIik+Bz/TXgwYOQP9jsru2o+//B9iovz7llJOBzsOyaievtGwDXtRfHnPMMdD6nJCX3XTkeyL2UhWQ0nQmTSrsc9JtHJSvFeBt/fXJJ50IdD2gQD0sYZ5Kv8w0zqV8PxgRIYQQQgghhBQ7RS1Q9u7dG9FoFLW1tZbltbW16N+/v3SbAQMGoKyszBLOffDBB6Ompgatra0oLy93bFNRUYGKigrH8rKysrxORtSI3kcFQKysDGV5FCghhI9HIxFES2yS1Rbke7yJPwKPQySdizESURDJdgyFlI5lsShQjNdCIn0fx2KxvPexQ94T4nVQJMfuGAfh37eyNrgOSh5NA3YtBbofDEQrHR/7HedSvB9Krb+EEEIIIaTjkseg4twpLy/HkUceifnz55vLVFXF/PnzMXbsWOk2xx13HNasWQNVTRcOWL16NQYMGCAVJwuJWLVbzXfV4FIoAELCJb4XaN5a6F60ASFV8Q6tnTzC4igE4HUQlK/nAq98E1gyPfO6hBBCCCGEkIJQ1AIlAEyfPh1/+9vf8Nhjj2HFihW49tpr0dDQYFb1vuyyyyxFdK699lrs3LkTP/7xj7F69Wq89NJL+OUvf4mpU6cW6hA8UFL/16DmXQ8pAfGFhMvT3YFn+wEtOwvdk/yi5aOKd5HeI1pSfFOwbpQ0yeZ28AwM6ZrvKDRu0P82bCxsPwghhBBCCCGuFHWINwB85zvfwbZt23DbbbehpqYGhx9+OF555RWzcM7GjRsRiaR11iFDhuDVV1/FDTfcgJEjR2LQoEH48Y9/jJtvvrlQh+CK4aBUFM2s6J03SkF8IeFijHndMqDfKYXtS14JS6wpBVdaKfSxiNn7JfDifsDQC4Hj/lXo3mQPHZTBMIR9LVHYfhBCCCGEEEJcKXqBEgCmTZuGadOmST97/fXXHcvGjh2L9957L8+9Cpe8Oyg5oe3AKJlXKWlCcgeXgogvOih5Hwdn1Z/0vxv+7SFQFunYWxD7yOsgI2rC+pcQQgghhBBSdBR9iHd7xnRQAvl3UKIExBeSJ9q5QJmPEO9iFf9UhngT2K5VXgcZMR2USe/1CCGEEEIIIQWDAmUBEWWjvM8xNeag7LAo7VygzEeId9GKfyUgopI2gEXPAsEQb0IIIYQQQooeCpSFxBSOtPxX8eaEtmPRoUToPIR4F+v5Y5EcAvAHp6DQQUkIIYQQQkjRQ4GykCj66Xet4h2vB9b9U/+bK6UgvpDwsEzE2/ltnpcq3nkQ8Rs2ASt/D8T3ZN8Gc1DmSDt59uX7Wm1vGPcNc1ASQgghhBBStJREkZyOgCabOL9zMbDlJWDQFOCkF3PeQxpOaNs9ooDBEG+fzeRZxH91NNBcA+z+FBjzf9m1UQp5MkudkvgBR3RQ8jrICB2UhBBCCCGEFD3t3FpV7IhFciQfb3lJ//vVf3PfFR2UHYsONREPK9w1z6605hr979fzsm+DId450k7Eej7Pg8EclIQQQgghhBQ9FCgLSnqynPcclFopFAAh4SGOdzsRZdzISxXvPN4jSg6PXTooCQA64gNCByUhhBBCCCFFDwXKIkCB1gYmGIYEdig61EQ8D0Vy8ini5yRQ0kGZG+3knNFBGQw6KAkhhBBCCCl6KFAWklRuQEVpgyredFB2LEQhKxdBrCQIyUHZZiJ+SAIlf2jowNBBGQgWySGEEEIIIaToae/KRcmQdxMMBcqOhcYQ7+DtlIKDktWbCRjqHxRDmOxQznJCCCGEEEJKCwqUBcUoktMWId6c0HYoOlQocB6K5OTzHgkrxJuhvVnQXs4Zf3AKBEO8CSGEEEIIKXooUBaUdBXv/Id4hxUGS0qCDpWjrtQclNEcNuYPDQTWe5rXQWZYJIcQQgghhJCihwJlkZB3gZLCRsfCMhFv5+NdalW8cwm571DO2EJRCueVDspAMAclIYQQQgghRQ8FykKiCCHe+d4Xc1B2LDpUKHA+qngXaYi3yiI5BHRQBoUOSkIIIYQQQooeCpRFgpb/KjnizvK8L1JwOlIxldDSF+TBQVn/BfDRdKBxS3pZWCHe7X1c80G7efbxBycAwJ41QOvuzOsxByUhhBBCCCFFT6zQHejYGKGeGtS2rOJNx037R+tITrt85KAM6ZzNGwu07AB2Lk4vY5Eckit0UAL1q4HZBwLRKuA7Dd7r0kFJCCGEEEJI0UMHZUFJhXgrbaA1MMS7Y9GhBOk8hHiHdUO27ND/bl8kLMxFoMyDiKppwPpZuuBDSoMOdX+7UDNP/5tszLwuc1ASQgghhBBS9NBBWSTkv0gOq3h3KDpSMZV8hHiHfs4i6faLzUG54Ung3Uv01xe382vFF6VwDvg8DyTMMsSbEEIIIYSQoocOykKiGH+0/AuUdNx0LBjinUUzebxHFKFyd1gCZVgOSou7s73TTsQ8Ps+DHbfhnNSSTI1ACCGEEEJIkUKBsqCkRQuGeJPQiO8BGjel37d7ASMfVbzzeI/kUiSHwhQBwCI5QCCBvkP9YEMIIYQQQkhpwhDvIkCB1gamDgobHYZn+gBqi7CgnY93aMJiPguPKC6vA9KRQveLAU2zul+LBRbJyS7E23ydw48EhBBCCCGEkLxAB2VBSRXJCSvEW9OA+F73z9Jvct8XKV4s4iQ6gICRjyreYd8jeQjxbvfjWgwU67OSPzgFqshtuW+Yh5IQQgghhJBihAJlkRDKNHjxD4CnugI7P5J8mIcKxaREaOfjrYUU4p3Pe8SSgzKkEO/2Pq55IeA5K9ZnJX9wykGgDLAdIYQQQgghpM2gQFlIFMNBGVIV77V/0/9+9gvnZxZho0COm5adwJIbgV2fFGb/HZV277DKh4Myj+eMDsoSoljFPzoosw/xpoOSEEIIIYSQYoQCZUFJh3hr+a+SI7ws0KT7w/8BVv4v8PKowuy/o9LuBYx8VPHOY4h3To9dClNtSrGeYzoosxcoVYmDslidsoQQQgghhHQgKFAWCR2iivcuWeg5yT9FKrKERVgh3vl0UIquybAclB1VmMo3JSH+UajOvop3QvKcKNZxJoQQQgghpONAgbKgpByUigY11PmRpOqsxglth6Xdu4PCEpTayEHJEO/CEXhci/TeYRXv3EK87du2+2ckIYQQQgghxQ8FykIiFM4IJQelJ6XgCiL5ob0LGPlwUObQTsNGYMlPgIYNwsKQQryLwQldDDRvA5pqcmvDz7VSrOIfr4McBMoknM/EIh1nQgghhBBCOhCxfDW8adMmKIqCwYMHAwAWL16MWbNmYcSIEbjmmmvytduSJVR9UilSByVdKoWhWEWWsAgrJDese2Th6UD9CmDLHPnndFDmhpoEnu2rv/5OExCtzK4dTfVRUb1Yn1lF8DwvNEGqcasJ62s6KAkhhBBCCCk68uagvPjii7Fw4UIAQE1NDcaPH4/FixfjZz/7Ge6666587bbEyFeRnAwCZdFOukleaPcCRh5CvHNpp35F6u9K+ec5CZT5EKZK7HmQbEi/bt4WcGPxWH2cv2K9d0oiT2a+ycVByRyUhBBCCCGEFBt5Eyg/++wzjB49GgDwn//8B4ceeijeffddPPHEE3j00UfztduSJdwclNI9pF/SLdLBaO/jnYcQ77CFKVEgYZGc4sD1WikF8Y8OylBzUBbtOJNC8uabb2LKlCkYOHAgFEXB888/b/n8iiuugKIolv9OP/10yzo7d+7EJZdcgm7duqG6uhpXXXUV9u7d24ZHQQghhBBSOuQtxDsej6OiogIA8Nprr+Gss84CABx00EH4+uuv87XbEkNwUOY6QbJMtmUOyoDOIVKayESX9i5g5CPEO2zBwiJQZgor9tlOex9XP8jSWfjGj4OySIUrPs9zy0HpCPHuoOeQeNLQ0IBRo0bhe9/7Hs477zzpOqeffjoeeeQR873xvdfgkksuwddff4158+YhHo/jyiuvxDXXXINZs2blte+lxB13FLoH7hRz3wghhJD2SN4EykMOOQQPPfQQJk+ejHnz5uHuu+8GAGzZsgW9evXK125LC0uRnBzbUlsyrZB+WayTbpI7sol2u598h+V4y6NzThNy4Ml+QPDdDlM1hHY9+2qnWO8dPs+tjmfNW6wWBUo1Aee4dtBzSDw544wzcMYZZ3iuU1FRgf79+0s/W7FiBV555RV88MEHOOqoowAAf/zjHzFp0iT89re/xcCBA0PvMyGEEEJIKZM3gfLXv/41zj33XNx77724/PLLMWrUKADAiy++aIZ+Ex0FQHnzOkDrlX34ZzKDQFkUwgYngflHJqgUq8gSElophHgL7bFITm5Yjjuo2BtQhC5W8a+jOih3LQPKq4HOQ+EIc/dyJjsclLZxLdZxJkXP66+/jr59+6JHjx449dRT8Ytf/ML8EX7RokWorq42xUkAGDduHCKRCN5//32ce+65jvZaWlrQ0pL+PldfXw9Aj0qKx+NZ9TGSt2RO7Z8sT3mA9uOWv6T9wLFtv3Bs2y8c2/wR5JzmTaA8+eSTsX37dtTX16NHjx7m8muuuQZVVVX52m2JoU+uv9PzVVR89hLQcDkw9tHsmko2C29kky1RxOlAE9qOBh2UwJKfAC3bgWMeDRYCbHdkhYrYXrEVySkxglRv9mynhIvkdEQHZeNm4OXD9dcXa7brQAXgV6Ckg5KEw+mnn47zzjsPw4cPx9q1a3HLLbfgjDPOwKJFixCNRlFTU4O+fftatonFYujZsydqamqkbc6cORN33nmnY/nChQuz/u6c8geQLJgzp232M2/evLbZEWlzOLbtF45t+4VjGz6NjY2+182bQNnU1ARN00xxcsOGDXjuuedw8MEHY+LEifnabUlSEUmFf657LHuBUhUESrXV+XkxOCg7ykS6kEgFlfZ+3m3i+8r79NcjZgDdDwrQjuryuohgkZzwnmW+xMciPccd0UFZ95n1fRA3MXNQkjxw4YUXmq8PO+wwjBw5Evvttx9ef/11nHbaaVm1OWPGDEyfPt18X19fjyFDhuCUU07JOj3SzJlZbUYAzJiR3/bj8TjmzZuH8ePHo6ysLL87I20Kx7b9wrFtv3Bs84cREeKHvAmUZ599Ns477zz88Ic/RF1dHcaMGYOysjJs374d9913H6699tp87bqEyKXAgw0xxFsW7k3nVQehAzoo3cQaLaA9P68OSpEcxoMh3iGeA7dtWcW7OLGHZQc4B/YclKziTfLAvvvui969e2PNmjU47bTT0L9/f2zdutWyTiKRwM6dO13zVlZUVDgK7QBAWVlZ1pMltaM8IvJAW81PcxlfUtxwbNsvHNv2C8c2fIKcz7xlplmyZAlOOOEEAMDTTz+Nfv36YcOGDfjHP/6BP/zhD/nabWmRUwVaG2KIt7RgTilMuknOdPQQbzWHYjRt5TLOSfwsAid0wQlJnHMbh1L4MacYHPGFxnIOgjgoE3Cesw56DkmobN68GTt27MCAAQMAAGPHjkVdXR0++ugjc50FCxZAVVWMGTOmUN0khBBCCCla8uagbGxsRNeuXQEAc+fOxXnnnYdIJIJjjjkGGzZsyNduOy5qAAclJ2P5I14PxLqGKz4HQSqoFKnIEhpiiHcu+QnbSpiigzInHLkHs22nhB2Umi2tQUfAISgHcDxrwg8XDPEmPtm7dy/WrFljvl+3bh2WLl2Knj17omfPnrjzzjtx/vnno3///li7di3+3//7f9h///3NNEYHH3wwTj/9dFx99dV46KGHEI/HMW3aNFx44YWs4E0IIYQQIiFvDsr9998fzz//PDZt2oRXX30VEyZMAABs3boV3bp1y9duS4w2dFAWhSuoSCf7YbH1TeCpamDpzQXshMxB2c7Pu+YmUAY87lJwUIYlzpUyuTzL/ORubLNQ/1wohT6GjUeIdxAHpSzEu8OcQxKEDz/8EEcccQSOOOIIAMD06dNxxBFH4LbbbkM0GsUnn3yCs846C9/4xjdw1VVX4cgjj8Rbb71lCdF+4okncNBBB+G0007DpEmTcPzxx+Ovf/1roQ6JEEIIIaSoyZuD8rbbbsPFF1+MG264AaeeeirGjh0LQHdTGl/2SIgCpShKMsS7MLw+CYAGrLgXOOI3helDRw/xzsVB2WYifkiuv44qquTkIvXjPCyBAjQdsUhOWDkotaSzLf6bSCScfPLJ0Dyes6+++mrGNnr27IlZs2aF2S1CCCGEkHZL3gTKCy64AMcffzy+/vprjBo1ylx+2mmn4dxzz83XbjsuooNSfG1QFMJGO54EaiqQaCh0LxjiLYZyBqatHJQhhXi3+3F1IScHpY8xLopnZSZKoY8hY3FKa8GEatWWg5JFcgghhBBCCCk68iZQAkD//v3Rv39/bN68GQAwePBgjB49Op+7LC3yVSRHloOyQ1Z9bUN2fJB+3Xts4foRloMy0QDMPxUYMBEYeVfu/conriHeORTJyes9kkuIdz6EqRITZ4KE9jrwMcY5td9GdHQHpabm6KBkDkpCCCGEEEKKjbzloFRVFXfddRe6d++OoUOHYujQoaiursbdd98NVeVkIHSSGUK8WSQnv+xdm36t5FX3z4Ds3spivL/4C7BjMfDZ3Tn3KP+EEOLtEPvooCxacgnx9iVq2Zx6RUlH/8FJDXYvZMpByX8TCSGEEEIIKTh5U1J+9rOf4e9//zt+9atf4bjjjgMAvP3227jjjjvQ3NyMe+65J1+7LiHCzEGZyUHZAau+tiWqrUpsoQjLQdn8de59aTPCyEFZggJlPu5jTStcBXrfhBXi7cdBWaTClUU4LdI+ho7dQZmwvvfcNEMOyqIVogkhhBBCCOk45E2gfOyxx/B///d/OOuss8xlI0eOxKBBg/CjH/2IAiWAcKt400FZUIJMlvPaj5AEypadufelrRDFBTXLKt6Oqr4dKcTbsgOE+lzKB7m4SH2dv1L4MacDOii1sARKmYOyg5xDQgghhBBCipi8hXjv3LkTBx10kGP5QQcdhJ07S0j8KBXEHJRqq3PyXQwCZXt2qWhF4qCUTrSzmHy3ltI9Koo14jjkIFCWgoMyH6JKKYhdOeUKbS8OyhLoY+jY8m6KrvUgId4ygbI9/9tECCGEEEJIiZA3gXLUqFH405/+5Fj+pz/9CSNHjszXbkuLMEMpVVvlbrXVtkIpuIJKmGLJDSibaGcz3qUkULoWyQly3G3ooMyp7Tw4KO3OtGKnTXNQFuv5KIU+ho3tmNW49b3rZqpt2yRzUBJCCCGEEFKE5C3E+ze/+Q0mT56M1157DWPH6lWNFy1ahE2bNmHOnDn52m3HxZ53Um0BohXp9x3ScdOGFEsOyrCK5JRSiLdbDsogwk2bOqpyaFvNtxBeAmJXLg5KPyHepfCsbLOK80WE/YcIvyHe9uexmkCb5pwlhBBCCCGE+CJvDsqTTjoJq1evxrnnnou6ujrU1dXhvPPOw/Lly/HPf/4zX7stMSQOymyFkaTNQekolJPv3HUdnGIJ8Q4rB2V7cFDmIlBmEiw0Ddj5sUtBqqD7CrJtvovklIDYlZNb2UeId0lU8e6IRXIE7Dkova4D+/NY5qAsheueEEIIIYSQdk7eHJQAMHDgQEcxnGXLluHvf/87/vrXv+Zz1yWCTKBMAkoWw2IvjGMXLDVb/q6C0I4n0mo7K5JTSgJlGA7KoCHeX/wZ+HAqMHAScPJLAfYj2VfW2+bhfioFoSY0B6WfHJRFej46ooPSEeKdpYNSS8A5ru343yZCCCGEEEJKhLw5KEmWOHJH+sQuSNoFy7xX/w1IMfQhTNqbg9J+PRU1btdSHh2Uq+7X/27JIl1FLtd+vh2UxSrIibRpDsoifU5pHdBBaReOsw3xljooO8g5JIQQQgghpIihQFlIZEVy7MKiX4KEeBeDg7K9uX7yLhz5JYQclKU2Nr5yCWZqoy1DPnMJ8aaDMrdnmY/zVwoOSvgRWtsZ9mdstg7KXHNQairQ9LX/9QkhhBBCCCG+oEBZUCQCZTJLB6Vd2Cx6B2XILsO6T4EvHircZL1YiuSE4aAsqQI5gLvQFGAcAlf1ldy7We8ryLbMQZnXEG/7s7EYnpUySq3yehjYx06Lix+6b6eGnIPy3UuA5wYCX7HYHyGEEEIIIWESeg7K8847z/Pzurq6sHfZvggrxNvhoCyykEAtAaA8vPbmjNT/RiuBfa8Ir12/FHOId1AXWMs2W5ua3O1bNITgoGzTnHRFHOJdCmJXWCHevtzGRfCslFIClcbDxjLuydxyUAb+QUJgw7/1v5/PBAZN8r8dIYQQQgghxJPQBcru3btn/Pyyyy4Le7cliizEO1uBMoiDMsOkfvfn+t/uI7Lrix/yJeLt+KAIBMoiC/EO2p/EXuf2SjT7LuUbV5dbPkO8cxEZiy3EuxgKaAUglxDsjA7KEqnu3BGL5Nirt2dbxVsW4p2NU1ZhAAohhBBCCCFhErpA+cgjj4TdZMdCbc3OsZbYY31vFyz9ChvJZuClQ/TX324EYp2C9cMLS1hingRKNZ55nXxgd/cUCqlYEXDyLTqTgNTxFLFAGYqDsg2dc8UW4l1qYldO5yDTc7BUHJRF5ohvC8LKQSkL8c5KmC9mVzkhhBBCCCGlBy0AhUQmQq77J/B0T2DDk8Haaq2zvncU2/FZmTZeL38dNvkS8bRE5nXygerTzZNvwshBaT+HhRRcfVFqRXLCcl+GJUyVWLhwXnNQtmWofw6UmqgcBnaB0ncVb/vzTBLiTQclIYQQQgghBSd0ByUJgkSg/Hym/nfN34I11bpL/xvrDCQavEO8PQW0fLpC2sJBWSCBsphzULZ3gTIfVbzpoCxewspB6cdtXLTno8TC8sPAS6AMEuKtJSXrZ3O/00FJCCGEEEJImNACUKx0HmJ9n8nhEa/T/1b00f86Qp39VvHOpzAjTBTzJSQWg4OyoIJeGAKlbEJfzISQg9K+bqb7LZfqzhQocySHHJTIcKxhOOvaAs3v87yEWD4TWPIT98/tOSj9hHg3fgW8cqR1mZqQnDMKlIQQQgghhBQaCpQFxWOCo9pFIo+JuBrXXZMAUNk3vcyyvU/HTV5CSI3m2kDEM477i78Ab1/Ydjkpi6VIThhVvKU5KIuZDhTinZdw7CwFP03TBaC2FshCq+LtJwdlsQq2ohu9WPsYAE0Dlt0CrLwvXaTNsY69irfwbHc7B8t+5kxVInNQZnMOGeJNCCGEEEJIqPAbdiHxKoSjeTgg7Yj5Jyt6p1a3C5Q+HTf5LPbSFoVkDKHwgx8CG58E1s/Kz34c+y3iIjlBBaRSCvH+/DdA7UL5Zwzx9tlmlg7KDf8Gnh8MfHJbOP3wS1vmoCxWd2I+f0gqBOJ13bIz8zp+c1CqrZJ2JDko6aAkhBBCCCGk4FCgLFbsEyuvibghUMa6AtFO8u39Oq/yKbS1RRi03f0X3x1Ou1/NBuqW+9uv62Q5AWx9U6+UnjdCcFCWUoj30ps9Psyjg9Lrx4XMO8t+U4uzOiRhKlvBb8W9+t/lv2hbIc93Pl0ZmbYtkSreWjtzUIo/qBnRAHayyUEZ6yxvh0VyCCGEEEIIKTpK4hv2Aw88gGHDhqGyshJjxozB4sWLfW3373//G4qi4JxzzslvB7PGK8TbwwFpxyiQU94DiJRl3t6z4mk7cVCahOBy2fsl8MYU4J0L/e3X7diW/Qx47STg3Utz75NrP0LIQekI8S5QXs9cCXTcAR1VhcpBmSmHYjZkK1D2Pjb9eueH4fTFD6EVyZGMYZuG+udCLiJtESJGDCQbXdbJIgelVKBMIJRQfgqUhBBCCCGEhErRf8N+8sknMX36dNx+++1YsmQJRo0ahYkTJ2Lr1q2e261fvx433ngjTjjhhDbqach4FbmxYxTIKa8GlJRA6QgR11xe23eThcuxcbM/V2Bb5KDMh0DZvD31t8bfft2ObeX/6n83PZ17n1z70QGreLvh57jVBPDOxcCq39s3zkuXUjvNflO7SBMKWYpd0Yr066/+G1JffBBWiHdJOyjbWZGcwA7KpL8Q71gXyb4kId5ZnUOGeBNCCCGEEBImRS9Q3nfffbj66qtx5ZVXYsSIEXjooYdQVVWFhx9+2HWbZDKJSy65BHfeeSf23XffNuxtULwclPYQbw+RyAjxLq/256AMM8R79wrg+SHAS4dlXtfSdp5cefbjzikUN4XR72RL5nUAD9GkLSa0HqLLuieAte73TbqJdiJQ+hHbNj8PbPgX8MWfrcvz6ZwLy30ZljCVreAnXhetIaVSsNO8Fahf7b7fvOegLFZ3os+iZ9myeyXwxtnArmXhty3DIlDuka8jjrv9GeUa4i0RKGUh3tkI0XRQEkIIIYQQEiqxQnfAi9bWVnz00UeYMWOGuSwSiWDcuHFYtGiR63Z33XUX+vbti6uuugpvvfVWxv20tLSgpSUtPtXX61U/4/E44vH8VYHWVBVRl8/UZKtFPY63tgCQ9yXStB1RAGqsOzREEQWQTDRDFfoeVZNme5qqIuF2XPFmlBkv481AhuOPbHhKP4a9azKeq5iaMCU6P20HweizmmxFMh433yeTquU8yDD67dZ/JdGCGAAt2ex63qLCeGlaUrpeDIpw/Pm5rpRE3HFTq8kEks17Ubbou/q++52RLqYkbaPF0ka8NdyxciPTOMgo8/gsEW+BlqEtpWW39CGYTCY8r5sY4DqWXn0C3K8PP4j3kKomkAxhXKJqwrx24/EWQHjueY1FJBk3n1/JZDzjfZYNZc/20/sx+UugajAAQEmmr/FEsjXjGItEtfRzMJGQbBtvtYxfIhEP1H7YuI2D+DxPJpOhn/uylw4GAKhNtUielvnf0JxpbUw/s5t3So8nkmg1r7dEa4Plvk3E5ddBRKlw/BurJluhJq3PyUzjLI6D+W+NpoVy/+WbfH6HIYQQQgghJEyKWqDcvn07kskk+vXrZ1ner18/rFy5UrrN22+/jb///e9YunSp7/3MnDkTd955p2P53LlzUVVVFajPQRgeX4mRLp+t3bIVB5Sn38+b9yriSlfpuvu3vo9DAGzeuhcJbMG+ANasXoGV6+eY6xzbtA19Uq9bWprx6pw5sqbQLbkOp6Rev/3m66iPrPc8hm+0rsTBqddzXNoEAGgqzhZcKu+89SZ2R7d4th2Es1N/d+/ajjfnzDHff7Z8Odav9uiXwLx586TLeyU/w/EAFC2OOS/NljpnxjR/jf6p1wo0zHnpJYd780wN5mTZ81zlQJ/ExzjWtmzTpg34tPYlnJl6v3Def9EU6Wff1GSf+Mc4Qnj/5hsLsTfyRdhddcVtHGSc7fHZ0qVL8NVn8nvGYEj8U3xTsnzt2jVYscl9jE5r3AvDm2UfS68+AUDD3r2Yn+X4n9K4G91Sr2tra7E4hOvo6OYtGJh6/dZbb2BPZIP5mTgW3ZNrcXTLr/F5+WXYEjseI1u+xPDUZxs3rMMnNeFf08a5/Oi1/0NtbDQAYGh8KQ5PLV+29GNs/qy77/ZOaqpDder14vffx7aY1alertXhDOH9+++/h+1Rl5DjNsR+T4xt2oa+qdebNq7Hstrwzn1UazKfFbvrduDNPD2rRKrUGoxPvf5y5RJ8/qVznwe3rsY3Uq8Xv/eW5Tm3ePF72BZ1phnZN/457N7+mq+/woZt72OssOzdd97Grui2jP2cN2+eeU3W1m4L5f7LN42NLjk9CSGEEEIIKTKKWqAMyp49e3DppZfib3/7G3r3dneI2ZkxYwamT59uvq+vr8eQIUMwYcIEdOvWzWPL3NBWfQl8Iv+sJWkNqx0/7jQoDesRWXUfkiPvAToPNz+LfPI2sAoYNPwwXTxbDey/7z7Yd+Qkc53o678DUvOvivIyTJo0CVJ2fQy8pr88/rixQI8j5OsZ+17+AfC5/tq1TUAP4Xsm/fb448ZC63m0Z9uBeEr/U92tCyaNn2S+P/TQwzBiP49+QXeYzJs3D+PHj0dZmeCf0jRElv4ESkstsElfNOn0cUC00tFG9M0HgNr0+0mTTgcUq3cn8mwMSMZTn3v3KVuUryPA29ZlQwYPwsDDxwPP6+9POeUUoPMw9za+3AJ8lH5/4gnHAd0PCa+TjZsQ2TAL6n5XA+U9zcWu4+DFU+4fHT5qJEYN9T7PyoZdgKTm1n77Dsfwke7bxuZ0BlK61aQzToey80No1aP0vIwefQKAzp2rMOmMYOMfWfMQlB2LoCjlwF59Wb++vTHp+Nyvo+g7DwOp3wpOOP44oHqUdCxiL30DirYVR7f8FvGzfonoh/8F1unbDR0yCIOPysM1nTqXRx09BtoAXTqMrN0MLNGXjxp5GEYO87/f2NzbgFQ0+ujRR0HrP9G6QnMNIKTTHDP6aGj9Ts229znjdk9E3/g9kErDvM+QIRgU4rlXNj4JvK+/7j5kLCaNzs+zysKeVcAr+st9h/TGsCOd+zT+nQOA0UcdbnnOjT76aGj9xzu3Wf0FYItS79+vN/oOPxJ4J73s2GPHQut1jGv3xHEwnqP9+vfHpGPb4NzkiBERQgghhBBCSLFT1AJl7969EY1GUVtba1leW1uL/v37O9Zfu3Yt1q9fjylTppjLVFXPNRWLxbBq1Srst99+ju0qKipQUVHhWF5WVuZfKMmCZNQtwBuIwZpjqywWBebrnpFI43rg9A+EhvScXdHKnkCySX8NFVGx70ravagomvtxRdOuv7JoBMh0/IJJ0PNc2QTXWETJ3HYWKEigLJa+rKPRmPU8eOAY75oFwJo/WdeJJF36bT2+slg0nQ9U6J24r7wQdbo7IwoQiYn7Lvc+94o1H5uv6yAIr48DGtYhuusD4KQXHB/7vu8y5AeMRXz0O+a87wEgGol4Xzfidb/q18CntwP7fAs4/j/e+wOgQA0+/h9f51gUUYBIGOMiHks0ajlnlrFo3WFZLl4nEUULpy8iQo7NWKwi3S/hEo8FvTaFPkuvj4T1n8RYLJqX51RQHPeEMGaRSEjXgUFD2i0dQTJz2w0bga/nAsMvtRZOyoSmpt3owphGk/Xye08cd8V677teB5I0kRFoiNiek7Gov3EWxyASiYZ/zeeBfH6HIYQQQgghJEyKOst7eXk5jjzySMyfP99cpqoq5s+fj7FjxzrWP+igg/Dpp59i6dKl5n9nnXUWTjnlFCxduhRDhgxpy+77wL1oSpliK0wiijF7bEUjzCI5PdyreIsFNbyKawQtkuO3iERbFV6xV2jNpUhO607nMrdCOfbjkR2f377s+BBY/AO9QEhQpOOh2aqzZygI4edYcqEhZbv7+tXc2snYLx/Xpmuhi0zbCmO54l7978YM1kmDsAqv5KNIjlexENUWQpvvoldi+xHhx5ycqnhnKC4TSnXnFF/NAd48D2jOHDocmFzOQca2hbG0F2uT8erRwOKrgc9+4X8f714K/PcAIJEKPxaL5LgVXFLFIjn2InIu50BaCCkBx3We1Tks6q9PhBBCCCGElBxF7aAEgOnTp+Pyyy/HUUcdhdGjR+P+++9HQ0MDrrzySgDAZZddhkGDBmHmzJmorKzEoYceatm+uroaABzLiwHN4bBLU6bYBUYPMSaeCuGKdQUidfprezVr+JzQWoQsPwKlT/HKIXql9rPrE+Cj/wFG3gP0Pd5fW577SdjEkpArZ9tFGnG/lvey8+JzQvtqKvS9ZTtwwjPe6zpwEV20AOPq61iKAEclXxt+RAc3gTKjMCV+HvQaC0lYDK16s99ng8czKR/XiChCKcI/VblU8bYcq2wcwhCuUrwxWf9bXg0c83D27UjRXF6HgBpQoDR+SPnqBWDU3f72sf5x/e/mF4BhF1mvrXidW8fSLx0/FLkJlJLrMrQq3iH/20IIIYQQQkgHp+gFyu985zvYtm0bbrvtNtTU1ODwww/HK6+8YhbO2bhxIyKREnUyeAiUMS8HpR1DUIqUp9v0dJiE6aD0K1C6iF6vnwE0bQFeOwG4OISJtppngdLNQelwiErGy9Wt58Lu5cHWd9uvpgZzu7WV2zVXMgqtfgRKlzQLYbvS8tF2PhyUQfoWRPQGgEQTEOvkv32LQOnioAwq0mbaNgzhyk7T17m3YaeYHJQGiWyKCaXOr+j4j7s4KMVrbMd7ts8CCJR2l73Yj0xoufwwQQghhBBCCPGi6AVKAJg2bRqmTZsm/ez111/33PbRRx8Nv0Nh4emgtItIPpxNkZguUorLDPImUPqcHLuFDTeFUMnbMllPWAW2sF0uqluItx9RL2hfsui7dDxUmysqk/MwzyHe6YZz3DyEEG9XV2uAvgW9xkITlMISOrMU/II8K5bdCiy/Bxj/LtDHmZ5DikWgFMcplxDvTNuG6KA0CPrDhB8sx1FgB6VBIodq0ZYQ7zr5OuI19sWfbZ8FDfHONpRfTB9Soj+MEkIIIYQQUqTwG3YhMcRECVk5KJWY4KC0h3iLOSh9tAXkOcQ7RNFLPB41nmcHpVuIt4/xyiRk1bwGvHaSuIH/fpkTbNmEXCvSEO88C5S+HJRu5zhI3woU4p0PoTOQg1LMCZgAGja5r7v8Hv3vkukBuiWIY25iaC4CpWwc8uGgzMs/sxlyaebUdBs6KI3nlhrQQekgaIi3fVyzyaVMByUhhBBCCCFhQoGykHgIlA4HpZ+8kRFBoHQUySlwiHc+w4bFtppr9Iqy6Q/D2w8QIMRbViQnw+22YDyw9U1hfZ8T4A+vA14YpjuP3ByU7TLEO4QclEGcV9YVhNeFclAWOsRbuC42Pwe8sA+w4rf+t8mEq0CZS3hzQAdlGOc47w7KIhEok9k4KCUCZaJB8gMbvK+dXEO8/TooxXNDByUhhBBCCCGhwm/YhcQuUA6agt/X6aHsgYrkmA7KsnQVb68Qb08HZQ4Cpd/q4EDmMOMg2Nt+9+L87AdomyI5Qddf/UegcSOw9v9cQhqDhniXikAZgoPSrY3VfwTWPR68T77IR2h2WO0EaFOVnLuPb8qwrwDXUlIUKMXnl9hGyCHeDuGqSEO8i6lIjtmNHJ4T9h/U4nuCtR8oxDsJ53WThUBJByUhhBBCCCGhQoGykCi2HJRHPYD1yQMAAGVZFcmJuRfJ8TuhDSJk6SsF66P5Pk8OyiCfZUNORXICTmizyW3oWiRHDPEulhyUORJGDkqvNhZd6q8fgccpoKDkun4eqoHvWAy0uoTYGkQrU7vPQvwvtIMyo0BZZA7K3Z+iOvmFc3kxOiiz21lqPzaBUuaglAniVfukmgkS4p1DkRyGeBNCCCGEEJI3KFAWEruDUolCiejVaiNKgBxZxqRJ8Vkkx6/TMbCDMoBQWLICZS4OyjYokiO9Too0B2WuxT0yOkFzFCg9yUWcyCVvoo/lQRHb+fgm4OXDvdePGAJlNucuiEMzDzkoLfuXXX95cFBm+8+spqFs7pE4qfkmoHWn/UOX1yEg3lfJPAiU4n0vy0EJyMVv+/XW9RtA1wNSb3wIlEMvFJZlWQwpG1GeEEIIIYQQ4gsKlIVEIlBCicrXDeygtDtQfIZx5iJQrn8CqF8tXy+vOSh9Hk8YuFbxtu9H5qAMeLtl47xyDfEuxhyU+S6SE2IOVeeGwsuQBEfX9V36mJcQbwAN6yXrCMcbzUGgDNtBGXqIdxE5KMVjbtpi+yyfDkqx+FE+BEpJfx0CpUtxGxElmj63mUT8ET8FDrg2tSykHJTF6iwnhBBCCCGkRKFAWUC0iC3EW4lCyUagtDgo3XJQSlwr0v0EFSiFfr3/PWD2gZnb9du2X9o6xHv7YqB5q20/ReKg9BPizRyU/tvw1Y+gIlnQEG+3PrZhNXCxAErBBcqwqni7FJSyrF9AgVJ8hjvOWw4CeSaChni7/Zvl2r54LKnjcBR18+Gg9CVQprZRIvq/j4D+Y00oId4hn3dCCCGEEEI6OBQoC4ldoIzE9P+keLkEU5M7JZbOa2mf8GUMbTQ+CugQ8Ss4lGIOStmkd+vrwNwxwHMDrMvtop8sX1pgoSKsEG/moMy+jTD6YSeoqOYyXqEJUz7aie9Nv45WpPafRbir7L5wXdelSI7fvLfyRr23dSwrZIi3eM96hZ6HHOLtJlAmGoCv5zl//Ip1FtYP+m+GWw5KHwJlJIb0uc0kUArRCVoCjnPmO8TbSzQmhBBCCCGE5AIFykIiy0GpuAiUXpMhVRbibXO+tEUV7yDrhZnLK28CpWTbLS+lPrMLBvbjkZ3jPBfJATwclGLYZnsJ8S5kDsoc2tBUYNcnwNIZQLw+h/bb0EGZEARKY/0gYqO5bcgOypzyecrOn124KmSId/r6VrwESs/nuaZXo69f5X+/blW8370EWDgB+PQO6/rRqvTrRJbXc75CvM3rI5L+8U8LyUFJgZIQQgghhJBQoUBZSDyK5Djwk4PSb5EcTwdlQIFSVm1Vul4J5qCUbetWNMKX67BAId7Qgk2s7cJfNkJUW1DIEG+LcBXUZacBL48CPv8V8PHNPlZv4xyUMkSB0riWClYkJ59VvPNQJCfrEG8v17PPIjkbntSr0c8+yP9+3RyUm1/Q/37xZ+v64vEFFdxzKZIjCpRu58DioDQEygSc12E2OSgZ4k0IIYQQQkiYUKAsJNIiOW4OSj9Fcso8iuT4dAYFdYj4LaJQijkoZZNkt+N1CLAhFMnJKsRbMq72HJRhhHiH4SzLtY0wBMowxNdsHJQGu5ZkXt/N8dqWId4WB2UOAmWQbZIFECgd90+W16jYdggOSkdxLr/nYNvbwfdrd1Da79Oy7ra+COu37s7cvjQHpf1HkZBzUEaEEG+1AEVy1Lg/8ZYQQgghhJAODAXKQiIpkqO6DUnORXI8iiy4rednApZ0qWrtaDePrry2DPF2Eyj95NgsaBXvAEVyMrld934JPDcQWP7L4P0Lk0zH0WY5KLNwUAZavQhCvON7hPUT1r+ZUAM+U8zt8lAkx3JNiCKfIZRl6axz7EZ8/oZQJMfxnPVbJCcLEdvxLLO9L6+27UL4PHDKAjcHpZ8QbyEHZaYq3hCK5GhJOMfV33lS1CyvvTkjgae6A83b/G9DCCGEEEJIB4MCZSGROChbVTfXnB8HpSBQ2ovkJJtt24QkULZnB6VUAHM7b4ZI7DVhzkOIt30cpfu15aDM6KDMIFAunwk01wDLfpa5f/kkHyHefkXhsIov7VoGfHo3kGj0WL/EQ7xdi90E2c5NGMrFQZm6d+pXAc/2AT7/NZxO8yzPsdj3fDsoPVN2hCBQ2sVRLwdlPKCD0sxnmmuId8AiOW3toKxfqf+tXeB/G0IIIYQQQjoYFCgLiUOgVJBQs8hBKTooFZciOXaB0nVCF8BpJ9uPG3kVKNswB6V0PS3dB2NMw8hB6adIjsONJHNQaggW4p1BoLQ7f7OmCEK8HcdWmdu+/QgdYr/UFuDT24DPfuGxvtt4hVW9Oc8h3qK4lrWDUhQWc3BQysTNJTcCLTuApT/Nj4MyjByUdoHSr4Mym+ef/blvD/O2C5RqLgKlcS3lqUiOsVyJhlMkJ1uB0t4fwL8oSgghhBBCSAeBAmUhsQuUAOKaiyjl6RJMTe4iHkVyCu2gdIQNl0IVb5+h0OI+IhXu+xWFCl+T06ACJdwdlIGK5GQQk8t7CE37LJLkRbbh/hmvIYlL6p1LdCHKXGbbd9SvQJlLXkjJ2O9Y7LF6ARyU9utTDPEO6qAUHXhBrhc3B6UlNNvjHNQulFSvzlQkR5Kr188PNXYsfc82j6WwX7uLMZuiZ9nsF9CPJV6Xfl/WzX19xw9hGdo3zq39uvCbg9J4RmbKQalEMuSg9HkvWZ6j2bhThW2e7Qe8MAxo2Bi8HUIIIYQQQtohFCgLieJ0S7aqAXNQaqrgEnHJQalpgGoXKBNAokHSXlCBUpKDUjYhL1SIdy65LjP1UW0Fmrfq+cUMooZAKSuSIwiOfgRaP84rh9vRJQdlEGdsphyU0ar066aazH304ouHgKd7ANsWBd82qIOybhmwYVYqlNelDd8Cpdu+/bg2XUTkfOzLD7L+2H94aNkhrG+ISh7XUf0XwLJb9e3EZ4QfAUvWB9fnkos4t3slMP9UZ/VqmYMtIhYms52Lmtf03IFrH/bb61Qz4vM3y2eQ2IZ9PPwWyclGRJM5KJu3p9/bn0tB3NmAcyzXzwJW3Ovepmw7QB83v0VyLFW8ZTko8+igtPx7mOpnogFo2QY0bHDm9CSEEEIIIaSDQoGyyEi4CZSZcmwB1hyUlslt3DmBe3U08NxgqzPK3l62DkppeJ6PIjLZkquDcs9qdFa/ci7PKOS16PkY61ekl3mGeAtjm40rS4ZjP24CpbBessl7/5nGKtmUft0kOW9B+OBaILEHePeS4NtmEp/t17zlnnBxAeYqUGbroPRy2blW8c5jkRyHQLlVWD9p/Svj5ZHA8nuAJT+xOShb/P9o4CcHpdv53rNKvo4sxFsRBEr7OV3/TyDZCLx/la8up5t2c38GQLgPFa8Qb09x2ys1SBzY8B+gqdZ1v/p6rUDLdut2YvuWlAU+HLKWQjMJ+b0v/TfEdixKFOlnqo8Qb68clHkVKCWuS+OHnWgVEOvqrx3S5rz55puYMmUKBg4cCEVR8Pzzz1s+1zQNt912GwYMGIBOnTph3Lhx+OKLLyzr7Ny5E5dccgm6deuG6upqXHXVVdi7dy8IIYQQQogTCpRFhmuRHLeJpiheKGVCkZxEerJtd08CwO7letjejvfd2/NVxVsiUPoRLfMhUCoRoOdRwfaTaELZK4diXNNUSRhlhm2TLc4JuRm2H4KD0lcOUB8OSthyUC65AfjvN/xdU4DzPIgFXZq2ZO6jH7KqWJ7pGrILGoIQZQg+mQTKTM4s5wcZ+uS2WTYie0gOSlk79nu4WRQofYR4G07JXR87Xday55G0W34clC7nQBR9Wuvk65t5Yz0clNliEfGy/DFCvA+9Qry9hGqvMVrxW+Cd7wCv2p+ZMoFym/W9W/t+nlniNnEXocZviLdvB2XEOs6OH2F83reWfvkNC5fkUm1OCZSd+vvLNUwKQkNDA0aNGoUHHnhA+vlvfvMb/OEPf8BDDz2E999/H507d8bEiRPR3Jx+xl1yySVYvnw55s2bh9mzZ+PNN9/ENddc01aHQAghhBBSUsQyr0LaElcHpWUC5iJ0RWKAJhQwUeNAtNw7rLK8p20/IYR4y1w0mUSvXDDOTdU+wMBJwM4P/e9HzK2W2Augi7BtJgdlKxDrZF3m10HpKxTShxvJMl4a5O481Xn+G9bp7tny7pL1U21GylLuW7uDUhAoG3N0UBpI0h1IafwK2Psl0PeEzOdQ6rhKkWwGYp0lYaM2gVJN6PeQo+2w80JmEeLdlg5Ki0CpOl25blT2dbaVaNTPvWt/NP26kwk7rbudfclEyzagoqdzfdFdF6Q9P4TsoPQskpNVegAAm1/U/zZuti6XhXi7OijtPzwFFChF4TNTOzkJlFHrDxSOnMw+x90yJlk4gY2xavpa/9tpgL82SEE444wzcMYZZ0g/0zQN999/P2699VacffbZAIB//OMf6NevH55//nlceOGFWLFiBV555RV88MEHOOoo/YeAP/7xj5g0aRJ++9vfYuDAgW12LIQQQgghpQAFyiIj7stBKVZwFR2UMWvhHS0OIINA6eVszDbEWyZQZuO08YvFJVMm/8xfQ9a3mfqYbAGiokCpCA5WWZEcYWx9hUL6mez7cPS4ikluhZJSbUYq5AJlIR2Uzw/W/45/J/PY+nH4ZXRQurThGnadpcDl6YILeV9+2vEK8Qb08+LaL+FYKvo63X+iwC3jnQuBr+cCg8+y7g8Anq627cvlHIjPvOatQLcDJec49V4UrsKqjK5J0gkExauKt+9q0B7XiEx4B5zjmrQ7KD2Ozc9zzZdA6aeKt5CD0igEtuwWoO/JwKBJqW0kId6A5BrMIsTbt4NScr6MEO/K/v7aIEXHunXrUFNTg3HjxpnLunfvjjFjxmDRokW48MILsWjRIlRXV5viJACMGzcOkUgE77//Ps4991xHuy0tLWhpSd/v9fX1AIB4PI54PLuidBHGSmVNlqc8QPtxy1/SfuDYtl84tu0Xjm3+CHJOKVAWGe5FcnwIJUpUD/M2P0tdCMZkPVrpFCvtE0q/ObbUJPDhNKuzxvxMFuLdBjkolWhwgdIy0Q/YR7XVKlCKlWLditWY2+bBQakmXcIFVbmY5NYHY3m0QneVejkoCxXive0toMt+3us4KvVKBJ+MAqWbCBdy2HUphXgD+jXi1q/47vTryj5OcS3RBE82/kf/u+6f6WVa0iXs1y1NgbBPQwRzq9ycDwdlMgwHpXD/20XeMHJQRtwESvuPSTYHpdgvxzMzoIOyOUcHJQQH5ba39WI7K+4FJrwH9B5j+/FK+LrjKBCXRYi333EV7yXjumxOOSgpUJYsNTW6yNyvXz/L8n79+pmf1dTUoG/fvpbPY7EYevbsaa5jZ+bMmbjzzjsdyxcuXIiqqirJFpkZNSqrzQiAOXPaZj/z5s1rmx2RNodj237h2LZfOLbh09iYwaAiQIGyyHB3ULoJJanlSlQXpyJeAmUnSWibhyjnNQHbMhtY85BLn3w4KPMmUJbLP3Pd1qO6dcYQ7xaroKUlkZ4wZ3AA5SMHpZYAIIgtsS4pgVES4g24C6BmiHeF9b2BOLk3QhVzxW+It7l+LPP5cRTJEXP65eigDDvE27MSc4FDvJOtQOsu2zYeAqUY9h8pC+6gTO9EeJl0cYL7cVAaIph9XYlAmQ8HZRg5KD2reGeZg1Ipky+XhXiLxdQsgpt93ZBCvGXh05lCvBNCPstPbgVOnWf9t0Ec54TtGtRUoGWnXlF8yHlA/edA35MkP3jlKFAa16XhoGSIN7ExY8YMTJ8+3XxfX1+PIUOG4JRTTkGvXr2yanPmzLB61/GYMSO/7cfjccybNw/jx49HWZnLM5mUJBzb9gvHtv3Csc0fRkSIHyhQFhmuAqXMlQikRUgjTFFR9ImYlkxvIwqU2CXf3nzvU0SzV//2alPWVpgCpSg02CfdGV2QopBgd5P6cFDaBVFPB2VQgTJoDsokzPykB1wLDD4HWDgRsBfJMdvPIHq7CZSiwJTM4IZztO0ipmQjUAYtkqNlIVC6uvbcwuOzdeBlIzK1UYi3zCXtJVCKld3VuNNBGfSaAdwFSj8OSsP96eagtBRPKdIclI5jF8Vbnw5KTbM6rDOFeEcq9POottpEydRzSZWE+Qd1fcuuLbEPbtsBQMQmUIr3qun4DRDivexn+o9uH/2PvuiI3wIH/8S9D+K5je8B9q4Deox09luVOGHFIjmkJOnfXx+72tpaDBiQFppra2tx+OGHm+ts3Wp1nycSCezcudPc3k5FRQUqKiocy8vKyrKeLKlhme07IG01P81lfElxw7Ftv3Bs2y8c2/AJcj6ZmabISKpAQpMMi5tYZU4mhUm24fowJ5KGQCkJD/JyDXpNrKOd3D+T5qVsixBvWQ7KTC7IuPw14C8HpcPV49NBGTi/pI91tATSolXEOnmX9ifDNRV1c1CKAqWkSJJnf93GPeCjyBDhPfeVhUBpL5LjRyjx2qdfPNMp+Mj1mBPOPivivWC43Cr6CJu4hFwD1rB/mUBpd69ZuuLhTJUV5HLNQekjxNvMQSkIV24/BAXFK0+j7zbS2yleOSj9hng7ikZlcFDGUv9eqK3W86nGgS/+AjzdHahd6Npn9z6FVSQnhvRzQ7U+z4xxNLcxnocpgdbhoNSAGls4z5q/OPvgFuL96tHAy6OAmvmSbURx13BQGiHedFCWKsOHD0f//v0xf356zOvr6/H+++9j7NixAICxY8eirq4OH330kbnOggULoKoqxowZ0+Z9JoQQQggpdihQFhlJTUNSKlCKE2ex2IoR4i0KlClnjDFJNnK+2StOA96uQa8qpV6Fd6RVvO0hiiEJlA2bgI1P6a+zCfH2EigzbtvinJCbDspMAqWfIjlBc1Am0iKEEoElP5tMOMiUg9KtIrnoPlI9rgNpf1326ScHpSjIRbIQKMMK8c4qX2QGsgnxzquDUuJA7NRfEL29QryFqtBawpqPEZCHeG/+L/Dhj61hupY+5hLibXPTmW0a94pHdeds8ap07ReL4GYXZ7MI8XaI8RkclFFBoLSIbK3ABz/UUz28e7F8Wy/cXIhu67gts4R4a7ZnuU2gjKSey8YPeTIHZZ/jrYv6nuzogmL5QUjoe/0q/e/6Wc5+y0K86aAsCfbu3YulS5di6dKlAPTCOEuXLsXGjRuhKAquv/56/OIXv8CLL76ITz/9FJdddhkGDhyIc845BwBw8MEH4/TTT8fVV1+NxYsX45133sG0adNw4YUXsoI3IYQQQogEhngXGUlVg4ooAEkeMBNJFW/ROWhWkvbjoPQQ5bwm1m5CgqxN6X5CquL94nDBQZlNkRxxwhmwj2qrcx3RtejYl0dBnkx9c+2DzdEjCpRmOKdbkZxsc1DmwUHpJ8TbUrE+6uP8hBDiHdhBme11nUWId1tV8TYEvoq+gLIy5cj1ECgbNgjtyByUkhDvN1MVu6tcJu25hHgHKZLjJ/x89+fAZ/cAh92mVweX7l8M8Q4hB6X9PtMkRXJad+uuR/EZ6EgrIYiSYoi3GP5t9DfmJlB6/HAS9EcV13Z8FsmxVPGWhFLbx1iJAojLc1DaBduoM8w2Y4RBpudsslk/18Y9VdnXuT4pGj788EOccsop5nsjN+Tll1+ORx99FP/v//0/NDQ04JprrkFdXR2OP/54vPLKK6isTP878sQTT2DatGk47bTTEIlEcP755+MPf/hDmx8LIYQQQkgpQIGyiIgnVSRVTR7iLU5QxcmpzEGp2EK8LTkobWRbJCewQJknB6XYTjYCpdBXxe4YzbRtskUiUPp0UDrOu0SgCpyD0hbiDcFdFGqIt1AkJ6jjzM216UegFPelxDKH4zoEKdFt6lLFO2ITJYI6KP2MmYysRM82KpLTsE7/22lg6jkTTx2ny/7FEO+N/wG+fMT6uZcIKIqblj4mA4Z4C9dKphBvt+3cmH8q0FwL7HgPOGutfB27g3LjM8CGfwHHPAyUdcu8D8A67l4h3pqmFwJ6ti/Q/RBg8mdiR+TtAdYQb7U1fb8b96irgzKeygErExEDOijtGO36ruJtiKo2gVIW4m20D8gdlPbnofTfsUwCpWSZvYp3sjm9Xll35/qkaDj55JOheTiUFUXBXXfdhbvuust1nZ49e2LWLImzlhBCCCGEOKBAWWA0RKCkJpHN8aTuoJQIlK3xZsH7Ipl0SnNQ2ovk2NxhgLezMWuBUpaDMk8CpYgsxNsrTB2wCQlZOChdQ7wzFMlx5OSUrR80B6XdQSnmZ8shxNt+DgsV4i2K9F8+Amx7O8O+QgjxDhIaD2SfwzBIiLcSSbkYw6p84CZQpp4juz7W//YYBWx+Xn/t5Zw1KhQDVjHb0rYLbmJA0BBvS4i6SxVv4/yJ59ePg7K5Vv+790v3deyVrt++QH/ddX/g8F9l3oexndiepgFvfwuIdYFVXFXTuQ93L7e2IV4j9utW/DEn2agLlJpQUMv4QUtttZ5PLQ502RfYs9q7z5oqv689cxtX6v+2+ArxjsE8D1qGHJSmgzL176TjuhRCxCv76g5H2XXqFuJt7lf2vLCFeIv/dsqiGgghhBBCCOmgMAdlgdGEfJLNcd1BmYTTTfbyMjFsMuF8rUgEysbNQO0b3g5KzyreXgKlRHgwt/MR4p1JOMwGaZGcTCHeOeagdA3xDpiDMkgItmubCZgTdlGgdM1BmanwksRBqWm5hXi7Oih9PIpEMTSTOAlIHJQSgdJ+HeaagzJbB6VnoRO33IFt5KDcuUT/2+OI9A8hXsK0IeC54fixQjyOgAKlnyI58d3ydU2B0qtatkCQYjduOSjNfJg+sPSrBWhYD2x6Blj3mFVI1VRrfmG3Z7jjGSPkMjbuafEcGSHeSUkOyqrBLn1OHfeyW3VHp8wR66f4mp8Q74jgoLSHeJsCpS3E28hFKSuSY2xjOkcz/DuWbYi3IVBGq9L9IYQQQgghhFCgLDSaMATN8aRrkZz12+qEjdKToDdX6eGUcU2Y6BgCxlvnAfNPBr56UX8vc1C2dYi3mR9TNgENKLg4qtJGnZVpM4qMotibRRVvNwelTHDyykGZbbikxbFkL5IjhD9K28+Qg1IW4q22wCIiBQ3xdnVQBgzx9oWHQKn6dFAGzUGZDwelw/lmFC/KwkHZWicRxz0EytZdujAGAD0OF0JkPYTp5hr3z8S2DSyuRTeBMmgVb+FaSTbpIpvj+RLQQRnk+nPNQak4VnVvw1Ykxy4Spndg/fEpUe/SD3sKC6FfpkApLBNDvMVCR545KFPbL78HaNkBfHqHcx0/AqVU6PMqkpMhxFvJFOItbB/rbG3D7LcKNH1l3caOtN82B2V8j/66rKtzXUIIIYQQQjowFCgLjEOgVDUkJcMSU+yhvPpk+901ulupUZwz2l2EW+bof8MM8Y5nGeIddZn8ZdqfDLuLU4laCz/4adMzxDvTtpIiOfDpoPQShs1lPsJ47YKyub4Y4u2Sg9JNgPVyUNrPudoSTFh2PacBQ7x97ctPiLftHPgJ8c5LDsogDkpD5A8oUDbVAk/3AOaMsn0gEyj141DqlunvOw8DKnoKDkqvMG0fzmMR8ccO12syhyI5QMpFaResNec+gwqUK3+vi76O/bs47fw4hc3tbDkoPYslCcKn2B97qLlb+0mJQOlVJMfthwb78qD3j3H/Ba7irTrPuZoUBMqo9a8hyJopOYQQb1OgtN7Lh7Q+iujah7yPI1OIt9qSvt5jXZzrEkIIIYQQ0oGhQFlw0hPLJkOg1JxusjLFPrlUU3/0SVRC3MbuIjSQCpRt7KA0XCMysSmoQGmEbhpk46C0hHj7cDWKqB4Oyowh3j4m8l7LZX1U7Q7KLEO87TkoLQKl3XmEYK7BbB2U8Xpg54f+9wNIQnolVZEzOih9hnEatEkOSuMaD+g4/voV/W/9ioz7VlLHodSv1BdUj0x9kBIot70TbN8iSds5MhxlgPtzJWgOSvu6rXWS45SEeMsqjJurS/a/5Hrg/e9L1nVxUPpxCpttpLdTki3uDs7dy9MuV8BdoPQSDw1hVnUTKIXntSz3rvlZhtQVuz8HNj0n3xYIFuKtxNwFSkB/tjuqeBsCe+p4zPtdEChdQrz3T7wo74+XMx6wXu9iiDcFSkIIIYQQQixQoCwwooOyoUWf8KiSYSlX5BM/Lakvl4Z424nIQrw9XINeQkxggdJwp6QmZdJwzQA53gCg1SZQIosclF4OykzioNrq3Cbis0iOnxBvWZ8820zCFF3sOSjdQrxXPwhs+I+8TVmIt+G0EoW8QKGvbgJlhtDXlw4D3rvC/34A59j7KZJjv0eChnhnGi9XvARKl+ImQR2Ubu49rxDv5lRFbiPnoCH0LJsRbN+ytg0SgkApipWWPgas4i1zULrmoMwxxHvTM5J+ZeGg/OBHwCtHy929aov8xwGDxdekXzfXCu5QDwel+OzLFOKdrYPSfq+8c5GeQ9ONoA5K899KyTMuKbhOzRBvm0Bs7k9NH6MsxFvq+FfT+zHXE/qw9Bbg5W8C8TqhT4JAWUaBkhBCCCGEEBEKlAXGKlDqk5uEJAdlRUQuaGmpCVhCFSZe5T3kO/MI8X76o834+9vrgjsoT3gO2Oc70jatywwHZbfU+zw5KO3ibBCBMmiRHFkOSs8Qb7Girs99ZRRJXXJQIiKIfqq8/frVwIdTgXds4+cZ4p0SMsqqhT4ECL12FTYyiG2NG/3vwySLHJR+rh+vAk92d6AdsZiVZT+S42/YBNQskPQxSwelGAas2apA20mNqWIUvOk0INWEvf8BciqabXs5KOshJZciOUBKJHIRKFXJdSFt00eFb3NdF2HQy0H5xZ91l/DGlOBpz6nod/+vnwG8c2GqH2L1bR8h3mJfjX8v7AKllsjsvnZ73/S1d9+9clAGCfE2+m0P8Y7Yrt+I4KA0RGVDoBRFZnt1dLE/lgrnQh/XPQbs+hjY/n56mZiDMsYclIQQQgghhIhQoCwwFoGyVZ+UqZIQ705RFzeYajgohaGs6CXfmUeRnBufWoa7Z3+OphaXyZYd0QVin3R75aAUQ7wdjqYwBMqgId4eRXIyhnhLclAqWToo/YZMerYp5KD0E+Ldsl2+H2NdLwdlrEtawAzioHQVYrN1HkIXzw7/ldOd5qeKt7QysNivkB2UQVyML+wDLDgNqF1o62O2RXIEMdHiHPZyUKYEysr+qX3bBJ4gIctm23bxUHRQugmUao4h3hIHpSHwWgT4EIrktOwEvnpB6KL4PPTxT65RZMieksBR2MWDjf9x7ttXkRxD0Iul/71ItjgFXzexNNMPL5lEVvOZk4VA6XDAt9qeh/BwUIo5KAXnqLErIxerrD9JW/i7gfHvk+gQZg5KQgghhBBCXKFAWWA0QbDYm3JQyorkVNoFytQETk0JKK2qKFD2lu/MxUGZVNNuqnjCJTTRjlEsJdbFKVrIRBpZiLdXCK4f7GJGJAuB0iLU+ChcI5JskQiUPovk+M13mUkkteeglIV4Q8s84ReFA9NBKctBaYx7lSBg2IQbr2I2mQrz2NnxIbB8pnt7ALDPt4ERN8Ph5vNVJMc+TvY2ZOcthxyUrgKlh7BZ+7r1fRgh3sZ4uxU4MnJQGo43Nwel/d73gyPEW0gX4SpQ5hriXecR4u3XQelToHzzLGDX0vR7sbCUnyI5xg8HFmdni7d46obvHJS2EO9ILO1mTDZJxsxFLM2UuiKTQKlJRGNzue1ajcRgCfH246C0X7+WHJRGITdnDkq5QGk4cIXrwhhrNZ5+LQrwDPEmhBBCCCHEFQqUBSctiBgh3klJiHdl1EU8MxyUvgTKTs5lagIJNT1x1ywimg8HZayzc9LnFeIdExyUsgI9DRv03F2ZQgEBp4MSkRxDvH26Gs3PhWOo2geY9Jl7kRz7BDsvDkpbkZyMOShlDipBxPGq4h2tSrudRDGodiHwVDe9urG0vwGLAb16NLDsFvlnBqYrKoNA6cdBKXP12sO2c6ri7fLI9QoNt4ta5jUeNMRbWN8UKN0EPpuDslNAB6VbKLvYtoHfHJSBQrxT6xo/iLTKqnhLclCG4aC0FxASBVg3gVJ81rZsS/XLdr0GcVCa7frMQWleD6l1LA7KZueYuQmNXs81Ne7DJS8Juwfk45xNiLdnDkr3Kt5mqgNLnyQOSmOsRaFdfM0Qb0IIIYQQQlyhQFlgxBDvvakiOUk4J/3lERc3WGpC2JKtg1KzOiitOQ09JpNxIUwtmxBvmYNSSwALxgOfzwTe/rb7vg3sRXKyCfEWJ6FeBYOk2woh3gf/BKg+xD3E2+EW9bmvjGHmtvESc1CKId6y9kUXlKyKryzE2wz57ZPO3yYKN+9frZ+XJddn7q9I0AJJIqb4YM+H6OGgNERVr7BXQM8991QX4KMbhHW8BMpMDkqXnI2eeTxt2yhZOihFIcUcMzeBMlUBucUW4m0XHt0Eysp+1vfdR6RcrvCu4u3loAwiUBrn0+iHzEEpE8PCcFA6+uIRZi1bx3BQ2ovkBMmBaeA3B2XCloNSyeCgdOvLjsXAp3cL+5Dkr7UTrQQGnglUHwb0PNKlr5Lz5kegFB3lgCQHZYWxA4lA6bNIjnjvmq5J4d+mhE2gZIg3IYQQQgghUihQFhhRoGz0cFC6VfE2JpStySxzUKpxxJNpgVITnTyuk+mkLRdhAAelJQelZBK65wv99ba35fsWkeWgVILmoPQqkpNBNBNdoIZw4xbi7eXm89pXUAelJcRbKJIjEwbF0FO7gwqQh3g3fqX/rRqcFjBFEcSY3Lv2N8vj9MIUzUJwUNrP0ye36X1bdb/QTh4clHYBRAy9touasiI5uz4Bdi7x3rUopPhwUJajHoqWBKAAlX1TfclSoDz+Kd1lbO8HYBUoXVMdJF1SB2RwUBr9kOWgNMOJRaHOy0HZlHl8M4VhuwnYMoFS/PEEmrt464ZYmRrwTithr+Ithngn9jqveTc3Z7IZ+PQ2YR8+KqRHKoGTXgTOWJb+0cPx/JQJlDHBkSrJQWmp4h0gxFvioPRMkZGUhHiLP57FXXJQMsSbEEIIIYQQCxQoC4y0SI7EQRl1uPtsDsqkIGIEykGZsDgoLSHebkKMODmVOih95qAMGlJtRyZQRoOGeHsVyQngoDREWvNcZHBQ1r4BvPxNYGtKiA0jB6VrkRyXHJRJiYPSIlBKHJRNm/W/nQYJVX6FCXp5dYb+ZukU9cJviLdXDsqDpgNnb5T0QxJG7XWdZhKwXHNQJq1hvhYxxyXEW6xC/fIo4JUjvUUsUUjxIVBWanX664reaVHUXkTITaA0QsLFPhv99grxdkNT5e7GTFW8/TgoLWktMjgoM7kYGzd5f+4qUAria3MqxNt+nbXWebdtJ9EIy/XrJcbbq3iLId6ya8qve9fyo4DLuYtW6CK8oqSfo7L0H3aUKCzPOGmItyqsC+f1alYNFxyYZg7KTA5KSYh3skHvS7wuvcwiULamzycdlIQQQgghhFigQFlgtkVHAgDqk1XY02wIlM5hicI++dLXVVITwOZkFJrhCBIEyi3JIeltIvIQbzEHpdU1IkwKt70D1H2mvzYcIEpEn8Rmm4PSj0vGC/vEOVcHpaMKrG2SbM9vqQZxUNreb5gF7PoYeO0E+b7MfQRwUKpCDkoothyUshBvwUFpr+ILyEO8TQflIHmId1m10DeJuJetEOuFKT7Y7xsfDkpDFOxzAtB5iL9+5BLibRdR3bZ1C4cFBAelJMS0ZYdH+xIHpYsDUVFbUaHu0t8YBXIAiYPSJddk52HW95EyD4FyLzKSc4i3Vw5KYczrPnXvg9rsPi7Gfdq40X17wD3XqChyNW1x9gsAWnd6t23H/nz0E+Itc1CKPwSZIdE27M9GA9WHQCm26ZrDN0OId6YiOca/qXa3f1QI8Tae/zIHpfQ5rKWEUTGMXtXfiw5KuwBvOGSZg5IQQgghhBALFCgLzPLy7+GNTtNx5he/N4vkqJIQ75hdoDSL5OjbJLQoGlpTywSBclniyPQ2jqIyANQ4EskMDsrm7cC844E5h+kTWbGCt6LkkIPSyyXjIeS4HY8Sya2KdybXjuGsMRDD1BWbg1J0wzVvA5ZM9+5Hts5Ci4PSpUiOa4i3IAzJclDKQrybMoR4l3WXt2/gKsTmIFAaooOjQFKAEG9z3HyEmucS4u1VxVm8b0R3qz0k2qzinbpvLaHPHveNLAelh4OywnBQiuHafkO8u+xrfR8pF4oqeeSgtGNcTzmHeNc5j3X7IqB1l/8fRrwK1RjH0JBBoPQT4p3Yq7dnv/eN4jl+sQuUforkyHJQis9ZtxQOMnc+YD23rjkoRYEydX35zUFpXO+yQmB+iuSYIeWSHJRe6T/MfqnO6zLRIP+31sAQKBniTQghhBBCiAUKlAUmoXTCp91+gI2tA9CQKpIjDfF2CJRWB2VCi2BvyoEp5qBsSKYnjlq3g50d0Gwh3pYclKn2DFEKAL76r1CFNDXB8pWDMrXMFChbJZNQ4b2XkGNgTPTMbXIrkuNwUNr7Z5+c+w3xfu8KYO3fvPsh7usb0+QuHul2dkFZLJLjMXkH5EVyTME1IogFooNSEuItOtvEEOCmGltfVWD3Z/LjSB3nPvF5iL0yEtj7pf+8lMY5t4skhiBVtxzYvdIWzm8rkmMKlHl2UHoKlJJiG4DTeWbPQWlxcHn0LWCIdxSG61kQUvxW8ZYJlIaA7BB0PARK48cWLRksxNs4J50EB6V93bpPgPe+518cT3o4KA1BKmuB0nZOmrY4r//mHAVKr+dtslH/UWXbW/p7McTbcANGyqxiooghZtrxE+ItOiiDhHhHhByUrlW87SHe4vWrWO8lY2yMH6KSzhBvrcv+tk5IUg9kFChTLmeGeBNCCCGEEGKBAmUR0KlcnzzVN+sTLJlAGVNsE7amGmhrH0UsJSIktSj2pLbXhElYREsAZ28ATp0Ptcc3zeUtMPJsxdFt+XT8bejdUKBKBC9YJ7rrZ+muIwAo76H/tYkU8YTT6aSpTrFj++4620rCvt2Ej3SDemEQEUvIn6RNWRuiaJOpSI5DoJSEeJs50YT9bn3DvQ9mW6l2uh0EHPVHoNNA6/JM2xmvpQ5KtxyUYoi3IVgZgl3MGW4Z35u+FqoGpYUFS+iwmEuv1rq/ZbcAH98kP45U/45ofQDKnpW649Rv1WKjn47wU1U/rrljgXnH2frp4qD0FeKdQw5KvyHesvygBqJTVNNs17CHQBogxBvJFkSMH0XE/fl2UA63vvfKQenloDTCy4OGeMsclLJj3ftlAAdlk/s1aQhSmUK8/TgoAf3HF/u9b/9BJhOJDAKl2H6iEVj5v8DHN+rvLUVyUuMTKXem0DBwdVD6zEFpkG2It6tAadzfEWv7xn5l20sclErqdfKI3wPnCHlGZc7exF7vfKEM8SaEEEIIIUQKBcoioFulPumva0wJlBKXlSPEe+EEKO9fiSt6/xcAkEAU9SkHZUK1FUbovA/Q/1TEk+kJeotiOBnj6L7pLxjf/X0c1mmNLWQ4NbkTJ1s1c9OhhqZAaRUtXv7EOkn/fEs9anYZhQHSk7LfvmSrOixOmDM5KPd84XReycQS19BpDXh1jD4pl+1ftm3MFuLt5aAUhRNpaKq9Pzah0wzjDeiglAmUmmoNOTewuPQkRTIiNrHAcNLGugJl3eQOSvG1XaD8/Ncex2E794lG7zyMIsY5kzkom2v066R1p1UIswuUEReBUsyjaZzDvDkoW9P7sYyNTZgTRSJ7iKndiScirpfI5KCMIwLj2hYESruD0v7eQMxFCgTPQdlpINDjCGD4pal+BgjxVpPpMfIK8Tb27Tf/qaeDMvV8M1zDY/4PmLxc0jeX8bEfW8uO7EK8R/1SyLtpez46RD+bo3jlfen3StTpioyUOx3qBn5CvF0FSmHbwCHeHjkoM1XxjpQj/YOBJMTbkv4jdc1GytL/7hn9so9pJgelme6EDkpCCCGEEEJEKFAWAV1TAuXORn3iInNQltkdlCn6lemFE0QHZVLVUJ/UJ1nvNh1trisKl01ITY4EIaRTpMUpeAHWyVayGa3bPgAAfLbdcKVYRYrmlpSgkmwFPrkDj7z0fFpgFSZlm7bZHEGWSWiGS3PHh85lMvHHzYHYuAnYaWsjU5Ecu4My2ZKexHoVyQlSeMUUOl1CHR3b2XJQQhAoIQiU0hBvSRixKZRGnW4mI7y7alCqr0YOSp8CpRf241Qi7vn+7HiFeIuuM0texyxyUCZtLlMZmQRlL4Ey2QJ8/P+AZ/sA9Svd17OIRKrNGenloGx2vvYIkY4Y10I0CwelXchSytxzUMoK+4z+K3DGkrTjOkgVb/F8GGJdot49D2sYOSjnnwI0bABaU8dS0Tvtgrb0LYCD0lEkZ1fmPkbK3atve+agbLYKkmKIt9l2hXsxHNcQbx85KKVFcnwKlJZCYD5CvMUUFJFU9XAgdW5S/z4aP0SprekfKFTRTSzcw3YXPpBZoDRgiDchhBBCCCEWXOwvpC3pVqlP5lsT+mRKKlDC25mV0KLYmyqyE0+qOG3Vn3Fg5QasiaQFyqRQDGet8k3009ZaJlKdIi2IqYLLReagBLBn40L0ArByRxkOBayTPgAxJbXdmoeAz+7EvZ2BXYmUczJSoYsXahxdojbniSUHZYYQb0Nc7DQo7ewL4qDcvcK5zBHinalITpNTUHQLUcyEKgiDQFrgCVzF2xhjW5EcvwKl0Y+IEOJtOAcNsa+yr/7XEDBUFwdfsy0HpRf241SiARyULiHeWtIqUMpciRlzUIpO2EZdYPcaWy+XqL4j94/UVmDFvfrrpTPc13OEeIfvoFRadyCCAc79+ckNC1iFTCWiPyNkOSg1DWj62rm9IXiJ95PfEG9xPUOg1FRnyDOgpy2o8OugbHR3AWoqsOTGtNha3kv+PHITkO3jJgvx9oOXQOmVg1JtsYqMYoi32LabgzLi4qAMWsXbzEFpu8dkz0FFyEG58T9O8Vwa4i2sE62AKTaK51/8IUpL6tuYAmWZdVxlzt7WndYq3mK7xjMoUi4XsAkhhBBCCOnA0EFZBBgh3gaaRKDsGZNMrgUSWhR7jBDvpIZtiZ54e+8RiAuiZFxVccrKv+CSL3+BL9VDUwvT7T46/A50x1ahIxIHJYBezXpo9u5kygFim4ibbs89a8xlPWJCHrPUhLTKIVCKOSgzXJpGsZVeo4VtAgiU9Z9L1vWYwANOB2WiQRD0UhN30dETBEc18NTfT27VRRQ3RAFALJKj2IrkyMQOS+VgHzko7blHg4Z4e2E/13WfeIt0IhEvB6XgzhMFysReXRyzC5TlQhVywHo8CVsYfDb4reIdr3Nfz8tBGVYOyqav5SHe9vvJTUAX+2gKyJIQ78ReuSvRGEuLQCkRX70clEpEd6kZfZE5EJONmcPyDVrrvEXzumW6OAXohcrsghngvi+7cNmyw0c+UwnRirTgl9FBKYqHLRIHZQgh3rJK4bI+i/sFbM5wTb6tPeew6fgVHZAZQrxNB6VwbUWF57yZdiGV39khUNruPwDYu17uoOx5VPp177HOlCGEEEIIIYR0cChQFgHdOlknfaowLK2aPqmPKt6CVwLpEG8xlNtwZQJ66Pe61kF4Z+/haE6mJmZeoWi7P8dfn30CH37xpfTjumQXaJoGuyvMFChlwkOk3JyQegqU8d3Am+dYRE4LhthQNTi9LEcHpZLJQekI8W70yEEZ0EFpb8cQAnZ9nCF3oy2U3LVITob+mOHLRsi6JMTbLlBKi+QIgp5XoQg7doGycTOwZba/bc0clJIiORYHpRjiHU+F0QqCLADs/0Ng8Dny/RjXc9CxtXbW/SOLs9DjfhdFIk3zL1AGqOKttO5ATEutIwqUdiFN5swd/VebkJU6ZvN6EdqQuScB/w5KmcBqrBep1AUoIx9my075vuxC3uTPgYN+4lyvdad32oG9a9P3SIWLg9JvFW9ZiLcfsnVQJputImNEEuId9SiS4yZcSkVx+7YeId5qEnh1NDD/VOd2lhyUAsZzOtkCM2zbFChtId7GtSle16JwaPybYD4Xy2yiqEQ4b1gvf/b1SkczoJ/keAghhBBCCOngUKAsArp6OCi3JIf4aiMpOijV9KS9KZ4WU8QiOU2J1NDbJ7E2rmn+LjZsWae/6T3W8tnuZBe0JFSYk8AUZsXxhg3OBiNl5oS0S8Q2sbM7fDa/ALx5rrxjRr8reqeXBXJQ+gjxzpSDMtEgcT5KiuT4wS6UiU6fnR95bGdzUJr7tRXJySR2GMKL4TKMdU4fy64lwPKZafGlzMNBKQoSfvKwmduJ4ekBMR16GRyUYtVyQM9DandYxToBJz4HDL/MuR8/OSgz9tWng9ILSx5AW5Ecr4JMSYmQ7HGddtK2OfeXKU/reduA/a+2uQcNgdJwUPpIAyBzUPrNQWkcpyFYl6VcsW45HA236tALgTNXA90PlgtuLTvlDsr9f+jsS3kPeQGhLHJQJuDiTpQhONQdLlwv96s9xBsRiYOywpqP1A/NtcCSn+gCoJv7VFokJ9XX3Z/p6TxkBYLsDkoDM4ekcL0Y60VsId6KPcRbsfbHOEdikRxFQdqdLlyXxnnfu07+7KselX5t+7eUEEIIIYQQQoGyKOhcHkVEMFapgtC2qnVfX23YQ7zN5apmuijF5Y0Jw0HpLVACwOFVq/UXEoGysTXpEJbKlCSSqgY0WKt5A7BMoDvZHZSy/HlGKLddvDImgBaBMnU5H/5roEvqvNkn5V/PA9Y9AeyWhXinJqONW3QxIlMOSrU1LfTYi+Ss/gNQ+7pzH27Yc1CKwk1FL/ftvByU4u2dKVzUEN+Myr9lXa2C77Jb0q4gM8Q7Q5EcMQ9bRsHWJYzTD2aRHHsOSruD0i5QbnYKlGabEnEpEYaDMgyB0lbFO/QQb/3ZUKWl0j2IAqW9fbtgWdnb2UcjjDYqCfF2dVCmxlIU/IOGeBuCUXm1/rfVxUFpXLMHTQe6HZDar2ScWnfIr9HRfwaGXJB+X9YtJWTJina5hXjb8xjuMJ8JcSVAKLDooNzzhfWzjA5KQZBMNqbyhgrjGPFwUHqx8j5g3T/8hXibOSiNvnn8aBGJuQiUqR+SEsL+pA5KoYq3ec2UW93nthBv816QXZfdDtL/NrgIlGVdgaP+BBx8I9D/NPfjIoQQQgghpINCgbIIUBTFEuYtOiiXNw7z1UYSEVOgFJ2SQNpFaanibTgofYgt+1WkitBUH2YRK3Ynu6batguUCTS3JoBGF4HSCPG2Oyjdci2ufQR4bgCw4wNhXQ8H5Yj/B4x7S39tP76FE4BF35WLFWpc78Psg4BXx0hyUEqEAkOEs4d4A3plX7/YnZhiFWc3YQWwOthUWxVvRVS9MwiUxmQ+kRIoY12d7jgzxLta/ysrkiMKlOIk3RA+vfDKu+iFGeJtd5plEiglDkqzTYkb1xQoc8lB6VUkx8P9aGlDFInsRXJCCPGu0l3bVapMoMzgoDT7KJ4/w70mEyhdHJSGG1YRnlNBQ7yN6yGTg1LaZ2GcjO3dHJSA7ro0KBd+UJAVbpFhjH1Zt9S+tpvnOo7O8m0APZzeKAQEWAXK3ctt+/DIQWm/9hKpZ7HoSrbnoPRbMAnQHZCBQrxT96WXq9otxNvIIZmUCZRiDkpJiLdxfMa1+vFPgOatVgclYL0uVZtAuXedPMQ7Ug58YypwxL3Bzh0hhBBCCCEdBH5LLhKMSt6AVaBcumcfaF5561LoDkp9QptUrZO6ZlOgTE/mG+OZ23RQ3hPosp/5dneyC5panQJlTEmgpaFGLiiIDkq7QJlwEbHe/54eKrjoUv19UnAuuoV4y3JBSgUOAS2hO7oSe4C9a5zuUsOZE+ucnmAalYHNia/tlrJXo3Xdd2o9WVho05bM2xmvZSHegNPpZifZBNTMB7Yt0t+XdQW6DE9/XlbtnoPST4h3JnHIvn4QXKt4q7oTzcAQlwynmJeDUjYOyUZrYZ2syIOD0u38O9q3OShbd8n32VkXKMuQOl9eAqWbWCsKscZrY3w0NX1fNKcclJX9rdtnE+KtqcCiK3S3L5B25mVyUJr9tFUeN+iUqmae2ON+jXY9IP26oqfQju26chsfYxyMys4tO8xzG1fcBEpFD6cfLKTBiAhFcpq3Wlf3dFC2WM+v8YNCTKzsbRcoxXskQ3qG8h7ZFcnxclW7hnjLBMpIehtxv2aRHLsAmfq74d/AoiugmDmC7Q5KIQdl1wMAKKnCS5JxdsvTSQghhBBCCAEASGbhpBB065QeCk2YRG1p7YVkWW/E4s4cXE2dDkSnplUArDkoxcrdAFIiojXEuyErgbIa6PYNM39jXaKr3rY9xBtJxOvXS5tIIoao6aC0CQ4JFwelgSEYiiKBGP4sEygBXbhQItaQYxlqwrsIhhHiHSkHENEFC0MgMYvb2AQJN9FVtm8gPUGvHqVXBQYyCJQJ62u3EG97cRM7uz4GFoxLv4911cf6mMeA9y7XJ9yuVbz9OCjrvPcPQPGRbkCKZxVvwUFp5KDsPEy/hoM6KHd8ACy+2iqKB8XLOeWVP1LEkoMySJEcYb3dK4CnewKdBjnXq7LlvfUK8Q5SadrSTgsQqUqHeFcNsaY1sId4q63yfYkC5da3gHWPCfuzOSibJXkMRdwclBV9AKwCoAFNKTd5eU9d8DzmUf19l/3T64sOykjMJgxnCPHuNFB3T7fuNIXRhFuItxk6Lwh80XLnfVBWrd9/dgelPcRbvD6MZ7EY9h2tsI5hpCx9PWTKHxvr4u4+FX9YsId4ez2PXQXKLqltJQ5K8YcH4zkOWEO8AauYWDNPaMfmoITwA0F5d338jGvEjuW+JYSUAnfckd/2IxFg1Chg5kxAlWVcyUC++0cIIYS0NXRQFgmigzKipCd7OxLd0RzrK92muTxdwTqhRbG3xVkkB5CHeGfloCzrDnRO58TcnewiDfE+qNN6RLe/qb/pPEzoYwQtSTXtoFTsAqUtBNdOcy2w+b9pJ1SsszUvpDhZFYVCQ4TKJJKpce8+GM6cSJkz3NteJMfss83F5IYZ4p3a/qQXgUNvT7fhJgQ5HJSG4GZzUGZy5+1da31f1lX/O/B0/W+yKX3eTYEydQ7EPIKiQJloSIuafip6Z3JQ7vNt4OQ5zuWKi0AJW5EcQ8wynKFBc1B+PlNvr36Vdz+98Arx9puD0+Gg9CGAAdax2bVE/ysTU6r2sb6PegiUvsLdbSHeYjtGiHdnuyhqEyhd70vhWde4yfqR3UHpVpDH3KeksA+gi3RGG42p8zXip8AFdcC+l+vvRQelWEzL4aDMUCTHcGsK4rpriLchwFoEvnI4crEaoceWZ4VqFXfVFuv1ZzgoLZW9vRyUGVDjHg5KsUiOzfnuJmqa+5fcT6aDUiyS41LF27gfjXvIHuINWK9x02FpCOdCiHekAqhK/5vsgAIlIYQQQgghnlCgLBLESt7dImknWV2yK/ZG5ALl3k6HmK/FEO+ELcS70XRQpiekDYksHZSVfcy39cnOukApuGc+aBiBTpEW9P7iVn3BwDPMz2KKiua4ak6gO9kdlJnyFGoq8OZZwPJf6u/LutsmzG4OytRkN5ODUvMpUCplzorebiHesmrhdtRE2jFkiCSd9wEOuy3drluuPtXmgtr6eqo/EVgm75lCvO3EUgKlKAAbAo8hUPY7WT/PO94Ddi7RJ+t2werpnsDqB91DvMV+ZhKQIxVyUcRYZg/xVpNWB6VBZ0OgFByUduerzEEZCh6PXD95OoEMRXJ8hnh7EbaD0gzxFvpttGOIilVDhfVjzpyubu5qUWSzV3o2rgfTQVmboZ8uId6Rct0xKfY32kl3zBmITm7xOpbloJS5DY2xiXVJh7unhH/XEG9DaI/aBEpLNXtFd0ID1ntTljvU4mBO9VF0UNqL5MjSILihtnrkoJSInkZf7QKlxcEZIMTbuO8Uu4PSFuJtHJ9bOLY9xBvC/RetSLltXWCINyGEEEIIIZ5QoCwSRAdlFyUtVGiIoE6TV3He1vUk87UqFMlJuIV4C8JlSzKLoS/rbskVl0AMTa0JiA7KmV9fad1GzI+GVD7MiItAmSnE22DdP1L96WadsGYSKP04KL1CCi0OSptoICuSA+ihtJlQWwQnn00kMRxVbmHervkQ7Q7KgAKl4aC0VPY1Qhmr9b+d9wH2+Y7+evUD7gLYh1MlDkpFr3w85Yv0ucsU4h2tkIsibg7KRL3csWa4nJprg+WgDAOvEG+/Ie4WoSNAkRy/AqUxvub+PHJQ+iIlAimKtVCOmtRzvQLWIjMWR52R6zWDQLnzY2DbO9bPjHaM48l0ft1CvCNlaYHSuA8dDmph/RYh16VD6HbJYWrmQKwA9vu+5SNXgbJrKqzc7nIU31cNSj+rVFs6CDsyB7NFoKywumkt4muG2EQvgdIiGmbIQelwpwbMQSne15V90stVDwelrK+y4k2RCssPeI4fTKIu40gIIYQQQggBQIGyaBCreHdTrBPFDeoB9tUBANsqjjJfH1i5XhAorZPFhlancJnQ/Akwa5qFkLVYV2Cfb6G+bD88vfM0AHCEeC9pPBibWoWqsn1PtLTXHE+ajp9KxaVyrF8cDkrhcpY6KOu829MS3g5KI1y9arBzsmmfuBr4cVDWvgEsud7ajoFRNEMMoxZxLVIS8RbDMmEIlJGoc7JuOCgBYJ9v6X/rPrWGVNrzNNrF4e4jgBOeArrsa7qWlEwh3q4OSkOgtAkCMvckkBasEo1C7k8fId52RPHGNx7OZb8CpT2/qiXHoe2eSjYDy2cCG/6TuUiUgZHDz8DLQekLUexLtZVsARo36IJnpEK/DgwsRVOEHJRA2tlroKm6M/qVbwKbnrH12+agzIQoXim2PouFbwCrs9ige8rRPkQsWiO5jmTn0Bi3aDlw4HWWc56AsC8xhNgoWGYJ8a6wnr+qIU5Xov21gVSgFMTOaLn1mhfvkUzCtdriHq4t+2HJKKJk/8HILlD6cVAqkfR4ivuqGgzz2jQc3rIclMYhIOZsRyySE620PvfE6uqAM40BIYQQQgghxAIFyiJBdFB2tQmUy1q/Kd2mVYthbYte5OLdvaPQmlTRkkgibgvxbpDkpkzAXwjro9vPwpy6Y7Gy+1W6WFXWBf8d8hpu3HwDAKCpVXWELF6/8SdQlXJg3+85RKMmwUFZFbG5YwILlFYHZSIhTJKlDkpvAUxR4945z7p+A5i4GDj+aaeDyhQibAJU/UrPfQIA3pgstGMbl8pUeH+LSy5LNwelvUhOUEQhSBRjopVW0cIIM21YnxbKlKg15BUA6ldb34vn2XRQZhAooy4CpSGQR2wOSs3F1VVWnd7OnvvTwE+IdzbFcrxEY78FlSzteBTJ0TS98NGyW4BFl2XO8WpQZhMBc3VQ2sU+o5/GNdH1AJvIJslJaPatC3DqPD0HpN6Qe+i23UGZsZ+iUGYP8bZdzzGJOH3qfGDsP4ARN8vbNJAJlKKDsrKPJaelxUFp5JME5AJl1Bbi3WmAXKBUJc9KmYBtD/G2vBdD9jPkIvV0UIrnPUOIt0WgjMnvJ+N5lTD2J/5wJTw/qoakr03jOR1xD/FWLf9mGg5K1eqgFEO8RYGyrNrpuieEEEIIIYRYoEBZJPTqkhYBOitWJ9WnDfLE+/GkivPW/Ba/av4d5uw+DgCwpzmBpK1Ijlk8x+KglAswHzcciL29J5nvtyWq8aONt+DtbrektxWalxXJ+ahxBOYe+DEw+i+O9sUclN+I2dyFbo43N2wOyg/XC9uLk17Vp4MyU5GcSAzodTTQqZ8kB2WqH3YXnJ8Qb0s7NgGurFuqXRfxym+It0H/8U4XmgxRpBLFWNE9CQCdU7kDW7alC9JEKpyuta1vWN+LzijjmDPlCFUickeacQ7sDko3jHNqaTsLB6VXvjk3wgjx1hvS/9iL5IjiV8O6dNiz3/BuQJK+QBAoD7s9QB9T2MU+QO/nHlGgFM63rGiK+VkV0H8cMPgc/b2muv+wEXVxULrlArSMuSCqdt7Hn4OyUz9g+KW2/kuuo9oFwOe/Tj+XAGcVaYtAKexLJlA6QryF+6CyPxyVse2vZcdywrOpzzwESvHYMuW4TbZ45KAUnau2Ijl2B2XUh4OyzFbF2y31R9UQOH5QMq8Np9NZg6QdLQkkU/9mxDpbQ7xFgdL4oYkQQgghhBDiCgXKImFA9/Qkc3byewCAj8q+DQDYtqcVCc06VM1aJ8STKnYnu+KLyHGoqtAnpXuaE4jbclDKqnvHXUK8b9/yA6wd/lvzfV1SF6qMPJZ6O+n27TkoDerVrlIxqUVwUDrY+qZ8uRvl3S3iybY9kpxjgG8H5c69jXh/zSb3FcTJrVsOytad1uVBXHGAU9AwxEQ3gdLNuWQvkgPobqqTZgPHP5m5HxYHpSBK2AXK8uq0AGRUt45WOsfYHuouihURnyHemiYXfIxz4Kji7UKkzBme7chB6cNBWZmFQBlGiDcUIQeeR5GceEBHsoFdsBIFygN+BJwZtIq5WBE7dV2IDspu37CKhp4CZWrcxON3O29GO3ZHqEyQs+9LfH50O9B53dsd1G7IQqnfOh9Y+lNgxb3pZWaYcOr8GPklAcTFEO+uBwqvDQelWDjGloPS4qAUq3gbRbnKnPfN2RvTYeoW8bPCetyRGDDwTP31gT92HqdItjkoPR2UmXJQppyNbqk/qoY4BU7jOpS4SVVICrJparqPsc5WV3UnUaDM5llBCCGEEEJIx4ICZZHQXxAoX9auBE5fgsW97wQA1NQ3Y/yqP+PF5m/j6156UZIH915vCpFl0YhZBXxPc9xRJGevkZtSEBaTLg7K9a0DsbclLWTuTupulMZ4enIrOjTtVbwNWoT1W5EWq5oTHgJlUGLdLBPOZMIqBhii7uadKXEvg4Nyb1MTFn+x2fVzTRRa3Kp4Gy7CbLGLuoa44iZ0ujooFevku3oUMHlFKgTURzVZNwelGR4tYOTm3CMIlJnC9UWnn3HuMgmU0OR9N8UWn9dVJCYZv2wclCGHeAdyUAoh3qKYIp7XoCkTDOziraUQlVAV2jcuOShNB+U3bCKVmIPSdr7sAiU8BEqjHbsg6SYuRlwclF2/4XRhuomcdsTq9fZrbs1f06/NEG+Zg1LICdp9RPq14V62OFQrrKKi6KCU5aBUYk7nsV2UNOiyr9NBeeKzwOTPgWHfhSdJvzkobW5PR5Ec4Vy4OSij9hyUQvvieHQaCKeD0rg+nWKqKhOwtWTadR/rbHVVi8+HbNzWhBBCCCGEdDAoUBYJA7unJ35JTQF6HoHeXfWJVl1jHOtaB+GfrT/GlgMfxNGf/wPP7x6PeKoYTlksLVDubU5YnJKAPMQ7bhMo/1D7HZy48m+oT3bBnpb0enuT+kRcdFCKDs2mVtV02+xGOoytOS66NSusy/2G4maivLslv10iaRUokymBctHaVP7GDFW8o0oSVfbK4gIWZ6rDZWY4KHMUKO1CWSYHpWeRHGHyXX2Yfr6AdDi6wYDTnduLAmXUI8QbALoM0//uNvK4VbiLjQMn6cd0wvPpZV5VvAdNSb92c1CaId4+HZRKzFkIpq1CvENxUCI9tvYQ76QoUKaEkypbcQ7ZGIrY8yu6VTT2i1sOyuaaVP8G+3dQmn3z4aA0xDW3HxMc/RT2JaZ66PYNZ1oAvwWSxHbsAnrDunTFbzPEO7VOF8FBKQqUPQ4HTpkLjH9HHo4cKbfup9MAofCMJMRbiTlzt4rP510fp18Pu8QW8h3T+9D9YOsYG/Q9CTj056n9+XRQZgrxFscy4pKD0njOGc97cVzFYmPRcjjuR+PZaN8vUkVyZP1MuIR4iz/mUKAkhBBCCCEkIyUhUD7wwAMYNmwYKisrMWbMGCxevNh13b/97W844YQT0KNHD/To0QPjxo3zXL9YqK5KT9C37dEnq326Wie0ZdEIunUqw7ZET9Q3x9MCZURBlwp98lTfnHA6KFsyOyjXtwzExtYBehuCQLkrFeLd2Jqe3CbFEO94Auj5TWDKF/hN2QvCcrmDsqk1GTzXpBs20UC1hTsbRQ2ShnBpy3GYjFhFxjIliU4R91x94jG5ih77/0D/23+c7l5y4CFQAU43qpmDMiXCrPhfYPaI9ETbs0iOgFj91+7SPGi6HrorOn5iwrn1CvEG0g5Ko9BEtNJdNNr/B8C36oCBE4W+6v1R7AJy77HASS8KCzSXqsgBHZSKHwdlnkK8Q3FQKkgLdEn3EG/DQVk12Jq7sNMg7+bt4ls0R4FS5qBUW9NutvKeVtE82xDvAROB7oc62/FbnEQUyvasSb8u7+F0UPoN8RaR/TCza6n+N+nuoFRRhsQJs4GTX9ZzYQ4YD/Q5Vui37fxaQrz7exfJiUgclKJguf81+t+Dpuu5HS0OygxObCWWvidVjxyUWRfJcQnxNpylhnAo3nPNX1vXtQurxvMl4eyrZhEohfvPyEEZrbIKkaLozhBvQgghhBBCMlL0AuWTTz6J6dOn4/bbb8eSJUswatQoTJw4EVu3yqsav/7667jooouwcOFCLFq0CEOGDMGECRPw1VdftXHPg6EIEyU3gTKWEigBoL4pjtZUtZqyaAS9u+jrbqlrcjoojRDvpOBqhFXoaRJcjjvjVcDRD+GtXr9Gg6pPxBtdc1CmlnfdH01aemLbLIh5caQFjuZEMh3amSs2sSyRtIp1hoOyPJ6q8msTwOJR6/YxJYFOhoPSEN0ERBepZndQGpPswWcBZ67Ucz3KxKhMwpc9h6U9xPvjG/V8jqv+oL/3zEEpIAqUdmEh1hk4+gHgsDud+wW8i+QA8hDvXqPl/Srr5p73ze4SdZw/Fwel4Zhyc1DaiwL5CfGWCaF2sgnx9nrk2gVKt8IaipIeh9ad7kVyRGeX6ETtNMC7i14h3lkhCmhCDkrDPVjew3q+LSHekiI5gDzEu9MAa8XuqJuD0mUMxH3t+cL6mcNBmYVAKTuPRt8NYdnoc1VaRG5VOkPrPwEYKHE6A7Ce36hNGMsQ4u3IQalYt9/vaj01xBGpvMR2B6UXkVj6mL3Ed/G82wv6OByUwnl3C/HuPNy9/W4jrJ/ZK6obzyCpg1JSJCfZpIvkgH6dic5ssVJ61T7OfhJCCCGEEEIsFL1Aed999+Hqq6/GlVdeiREjRuChhx5CVVUVHn74Yen6TzzxBH70ox/h8MMPx0EHHYT/+7//g6qqmD9/fhv3PHu27tFFsj5drAJleVRBt0p98qhqeug3AJTFFIwYqE+gP9uy21EkpyHlfowLwqK9inezmt5XfVMcOOAHWNnpAnOZpUhO0paDMoXVWSk4KO0h3j2OlBx1FthCk9Wk3UGpX97nbf82sHOJI+y4NVpteW9xUFYf5tideEyrdggisBK1OnG6HagLDcMvc7blmjMyRcs263tDKIzvSU+EAWiywhcW7AKlEOZrz+NoiAiikypIiLchUBoiRKQCGP034BvXAUc/aF3X7kQDPHJQSh5PoihSNRg44Fo9bBxwFyirBjr3lzHE24eDMqsq3h4OWnueUakD1/gsVYCjudbqmhTFSlOg7AIcMkN3UQ67NJV7zwP7eZQJa71TDr5MYidgPWbDjRnfkxaBynsEL5IjC/GOdbNua7yOVMDqXHYZA3FfB92g/x16of7XLlDaw+AzEessP4/GmJs5KFP3oBIBzlyF+Lj3kVC6OLezYDsesThSZV8hr6OkSI7ocgT0c6bYBM/uB6WXuVXxlnarLP1M8cr/61XF2+6gtOzfo4q3+OOBOK6j7gYOuQWY9Jn+vtn2Q6eYo9KGKgtFF4XXWGfruVPj+o8+/U4F9r3ctV1CCCGEEEKIjg+bUOFobW3FRx99hBkzZpjLIpEIxo0bh0WLFvlqo7GxEfF4HD179nRdp6WlBS0t6Yl9fb0+6YjH44jH426b5YzRtn0fqqYv61quQFHSUb8RBYgiibKognhSw/aUkBlVgIP66S6hzzbvxqEDdHGpsiyC5riKPU36cbTG9UnpoOpKbN9tdY6IAuXuxlbE43E0t6b71dCSPhetiaSwPJFeLgh4jcLyFi09MW9sbkX8iFsRKe+N6Ge3BTpfAPBa/dE4rNM69Bo3C1qsFxCPm7VVk8mk5Vx2i6bzv2lvfQtINlmm8s2RaoiyQ0xJmDkok90PR/Sr/1r2vaexBfG4fizr6zQYQbOaEkNCdp0c/HNEKgdDHXgmymYPM9b2PD6taaulLUXphBgALV6PRP0G81hXbI/ggHgc0UST9FeGRFKFFo8jFusKJbEH8erRgNGuqon1aBFXFSAeh6KmHwhxtcxcPxqpNPeRjHaFaj/WykGW9pJKOeat1TD6wF+ha9On1n0pndL9SBFTolAAKHGrOKBqQNI2vmoy3ffk8KugjvgZkEgCSAJaFLKgU7ViACJIV56OqxqikU6W8xZPqIAinHdVyfhwTMR6BH6Aqpri/quQTWxWK/q6jG0CkdRniYYtiCSazfXUZAuSqfMbadmNKAA10glJpQswYRmgKIgsvRFe8ms8kUQsUgElJXwa14eFY2Yhsvp+qPtdg7KX06408f4zxkLTFPOajirliABINmxGFHrhqYRSBSQbzPVVpdw8BiQttZOhRir0z5JJlAHQNBVqSx2iAJLRzlBS7QNAUouZ12os1hlKKuRd05wSpYYIEmKRrYHnAxM/Abrspx+70sl2z8Sc50SCeQ7K9DB2+36TzbugxuOIpsYwoUWgGe12Go54LA7ga89/hxQ1mb5v43FEmneY4xtPAhFN0d+v/ycSvY+HNvxKKK3N+nNFiUETzpkWqZA/y4x9odzcl4poepyEY4X5eQSaFtWvwdY61+s+3uvE9LlM3d+alkAiHkc03mDZLomYcGwalETCcQ/G43FEq4YhkkoloiGSPialCzDiDmNFRBtrLO1rLTuREJ45Ihpi5jjEoEABkGiu089jpFxPrZKMI9r7BES2v4X44Av0XKIHzQBUWB2VbUg+v8MQQgghhBASJkUtUG7fvh3JZBL9+vWzLO/Xrx9Wrlzpq42bb74ZAwcOxLhx41zXmTlzJu68807H8rlz56KqKotQvoDMmzcPAFAVi6IxoU9h58yZAwDoHItib1xftr22Bi+/vAUVShRxKFi5/isAEWxavx77tX4JIIYvtu7BR58sBxBFpZJEMxTU7tyNOXPm4NMtCoAoOqmNjiI5TWoFIooGVVOw8ssNmDNnHZZv0tcHgK+37TT7tGZ9BIaDaUvtdnP5V1+nl69dvxFz5qwHALxWexX+OPAm/L72Qny+YzXmNKgARuJsyblYVHErxrb8wrF8Y+xUTFl2NXYnu6IsouK31Y0A9P0a7STVuNkXVQPOFbZXGr50tPn1bqCvMBOtirTgxK56UYiP1rZAqbgRkYZPcWTsVQDAgjffwrqUsbBuWz1gFItVFXO/TgYDq5ZKj1VG8+6NmCu01SO5EicCaNxdg4/nz8LxqeVLv/gSnXZMwfDEW9J23nt/MXZEGxAr/zOiZXG0zH/f/KyLuhmnCeu++fZ72BvZjEGJFTgqtWzOyy+bn49s2QYjaHLZio3YtMZ6rDFtLyYL7z/fshc/XLAU+3dTcfPBWzBe+Gze64sRj6yybH9iUwMMX6aKKCLQhbodO3fh3TlzzHP3xYbtWLfldZyRer9q9Rp8sT7dl67qRpwqORdf7dQglol58613cWBrPYygdw2K5XgBYJ/4chwhaUvkjfeWW85jJjQo2LmrDn4Dw5ftOhBH4DXn8mWfoE+yFfsAWL3sLQxIbjXP387tNXgndf18o3UpDgawccsOLBOuqYNbt8CrDvecOXNwhhpDOXSBcv6Ct9ASkRXWORn4arXl2hbvA2N5c0uLeU0f1dyIQQA2fP4m9gUQRxVefvlVlGn1SPlgsfnrrfg4tX6luh1CtlKs31SLT7fOQRf1K5wGQGndiejaPwMAVq7Zgmq1DkZw9GcrvsD61LU6MRmD4a1sbGo0bl0TFRGXe/jLVD+2mf1IohxzXn5Fsq4T4xzUt0ShtTSj2vb5qs8+xBer5+Ckpm2oBvDBR59g6zKnPGb8GyGjq7rLvO7nzJmDCrUHxqMM26Ij8f6cORgeX4mRqc9jH/4AL6zol36uNLVgb0s9jH9hWxIKXnV9lgE9kqtxYur19p11WCQZb4Ova7dj6/aVOAJAw66vYEu0gA8rfoKa6GgkX0v/2Fiu1eEMAIqWxJyXXsJJzTWWc7Z67SYcnHr96tzX0Dv5GY6xtTtnzhwc1VxhXgfNLa2WZ6rISU1rLO0nm/V/z2TPaxUxcxxOampANYAVn7yPwwDE1XK8nNqHov0YZVXfR+ubqwGElM4kBxobXaqnE0IIIYSQ/8/eecdJVZ3//3Pvnba9s4W21KUXQaqKBUWxxxKxBHtU7InfiNGAMV81aoox/kyMsXxtJBo1xo4FYsEOAoL0DrvL9jI7/fz+uHPOPefOvVsQWITn/XrxcvfMmXNPubPOfObzPA9xgHFAC5Tfl3vvvRcLFizAokWLEAi4V/idO3cubr75ZvF7U1OTyF2ZnZ3t+rzvSzQaxcKFC3H88cfD6/Wi75gm/OKllbjlhEGYNtgMH31k4yf4rsp0/vTp1RMzZ47EH9Z+hJbaIPT0HKCxGRWDBuC86QPxpzWLUdMSQSSzFEA1SvKy0FDVAngCmDlzGrZ/uAnYsg7D+/XEphWVpptLMx19bcyPggw/drdEkJXfAzNnHobvFq4Dtm8CAPjSMzFz5lQAwJevrQZ2bQMApGVlY+bMyQCAV+q+BupM10pBjxLMnDkGAPDko5+hYsW/EGZ+XDa1L2aeWGFuwAupezL+tF8BL6QKlL2Kc9CYLNgTYzpmzpTCu5PjaGCYOdOUOULROPBK+/vvze0DtH7s+NhhE44EK5mBrz5+CthpCpRjxk/E1AEFAIAPP9gJJGv9GF6/uC5jDGurWlBekA6/VxKBHdbqhG/IFZg5YqbV0NgbeOdWpPsSmDSiGPjSbC7IMtAv9rbrOJMmTwErnOr8YMt6QNLjjjp6OpDZH4hNA3v7X2D54zFzsjUHfdkHwDrzWqPGT8PInjPtI4K9MgdaMkS7JmK6cdc36Tj6hDOAf18l+s1bkY/fnj0ORw6yZDrj/XuBWrMoSa0+FEUJM/yyoLAHZk6bidjmv0Pf/i8MmPgwBrA4kKzFVFExBIOGSnOxrYtTNnA8sGaxtd5px8JY8zWwKSnuaoY4P462pQ7ooLbWUSecDfz72vY7KYPqyM8vEPeNGyxrMOKj78OIginAvx9OeXz06NHQGj3AmvdRUZ4HvToAJKPj83MzMPM4cy368g+BNUDv/sPQc7R0nqu/AVa+6Hr9mTNnwvNqOhA2HcjHnXCSWcjGDeneVvYx2R4IpIt248v/AJs+QXlhAqgEvBk9zMeiTeL12qtXH5QenhynbRfwmjVk3wFD0XvUTDNHpE0jHDJyArTaGLDlEwDA8FHjMayfOY7njXygtQEAkJ6eCbSqob264Uu5BxSijcArVyT7etvvK5Pcg6yCPmaqhEr1i5KKAWUYNHImPG/fBjQBh0+cClZsyez2/0e4EasaCJZWipnJHIuJ2BkoMNIwU9Ohb9gOfG31nTlzJrTd2cAiID0jG2nZvYCdZgd/Wk77a2voBSS10sKiUsw8MvW8OaVlvVFcOh74HMj0xQBb/bExh00A63Wm2hiuBV7l8zwRnrdvBaTMB4OHjgKWPw8AmHHiTGg7GfCpOsTMmTOhr/gY+M782x5Iy3Rdk+c/1wAh6XdEzL4Of68TMMQ5GO/+GqjfhGGDegHfAt603M7fE/sZHhFCEARBEARBEAc6B7RAWVhYCMMwUFVVpbRXVVWhpKSd/GwAHnjgAdx777149913MWrUqHb7+v1++P2pFVa9Xm+7Hwr3Fvw6Y/oW4O2bpimPFWUHhEDp93rg9XqRkyyUU9dqhm75fR74fD5UlGShZn0tNtSYwkJuhhmO3BqJw+v1giUDDNN8HhRk+IQ4CZgOyvykQNkSNvsnpIDEtmhC7IVrO7Paw3EmtQPhZB7KSByi/Td1t+L2/HtT9sIJPWQVOWLMuZ8Oay5tHaR6BIA25p7bzePPBrxe7Mo+Hq+tOhLLgoMwcYwmxvcEcq35aB7R/trynbj2uaWY2C8f//jp5I4nkaQpnoHsY/8Bo+R4GIYXzaEovtnWiMnFpnNNizXD07ZZ9A+w5nYLgns8PsDrxXOfbUV1cwg3Tpc8cz5b9XJ/BuD1At5c4PQN0KBBl3Op+ax98qQXmX1hVof/48K1OGV0GcZklAMN3wBQq7Z70wqUa+0Oarj0/77G5nslz6WUf7DaGCcESl3XoXu9wKBLgUGXmt5cKb+eYRgw5PvAb/doJftl9FZ+9/rSAJ/1xYOmGan3k0f6QsOTmazWq1b29aZLOSi9OcDo/wW+dBcsNU2Hpnec21LLGgRPn9MdKwkDgMfwAOlm7kcjslsp9KGzqLlnAJAwn2/4stR9KhiP9vB6vVaOUwBeX4Y4c0eG3gKsvh8YNtfxdalp1usGAVPo1IPmFx+aL898TLNyC+qG11pDXP1iyfBmmmvxpv69NgJ5ShEVjy/dmrfUrjlU4Ha8B5TBLQepxqKd//9C7iigYTn0gVeY909lUlX1ZAGxZhiJoLkelgwddtnrDv9f1MtWQMeba/3sUffK6zFEOgPN8EHzynsTaP86fut1o5yTA7rhg578W6PxAmW6V4Q6e7z+1LUy6z7wGlrKa87wWt5XrzcAaKlFwrxeL5A90FqTpruvqedJwIa/pz6/xzSgerHSzmBY55DMQWkkTHei5knfL+8V9oQDdV4EQRAEQRAEYeeALpLj8/kwbtw4pcANL3gzebK7+HPffffhrrvuwltvvYXx49v/MH6gI1fy9himaJSVLJRT22paUryGeYz5GWbfmmQV8Nw0U6BsCceQSDBRPMfQNeRnWHkho/BjW6QYeRnmuM3Jqt8RqRiOXMVbLobDK4QDUKqHy1W85arfcvsbzdMx6tsFaI27u1sFWYM67GJoCUSTc47GEniy5hSsC/XGK55fWnPxWZWRG1AA6D4wzYPVCZvbMFn5N5rQce3WX+Cxmh8pRXJaPZZALld3fe6zrQCAzzbZqnF3QGW0AOh5sigiMvvxz3Hh3z/Ds18lx4mHgCYrNLoIW9ofMFk84raXV+CP767Dd5WSi8ZexVupmqynFHKpD0uimlQk58F31+KxjzbhjIc/BjLLRfvKyHBp7E58ByL1qTMsIXVnYxgzH/xQFI0y+7bzYVtPFa2g+wC/KpKaRXKkIF+ngjhKZWGfcwEeuUCHkQYMnuM+N/MJLhWkbUozL6biVj07rVQtkhOX9sexirdNiC87CRj/MIJ6O8Hm9vW3x5h7gZO/NQVaJ+Rq5Pz+aTEFSviTzkz5XOVr28+GF6dxLI7iUiQHUM97xO1A9lBg9D3W+B1WpJbm0ZVcgtMXm//KLwBktyDfB15khRc3+t4V0x2wr616MfBBMmDdXiTH6TUkIxcH6lQVb161Pbk+pUhWB0WwWDy1mrZcyEv3qPe+jFwUrL2CV4f93izkddjvzd8nPWX+98h/ARMfA8qsL1ISmsM9Kgo02ZMGEARBEARBEATRVQ5ogRIAbr75Zvztb3/DU089hdWrV+Pqq69Ga2srLrnkEgDAT37yE6WIzm9/+1vccccdePzxx1FeXo7KykpUVlaipaXF7RIHNH3yLXcLFyKz08wPcVxw9CaFy7x08wNUU1I05IIjYFby5sKi19CRn+FDZdQUB543/hcMuhAtm0LJ4j2KQGkJkXKV8NrWCBLJcWNSe5uLoBmKWWPGEgxN8Ux8Fyo3G+xCEgCctBQYeBUw9v7Ux2CGVO+O5gIA3m2aKMTVWIJh/s6rcPzaR7A6bmUTbMuZYO1JzA+cXY/YGbvxcux/8FrDkdbAycrV8ppC0pqaNKt6sSZ9iPZ79uwl1RJXqwJ/vbUBAPDMV5LQudOKdS3TNnUwog7GrLnXtUjClV1Y6EAUWbpTis2UnFkbdltFiGQR4L9tR6FLxKxxmvS+4ue1VUGs2tWE378j5XFTKgfbhD0nEdFfqIoagLn+jgRKeY8Mf8fCUSLc/uPmheBoe+11hvo7n6/dbTn2d8DI+WZV4LSuVPG2iSeaBgy+BrsCU9qZqixQduDA0nQgZ1hqhfJpr5vVvqc8a7X5cs3/ciGVC3VKhWSHaskcoyOBUhbQpHOX9yC9N3DKKmD4rZZ425mq7QKmvLbaxZcL9DjK3JtAITDgMnMu5ReYj0ebTFcwdxg6uDu/N/Zq219b6Uyge9xFXSeMtPYft1/X/rpRqqEnkII810Q4tYq3Lld411MclgIu4AOpryP7fAZdbVZtPzcI9P+J2e4vMM9KEjqZXFqK338kUBIEQRAEQRDEXuOAFyh//OMf44EHHsCvfvUrjBkzBsuWLcNbb70lCuds3boVu3btEv0feeQRRCIRnH322SgtLRX/Hnjgge5awvfiuCHWBy0u9PXOs4XoJoXL3HT1w2C6zwOPbooGreE4okmHo0fXkJfuw6Wb5uPtHk9habLUB3++EPlkcS6aQGNbNDkP64NlPMFQ2xpR5gdYIimgOiib2qLKcwHghq23YH3OhcDxn6RuQN4YYMIjpmvMgViC4bi1f8EZ636HRc3jxBwjkhC6JdpT/BzMGCN+bon7TEHAk4E4A7ZEpLQByQ+c8lplkTYM64OyEbfEb7+nK0KHRUsiHbF46gf21phhCS2SWyhXUx2aX7dWYG2oj9XA4sq+h+WxfXlgXXDIJXTpfpMclIoYmz1E/NgIKfS5E8QbVomfY5rl9uOpBLhgDkAVpuyCmIP7i/kL8fDirWqj5gGMLjooO3KWyc5FNzSbg3LIzaZja8Qv1X5u5zHkRmDkvKTYlfy70LjKFCnleVR/CNR8DiSrVqc4KJMwvR0xSnExtpNLoD16zgRO+BjIltILeG3FdoRAKV1DcxCCOEIg64yDUjozQ7qH5T7eZFoAu4jXAb+TRfOuMOFR4OxGU9AFgO0vAy9kmUKXJwvIrtizcdvDfn8rDlBdvbc7FCilfUzYwqvHqOk6oGmpXw7IDkrmIFDqPutvfcO3qQKkfX7clZo5EMg/3JqDLFC2lwtDxuMgvkru3wQchPNYMkEmCZQEQRAEQRAE8b054AVKALj22muxZcsWhMNhfPbZZ5g4caJ4bNGiRXjyySfF75s3bwZjLOXf/Pnz9//E9wIjelqOk0831gIAhpWphXsy/OYHJ+6g5HgMTTzWEo4izkO8DQ15GV6sCvXHt2ySELLy062Q8HiCKSHeALA5mdsymlDdQzwEV25vlIRIWXirarJENn7d7dFiLCn4jSpkJIk6iHbymPGkC3NZWwUATQig8vNaIhow9R/AmHvRkGPl+GyJWR844wlgR0QKReUh3rIrNGqNKbfL+PbQQdkcT0cwmpo4MxSNWyIKgK9ah6T0AYAX66fj5HUPWg3xoCIwy4JtlBl4uUkq6NCBQMmSokSUGcoH8YBcBGjYXGDoz4GTv4XH6NoeyAIvALCkiPV+8+HJuUt7LQlZshsXgKPTL2Lk4fMtrbZ+HsAriXZODis5h5/mTRVa7HRGoISuCm4Z/UzHlseWO9NJDDXS1OcqAoxEcBvw7lHAe8cAbTvNNhfxJN6uQLmP/tfgswuUDsV3FBHa7qBMT+3DsQuUukuIt+wC3CMHJfDnD9Z3qb9A0837zeuQL7XHtI7dqntCzBY9IAuDbTttYdMd3Ofy/tpD3Yf9Aji7QboOc3BQdiBQahpQkPz/++r7ANj+ztpfhxm9gbPrgFNWAyd+bs4BAPzSlyTJ4l17REcCZZQESoIgCIIgCILYW/wgBMpDGU3TcPIo01FywUTTITe0VBUox/c1P/Tn2RyUXl1HZlKgbArFhCDo1XUhRta3RoRLME/KS9nUFk0R4TbXmkJPPG4XKM2wUtlt2NgWFWGQspNvZ4PliJEdl2EHcY7Pw4nWsNnfLmA2CoHSGjsYiQF9zwWG/QIxyfnYErPEgDgDdsck8YSHeEtrknNQxuIJNMZSP5TuaYi3V4sjGE7dg3AsoXyQX1A3w/H5H7WMQZRJ4kasVThmxThJGtuiuHvbuaiLZSOePVwIdLF4Apc9+QX+sNDmDkuKEo3xTEUuCHiltXozzTD8nGHCtdtVdsfMEP/YjKXAEf/Ec7Vm4Q/5DGS+2WYTHjQN6H8pkGeF9DNfASLMJvp0xkFZOMn6uWV9J0K8O5GX0F8Axc3FRVK7K8zpWpn91N99DukQZOJBoGG5+bObQKlZYk8s0yZ87zOBMtf2e15qH72dEO/2clB6stwdlIpAaSuAZL/m/sCbndpWPC21bW/AhWpO62bpsR1dC/GWna7M4Z5XBEQngVISZplLJTMuUO74T/tz4fjyUs/PkK4bruncOE5IAiVzcvZyB6VBAiVBEARBEARBfF9IoPwB8LtzRmPBlZNw4SQzP1//QvXDUL/k77kODsqyXPMD5/+8uBwt4Zho52JkXTAinHZpXgPZAfODXm1rOCXkeFPSQRmzOSh3N5kCpezYiycYWpM5G+2h361hnifSGt/u1pT7A0jJ+dYc5uHmajsPw466FPiRK0w3RyQHJQM2hHtZAyU/qLsV+IklGKpiqSKR7KC0z609hgQ2oTWSWpE2HIsDESuce50cxg0AY+9HzXFrsTViD4HXEJVEScVBGU+gJpaHo757DNVTPhLt/123G+99V40H31unDpXMi9cYz1TOSQ5nl8/H6KJAecu261EVzccNu+4yG9LKgD7niOJDbi5axVnJmfR34DirqFbcm4+wk0ApC1YZNvEPMIUW2dnYkbOsPY5+yxRNp72qCms8v519bCeB0jbHYIwhOOpBYNA1QNbg9h2ALiHeMUmgbOp7LTDhr2axG6DLjsJOk+KgdBAo2yuS02GIt+SOVIRIlxDvPXRQfm9SXLO+1Fyke4u0MvV3e9h0V0K8ZZxcw+0V4ALU83FyUAJAoRUhAd0HTHoCOHUdcMYO05XZVbpS1MiOvyMHJeWgJAiCIAiCIIi9BQmUPwACXgOT+heI0Fk5hDY33Qst6WqRK3MDZq7JeacOR266F+urW/DJ+hrRzvvWt0aECOfRNRRmJiuBt0SEMMQFUEugNNu5DsVDvO3CJXcz2tsrk2HesoAXsYfrAmhL+B0dkQCE2Gpv585KtwrkskDZFFUFyo3hXrh9x9X4a+zXwikUszsxk0TjCVRFU8NTZYHSSXC0szYyEACwqHm8UljIuo60vkAJWhK2PGlZgxH2W8Lqf3A1UHYKUHaSsu9t0lz4mloS6WiJWsKMrAPKgmNDYCR2R3PxQdN4xeUpu0Vlh2ZXHZQv1J+Aiav/D8uD/R0fjzoJkUi9ryysecU8BYgye1EgD+K6tI8lxzkPM3K++d+swaozLNADGPeQy7UdKJsBnPS1mU9V/pObnjw3e9iqUzi5zUF51H0fYNgzA9Aw/A/AzOXAaRvFY61xm8jkIp5EmXWdsJEPDLzSyo3oINhtqmnFeY8uwYfrdjuO1ynsDkqnwlidEShlodebDYz8temaU0K85dBl6W+jY4j3/nZQSgKlvxCYuRLIGrhvrtVvNjDiV0Cps/taud8ynV+DjthzUAK2dAkODkpZKHZzUOaPt+7ZmcuB/hebe5NepqZm2B+4hXjzdXF35r4obkQc8MyfPx+apin/hgyx3OihUAhz5sxBQUEBMjMzcdZZZ6GqqqqdEQmCIAiCIA5tSKD8gXLTdDNf45/Os8JZ7SHeHkPHiJ45GNnTzPu1KykMegxd9K2TBUpDFijDiCSFrEE9zA+FPAclFxZLc8wP+lXCQamKjA3B1OI5ALCroX2B8ubKO7E9UoSfbLzTMackALQknZUpDsqky1F2DyoCpSTKtEQlt2NymGdqT8YHoemiXV5TW0R1ISqOyyS6FALZEupYoLyx6h78asdP8UDlRcJZmsKga8z/Tn4KrXaBMrOfEnL/7+glwNH/AXSPsmet8h5I7XIxI1lwDEn5NkNGESas/j/8ZtcVSp5MrySUt0hzlwV0xpiowH5f5U+c15ckHHMWLKIuId72XKgCSbyKevMdQrwN7GiR/vQVqwJlY1vUrEw/5CaziM3Rr6ti15mVQMW17gtpDx4SCgBpyeJN7YV4c/db+YVKl5pkVfavttSbApNUvOSR3WeLvKEAXB2UUUmsb9Os3ICMMbTFUkXmn7/wDT7dWIeL/v6543idQs7tCQDZw1L7KKKkvVK7g0A54VFg5B3mz7pziHebLHI7FsnZzw5KOcQ7dxSQPWjfXcvwAaPuBHr/KPWxvueZgqDmAXqeBgyf2/lxO3ImMuYgvst/v1wclN4sYPp/gRO/Si0aVHws0O8n4m9Ku3TFDeqGJFAq8NdlpN78LzkoD1mGDx+OXbt2iX8ffWRFJdx00034z3/+gxdeeAGLFy/Gzp078aMfObwOCYIgCIIgCADAfraNEHuLa48diJ9M7qvkjUwJ8U462YqSoiM3xSkOymAEBZnmz4auoTDL/LmmOSxEvkHFmXhnVRU21bSCMSYceGW5AexoaOvQQcmFst75adhW14ZdjW1IJBjk7rJAubBpIl6qHuc4Bqc5nBrKDQBBET7u7HyUw31DUrtsVJQFzahbiHec4fdVF2JoYBNW+U/BJaJdEgXD8vgGDC0pnmYOgbflOyC9N6qjufi/llNTrqsw7k/A8LlgaT3Rmvin+lhGP0QbJDHRpZCPPBfZFdosVciWBcfmcBRpPlO0icQZWPK7jKCyJnV8Lm7LId7ROINvyM+APufisbtXIqXohYRb4aGYS7tbf0WgNPIQkW80zQNoOsKSUB3Nmwh+V2ytDeKo+z/AEQML8czlE80iNoDpsqxelBxjD6taA2o+QO4Gs7vMZFHnpGVAcCuQP85xOCE8Szn3vmgdjnD2FgTqPzQbXMSTMKznyALl299WoXR3CKNtprCalrDjOF3CsK01y0GYk92MvPI5Dwf2OBTJkQvtuBTJWV3ZisNEu5ybsgs5KCc8Cnx+JebvuLLjvh0hh3g7FQraF/htYtvQW4BRvzHP5JzGrrsAnXJQqh0c7u1OhHgDQP5hzu2aDkx+qlPTg7/ILBr1fZBSEHiZVGyLp2fgkEB5yOLxeFBSUpLS3tjYiL///e947rnncOyxxwIAnnjiCQwdOhSffvopJk2alPIcgiAIgiCIQx0SKH+gGLqmiJMAkOn3wKNrVjGcpOBUlKW6WDzSc+tbrWI4Hl1HQYbZt7Y1IkK5ByYdlE2hGOqDUdFelpsGoF4qkmOO4/PoiMQSwv3I23vlpicFypAibgFASHLPyWJlU1JAs+eodHVQRlJDvNuicSQSDLquKWGtspgnD68Ii3KYtCRQRuNm9fAfb/wtRvfKEQJlRBLNZFdhAgYMmM/fPuof6Ff9MDDkZ4gt32xd1y0kXDeA9F6IxxMIyuG7mlmNOha3XHkhWyEfa03y3KUK57JwKTkVW0Ix9MhK7S+LqDGXceQQ77ZoHD6PF8joA0NfBcRdRNh2cK3k7uKslI3hYSMfEdZkPZQM524ODMXD1edgQ6gXfhn1oCB5W7z49XYAwEfrbYU1ht6C+ogfmf1Owfeqsxzckdqm6aaQw3P6yaJOoMj8JxF3Ct33ZCKSNQJbdzfgq9ahaMvaKQmULjkoJbE+KAmULy/djqtYqrl+TwtAuRFhPvicKqinuBklQdgpB6Wcx1J+riT0akrYuDReV3JQDrwCY5/MQn3MoQJ3V5EFLaeK3jYYY2CMiXQee4Q9nL5wsiUY70mIcmdyO7aXgzLHwT27N/EXfn+BUhLCvZAEygxbLmAqknPIsm7dOpSVlSEQCGDy5Mm455570KdPH3z11VeIRqOYPt2KyBgyZAj69OmDJUuWuAqU4XAY4bD1ZVBTk/n/r2g0imh0z/Kp6hQrdcCi61Hlv11lD28JYj/AX697+rolDlzobA9e6Gz3HV3ZUxIoDyI0TUNuuk84nbiTLUWgNKwq3pG4JSS6hXhnB7wozQlgV2MIm2pahQDKQ7yrkyHeXOgszPBhZ2MoJQdl7/w0LNkIU6BMcVtaxXBkcdEtByUPH7eLVKJIjiRyMmYKoOk+D6JMw8fNo1DircPS5j4i16I8vJwLUhXnnMW8ZilM2k38+yI+DVM8C7Eu1BvNem/0m/BXc5z4Jmt8hyreMrEEQ0x+ySbDD+VryrkgIy5zl/t3Zu7yXrYq48gOSue5h6Jx5KSZQtieVveW55VIMCFN1caLnJ8giQphPRcRFrQe639JckyG+ytnAwCubYuiIHnfK5XJJRatb8TF/xyGIwc14enL2plsznCg8Vv3x+NB53bdLwmU7RfkiToJz5qO7RP+i+P/8BEYdDQHhkHIdp4M/P2jTeidl4YThltOHyYJTEFYIpnfYyDukP0j4N27YdA1rBRK+ZYeR5su1fLz1Y5yrkKet1PGqdAOoLgpdSchFLBcrJ3MQdnKcuAUmryrsQ1vr6zEOeN7I8NvjfXUJ5vx+Meb8MxlE9E7XxIBZaHRqaK3BGPA7Ce/QpwBC66YBF3n+XETOPevSzCgKBP3nzO648nbnZouwnWn6VCgZKmOWU+6mVeyeQNQcPj3u35H+Au/9xCfbKjBlOTPioPSLlCSg/KQZOLEiXjyySdRUVGBXbt24c4778SRRx6JlStXorKyEj6fD7m5ucpziouLUVlZ6TrmPffcgzvvvDOl/YMPPkB6+p7lOh3diT8PRPcycuTCPXreG2/s5YkQe52FC/fsbIkDHzrbgxc6271PMOjyGdgBEigPMvLSvUKg9BrmB1kuOnI8uoY0n4GAV0comhAOSI+uiXDvmpaIEKY8ho7yggzsagxhc02rEBd5hfDdzWEwxhBPinb5mapAKRyUeeab68rGtpRwcEtwZEqh1sZgUqC0FdHZnZyzfRwe8moXNIMRU6CMxRO4YNP/QkcCCRgIx8xa0XGmpYwBqOHFbS7h000uIp/sxPxr2//gnaZ+eL3xCPw+7NLfxUEZTzAYupbqJEwKlPF2wtA5qlPSOcRbroot58+Uw9xlEVVxXIatceTryvMxjD0VKK3xookErt98G8ZlrMbnOcfiUqcnSAJlyMhHQ7wZuyIFyE9j8Pc6PWVMfp8CQMBWmZw71p75dAsA4MN1NmelnaNfB1b/Hlj7p84uz5xnwgchp0kOyg/WVOPrLfW4afpgS5hKyMKwdE7MI0Lxd2dOQx8A8GRi5a5W3PXaKgDA5ntPFv31hFXNuS1u/Y3weXTE94KD8qN1NfjL4g24+8yR6FOQ+sF6ZWycIlBuHfMqNldV4ShbQSBBz9Ms0VF24qX3tH6WhT9pHzXJQqQ4EXmodSdzUOo6AAct/py/LMH2+jZ8V9mMe88aJdrnvWqK1b95fRX+etF450E7ECjDcWDJxjoAZoEx07kOfL21QfzrlEDp388CJXMJ8c4daf7b14y+G6hcCAy+vsOuX26uw9yXVmD+acMxdaAlbJ7/t8+wOXmc7YZ4d8IFSxx8nHTSSeLnUaNGYeLEiejbty/++c9/Ii0trZ1nujN37lzcfPPN4vempib07t0bxxxzDAoKHIqKdYJ77tmjpxH7AV2PYuTIhVix4ngkEs7xGUfmux/gEUega7mDif1GNBrFwoULcfzxx8Pr/V6xN8QBBp3twQud7b6DR4R0BhIoDzKKsvxYV90CwAzZ5m0yvIhJYaYf2+vbUNdqioOGrjoouQDlNTSUF2ZgycZabK5tFeIOd1BG4gk0tkWFMJWfDBNvbIsmhUsuaCYdl81hpbALYObCBFLDed3ad7fwwjzqOG1CoLTnpowDmby/BlOWNIW7HL+uOChbwzEhYshiUMg1vNlZnGuRxLzmeBqerD1NjC/6K7ky1QrZ3A3ZEo4hJ82bmovRX5QcQ8pBGXMWEJW8mpLY29IZB6XsxHQVQOW8nWp4Pcezh3Fm8vVjcYa3m6bg7aYpmJqh7sfN/1yGLbVB/OOKifBk9AVirQgaPRBlO3Hc2r/gztOG4pykwBV1cOkCqkswFE2IPJx+F/dgHF4YkISajL7A+Ac7Fiht4bYtMQMBvj1SaPIlT3wBwEyzcPqYnsk9cC5+JK+pSSsATl0PGAHUbo+Idlmc0xMh0S4XKPJ7dCQcHJR+j/Me7Gpsw7OfbsWFk/qiJMdyLV74988AADf8Yylevmaq1b7xLpyeuxj/8V6NE6RxjnrgvwCA5y7PxRRJJEJmf6BlIzByntVm+IDTNyd/tq5Z1xqBkOEkoVqXckzK52rlAe3c/wrNezjVQbm93hR7F66qwr1npT5PvscEWYOA5nVAn3PavaZ8NfmMpZSxiMQS8HUkINsdlN+zInY0Fukg3UEHOSj3NQXjgXOaO+VuvOjvn6MtGscFj32miPgAEGc6DC2BRr0cYvZ2J2/BxL0zZ+IHTW5uLgYPHoz169fj+OOPRyQSQUNDg+KirKqqcsxZyfH7/fD7U130Xq93jz8suWZDIQ4YEgmvq0CptZOv16sDoA/RBzTf57VLHNjQ2R680Nnufbqyn5SZ5iBjcn9L+PAYziHe3FlZnB2wteso4kVyWsJCiPQZOvoVmg6oTTWtwimZ4TNEYZ6qprAQ2wqT+S0b26KKAMcdl2ZfmxDZmsw1aXNKNiQdlGFbOw8rT3FQJoW1lJyVvKiO7bq8v9w9lmDiekoVb1mcU1yLCSEcyNdtkdyJUQcxTxZvATXcWjaCuc09EShNGVvOq6k4IsPOjkg396dbiHcw7Bbi7VyER56PHOJtr/jeHvJ13MZmjOGlr3fgqy31+HJrA3DSUuDk1Ygw814MJtLQHEuXxnQWKL2Sy7Mp5OyslIl1NSPl2N8B0ICpC5RmPk8AqaIOgI27LfdWxMWlK6+pLRIHsgYA6T3hk5Qs+R5eaxwFANgZKVReX36PgY9axgAAmOQsdHNQPvPpFvz5g/XCZWpnS61q6f+oZSxu2X4jggnncMUlG2vVhuPeN8OC7YVTMvqa/ySaNUuEk19btZlHip/ldAddykEJoKMsBfa/Oxz5XgXMVAUtx3wGnLJWcRN+s60BC1dVKX3lP33yOfkMa87yfeCK4VfFuuTa11e34NSHPsLb37qHnTqxraX9VAQATJFYDp/fTwJlLJ7A/y3ZjHV1rFNFreTXhZ0Za/+Mx2rOwkr/5VajvUJ4hs1RSRyStLS0YMOGDSgtLcW4cePg9Xrx3nvvicfXrFmDrVu3YvLkyd04S4IgCIIgiAMXclAeZBw7tAd+t3AtACCRjJUusoV489yUPbJS20WRnJYI8pJ5KnmINwBsrrVyUBq6hh5ZfjQEo6hutvJK5guBMqaIBD2TDsra1jBCKUKk6fJyEyjtjkgelm4XuvgHTTfHpT33ZXMoBmT7YDcnNoWiCHgNRYhsUgRHW1XxUAz5GT5byLazu61VVCBXL9rkJmjaCgI9XH0Ofpz/DvxD5iEL7YR4Kw5KWUDsWg5KJaQ44jx+u0JZErm6dyiWQGZSOONFlcwxoazVfp2Iy9hyeygaB3wFybVaYo+9yBGnqc1535tDUSHiu+WmbNB7ojixzvExJ2p7z8EnbafjhMJyyK++iFSwxkmgVJyxLuHp8tzlvZFF15ZwDOk+88/+Tq0Cx675C6qj+fjFEEmg9Op4dPeP0BDLwq0XXyOyU8ru0nAsLhyV/B6qbXWu8h2W9p1J+RvshbLEWm1iUTTQGxFPAp3J8tecPha/q7wAWyMl+HUohpzkFyi7A+Nx9vrfYmukBP86MQ7xNU7REUBaT6DsFGWcrbVB/PKVFbhq2gAl5NfoQKG0//3i2L9gufLpL/Hu6mp8+D/HQJa2Tn/4YwDAwpuOwqBic+flPxOyEzrB1C8g7EXTHPHmArGk2J0UKH/xr+VYsaMRP336qxT3oBMXb5qPG4ufxfyqn+EVqT2RYKgLRiB2Kzk/ZvihxZJ/H/aTQPnc51vxq3+b4fWdWVN7rA/3wd27LsEf+rqIwD2mfa/xiR8uP//5z3Hqqaeib9++2LlzJ+bNmwfDMDBr1izk5OTgsssuw80334z8/HxkZ2fjuuuuw+TJk6mC9yHE0QXz232caTqaQElCCYIgCIJDDsqDjGGlVj4z/mE2J82ruNd4qK1doPToGgqTbcFIXAhmXkNDv0JTHthSExQCicfQ0CPLFHCqm8LC4ccLjtgdlEVZfnh0DYwBVU1WeClgCl+RWCLlg7wV4q2KGdXNIcd2LpTZw6F3NbQ59udCnF2g5KKLPP+GYFSIe/bxeS7HzoRJ83a7i7RZEiLjDnki+TXvr5yN8aueQdBTljJ2WHJqxRSHo1sVbykHpSLOxaR2WRR0dkq6rVXJQSkLlFK77Mzj05T3RnZwKqKoNJdg2Fl8VN2lznuginxSmLS0B7I4J5/NAt/v8EnLKMzefDc6w4V//xzX/fM7/PFdVdQs1ndZv2QNTHme27nKc5eF56CLGGu/DzaGe6Elka6IiF5dQ5R58WzdTDR5LIeiHEIspwbgolxTm7OAI38ZIb++E876ZIqb7dSHPsLweW87h0nbiMYTeKh6Fv7dcIwi+MfiCXwZHI7qWIEi8kUCffH3wv9ibcFVyjhzX16OD9fV4ILHPlPajQ7SFLg5KMO2Nb27uhoA8M8vnatMb9jdIn5WBMpO5K9tCccw88EP8bt31qQOLDv/kgKlfJYyn2yowRG/fR+L1+5W2hc1j8cZ6/+A70JqoZib/7kM43/zrtSSTFERkfZsT6qF7wHLtjZ0qb+3g/y4jvfq4X8x3a+TnujStYiDh+3bt2PWrFmoqKjAueeei4KCAnz66acoKjLTr/zhD3/AKaecgrPOOgtHHXUUSkpK8NJLL3XzrAmCIAiCIA5cSKA8yNA0DQ+eNwYnDi/BKaNMAUuXcksCVqhtD1uIt8fQkeEzhGDERSqfoYv8kc3hmBAKPLouRM5/fb1dFLfhIeU1tlyTPkMXj+1qMAVGv0cXYZMNwUjKB/yGNlX44xWha1oiSNiEPMASZe0Ox12N5vXsjkshCroJlCk5MXllcudxoi6FZpwKm6Q4KNuc3aLNor/crgmXnCxYReIJJJLXcg8FdhYilWrdrlW8XYROl7XKoqCb0xPSNoS4QKkUxpF+dtlfWZBTc3x27Lh0EyjlvfG7iHO7WF+cv/FuLG4apazp1tp7sStSgAs2/kZxDa7eZSYIfunr7ZBJ0837M4wAkDcGduQckXKof0ObLDBLwnAH7l1APQ9ZOIw63Kv2/rLQKQTKkLOAKD9PnmPCRaFsi6j3/3eVzQCAz+yh3w6oxaukc1VyvVpr+r8lm3HX66txwh/+q4zDi3DZkfM+MgcHqIspNMUVynETNOUvC9wclG4pFl78chtW7WrCQ++vTx1Yym/KxUq33JUX/f1zbK9vw+zHP3d83P4lzSvLdqodkpvRFt+3Id5vrdyFdVXNSpuniwW5OnLGOjLop2bqAbeiTsRBz4IFC7Bz506Ew2Fs374dCxYswIABA8TjgUAADz/8MOrq6tDa2oqXXnqp3fyTBEEQBEEQhzokUB6EnD6mJ/5y0Tgh5gFmkQ0O//Dm5KDUNC2l6rfX0JHh9yArYH7Q5IKOoWsoyjb7frLBEg/6Jiv27mxsUwQiQ9eEKLoz6Wj0GrqYZ10w4hDiHQFjTHyQL80JQNNM0aMuGEnJy8g/wNs/+O9qNK9nz1nJHYSpAiUXC9UHeEEhe7sQKN3Cnh3Ev1SxlIuftjk6uDkBa612sdTKn+lcgEetvt21EO+gm2Dl4qxscwk5VwUxSVhzEijjzn1lAVF2droJjm2dcFBGFIHSWXBrUiqfS45LaZxvElMw+bun8HHLGMf8dvachPfVXo9Vbf1wR/hpx2u6OSjdwtODbkKyy7nKDj+liJKL2NscTt2zJhcnnts6UqrSJ5HD2WXs+/jA22tSXIJuArOSS1bamxU7Gh2vleZzzn4iF3qyvx7bw57SgiPfP3FFiJddw1Z/+XWmnqv0RUB705LTByRzM7oJlPYvf+x0dv09vPXWL5kD3DsmMZ307jkhZT7bWIurnvkax9sEZo/hvKbvKptwxG/fx7++Ur8g8HaxgNeH63bjuueXitQkBEEQBEEQBEF8fygH5SHC6N45+Gh9DQBTFARSHZTcRVKY5ceOpIAIWIJmaU4AzSEr9NBr6CjOshULANA33xQoG4JRIVoYSfGzOCmK8vENXUNehg/1wSjqW6NIT1bXzc/woa41gmicoTUSFx/Y03wG8tN9qG2NoLrJcmjyqtfcHcWddrzdclA6i39uDsq4Tfwz8+xlOTgoU92PSgEa6cO85cK056Dkc3d2Z6ZULI86C5ehaBxpPiMlHJpXcJZdiK45KF1CvBXhS7quLJCo+SCdBZiQSwiyECiluTNmCkweQ1fmGIknRC5EeV6dycso77HS32VvFCEy5CxoNrZFxWtKDhltDll5H8VcbGLbgoZT8P92nIDRvXNFm7JfsY7F1c6IsWoxIxfB2EXQjLi4V/neuAm6MrLw5FaYRHZ/xhPOomtzKIo/f2A6BGdPKRdfqrgLlM7jpLlUZ093aZd1rHAsIf6WdoQ9xJsTcdl3xWnq4qB0ywHrc3EPNoWiqKuPotzW7lb8aE/5JjoOo71fAYPUsPkofPBmqleXq8oDpqv2mAcWoS0ax2e3HSf2NxZP4Jy/LkF5QQb+8OMxov8am3OS43VxRM59aQW217fhZy98g7PGWZW4vR4dcDbNOnLR301XacCj4/5zOs4f911lE3Y1hnBMRY/OX4QgiAOejnJMEgRBEATRNchBeYgwpnee+NmtSA4XVQptxRZ4FWB71W9D1xSXJic33SfclltqW5VrFtsclB5dE8V45BDv7IBHOHsaghEhNHmlMPHq5pD4kJ6dnIc9xLtPUiwVAqVdWHSo4g2YAkhlY0iIiZzalg4clJKIU9NifeKVxSDebneQuYV4N4Wcw8p5KKxduORilix+MCYVEFJciC45KN2qeMsCiYvb0K3yuVO1cXsl81BcS67VvibntXKhzE2gdHNzynNRHZSqCGb1dxa+3MXC1ByiMnanMN/jRsmR5ZZb1H5NHmqsFieSBWbnXKExlxDviBI67CKIRVIFbLcclDJhl3upM2KskjtSGqdB2TPnfZfveTkdQMBFiEzzObcbkpjmJjo64eagdBMl5XbpT4prqgb5XGXRVN7Xh99fj+rWVNejm4PS10nx1c7c+vuAM7YDPczK6Y0xM3/xO/rVSr/rnl+KY3+3WPmyIhiNY0dDG+paI+L/EQDwzfZGLN3agJeX7lBC62VxVW53yxXq5tr17EmIN4D1Uq7Q9jjxjx/ikie+wHeVTXt0HYIgCIIgCII4FCCB8hBhdK8c8TMXCVIFR/N2cArxBkwHpYxH15TQcbmdV+zeVhcUbeY1zbF3NkoOymSl3fpgVAgPPo8u2huCUfHB0mfowqW2uzksRJYcSaBMJFiKQFnZ6FxUx81BubGmFZPueQ/LtjUo7TzEm4twXNRtccgTKeexk6/LRc7UQjvmGPbwdEu47LgwD2CJf26h4rLg6Ca2tbqFeLuEcvN9sbe75aDk4o59TVwPs6+pzSV0n+970CXEW8lBKc1FdT52nJ9T7u8WVt2ZYjtu8PFlQVwW85Qq3tI+xhNM5AWNuoixspinCI5xWaB0Fp7dUgA43TedclBKQmubSxi67LpVBM2w83nw15N9nCY3oVq6V2SBUs6JKQuUcruSX9VFdHTCLVxacVDKIrES+m31d9uzVheBUr4PqppCiCRSv1ByE/nchMuOaIvpQHpP8fsFm/4Xt26/Fu+wnyj9/vPNTmyqacV7yYJBgLpu+WdZP5TvCbWyvNUuO5jlfeKV5+101glrRz6PzsBz0BIEQRAEQRAEkQoJlIcIcjg3d07mpXuVD6FcRCzIVB2UPMS7JKWojobRvXPxlwvHKe26JFBuqVUFSl71mxfJ8egacpMOynopB6UpUErtkiDYQzgow0J4yA5YIbShWFyINb2TAmVLOIamULTDIjkBr7kfi9eolWsLk3tSa8tByecoxEJJEKtuDjm627iDMsURGY0jGk+kCJdc+LLPnRdIcQrxtl8TMHN82vuHYwmx5265B9Vqwc7FWmokMVYN8XYWVLiYkCKuJrvbBR2ngkCA5dpzc1DKIoebuNPk0l8N8e44LL6pE45LJ+Qcq02hqFi76oh0njtgOQjd3IadEZ7dHJpujj2nIjnhTuQOlB8PJtMOAPZK8S75M2XXpnROta3OAqVbDkq3EG/ZNZzu0u4ksncVWQRUXZPOYehxZoltrRFngVk+J9k8KIu6HkNHUzwjZT4+j7PIt6cCpd0dvLJtIBbUnag4c2VkEVW+P9xcoS1KOLvVLr/+5CI58l66hbN3tagOx1786KlPNuPHf12izFFGdtgTBEEQBEEQBKFCOSgPId69+ShsrgliaGk2ALPi96VT++EvizcAsD68uTkoS3LUCqy8YMSJI1KrUvbMM/tu5Q5KkffSHJuLCoZhOShrWyLiA7LP0JHpN59TLzko5RDv3c1h4QLNDFjOoNZwXKn6nZPmRWObGbLNRRmfR0cklhAfJPnnxoIMM/+mLHIBprBa0xJBXasanp2X7kN1c9gxxDsaZ6gPRpGf4VOEk7pgBLF4QhI5vcI51xyKOYR4O+eabHDJZWm5OW0FflzD06MoyPQr4zjlGATsOQylcPbWiMgnJ4/T5hbGG3UWHLmZ0T7HNtFf3Zv6pDjnGuLtmvfSLTS7M4Kjs6DZKLkfVZdq+87CeIKJ6s+Mmf1z033K2C0uQqE5fmrRJbe1tihim7NTUQ0ddhvHXdT1Z5qCF8//ytdo6JpynQQzrxvwGqqQ7OL+dBOSa6VUCspc5D1zyWUpRWyjqS0qnNi69IDcHlVcp+07KO1VyvkeyGNElL8XLq8zJQels4tUPSfntXoNHXfvugzD0zai75S5Uru11pZwTLgS9zTE202kVlNOOL8uZaFcEZiVQl1xIGncV53NMfTISr1uWyQuzs9VoOxEiLeTCdaeR3Xeq98CAP7234246fjBANS12ou6EQRBAMCR+fdAY/T3gSAIgiDIQXkIMbBHFqYPK1bafnFiBeaeNATXHzdIuCwLpdyUXkMTH1TtId6G9KHOa3OglOWqAqU9ByXHo+sYlPxUuXJHoxXK7dGRm2a6ExuDESH4+Dy65KAMCVHGZ2iiwE5bJK6Mw+e9s8GqKs5FUf4hOJF0KeVlWGHlMjw0XYR4Jz+t8v6iwI1NQKtuDiGeYMqHW8a4SMkL+RjI9JvfFTS1RV1DvFMEyjY13JxjD0Pn1LSmOu0AS7SyF3zhdEbMi8QSaA7HkEioOSX5B3jGmC08NjUfJgAERQ7KVHepfY4AL1qkCqFqaK+z8GXP38f7uTnw1CI5bjkonfM+ujkoLXets/DcGTeg/FjERZhyFZgVJ61z+HuLiyCtFlGy2uW9l8Nv+Th2Uc/J7euW+1O+9+Rxdrd0nIPSrYq3W/Ejt+u6VaKX/xby59rv7RaHvxFuOSgVIVnJQdmxg1J1PMsuRA3bo8WYtuYxsMHXWeMnnMfxevbMVSjvizy2vD63gllhl/PojFiv3jfOaSncXKGyQ9MtT2XEodktxHu3JJq7VWcnCIIgCIIgCEKFBMpDHE3T8NNpA3Bz0u0BqEVyJvYrgO4iLsqiZIZfNeOWueagTBU5x5WbBXy+2d4gPiD7PAYKs8x5VDWFxYdbn6GLMPHqJisHpaFrolJyaySmhIRzgbKyMSQ+1PPQbO4E459nebvdGcOvaeWPNMfPz+DjqCIf/yBc3RRWPvDy9tqWiBAwPIYmigqZYeidDPFu5e1q/3oXIbKOh5a7CFxRW9gsf74sZLm5E+1r4vB9sYdsizyZLg7KlHaXEG9+Hq0uOSg7k5cRsETHiIvz0dVB2QnxVu4vu9L4Htjnwl2hbuHmnclF6ibGtrrkEHWr4u0mfClV2yVxU56n7E4UAqXtdSWKWkk2wSYXYVj+0kDNQSnnelUddVa7s3jrJkS6nauyZ9Ja5HMNOuQENdeVek5uORfVEG9rjLZoxzlj3YpaySKq2x64hU/LLsCOcLuXlLU6pHswf3a+lxSRPeK8B2791RBvSzSXnyuHeLu5tcMOWqRbHtJQxPn1Z78nCIIgCIIgCIKwoBBvIgXZQXnskB7i5/YclJl+jyIg5CZD6njONN43L90Lr2GFORqahv6FGcjP8KGuNYKlWxsAmB+Oe+eZ+SO31Qcx2Ge6LL2GLsLEd7eExYdIj6ELB2UwEhPje3QdpUmxdGdjSLTzkD+RgzL5ObPAVsGcY3dQ8nFy7TkokwP1zE3DpppWM0+mJM6V5QSwuTaImpaw+LDsNXRk+HTsagyZIbI2l48lbDg7KO2ioD1PJofP3Z4Lzkk4AcwQ+rLctBShjIeqpjg0W8IpleHdwtDDLnkyhUBpz0EZdXao1iQFyjaXEG81j6OzcMKfY4biy2KsczitIhbGnMVbN6HMY2jgU21siyLNZ6S4QnluUbujTuy7vRK9Q4i3ax5Hl6rcbjko3YvkOIt5igtRdr2FnB2UXDyK2ISsUDRuhn5L7YpAqYR4uxTJCTk7H2WRzx4i7DSO27m6iVPBSAw5ad4Ut1yzgwje1qlQfGuMVteQ+47FWA1qKDf/UkkOM5fHl3NTtkXj4gugjnATIu1nzAm7OCjdviCQ1+ou4jvvseygbA3HxN9vOaQ/FIkjO+BNuVedBEr7lwscNwez/bVOEATxvVk+v/3HR3Xw+Pd9PkEQBEHsRUigJFIoknJQHjfUEihz071KXjmPVI0h0+ag5AIgh4fQaZqGHlkB7GiwqnhrmobD+uTh3dVV+HhDDQAzVxivwL21LojyArO4g9ejifnJDkqPrklh0jEhDng9OkqzuYPSCvG2Ox/5Z/r8DFVg4/D8m7ttBW64oFnfqlbmtgTKkCJUlEgCZXHSlenRNWSnWSHeOenq3lkh3nanXdL96eKgtPevcQn9tkJP1XEqm0Ioy01LzYnZFkWeJOYZuoZ4gqG2Jezq/kwphuNSJKct+bnePkcuQkVTXJvmeciCQDAZ4u819E7loJTnKQsIchX2zoQCu7Xz4kT2cRraIijJCaSIFjyXpV38aAmbwldKoaAQn3vHbtHOhHjLoqtbbkM3kdbN6cmdyvY1tUWcheqmUDQlN6VbiDcP8wfcheRYJxyUTS4hwvJ13YrkqG5G5zU1h1LvMTeHqhyW7JaDUj4/xVUove7lIjl2d2JxNh/HeT723JRcoNQ0iJyp/HXmRlQRIp2FOvkek1+j8vmp1dyd991N0JTPWzaCNocsgdLJad0ZgdKNVpdcoR3lLSUIgtjrLJ/f3TMgCIIgiE5DId5ECnkZPsw9aQhumzkEfQusqq+apqFEclF62gnxzrYJlLLbkjsg5TFG9swBAGyvN4VLn0cXFbi31QWVIjn8+W3RuHBVeXQdZbnm3LY3tFk5KA1NOCh3SSHe/IMpF+csgVKdN6dXsuhPQzCKUDQuxqkoMZ2da6taEIsnhGjAq5hXN4WFYKBpVoh7TXNEiG0eQ0dWgOeyjCrFcwBTcGWMpYp/PFehTbSrC6oOSl6pmBfJsbsTrRyG6ofn6qaQY/9Gm2DKq7vXtERSxuDiqj3Em4sGKU7JmObYXp0UC+2uNO4WDdpywTkJo3LVaLsY69S/ttUKz3dzRDqFAjPGlPHrpLQA8rpErknbmhqC7sWM7Nc021PdpZ0J4VVyD7pUZ290yefZHHZxUEp74CQWpjoonZ2xTm7DBheht0Z2UCpiqVtOQmcxtqmDvKv2PKp8LYmE2t7qkNMVcC5e1aq4AZ2FYfmloM7dWeST1yT3D0Wd7wNF5OtE1XavnhrO7hYC7pYL0i2VgluRHLdwdlf3riw4uoSEq/lVUx2XdjE93AVtUclzKr8uXap7EwRBEARBEARBAiXhwk+nDcCVRw1IaS+Rckh6bCHeMnYHpdyXOwcBS7jsna9WCPcZOvoUmAJlTUtEuAV9ho50n0dcb2djmxi/VzIkfHt9UIh2XsMqkrOrMZRaJMfmoMxzCfHOz/AJoa+yMSQ+6A4uzkKa10BbNI61VS2iP69ivrvZchV6DV1USK9ptcLTvYaG7IDl/uTtBcm+kXgCoaglbvExeK5C3j8jGeJeZ8tByUVlK8Tb2dllF4kqG02B0i6gWTkxmTJ+TUs4xbVpCX+qgMHdiSmOOuGgtAmUQiy1Oyt5kRz1g3+NyLdpjZNglhCX4lp0ECgZs8Zxc9RFHcQ599D6jq8JSCHeLiHCbg5K+ZxCLgJJq4sAxV239vnI4p9b4R+nMFu3yvL2Cs9Bl1B/p/umVSp+pQqUloNSvj/kXJZKiLfL3rjlvrQEeVuaAu4Ctt2TrZFUIVIex63gi5uQJd8GikDpUtzGzYkZkva+xcUBqwqmccd2Gb6X9i8gIg4OaTVvqbMQ2eUiOS7uXbcclG7jyGfL12S/V8Nx56JBdre3/Zry67I10gUbJkEQBEEQBEEcYpBASXQJLkbx0GyOXaDkRV84soOyWHZQJtu545Dj9WjIDniFkLhxtyn+8XDComSuw10NpnDlMTThctxe12aFeMsCZUNbSpGcSCyBcCwhBMrCTOcQb49huUdNodN8gt+jY2ip6aJcuq1e9OdFgqqbLTHTq2soyDSvW9MckfJkaiK0vEZy7eWmecW+mcVzEsraG9qiYMyqms3dmfYiOTwvZE2rc5Ecy9lltvPw9youInbgNuSidW1LxDHEW54jpzIpLKc6KM3/2kWfattc+Jq4g9L+wZ8LoKmFgpJ74yK6yoIHYDpggdTQbI6TcOlWadwuOHJHoN1V6FTFWx7fNQeltJfBaFxyizoLlHFpnLpgRJyRvAdy+LQi/rkUlOGikv0+cHNQtoad2629VM+V743c3hyKCTFJdW06F7cJuoTfduSgtK/JqkBucwe7hHjXO5xra0Q6J5dQfLWKdyeK5Lg4acNK+LTz+bkJ2Pz8GGNqCLmLGOt0rm5jy0KkW5EcdwelS1GdzriJ3XJWRlwclMlh7G5RxxywruH/5KAkCIIgCIIgCDdIoCS6hCxQytgFSrPwi1VkwaOEeKc6KHslw7k5PsN8Ls9DuWF3qxgXsASqLbWtYnweEr69Xg0J53NujcRF+HNOuiX+1bVGRCGK0pwAdAejjEfXhRBX1WSFinsMHSOS4enLkgV+ACnEuzlsCZGyg1JyG3oUETUkRDivoQtnZWNbVDimCjMtcbUtGhfjc+GyLqjmw7Q7KHl/ri9bVbzN9t5JobfK5lqUhVGzPdVBae27FaodlJxvnF1Jd6YocpTcdC5Q2vNqirkk+/P9qnUokgNYwqJdOOGCG5+PEICTawrb5smvK89fLtYSdXDa2cVPvu92EY6nM0hxUHYoXNoclA7CaDzBhDtYzSnpnJeRMcuRq4pzlvgXdXC9McYc3XBublE5hLe9NVl5OG35VR0qnMvjy2sNRROOTj43d5sqUKa6S+2iOT8fu+DPHZQpcwymOmnjCWY5MW3CpejjEuKthNy7iHOtLiHezS6CplvoNxfW4gmm5HEU4dAdfOnB+3IR3C2na9jlPNzW5CZ0OgmO9nHc9qCjHJT2L1WaHFIvyCK4kj8zQgIlQRAEQRAEQbhBAiXRJXjBGY9NxbOHaANqHkqPVEhBrgbOC+0UZ/mVMXm1VS5c8jBOr8fsM6Z3LgCzMjcfnzsot9VLOSg9GtJ9HuHE3FxjCpp+jy6EuK11QSECBLyGcCLKeHTVQcnFCq+uYXhZNgDgm+0NAABds9ZY1RRSxFJe4Ke2VQ791lCaywv5hJQCP3wPm9osB2V2mlcIgA3BaIqAKFyCXKBMrqchaI4hOzQBK7SVf8DmofJCnEvOh4ur9pBX2UHJr5kdkObYFk11UCbH5tfkztIo0xCOxsWa+C3BHZR8z/gZtUXjCEZi4oM/d+6K/jZRqU4UEDLH4W5Ru7gY8Jr3n+UiVcUgfhZhm/AVlfaXw9dvF+fWV5uu4NTq7C6h37aQfnk+8tw5W2uDKeM0BKPCAWYXWrjYm+IAdShQ1BSKpuTaBOQwdzeRVhWShfhnm3u9g5gHuKcGaHRxnXKnoFPuT3t/t9B9vn67aO7k2gQsEdHNQWmfu1POytawlS9VFSid86i2hGOO+VLlUHG5AI3sHpSFajXEWxYFnUP33YocOQmUcrub8zHsUiTHrdBTZ6p4d8ZBKe+BJVDaQrwTqdcErJy0bmHlTgWUCIIgCIIgCIJIhQRKoktwkc4uUF52RH+cNKIEf/jxaNEm56GUHZelOZaYqSfbPZLTEbCK0pTlqGKhLyl0Th1YqLTLDsq61ogQebgA2r8oE4AlEnh0HeWFZgGgzbWWQOnRNUVAFeNLId47G9qEi8hj6BjYwyqUA6iFfELRhBBhvIZmOSibI+KDq0e3HJQ7pUrjPkMTe2iGeCcFTV1DTpoprDUEraI6XLRrjcQRilquxcJMv9j/GikM217JPCoESnPvRQ7KuOrcFFXFk/2LHRyUHmnujZKIym+DhmAUbRGr2FBums9ydIZj4pplkhNVLrySk+YVImJtS0QIAn2TeUt3N6fmoASskPCIXXS1CVxcpOW5L1NEiVAsKdDZxYqomCN/jXB3oj1ceV1SoEwN/XYWsuzCpcij6iDCAcCWpEApi6ixBLMq17tURLevqcahPRo3nX+p1bedhSku8tldaXUOrk3AvYAQn4tbSLhbsR17VXUu8smiYIOLY0+s3+6gjHJB0E2kdXZ/2ufIRSv5vGMJ616PJay/nQlmCWipAqjqnAasHJ+ALQelq4PS2W3Y4iCiAu5ibKuLQCnEWJfiRPK5uhVici2SIwvMcqGgaPuV5e3ztHJQ2uYe1RzX5OSgbA3HkHBwi7aSg5IgCIIgCIIgXCGBkugSJUlx0Wuot06az8AjF47DmWN7ibbsgOSglARKOd+kVAgcUkpLnDC8WLkehzsrDy/PF2IlYApi2QGvEMW4OMPnOaAoQxnHY2goL0gVKL2GLqp+y3gNK8R7e31QatfQrzAjpa9cyGdHQ5to505Bs0K05KDMscKq+QdjM8TbXI/plLRCxbk4VR+MiFyC+Rk+IUTKzkq/1xJA5fB3LlDai+Rw4XZ7fRviCSvnnOygTCSYqC5sVfEOW3PULfdnY1tUcSymJ0P/K5tCyhyz/DycPSbEs7LkvkRiCXMcvmceK1y+ujkkRBtedb66WXVocuwFhLjoahe4uEhrhYqnuhnt4a58HLl6em7ynOpaI0qILWC6eSMOIp+b2FZvq+6dJ87PWTzanEx/kOJObFXH567TmlZV4PJo5n9rWsKOa21qizoIt865POtaubBonhO/B6ycks574OqgdBE03Sqcy6JdJJYQYcLyPOtcCgXtdii4JI+dEtLvIkS6natTdW/AEi5tzZYL2C6AtjqEGisOylTB0d7fLQ+nEBxTqrDHHefS7CBEyuPYHclcMA67OSJd0hQ4pR0AVFG+za1IjlJESU4N4CxQNie3xvVelfonGNDikJ+TclASBEEQBEEQhDskUBJdYkRZNmaOLMFlR/brsG+2i4OyOMcqRNOkhPdZH/B6JCt9l9jCrbmQl+YzMLp3jjS+eSuP7ZOr9PclQ8IH9shU2r2GhvKk226LTaC0uzb5/LlDcVsydyDvn5fuVYoC8dBmnidzp1TIR4Qyx5kQbTy6jh5Zfuia2b5LCluXXZty1W8+l12NUj5MXUMBL3DTFJIEUB29k47AbVKFc14oyHKZme39CjPgM3SEYwlsrQsKYUrkoAxGFDcZP6OmUEwIIj6PLkLIZWHRo+tSqHybUigoW3KLcnEk3W8I0dnM58ndpWp1di7m8DO1QsLN/rJQKK+1yBa2ztu5QFnVrLpIOQ2SEGnupbVWEdLv0YUIXNsSkULo05DhMxBLMGypbRVCCM836hbezF2eXNTNT56fvYo3v++cQrwBS0CzF1fiDkp+3dzky1R2+wLWlw1NoZirIGh3G4oQ7+Rr3C0vKsfNbcjFQtcclCkionNhoXqHyuq1UjVwe3XoUDSeIsLtaHDZX5dQeSt/pjqOazh0ci/dBEq3MHp5D4JKDkrnIjlq1XZrD9SK10kh0ua6DbnkoHQL9RdOTJtb1CmsWhYuZfexW/i0EqLfiXBrOQdlzGHP7CkZhEDZScezU9qBIFXxJgiCIAiCIAhXSKAkuoTH0PH/LhiHa44e2GHf7DRLtCuSqmP7PVbxHO50A4C7zxyJ7IAHf589XrSV5KhVtXkIL2CF/wJm2DMA3H/2aIxMFq0ZXpaNISXZAFIFSkMK8TYFSh5qrikh6Nb4Vo5LnjsQMMUaTVNdlPZK41zI8Bk6/B5DCFGWEKnBY+hClN1ax92fmigStKXWEhY9ui5cqDvq26x2Q0f/pFN0fXWLFEKuiRyh26QK5yLEO6SKBgGvIdbz3a4msS7ZbSh/oC/I9AnRigt6Hl0KT2+T3Z9WCH1lo5Wf0yMVBGoOxRQnJq/6Xt0UFsKXGYpvrmlXQ0jkoOQOShHinRxHVP1usTkos1SBkouFVh5OVRDjAnBjMKqIMrK7NCIcsJZgXNcaEYJHwGtgYLGZFmCddE5FWapD1S542EN48zPsArPZn9/rwkGZ4rRT90DOISq35/nM5+2WQvfl6zaFHByULsJUra1QkJUX1bnojZvoU9Ps0t+lQJEQb2174CTyNYWc8zjy/naRkxc5cnXpJufCneFODkfA3VlpCclKs9jLaMJF1HUR55QiOSHnsGc+dlxySJtrchaG3UK8N4j8qi7nlCJoxlLmGE8w4XRV3UWkbQABAABJREFUxVJn9ye/l+zjuxbJkfdAOlu3wj/NIsTbpXCTi6s37DJ3giAIgiAIgiBUSKAk9hlyDsrRyaI2drgABADThxVj+fwZOG5osWizh3j3kap9y6KnkXQtFmX58fI1U/DNr07A69cfiYxk2PDAoixlHK9uiYobk4VzzHYdeRle2PEYGgb2yEypXs5/5+HigCVQcmea7KAELFGMF6Hh/bmrbFudJWhyQXZrXVC4AQ1dQ8+kWLqjIag4Kwf1sISvmOSg5ILbtrqgEFryXEK8vYaGAT3M9ayubBbrKsjgDkpVoJRD12X3Jz//hraIUq27JDtN9JULBXGBUhY0vYYmhNuqppBwtnk9loNya11QCCWDkuIcFyj5mvhZ2MWEjkO8Q0ruS6uSueoqLJDGkYsi5QuBMizcYX6PLpyeO6SCTnxsxkzxhF/THg7NBRJ+zWAkjlg8IfaMC5Rc6HZz8nEHJc+XWmtz4AkHZUtYEWVkYTTVQekW4q0WyeECpT0cmqdwaLCFeHORr8bNQeniuOQO7ZRiSS7CqJOzkl/X7h7kAqXdWVlnC8XvYRPBO1sQqIaL7HYHZYtzsSQn0VWpJu1SgMbJQWnfx10NqdXsASuPo/28eT7eFAesS5EjK7eo6jDkOWDdwqRlN2prJO5Std15rdwxm1KZXDgozf9y9zV3ULoVr3L7QsHuoGT2XAkEQRAEQRAEQQAAPB13IYg9Q85B6SZQ2j/U2eEf7gFT3Ap4LfdlkfSYV5fzUerISVe19555afB5dHE9j2G6EH2GrggDXo+GTH+qQGkkr92/MEMUN/EapnsSsPI28nZ57jwHJS/YU5jhx8bdrZaYlxQ5y3IDWLZNzZ/JC/9srQuKyuVeQ7MclA1tSPd5xPiDik1xan11syKUcQfl9norrDrfFuLNBRifoWNAsqjQmkrLQckFwd0tYcVxZDoF/ahqCovCOnKBn8Y2NX+m7KDke+Q1rByUdcEo9OReGbom+lQ3h4XQ5NU15CfbVyddnpoGDEiKcy3hGFqlYjsijNkm4nDBu7IppFQ454JubWtEOKoA8577rrIZDUHVKZmbZhUQ4tf0Gbq4R3c2hsQcfB5d/FzVFBLpATL9HqT7DAQjcVMAjVkC1+baoCRQ8rB1n5hXcygm5sNF2l2NprM0KgmdwUjcErJsVdgrG82co1y4zEsOv7s5rIjjsjM2klyfpiWF1WRxEP6aykv3oj4YRTBZuInPkYvx9nyYRZl+7GhoSxF9ynLSsKOhTQhcbkVyOspBqWtmfkC7i5RT2xpBj+yAUoioPhhFTUtYnFmm34OWcAyNbVElHQGnToic5n3TIyuAqqYwWsIx55yjbm5RLqB1MsTbSVyVq0bLRXJqW6VwdsWFaK7Hvr87G51THbhVfl9bZX6xYR/HTWDe0dCGET1zUsKqq5vD6F+UqYzT6lLgBzC/OOiRFXB1XMpCKv+77H5+1r3XEIyiOQLzy4oUIbL9nLFy/3jCLC4l/3+MIIgDl6ML5nf3FAiCIAjikIIclMQ+o0nKcza0NKudnu7IxXj8HvV2LZQdlDZnox1D19BfEhF5WDUX9ES7rmPKgAIRmmufx7CybKUvp79DiDcXNLYkQ215UZ/CLHPsSsltCEDkieRii0cK8a5sCokKsB7DCjffXt+muBO5e25tlRTibWhqDsq4JRJpmikq7W4OC2eXVxIov0s6KD2Sa3Nng+X6M5Ih7twVKjsouUPTFLisXJMlUu5IKwelbgmRTWHF/VnEQ7ybQ4royosZcYEyJ82L7IBXODG31VvuUh4mzkNVuVDRMzcdPo+OeIIl52P275HlF0LzzgYr56iVh1N1SvJ0BnKYsM+jY2ipeb+s2N4oRBa/x1prlZRX02tYeTvrg1FF4AJM8QWw3IBpXkPcp9sVJ2ZA5MTcuLvV1UVqz0H5wZrdeOzDjWKtuX6rSI5cmZznCpVFUS52M2ZWKhYCX4ZP7GNta0Q4GrkAzEU7Pj53c9rFWC7I2wUu7m6zhw5b6QLUdr6Xrrkvbe3cwV3TEhH3ZE6aV7hIt9e1ibnzv0Hc4cjvsfwMq0J9Q1skNRS4zV0sBVIFSrvTld9LdQ5VvLkQGU8wZa3cEem0B7UtkZS57HQR8+xOZS62b65tRSgaTxnbrcjRjqQb1S5oVjWlOjeb2xMoRbEk67r8mvbr7qhvA2MsZQyev5aLpfzvXpRpaAnHUx3JtvQFHHvKBA4VyiEIgiAIgiAIZ0igJPYZ+ZLDS847CQC/OWMEAOCaowd0ejyfTaBUHJRG+wIloOah5I7LYaWW4Ng33xSrMvwefHzrsbj95KHiMS4+DJX6e6RrysIl7zskKcryyFAR4p2phtTyuZfbqoH7DB0FGT5k+AwwBmyuSTorJbFwV0PIEo8MDYOTuQ231QeFe8xraMKJuavRqnidn+FDRbL/55vqxDhejyVQym7O0pw0aJqZJ46H5nP3Z2GGTXTVNQwpMcdeuaPJyh0p56BsarMckYbslAwpuSaLk6KSWSQn1YnJ89Txoj/cRbmhujXFQVlnEw38Xh29k3u5pTYo+psiYmpRJN4mh3L7PFI4e9ASoLyGLlyv32xrEPvu8xiKgzIiibFc1N1U0yJEOC7QVjWF8fAH65XiR3yt63c3Ww5YjxXqv77aGofvb30wCsYsp+TkAQVifc9+tlX8nJt8+crinM/QhQgluwezAh4hwDeFYkp/fi51LRFxfwwuzgT/TqEhaLlFedqGplAUcSkPZ2mu5YCVHZr28OmYCH9PjtOmuoN7CKFadcnx86ttjSguUn6PmSHe1j1sfUFg3TPcidpsE10DXuv+aAym5u20h+5zuLDG9S0hRLaqoqCbqGvOO4JwLK4UyOHrDEXjylr5a7nGlnMUMPfXdCQ7i3l833vmpiE33YsEAzbsbknpX9OS6ioELDejPcTbKSS8ORSz3KIuIfrydauawiKsWg6vb43ElXvVviYuOOakeZGRTLNQ2xp2DeW2t7s5XWVXK0EQBEEQBEEQFiRQEvuM2VPLcf7EPnj+ikkpj10wsQ8+/J9jcMuMik6Px8U3jixQGnrHtzIX3cz+qYLj5AH54ueA10CaLzUMT+4vuzvlsXkOybF98iAbO3n/if0sQUiei5zHkvfXNEtcXJMMm/QYOkqyAzB0DZF4QnEtFmT4kJfuBWOQQtF1FGX6kZfuRTzBsHF3q2if1N+cy6cba5UclLzYjjUXzQxNzlJdoV7hClUdlD6PjpG9cgGY4Z7cNWToVnhzZaNaaZy7HKuaw4gLIVITAkx1Mgybz8dezIiLQPwsNu5uEaIS79saiaM5FFVERO5S3bDbKn7k9ejiujzXoK5ZRXIaghGR/85r6OiTPLt/fb1D9PcaGiqKsxDw6mgOx/DdLvP8/FKId3VTSAgbPo+O44b0AAA8+O46IbzKaQ7uf3sNvtnWIMbn4dzrqlqkkHNJuKxusZx23EHZGlHyKRZnBfDKnKkAVLcZL5IjC1YeQxOpG5pDUeWa3EXaHFLFW+42rGkJC0dcWW6aCFGvl9yohUoeTitcnrtu4wmG+mBEEl15sR1VnMu35VeN2dyGdlFJ5MRsVd2DQqBstkRaQ9cUBzMXvQoyfeK1XB+MQHbG5ilrtVWEt4V4pyXDf+0h3kJkb7Wvye4KtYmITWFFoOROdLlIFWCJwDUtlgiX4TNE6oVdjW2uIqq81oHJ19/mmmDKXHYni2h15KDkDmAnByVg/W1LDatOFWnbonHhurRfd2dDW0rFeTFHyfHM873WOLhL3Qo6cUer3Vm5W6qUThAEQRAEQRCEBQmUxD4jO+DF3WeOVBxaHC688RyO7fHUpRMwZUAB7jt7lNIuC5SdGEZ1UBqpAuWU/vlKf5+R+vLgFcIBtfiCHGLOhaVMv8cmaJp9Th5VihunDxLtvChDv8JUgRIAxvbJBWCJAYauCZESgBAceUVx+Zpmuw5d13BMRQ+13dCEQPnxhhrh9PQZpouUizPyXLhzk7s5uSu0QBKh+FzKcgIozPQhlmBYsaPRHEfXJFdaBP9etkOMI4d4yxXLezg4KL1JMVY+Ix7uy8VV08HFxGNcwNzZEFLEWCeB0mdYYuz2estFyoWKnZK44zN0nDOuF4aWZqOuNYK/Lt4g+nsMXdwzn22qBcAFSssVKYt8V04bgIIMHzbXBvHhut3m3NPUdANWTlNJoFSqtutSe7MYvzjLcg/GJYHSMDQhCHGXp6FryE5etiEYFe1eqdp6Y1sUYUmIzArw3JQxRQDme7a2ynR5apr52uXXlEXBDJ+BzKQgVh+MCnEnw+cR/U1XIA9nVx2U3I3qVuG8yEXMK86xHJqyAMVD0Xc1tlmFm3RdpEzYWhdU7gM+RzlM2mfo4t6sa1XFP8AK8eZ7JguFgFUkxxIok6Jgwu6M5eOoImJVUwghSQTn4fI7pTUBlogvi3Bej44y3r8hlFKYh4ttEen1ZDnEwynuXSFo2s5ve4P5GuNh1fxLmWqXojo8x2VEcuoCVsEaJ5FWbuevv50NbSkOytpWs6gXd3P6PLpYk7w3Aa95TbciR5ZbWx1/h5QygiAIgiAIgiAICxIoiQOeaYOL8NwVk9DX5jDMk0LIuUDRHrJAyfM+yiHeE/rZBEpP6ssjX8pNGbIVdJCdbpzDy60xZcflDccNwsmjSgFAhAEXZ/uFe8qcoyn+zTlmoDIXLnSeNKJEuRYP0RwuhZvL/acPK1bbdR2Hl+cBsEROeZ6yK1QIlLk8HJqLoql9zbmb7s9RSRfl0i0NAEzhK18SFpdv58KlrhbDkRx7xdmWcCmHs+u6JsQcACJ/I5/LBin/oke3CguZOTQtYaO3k4PSSHVQyjklV+9sEiKZ1zALKP14fK+U/oB1j/FcmT4pfLwtGheCk8/QkOn3YERS0NxWp47D4RXLzRyqpqt4Q3WLEuI9UHFQJl2CvDBNUBXhPLqmFNzha0r3WMI7D832GroQqTfubhVCk8+jK3kf5X3n4s7Kneb6izL98Bq6mM+W2lZpL637YOWORsWZVyRcum3CkZtSIZs7MZOiaHOyynRM5Ns0+2+qaUVbJG7tjZSjVBaUBiTFblmI9Bia+Fu0pbZViOlq1faIIqaXKnlXeWi2zf0pFQQCrByGloNSDU+3j+PmoKxsCgkHZcCji9fMroaQEvLM51jbEhbuYJ9h9d/Z0CYVjlEdp0rl+uTemyKtOU5ZriV+ApaYx7+UsRyU5jz5a5I7KCNCYDb3YH3SQRlJ9i+S0hckpHyb/Dy4+5rrsfz8dkgCZcCrw9A1MGbO03JQGsoXMELwz7bSPZjXdA7xtourO0mgJAiCIAiCIAhHSKAkfrDIrkWee6w9+hVmiJBrLvrkpHvx+E8Ow9VD4ymFcYbZnIicMslZKHPdsQMBqELn8ZIoKIfPapqGP88ai8W3HI0fH95btMl5KLmI1ysvXRmHi4K3njQEo3tZjk6nQj5y+5GDChWnqdejoSDTnyKsWgJlhtIXkByUIsTbbJ/QP18JZ+dzH5sUXz/fXCfG1jRNEXoBLkSa+9oUionKux5dU8Q87pDic+TuR8Cqai2HeMvCCRdJttUHhYNQCfGuNtdk6BoM3ZrP9mTIvs/QMbBHJnweM2R7424rhB6whCL7PvLrcnHE7zHTB1jFfFQh0n4ePo+OP/54DOx4DUuI3FzbKkQ7r1T8aXNtUAia3CG3o75NuHb5Hmf5PSnpCOSQ9l1CoNQwvMy851btalKcksJBaQvx5vu4bFt9ch7m71yY/2pLvdL/lKRw/+xnW5Twdy50XvzEF1iy0XSjWkWLIopIxO8vnoOSt4/pnQuPrmFtVQt++cqKlPyRspvT0DUhoG2tDVqFngwd5QXpYn9FcSkpnL1OCkP3ejQhOsoFpuSCS/Ic5byXABBPaMocee5aPp9eyfurqimkiIUi12ujJFB6DXEfVDZZYqmuWfedXBRJft3slFIyFGX5xd9fxS3q0cU9I+9lWa4l5n2yvkbsTb+kUGhWeo+JL336uDgo+Zcv3EFp5Zi17gM5ZJuH4lc1hxThuZ8kUPL+PkMXwvbu5rBS1Er9kkRNF5Bg5j1vD9Gvc3Fz7qgngZIgCIIgCIIgnCCBkjg46ESId8Br4ObjB+O8w3srIuORgwoxJJel9B9UnIVnLpuId246Smm3C4CcCyf1xVOXTsBfLxwn2qZI4e1yOCVgCpJ9CzKUMHfZ5ZmRDHUFIEJ2AUsU9Bg6ThhuuSi543JYqSVayu1ZAS9KJRGNC51DSu2Cptl/gENRIe5C/HprgzJ2dsArck7K7SeNtLk8k+2VTSGl3WvoyPQb8OnmHnGXkcfQkSblwdsh5XcEgL4FskDpFW0eXUNrJC6K+Xilyuebaiy3qMfQxDr5nPjYXID4hrs8DR1eQxfFf5Ym98Cq2m4Xes1xuMDD4TkAhQAqhZADauoCwHRWnjG2J662FZTibsMsvwcJZrnTPLoZ/p/p9yCeYELsHVaWjR5ZfrRG4vjvuhoxjqGbblTZRem1he7vbLTOY1BxJjy6hoZgVBRR8nus4jnNoZgoemPmFk0WG0o6Qrlzclxf07371dZ6RUg+b0If6Brw6cY6kRrAJ4UOy3CxLcGSzlBeVTzDn5yLmq9xQFEmfnfuaADAf9fuFuPwe2DVziYp5N7K/9ocjolwZq9ufZGwrS5oFT+S5ljdFHIUx3c0tKWEp1thycnCPMm+PDcn19X4GM2hGFZL4nDv/HRk+AxE4wxbalvFWnkYelVTSAh/pkCZ6oiUz2lzbVARHPnfyl0NbcLZ7PcYipjHz9uja4qLlO+lfHbnP/aZEBgLMn3ivtlR3yYclEKgbFJDyLlAac9Byb/EMN3B1t9ZIVBK7msA6FuYntwD9ZysdBIh8brJ8BsiPUJlc1jcY+k+j0hHIK+1VHKXynPkf8PIQUkQBEEQBEEQzpBASfygufdHIzG+bx6uPLJ/p/pfe+wg3HvWqE7lvgSAIwYVphTnuSJ5Ldm9CJiC47TBRaISM2979+ZpOKaiCL+UqoK7ccNxAzFrQm9cfkQ/nDyyVLTLIdQ8PB0Apg4stK6VVGn7F2UIEQxQc2nKYfK8fWiJtT6voYm9qZDWzfM6ctebmItUnEgWY/meDeyRpYSc8zB0WYiVr5uT3DpRaCbZn+cI3CmFGgM2B2UyxFt2RYrrGppwcXFRjfftX5ihCNZ87NG2tXKHI1/P0qQj0Cucj24OSrXdLlDKldLNcVIdlIBV4dpak+lG5WKL1d/cywG2PfZ7dMxICtqvLd9pjqFb5833T54LF6F2NVj77vcY4vyWbjX3wGfoonhOQzAqXGleQxPOPw7//bA+VnoBLiT7kmLeScl7n4vJPo+eItwCQLrfEsqqHNxt1c1hxBNMqcDNXzNc0AWAKQMKYegaNtcGlfMIeA0hPHPHrKGba/J7dMQSDJtrrIJRXDDfVGOlF/BJYfE7JEGMz7E1EleqfveQ3YmtEZGDMj/Th5lJwf9/X19tVUr36OI81la1iHF65XOnZBhh4aDULSetLdXBYUnB+MvNdSIXpM+QHZSSoOnRxHnsbglJ5225SGtbreJKfo+u/B1ascNKd8C/9Nguibd9k6/flnBMKdLE17m72Sz8Y69kXh+MKmHrvSSRVnYy8muaod9SQS7pvuGO96IsvxDVqxpDiqjLvxSR86Xy13V90Mz1ytu5qE05KAmCIAiCIAjCGRIoiR80503ogxevnoICB3fVvmJi/wK8ecORePryiZ3qP7BHJp64ZIKSj9K9bxbu+dEo3H7KMOSke6X21ArkgFq0RxYJzhrXS7R7FIEyXWo3xxlSagmRsuA4oV8+5p06DHefORIPzToMADCiZw7+3wWHSf2tuVwwsQ+OH1aMe380EtdIbj9emVoe/8/nj1UEQD5HLlByYYpXZ5fFWnmcPvmW4Co7APvbcmJ6dR09c5NhuTVqvk1N03DU4CLRxoWUwcVZ+M+1R4j2BDOFDB7ivLaqJdnf3INU56M5jr3aOBcoeQh9WApjBtxDxe3jc/HWnpuV9x9o3wNDx4nJvKWL1pjuQfleyk13FygrG1V3Kd+DZcmK4j6PLoTS1buarLBnqTo7pyS5H7npPnFffyFSAJjj83QJ8nycHJR+yfVmhvGa160oyUKW34NgJG7Oh4tQuhmCLOd6BcyQ8FHJLxwWr60215Tcg77Je4znafUaZtEp/lribj6PoaNfoRVyL+eg5ILYjnorxLsw0y9SLjS2WSHCAa8hnH9baoPCQekzdPzixCEAgI/W1wiHn9fQRC7SdVVWWgPhoGwMIRSzQrx5+P93u5qFI9Lr0TGiLAdpXgP1wShW7Uq6hj2auH93NYSUfeSCeVVTWAlzL8iwcmXKYt5dZwwX+83D130ey9lsOijN/rnpPgxOzvPrLfVK1XZ+dpWNIdG/h+RGlUP0uZuxuimsCMZcJDcLb0kOSimUmwuUhZl+0b9SHsejiTzIDUFrrfx+Z0ydT7kt3yZBEARBEARBECokUBLEHjC0NFs4xvYHcoXvJqkgkKFreOCc0ThnXC9MHWC5KW85oQKAmQ+NV1EGgD6SQMlFKLnqd7rPEm40TcMlU/vh/Il9kCa1HysJjvVBay698tLxt5+Mx3kT+igO1V6Sm9HgomhJNp6+bIJo5wJHeZYpgHDhgYuoVx89QAgZgCkOAKqDUhZ0B/RQRTvZQcnzZ/Jck4BZiMmJET2tveGuzhE9VecsFxYDXkNUCgdUR6SSnzPZv6JEDa33uQiRwkGZleqgBKx8evbrciFKHt+eV1Uu3CQLvFx05cI/D/G25znlLkSvoQsB/ovNdUKs8Umhwxz5HPm9x/NkcjfqkJJsJa2Bm4PS55FFJUugDHgM4Qj8NJmv0pyn6RiV7xtdM+8F/vr579oaZa38NcOLKHkMVRjmBVu8hoZ+SZF20+5WxzyONS1hISz6Pbq4X2Qhy2toQlxev7sV3Pjn0c15c4GbhxB7DV2IeWurm8VriYu/a6qahdiWk+bF0NJsaJqZ0oDnFvXoGnweHYf1zQUAfLjO2gP+utkh5c/0SmLsd5IA7EspFGSJ7z8+vA9+Ok11uvtk8bahTTg9/V7dlgLAcouWSUV7rAJQVii3XLyK33tVTSEldF9uj0n5aHmu0G31QSGiFmX5hehYKY3jkwo6VTaFRJh7ut8j8svWS7lIed7S5nAMTaGOi7oRBEH8IFg+v+N/BEEQBNFJSKAkiB8AAcnxtd3mwDl7XC/cf85opdpzXoYPX/xyOt6+8ShFSOWuKsByq1UUZ+HaYwZi+tAeuP2UjsPQ5bnwD/Ht0VPKweiVlDp5XrwYxoQitaAEn2PAa2DWhD6iXTgoZUeoNPaAwlT3IBeUeCpQPjYAHF1hia61UsElp1QAQ0qyFOehLPLJ4dm8sJBHcmzxtQCqc1Wej1vRIruD0CkPp3m9ZBi9zUHp85ghqVw8AdwdlHx/i5LiNr/n3CrF+zw6RvXKgc+jo6YlItylHkMTAiJnbJ9c8XNFceo5cXrnq2J6oSS0y9ctzkp1yXk9mqhQLwuUXNSVxxbV1kV+w2YxBmB9ObA1WSyJ7w0XnHjIrs/QUZ68x3Y2htAciolx8tK9wvknh5Dzs95WH1Qcfjw8f8PuVuGg9HpMt699P1UHZbMQ80b3ykWa10BjWxRvf1sFAOhfmIFMv0eI2twBy/d9Yj8zTcNH62vEXHh4cziWEDkhfR4No3tbLtqIJK7yL0Tqg1ERKs7H72XLx+rzSOHvkoPS79FFCgDZQSmHnMsh6vyLhK11QdRLhbS4sCi7a72SqB2MxEWBNY+UW3R9dYsoclOUaQmUwUhcEYa52L6trg2ROM9Fqku5SK17MifNK3K6bpXSTBAEQRAEQRAEYUICJUH8QDh7XC94DQ3nT+zTcWeYzp8+NvFKLtjCxRpN0/DzGRV4bPbhOHNsL+xt5GsauvOfnKpkgZriNGCU5FqU+586qkz8zAWLTKmQkBwGb3dQeg2zeEd/yYmaJQmkaT5DuLvs1dzt+SwDXkMR/2RRTXYLyu3cNQdYYmiFLbepz2MKWJ11UPLxZXctoIaoq/01UZhJbuPkpklFcjy84FKqEAmkForyeczclDwvKy9Aw3NWysiC9SD7Hkh7pjpm3R2UsrtNDqsen3R0LtkgCZRJgbV3viSaiyJH5tlZArbZPql/vtLOxygvTHWu5mf4hAC8LlkIxpdMI8Cdf7xAjM+jY2TPXADAsq0NIn+m19DF/bVREij53hQ75Drl57S+2spBmeYzhCPy/e/MsHWe+mB4z9QQfcBM6wCY4cm83SyIY+7xlqT72KPrGNvbFBBX7WoSFeQ9hi7CnuMJZoVyJ+feM88mUBq6yBO5rT6opDvgDspvtjcKx6EcLr+tvg3xBM85GhB5ZHlRK58c/q84K3Wk+zxWcR6pIFe5JNwyZgr4eek+pPs8SDfMa3Gh2uvRhdC9rT6oVJzn7Vvqgpab2KOLe2YLCZQEQRAWy+eTA5MgCIIAQAIlQfxguP/sUVj6qxNShKeuIIt4ciGdrmIX7dqjVCpAE4zEHPvIoeWHSQ47WUDrU5COowYXIc1rKPkrl8w9Fm9cf6SS63GAzT3IxVguwADAMRVqWPctJ1Rg3qnD8OhPxint503oDUCt0j1cElFlUU0OhZeFTi5MFGZaeRcz/B5lH/la032W6CqPnx3wKC5Zj0MOSl2zHJq989Mc3ZKyaC07KPMcclBOKM9ThEJ+zeyAVxH5+ByPSYb/c0esLNICZiVj2ZVqF2m9bgKloacUCeLtPGenXB3Za+gY2TMHumYWobGPL+87n3tJjir88fbRvXKFmAVYDtVyh9B6TdOEYPxdZbNyzQlJd6IsonI36dJtDVYIskd2ULaIIjkiZUB2aqh/cXYAPXPThIjK5z+hvEDpy4tdcQfsl7bcn2N65yr3M79mz6S4KsQ5Q0fv/DTkZ/gQjTMsl0RBn1TRvbIprIzfK0/9uyEXyVm6tUGMkZ/uQ7/CDJTmBBCJJYQb1Qzx5vk55VyymhDNl/FxJKdkJJYQ6QiE0Ju8b3hOSK+hodxWcKogwwc9ec/nJLd9W3IPfIYlRG6vCyouT+6u3VzbiohU0b6v1E4QBEEQBEEQhIqn4y4EQRwIaJqmOAb3hJw0ryj8YhePusLpY8rw0PvrkeEzOuwrh4RzpyTnyUsOx18Wb8C8U60CGrIA67E5Lh/7yXgkGFPGLM1JSylEk5vuw/XHDcLjH21CSU4A6cn+48vzseCLbQAgKkWLaxk6LpnaL2X+Pz1qAPLTfZgsVSkf2ycPL329A4DqbBxamo2Fq8xw2gsn9RXt/3vGSDz20UbcMqNCGXtCv3wh+sji5oCiDGxIFmXhooamaehXkIE1SQceF10LM304e1wvbNzdgquPHij2RtM0nDSiFP/4cpv4HbBCkwF1f+UQby5e67qGc8f3xu8XrgUASPoX+uSnY1udKe5wcejiKeV4eskWkdtQdn/ubg7jzMN6KuvvncypaC8UBKhils9j5TaUMR2UptAkpz7wGTrSfGa1cR5urmmWICuHxYviRFlm0RpmEwQ9ho4jBxXijRWVSnuqg9Ice/rQYuHik/tfdkQ5nv98q9J/aGkuAFNUK0qKaT7JQVnZFAaXcz0iBYDNQZlc09g+uUqFaK9Hw+QBBfjDu1Zf7qCc1N+8l5t4GLph5VEd0zsXnyeFS7nQ0zfbG7E56fzjleLH9M7F+99V48stqtBZkOFDcygmXu88t2hPW4i3V6pwzhnQI1Pc20dX9FD2TA7xlotd+Tw6hpXl4N3V1Vi2rV6M7fcYyEv3oj4Yxfb6oOhr7qMf66tblPQFWQEvCjN9QsyUX9u5PoZdQU3aA12kzNhW3yZeA7JTcrOtmjsPrZfnThBE93F0wfzunsKBz/L53T0DgiAI4hCCBEqCOMQY2Sun404dcP1xg5CT5sXRFc7FZdzgzjrO0RU9RMhzNGqGccp5CeW8koAqYHXEzccPFtWguQvqiIGF0DXA7zGUokLtYegazpughtWfM64X4vEEPIaOU0dboeezJ/eF36PjR4f1VIrOjOyVgwfPG5sy9m/OGIFRvXKwvrpFKdRz4/TBeP+7asw5ZqAi1B3eL08IlFwM0jSzUJITs6eU4x9fblPckX2lyuceOcRbmu/Zh/UEdu5OrqlcCJS8IAwAnDi8BB+vr8XJI0txTrJqfLrPg4unlOOeN79T5vjMZRPx+vKduPpotTq3oWsY0TMHX22pT5mPmi9Vh8fQ8efzx+KbbQ3424ebACRdckkRaZ00N37dkT1zhUDplcRYuUCRLEgWZPhFWLLs3j1qUJEQKPk9WZodgM+jS8VXzPHnHDMQWQEP5v9nlTl+UtYd2CML0wYXYXEy/N1n6KgozkKa10BzOIbm3dwlqCEn3Yv+RRnYuLsVLClRuhVR4tcd2ycPry3fpbSP75uniK6lSTF3dK8clOUEsFNUZ7f2ZmL/fCFQcmFxQI8M4FtIe8MLbGXh/e+qEbLlmuyRFcDm2qCSrxEwXcNcMDTXalZVz/J70JwMEx9SYn1BcewQm0ApFcnZJIl8Xl0XrtANotq6uW/F2QHUB6NCTJfbASvEWwjPBRmOAmVxGrC6way4zsfhLuK61ggaglYYuhzKzUPevR4dfblwSQ5KgiAIgiAIgkiBQrwJgugyXkPH5Uf2x8AeXQs3twuUTsjVmxuliuV7gtfQFfGlJCeAN244Eu/cdFSXxE47Aa+Bi6f2w4WT+iqVuwsy/ZhzzMAUR2d74/xkcjl+ffoIJSfmqaPL8Icfj1FC8gGIStlA5xyww8qy8dp1R+CVOVNFGw/zBYBxyUIkAHDU4CJMG1yEu04fjjPGWKJrTroXf7nwMPg8Oi6VHKbnT+yL168/Ag/NGquEbU8daAm/WlJcqyjJws0nVCguUc4dpwwTP8sh6XKIN0sqbKeMKsN1xw0S7dxRZ69lxJ2SoyQxXhY/y6Rw7krJ1VuSYwlS8v4eJYnH3N2n6xr62gr58PaLp/bDVdMGID/DJ9yKAPAjyUHKRVee49I+zik2h689V6Z9XXJqBMAUUnVdw0+PGgAAyPAZQqjXNA0njrDGl8VYWSjnzWeP620b26q27jTHUbYvQeSwcTnvqC9Z+Efe3wpJoJwyoADydxQ+jy7C87n7k69zYr98x+JVPYTDNmhrN886RaCUnLF5kmhfniX7hwGfYSAr4BXO441JYdTnsXJZbq5tRTjGi+dowkG5qYZyUBIEQRAEQRCEHXJQEgSxzxnUIxPrqlswoTy/w76yiFXdHGqn555hF1V+SMg5NOMJ1k5PixE9VbHosD55uP64QeiVm4azxllFkXLSvHjq0gkALDcr58QRpVgxv4dS8MbQNQwvS3XjyoV1Vu9q6nB+Y3rn4rnLJ+LrrfWi8AqghpzLYmF2wIvbTx6KcCwhXJ/3nTUKt7y4XPThgqkslBmSiulUnR0ASrIDWLnDnLMsYJflpol7WHb19i3IEM5Nv1cVjG89aQh+cWKFcq3pQ4vFzzysfcbwEnywZnfKWk8eVYY/vb9etFsh3s4OSntuWn7dn50wGFkBj5KiAACmD+uBxz82nahyqP8YKb/rtzvNvehXmIFjKorEPHkRJdntaM7FbB/XNw+PfbQpZY4AMKIsB59vUkPITxhejNdXmO5PWaDM8HvQvyhTOHe9hoaeuWnICniUvJSA6QAe3zcPnyXH5mkKipP7xUO5eX9eMIu7PPn+nnd4b7z41XbzuZIY3C9Tfb3xPehbkIGGYINwNvs8ZnVvQ9cQiiawtc66bt9kjsualjA+31SnvJ4JgiAIF5bPb//xUR08ThAEQfxgIIGSIIh9ztOXTcRzn2/FBZ2sQP6z4wfjqSVblDyOhJkLcFSvHGyvb0up3t1ZdF3DzccP7vLz7NW42xs/3WcgGImnVOl2Y8rAQkwZqIbca5qGRy44DJtqW1NE1suP7K/8fs743pjUvwBnPPyx4oCTn9ccVgs09S1IT6mm7FaFHQCOHdoD66pbFOH0pBEl+O+63RhWmo2TRpSkrMsuhGb4PbjztOH4YE01jhtqpjaYPqwYeGkFACA/wyfOtaIkC2eOLcPLS3fC79FF/ll7kRw5fNoJr6FjzjEDU9p5lWwA2Fhjhcd7pHXLkty543sLgVKuZO4zdFGpms/lMGlsQBV7h0sV4Hk7T/MApFaOH1GWLQRK7rgcWpJthaFL850+tFgIlDyVhT1HKe8/xZbigYu048vz8fRlE/DUJ1twznhLwM/1m4WyqprUyuQ3HDcQVz3ztQj19ydd24N6ZOK7ymYlLD474MWEcjOE/pInPsdHvzgWeQ65VQmCIDrDosXdPQN3jp4m/bJ8/r69WEfj72sB0+n6CR3AaODbewA9QSIqQRBEJyGBkiCIfU5JTqBLoth1xw3CtccOdHW6Hcq8fM1URGIJx3DpA4W3bzwKr36z83sLzPZCRu3ROz8d//2fY5QCSl7DzFHplFpg6sBCbKndqrSVKAKleu/NOWYgeuWm4ZRRVvj7WeN64UeH9ezSfTp7SjlmTykXvxdm+vHTo/rj6631eOCc0cr87/vRCPSNbsWUKVOs6uy2SthyWHO/wgwlN2N7yIIzF904/54zFfNe/Ra3zRwq2niFdgCiarfXMCuOc6esHIbeKy/NKkAj7aUsGnOhMyfNi+cun4hgJJ4Swj6iZw5eWbbTHJ+HlpdmOQqUZ4ztiVe/2YmxfXLF3IuTgi4XCrmw2Ds/XRGp5fM+clARjhyUml93Qnke/rPczEXKxdVjhxRj9uS+Sl5UALjrjBG44G+fCfGWX/fvF4/HyX/6CFvrgvhySz2OH1ZsvwxBEASxP1k+v/3HSVwkCILYb5BASRDEAQmJk84YunZAi5OAKf44ufb2NU4uwsP75eN1qXgM57aZQ9EciuE0qcjRKCm8ucQmlGUHvLhocnnKOHvjPp0rCYF2+mUBY6V5Zfg9uOOUYbjrtVXIsN0H5QXpnRYoAbPq+pOfbMYRNvfq6N65St5SwMyXOr5vHr7cUo+TJeH48PI8S6CUnJI/P6ECD72/Dl5Dx2FSrtMBUg7UmJSmwO6g5cih6TyH5lDJZZnht/agKMuP/1x3hPL8cX3tOT6t8zpCEqk9NsesE1cf1V8IlKFoXJk7Fyi5YHp4eT4unNRXhNHzvckKeDFlQAG21gXxFQmUBEEQBz7L53f3DAiCIA4ZSKAkCIIgDlqunjYAry/flZLvL9PvwUOz1Mrq0wYX4e0bj8KaqmYc6SKYHQhcOrUchZk+FGaq4d7lhRmAlM+yI3558lAMK8vG0YNT3YJOPHP5RHy6sVYphHTp1H74vyVbAABNUlGrM8b2xBlje6aM4TF03HHKMHy7o1ERLt0YXpaD+84ehWypiNRIyYU55+j2hfBhZdmYMqAAn2yoBaDmuD26ogee/cwUKL16x0LzoOJM/M+JFXj8o804TsonKufW3S3lzT1/Ym8hUOqSkH1Y3zws+GIbvk5WrycIgiAIgiAIggRKgiAI4iBmRM8cLL7laBTYxDw3KkqylEItByKapuH0Mani34/G9sITH28WYc0d4TV0nDu+d8cdkwS8hpIvEjBF0XPG9cILX23HUQ5h0U5cdkS/jjtJ2Oc4omcOfnPGCPTJT1cqgLtx0/GD8eWWz9C/MAOXHWHlLz1ykCW0rq1u7tRcrjl6IK6eNkBxzmb4PRhelo1vdzZhspTbcmCPLMye3BerdjVhaKl1T/H8n99sb0AkllBydBIEQRAEQRDEoQoJlARBEMRBTd+CPSso9ENjZK8cvHbdESjJCXTceS9y39mjcPspw5CT5u24816iK/lNDy/Px3e/PhGapobky/k+61ujTk91xCms/4WrJqOmOYI+BWqO0DtPH5HSt39hBvLSvWgKxbCxpgVDSrJT+hAEQRAEQRDEoQYJlARBEARxkGCveL4/0DRtv4qTe4LuEsK94MpJuOXFb3DnacO/1/jpPg/6FHTuLZWmaXj+yknok5+OdB+9DSMIgjjoWT6//cepEA9BEAQAEigJgiAIgjhEmdS/AB/+z7H7/brkmiQIgiAIgiAIFRIoCYIgCIIgCIIgCKI7WD6//cfJYUkQxCECZWYnCIIgCIIgCIIgCIIgCKLbIAclQRAEQRAEQRAHFUcXzO/uKRyyLFrc3TOQWDw/penoaft/GgRBEETHkIOSIAiCIAiCIAiCIAiCIIhu4wfhoHz44Ydx//33o7KyEqNHj8ZDDz2ECRMmuPZ/4YUXcMcdd2Dz5s0YNGgQfvvb32LmzJn7ccYEQRAEQRAE0fX3sQRBEArL57f/eEc5Kjt6fkfsjRyYB8Ic2mN5B+NTHlCC2C8c8ALlP/7xD9x88834y1/+gokTJ+KPf/wjZsyYgTVr1qBHjx4p/T/55BPMmjUL99xzD0455RQ899xzOOOMM/D1119jxIgR3bACgiAIgiAI4lCkq+9jCYIgfnAsn9/dMyCBkSAOEg74EO/f//73uOKKK3DJJZdg2LBh+Mtf/oL09HQ8/vjjjv0ffPBBnHjiibjlllswdOhQ3HXXXTjssMPw5z//eT/PnCAIgiAIgjiU6er7WIIgCIIgiEOVA9pBGYlE8NVXX2Hu3LmiTdd1TJ8+HUuWLHF8zpIlS3DzzTcrbTNmzMArr7ziep1wOIxwOCx+b2xsBADU1dUhGo1+jxW0TzQaRTAYRG1tLbxe7z67DtE+dA4HBnQOBw50FgcGdA4HBj/kc2hubgYAMMa6eSaHJl19H9ve+9E9n8MeP/UHT0vowF4803QEg0HooQg0luju6RxSvPb2vh2faTqCPYN46909O9vJk7v4hNra9h9v+p6vhX09/v6Yw156/vd6T7D6d+0/PvRnXRtvL4+/5PH2n/9Jfcfzm5LXwRz2EPGa+L571A4/5Pd7Bxy2e7G51Xxv05n3owe0QFlTU4N4PI7i4mKlvbi4GN99953jcyorKx37V1ZWul7nnnvuwZ133pnS3q9fvz2YNUEQBEEQxIFDc3MzcnJyunsahxxdfR/r9n508ODB+2yOBEEcDNzzAx+/M3zfOXT38w+Ea/wQ9qAjDoQ5EHtKZ96PHtAC5f5i7ty5iusykUigrq4OBQUF0DRtn123qakJvXv3xrZt25Cdnb3PrkO0D53DgQGdw4EDncWBAZ3DgcEP+RwYY2hubkZZWVl3T4XoBPb3ow0NDejbty+2bt1KAvNByA/5bwvRPnS2By90tgcvdLb7jq68Hz2gBcrCwkIYhoGqqiqlvaqqCiUlJY7PKSkp6VJ/APD7/fD7/Upbbm7unk16D8jOzqYXwQEAncOBAZ3DgQOdxYEBncOBwQ/1HEjY6j66+j7W6f0oYJ7hD/HeIzrHD/VvC9ExdLYHL3S2By90tvuGzr4fPaCL5Ph8PowbNw7vvfeeaEskEnjvvfcw2SU5x+TJk5X+ALBw4ULX/gRBEARBEASxt9mT97EEQRAEQRCHKge0gxIAbr75ZsyePRvjx4/HhAkT8Mc//hGtra245JJLAAA/+clP0LNnT9xzj5mP4IYbbsC0adPwu9/9DieffDIWLFiAL7/8Eo8++mh3LoMgCIIgCII4xOjofSxBEARBEARhcsALlD/+8Y+xe/du/OpXv0JlZSXGjBmDt956SyQc37p1K3TdMoJOmTIFzz33HG6//XbcdtttGDRoEF555RWMGDGiu5bgit/vx7x58xzDeYj9B53DgQGdw4EDncWBAZ3DgQGdA/F96Oh9bHvQvXdwQ+d78EJne/BCZ3vwQmd7YKCxztT6JgiCIAiCIAiCIAiCIAiC2Acc0DkoCYIgCIIgCIIgCIIgCII4uCGBkiAIgiAIgiAIgiAIgiCIboMESoIgCIIgCIIgCIIgCIIgug0SKAmCIAiCIAiCIAiCIAiC6DZIoOwmHn74YZSXlyMQCGDixIn4/PPPu3tKBxX//e9/ceqpp6KsrAyapuGVV15RHmeM4Ve/+hVKS0uRlpaG6dOnY926dUqfuro6XHDBBcjOzkZubi4uu+wytLS07MdV/PC55557cPjhhyMrKws9evTAGWecgTVr1ih9QqEQ5syZg4KCAmRmZuKss85CVVWV0mfr1q04+eSTkZ6ejh49euCWW25BLBbbn0v5wfPII49g1KhRyM7ORnZ2NiZPnow333xTPE7n0D3ce++90DQNN954o2ijs9j3zJ8/H5qmKf+GDBkiHqczIPYXXX0/+MILL2DIkCEIBAIYOXIk3njjjf00U6KrdOVs//a3v+HII49EXl4e8vLyMH36dPpscICzp5/lFixYAE3TcMYZZ+zbCRJ7TFfPtqGhAXPmzEFpaSn8fj8GDx5Mf5sPULp6tn/84x9RUVGBtLQ09O7dGzfddBNCodB+mu0hCiP2OwsWLGA+n489/vjj7Ntvv2VXXHEFy83NZVVVVd09tYOGN954g/3yl79kL730EgPAXn75ZeXxe++9l+Xk5LBXXnmFffPNN+y0005j/fr1Y21tbaLPiSeeyEaPHs0+/fRT9uGHH7KBAweyWbNm7eeV/LCZMWMGe+KJJ9jKlSvZsmXL2MyZM1mfPn1YS0uL6HPVVVex3r17s/fee499+eWXbNKkSWzKlCni8VgsxkaMGMGmT5/Oli5dyt544w1WWFjI5s6d2x1L+sHy6quvstdff52tXbuWrVmzht12223M6/WylStXMsboHLqDzz//nJWXl7NRo0axG264QbTTWex75s2bx4YPH8527dol/u3evVs8TmdA7A+6+n7w448/ZoZhsPvuu4+tWrWK3X777czr9bIVK1bs55kTHdHVsz3//PPZww8/zJYuXcpWr17NLr74YpaTk8O2b9++n2dOdIY9/Sy3adMm1rNnT3bkkUey008/ff9MlugSXT3bcDjMxo8fz2bOnMk++ugjtmnTJrZo0SK2bNmy/TxzoiO6erbPPvss8/v97Nlnn2WbNm1ib7/9NistLWU33XTTfp75oQUJlN3AhAkT2Jw5c8Tv8XiclZWVsXvuuacbZ3XwYhcoE4kEKykpYffff79oa2hoYH6/nz3//POMMcZWrVrFALAvvvhC9HnzzTeZpmlsx44d+23uBxvV1dUMAFu8eDFjzNx3r9fLXnjhBdFn9erVDABbsmQJY8wUm3VdZ5WVlaLPI488wrKzs1k4HN6/CzjIyMvLY4899hidQzfQ3NzMBg0axBYuXMimTZsmBEo6i/3DvHnz2OjRox0fozMg9hddfT947rnnspNPPllpmzhxIvvpT3+6T+dJdJ3v+14/FouxrKws9tRTT+2rKRLfgz0531gsxqZMmcIee+wxNnv2bBIoD1C6eraPPPII69+/P4tEIvtrisQe0tWznTNnDjv22GOVtptvvplNnTp1n87zUIdCvPczkUgEX331FaZPny7adF3H9OnTsWTJkm6c2aHDpk2bUFlZqZxBTk4OJk6cKM5gyZIlyM3Nxfjx40Wf6dOnQ9d1fPbZZ/t9zgcLjY2NAID8/HwAwFdffYVoNKqcxZAhQ9CnTx/lLEaOHIni4mLRZ8aMGWhqasK33367H2d/8BCPx7FgwQK0trZi8uTJdA7dwJw5c3DyyScrew7Qa2J/sm7dOpSVlaF///644IILsHXrVgB0BsT+YU/eDy5ZsiTlb8aMGTPo/eMBxt54rx8MBhGNRsX7JeLAYU/P99e//jV69OiByy67bH9Mk9gD9uRsX331VUyePBlz5sxBcXExRowYgbvvvhvxeHx/TZvoBHtytlOmTMFXX30lwsA3btyIN954AzNnztwvcz5U8XT3BA41ampqEI/HlQ81AFBcXIzvvvuum2Z1aFFZWQkAjmfAH6usrESPHj2Uxz0eD/Lz80UfomskEgnceOONmDp1KkaMGAHA3Gefz4fc3Fylr/0snM6KP0Z0nhUrVmDy5MkIhULIzMzEyy+/jGHDhmHZsmV0DvuRBQsW4Ouvv8YXX3yR8hi9JvYPEydOxJNPPomKigrs2rULd955J4488kisXLmSzoDYL+zJ+0G3+47uuQOLvfFe/xe/+AXKyspSBGmi+9mT8/3oo4/w97//HcuWLdsPMyT2lD05240bN+L999/HBRdcgDfeeAPr16/HNddcg2g0innz5u2PaROdYE/O9vzzz0dNTQ2OOOIIMMYQi8Vw1VVX4bbbbtsfUz5kIYGSIIj9wpw5c7By5Up89NFH3T2VQ5aKigosW7YMjY2NePHFFzF79mwsXry4u6d1SLFt2zbccMMNWLhwIQKBQHdP55DlpJNOEj+PGjUKEydORN++ffHPf/4TaWlp3TgzgiAOde69914sWLAAixYtov9PHAQ0Nzfjoosuwt/+9jcUFhZ293SIvUwikUCPHj3w6KOPwjAMjBs3Djt27MD9999PAuUPnEWLFuHuu+/G//t//w8TJ07E+vXrccMNN+Cuu+7CHXfc0d3TO2ihEO/9TGFhIQzDSKkGWlVVhZKSkm6a1aEF3+f2zqCkpATV1dXK47FYDHV1dXROe8C1116L1157DR988AF69eol2ktKShCJRNDQ0KD0t5+F01nxx4jO4/P5MHDgQIwbNw733HMPRo8ejQcffJDOYT/y1Vdfobq6Gocddhg8Hg88Hg8WL16MP/3pT/B4PCguLqaz6AZyc3MxePBgrF+/nl4PxH5hT94Put13dM8dWHyf9/oPPPAA7r33XrzzzjsYNWrUvpwmsYd09Xw3bNiAzZs349RTTxX/3/+///s/vPrqq/B4PNiwYcP+mjrRAXvy2i0tLcXgwYNhGIZoGzp0KCorKxGJRPbpfInOsydne8cdd+Ciiy7C5ZdfjpEjR+LMM8/E3XffjXvuuQeJRGJ/TPuQhATK/YzP58O4cePw3nvvibZEIoH33nsPkydP7saZHTr069cPJSUlyhk0NTXhs88+E2cwefJkNDQ04KuvvhJ93n//fSQSCUycOHG/z/mHCmMM1157LV5++WW8//776Nevn/L4uHHj4PV6lbNYs2YNtm7dqpzFihUrFMF44cKFyM7OxrBhw/bPQg5SEokEwuEwncN+5LjjjsOKFSuwbNky8W/8+PG44IILxM90FvuflpYWbNiwAaWlpfR6IPYLe/J+cPLkyUp/wLzv6P3jgcWevte/7777cNddd+Gtt95ScqATBxZdPd8hQ4ak/H//tNNOwzHHHINly5ahd+/e+3P6RDvsyWt36tSpWL9+vSJYrV27FqWlpfD5fPt8zkTn2JOzDQaD0HVVLuNCNGNs3032UKebi/QckixYsID5/X725JNPslWrVrErr7yS5ebmKtVAie9Hc3MzW7p0KVu6dCkDwH7/+9+zpUuXsi1btjDGGLv33ntZbm4u+/e//82WL1/OTj/9dNavXz/W1tYmxjjxxBPZ2LFj2WeffcY++ugjNmjQIDZr1qzuWtIPkquvvprl5OSwRYsWsV27dol/wWBQ9LnqqqtYnz592Pvvv8++/PJLNnnyZDZ58mTxeCwWYyNGjGAnnHACW7ZsGXvrrbdYUVERmzt3bncs6QfLrbfeyhYvXsw2bdrEli9fzm699VamaRp75513GGN0Dt2JXMWbMTqL/cHPfvYztmjRIrZp0yb28ccfs+nTp7PCwkJWXV3NGKMzIPYPHb0fvOiii9itt94q+n/88cfM4/GwBx54gK1evZrNmzePeb1etmLFiu5aAuFCV8/23nvvZT6fj7344ovK+6Xm5ubuWgLRDl09XztUxfvApatnu3XrVpaVlcWuvfZatmbNGvbaa6+xHj16sN/85jfdtQTCha6e7bx581hWVhZ7/vnn2caNG9k777zDBgwYwM4999zuWsIhAQmU3cRDDz3E+vTpw3w+H5swYQL79NNPu3tKBxUffPABA5Dyb/bs2YwxxhKJBLvjjjtYcXEx8/v97LjjjmNr1qxRxqitrWWzZs1imZmZLDs7m11yySX0RrGLOJ0BAPbEE0+IPm1tbeyaa65heXl5LD09nZ155pls165dyjibN29mJ510EktLS2OFhYXsZz/7GYtGo/t5NT9sLr30Uta3b1/m8/lYUVERO+6444Q4yRidQ3diFyjpLPY9P/7xj1lpaSnz+XysZ8+e7Mc//jFbv369eJzOgNhftPd+cNq0aeJ9C+ef//wnGzx4MPP5fGz48OHs9ddf388zJjpLV862b9++ju+X5s2bt/8nTnSKrr52ZUigPLDp6tl+8sknbOLEiczv97P+/fuz//3f/2WxWGw/z5roDF0522g0yubPn88GDBjAAoEA6927N7vmmmtYfX39/p/4IYTGGPlTCYIgCIIgCIIgCIIgCILoHigHJUEQBEEQBEEQBEEQBEEQ3QYJlARBEARBEARBEARBEARBdBskUBIEQRAEQRAEQRAEQRAE0W2QQEkQBEEQBEEQBEEQBEEQRLdBAiVBEARBEARBEARBEARBEN0GCZQEQRAEQRAEQRAEQRAEQXQbJFASBEEQBEEQBEEQBEEQBNFtkEBJEARBEARBEARBEARBEES3QQIlQRDEHqJpGl555ZUO+91xxx248sor9+q1Fy1aBE3T0NDQsFfH7YhVq1ahV69eaG1t3a/XJQiCIAiCIAiCIA5eSKAkCOIHycUXXwxN01L+nXjiid09NYXKyko8+OCD+OUvfynadu/ejauvvhp9+vSB3+9HSUkJZsyYgY8//rgbZ5rK0UcfjRtvvFFpGzZsGCZNmoTf//733TMpgiAIgiAIIoWLL74YZ5xxRndPgyAIYo/xdPcECIIg9pQTTzwRTzzxhNLm9/u7aTbOPPbYY5gyZQr69u0r2s466yxEIhE89dRT6N+/P6qqqvDee++htra2G2faeS655BJcccUVmDt3Ljwe+t8IQRAEQRAEQRAE8f0gByVBED9YuPtQ/peXlwfADL9+5JFHcNJJJyEtLQ39+/fHiy++qDx/xYoVOPbYY5GWloaCggJceeWVaGlpUfo8/vjjGD58OPx+P0pLS3Httdcqj9fU1ODMM89Eeno6Bg0ahFdffVV5fMGCBTj11FPF7w0NDfjwww/x29/+Fscccwz69u2LCRMmYO7cuTjttNMAAJs3b4amaVi2bJnyPE3TsGjRImX8jz/+GKNGjUIgEMCkSZOwcuVK8diWLVtw6qmnIi8vDxkZGRg+fDjeeOMN8fjKlStx0kknITMzE8XFxbjoootQU1MDwPwWfvHixXjwwQeFO3Xz5s0AgOOPPx51dXVYvHhxR0dEEARBEARBdDOLFy/GhAkTxPvZW2+9FbFYTDz+4osvYuTIkeI98fTp00U6n0WLFmHChAnIyMhAbm4upk6dii1btnTXUgiCOIghgZIgiIOWO+64A2eddRa++eYbXHDBBTjvvPOwevVqAEBraytmzJiBvLw8fPHFF3jhhRfw7rvvKgLkI488gjlz5uDKK6/EihUr8Oqrr2LgwIHKNe68806ce+65WL58OWbOnIkLLrgAdXV1AIC6ujqsWrUK48ePF/0zMzORmZmJV155BeFw+Huv8ZZbbsHvfvc7fPHFFygqKsKpp56KaDQKAJgzZw7C4TD++9//YsWKFfjtb3+LzMxMAKbgeeyxx2Ls2LH48ssv8dZbb6GqqgrnnnsuAODBBx/E5MmTccUVV2DXrl3YtWsXevfuDQDw+XwYM2YMPvzww+89f4IgCIIgCGLfsWPHDsycOROHH344vvnmGzzyyCP4+9//jt/85jcAgF27dmHWrFm49NJLsXr1aixatAg/+tGPwBhDLBbDGWecgWnTpmH58uVYsmQJrrzySmia1s2rIgjioIQRBEH8AJk9ezYzDINlZGQo//73f/+XMcYYAHbVVVcpz5k4cSK7+uqrGWOMPfrooywvL4+1tLSIx19//XWm6zqrrKxkjDFWVlbGfvnLX7rOAQC7/fbbxe8tLS0MAHvzzTcZY4wtXbqUAWBbt25Vnvfiiy+yvLw8FggE2JQpU9jcuXPZN998Ix7ftGkTA8CWLl0q2urr6xkA9sEHHzDGGPvggw8YALZgwQLRp7a2lqWlpbF//OMfjDHGRo4cyebPn+8497vuuoudcMIJStu2bdsYALZmzRrGGGPTpk1jN9xwg+PzzzzzTHbxxRe77g1BEARBEASx/5g9ezY7/fTTU9pvu+02VlFRwRKJhGh7+OGHWWZmJovH4+yrr75iANjmzZtTnltbW8sAsEWLFu3LqRMEQTDGGCMHJUEQP1iOOeYYLFu2TPl31VVXiccnT56s9J88ebJwUK5evRqjR49GRkaGeHzq1KlIJBJYs2YNqqursXPnThx33HHtzmHUqFHi54yMDGRnZ6O6uhoA0NbWBgAIBALKc8466yzs3LkTr776Kk488UQsWrQIhx12GJ588sku74G8xvz8fFRUVIg1Xn/99fjNb36DqVOnYt68eVi+fLno+8033+CDDz4Qjs7MzEwMGTIEALBhw4YOr5uWloZgMNjl+RIEQRAEQRD7j9WrV2Py5MmK63Hq1KloaWnB9u3bMXr0aBx33HEYOXIkzjnnHPztb39DfX09APO95cUXX4wZM2bg1FNPxYMPPohdu3Z111IIgjjIIYGSIIgfLBkZGRg4cKDyLz8/f6+MnZaW1ql+Xq9X+V3TNCQSCQBAYWEhAIg3eTKBQADHH3887rjjDnzyySe4+OKLMW/ePACArpt/mhljoj8P2+4Kl19+OTZu3IiLLroIK1aswPjx4/HQQw8BAFpaWnDqqaemCLzr1q3DUUcd1eHYdXV1KCoq6vKcCIIgCIIgiAMHwzCwcOFCvPnmmxg2bBgeeughVFRUYNOmTQCAJ554AkuWLMGUKVPwj3/8A4MHD8ann37azbMmCOJghARKgiAOWuxvnj799FMMHToUADB06FB88803IgE4YBac0XUdFRUVyMrKQnl5Od577709vv6AAQOQnZ2NVatWddh32LBhYi5c+JO/oZYL5sjIa6yvr8fatWvFGgGgd+/euOqqq/DSSy/hZz/7Gf72t78BAA477DB8++23KC8vTxF5uavU5/MhHo87XnflypUYO3Zsh+siCIIgCIIguo+hQ4diyZIlyhffH3/8MbKystCrVy8A5hfsU6dOxZ133omlS5fC5/Ph5ZdfFv3Hjh2LuXPn4pNPPsGIESPw3HPP7fd1EARx8EMCJUEQP1jC4TAqKyuVf7wKNQC88MILePzxx7F27VrMmzcPn3/+uSiCc8EFFyAQCGD27NlYuXIlPvjgA1x33XW46KKLUFxcDACYP38+fve73+FPf/oT1q1bh6+//lo4EDuDruuYPn06PvroI9FWW1uLY489Fs888wyWL1+OTZs24YUXXsB9992H008/HYDp3pw0aRLuvfderF69GosXL8btt9/ueI1f//rXeO+997By5UpcfPHFKCwsxBlnnAEAuPHGG/H2229j06ZN+Prrr/HBBx8I8XLOnDmoq6vDrFmz8MUXX2DDhg14++23cckllwhRsry8HJ999hk2b96Mmpoa4QzdvHkzduzYgenTp3d6LwiCIAiCIIh9S2NjY0p0zJVXXolt27bhuuuuw3fffYd///vfmDdvHm6++Wbouo7PPvsMd999N7788kts3boVL730Enbv3o2hQ4di06ZNmDt3LpYsWYItW7bgnXfewbp165QvwwmCIPYa3Z0EkyAIYk+YPXs2A5Dyr6KigjFmFrB5+OGH2fHHH8/8fj8rLy8XxWM4y5cvZ8cccwwLBAIsPz+fXXHFFay5uVnp85e//IVVVFQwr9fLSktL2XXXXSceA8BefvllpX9OTg574oknxO9vvPEG69mzJ4vH44wxxkKhELv11lvZYYcdxnJyclh6ejqrqKhgt99+OwsGg+J5q1atYpMnT2ZpaWlszJgx7J133nEskvOf//yHDR8+nPl8PjZhwgSl2M61117LBgwYwPx+PysqKmIXXXQRq6mpEY+vXbuWnXnmmSw3N5elpaWxIUOGsBtvvFEkUV+zZg2bNGkSS0tLYwDYpk2bGGOM3X333WzGjBldOC2CIAiCIAhiX+L23viyyy5jixYtYocffjjz+XyspKSE/eIXv2DRaJQxZr7nnDFjBisqKmJ+v58NHjyYPfTQQ4wxxiorK9kZZ5zBSktLmc/nY3379mW/+tWvxPtagiCIvYnGmOT1JgiCOEjQNA0vv/yycBN2F4wxTJw4ETfddBNmzZrVrXPZG0QiEQwaNAjPPfccpk6d2t3TIQiCIAiCIAiCIA4CKMSbIAhiH6JpGh599FHEYrHunspeYevWrbjttttInCQIgiAIgiAIgiD2GuSgJAjioORAcVASBEEQBEEQBEEQBNE+nu6eAEEQxL6AvnshCIIgCIIgCIIgiB8GFOJNEARBEARBEARBEARBEES3QQIlQRAEQRAEQRAEQRAEQRDdBgmUBEEQBEEQBEEQBEEQBEF0GyRQEgRxyKFpGubPn9/l523evBmapuHJJ5/c63MiCIIgCIIgup89fZ+4r7j44otRXl6+X65VVVWFs88+GwUFBdA0DX/84x/3y3UJgiAAEigJgugmnnzySWiaBk3T8NFHH6U8zhhD7969oWkaTjnllG6Y4d7hjTfegKZpKCsrQyKR6O7pEARBEARB7Hfk932apiEQCKCsrAwzZszAn/70JzQ3N3f3FF355JNPMH/+fDQ0NOzVcY8++mhlT/Lz83H44Yfj8ccf32vvGe+++2688sorne5/00034e2338bcuXPx9NNP48QTT9wr8+iIhoYGBAIBaJqG1atX75drEgRx4EECJUEQ3UogEMBzzz2X0r548WJs374dfr+/G2a193j22WdRXl6OXbt24f333+/u6RAEQRAEQXQbv/71r/H000/jkUcewXXXXQcAuPHGGzFy5EgsX768m2dn0tbWhttvv138/sknn+DOO+/c6wIlAPTq1QtPP/00nn76adxxxx2IxWK47LLLcNttt+2V8bsqUL7//vs4/fTT8fOf/xwXXnghhgwZslfm0REvvPACNE1DSUkJnn322f1yTYIgDjxIoCQIoluZOXMmXnjhBcRiMaX9ueeew7hx41BSUtJNM/v+tLa24t///jduvvlmjB079oB+w9Xa2trdUyAIgiAI4iDnpJNOwoUXXohLLrkEc+fOxdtvv413330X1dXVOO2009DW1tbdU0QgEIDH49kv18rJycGFF16ICy+8EDfddBM+/vhj9OrVC3/+858RjUb3yxxkqqurkZubu9fGC4VCnXKDPvPMM5g5cyZmzZrlaFw4UOjsegiC2DNIoCQIoluZNWsWamtrsXDhQtEWiUTw4osv4vzzz3d8TmtrK372s5+hd+/e8Pv9qKiowAMPPADGmNIvHA7jpptuQlFREbKysnDaaadh+/btjmPu2LEDl156KYqLi+H3+zF8+HA8/vjj32ttL7/8Mtra2nDOOefgvPPOw0svvYRQKJTSLxQKYf78+Rg8eDACgQBKS0vxox/9CBs2bBB9EokEHnzwQYwcORKBQABFRUU48cQT8eWXXwJoPz+mPZfS/PnzoWkaVq1ahfPPPx95eXk44ogjAADLly/HxRdfjP79+yMQCKCkpASXXnopamtrHffssssuQ1lZGfx+P/r164err74akUgEGzduhKZp+MMf/pDyvE8++QSapuH555/v6pYSBEEQBHGQceyxx+KOO+7Ali1b8MwzzyiPfffddzj77LORn5+PQCCA8ePH49VXX1X68PDxjz/+GDfffDOKioqQkZGBM888E7t371b6fvnll5gxYwYKCwuRlpaGfv364dJLL1X6yO+b5s+fj1tuuQUA0K9fPxGOvXnzZkybNg2jR492XFNFRQVmzJjR5b1IT0/HpEmT0NramjJ3mc68F9Y0Da2trXjqqafEvC+++GLH8fgeMsbw8MMPi/6cjRs34pxzzkF+fr6Y4+uvv66MsWjRImiahgULFuD2229Hz549kZ6ejqampnbXvHXrVnz44Yc477zzcN5552HTpk345JNPHPs+88wzmDBhAtLT05GXl4ejjjoK77zzjtLnzTffxLRp05CVlYXs7GwcfvjhiuhZXl7uuA9HH300jj766E6tp66uDj//+c8xcuRIZGZmIjs7GyeddBK++eablHHbe5/PGEN5eTlOP/10x+fl5OTgpz/9abv7RxAHE/vnqyGCIAgXysvLMXnyZDz//PM46aSTAJhvLBobG3HeeefhT3/6k9KfMYbTTjsNH3zwAS677DKMGTMGb7/9Nm655Rbs2LFDEcQuv/xyPPPMMzj//PMxZcoUvP/++zj55JNT5lBVVYVJkyZB0zRce+21KCoqwptvvonLLrsMTU1NuPHGG/dobc8++yyOOeYYlJSU4LzzzsOtt96K//znPzjnnHNEn3g8jlNOOQXvvfcezjvvPNxwww1obm7GwoULsXLlSgwYMAAAcNlll+HJJ5/ESSedhMsvvxyxWAwffvghPv30U4wfP36P5nfOOedg0KBBuPvuu8Ub2oULF2Ljxo245JJLUFJSgm+//RaPPvoovv32W3z66afizerOnTsxYcIENDQ04Morr8SQIUOwY8cOvPjiiwgGg+jfvz+mTp2KZ599FjfddFPKvmRlZTm+GSMIgiAI4tDjoosuwm233YZ33nkHV1xxBQDg22+/xdSpU9GzZ0/ceuutyMjIwD//+U+cccYZ+Ne//oUzzzxTGeO6665DXl4e5s2bh82bN+OPf/wjrr32WvzjH/8AYLoDTzjhBBQVFeHWW29Fbm4uNm/ejJdeesl1Xj/60Y+wdu1aPP/88/jDH/6AwsJCAEBRUREuuugiXHHFFVi5ciVGjBghnvPFF19g7dq1Sph4V9i4cSMMw3B1Mnb2vfDTTz+Nyy+/HBMmTMCVV14JAOJ9pZ2jjjoKTz/9NC666CIcf/zx+MlPfiIeq6qqwpQpUxAMBnH99dejoKAATz31FE477TS8+OKLKedw1113wefz4ec//znC4TB8Pl+7633++eeRkZGBU045BWlpaRgwYACeffZZTJkyRel35513Yv78+ZgyZQp+/etfw+fz4bPPPsP777+PE044AYAptF566aUYPnw45s6di9zcXCxduhRvvfWWq/GhI5zWs2rVKrzyyis455xz0K9fP1RVVeGvf/0rpk2bhlWrVqGsrAxA597nX3jhhbjvvvtQV1eH/Px8cd3//Oc/aGpqwoUXXrhH8yaIHySMIAiiG3jiiScYAPbFF1+wP//5zywrK4sFg0HGGGPnnHMOO+aYYxhjjPXt25edfPLJ4nmvvPIKA8B+85vfKOOdffbZTNM0tn79esYYY8uWLWMA2DXXXKP0O//88xkANm/ePNF22WWXsdLSUlZTU6P0Pe+881hOTo6Y16ZNmxgA9sQTT3S4vqqqKubxeNjf/vY30TZlyhR2+umnK/0ef/xxBoD9/ve/TxkjkUgwxhh7//33GQB2/fXXu/Zpb2729c6bN48BYLNmzUrpy9cq8/zzzzMA7L///a9o+8lPfsJ0XWdffPGF65z++te/MgBs9erV4rFIJMIKCwvZ7NmzU55HEARBEMTBify+z42cnBw2duxY8ftxxx3HRo4cyUKhkGhLJBJsypQpbNCgQSljT58+XbwHYYyxm266iRmGwRoaGhhjjL388ssdzoGx1PdN999/PwPANm3apPRraGhggUCA/eIXv1Dar7/+epaRkcFaWlravc60adPYkCFD2O7du9nu3bvZ6tWr2fXXX88AsFNPPVX0mz17Nuvbt6/4vbPvhRljLCMjo0vvuQCwOXPmKG033ngjA8A+/PBD0dbc3Mz69evHysvLWTweZ4wx9sEHHzAArH///o7vJ90YOXIku+CCC8Tvt912GyssLGTRaFS0rVu3jum6zs4880xxPQ4/84aGBpaVlcUmTpzI2traHPswZn62cNqTadOmsWnTponf21tPKBRKmcemTZuY3+9nv/71r0VbZ97nr1mzhgFgjzzyiPL4aaedxsrLy5W5E8TBDoV4EwTR7Zx77rloa2vDa6+9hubmZrz22muu33K+8cYbMAwD119/vdL+s5/9DIwxvPnmm6IfgJR+djckYwz/+te/cOqpp4IxhpqaGvFvxowZaGxsxNdff93lNS1YsAC6ruOss84SbbNmzcKbb76J+vp60favf/0LhYWFIlG8DHcr/utf/4KmaZg3b55rnz3hqquuSmlLS0sTP4dCIdTU1GDSpEkAIPYhkUjglVdewamnnuro3uRzOvfccxEIBJTcm2+//TZqamro22CCIAiCIBQyMzNFNe+6ujq8//77OPfcc9Hc3Czem9XW1mLGjBlYt24dduzYoTz/yiuvVN4XHXnkkYjH49iyZQsACEfia6+9tlfyO+bk5OD000/H888/LyJR4vE4/vGPf+CMM85ARkZGh2N89913KCoqQlFREYYOHYqHHnoIJ598crtphjr7Xnhv8cYbb2DChAkiHRBgntWVV16JzZs3Y9WqVUr/2bNnK+8n22P58uVYsWIFZs2aJdpmzZqFmpoavP3226LtlVdeQSKRwK9+9Svouiph8DNfuHAhmpubceuttyIQCDj22ROc1uP3+8U84vE4amtrkZmZiYqKCuVzQ2fe5w8ePBgTJ05U3i/X1dXhzTffxAUXXPC95k4QPzRIoCQIotspKirC9OnT8dxzz+Gll15CPB7H2Wef7dh3y5YtKCsrQ1ZWltI+dOhQ8Tj/r67rKaEsFRUVyu+7d+9GQ0MDHn30UfEGkf+75JJLAJghQV2F58ipra3F+vXrsX79eowdOxaRSAQvvPCC6LdhwwZUVFS0m4x9w4YNKCsrU8I+9gb9+vVLaaurq8MNN9yA4uJipKWloaioSPRrbGz8/+ydd3wcxfn/P7vX79Qt2bLcbUwHE5rphmCagRBCCoQEUoCQhOQL/EJCQi+hhp4QEkILmE5CKAZcKMa4YAy2ce9dkot6vbvd/f2xOzPPzO5KsikWeD6vFy+k0dzs7M7s+fZ9n+d5ALjXrKmpSQpnClJJSQlOP/10Ke/P+PHjMWDAAHzzm9/8HM9ES0tLS0tL66uulpYW/vluxYoVcBwH11xzje/zGfvCVv18NnjwYOn30tJSAOBfDI8ZMwZnnXUWbrjhBpSXl+OMM87AY489hs7Ozh2e83nnncdzKALA5MmTUVtbix//+Mc9ev3QoUMxadIkTJ48GdOmTUNNTQ1ee+01HkoepJ5+Fv68tHbtWt/n566OF/T5MkxPPfUUMpkMhg8fzj8vJ5NJDB06VAJ2K1euhGma2HvvvUPHYrnbu/t8ur0KOh/btnHPPfdg5MiRSCQSKC8vR0VFBebPn88/L7M5dfc5H3D30QcffMCv5QsvvIBcLtfjfaSl9XWRzkGppaXVK/TDH/4QF154IWpqanDKKad8rhUEuxKrxPejH/0I559/fmCf/ffff7vGXL58OWbPng0AGDlypO/v48eP57mAPi+FfbtqWVboa4K+3f7+97+P6dOn44orrsABBxyAgoIC2LaNk08+eYeqFp533nl44YUXMH36dOy333545ZVX8Ktf/cr37beWlpaWlpbWrqsNGzagsbERu+22GwDx+ex3v/tdaLEZ1pcpEokE9mPuRsMw8OKLL2LmzJl49dVX8dZbb+FnP/sZ7rrrLsycORMFBQXbPe+TTjoJ/fr1w1NPPYVjjjkGTz31FCorKzF27NgevT6TyfS471dFPXVPOo6DZ555Bq2trYHgcfPmzWhpadmhdelKXX1mDtpDQedzyy234JprrsHPfvYz3HTTTSgrK4Npmrj00kt36PPy2Wefjcsuuwzjx4/Hn/70Jzz11FM4+OCDA8GwltbXWRpQamlp9QqdeeaZ+MUvfoGZM2fyZOZBGjJkCCZPnozm5mbpm+MlS5bwv7P/27bNv7lkWrp0qTQeq/BtWdbn9gFx/PjxiMViePLJJ30fdKZNm4b7778f69atw+DBgzFixAjMmjULuVwOsVgscLwRI0bgrbfe8iXPpmIugYaGBql9e75Fr6+vx5QpU3DDDTfg2muv5e3Lly+X+lVUVKCoqAgLFizodsyTTz4ZFRUVGD9+PEaPHo22tjb9bbCWlpaWlpaWpCeffBIAOIwcPnw4ACAWi33uAO+www7DYYcdhj//+c94+umnce655+LZZ5/FBRdcENi/qxDbSCSCH/7wh3j88cdx++234+WXX8aFF14YCks/D/X0s3B3c9+e46mfn8OOtz167733sGHDBtx4443cjclUX1+Piy66CC+//DJ+9KMfYcSIEbBtG4sWLcIBBxwQOB6LmlqwYIEPXlOVlpb6Pi8D7mdmtu+604svvojjjjsOjzzyiNTe0NAguV978jkfAMrKynDqqadi/PjxOPfcc/HBBx/g3nvv7dFctLS+TtIWFi0trV6hgoIC/P3vf8f111+P008/PbTfuHHjYFkW/vrXv0rt99xzDwzD4JXA2f/VKuDqP/aRSARnnXUWXnrppUDgtmXLlu0+l/Hjx+Poo4/GD37wA3z3u9+V/rviiisAuBULAeCss87C1q1bfecDiG/8zzrrLDiOgxtuuCG0T1FREcrLyzF16lTp7w8++GCP580+TLMxmdRrZpomvv3tb+PVV1/FRx99FDonAIhGozjnnHPw/PPP4/HHH8d+++233Y5ULS0tLS0tra+v3n77bdx0000YNmwYzj33XABA3759ceyxx+If//gHqqurfa/Zkc9n9fX1vs84DHZ1FebNckkGQS3ArUBeX1+PX/ziF2hpafnC82z39LMw4M49bN7bc7wPP/wQM2bM4G2tra345z//iaFDh3YZdt2VWHj3FVdc4fu8fOGFF2LkyJE8zPvb3/42TNPEjTfe6HMosjU98cQTUVhYiFtvvRUdHR2BfQAXGs6cORPZbJa3vfbaa1i/fn2P5x6JRHx76YUXXvDlRe3J53ymH//4x1i0aBGuuOIKRCIRnH322T2ej5bW10XaQamlpdVrFBZiTXX66afjuOOOw1VXXYU1a9Zg1KhRmDhxIv73v//h0ksv5d+eHnDAATjnnHPw4IMPorGxEUcccQSmTJmCFStW+Ma87bbb8M4772D06NG48MILsffee6Ourg4ff/wxJk+ejLq6uh6fw6xZs7BixQpccsklgX8fMGAADjzwQIwfPx5/+MMfcN555+Hf//43Lr/8cnz44Yc4+uij0draismTJ+NXv/oVzjjjDBx33HH48Y9/jPvvvx/Lly/n4dbvv/8+jjvuOH6sCy64ALfddhsuuOACHHzwwZg6dSqWLVvW47kXFRXhmGOOwR133IFcLocBAwZg4sSJWL16ta/vLbfcgokTJ2LMmDG46KKLsNdee6G6uhovvPACpk2bJoXon3feebj//vvxzjvv4Pbbb+/xfLS0tLS0tLS+XnrjjTewZMkS5PN51NbW4u2338akSZMwZMgQvPLKK1Jxk7/97W846qijsN9+++HCCy/E8OHDUVtbixkzZmDDhg2YN2/edh37iSeewIMPPogzzzwTI0aMQHNzMx5++GEUFRVh3Lhxoa876KCDAABXXXUVzj77bMRiMZx++ukcXH7jG9/AvvvuixdeeAF77bUXDjzwwB24Mj1XTz8Ls7lPnjwZd999N6qqqjBs2DCMHj16u4535ZVX4plnnsEpp5yC3/72tygrK8MTTzyB1atX46WXXtqhtD2dnZ146aWXcMIJJ/gK2jB961vfwn333YfNmzdjt912w1VXXYWbbroJRx99NL7zne8gkUhg9uzZqKqqwq233oqioiLcc889uOCCC3DIIYfghz/8IUpLSzFv3jy0tbXhiSeeAOB+Xn7xxRdx8skn4/vf/z5WrlyJp556ype3viuddtppuPHGG/HTn/4URxxxBD799FOMHz/e58Dsyed8plNPPRV9+vTBCy+8gFNOOQV9+/bd7uuqpfWV15ddNlxLS0vLcRznsccecwA4s2fP7rLfkCFDnFNPPVVqa25udi677DKnqqrKicVizsiRI50777zTsW1b6tfe3u789re/dfr06eNkMhnn9NNPd9avX+8AcK677jqpb21trfPrX//aGTRokBOLxZzKykrn+OOPd/75z3/yPqtXr3YAOI899ljofH/zm984AJyVK1eG9rn++usdAM68efMcx3GctrY256qrrnKGDRvGj/3d735XGiOfzzt33nmns+eeezrxeNypqKhwTjnlFGfOnDm8T1tbm/Pzn//cKS4udgoLC53vf//7zubNm33ne9111zkAnC1btvjmtmHDBufMM890SkpKnOLiYud73/ues2nTpsBrtnbtWue8885zKioqnEQi4QwfPtz59a9/7XR2dvrG3WeffRzTNJ0NGzaEXhctLS0tLS2tr6fY5z72XzwedyorK50TTjjBue+++5ympqbA161cudI577zznMrKSicWizkDBgxwTjvtNOfFF1/0ja1+pnznnXccAM4777zjOI7jfPzxx84555zjDB482EkkEk7fvn2d0047zfnoo4+k1wV95rnpppucAQMGOKZpOgCc1atXS3+/4447HADOLbfc0uNrMmbMGGefffbptt/555/vDBkyRGrr6WfhJUuWOMccc4yTSqUcAM7555/f5bEAOL/+9a997StXrnS++93vOiUlJU4ymXQOPfRQ57XXXpP6sOv9wgsvdHtOL730kgPAeeSRR0L7vPvuuw4A57777uNtjz76qPONb3zDSSQSTmlpqTNmzBhn0qRJ0uteeeUV54gjjnBSqZRTVFTkHHrooc4zzzwj9bnrrrucAQMGOIlEwjnyyCOdjz76yBkzZowzZsyYHp1PR0eH8//+3/9z+vfv76RSKefII490ZsyY4RvDcXr2OZ/pV7/6lQPAefrpp7u6fFpaX1sZjqN4i7W0tLS0tD5nfeMb30BZWRmmTJmys6eipaWlpaWlpfW56r777sNll12GNWvW+KqJa2n1VJdddhkeeeQR1NTUIJ1O7+zpaGl96dI5KLW0tLS0vlB99NFHmDt3Ls4777ydPRUtLS0tLS0trc9VjuPgkUcewZgxYzSc1NphdXR04KmnnsJZZ52l4aTWLiudg1JLS0tL6wvRggULMGfOHNx1113o378/fvCDH+zsKWlpaWlpaWlpfS5qbW3FK6+8gnfeeQeffvop/ve//+3sKWl9BbV582ZMnjwZL774IrZt24b/+7//29lT0tLaadKAUktLS0vrC9GLL76IG2+8EXvssQeeeeaZ0CToWlpaWlpaWlpfNW3ZsgU//OEPUVJSgj/96U/41re+tbOnpPUV1KJFi3Duueeib9++uP/++3lleS2tXVE6B6WWlpaWlpaWlpaWlpaWlpaWlpbWTpPOQamlpaWlpaWlpaWlpaWlpaWlpaW106QBpZaWlpaWlpaWlpaWlpaWlpaWltZOk85BGSDbtrFp0yYUFhbCMIydPR0tLS0tLS0tre2W4zhobm5GVVUVTFN/J/1Vk/48qqWlpaWlpfVV1/Z8HtWAMkCbNm3CoEGDdvY0tLS0tLS0tLQ+s9avX4+BAwfu7Globaf051EtLS0tLS2tr4t68nlUA8oAFRYWAnAvYFFR0Rd2nFwuh4kTJ+LEE09ELBb7wo6j1bX0OvQO6XXoPdJr0Tuk16F36Ku8Dk1NTRg0aBD/XKP11dKX9Xn0s+qrfI98XaXXpPdJr0nvkl6P3ie9Jr1Pn9eabM/nUQ0oA8TCaIqKir5wQJlOp1FUVKRvwp0ovQ69Q3odeo/0WvQO6XXoHfo6rIMOD95+TZ06FXfeeSfmzJmD6upq/Pe//8W3v/1t/nfHcXDdddfh4YcfRkNDA4488kj8/e9/x8iRI3mfuro6/OY3v8Grr74K0zRx1lln4b777kNBQUGP5vBlfR79rPo63CNfN+k16X3Sa9K7pNej90mvSe/T570mPfk8qhMSaWlpaWlpaWlpaRG1trZi1KhR+Nvf/hb49zvuuAP3338/HnroIcyaNQuZTAYnnXQSOjo6eJ9zzz0XCxcuxKRJk/Daa69h6tSpuOiii76sU9DS0tLS0tLS+kpJOyi1tLS0tLS0tLS0iE455RSccsopgX9zHAf33nsvrr76apxxxhkAgH//+9/o168fXn75ZZx99tlYvHgx3nzzTcyePRsHH3wwAOCBBx7AuHHj8Je//AVVVVVf2rloaWlpaWlpaX0VpAGllpaWlpaWlpaWVg+1evVq1NTUYOzYsbytuLgYo0ePxowZM3D22WdjxowZKCkp4XASAMaOHQvTNDFr1iyceeaZvnE7OzvR2dnJf29qagLghljlcrkv8Iw+m9jcevMcdzXpNel90mvSu6TXo/dJr0nv0+e1Jtvzeg0otbS0tLS0tLS0tHqompoaAEC/fv2k9n79+vG/1dTUoG/fvtLfo9EoysrKeB9Vt956K2644QZf+8SJE5FOpz+PqX+hmjRp0s6egpYivSa9T3pNepf0evQ+6TXpffqsa9LW1tbjvjsVUHaXgDxI7777Li6//HIsXLgQgwYNwtVXX42f/OQnUp+//e1vuPPOO1FTU4NRo0bhgQcewKGHHvrFnYiWlpaWlpaWlpbWZ9Af//hHXH755fx3VvXyxBNP7PVFciZNmoQTTjhBFzboJdJr0vuk16R3Sa9H75Nek96nz2tNWERIT7RTASVLQP6zn/0M3/nOd7rtv3r1apx66qm4+OKLMX78eEyZMgUXXHAB+vfvj5NOOgkA8Nxzz+Hyyy/HQw89hNGjR+Pee+/FSSedhKVLl/q+ydbS0tLS0tLS0tLaHlVWVgIAamtr0b9/f95eW1uLAw44gPfZvHmz9Lp8Po+6ujr+elWJRAKJRMLXHovFvhIPa1+Vee5K0mvS+6TXpHdJr0fvk16T3qfPuibb89qdWsX7lFNOwc033xyYhydIDz30EIYNG4a77roLe+21Fy655BJ897vfxT333MP73H333bjwwgvx05/+FHvvvTceeughpNNpPProo1/UaWhpaWlpaWlpae0iGjZsGCorKzFlyhTe1tTUhFmzZuHwww8HABx++OFoaGjAnDlzeJ+3334btm1j9OjRX/qctbS0tLS0tLR6u75SOShnzJghJSQHgJNOOgmXXnopACCbzWLOnDn44x//yP9umibGjh2LGTNmhI67s5KS60SwvUN6HXqH9Dr0Hum16B3S69A79FVeh6/inHuLWlpasGLFCv776tWrMXfuXJSVlWHw4MG49NJLcfPNN2PkyJEYNmwYrrnmGlRVVfFURXvttRdOPvlkXHjhhXjooYeQy+VwySWX4Oyzz9YVvLW0tLS0tLS0AvSVApQ1NTWBCcmbmprQ3t6O+vp6WJYV2GfJkiWh4+7spOQ6EWzvkF6H3iG9Dr1Hei16h/Q69A59Fddhe5KSa8n66KOPcNxxx/HfWW7I888/H48//jh+//vfo7W1FRdddBEaGhpw1FFH4c0330QymeSvGT9+PC655BIcf/zxME0TZ511Fu6///4v/Vy0tLS0tLS0tL4K+koByi9KOyspuU4E2zuk16F3SK9D75Fei94hvQ69Q1/lddiepORaso499lg4jhP6d8MwcOONN+LGG28M7VNWVoann376i5ielpaWlpaWltbXTl8pQFlZWYna2lqprba2FkVFRUilUohEIohEIoF9whKSAzs/KblOBNs7pNehd0ivQ++RXoveIb0OvUNfxXX4qs1XS0tLS0tLS0tr19VOLZKzvTr88MOlhOSAG3LFEpLH43EcdNBBUh/btjFlyhTeR0tLS0tLS0sLADpyFt5cUIOWzvzOnoqWlpaWlpaWlpbWLq2dCihbWlowd+5czJ07F4BIQL5u3ToAbuj1eeedx/tffPHFWLVqFX7/+99jyZIlePDBB/H888/jsssu430uv/xyPPzww3jiiSewePFi/PKXv0Rrayt++tOffqnnpqWlpaWlpdW7dd3/FuLip+bgqv9+urOnoqWlpaWlpaWlpbVLa6eGeHeXgLy6uprDSgAYNmwYXn/9dVx22WW47777MHDgQPzrX//CSSedxPv84Ac/wJYtW3DttdeipqYGBxxwAN58801f4RwtLS0tLa2vkyzbwW1vLMYBg0px6v79v7TjPjd7HZ6Yvhb/+PFBGFT2xReWA4AN9W0oTMZQnOpZCPOSmib84cX5+MWYERi3n7g2z320HgDwv7mbcN/Z3/hC5qqlpaWlpaWlpaX1WXX99V9M396knQoou0tA/vjjjwe+5pNPPuly3EsuuQSXXHLJZ52elpaWltZXXLbt4G/vrMD+g0owZveKLvs2tufw93dX4qjdynHUyPIejb9ySwuen70eFx4zHOUF/lzGX6ZmrtqGh99fjaF9ar80QGnbDv7wkus+fG72evzupD2+8GPWNnXg+LveQ9+iBCZdNgbJWKTL/o7j4OR73wcA/Pn1xRKgZErHux5jR9WetdCWzaOPsjemr9iKgaVpDCpLwTCML+TYWlpaWlpaWlpaWl8lfaVyUGppaX01taW5E39/dyW2tnTu7Klo7WJ6cc4G3DVpGc5/9MNu+74+vxoPvbcSP3pkFp6csaZH4x9/13v4x9RVePCdlZ9pnjNWbsNzs9d137ELfbqxEQDQnrN61L89a+Hv767Emq2tPer/yrxNOOyWKZizto63zdvQwH8uy8R7NM4T09fgDy/Oh22Hf0HJ1JGz8MOHZ+KKF+bxtk83NKIzb2N9XTsemba62zHeXFDDfy5JBzsuq0pSPZh5uLa1dGLO2nrfl66n3v8+jrz9bdS1ZnmbbTu46Mk5OObOd7C0tvkzHVdLS0tLS0tLS0vr6yINKLW0tL5wXfDEbNz+5hJc/vy87jt/CbJ6AEZ6q+pas1hf17azp9Gttvcaf1FrMnFRTWB7e9bCn/77KaYt38rbapo6+M/TV26T+juOg+W1zchbNm9ry4rCKrXktdsrx3FwzsMz8YeXPsUyBVgtbzQwlcwxb9n4yWMf4sCbJuGalxdIfRd4gDJv9exaXvO/Bbj9zSX4+ROze9T/t898gpqmDtz42mLe9gaBfxFTOAFXbWnBy59s9EHI9qyF615ZiOc+Ws+Balf6z8cbMX3lNrwwZwNv21Av9v+LpD1Mj01fw3+mEJWuX//iZLfjdKVv3vUezvr7dMzfIM5p5ZYWrNraio6cjZVbWnj76m2taOnMIxkzsVtFwWc6rpaWlpaWlpaWltbXRRpQamlpfeGa5z20T122pUf9a5s6MGlRbY8cVturDfVtOO4v7+IXT370uY/9ZejAmybh6DveQT1xZH3RamzL4c+vL/LBszA9MX0N9rnuTcxcta37zgD++J/5OPjmSdjcQ8j36YZGfLKu3tfe0JaVoBOAUAj2l4lL8fSsdfjRI7N4W12rcPjmFMh342uLcMI9U/HUzLW87X0CDkdUZHo09yBtqG/nP7dnhfuxvi2Lvy6K4Of//pif18JNTXh36RbUtWbxrOK4ZIAyRyBqV2Jwb+WWnjkomYqSIjvMnLViHdhxHcfBxU/NwaXPzcWjH8gOx1mrxZ7oSWTzWwsFAGUQe12duF6tpPq24zh449NqPDlzLaob3T7Lapvx4Wrh+KTXZh0B/Zl49xlvFm5qws2vLcLabfL1amjLorE9B0Deb5MX1fKfTe9kf/b4bBx/13sAgL37FyEa0R/DtLS0tLS0tLS0tAANKLW0tL5EUbDRlY6/6z1c+O+P8Pqn1Z/r8W3bwRl//QDr6trw1sLa7l/QjTp6GEr7eWlLswBo6+s/m4uypTMvuQG70q+f/hgPv78av3m66/y/gAuJrntlITpyNh7/YE23/TtyFp75cD3q23IS8Ouq/+l/nYYzH5wuwbzX5m/CoX+egrP+PkOaS21TcFqBGSv98JSG4eZtW2p/zDuX/3yykbdT92WuhzD91jcW45g73sE2L93Bja8uwtF3vBPYd+oyMX6bd64UzOYsh4cUN3XksGZbmzd3h7wuj9888wkuffYTDjBVRU2ZFK7Y3IwL//2R1H8bSc8wwnP9OY6DFZuFM5BB3YWbmrCs1m2/7Y0lHN4ByjUjEPi+yctxw6sLpS8lsnkb75EvNRhcpGCRAscJn9bgl+M/xjUvL8BV/3XdpR+skPcUdZeu2ybGoes9cWENDr9VDmUHgLsnL8e/pq3GmDvfxeLqJn4N3l0q5tjHc2g6jiO5S3OWjaaOHN5espm37TegGFpaWlpaWlpaWlparjSg1NLS+tJUkpZz1GXzNhZXN/nytrV4rijqzvo8tGJLC7aFOA/zlo3rX1mINxf0DIpOWVyLfa97C4+SHHjz1jfglPve77FzcHvFoAgARM0de/vOWTYue24uRt0wEb95RgaOy2ubA6HrNA/y0Hx5TR05PD1rnQSu3DmKPsN64Cr8ZF0D/7msQN4fc9c34A0FUm9qEO455ip849NqXPL0J8hatnSN1hAApRZBCQK821oIoCQgazxxTe5LoNIa4qQLgr2qA7gzb+Ef763Curo2vL1kMxzH8TkMKXCbskSALzafT9Y3yMfwDrFoU5OvL+A6EF+dtwkvz92EW98QodkNbeJc9+xfKI155t+mY9KiWlz23FzeRtc1EXP33rbWrAQf2TX431wBcfO2g3lkztNWUEDp9p+3vgH3TF6Gxz5YI30pQeEn7U9DvBnkdBwHD70n8oAymL/ay685sq8LVRlI7shZePrDdb5xAOCiJ+egurED17y8kLc5DvDJegFs53rndONri3ApuU5Zb46TF2/mfdjc1Vyf+2pAqaWlpaWlpaWlpcWlAaWW1i6mT9bVS8Dgy5RaoOL/nv0Ep9z3Pp7/aD1v6ySAbIBSuGLqsi24Z9KyHuUrbOnMSxAGANYSYBVRXGNPzlyLx6evwcVPfSy1O46DNxdU+3IM/vyJj5C3Hdz42iLedtXLn2JxdRPO/ufMbufHVNeaxeRFtRKkzebtwIJCFL5Rx1fOsvGHF+fj2v8t8L2moS0rQcdP1jXgv59shGU7Ehycv6EBJ9wzFcff9R7qyXWjrs1Rg0r4z/+evgZ/+u+nOOK2t6XrPIEApkS0+39iZqyk+RXFNXAcB9/+2wf45fiPJbBT3SjWIW87aMvm8f9eCM5tSq+XGk3c3JGHKuqgzBJQ+BbJY2mROa6v84MypjveXIJRN0yUINvHaxv4z6XpOBraclDFxqlrzUq5Jxmco2tG26nbMUf2BgWX9JznkVyJaSW8udn7goDuQWnveXP0AUTvvpy1WnYe0tDv1Vsp1HX7/3PqKt527+Rl/F5QUwrkPcdokINy3oZGKbyatbPj7d6v0BvDbX9u9nrJ+cjuJ7rfbXJPNmbl68fGeYc4It3juq+5b8oy3zVYrQGllpaWlpaWlpaWVqg0oNTS2oXU1JHDmQ9Oxxl/+6DHeerC1NPwZuoiK07JgJKFQD5GQoHXk3x8tL9tOzjv0Q9x35TlgeG5VHnLxmn3v4+Dbp6M6wi0o7njLNuRoGCYW3Piolpc/NTHuO5/wk1Fxxk1UEAGagRt6pDh0+w1dfjlU3N4bjy3v4ND/jwZF/z7I6koy6XPfYIjb3vbB5IpJKJA7M+vL8ZzH63Hv2es5e5TAKhubMfoW6ZIwJRCNQppGWza2NCO298ScIUWISkjgHmhB7468zb+PcN1GFq2g/98LPqrwJHmC2SaQdymFgFrNC8jBUMbSXvOsrGpoZ2HP4tx3OMuqRGAi4Y9q45dJinE27s/NjW0Y8FGCobd19q2g43EzUmB8YKNjXjw3ZVo7sxj2nIBwaatkIEYPUd1nLsnLUUrOa+cZaOhLSvBOTqfhQREOo64BtT5mM2LOU5fIRfeYaI5PBlAs2wHLxNXJBubFn5hcwQE5It7+RVZe1N7Hp15GazbtiMVMlq5pZWvgwooc5aNutastN5s7GXeWjMoztpXefk1R/Yr8M5VhquDylJef7f97SUi9QO9XhvaZMSd9WBpjfLFRc6y0ZGz+HVnX7Jk8w6fCwD8/Khh2LNSdq5qaWlpaWlpaWlp7crSgFJL62ugnuYSpHCKPnxvr+atb8CoGybijjeXdNu3gYSAFiVjgX1oZd11BNpYBCTJsEnOD6gWrahu7MCabW2wbAdPzFjLcxWGwZ2gvzGxHHbbSAGViSR/ZUWhqP5bSHJsvkfcWdm8je89NANvLKjBkzNEuPDMVXUEJAnANOHTGnTmbVzyjOzmpLCJrXlHzsLjpEpxjqzrw1NXozNvS6GmFKpRJyqFh8xpuqy2GXdNXCqOSfpT9x8L8/5gxVZsUhyOgAsEL3tuLva7/i3M3yDmkrNsxfUmxg/Ll7ihgQJKB1u9sOyKwoQ0LiCglTr3zcQlx6o327YjOUdZ/3eWyg45BlFrmzuk+TLwVd+alUJ+E7EIn9OET2lOQgcbG/x7Lm85yOZtPD9brk6dsxys94rD0PslH+CgZMdzHEeB2mJvTCIFXOi1oVWoywvca/ryJxslAMrGUR2Uee+YLFS+0ru27DoFwbxtrVnkLAeGIYrmsP4qoMxaNg+rZyDS9mDs2jq3fTcWym056MhZ2OR9IbCH56Bk7lLmxB3aJ8PnDgAfrRFfVKyvb+PtG5U6QgyWduRsGAYwdq++vH3F5hZYtoPiVIwAUOGg/OMpe+Ka0/aG0ZMqQVpaWlpaWlpaWlq7iDSg1NLaAdU2dfQY8DmOg/V1baGurc+qy5+fi9G3TJHcX2GSHWJiPh05C5c++wkefHdFj4751My16Mzb+DigkrIqKUdhyPN4KclNuVYqXCHmSN1n9MH+nH/OxAl3T5XObb0CG1m4rg9QWt0DSlYBWIJnmwTEobB0K8lhSMHbS8RVSF2LtCI0g5vUmbq+rp3n27NtB6tpzkPv2tCcjLQdAN4IyKdJHYi0bzMBlOxcZ6yqk/pQoEmdoOz60qIggIA+42etw8tzN8F2gFmrRPjvstpmdOTE9aPXkgKxrOVek9bOPBZtksN4GQxjoJGeFwVc1DGrOv8AF6TTzAHsGrBCKqyQDAtjXl8nX3fW/463lkjgjl+DmWulEN+cJRyUp4+qwgFe+HzOsrGurg1Zy0YmHkFRzOHtDGgOLktLx23L5n3nlLcdjJ+1Tsq5yua4cksLVklzESc+O6Di9XTFsczum089mMngcM5y0NSe5/uhsijpzcX93Q8oHWxudtv6ZBJIEZgLyDlPWf/lXvGdvauKpHmyyt6sgE/esrF2Wxscxy3O1ZfNhcNStz+7lmzPrFauCwOZm1rlN69c3samBvdvFQUJZBLu/ZvN21jqgfE9KwsRj4pzYmMPK9/xiu9aWlpaWlpaWlpaX1dpQKmltZ1auKkRo2+Zgp8/MbtH/Z/+cB2OvuMd/PXtnsG/MN0zaRmOvO1t1BCHmm07+M/HG7GtNYt3FadXkGhOOZqn7p9TV+HluZtwx5tLg14mKZu38dZCF0T1JBfk1hZ/2Cwgu7lKM8JZSUGhRfrQCs9snPashaW1zch6UCdoDNqfVu0FZCAWlA+wsT3HIUkYPKPXgF5fCrBfnbeJ/0wBJXU2Zj1wQnMs0jlva81KY7Lrp/Zn89zY0C79jc1TdlCK8Vpofj2vnV0TVpmYwR3HcaSxs3kZCKrQ5/X5ApTSoirUreeek7iWCwmIzOZduPitv07D5MWbSbvI1dmviABKL8x2jeKsZfOheRxFzkc55ye7vsxVySAcy0GpQnB2zeZ6hVREqLHb/4U5qiNSAMqBpSkeDp23HQ4bh5anwdJ45m2H9x9QmuLA1LIdLK1phu0ApSQE/5N19bj6ZTknKTsnVsSJ7UV6X75G1or1ZxBRFJqx0dyR4wV7jh5Zzq/BFm89CpNRZBIMzrnXQM3jmrdt7mbtW5hAzLsGWctGa2c+AALbWO7B3737F5FxHKzz1poByqzlSEAwFpHPlb2PDvL2KpujmieSfWGytdN9/cBS4Yhk91JVSYrPPWc5WFLjvj/sWVmIuHfcbF4UydGAUktLS0tLS0tLS8svDSi1tLZTj05bA0AGZl3pqv+6kOCuScu66RmuBRsbcd+U5djY0C5VwaU57PqSUGPHcXDlS/Pxs8dnSzkgNzcJCEPB2mNKJeGu9P7yLWjyYBYFSg1tWVz50nwJLAFyaDQ7ZjZv89BpACj0Qr8tWy6wwYDS5qYOycWV57BNQCJaqEWt0Jy3HVgE8PB2b/4tAbkRAeDjtfU8ryTr2561sIq41RjEcXMEkorGtgjB/ojkt2TjNLblJFjIQrNVRyRzo/mcklaIg9Jrn6VUEmfzlPMmEgelVADEm6MHE1moLzun+racL5eg4zhY7gHKvbyq0Kz/JuK2pBCXhnsDYn/kLVsq4pKzbDS157FyiwyPXAclA5Q0xNvBis0tsB0gTgr1sPGn08I83hxpBW82B3auQdcgaC/ZtoPVW929wfI35m1bKg7DQ40th7tjB5amEPVAVs6yea7CYX0yiPCwZwHEBpbI/Rm024tAO5rv8NrT9uZ9AQG9GeRj7UtqmiTXItsHLKdklZdP0bIdnp5gaJ80hnvALZd3eN+KggSiDLoyQKnA9GzexhbvPalvUYJAPpGWoKo4ydc27FxzeRtrPWA8om+G92VgtX9xile9z9kOOvMW/+JkUGnaOycbjW057jg9fHgfAOCu5W3e1CkAZffegNKUNHeWjmKPyiLevq01y13Kg4gDVktLS0tLS0tLS0vLlQaUWl9pvfDRepz9zxk9Cm/+vKTCr65Ew3VHVPTMNbNqSwtue2MJmkmRFeq+ZE4gwB8CyfTusi14dvZ6vL1kszTfWgIoaZXb+gD3IOBCmtVbWyXnHnVYUch5x1tL8ezs9Tj1/mnSGFub6TEd73yW4yePCQcqg6jv1RhYvlkuZAMAL328UTpWnrvYBCSicHBdgPOqtqkDWcvmzjPABXSO4+DGV0UBHBZmCriFbegYgAtxqHGUzcUPuIRbL8j5uLimSe5v+wEiHUdtz3FwqbrS3P4fKpWU87bjK+xCr2lLQIg3h3OFcWlsH0TN29jS3ImmjjxMQ1RMtrxjVpM5UkDJ3IYsYp9BwXeXbpHuaQqb1HPa6vUrL0jwtc3bAhLtUyU77TrzlpRnkIFhdjweyu1dgwbFQcmuwSoPRLIiKDnLxqbGdnTkbMQiBs9t6IYxd6ItayFiGtw9Jzso0xzm5SyHA/DhFQqgJA7KmCkclyykfPd+hXz+DBSeuHc/HLtHBQDh6mUOY/aexM71AcXlzcLZNyuAMm85/AuGo0aWi7nbwtFaXpAQrsWQEO+8LUK8XQeld+3zDi9addDQMsmduKJWhE+zfbOtNcvvfxrize7JsoK45KBkX9QkoqZYV0ukUOhXlMBQb53qWrJobM+h3XJfT9eP3UsDSlLcKZnzQssBNx8mmzsDxuUFCSTJe4yWlpaWlpaWlpaWlqto9120tHqvrnhxPgDgX++vwu9P3vNLOeaGkFyFQaKQaGTfnlVs/cNL8zF7TT2G9EnjnEMHAwAWVgcXEqE59mgI8v1TlpN20b+WQB6eR47kSqQ1G7a2dOLke6dia0sWJ+9TiYd+fBA6chYmLqSFPsQx15DQyOaOHAqTMXTkLExZIkJy2RzvV0GIN5eF9fJ3Jmzubyq5FNk4FL5SB2VQrknm5KwsTqK2yS1wkrfdIi3Pf0QqT5PrSEEWm4tajIS1U/BGz2mG4mRkjkhavIT231gfBiKVdpuFhKtA0+2vAspc3sbWbF6CpaEOSu8a+ByUIWHo1Nk2pE8G6XiU99/a0snPGRAh/+1Zi+/fgwaX4qO19fwaPP/Ret/4FK7Tc2IOyj4FCUQjBvK2g7zl8LH3qSriId15y8b8DU2y+9OWqzoPKE1h7bY2EuLNroELaRnUZWH++w8sxsaGduRtUaV5SJ8MEjHhHmTuyQElKaTjEd5ex+FqHDESbs3yQw4vp4DSkYBYlAA35lzdrW8BvwYMUJam4xLgA8T9MZzBPNvG+8u34PX51YiYBi44ahj+MXUV8paNbN7m86ziRW9sXphqvwHFfO+w9QZcqM1ci9m8jedmr8P4Wevk9bNoiHdSCvFmruODBpfwe66+NcuLMI3sW4iYaSJr2RzolhfEUZyK8XNl8+6TiUtuTrZ/+xcnhRPVtrn7dVh5hofo522bfxFSURBHER/fFs7S4iSHzVmv2joAlGVi/JzY2CxEXEtLS0tLS0vrK6P51/e87/7b0VdLS5F2UGp9LWT1sADNrFXbfLCrK70+v9pXCGZTo9/JBbhh1Qs3NUquSZrrTp3jO0s248R73sOctQIkbahvw2wPirGqyh05SwonpXkD1SIggOsEo8elhWA2EwcTg1A0JNtxxDhTl23hMIkdZ+aqbWjNivOjDjyT0M0PVrhQbvysdUruyOB1YnNp9kyQzKUk8ia682agjIEWel1otXAVIudtm+ec61+cJLn3HO5AHExy0TmO67SbS0KQ2dyZs2uUV9SEzX2LAijZOjGQkyFgCggClF2HePudlSx82u9Kq2/NYtXWVgk45yyb7wuW71B2UJLwdOYeDAnxDpqjBMm4k9HxzZsBrIWbGmHZDioKExzaMMclS2NQ5BUOypJiKkeM6MNdkVnikqsoiHNXIQ2z3UvJVcgqsPO8iUqBn7F79eN9AQG+xd6zpTD/UaS4DcsdObw8I4CjLXIPDi3PSBCOuVYLEzECyoSDclh5mgPKPHXslaYQIQ5KBodH9i3g14Bd55J0jIe5s1B8FuI9vIJVsBaOxdP37y8V7GHjRE1D5OG0HdR54LYsk+Dj5xUHJYeotoNr/ydcygVeQZmc5XA3oxvi7fbvzFv4xJvPwUPLePtKCiLTMd7O9mNlsQw5GaAsy8RJkSObQ/3K4qRwoloOVm9t8657AT+nrGXzL0IGlaUlpyTLqVlZnETM69+Zs3kod1EqhnjU7c8gtQaUWlpaWlpaWlpaWsHSgFLrKyuaW5FWgQ5TR87CD/45Exc/9TF3vjiOgxteW4znVpm+KtsLNzXi109/jO88OJ23dVWJ+7X51Tj1/mm4+Kk5vI25ZgC5EMXmpg789PHZWFbbgsc+WMPbXyHFVBiEW7OtFfSw1PVG3Xysfc5aGahSR6Ac4u32X7QpONRYKiTCHIse+CvLyCG/gOxmZOGfy0g1W9afAlx17i0eI+tLwmkt2+EFTKpKXAeXpcwHECHe9a1ZnkdOVAV2CJSQw2MZWKwkVaAt28G89Y2BodnM2XXY8DJ3DCVPn7hmAhgDoogLG2eNB4lYARoOKH2OyK5zTQa1M5BVXpAQxVpsh1cN/9YBVfw82Z6mId4MXPrzL7p591go8NA+LtTN5kWF4hEVBRKY4pWOvTXd2tIJx3F4jslRA0ukEOG1dW1oy1pIRE3sN9DN45jLBxdTyRHHXh8FiNG9x+BUNm/zyubfGuVeA9tx4dGi6iZETAPj9uvP18NxHL6vKJxb7IX5lxckePXwvCUclMMrCvg5ZS2br/XQPmk+x86chTYP9hcko/yc2rN5ft1dmO6uSVNHjs9lQEmKw7mm9hwH9SP7FfLxt3BAKRyUedtBfVuOAzQarsyKJPUrSkrh5uy6V9Dr7kFwwHUJCqekg63NAujywj+WLblWWY5SGrpP17W6oQPNnXkYhrt+rJ3d7/2LXcjHoCC734tTMX5MQBT3KcuQa2A5AiwWEQel5aCmkTlUk/z6ZvM21rNQfFIMJ5t3uMO4OCXGr2vN8vfroqRwULI1HaABpZaWlpaWlpaWllagNKDU2mlqDMl72FNRxxytXhsmGmrblnUfxqsbO/DUrPWYXmvio7UNUn8W8klFK1IXJuQMCSxP5LtLt/A2WhGWQbh3lmzGobdM4e0MUAHAK3MFoGRAc5VSGISBqaU1zVLREAaVPlpbF9jfcRypii6DUz5AyXInrqd5+tw2lkOO5d1jMDNP8uMBIjS42uvP8vHlbcdX/Zi9Pm/ZaM27UIDBPMu2sa21E7YDmIYoBMTgalCI9zzP9Ti8PIMSb1/kLUd2UEYMPv5WEqbJ52M7mLzYddoxuMocUxvq22EawCFDynhfwB/izdZPBVzMEcncrKIqsOseZOtBweXTs9bhLc/5FyFOMMdxUK0CStuWAAwDNuu2teH95VthGMBPjhjK+7N9Q0O8c74Qb1HF+7+fbMTWlk7s3q8Avz5uNz5Hdj/2IW61vGVzgDrKg40dORttWYsXyBk1sJjkB3S4s3SPykICmG3itBPnlCMOShrGu7Wlk+/V3Qm0m7e+AbVNnShMRHHcnn35+TKgfvCQUg7Hc5brhGPrSyEtC+/ep6qIwD9xroPL0hIQ4w7KPsJBSd+/MokIb2eg2zSA4mQMEUMOq0/FIihMCscle48pTsVcp6AyTilxGgLChdi3MIHCRIyfE1v/gkSUgF6b79O+hQkprJwBytJ0XOrP7oMK0r+ZwO+nfj5ayhMpAGiSuxZZKgp3bFMASu89hr0/RBW3aHFKXBdAvF+510XAa75nlDyZWwLCzXOWAJSDyuRiOKxYWFEqyp2VLJVEImoiGRPryjSwVBfI0dLS0tLS0tLS0gqSBpRaO0X/m7sRo26ciH9OXbnDY1AgZNBY1hC9K+VCdB/6V5JqzC99slHqHxSOvGabAIKRiHxMNZTVcRyeS46Op1b/ZnNfUtPEw1IB4cBbqYBSNvf/zd0Y2D6H5E102z3QubXVV3l5Q307dx/RebZl81hc7c9vyRyYDKrRnITUTcn7N3Yo/W0J2tJjspBR05BhEIMGZZkEyevHHJT+EO95XuGVUYNKpPxy1dQ1xUOBhYOyf4lwNuUsGxM+dZ12p3tOu7zl4GPPPblnZRGKOfz0QrDrhWuRXpt6D5z2LRJw1YXF7Fqy0HIbS2ubUd+WQzoewTcGl/L2+6aICvCsAE3ecl2IrVkL6XiEQGMxdr8iAYlYqPmg0jQvAML6A+AOOja25QhoWU7cgwwEH7tHX36uWcvmfQuTUZHvj4R479ZXAMetLZ18H+zZv4ivR94WgHKvyiIpNFly2nlhsy2deQ6/+hQkeFj1Gi9Ut6Iw4cI8kwEut31kvwJk4uILhhq+T2Ww2NDq7qlkzORhyXmLVisvkorq1LWJkGJalIW5dweWpvg5MWdtPGoiEY3wcRiwK0nHYZoGD/FmgJa5l5kLmB2TfUnDrgEN8aaQjH3xMrgsDVoJvNkL8S9MRgUAzjsSQOQuz5zFr7t8ruJ+cosWebC0SYSJH7lbHxKG7RAoKByU7H2Dn5M3PsudWVns7jsGBdkXR8Up+VxryTVj1wsAeU8ReTLp3CW3aF6EoVcWiVDunGUTB6U4LnOQslyYfkCpHZRaWlpaWlpaWlpaQdKAUusLU0tnHje/tghvLqj2hUb/37NzAQC3TFjS7ThTl23BD/4xw+e8oyG1NJdemN5bJpyNrD91Sb6xoFYKG6cFYJhof0sBmDREFnDDDqkrjR2zyavOHSfOKwB4bZ5SCMYSYJGKhTe/TYArIELeKeR0xxHOTanddvCml3dv9LAy3p6zbSzc1CRdUxbqXOtzULJq2vLa5Di4FGCGzYVB3tNHVeFP4/bk4zBXU2k6ziGOpYSY0tyGTR05DggAEULJnHn7DyyW8stRByUbx7Id7qDsTxyU8zc0YkN9O1KxCE7cux+/LsyRNawi46v2zCr37t6vwOvPchi68+Jh654zr90LdRcOShszVrou34OHlvFiKjlLhJJefepeYnwCUY/fqx9SceE2ZPPsVySgEg2DVSuZv7mgWgLVedtBO9nOfXhIv8ibWJCICoCYd3jV+cJkjOT4FHCuf3ESfTwn5taWTg5Ei5JRqT8HlP0LJZAl7wPZJRgxDRQRMMpSApR4kIiBOLZHqAMREPu6T4HstBPwT25ne8kNBRb3cb2U81CEQ9OchOzas33BwCebO4NhDM6xadYSNyA7Z2mcpDwO25c0xBsQX3jIgNIhgDkm5WvkofVFCX5MBj9NQw5jzlki12tlkQiTZmOUZuIwDIMft6k9x780oaCT7d8+GfeeCQvxpo5ZgM1FrCt73+qTSUjrvZkASu5IJkVvKgoTskuXFNthULS5QxSckgAlcXMCAqIyVRVrQKmlpaWlpaWlpaUVJA0otT43zVy1jbucAGDK4lr8a9pqXPzUxzz8eUd03qMfYtbqOlz63FypnToo890AyuaOHM8DBwhoR4FjW9bi4betnXmpGAzT8lqSU7KbY6pOQRY22+TBptKM+wDLQODyzS5YZC4zNj5zvtHCEoAoEMPhn+1CIgaQWNEXNg4NPXfHsTHBy8d36v79SViucLGN7MtgmBfizcAMcUQCcqg1a2/L5nkIJHMJ5m2bg7yhfdJIRCO8nUIACiKpw4o67TbUyY7VRiXEmzoo8xSc0Mq9ls1dU30Lk7yoDCtSsndVEQqTIky8qZ1BNQEk2Pqx9d6trwhfzeYF0KOhwyxstigZRaEHlnJ5h6chOHx4Hz7HjryFjpx7nb9z4EDpGjDAPG7fSgm6bg4AlMyxV0iAIAC8u3QzLn7qY+la5iwbrR6gLExGxTpZDlo6vbyJiagEpqiDMkZgXiPf73HuuNzSnEWrl2Yhk5D7L/PusT37F0njB4XfsrbiVEwCX2wvsYrLAgq67QXJqARpawmwojBvK3EzRvj1tXn/viSHYd5yOAAtTcekvddCwqdFTsIsbwOES5DtR5ZXNwxQMjjXoIwTVYBYqTd3Nn/mGncLvohzZeskOWAth7sZ+xUS4Ngk5miaBt+THXkR4t2vOCGgK8sF6Z1TXFm/eNREOi7CoWtVt6jXzmAmC/Fm82FfbhSxfWAq1yATDKRphfOsZXMnpuugNHh7Hcm3qe69iGm46xqQD5POnYnd71paWlpaWlpaWlpasjSg1PpcNHXZFpz9z5kYc8e7vI3ma/xwTV3Aq4AeRGZz0WrTgOygtLuBhWo+ySAHJeACkrZsHkff8Q5uem0Rb2cOUAYR6RiAyGkJCHehCihVB2VZRg4FZg/ZrFgLc3Cy3HMMOFqee5CBr0GlrF1AOAq+8rZbkfrD1e4aMNfbthZR7fukfSqFi80WLrb9B5bwNiAoB6WcHy8ZE/CMzSUdj3AY64Yf+0Fh3nIEoCygMMiRXE00bJZBUQZm6tty6MhZfN+NqCjg4KSTVNztX5ySHZQ0Z16Au40659jaFaXkPH0dOYsXtxnJAaUIhzYNAVuyFg3BJrnuSLXnfQcUcYjT0KbmKmRFVmwe8nrIsDLpWgoHZYJAHOEyi5Abb8FGOQcpO9c2b0ur7sGWDuHYY3upMy8Dyghxrja2C6ckA4atnXm0eqAzk5CBGAN3cpgtyYVYlOCVkVW3GnPM1hMYC4hchexaFiaiMAyDADdaTEVcGwELY3IoMM3LyOBczpJgLA17bu4QYJtdMzZHDhZN5qDs4GO47e5c/NBOdoUWJORrwMTysLL9xPLWug5K0ZdBuAIS4p23bL6vB5SmxP3kgcJSZS7VDe2wHRfa9cmIkPstHPTKjtbNPNekCxbjkeBroEK+Sg4o2XuZsg9I/8KEC9jpdaEOSjYXx3HfDwzvXqWh3HXkvUk4ktn9FPXmLv9jxucSlefOvoDS0tLS0tLS0tLS0pKlAaXW5yJWUKSdVGhmjjYAsEOqXzNHVZhoxeeOnC2Fim8JcVA6joO7Jy7FeY9+iM68+/rlPhDpPmCv9BWgcR1+dUpeRu5ylByUIgR8HQlxziTcB9AtSugwcyEyF14ZgXaAcN6IUGAHeQLWuCOSFB4pScckEFlD4R8Jm129tRVZy0ZhMoo9vByGDO4UJKISKMtbDhZ5+Sf3Z1WUvbyUzaoj0junoHyK8lzE2JuJE446Jeu6cVBWFCZIOKYotrNPVZF7XTtyHG6xkF8GTmq9HJlufsu4FAbLxyc581g+y4JEVAIbDCoV0TBYby6OI64l4AJHBo9K0nEkmDOWuDnVisnsuNTZxY4Zj3i5Cr1zouHthcmo5KwMAqDbCLQzTQPMZFaQ8AMTxwFacoY395i0l2iId4w4UZtIiHeMAOAmkqcvTlxpwkEZ4f2zlsXHL0qK/s3E0VxekCBgSnFKqg5Kz/3K9g3bHyq45CHeJDQboIVmBMjKksIu/UgY8zbyhUxJKkZchRZ/X6TOzTri5gTA82oy1zEL8WbrRCtSu3OXXaH8nBRQVhKSx3FIn7TkNJT2Nj9XkUO0qiTpcyYyR6RwSgq3c8Q0fBWsVUfkZnJ9aXsNWQ8AHEgzsS9xWH+2NwSgFP3LvLQCdM+ze6csE/MB3TLv3mNjt5C9V0buy63K3lMhalg7S8WgpaWlpaWlpaWlpSVLA0qtz0XtAeHQ9cT5ZSkAkamCAMqOnIWJC2v4gzIgF6UB5EI0cg5KAQufm70e97+9AlOXbeHusCAHZX1r1l952XYkxxptb+rI8QdnALAd4dxcE1Ctm4EDBmHZHH0OSqUKNAOaedtBbbNbwToWMfhDed52UN1A3IARAYNEGHNKAl/s/Ef2LSD531wQwh6YKYBZWsMclMX8vFgRmHQ8wqEHW9fNqrOS5HysLEpK7kxejZm4z3KWCA3tUxCXqmwHFdGwbIeHuO87wJ2j44iiOczVxK4BA7rlBW7YKQMtje05Hj5dXiggFNsDmUREAj40dJjN3bIdnj5gaLlcZIVB15K0gG05y+ZVivsVJUV73uZ7ppQ4u+rVHIMcoma9313nGYWItV2EeIuwZ7e9ieRJpWr2boOStAC6arVn5njrJKHsNKdkzrY5oCwiefoa23NgbwMFCeG4bGgT7W6ouAwQoxw8yy62Yg4o1XOVw6cbFLeheo1VByVzBpdmBKTd0tzJ71m3UrV7zGZy/m7lafmY7FzZ3Nm+4yHeCihj7kE2HfYFhwCUMvxTc1kC7r3KwvPjipOPVhoHBOSjIfo58mXIwBLZccmuCyADQYBU2Vb6lyggkoaKA8JtyP65UM+Vib0XqjA2yEHJxgiaT5mSmxJw1xRwq3ADIq1FxHDcAkLR7o9J26mz0jDEuFpaWlpaWlpaWlpasvQnZa3t1orNzT6wR52TTA3twYCSgktWHRgA/jl1FS56cg6+cdMkTF/pVrpepTgcF2wUYd4yoHT/n83buOOtpbydhUmzqrtMtFrwoNIUTMOdHw3LVfuz4hL0AdPyaMr8DWJeonqze54VhXIoNwM2ZRzy2ejMWxz89GMg0rJR7cGBfkVJUTjGdkTYZUkSpkFCezkUTEhh0sz5ObJvIQctDJSyYiwMAqzc3IKOnI1kzMRIz20JiJyXlSTvHsuryYAby02Zo25OAsmyJEddX1Jh2rKdbnNQ0hDsnG1jQz2DghlkvHNg+ToZCGH9N3nXha0FO26Ndx0LElGk41EC0Ny5ZEiIN0DAV1K05yybH3dIn4zU3kCLrJgs152oCuyGYAvgyCBUaTrG4V9YrsJGDlGjUv7FtqzF50kLldQpYc9sfzQGAHmAAEpS2CVvO8JBSUK8KXCkIDKbtzm4o4VEGLQzDDfkNarMMR41kYxFOLCqaRQhvzQ0m7vYmAvRVM+1awelCpX6ZBJSDkP2hUi/QgHZO3nhFddN53MVKjki2ftAImoiHjV9Ib9d5Y4EAIWHkfH9eTXpNQBEkSD1XBNRkzuS1TQbtOo5A/iGwXJKBp9reAi22l8GmuzfDTVsvavx0/EIuRe6dy2yvoB8bQzDKxgVAii5m9PbewVReHsvGET2ZC6pWATG9uQ10dLS0tLS0tLS0tqFpAGl1nZp7bZWjL17Kg6+ebLU3hEEKAnoo4CSPfABInwRkF2I05YzQCk7H9lr85aNuV7+RHd8FxpMXlwrhWczKOgL8bYcLPIA5Z6VhdyllLdtCazy8Umo47DyjO+8PiFzYYVjhIOSVUB2YJOKvswhlbNEBeuoaZCKyTS8MiWFNzNXE82naDsqFBSuRe6g7CfyMjJQynKisf70mBTGsvbywgR3e7G8bSykWDgobQ6haE7J+rYc8l6et3ISUk3dhm5lXQFjWSh/RYFwq+Uth7slB5WmuAuUOe1UOMCuFwufZ+OzsXluPOKsBPwh3nVSiLdwFbJxKouSUjstmsJz2uVFiHdf4sCjBUNSMVEwhEG1jOKQY3PMKICZVc2OR02UpAUUVMOe1XNV1ZQ1+Nxpzk4GKAuJG5ApFjGQiJpivcm9SAsLsXsjE5dzQdYpc1RDgfsowIpdm2IlxDtsHL8bVQFiBTL820S+IFDPta+SB5GpVAnBVvNhxpRjFoTA0jKlSA5v9xXJUR2U4gUM0KrjDy5Lc1CmOjdpwRemioKEm15ALT6jfBHA1K8oEdiuhnLz9oycJ1M9V+palIBjaN5H0U5zPlIHZUnKrTavnj9z9avh4wUxSO1M7L2mJ85KnX9SS0tLS0tLS0tLK1waUGptl2as3BbYzsIVqWhoo0VSUNaSMGkKLoNySq7eqlSH9trnbWjgoM8dx/3/i3M2SP2Zw0/kOmN5H20s9vIs7lVZyEEpLdIhH9fmYdWDvFyQbD6W7WC+VzmazpHBEBbinbdstGTz3GnGYItlC0BJizDkLJuHmFaRnJIWmYsLLmnuQRHiTQuVsOI+u5EQb+bYZOBLdZnRPIuAWLdyEoINAJ15i7sieUVxy+HOPBdM+XPXxSKmVGiGFVNRHXtbaMVkGsbcLK4BC2Nl61yiACsGmrg7SimGU6iArAbiTqS563hhDFIkh+ZZpOdq2WIvuTklxbmuIY5LXqiF7FHqiKSFXejcGVhMK2CKu/6KEpLjK+s5/7iDkoX6dwjQ+dpvjuJrxByUxWk5L2MDgXz+CsXB1bRTsQjiUZNDHAFd3XWLmDJwZKHZLDyWAV3VUcfuJRUGMZcjG4ftbX4NAhx48ajJYS+DViytQV/iSGbiEC7E+chD630OWOWaJYJhKQ/xVv6V5teA3CNAsCs0SfId0nuQhUir8+cuT2UuVd4XD2qotchvKbf3Kw4O8S5Vclaq7T5IG+KgFOcU4mYk80yFXAM2Nr2/5WPK1yATc7o+Zlg7gb1JDSi1tLS0tLS0tLS0QqUBpdZ2qaUzOF9dcIg3dVAKgFlNHJR5Qi5pyDYLzd6ihJKzENipy7ZK7Wx8FgLOoujyllvBmoEJ9iBskRBv1UEZFPJqETcjq5oNuM7K5ZubucvGHSM4B2WegKx41EQ6LorbbG1lDjHZJVhN3IzUmcdCvGnhCst2+LWtLBYVdHNekRzABZTsQVoN8Q7KVUjdbbW00jEBANWNHXC8yr20QAwbvzgd8wGJvkoIKM39WZyKwTQFRGV7zs3354Emy+GAriQd405PNsdiJc/iJu5YdI8bUQGlAvmYizajQCUG4ouSsUBnZXGK5F+0bO6eK83EpXyNDFAOK8/w9mZeHEWGMg0KzOvOQclC8fsVBufpEzkoZdD5m+NHYt8BxSIFQE44KCOqjQ9eDkrFaacWn2GgUIU49W3q9Q0OzWb9/aHAPYNEbBx1/4nxxXn18cLHAQGV2HFpQSemvhx2dw3bBBgOruqs7jExjhee3k2IN1MQAE3FxM+0PROPBraHXS/mjFahXSoePHe298JCttWK190BSgpG02TuKjAtCoK00Uhg/7DclAz4q27OQs9BqbarLl2moByUukCOlpaWlpaWlpaWVrii3XfR0hIKBZQBRXIaWmkOStFOC99YpGDOVlIFlwHKrc0idLamqYODyI/X1QNwQV82byNvuyCSudCGlWewaksr8pbNwQ8g4EzOEmHPe1QWEEAZ5qB0eOjsoLIUabex0CvE07cwgc3NnXzuDH6xPJsuoBQVirl70LK5g9INnxZOOwbW+hcneTGTPKk83bcwyeGRr0I2qfbMwG55gXCCNSoh3gzAbFPz+kVM5CwLNV4Yd1lGdpPR8GkGrPKWwwGl6sRkfQHhnMvR/qkooo3MOdfO55iJk7Dn1qzknmMVslmouVq5mEEiNb8cg+hqyC/P65cQ8JbBNsAFORTaUahLi+Sw9aCVw9fXtcF23GMNLE350g+wuTMQwkzGBUnZFdqoQFSeN7FehCXTccTclRyU6jgRwy2G490Gbv5MP6DMxKPSNaFj+6GoDH0a1bBksg+AYNAEhLvbihXoyo8bEsrN1zusmEqAW7JDOVdW5MrvoIwFthcoDlhfu6/6dnCId58AaEfHoXOnjj0Kk9MhrsKikPVjjkvVRZr0xgyD4H5gLK83U1ixHVE8R7TTuauh32w9pRDvOA3xFu1sbMBdE/auz7+wUXOFep+WYj0skhO0h3WIt5aWlpaWlpaWlqrrr9/ZM+g90g5Kre1Sawig7MjLgDJn2UoItni4Z85Ft93h/69rJSHellzZmj0gM4DB4N9Az9ljOQ5qG92+iajJAViOVB3OxCP8Ib25I4+sBxIrChJSiHdQFW/qThxQkuL93QrOLnDdo7KQtzmOw114PAelZcsQjjgit3nnWZ4RICtHCvb0IWCRug1pdeG8LRx7fQpEWC5zq7H8gAwC+IrkhFZ79kKQPXciLWID0DDYJHeT5Sybgy/3XMPcZwLSckeklxsOEG7bikI3XJm1s33BqhQzB+Vm1UGpOjc5GGX5AVmItwwT2D5jTrMg4EbPSUBdAp5tm7tuB5Sk+XVnwHFwWVqq9swk3GTBIIs6Md1rIM9xQ4MMKP3gRKnirTgx2RqKEG8/YC7wQt/VORYmgkGhGu5aT3JQ0rnzOQaExwLhDsowIKbuYTp/tZ0CSjpO1DQCIS0H++r6hUDUsFyTQbAUICDZcJT2EADaDRCTwqQTwa5CNoYKtcPC09kXA6qLNKO8pzAxYKqua5CD0jCApOcApfORgCM5LttjansixEVK83NGA66Zb526CfH2fxGgc1BqaWlpaWlpaWlpbY80oNTaLoU5KDsUB6VaeIPmmlxaIypqi3yNWZAuyFluQRlajdjt70KZ5g4R3gu4odYbGtx8lQNKUsTFZvO+hcTJR12VyZgpOSiDAGXeFoVp1HBrGj7tHtNBU0een3NFQIh3UVJAuLwlKlu7YJHmNiSOSxI63MBhnihA09Se49ezNB33Vfnl+QE5mHLHFmGaXpgtyUEJCOdVDQnxpnnbNkkFX9gcxdyLUzGfY0pU03bbWzrzwj2X8q8TA4vc5entC5Zrkod4e45FkYtOhjjCQak6JYNBmerwY3OgRWzofFyYJyAng7cDSlM+gMGKLantJSF5+pibUwVlGaWduUVZjkQVBrF15ftGzUXqXYNOm4V4x33OORpuTv+kujPVY/pzfAafUxEHZXK7WiSHSS2So44T1h6e21D071uYgGkavjmy+0KFc0VKegEmDsHVsPiQHJQMxNFTpQWIfOMn/NdABpTUhUjCpKWiOl3PRb2OiRAHZTrELcoBZQ+K56RJxeuehaeL9ngIFKTnlUkEt6tFw/jcI/6xAeEOVp2V3eUE1dLS0tLS0tLS0tKSpQGl1nappVOASIeEZ6s5KFXIx2BdfWuWh70CgG3LTkkmt2iKAG4MtjBnpZqvz3IcbCKFY2iYbYvXtzAp8gMy0JryHoKFg9KWcmcytXbmeQg6rahNHZQMUOZIHstULMLBT952OAxyQ4HFGLzacyZOIJ/quBQhuex6lqRjiBjMVejOLxE1kYxFfEVvVAjXrDgoWX/hoJThHK/KrVQR3tIsnJW0EIwU4q0WnFAK8zAHogkHmXjEB7jU0GwW+l/srT8DH+K6BOfpU3NQ+qs6h+UHJGGwXm5OOsXGgCI57TmLF/IZUJLyAUcGKP0FQ4IdWQWJYFeocFDK7cx1rMKgMAgVBGMBFwK7YNvw9QWCXWnd5eNj7mW1MjlTWF6/MHeimnNUHSeihkMHgMuwas8sX6o6djwMzoU4K9UiR0xhOSjZfOgwYRBVOifJPRjioAwpNMMgp3r/iTQQIY5IZe5hDspUPLg/T8lAQ7lD9ljYNcjQ/mQcGuZOzyss9Ju1q/cf6x6W5zQsnD0uVRTXH7m0tLS0tLS0tLS0wqQ/LWttl1qlsG0BKNUq3o0K5GO5JpcQ9yQgHJRbm+X+ecvh0LIoGeUuv5wCKEtI0ZtNPJw2JdyGts2hYGFSQD4OKOMyBMhZAhZSscIjiaiJ0rTsZlzjVRof2dcN8XYcUe25hPTNWzZxUEalkG0BUYUDL2+LQjDFqRgiDM551yUZ80BkRA575m4kJcRbDWtly5dWQnt9la0VZ1BZgVxFmOXDLM2IsPJcnpxryg8ok4pLiQGrVBSSy5NJOC49uNopKn4DwsnFFBbirTo3GVgPc7FlApyVDDzQAkJMbnVvd+xs3objuHMrL4j7+rJq8D3Ju+fOJdjZVRDS3rcwOMQ7zOUoiiUFz4degwISHhsPcLH5HJQhIdiZEGgX1p8XWQlZb1/IedLvpI14Dlh1/JRUfEV2UALwFQpie06do1p0iqkgxEEZFJodi4jq6zKgFHNUr3FQLksKXeMhkE92FYq9HeRCVO8PcQ2CHZS++z4sZ2XSD+XDQGQqBK5SQEnfP0LD3GNd91f3WNz0zwXwp15Q56NDvLW0tLS0tLS0tLR6Jg0otUK1emsr1te1SW0tJDQ6b4c7KFluP1ZNm6WgXLHZBZTMYcNyU6oOypxlc0dgeWFCcuZ15CwOtJjbTA3B5rkE83KIN3NSsTbVpZS3bR72S8WKtfQrSrq5EHll6060dOZhGMDwigzvz+ZekhY5JW1HVIcuImHPecvhwLQgEeHn2pGz0OaFzrtVo2UQWZJixTKUdqVIR2ObnGcxzNUUGgZrBkOiiAoo0wJEtmYtDkCDQrxZbjlf6LDHDNR2NcSbiZ1rQnnw544s0j9qGuJcw9yDoYVNxPyLaO460w/KVNg2oCTlAz4ACQ0NcZOFhQir7QxaqddMhHjT0N4IX/8wwEWvgWkEH7eQwCAKcjhYDAl79kG7RPDcg8JjAbfKvToXOj6deyxiBO6zgkSUhA7L1ybonPoUyPeZ2kdtT4fA7lCnXcB1pyAr3EEZsj/IC5Ih+RflEG/RXpAIdhUmuw3xVvZkLNiFmAxp7w7mhYJI+sVBiONSdsYGr3cQAFWvLweUIZXr1WOy3+le0lW8tbS0tLS0tLS0tMKlAaVWoJ79cB2O+8u7OPPBD3gYNgCl8I3bTv/OxGBZX17BmoFIESZNx/CHeAsHZXlBQnIVMvekYQgwYdsOL0hSVSIqWNP+hckoKRAjOygZS2hqF7kQqerahJMREPBhxRa3AnNVcUrKjUahHYUjrIiNWkxFAMqYr1gNm7soEMPgpxy+qrazOdbzHJTBbjURktozqKTmixPnGvc/1EdMnjfPIIdNRoMhgMfsfHCHOx/V8GMW4t0DB2VSykWnutjCHH5+MENz3dF2lptSvY4DSlOBY6fjwRAurCpwQUCOQWmOyjn1CwhNDpu7O44fxhanYjBZzsOIDPmY6DzZ3FW3YVGIezATcg1E/lPVzel3ShoGdcBS2BYNzGFY0AOnHd1/vChNiHsw3IkaDGPDKoqH5YgMA5RRBb5zWBjmHgyp4k3zfMouRD8wVc9JdULz8UNyiyYD3msSUVPk8wwBkWHAMS71D67uLRXJoddGCvH2t6vnyg4bXkAoJNw85D1IS0tLS0tLS0tLS0uWBpRaPrV25nHlfz4F4EKvHKnALYV4O24Rm9G3TpFebysVrwHAMzzykGXmSmKAkvVn7rwcKRxTXiDyMtKiNwXxqMg1aTsclPUrSkrVmJtJ+DSDCaxNdVCqoJSpXsnLyKtMEyhKQYUM7UQ7K0BTTArBWLZwUGYSEQ4QWeGVgkTUq/ZsStdMBZHs2pYqRVb8OSiDQZm/UIk/xLs4FROhp6y6t3euZRl/peOilIBEEvAIcTKmo+65qYCrLCOHeDMJB2Uw4JLdZMFwBwiHR0EArSAE4rDclOoc2T3gA4JxP9gASCXl0BDv7tfPMASEog4uCk7UvIxBFbXlysghAIbMsygEgmd4GGwIXA0IlQcCwCXLZUnOqdCrKO72D55jRAKOFB4Fhw5HA9ZbBZEMCKqh/mHVvUVOwjBwGQwQ6SUIm2MmxBWaigfDvDAHZXdQMOwaqI5Zdiw6djwiCvzEQoB/mKM1rD0aCle3z0EZBGNVEJkwg6t4FwSsK3WuhhXs0dLS0tLS0tLS0tKSpQHlLqSP19Xj2Q/XScVtgsRgIRPNNSkBSsvBu0s3cxjH2x0HNd4YA0vT3hguoWTh031IZWsAmL+hAQCw/8BiAC6gZIVTygtIiLflSDkl2bOfZTs8HDqTiJL+NMSb5KBkId6KgzIMUKqVrdkDMIOCBeSYAFDT1O7NXa6AXNciQGeUQFR2XQuJg5JVYy4KyevHQrx97Wk5JJU5QsOcYGGwsDjlByesQA4ACQ6z44a5MNVx2AO8L1w5xEEZ1l/koJQf/AsCwtMpNAiDR9GIDImEezAYHtH24pDwZlZkxQcWQ4Ajr8YcGuIdFppNzjUaCQRWYXAVCHZiMgAMyPssHQK+uIMypMhKWA5K/7n6XaRxr/iT2l5M5hi+TiEgMgQKxgMApVooiAKpoCIuvoJLIW7RIDAszdHw93XHJ3AuZE/S1AfxaPdwLhMSQp6Km76+dHxTaQ/ae4mQcHO6J6Vw6O0s8BPm6pW+mOjBOHyvKl8QsOn7UiMErGsyZO4aUGppaWlpaWlpaWmFSwPKXUi/e34ervzPp1ixuUVqn7+hAdNXbOW/b26WASUDXI4j3IiAC6boQyGTZTuobvIclKVyKDfLwchAl2U7yFk25m9oBAAcMrTMHdtyeKGZPpkEd3vlbCcwpyQFlOl4RBRroSHeiSjvT6t4A0DECA41Z2pQwqTZQyqDpczBxNpZRfGKwoQErOoJ6JQclB3CQRlWYESFA6WZ4HY1ByVTUOVidkygizBb0k6BozrPsoy/EIyUr5G6GaPBLrN0JHjsREh/dq5JxUEZVEm5Jw7K0DBNyf0Z3B4GksNCfkOBVch6hFV7TgcAZgnGkv2XCSmykoiaHN5IFcuTwa60sNyGYcWJWP9QQBnqLvWP3WW7BFGDYV4YuAxrpw4/es3ika4LzagOvKCw5wyB4GFOO7qdwipPp8P2amgex+3L78juP/oeB/hTK6jqSbGawmTwuoa5Oem6hvWXKmdTEBlyHwftg7AclKqEe5eAYQleB78HaWlpaWlpaWlpaWnJ0oByF5FtO1jnFbxpIpBxfV0bvvXXD/DDf83ixVRUR2Tei8/uyNm8OA3ggjW1OA5rZ0VlBnqAkpkwWYVoVuzCsh0sqW5GZ95GcSqG3fu5lbBzpIJ1STqmOCL9Vbkt20GHNxc3DyDp3+l3ULIx2MMoe4bc0uwvkAMAda2s+nZwWHVGKVTCHKTlBQmYJP/iNh4qLsLN87aNlqyXgzIZ9UO+EOhTHOKgLA0pshIUSgoAqVgwJAoqPBIGcQCgLB33jV0UEiKcCAmDFTkoFTgQUi2YXQPqoKTQJ7xYRnCYpprDUPQPvgaD4tV4bOh1ODSzgOTsDHYPRkxDgk1hVbN5VeBocHtY7sgwGEtdaQXJYGgXFg6dDoE49FpSGFOcDobmQc5HQDj2fKH+LDydwvGQ/JlSGHoPnK5hOQzDcpQWhlwz6gikpvSwolPsWoauR0AeREB2UIa5/sIclCpILo004pGhN6BvwxukfwiUD5kPddSrxakA+RqFwbme5DOVq3V3H/rdo9yiIePQNMrJkD3ZXX0bCdyG5P5U94SWlpaWlpaWlpaWlpAGlLuI6tqyPBSXPmDe9sYS/jMDeZublJBtrz+DcUx520Z71g8o8wE5KFmRnKAclJ+srwcAHDCopAsQKcKhaci2SSBfW1ZARxp+LOWgjLAQb9lB2V2Idz0BiwBxUDJAyQuMuO3VHqBlxV2Yi1IUyZEL9jDAUZCI+iFfSGi2KJLTdYi3GCc452FQSGosIopuhIVX0vFZtWc/QAyGR+Eh3k7gOQUVAAFIDsqQPIvhrkIlxDsA8oW5zOg1uLn8BhxXNAfPj7iS7w3VZSZVZA6AgiosDXNKhgHmIMeXHGIaDHHkuYTBvOBrlgiBvcUBUJvOR4WuYaHfPPxdyX/KFBRWrh43zEEZ7pwLzoXIihO5/UnIOfmZfnET5N4FBETsST5TKQelBCjD+ncPkmMRE/+v8ikcXzQbgxb+iIwT5loMHocqEeCgpOcXC4GlUgh9WA7KEMgXFqIfHuJNAWEwuMyT9WPthiGvX5iDMkj0utBzVd93tLS0tLS0tLS0tLSENKDcRVTbJMK2GSx0HAeTFtfydgYifSHeIZW2aVg1VVN7jkNB7qD0nv9YqHS5ByjztoOVXsj53lVFAixKuSZFxWvLtiXgyB742nM2d8Ek4xGSfzHYcckci0nFQcnOsZTktQNoDkolxJvnoJSr07K5M0Ap3JLuJF0HpduX5Zo0DffhWIV8RyQ+AN77FpLWNqm9NMStVhLiuCzgc+8+RLgwGQusgCzBHTJOaTruOUVlOBcGYJIhECcZ8fcFhOvMBxa9c6JAriAZBeZdBcy5NPCYQccNylkZlu+PApKBkQ3856IQp12S9KcV4oNcgrTdl8MwIAwdCC7iQgGJDKDCcg/2oHCMFOJN4A75wkNUuVddusEgkt9/PgelH9IWhYR40zQCdP3SIXNPh8DVMHdimIOSOiFzBHDxUHn1PguA4GFQjQJHOkyYK1Sae8g6RU0D/WMilQfePR3YOEFOa0DGMQmgCwOU6noC8nUJg6VSCD0NzabnJIWth0DUkC8mKNiWYH0IBKbrF3ROQDCgVO/dwGOSPup7r5aWlpaWlpaWlpaWkD+BoNbXUtQVyUBkR87mcAwQoGFzSIj3ei9EnPYPCvHe6FW2LkxEOTzI2zZsErbNqjLTit9VxUkBFm0buQ73uAWJKFo8d2feVovkMEekcHe6Id4CdNKclQxUMMdiOsRBWV6QQH2bGJNdp0KlOjQ7Hw4fFNBSXkCqTxMDalEyxkPSmVgeSxUs/sy+FNgIjOhMA/gxbxcFSeT+pZlgByVzgvlCkANCUmX3YLCrifanxVSipsH3WFhIOA/5VebODhvqoAzJ40iBXFECwMJbAADlA071HdMdn0IDI7DyshziHZJLzxCLmlFy43V6e6Y795m6HgwMxyXIIqq4+92G/r0XFmIaVsU7LLw5DCrR9vZs3tce8dIasPuMOyiVuQtwGeysDHNK0v5hDkrZFRoMXWMh4FnaByGh5XTP2QF1x3wh3gHnFDa2HOLth9qA4qAMSUdA19VSi6Nteg3Y9BpikY95E90fFNqluotvJgorHpQIgXaSg5K8NiyUOxUCHMPge3iRHHquXReOA+Rq6kyJWDBwpHuDvpdpB6WWlpaWlpaWlpZWuPTX+buIqIMyLGSbOyiVEG/28LZOAZSug9KFEz85Yih/+NpQ7wLKyuIkD8G2HaCpI8cf5MsywkFZ482tsjjFH1Bzls3DsF0QKdqlIjkezGFFb2IRA7GIyR9c87bioAyBc6y5I+c+lJ+2fxVGVGSgqkiBgk0dcoi3Oj4LZQ9y/vkqSYdATqZkdqP0eyakf1BVYHbMoP5BbrV0rPu8cHScsoDq3oAcXkkdWay4hgoi2fDq3LmDUoU+HpSgoKCQWJ2KLeFwDHNSFXhgWJ17WLirBIkMAZnDIFEYoGTHDAMYoaHAZOxfVryAvsuuAGxLDsGWQkzJuYa4Qouj7cDU7wBrnkUsYqAs0oiHh9yEffF+YH+6rtRFTcNiJdcpr4zcvVs0GTP5NQl1SnrtBmyckrsDWHCzO04IgApzwIaBZ8lNHJLbMN5NgZieVG0vCHEJSmkBwkK8Q5yPPQljppLHEeNLuSa7OVeqsJBm+f4LAf5hrtBQcEnuexKKT0+Vjk8ZbZoAzVzItelONPctVVgxHPX9TktLS0tLS0tLS0tLSAPKXUS1AQ7Kpo4QQKmEeLP2YEDpFaaJi+rTDIZWFCakB9Q6L/9iOh7hD5kWcVD2L07yB1fX+eiCxaJkTABHywkEjmpOSfaQns07vHJ2WSbue0BkD5Jq7YJ+RQlMvnwMjh5ZLrUXKZCPwVse4k0evItTMVJ9mjiJoiaSMX+17rDcg0ymIxfwSRK3GlWC53cMDgVWXYhBId6h1YJDwmbDXGwUDlDgwUO81bmbCG6PBvdnQCVJQEFhgkALu1ock7qaepSXMQzSBhvPCxLB1ybVTfI6CvWk0OwwkOW1m7Dwh/5PILbib8CqR5Qcn9271eg1GJWYD2z4L7DkbkRNE1dV/QsnFM/Cmc2/CpwPXVfVCczHDwCaYQ7KMEhGIWBh0n8NTiqagQNaHwfmXwtYnaEhvJLbsAdFU/LEUUerTEtVvLutYK3CdwHl2ZLT/JY0LDk8B2Uw5JPyZ4aA5JzlIMgnGDHong8O3VfzMXaleMgeluA7OVfZWUmvwfaGeNP3GgEc6b2QC8gVqrZvj8LAbVi7LpKjpaWlpaWlpaWlFS4NKHcR1TbTHJTdOCibVQelF+LtOSNpf1YkJ02AGwORJekYd1ACooJ1CalgnbVsHlZdWZzkD3AdOYuHj7sg0nNQ2iJkm1bCbvYclAyssQfdrS2dHCL2KYiHVgtWjS0l6ZgvnyI7JhCeM48+gLL8k4D8cM6rPatVtpPBzkcm05HXi0E5vwsxOBw6qNiOaQSHT1MQEg9pD6v+G5qr0PIDSlOpbB2PsCI5Suh3SJEcBriog7IgTuCjJQBlKgTuFIaE2YYBx3SIOyqsyEqYy0rVacVT8cDAm4Fsg2+MoMI/BRFyPy66EwfW34ErKx8DEF4YJKyAUDrihWnnmxGNGBgY2+KbX1iV4qBCWUBwZeSwHJQy0A0DVmq7g9/2e9ZrcYBcc7iDMqw6dMhxO/LinJKhYbxdr6t6X1LIx65DQSICLP87MP1HiCI4pJq+HfToi4OQc81ZNuKGCMen7Ux0f+R7EPYcJLpm9GfZKSx/YSP6B7/XMLRqwEbpp78EltzjjRP8RQOFq/S4NKUJvU47eq5hId5hDkqWk1lLS0tLS0tLS0tLyy+dg3IXUW2jAJQ2A5RtalVuBx05izgXo2jqyPOHvaAclEEOSgYoi1MxyZ2zrcVrT8tORsdxIVhZOs6L6ND8jwXEKZm3bDS1i/mpId4cFCrVtEvSrptRhQapEAdlWCXssDDpgoBw64oCAijJAXihnZBiJ2Eh3oYKKGPBYdIC5qkOTb+bk4Y3h1URlnPjBbsNw/LFhRVTkWBFxOTgIMxBySCpmouOAXAKiwoSBuBt93SeOChDqmmHhdn2JMSbqic5D7vSX4fc4f6w+klgj9/IuQoDHJRFZot4ccsK7I0V2Lsv8NCWs5CMVfE/xUPcohJIjnjgJt+KqGkgYvihY1i4bluIg5KGFDOYHpZrMgy2hTlXYxETY4s+xN6p1eSATYhFxD0nuRBDKl5TT2EqBLpSsLg9Yc9duQ7j3p4vSEaB2a5LNV1+MoBSb2w6d/G6dNj9R/YePSp1FucsG0URsmc8dRJoR88pb++YqzAsB6WcviAk9DsUrrrX/fCC+YivfRxYC2DPy6T3FHq/WkFJQRHulPy8Q7xH9C2Qfn/iZ4dizdZWHDSkbIeOo6WlpaWlpaWlpbUrSDsodxEFOSj9Id42zz+ZiJq82Ipl27BsBxs9ByV70LRsmwPKdFwUrGFOSepwdNvdsUtSbvXtYfGNeHjITTggtRT9ihMwTSPQIefmlDS9Y4pCO8VkfLUYB4Nwmxrc8+7ruRlV8JXqwkEJ+OFfmIMyKDxbCnsm51WUCq6+HRaCzeRzUIaFSUf90AeguSZFOw1fDQORoXnhpJDfHhTpCAEeNCSVDR9RYAYDkbIbUIydDMlBKQFKCZwQaBfqoOy+gAlVJiQkNSwHJVV/6liMpr1jdu3yLI60Bo4VNSx8Gw8A86935xLioEwaWTww+HZ8t3QykhEPyOVbEY2YUm5Ncdzg3KJq7RUmyojY9VaBFS/8IznqwvYYcfVGTVzS91lIyjXJTtceODHlquri2lBoR0XBV1gF5yCpYJatIQ3xjmZrA/vTl2ZC7r+w3JF0r+YtByUBgJK6CilUzYdAvu50dPJ94P2zgGwjoqaB75RMwV8H346kKd6/6HHoXpIriotzYmufMeX0I3TudF3DAGU2FFDu2Lmq7tonf34ofjFmOM45ZJDUPmb3Cpx/xNAdOoaWlpaWlpaWlpbWriLtoNxFFJSDUg3xzlsOzz/Zt0jkj8xZDqob25G1bERNAwNLU1izrQ15y0F7jjkXI7x/PXVQEtCktj889CbsltyAE4pn4ftN0wF0UdglwuZi80I2xakYd2sypZRcd8xZycKt1dDhMAdlKXdQiv6GIYqM+HMh+sOtw3LgiVBreS5hRW/48R05PDMR4qCMB4RDG4Z4mI6GhDfLRXK6D/GW8zV2D5V6EkYZ5KCkwCosTybtk0mQPjkCKKW5d+1OdMcJdhumQ3JQ0v7UxtYTQHloZoH4xUx6c+k6hL4wBFBWxrbhOPsxYAGAym8iGtmL/42GrY80ZuP0kvexR3ItFkV+DlgA8q2IxQ1E0LWDcnuqOgO0MnmwQ466ieMhFZCl+8luxQHp5QAAK1aGSK7OBZSRStK/+4rlFHDRY4Xl1aSVsM2Q+zRIKsxk+4xkgZByzNL9FhriHXJt8l24PEsizfLEDDMUxu5o2PMfMlcB6wGkBsI4+D7cPdgNx3aMZwEc5utPXZ5h7zV79S/ExWNGYHS8GmAc13HQmQ92uobNPcwpuaNu0YzyXnD0yAocPbJih8bS0tLS0tLS0tLS2tWlHZRfQ725oBqXPP0xh3N5kucRIA7Kdhl4WY7D80/2LUzyh+i85WDu+gYAwB6VhRwghBXJ4TkoU3EpxJsBUbcqt4FhiU38b5XFDMrIW1INqc6HOCiZgnJBAiLcOsxBqQJK5n6k4xQmohxKqKAzE+B+TIXAvO4clD0tklO46Wlg2g8QN2RXUTwgxDsViwRWjaaVkXtSJCcVUtAiPK+f93P9PNzd/0aMSKz3nRd1LzF+FlaMJKyYCgUxGeKgTOY2AV4YLwUh0rpSd2JIkZwiowF/H3ILjiuc3UWId3D+viCYpzrqRlNA6TllY6EQ1X1tUQigLIs0il8W3Cy7RYljrw/c+y9ldiLBHJR2J2KGE+ygpCHe5Fr+7sTdAQCXjd09cD5UdG9LBYEkF23w/pf3mJifkxro/pBrkp2SieC9KkH2vLuuY4tmScdqzynAqnER8O7p2Cu2OHBufLwQaKlWL2fnWBITeUQj5P4Oc1CmQ5zN0t6zgx2Rl40d4d8z8bIuXIU7Bu24WldLv1ZgXWC3hOSgDHbpGoaBK0/ZE8ft2V+80MlLgJkqzP0Z5pTcXgfl1afuhfKCOK7/1j7b9TotLS0tLS0tLS0trXBpQPk11MVPfYzX5lfjyRlrAQBbW7JSGKbdRZGczU0iJJq7Fm0bH62pBwAcPKQUpiFgIS+SE49wGMlCuYtTbpEc9ozMgGgm4QLKiCEeLku9kGo/oGSg0G3vyFkcvJak4z4XohrizcQclP6wZ3+IdyxiBFbILggpMMLOCZDDlVMhudUYEKPXho4fkdpJJV0FUKbm/BxY9zwGbX1Cmhebc1jRm7Dw5kgIoJRDvINdhRmpIIkCshwHeOMAnFA0Hf/X7xmoooCEvVRykIUU16BzpK64grgY23Q6uWuM9gkrriHn5xT9v9l5D04pno7Hht0QCBxtx5DCb2n+xaBcheo+3yO5lgzm3pcmWcugOYYBypIoCeOtnYJ4iNO1DK67NGl0IkGAXyrSId2b4rjB++PXx+2G9644Fr89frfA+VDRvUfXkl6PsOrYowaW8J8LoiSfabKP+4Ma4h2Sc5TeC2Oz9+OU4un419CbpGN1qg7Kjy4BNr2Gfw/8beDcxHkEA0oVuvIw/aj4csHMN4n+1EVKhkyEuEvVUO4gDSrIwTSUv8VLtxvyhem3x4+U1y4v5yzOGLJ7c+xefVFekMCxe/TlbXSPBX5RY5A2qxPfPmAAStIxnD6qSupmh+Qd+MzQ1dMFRw/H7KvGYjcl16SWlpaWlpaWlpaW1o5LA8qvsVj4W22T7LALy0GZt6mDMsFdgpblYM5aF1AeNLSMP0RSB2U6HuFhmjQEGxAwiAHRTCLiA4vc9RcW4q24MwG32IxpAicXfYC/Dr4dabOdw4eYAhArQnJQJgNCvFMhICsZEpIKBId4hzkouWOv4VM8NOQWjEy4cEpyfHnXJ2GQvG22cLxSgJTpXMV/DnMbyo7I7QvxDitcEe64JC6oeASonsh/d5yuw2IZmI1IDjIKtYJdfGEOSgAojLR5c6TXhoJI4SoMy4FXbG3kPwc5KLNODOmQKsJBxVJU+CLlBrT9TrogOF5oBgPKYhrG69hScSU6TqnjOigTZg5xk+TyMzsDi+RQ0WtvGAaG9Ml0WRSGKRYxcWzhbNwz6C98XQA156h8bT66eize/n9juMsaAEwvBN0xIjDixW5jrim0snwCOdw36E58r3SStFeL7JrAeR48tNQdg/Vt30T+Gg7uwtzPKnQtTEbxvdJJOHjN+aKxQ+QhpeNIX570wE0cGq6crfO3GdEe5dvsiS4/YXcsuP4kMkC79Pc0mqTfHz7vYMz44zelPUmvE3V3kwmLH+1OlGbi+PBPY3H/2QdIvX5+1DAAwGn795faw2BsVbE/rQJVkDHW2PQ6MPFIYOq3gWxD4Ouw8lFg1kVAvj3471paWlpaWlpaWlpaXDoH5ddMbVkBsfoWug9dKqBkxUp8DkqLAMqiJGKm+0DZ3JnDomr354OHlOIRUrCGh3jHohJIAkShGddx6XAgmklEfQ987ME0poBL9pDKHthZKF5BIuoW8zBNPDT0VgDAys6B2BS7EkC4gzIsByV1FslgkcC2kFyIiajJjyeHKIa4FhlMmHslTiqajpOKpmPo/FclKMiqCycM4pokDsqBSRHGG7WCnVdSjjpSxCYWBEsRDjTDwmPDAElMhYg1k/jvTVYGPRGdu1zxm4Ascq40B2VC4YdFHsgLWz+pSE5IFW84AtglAyr35pyIWFd07z5TgVVxlEBFW9yXsYiJjpwtzTHWnYNSyTOYy4n7n55Tse0BSqMTceqgNNoRDchBScNgk/FgENedYhETjw+7wf05ngZwuq+P6jgtL0igvCAhd/Kgq2FEgViRN8EmxNLBMH33pn9j/9L3cEbpe1gVu5kMFAysrjp1bwwqTQtnXmYo0LQUADAisQErOwcFvi4sPF1tv+Fb++CgD44BqMmwMxhQUh5eTuzBYcWr9idOU0mdAYDSySOb7xpGhykI2kn7WgGUKcj70jAMHxBMRCN48NwDkbNsXqRNni9ZL6vTf0xP+w4oxrzrTkRRUv6Is3dVEZbUNPv6P/bTQ3HHm0twaUiagsDQ/SX3Alvd3MnY9AYw9Bx/n1k/d/8fzQAH3RM4tpaWlpaWlpaWlpaWK+2g/JqJVa0GxENrbXOn1CesSA7NQVlBQrxXb2mFZTsoTsVQVZKS8kG2Z0WRHPUhrlipeM0AZUEi6nNbxSOi8vS+qRX4x5CbMSy+0VckJ2xswM29x6CEPwelC2tVByULWe6Jg5LmSgsN/Q7J1xgJAmI5ARkPy3wqARUGgxK08q3VDsDBqcXv4/lhl4rjEAelnNev+3yYknswpBBMGAiJqBWTW9cDU7+DfWPz5OOS84yZct7TMEVCAKUMbgigpHnsTBk6MZAXFuIdBmnpuoIAu6DiKDknJjlguwsllYCV48hQkQBK1o+CRXY9wgGlXKk5nxMUjF6/EgYozby0z+IIdlDSsPXtqWBNRdfywMQngX18wGnJPcDsX8nlwpmb2IxJgFLKIUr2cGl2If9ZKp6jhjx7Kk7F8JvjR2JouQfUs/K9qmpoH7fy+kn7Vvr+FnROBw0p83fq3ILj9qhAv6IEjtytD5kj8NGfjsO8a08MLRhF98dhw/vgkfMPxju/O1YeP8hB6Vj4yZGu23DsXv0C567q4PRC/HPIzRiWqO26o9UmrVnaaeyis9C4/frjjAMGBP+RuIFhdwb3ybUA076P4k3/hjHt+8CGV/mfrj1tb1xw1DC88X9HSy/Zo7IQj/zkEOw3sDhwyEBnbJ7cs53bgufCtPReDlS1tLS0tLS0tLS0tIKlHZRfM1U3CtcKA5G1jSEh3l3koOxXJIrkVHuvFy5EBihttOVEiLcatl2kQEQe4h1QBZkWdnllt8tgGg52S2zAs8m3eDtVEKC0YHIQpzoxyzw3jgpRg6p4J0PAYpgDj4KpsLDnmBRS7J2/Ia7D4QXzkYqTcE9PCUM81BpOHgkjh78NuV0+h/ZlMGHBRiQ0xDvdgxDvnvTv0lk56wKgZiKuT/8Xj+M1AF6hi5xweMYMec+Fibpx5RyUsnM16OeYwhKKPGAnAUql+FHQOVHoYzhdu8yyTlRa47A8gKOHlWHW6jr8cPRg0ZhvRpTmfCQQhs2zMOG/Z8KqeBcrgHJUVRKGAYyoIPnyrA5knK381wSx8plWa2CRnO7C1nskEuraL1LrAizflxXKAn58ufv/wd8H+h3r/swgruKgpMZVel8mcyKUm+7hYX2SgLgM4SLuxt2S/kJPz/3icExcVIuzDgwGa2F5NSV1bMGjPzkEedvxAbHiVAyxmBzyLOeAlV29xwfBxiBAaefxo9GD8Y1BJRjZr2f5FF/c7Q8AgE4kAfwsvGO+HXDEFxKpHgLKLkXgfSjwW3QrsO4F9z8AWP8i8EN3Y5Sk47j6tL23+7Bm0H7Pk/sv8Noq91DTEqB01HYfW0tLS0tLS0tLS2tX0U53UP7tb3/D0KFDkUwmMXr0aHz44YehfXO5HG688UaMGDECyWQSo0aNwptvvin1uf7662EYhvTfnnvu+UWfRq/RpoYAQOkL8ZYBJXu4zdsOtkg5KN2Hshrv9Sy8kEGitk6LG2RS8YjkLDMNAVW4g5IUyVHFwJBhGDzcekRyA3f4qSHban5LALAdk4dJh+ayjBi4uOJFXNP/YT5vNl+mVNwr7DL7Vzg89xRvT0oOypACMdS1GAI6K51VwNQzgc3v8baEkZP683ZTBnoFZpuvj2l3YEDcBSgU7qhVvJkohCvqQZEcI9+O+wbdiW+VvCe7z6TCI1GgeblvbqlYBMgJp1Hc6N5BaVRPgPnB93guxTA3JYU+FJiVpuXrKByUwe5SuVBQAEgGpBBv0SZoWM7x4FHjYuD9s3Bq4WQ8NOTP2C0hVy5+5CeHYPwFo/GLY4aLRjX0VgnxBrx7ZvlDLgR2bJSmY6EOSilcHEBh1MKC60/Cm9Q11irPK0ljja1WRAJCnz+XAiNt4rgRw3arYwPA6qdw36A7kTCy4TCvTeQB5eCLOiiXPYA+G/7Gu9CUCclctWiPiD2opgNAezXw/neB2nfk9k5BMaXw960fAlPPRD9jI3582BDp/qDiwHHNs8C0syVoL46xxQt77uaf5Y9/Byy4GZYTnJYiVGyfDToLGP2o+7OTh9G2Afuu+hkS26ZJ3Z/42aEoL4jjX+cdLBrJ3kya3XzZYLUDlvi3J4FW2QXbnT69CVh4q9xm98BB2byy58foRj85YigA4A8nBYR+0yJAQYAyL9+HNLeslpaWlpaWlpaWlpZfOxVQPvfcc7j88stx3XXX4eOPP8aoUaNw0kknYfPmzYH9r776avzjH//AAw88gEWLFuHiiy/GmWeeiU8+kUMF99lnH1RXV/P/pk2bFjje11E0xJs5nliINw3NBoCmDlENG3Ar19a1uQ9RFYUJDnFqGhmglB2UtMhOOh6VgFiRV8GbHpc5KAsCAGVQpWNAwCO16A3LbxnqoFSL2DBYagBX9n8cP6/4H/ZMreP9fCHem98Flv8dJ3Xextt7VgXaCOkvzu+g9RcBG16W5hcz8oHFV5Km/BBeEPEDSgCoirmAUg4BDStuQx2UtEBMcCj3bk1P4ozS93D/4DtDK5OHAZJkLAKQ6sSxHgDK6LRvA+tfwrVV/wQguykpiFSddvedfQCuPnUvDPfCbZlYMZaerB/bZ6YhA03DCYBzBJBkbe86vn0CsP4/+MvAu3By8Qw8Nfxq6SUFiSiO3K1cBu4q3CAQZlBpGoYBDO2TAWb/Elj5CLDxdUz7wzdx/AiWo0+5NxQHJewOZLycrVyta6QuCQjYaVptMAOqeIe5QruTdOu2yMdFkwcoZ/wYZ5S+hx/3eU0GlBRo5RrEz+wamcRBCWDo2ms42Obn6zhI5QWgjNkE7KrgedZFwPqXgCnfFG1WVkpTEKV7+L1x7r387inoSnyvTj8HWPccMO8afyerXXblBallDbDkLmD+NVLofhgYlcT2WaIP0MeDjo4FbPgvsP4/wJK7pe5jdq/A7KvGYuzexI1ZJ/6tXZ8LCcNmstokQCnNoTu11wCfXgvM+5MM0ynkC3NQWp9fQZrrTt8b0/5wHM4+ZGDAcchaBeX3VCG03bP0FlpfHVmWhWuuuQbDhg1DKpXCiBEjcNNNN8Eh71uO4+Daa69F//79kUqlMHbsWCxf7v8yT0tLS0tLS0tLaycDyrvvvhsXXnghfvrTn2LvvffGQw89hHQ6jUcffTSw/5NPPok//elPGDduHIYPH45f/vKXGDduHO666y6pXzQaRWVlJf+vvLz8yzidXiHZQWlLbf2K3DyMtu3AcRy0evkjmRuxqSPPeUBRMsbhnQooGdxp9gBnPGoiYhqSg5KNCfjD4zI+21J4COQQ+xPg/bMQ75TDKgNDvJ2IyEGphpuv/Rvw4cUSICuKWRx4mSqgbPdX9w0L/U4HVFgGlDBp2t6xzDd21MgLyLd5Kh4cfAsqY1ulKt5AOKBkOQN7FuIdAldD5p7MiS8MwtyMQWsKeOA51zNAqYbfH5JeFNjOpO6ZMw4YgAuOHu6DTkWRFvyx/6OoXHcnb4tGTMSNHG6o+juqtj0ttbvnI+dJNRAwbwJCso53Hds3Sl0qY3Wuy+79s/zOrupJwPvf48VXuOwcsOZp4INz8Y9z98Gky8ZgMIWu2XpkElHELM+hlZIrFatFcnyQCABa10q/xgm0i9htskuwbRPw/nexu/mxf5weSAKjbfJxJRcagOGJTQqgJPMgkJA7KI0YEBWAEgAOK1iI3fqScOW2DYg65BpIzjYFxDb680tS9yQAxGj4O8s9GOAeBoBjC2fjr4NvR3FUgcbEPR16rEW3w5x7hfx3spZpy/1SwoCNyEe/BJY9KPfNtwEfnOvuJUDAwXiZSC/h5MX9qUBrICCUn8y7LN1NmL/ioATgOlR7IjqXzVPFz5KDMsSRGLTfd1CGYWBgfi4i03+AlK18cdqdg1IFlE43jlOtr5xuv/12/P3vf8df//pXLF68GLfffjvuuOMOPPDAA7zPHXfcgfvvvx8PPfQQZs2ahUwmg5NOOgkdHZ/fPtXS0tLS0tLS+rpop+WgzGazmDNnDv74xz/yNtM0MXbsWMyYMSPwNZ2dnUgmk1JbKpXyOSSXL1+OqqoqJJNJHH744bj11lsxePBghKmzsxOdncKN0dTkPljkcjnkcl/cQwUb+/M8xsYG8dCUzVuorm/Bis3uw/G+VYXY2NCObD6PprYOAiNduNTQ6n5gjkUMGI7F6XVzpwsDytJR5HI57tdqbHOvWToWQS6Xk1yIhYkoPy+VLyUi7jnTjGomHN6ftduOgbEbzgIAZFo2AhCuo4KEe0zHErDAgom46V1PAjZiEQPRuW4eu76DvyHao1G+xnTuiaiBfGej7+aIR8RamSDFH2Jm4LnGTHFOdHzbiMN05IfrmJFHzHD7xyaPwbgSF4Q8uvVbUr8iJcTbMSIwHIsDpVjEEPuJ5EBLRM3A9lRMnJNBYE3MEO15W7TTvWqRax8z3LrjKrLI5/OIZpt4OwOUQXs+rrheB8ZdIGAawf2jZsi9k++U9taQeDXOLH0XWPkf5Pa60AV6toXL+o3H+eWvA4tfR27fi9xr4O2bTDwijU2LqfD2jiaxV2H69jTXxNEAAKdpGfInCsgXe+dEt339f6TrZuU7EZl+rjuP0oMwZORvpLHzVg5OLodotgEGADtZCbN9E3+9Cijz2VY4ynUym1eBIuWoLV5jWi1u+DU7t1kXwqyegFsKX8LTXm7R7XnPipliT5pN8nGtzibY5NwKI62IGuK+Qb6N/83qqIPN9mq2HVEAjhGFZaale/X+bzbCHnWY2Nf1i6S/59q3AQnXERexxftcLpdDNNfK14LPoXWTtK5RsoejsRIYnrMz6JqwiuW7FTyBXG4MH8dpWuq7VwAg31INJ14FOA5ic69EBEBBajdxLh31/FxGZhpw9iEDcWTmY2DFP9w5DLuQj2XOvw6RtU8Da59GbsD3EOnYChOAFS2GbdmIAXDsPOzORkQAOK1rke9mXSP18/j1Om63Iv85O5a4Vo6NXLZFunb5lg1wMnt0eQwAMJpW8vO0a96BNfAH7jnlO/j+CdrXABDJt/u+ef0s/8bGJh0JE8CB5jLkcu59CcdBNC/2it25DZZyDKO9Ttp3+Wx74Hy/aH2Rn2F2dU2fPh1nnHEGTj31VADA0KFD8cwzz/BURY7j4N5778XVV1+NM844AwDw73//G/369cPLL7+Ms88+e6fNXUtLS0tLS0urN2qnAcqtW7fCsiz06ycn8+/Xrx+WLFkS+JqTTjoJd999N4455hiMGDECU6ZMwX/+8x8JlIwePRqPP/449thjD1RXV+OGG27A0UcfjQULFqCwsDBw3FtvvRU33HCDr33ixIlIp9MBr/h8NWnSpO1+Tc4GtnQAVcr01lRHwDDRpwsWYs3ShQAiGJB20F5XA8DE0mUr8ErTMrDlb23YBsDE/MUrAJiIGTYmTJiA2hoT1GRbvXopJrQuwdYtbvviFWvcv1tZTJgwAY314tjtzY2YMGECACDbKdoB4MMPpmJ5AjiDzHvh/HmIb5oLQG5nitR/JP1es24lJkxYgS3tAHvctRwTiz51x6luAz+/QgL1mle9x3d9Pp/jc6Quva211Vj86Wzsp8yhesN6TJjgOsDWrjWRMrK4beADWNF0OCZMcJ1z1RvFNZszawY2e8WDnS2b8eDgx/HQlu8i58SRgAwo40YeH05/H6uS4vz7x7ciacr9KmLCqfNu8i6Myv4dpc4KDpSOzo3Hxv/egHnxi7G5w+TXoHrdGkyY4Fb73tYhrs282TOx1YuyXdJgAN7j/4fT38fqlNueqqsHvJ8nvP46L2qydI041/emTMLJHa3IKNdswoQJOKVtG1gwctzIYewAm193V+5cDKWoBMtF2tzUGNh//ZrVmDDBn2+uyFqF48jv5dEG/vPcSQ9gU+RIDG/9B37ZV+SvZeOvaHTHT1v1qPnPWNRED8Wm6FE4BuILDHYN0nY1TmBzhXtOQXuXyWhcgJmv/gV7Zp9B3kiB+R4NxcW3dvVysAyVqxZOw6LlIwCIffHpvHlYt2gCTmndgjiAzU0maO1otUjOhzPeQ//87Wg2B2F1zH2QPrBjBgaRPp1NNfwWba2vQbRIrEV7zUeB69q93HVyrDzvrx63cd5f0TH/aVR5vxdFWtFZvRwTJrgu46jTglO9v61f+SnmbXDH6WMtxFEAWto6MW/OIhxFxkysfACfrm/FqpgL9wfnJuMb5O8z35+Iuoh7vx7Vvg2sXvaECRNwaraJ/6PI5lxhzcMR9Kw8B+WECRMwxipDCRoAAG++/l/YRkK6AmzNStoWYcLrr/LfDTvYPfXhB2+h0H4YhfZ6DGXHc9r5vxHl1jwc6bV/MmMCDo8egcrWRfz1EyZMgOFY2D/7EIbmJ0ntozuWohLA/CUbsHX5NJwAwMp1Yt3KRRgOwMg1YuLrzyNvhBfLOaxjEdi/2A01q/Gusg8iTidOI79/8M6bOJb8PnfWJESdV1FqL8Pc+C8BI9h5vVt2Ivbxfm5b8yambHaPMzw3j78vz541DZuj/ut4THsNSpW2nu3XYLE1yzg1mOitg+lkcTr5kqq1bj3eVo7RN/8xDie/fzhrBrYEzPeLVltbsOte67PriCOOwD//+U8sW7YMu+++O+bNm4dp06bh7rvddAmrV69GTU0Nxo4dy19TXFyM0aNHY8aMGYGAcmd9Yf5Z9UV84a712aTXpPdJr0nvkl6P7ZS9HYG3O3hNd+U1Mbfj8vZUn8dl/LzWZHte/5Wq4n3ffffhwgsvxJ577gnDMDBixAj89Kc/lULCTzlF5ALbf//9MXr0aAwZMgTPP/88fv7znweO+8c//hGXX345/72pqQmDBg3CiSeeiKKiosDXfB7K5XKYNGkSTjjhBF+F1u5025tL8ci8tbj1zH3wXVI59pYF7wEeTNl9zz2xfHMrgE0Yd+Aw5G0HU2vWYtjwETj84AHAnGnIxCMYWNUPn9bXoqz/QKBmE0oyKYwbdwym/ncBPtoqnFnHHX4wjtujAm82zcP8ulpY6TIADRjStwTjxo3GMzWzsbK5HgBQ2bcc48YdBAC4a+n7qOsU4bCnn3yCW+H7BXE+ow85CGP36uv+QtqZTMjw6pBR+2LcoYOwob4dmOy22TBx1GGH4JiR5Vi9tRW3zfsAADC0SEC+3StygDtFlBRkMG7cOORyOXz09GTeZ7dhg7H3iMGABxcN2HBgYs+RwzHuRLdYwpJJy3GQfR3OKH0PwHvIjXOPMevVRZixeQMAYOxxYzCiwkU7Bz+/NwYYK3Bi8UyYiQqgU4ZIUSOPU0443q2U7p1/u53wAcpTiqcDcGHskaddgsjbzwN1Kzg0+W2fB4E8MPDw32B94hj8ee77AIB999od4451sVd1Ywdu/MQNmzx57LEYVOpS7rJVdfj7YhcEn3Li8ejrVW3/5NVXAO+5etxJY4BoAT9XVLvn+q3TxiH6ehpQnoXHnXIKoi+1gz3L79c/hb+deJKUDuD/ZkwEAKRTCQBtcCIpGF74dJHZgj5lAzFu3KG+/nvtsRvGfXM3+FT/Cd8TANAnKkKDDxzYjAOGD0JskoCTjhHFuHHjALiFYOomLMXZfSZh0MapGGRNRe5btyD65u+BZnZOJwBmHGhcALhTgWnY7hgBe5fqyIp5MNcHhBETDR1UCax2fx4+fASG7u/OjY293/77Yt+hpyD6UhvgAH0H7w+sFgA/bsrh6KOHtiOyxD3fvb71V8AwEHnnTql6dXHaALxbtLQwxlMGAEA6YfL1Z2LXqyuxdUolExg37lgAQOS9+4HNQIuVQkGkHWW2nO5gv3ILh59+sggt7twCvOL+OLiyCAMOd49r1CaBqUBBYTFGH3oCMFnO9blvfjz2/NaDgGHCXPAhsFj87fCD94HT3x0nMuUWoE6cU/QFcb+xczTWNQGzxOuZC3jcuHGIvHsPsMUF/ycfORwoUao0e2vWt6Ic4w47Cvhf19fs0AN2Q3Tm9VKbAYf/G2FszALuWwAO2rsK9m7jYKxvBWaKORs1kxB9X/7Sa9y4cYi8fRuwDdjv4DFwSg8EXgciJjB0YDmwxu134pF7+M+BKDL5Rv7+OaiqL6pGK/sg2yCd41GH7A68L37/xl4DEfHc7AMOu4Svgyrz4zcA77uHAmcTxp08FjDjMJcuBea77YcctD+cAf7XR9+6ClCiq3uyX0PlraEDU/xb3bmN70sAKIhnfccw1rfxdQGAQw8+AE7VZ5jHDooBLq3PX1deeSWampqw5557IhKJwLIs/PnPf8a557pO25oaN1VM0Bfx7G+qdvYX5p9VO/KFu9YXK70mvU96TXqX9Hr0VOGfD31au+NfDAO75pqM2o7L21N9hu/nffqsa7I9X5jvNEBZXl6OSCSC2tpaqb22thaVlZWBr6moqMDLL7+Mjo4ObNu2DVVVVbjyyisxfPjwwP4AUFJSgt133x0rVqwI7ZNIJJBIJHztsVhsu8HhjmhHjvPIB66T74//XYhzRg8F4IYT1bcROm2YWLjJfTg5YrcKzFjl5ktzDAOdFnB+n1exd0ENpkWuBQA0d3jhrYkoYrEY4lF5e1SWpN25eoVYlta6kG3vfgnEProARyd2w0wcCMDN18jOyVeBO5P0tSUT/mvgwACIS4WqtCCJWCyGZELAGMsxUZCMIxaLIZWI8/b+SZFfr7BjIf85Ewc/Jp1OJhFDxBFANWpYyDkm0mSOsVgU+6aEe4+102tWmE7w9gHGCm8sG07e/8B4UGYJKj79CWIHiTyJ7XYCBVFvPQ0TcGwOKNudNAricSDizV8BuFHkkUyI61mQFHOPRsU1KytI8XYzItxMdO77V2WAVd55Ou1AzPUn5YnxLxaLcWclVczMS+H2mZgNkLWh4oVEiKtqt+R6RCODA++PZDzkvlFCxQelW/k2imx9HxEFahhGhKwr8Ofv7A+sWQB46SRjnRsBUiQnNv17wF6/A2LCkR0xnB7dw2ZQLku1TyfJ+bn234hktwCjH+Ztbq7Qdn5dzcwgdQhJEVvs5RhagblXA1s/kPpELRHiXVVgI9Yu1swIKDoinWvDQuDT64H9bwCK9/b3jZiiv5eHr8EqQEHEP26ZUQPEyf7Iifvf3PAizPkDgQPv5sZuIxJHLFks+h/9X+D9M2HYnYhZdW44f8cG+VztNneh3QkFnxP9PV8vv96Dt7FYTCqUFJt0CHDGWiDjTydiGg5Mp5sCOACilv+9IYJO8W+ELf5xj2Q3IxKLARFS5CkaBfJKPsSId4/n3POIpiqAmGuJNpw8DLo/OjcBsYPl19fPAxbcBOx/k5Qj03SyMOumAyseAgZ8yy0WtN918vnk5PydkZyYWxR5sg6KSLV3AIihA4hlAALOo7CCX5/d5mva7n/H6z4BFt4CjLqFNzkwEWfrkFXCubN17rWn74GO/EEsajrh5/sF6sv4DLOr6vnnn8f48ePx9NNPY5999sHcuXNx6aWXoqqqCueff/4OjbmzvjD/rPosX7hrfTHSa9L7pNekd0mvx3Zq4a0977vPH7vvE6BdeU1u3Y7L21P9cceWQdLntSbb84X5TgOU8XgcBx10EKZMmYJvf/vbAADbtjFlyhRccsklXb42mUxiwIAByOVyeOmll/D9738/tG9LSwtWrlyJH//4x5/n9HuVLNtBxDTQmrWQtQRIsSwHrZ3uA11pJs7DmPOWg7ashRsGuHnLtpmn4FUM5FW5WcXrqJI8MqxIzmmZCcDqf+PXceBOL0cdLXRBqzAnY6YPTgJAIqBNVcQ0YHkVyAuTrCq3XCSHVbOlFaYrE+KGyLQLK1U6SuAHOU4qFgFywuEYgYUcYkqFbAP9Yv4HYVoQiFa8pgoCPoPitcDGl4B2UUSkw04gHfEehPuPcwuw1LtVdDuRQgHAi11EDNt94OeTTkmFgmiV3wz9mRTJoXsnTeaeNMiDdq4J8AJys3k5NJnCnijyyCMaUMk2pLAFvD3jOFLxieGJjdgYAD4BYEBJKnggpUhOoVEvpta4CGhxwfJKZ3+MMOaLgitUJvnCYvN78pjVb7j/jRUFQwaX+r/gCJ5bQDVwpniZW2yD5JNEx2Zg9RNA5fF0EFGUI5IC4mpAqyIC0bD+P8ByUUxlQ7YCA+NbYFpivx9UFePuNQDdFx2ZMsZ1lG2dAZwpYOCp+/fH6/Or8ctjR5C5uOvfZBUA2OIfq6PWPV4kKfXnWnovsPuvRcERIwqkqsTfB5wOpAe7gKtljQsolYJA0p6k6xq2NlkPUEYzQL5VLvSkhmqvfQbY+w/+MRxLLvITpo6tvqYo+bJEmntQwRnH8rdHPdcVqzQdLxNfAjh56b0uqFAOphznXoPGha6jlSnfDEw51v157bPenOQiUehQXGKtq8XPBPD7pBZTyjW51celIjkBVbwdx1fUaIc08XB3/CaRbsahWUNZtfVI0t2vjuVeD1JR3l8kR1fx/rrpiiuuwJVXXslDtffbbz+sXbsWt956K84//3z+ZXttbS369xfFzGpra3HAAQcEjrmzvzD/rPqqzHNXkl6T3ie9Jr1Lej16KLOLZxhVn/F67oprYm/H5e2pPs9L+FnXZHteu1OreF9++eV4+OGH8cQTT2Dx4sX45S9/idbWVvz0pz8FAJx33nlSEZ1Zs2bhP//5D1atWoX3338fJ598Mmzbxu9//3ve53e/+x3ee+89rFmzBtOnT8eZZ56JSCSCc84550s/vy9KKhhasNF98K1vlR/m87ao1J2JRzhYtGwbLZ3iYanccIEIA46sInNUcaL1KXCdTWpV5aqU3xmUoICS9KdVo6mKssvcSscNcvirHRUPsbS4b2GCgTkBxWyYSMVNYPV4lMz/Bc4uexO3DHgAfePBYCAdE6+NmG4o9+8rH8eh2ScBS5xT1MvvmIzJ51QZ9QNKVlCmIlqPojk/kivQ9kSkGnC7nUCGuR2jKaBwJP9bh+NBB9O9Dj8om4j7B98hxokkeRV2QF6P4nQMj//0EDxz4WESyM2RfSVBZPqgTX6mQFNV2uzwvxYIBJS3H92Ap4ZdhRf3ugUZp0bKyTggttm3D/83biX+c9CjOH2/vsEHV0GTLbud2B4bMczz0jsWeMUoPgaBCZvfkyp2c+VFW7zLd1LqqrLCuyW9MEAKKJk6SAVhx5YrMkdDQC1TljgA6+RK3Buz7jU0LAGFo6oDTwFBl44dKf+dVbJW4NS9PzgAb/zf0Tjv8CFkLAYo1ayWRK3EPRcEtDtqAdtbHzPmgq7TlrnuRTMCZLzjMTDJoFuBlw5A2pNkr6j7hIkB81gJANdBecAg92ffvmDrVDMFmEbyyzmWG/4cJi9tAjpq/X+i8fWBgJLsXTsr2ou87LxWp7u/2T5IlPH3DQByVXMV5gLidU1L5POtfsvfd4vszMXcK+XfacV6M+SDiuO4cJlq1oXA4r+EA0qrE5jxE7dY0PaCwKZlwPvfc12T6tiNC0lHcpOz+yXeR8D0rOy09b/37Xr5lL7uamtrg6kkjYpEIrC9zwHDhg1DZWUlpkyZwv/e1NSEWbNm4fDDD4eWlpaWlpaWlpasnZqD8gc/+AG2bNmCa6+9FjU1NTjggAPw5ptv8nw969atkz78dXR04Oqrr8aqVatQUFCAcePG4cknn0RJSQnvs2HDBpxzzjnYtm0bKioqcNRRR2HmzJmoqKj4sk/vC1NNo+za+XRjI0YNKkGdAigt23VKAkAqHuHuPstx0NYpQEmhV+Shqd19gApyISZjJncQ+sBlYQpQTDvxEECZCQCUtmNg5JIfAR2rJYDiADBjxfwBuk+sDZvyLowp8ByUURLabDkmUvEoMPlHSAK4zS3Ui5W24urxlI4JOBExgG+XvItf9X3RzV9WdCb5m+VdA9lBWRz1g9mc5cKC66r+geiGacCGF4AfBoepA0B1ri/6xwh8Ig/d7U4C6Yi3ppEUQGBtBzxA6TmhjikkD9deO73udD0A4Ng9/HAvFg2hbPRBm4SnZ/PKeZEH8JTZgSa7wP+QbvmB0w+K/wc0zgOagH0jDdLfBsQ3Y67ioBy14f/cH9Y/Bwz7kX++XUFAwA1ZBSTgCzsHREhosU0gR8MCASSoKKzp6pjRDJBv6b5fqj/QtNjvOgNkF6NjCzdcoszdG10pS4Bj3Rz+4+bkIehoDphPFw60BTecFPolg6pYxMRe/ZWQRA84Vpb3BzoWBL+wvRoo2t3rHwB12qvBoZznIEYRWcvMUGDL+y6YtPNAm+fqLN0faFmhOCgpoAxx93IYVQy0b8SwshgePskLg2brMugsYP1LQIfnMHx7rDyGY3cNKAeeCax5MnDtwx2UASDbzgId3ptx/3EuELSz7v5j4C5eJl9X6uwMApSfpyigtENAYrZeXHPmhq2d4v631xWin0UA5Yp/uk7j1U9s/5zeOx1oXuZzAKuSHZTe/KIZAI67Fp11Ao4DGlDuAjr99NPx5z//GYMHD8Y+++yDTz75BHfffTd+9rOfAQAMw8Cll16Km2++GSNHjsSwYcNwzTXXoKqqikcOaWlpaWlpaWlpCe30IjmXXHJJaEj3u+++K/0+ZswYLFq0KLAv07PPPvt5Ta1X6P3lW3Dd/xbi2qOyODb/KDDqz9hQXyz1yXlOtvo2+QE7a9ncbZmJR4mD0kFrp3hYKjQaAAhAmYm70IsCLhoiLLdHUJhK+uZNw6ElQBn3bznTcJDo8EL/fCGGAoJVxuuwqd0tCFSY9PImGuI8bJiBYdUDzeD8o6mIGPuYyHgcMfh58UcyDwZBJQdlcNQxX4sRifAHXQBA8b7Aof/Avf9+Bbf3v120ExCVdyJIRryH+EgSSJTzv3VyQBlyCzt5CTDHehBCf8zICpy8TyX2Gyjvr0AHZbYePzOvRrbgCExtOcibsAgVzUQ6gDz8D+mtq4FpPwBG/dmFOUvvdcOuPVVacrX2AbHNvlQDXNm64HYGnWLFwWG1DQxQkgI7Tg4AAZTUhZWtFyGdVJJ7rgtfvgQou+hXuBtQ+3bw39rWk7kpDsowQBkvdefeSa5TvfcFQL/j8U7idpTWBrz3dgEoOZxsXgHMuwrY+8rQvlybpwLL/w4ceC+HgEOrBvC8pj5RGBsEDds3AQnvC6cgFx51ULZvdKGwGQcKPegZFuLdqTiiPzgH2P23Yp2jLmztXxgBvAJSfK5pL+9kZ0DYOjtO0F4c/H1gn6uAbTNDAWWkOwelCllZe8FQ0cbOzUy4+4W+JksB5Zrg+W+vImkZ6hsREQbN5xoC7Nj846VAsq+cj5I6TO1OF3LOvjjYzdlTNXuFmtTwdAByDmQDxpZpwJrHRMqFaNrdW+2b/O9HQSHeSx8AWla5eVRDUldofXX0wAMP4JprrsGvfvUrbN68GVVVVfjFL36Ba6+9lvf5/e9/j9bWVlx00UVoaGjAUUcdhTfffBPJpP9zk5aWlpaWlpbWrq6dDii1utZLczZg1dZWHLvqNLeheRk2lMulYPOea08FlM0dxNEWjyASITkoO8VDb4FXlrW5k4V4ew5KAoYo+KN5H/sWJmAEQIIwB2VP3VeA61ih+Rr7RLYBGCCNozoo03E/oEyoJYg9paPeQ3q2AUeYz8t/JKHWUS/nXJJA1zQpE5s3C/iNlPcApYXg/JNcqSqg4gg05yeGdokaFlIsB6WZBJLCBZw15BBvnxxLuu6JMHckUcQ08NCPD/L/IQhQzrsaR0TewhHD38JJte95uSMFxEupId6xIvHzuufd0EkpfDJYA+ObpYrfkoyQa8ygU7wkGAqxv6sOSqkPAZSdW4KdjxTAdOWMjKR71q9gt/C/tVHg3VNAWeYCShp6ys6z8ni01pch7QQAPhXUBendce49svm97vtOHuMdOyuAY6yLvJkU/AYCymqRdzMI0DMw17oWaPeAX6q/ux8AyQUsrbMaXr32Wfe/IV56kLgH7uleYXNlhYrCAKXVEeygLBjmOjs5JNsOByUrBkOvkZ0Vzkrq5mPnlihzwRh93+jKQdkVUO9KyQqgz2j3XgeA9CA//HRCACVzgKb6yzkdAdk1anW6OT9XPrJjc9xOOTARmf1z90uWNU+6jdGMe383LnChPc0VG+SgnHeVC2lHXACU7POlzFvri1NhYSHuvfde3HvvvaF9DMPAjTfeiBtvvPHLm5iWlpaWlpaW1ldUOzUHpVb3stXo4OYV2FAvh5vmvHxHda3yA1+Tl1MyYhpIRE0OFi3HQUdWPNRmnAbpdbxIjhLizRQxaXskEBJwQNm5Db9LX4ejCj7xxo4Aretdd1JPRGBFiSHACQOUJmQHZU9AHJ87K5JDQFPW9s6FuAFFDkoBxIoc8qBMHvZZiLft0LyDAQ/5nhuywwqfb8zI4cihHtyKpoRrDD1xUFqS89DnoHQcYO4fgYW3hR6fKwhQEpDxwi8Pd8EIgT0Zs8PNE8ocU8T9CSAQTjoB0Kp/bCtiYUmZwwAlczNSMBikQlq8RYElNPw0rLgJBVpdgZwoybcYBigNUy72okpyUDo9C/FO9HH/H+Q0TfVH3rbRYQcU9+mqyAjL1ckAfkDOxFA1LhYwjcHCIEkOSm9dIilgqBfO314t2rt0UK4R93Y0I2AX3c80TDgotB4QY7DXS4BScVB2hADKfDOQa3B/ZrlGAZF7Ml7mvT4gB2UYoHRsdz18gNIDfBKgrJGPY4QAys6tsls4CFabAXvG1ycur3E6oNJ8dw7KZDeA0u4MBLo+hb1HAkCOODoTXaeBcQwTBi3yA7jvMeVHuD9/fJmbK9PKAqueEHCWzzcr3g8/L6eqlpaWlpaWlpaW1tdIGlD2crVllTxdkQQ2NsiOQO6gbFUdlO5r07EIDMPgjjrLdtBOHJQZyMn9mQuR5ppMEWeir10CRe5cOCj88GIcGZ+Cp4Zf440dBWb+RFR97UIGHAlQlpsCnLBzoQ7KeMSAsR1hc8mIB5W8vI/tdgKftO3h68dyUCYIpC20Rd5Ig4A5VjjGpvnKVCcNwB1gHXa40/LUfcoxtNT7u+qgND2wEfbwbeelaxFTY9K3fQgsug2Y98fwXHBB8+c/i/GKkjFfCPR3R5Xif5ccKfqrgDJIKVHlFJlhyDkRxM08Ss0QR1/ouXt7woxL85QUK3LXwPDWVC2s0ZNCG7Racnc5KJmCQsUBN3w4rGgIoABKq+cOSiAYsCb7I2c56AxyUAbl22RSHY2hkDjktRxQduGgtANCvAt3E+609mqxPkEO4tQA0Y/lCoykeYi2DCjJscJgKyuGFPMclMz5Z1tifuluHJS5JhFKXbSXaGd5ZRPeWpEvRngXEECZV95LHEtek846MUZmqGhvXOz+P+lWFe5y3aiLMqhSeHpg+GuZzAQvKgQgGL53ByiDHJRtioOyu3yzQNc5WkmV7u7ueQcGnISSuzeaBvoe482nHVj5L/e/mT8hx/e+KKH3/hed61NLS0tLS0tLS0vrKygNKHuRHMfB6q2tsIltklbbBgCYCTR6uSJjPGTbc1B6Id7MOMeL3rCq3N4f8raDTgIo044MKFmeyGhQiLdj46T2m3FO2ZuinUCC4kgL7hp4N3bPebn0NvxHGjsRM91CIEEiRWAA4VxkGhDfjMMz83HfoDuBDhdWmiRMMB6WGDJEHFB6LqqsHUWdVezrFw0okpNwhPOGzoGtheOQW2vKN/0HN918hzk73N2TjFgk/53soMwpRXJ88h7c96wsxK8r/4sD6xSn5Nbp4ucAKCLGcYIBpQqCFfD2/QPKMDxZA3zk5TjsAaB0GFgCgFgRanLuayoj1KlILMXdOSiNCL/OPjFgYniArisHZZioW64rB2WE5BpjLjpVscKuASUN03byPavizRyUQUr1R95y0GmHXJ8wqVWrqRuwO9lZUSSpSwdlQIi3EXMddYAbAszWywi4ZlHv3rA7iIMyLWBXe41b8Xn9f2VAGebG4w5KJcSbVpFmId5Wu7+aM+DeN2zti/Ykc/XgNYPJAYo6ITkoAT+gfHec96IC9xozAF8zyf1/xZHu/w1D/I2JuUC7BZQBbkhVZlyExAPu/lahdCig9CBkqsoPKOn9s/gOFwZ2J3r/qSL5b5Gt77KQjQHHD6AjGaD8MLlt0a3y7+w6SKH0a8LnpKWlpaWlpaWlpbWLSgPKXqRHP1iD4/7yLm59QwA8H6CMJNHiFbgpSTPIJTsoK7wCDiwHJStwE/HCfC1LCfG260CLAfAQbzWUGwA2/A8HdT6NWwf+VbQTJ9vPy1/GWWVvY1z9L12HkQJu3HFCtl2sMLjd08DYZjwz4k84o/Q94MOLAMjuxUQUXYMiCsBAQrw90JB1YqjLF6mvQoQVySE5KJMOAQUEEOS9tWCuSwBAvVJhG+BQNxfkYGOysgKgmEkJUBYVFkvj+ORdl9cu2hNX9H0EsWV/AdpIEYitM8XPQQ5PpnwrpEIRAQ5Kt1+L//dVj4vfqWssTNRBGU3D8IrYnLcXLaxBYIwKWJiYq8qIAJGQcFR2LDMEUPbEQUlhhWPJ8JSKQrAgeAW4IKYrQEllZ3sW4t0F9EKqPw4ZVooOZwcAJT2fpL8afKikHJQlXRwjwEFpxsWatVcLgBy0/82kGIc7KFMCBjYucKs9z/uT7NaU8nwS5UNCvOk8ExUi9Lk5oCiX1S5C5ymgZF9udAUoqYOShiQD7j6lYepsTxZ7+Q0ZoGfFl/qOEX1VB3Lx3u7/KTzrCACUNHQ8TGZcXuNI0vf+G3qPdeWgVNWysvu5IOS+BIDWdfLvXeRfTTgNLqSkiqbd//qfLNqkfWS4BdEApRiRdlBqaWlpaWlpaWlpqdKAshfpptdcN8fD74s8Vy0d/hBvBi1L0y7QYK69bS0yoDzIfBe3D7wPxXEvFyXJQdmZFQ/XMbSjwBQPwRnPcUnDgrmDsmG+NJ1UTA7xHhAj0IaAuTYv110yqoaEE3XzMDogLsKqUTPZ/b/qoOzCAaO6veKmC7IMD4J0OjFsy/sdlLGAEO+4LYCcAYeHFbPw+MJISCgvf5HbL+d0kR/NyQkIEklKjrhvDOg+ByUARLd9INpyTcDCW4AFN8vFTXKNwMf/D1j2N/84KrwMclBOOxuYdJTcL98qQmb7HNajas9OkgLKDAaOdEN6i5tnAuteAqaMBab/SPQJ2kdL7wfm/sH7uxnuoEx+3oDSDt97FN52BShVN+DR/wVO+tCfS9POEQdl6Y4BykQfHDGiHKceMCy8T5CsdhnqdJfnU3ptZ89CvANzUMaF67VzKzD3CvfnQAcluR7sekfSfgDcvFyGey0BYBEQjk7VQcnajagLSlkKhomKo46JhelTqMuAYzQTCqhDc1ACLqgNKiR0jFdIje5/Mya7/eh7hxkTRaNa1oj2IAdl6QGB85QUUXJQRpJAxRHK3HcgxHtH1FUYuFqop3NL6JdcSSVXMwABvY8Y7wewY14Hxs0HCrxct3TtNKDU0tLS0tLS0tLS8kkDyl4uf4h3kueWLPUclMy1t7HBfZAdVOpCgzv63YAflE3CdwpeBiBCti3bQWdOfqgtjwpwwh2XxEHJc1ASOBEzcq4jkjzQmTQse8N/xZhmJ6LIu8V2wpxv2wMoWbEB8pCbiCL4YZ1JAZTJCCuSQx2UfkAZCQzxVkMt3Xlcfere2HdAEarSnehSBnNQdpELzs4KCBJJSm6xiOON30UVbwAyiGzf5FaRnX+NnG9vwyvAkrtFODZVGKCkWvecvxBLvkUAvOHnd+uOBSA/4EfSwu21+T13zrVTgPUvij5BgHLO/4mwzS5DvBVAqYKK7Q7xtvzhz0wUUIbtz5G/9gOqzGCgzyF+l6KdVXJQBoSwGhE/BKzywn/LDuL34NC+XYSBBynfLsOVsHMOktUG7mYLCvEuGeUfk4Z4J/qIe5gXyenCQQmI6xRN+/eLY0Fy17HCP6pY+gIWqusoDkp2/XmOyhAXd4v3xVO8VLzX9fPSPxhGqKs0iu0I8Qbc65jyrhPd/8X7ifB3QL4e0QLh7Nz4qjiHIEdh6TcC5ylJzUFpJmX3JtA7AKU6h44t3e9p+u8XA/SJMj+AzQwCSvYV97UEKNd0fQwtLS0tLS0tLS2tXVAaUPZyNfsclEnuqjw4vQh3DboLifwW5C0bNU3ug+zgPrKrqV/UDS2MkByU2awM0EoiAqJkgorkMDjXJgBl2uxwgSN5AKyiDsot06RjFEVavZDwkG0XDYFYXnvaVKCf40gPmHETsiNKkiEVmQGARMQCFt4Gc+H1AICsHQvMQXnTgL/je6WTkIxSB6XqZHLnMagsjdcuOQoJ9e+qehLibWcFHKGFVgDZvRX4Wm/fUNhqazIAAJ47SURBVEAZFr64bRYZl1YEzgMfnC33ZWA4rPgM79cqAF6iokfhy05KdlCi/DD3de0bQ/KWKuGWtgIiunJQsmOF5aDcXgclbNn5R9VVjk8AOOp5YOgP/deIwQ/1HOycCPEOK5KTHujfM5UnAuMWAGNeJcfoooBIkKx2Ga50BXO2zgKm/5jMm+wtCk/7HQecOBPof4I3ZkiIt2EAJ86QjxG0r8wY+P5k1ymS6rqaMxAe4p317hsa4t2wAJj2XW9sD1AGVKaXxHIQpiqB05e77tg+B4u/h6wFd1DOu0bOewm4+1Rto8CaVtxWoTCFu9EMMPRcF7I2LQbWe7mDgyBicQ/SNag5KCMBgFL9UoCJhZUn+4vCRp9FXaX9UO/zzi1Arpv7tfxw8bMEK5X1Y79zQElCvDtqw98vtLS0tLS0tLS0tHZRaUDZC1XihW7nLBudeeXhKpJAs+eqvCJyAc4qfQfjOm9FTVMHLNtBPGKiskh2VDHzIwOUdhCgjIrcZiwHJQ3x5u5B4jLKmO0uuCSAciB1OSqAsiTa7ALN0ByUIQ+j8RI5PyFT5xYZUHbloIwV+sJRU2gG5v0R5papAFwHZVCI9wHpZbhz0H2SgzJmK7ng6IO81d494PJgSVGmixBZKyvy3zHYxCrzDvy2N07XRXJ49V4gvMIwLRRBH6K3zQYa5sl9GfDpDlDmiIMyWeE+yHcHiGilX5bbrfTA8P6qM0oFNT3KQenNyQcoe1AdmPZxLDmfIVV3wKPvsS58U2EbC1VWXYJ2VrixYkXBYdaZoX5nZTQNlOwj30tdFRAJktUuipiw38M08TBgzVPBf6OwLFEBlI8muSOpg5KEeANAwTD5i4ygPWUYAg4xB2UkHe427k7sSwIa4v32CUD9XG9s71h7Xt6z8VJVriu2zyFye8haRNHu3ksLb3YbaMEpx5K/VFDHoXBbBWiGAijjxcCIn7u/V7/ljR/wPtaTsH5fDsoEkB4gHzMIflod4non++64g5IC8O12UHaTnoPl9wREXlHAv37serNzVt2vav5LLS0tLS0tLS0trV1cGlD2QrHQ7VY1vBuAZSSQVaBlmb0eG+vdh/r+JUlEI/KystSJwkFpI5uXYU5xhAJKVvVbCfHOtUjhnRmz3W0nD4AD4uGVjUsizV07KMMeRiMpIB1QmKFxkeTCSUTQBaAs8j2gJxz5QTTrxNBoFQS/HnKId5iDEp3bgKln+F+85/+Tf/dgyX0/PDT0eG4OSm+ODAqcPAc44QNRlCE0B2VejMFEH6apaKGJbIP4mQK3b05x/8/cTWoVb1WW4qAEwt2MbMoUULK3Jpa/LUhqGLa69mqIN91fqoNSBTE9CfGmsjqA978bMs9uwv0ZOAtzUKp5Fu2cmK8ZBcwIcPoK4IinRZ/MkHBgIrXtAKCke4TCRHZf130s5woNUiQg1JjNJcxByV9LAVyIM5f16SrEO0zRkPcACig7SMVvdqz9bgD2vabrsY1oeEX7EAdlxOmAQcPqT5wprkdQDko6ToRct6gCFtUQb0B8ScDWNRBQhkB/tQ+F0MzJecZakcMyCFCyL2QAF5ruKKAc9F3gyOfcn7sElDvgoKS5cumXPmbI/RYU4g3oMG8tLS0tLS0tLS0tRRpQ9hLlLAHzWPEbX3g3AAt+yGPB5PknB5SkeK5JJuaglHJQZuWH2mIS4s1yUNJxUrGILwQyY3Z4OSh7BnOKIy1ekZztrOIdSbkhq6pa10gPubEIwmFQrEgungEg4cgPokMqSvGH75JqrNQpAwF4AaDIVFw2DAQuvU8U8KGqOkX+3YMDQyu6KBZiBzgoWa4zBgi7ykHpODIkDgOUVLkGcnxvXUv2d/8DXACrOraClG0QY/UQUNIq5Xwdu6oYrIIHNbxfDfGm438eRXJU1X20/a8BBIBUQSQDSiqEs7NiXRlkKhwBlI4SfTJD5PBeOh7VjoR40z2Spw5Kb0++eRCwZnz4GGZMBtzs/YDNJQhQ0mtDAWUYoGdjdRIHZXcOXsB9n1By1Up/A/x7gx0rVgAMUVIiqEr2C3//C3VQdgCtXv7K8sPdtWbn4gQByjAHpbL+aog3fS27l1SAt9cVPbuOZlwAXUDs13SVWzSLzV2V5b3fmTF3fjsKKM04UOEV7tquIjlbhYMzTKl+Il/q4O+LduXfFx+gzKuAUhfK0dLS0tLS0tLS0qLSgLKXqK5VPGQWpdwHGlYgJwrxIJeD+zdWLRoALES4g3JASUoCaQAQNd1cfax9fV07OjrlcNTL+43H5f2elMZmTszL+z2JgzrH+x7mMpF2D1D2IBwWroOSVsL2qSsHZRBcsTrkEO+uHJRRv4MyroRp9ykqxNH77OFWXv3WSj/gocOFOSjDCj+o7i32kN8VtLNoDsqQsMquqnir60KLuoSJhngzgGBE3QIl7EG7owa+t449LnNdlgfc7v7OYbYhwi3NLhxshilDKAapCoaGv2bVY8CcSwX8CHRQkjWkORnVIjnqugU5KNODw+fyWcTm4HNQpoLb7ZxYW7qv6LkGhXgHheZur4My3w5kyR7J03uoG1ctk2/PM0AZFOLtrWnkszooUz0L8c4MDXcIxvzpH3zz6QqoA8GpKoLGITLgIDrzXDE/QKy7Y/U8B2WXId4Fcn92/7H3gP1uBI5/G9j/5u7d04C7xvR+o3MMu+cAAbwjIXC+pzJj5Bp1kYOS3eesEFW2vvucsbFSYOx7wNipMqD0OSi938NCvD+8CPjgHLlqupaWlpaWlpaWltYuLA0oe4m2NIsHOMer/cFCvJOmAC9Zx31QL0iIh0vLIQ7K0hQiRrCDkgHKmqYORCDDq5JoC37b7znslljHQ5mjEQO7Jdbht/2ew5ENN/geKAvMNl8OyiB1wn0w5iHeYcUBQovkpIIf3lVAaWL7QryhPIgycFKyH1AwvOuHYxrmCoh5qBWXAaDyBD9I7AmgdHLCUaQWPOFz7qJIjq8ARA8clPS82LqaURdKMNdhe7UfUkRTQOU3RfgqA5SJPgJMdhU2HUnJYzKgERTaz7R1uutY3fSG/BouxUFZOFL8zGD49jgo6es/T/G9QPabmRBOuyAHJSsQRAGlCsp6FOKtQMuw8GYm1UFJZZg9C41X93yXDsqc/zU9clAqgDLSwxDv9MDgLyaMiPwlAZ0D/TnsPmXqElD2wM3KACjPndpNiLfZwxBvtg8YnLUVB2WswC1mFOnGBU2PS+9nuqZdAUr2fsfOIT2gZ8cLPD77eOOIf1RVsTkkGKCs695BGS9x83X2PVo+R3VPcJd7F+e79tnu7zktLS0tLS0tLS2tXUQaUPYSbWkRcMWy3YcpVgwnRapXZx33obIwSQFlBNWN7gNgVUkK0YgBA8I1QnNQfrvkHVzb/5+IGcEgYWiiGgmvWnXUNFAUIQ9rygNW2uxAKu6HErW5MjjETbXNcJ1nJdEWd+ywB8CuQrxVdwrgFaOhId5GePhxAKCMWoqjRQUTvuIkeWD2r4G1z/kB5YzzgeqJflhw/DvA0S/54UhY3kEqq1082IcVpuiqSI4KjrcXULJ1ZcdgcIUWSWFi145d47b17v9pWHVXIFuFaT1xUDItvhP45IqAEG8lB2Wqv+eOXSXgAc9B2QNAuaPhpl3KEACX7gW6V305KAnskRyU5FzTg4OL5KiKK65AtdKzKjUHpSQDaFnV9euBLgBlFw5Keg3oe0EYoOdFcurd/0d7GuJdEgwoI+nwgjPdgUW6DtvpoMwf8i+5gTsovXP55Apg3Qvh4/Q0xJu9hv2fAUrqot4eqdewK0C5/CHg7ROBeVeRlBbeXOOlbuX501d0D3+l48fkeyPsvcdRHJSddT1wUIY5aUP2hM8ZTdaneF8gGZKTVEtLS0tLS0tLS2sXkwaUvURbiYMyb7twsaWDOSjJ37wCOQVJ8dBjOSYPBy9KxhAxDcQNAVxYiHfUNHHv4Lvws4pXMK74g8B57JHaAMODN1HThAHiPFEcapn/3969x0dRX//jf83sbq4khEtIAMMduchNQQKioshNEEX5tEotIrX404JV08tHqAhqK7b9lqIWtbaAftoq1CrWVqSm0aAoF+WOIHJTVG4CYriGTfb9+2MvmZmdmZ3Znc3sJq/n46FsZmdn3rvv3YU5Oed9PGeDa0pqLv4O+Vsg4GsR+fm4VAIg2IgnmEF5BrqMMig92dHrewFRGZQ+OXqMdXdGByhVpapAdGmn9qL8s78Cu54G3r+l7jzhi/5ja4B3RqmDZCUTgKKrgoFXbSAlfGyzrC7l+GyXeNfoNIDQBih1SjWV2XHa4EQkQHkw+nHaAEf4fZOlDFCaZNeFgk7fyh2CP7efGPzTSln1kZXAjv8HHK5Qb9euQSn5QtmxHRXnNejiHX7tCocE/2zSOb51KWNRBi+UQTjlfGvfO8p1H5XvH2WAP7fEWom3stMyoAq++KH3mTurXgZASdSqO8IbCc9JuCFLyU2h8SWhSU7kZ4sl3r58/RJvb7YmgKw8vuav0ZaXqX/u8yvF8QuMz62XJd6kC3b6vlP3cziDMjzvB/6tcxyjDEqTEu/wuY1KvO2WWoczLcNB/dYjFefV/FJg48+AQ+XAx48BVTtCj1e8VwsuCq67aecXBHKGtQBlJPM9tO6o1QxKPaoMSuUvGDTvu7xudbdbXWl+LiIiIiKiRoQByhRx9FRd5l0oPhkJOmZJdUGvJqc34nclv0O7rOORbbWQI+XgORkeeCQJmZImcAdA0ZQbF2Qc1h1Ht6wv6h7nkSArMjG1pdm58llkZUQHKKtqc1EbLpkD8I0nlEHpOYksr6zu1KqkF4QEzDMoVWtQClsl3lGBFm3Wj/bCMqqpgRRsWqMUDlw2vQgYtEixq3YNytDPZuu5KYOtRlla2qBLuLGPqNUp8dasQanXeEgZFFWWeAPqAKU2+KnNoAxTNb4xK/EOzu+qrF+i5qr/Ah0nBbcbvSf0nDui/lnyqINNeuWpkWwuzdjCr12b64JrzY1aa7+ztxXK91iiGZSeLGDsduC6T4K3tZ8ZvddSG2xR/Hxe0vmFgVkGpagBvt2mf59SOGg2Zgsw/D2gzdjQ+E1KvFVrUCrmNFaTnMjPNprk6P3SwJMTWidVjj6vNuPu6jeDmdMTjgHXVALd76u7T7sOodmYQ455FM26tCXeusdRBsqU73+zEm9tgFJT4m07gzI0X+N2AyPeD/6iJnKfIoNSCHVA8OSu0Hh0XgtbAUptBqXBOpTaDMpkBCi1wd2mPepua4PZRERERESNGAOUKaLqXF2gTZtB2Tyz7r6m1Tswodk7eCDrvrr9hYyz/mAwKTfTE8yglBUZlFLwPq8iQpnrUXbfrdMla3/d4zwyZEmRQVmrfkyufDa0BqU6cHMqkANJEQz5Vg6uI5bnOYNsj2L9PC2zYIPlDEqzAKUmYKMJUIpYJd6a5w9fvk4pY+jCvs0Y9QW1UYm3FZ5s486/ytcsqxhoURq8rVfirX1tlAHK8PPQ6+IdHnt4DcpzB6MzDsOBEO1rnFmXSWta4h06R43UBKLwSvXzDY8zVraRNiCrLfHWBvqU27Ql3gFF5lirK4LPI9kZlFZuA8YZlEAw+JEfytDSBlJ0Myg1QR9Fhp9fL0BZc8Y4gxIAjq42vi8sPCeZLYBWl9cF6c1KvJXzKMeRQenVWYMyXC6t5MvXf5+GM1rD7xdlEEvVKCh0jKKrgr+8KBoafC836RK874Ib9MerN2YAoklnHJcVGXfaJjl6VK+P2RqUZiXe2gxKC99XmTqlylmFQKEmCBc+lgivk6v4++DUHv2xAsFGZ1ap1qCEjQzKE+ZBZMA4C9ZqiXduh7p5Kbra/FxERERERI0IA5Qp4qQiQBlegzKcQdkyOzr7o61Ut9ZbjfDgdHXwAizb54XXoy7xzpCCF/nK7t5N5FAQQHOx1cG3P5ht8vHjKPrqD5CVF49RGZTndJvknKrNgaS42K6VgsErDwLIVmSDRjELUOpmUJ5TBZa8HkSvQxjmy4udQRmrxFubWZNRoNPAJHT+WNmYdjKSzNZeU2XgeTXNM2IE1JQBymZ9g3+qmuSYlHhHNebQlniHx65sAGEQmI5l9PpgFmObMeb7aYMQ2hJvvWBWrCY5ytfXYrd6W4wCkUaZlYBxBqVWVqF6jvWCPtpjG2VQZhUH/6w+Wvc66AVqDv3XeDyRcxo0WjEr8VYGl+00yYn8rFPiXTw8mOmofF/58vWz7bQd1ZVZk/4YaxYCwMjVwWzK1qOM91F+xxWPBK7dBGQVoVbKhv/a7cB1n9b9osbs+0P5yxyjdTMB9esRPrcngQxK5S8jjL6Hgbq5DPij9zsZClDGCqYPeiHGa2mwBmX1MWDtVODLfwLr/j/g+Prg9khzMwGc/cr4uIBht3XLJd7eXOC6ncDYHUBOG/NzERERERE1IgxQpoiT5+qCSTWaAGWLLPPASK2Qcfa8osRbllVNcDwieBGoDFDmhgOU2UWqY+XI54Bj64DNM9B67yzkKZvkaAOUnrPBNSW1AcpANiRFsE8OXQjLUgBZskEHb8A4S8ewi7e6xDvHKxlnUMqZ0VmY2kCENnASlbmmKU335kXvE77gjgp2JpJBabD+pPa4kuKiXC+DUitbEbwqCAUo/Tol3pEAZehi+uzB6IxDoxJvs7GrmAQvs1oFsxj1gtRK2oCstsQ7ngClcp7C62I6SRm8kBzIoNRqfmndbaNlAvS6OQM4D0WAUrv+qOyLXt4AMP78KRkGKEPjC+gEKFUl3lYyKDXPVa9JjuQNZjqGg69A8BcZep+b8OsSeb8onqc2g1JPVstQNqXJkg7K76f87nW/NACCGZj5ii7yZt8fRhmUUSXeDjfJ8Sh+kVJr9j2v+Mxp1wyOZFDGKPFuOQjI7xG9T+QcBmtQ7v4jsOfPwLvjgd3PAecOhcaeU/fLlC+XBf80es5Gc6jq5m7WJCc72PyraXfj8RMRERERNUI2F5aiZKk6q5NBGQpaNssyz4QTIoAzoRLvnMzgGpSqJjmhAKVXEaDMCQcKM1sB2Kk+4JGVkZtNPYqLb6MS74D6gv5kbQ5kT9222lAc3INaZEkmF65mGZR6wRVNiXfrphnGARLJE7vbbqysR20GpSczuIaaklEGpVGTHCvMMihVWVDeuuPu+RNwcqf+Y8IiWUNQZ1CKALDhJ8A3m9TnUHbx1mbjGWZQ2ui8G0us9SijSry1GZRma1AalHgr56nzD4C8LsCZr4DV37c25lisZFBq3ytWMygBoMWldQEXo2CeN7eurFURnFNlUGa3Br7ZWBfQ8RUYf57yutatJahHby1QoO69U3MW2Pe3YHZbOOBv1CTHcgalTol3+H2tPLZRiXc40K33Gsbq+myVMrBo9BqFmc270RqUUSXeZmtQWizxluS6X/QoX5uAhQDl5y9Fz1/4FySxMijljBjfiz5NiXdojDVnDfb3AhnNg3NZfSy0nuvHwPGNwXO9e73xucKslngbNTwjIiIiImrkmEGZIpQZlNoS72YZ5gFKKeCPxMlyMrxRa1B6RPBiUZlBmecJZQMqLvpePDY6eOPwO5FtBR7FxbdukxxZfw3KcJOT/B7w1wbP65ECyDQLUBpmUDYxyKBUByhlmDTJkb0WApTaDErtGpSaDEo5I/4MyliBJSWzC1ptICt83LMHgM+XmB83X5HBk3dh8E//CeDzvwM75wNHKtVjDQcozx2JzpCKZFDqrP1nhTbQqyeeDMpYJd5Ga1BGMseUGapy3dqCTlEFJRWZWarAs/Y9Fg6ySOYZeQBQ8j+hXc0CWoo5UgTnaiXFaxfOMowEKJsaf57MSm8BCyXeZ4MB4J2/Bw68GdxmVOJtNYPSk228zILyGEYl3uGAq946phdO1x+DXUal2XrMfsFh1MXbtMQ7/Pm1WeJt9B4wK/FWvt6f/UV/H73vDVWA0hcjQGmwBqXhWr6ajOBOdwBNOgHtJgAXjDM+j5LVEm/LWeVERERERI0LA5QpokpnDcqToQBlfoZf9zERoi4ol32sEp0/+ymaKgKLnlA2izKDMksON5/w4aPeazH20yfwrxNXBLcdfjuyXzOvomGAToBSbw3K07XZQPubgWEVwIj3UCOC55WlADJEHBmUue0MMijPqgNLX/0TWHen8bFjBSjtrkEp+2ysQakNUCYjg9KnH4gyCk416QhcuzG4tl147cETW4HNM/THmlkYusAXwJkv1ft4DEq8LWdQWghQGq39FjmE3hqUsUq8Q89tQ1kwMBsWMMkcMwv2tbWQaaU6lsH7wKzEO/w5tBLkzu8KjP4o2NnbiHKOFK+hUCbYh7Ntw53SMwqMP09NOpmPKWaJtyK4FW7aZFTibTWD0pujk8XsiR6P1yCD8vTnoX01czHwj0DvR/THYJdRabYeqxmUdku8w2Ow2iRHdUxFsNysxFsvyBt1XL0Sb0VGr+wzf43C51AueQFE/5IpcrxQBmVYW4tBSSWrXbxj/T1ERERERNRIMUCZIlQZlKFsstOhAGVTn3mA0hMKUGb5ZHgqR6DV4efxk6K/Ru6XQ0FBWdbJtpK88Od0xsfnOmPz2VAWnSIrsbkqQBnM3BKht02u55zuGpQnAznBzK7iYUBmC/gD4RLvACSjC8TQWHTldtAPTgXUGZQ4tTd6n7CWg+yXeGsvyrVrUNrKoNQcy7E1KDUZlHrH1XZzDvPlA836BYNYOe3qtp/+TL1fpBTWU9ft9txhzRgNSrydzBaKNX/xdPFWzt/7N0cfS+89aRYcanOt+RjNzq86h0//NqAIUFr8+m7eP1iabsQgQKkKGocDlOEMZbMAZUaMDNNYGZSxHmOli7c229aTE/16Rd7X2gxKxWvQvSz4Z5c79c/X5rrY5dhWGQUW9VjOoLRZ4h3+3hIBdaMt7fnC3ae7/VixUQA5JcGbF4w3Hp+V7z697w3lNm0Jd9Q5NAFKaEq8czuq95d8gF+xnIm283iIgEnGslGJt/bzyxJvIiIiIiJdDFCmCOUalDW16jUo87zmAUofwg1y6i78umd9FrktB4IXZV69AKXsi5R+nwtEXxS38J6o+yEUGPF7mgIAcuUz8Hl0Srxr1Rdg/kBdiXckg6XFQODq/2jGYhSgbK8fvKg5G712oFazi4MddJv2jLmGoYjKetQGhrQZlBnR+yQlg9JikxzZqx88kzP0g0le5RqDRcEuw7HOkdVafx+jJjmmHciVXz8OZFBGdS2X1cEjvaCPUTaXtkGQ6jEmAUo78wqYBNhMSrwjY7OxTIAZVYAyurkWACBD0aEZCJZ4G32ejALiYbEyKGM9RtX4yKSxlurYofeO3tqe2gAlFCXe/R4HrnoTuGRe9L7asSTKqDRbj1mQzzCDUvOamDXJAYLfZUYZlFf+E7j6LaDnA+rt124MZs63vxmG9D5z2u8Jve+8qOdlFqAMPe/wd4w2g1L7HpW9wLfb6n5WZmuqxmDyHjXMoNSWeDODkoiIiIhIDwOUKaCmNoDT5xVrv2nWoGwSK0Ap13XwDsv11JXYSbXRa1BGKAKUAZ23w1Xto8v2/HJwLbBsOdzpVdvFWxugDB5XRqCuTNpXALQead4MJCyzUP+iTptBqadJ52D2JGChxDvGGpRRGZQmJd6OdvG2WOItGQQoJU/sNd2AYJfhzEKdxyvOkW0QoFQGgJSBR9NsIeUacVYClDHmT7v+qJU1KI3Kp81KW82CkHbm1exYRh29Vfs4FKBUZqaVTAAAiOwSeKAIUGqDw7584/nwNTU/n1HwTfveUT3GYA1KowCzrAkWhdfq1H5eAHVgW5tBKfuANqPrAmja82l/EZEIWxmUVku8FePVfhZlnQCl8vko1/jVvk99eUDrEdHv98wWwcx5s7VR9d7P2W00c6wXoNRkJdrJoAzPaY1RgNJXly3b8Tbj45pm+Ro0J4oKajODkoiIiIhIDwOUKeBUtTrAVysEcGovypr+Fh0zvkKOx6ThAABfqGO3MkCpJIUyKHUDlJJXkVkpIaApYfP5jyoGFirxDl3EesKZRpou3toMyvOKJjmRC8TwxbI2A1D3CUiWmuToUgWoYgQTYnbx1nTrNSvxjtnFOwlNcmSfQcafVx3kbDUUGLNNvzQ1t0P0NuXYs9vojyP8fCVJc6FulkEZo8GLVswMSs3nRPJYWINSs+38N6FjxVnibTeD0soalIZBzCRkULb7LjDsv6gZsRZe5Xqx2oCZJ8ukxLvA/HyGAUrNe8foMaoSbwtrUCo/P6qmR6HbyvUSvbn6a1BGzqcNNsV4T9qRjBJv5Wcsag1KvS7eiiDx6tuAqh2xz2eX3ufQk1XXiAnQz86NWtfRLAgazqAMPcfw31HhBlPaILrkBXo9CAx9A7j0WZPjmsy3asyKsWlfO5Z4ExERERHpYoAyBZysVgfZagMComI4JjRdgT91eBQ5sknDAQAZkQClwUVkrUmAUpFBGdqgvr/6a8VxguPIzWkCAMjLDJfPqUtrT2oyKCNNchCoCwZEMu40AYN239V/Dnrrqmmb5Ogx6pKsu2+MAKX/W83+OgFKqxmUTjXJMerirT238qK4eX+g4CL94+W21398mGEGpXKtO8VcKc/bfqL6MRdq1q+LJVYwKNLdOkSS7WdQVh8PDSfOJjm2A5QG70mzEm8r47BD+f6SJKD4GiCzpTqDMqrDvcGyAUAwO9qMWfDNsImJQZMcK128vYpMYVVAP3Rb+b6RJKDTlODtFqU641B+n8j2M2bN2CnxtppBqfzlkVmJtyroG7p9YLlim5XnafEXDrqfw0z1d0vMDEoJaHV57HNo16A0LPH2BV+3tmN0g6OBwqHBP7v8fybnNAquM4OSiIiIiMgKBihTQNVZdYCvpjYA6fQ+AECXrC+RjZN6D4vwSdEl3kqSqAUCfnhlnemWvPB66i4shfYt4Vc0yQl1dpVCwagsbyiopMk4unf0Jaqfq8NNcqQAIheK4QtHVYDSCwxaHFzzTUt50R2+uKw9F13Wq6W9OLzuU6DHz/X31QYVtRfl2s60emtQGmVQJqtJjqwJuOgdV/aqg1BmwY8mHaK32SnxBjQZlIqxl/45OLcTjgb/7PuY4gAOlHhr5yeqxFtvDUrN63U+FKA0y6CUTYJDTgWs6rPE2yCjS7UGZdTyB5kmGZRxlnibPsaoxNtCBmV+N8Vx9Eq8Ne+b7j8BrloRvUaudhxOlncD9jIoTdegVMyLtlzd6Biq9RN1npelwLuFz7DeOMLnVH63xFqDEgg2+BqxCuh8h845DNagDGfwa4PoMZ5f7ZBX8EHmbAS6/cx4J+X3gvIXD9rnGyvDmIiIiIiokWKAMgWE15rM8ASnI7wGJQB8W5uLjMAp3ceFZcQIUAYPetY4g1IyCVAqhTughi/MRSjYqAlQ/k9pT9XP/tDdHgTqHhPOttGWeHtzgmu+tb8luK3bvaEHK0s2m4Sek5USb83FYX5XoOVgg321AUqDwJDyfqMMykSb5CgDFKYZlMqLYp9BBqVX0wHXJLCiV8KtHGu4i7eW8piquVKMPTy3mS2Cf3oygMIhwfs66QQZtGJmUGpLvGXzteD0tp13IIOy42TzcSoZrb1ZnxmUF9wY/NOrbgxywBvqZJzfTT+DMtyxWcubDxRdY3w+s+Bby9A5B/zB+DF2MyibKr6P9Eq8azSZt7IXaDNKP9AqGQRKnaAKEiZS4q04jjK7XZutq2qSo/yc6Hw/OJkpqrduqJypbsClF/xu1i96W+EQIKedzvEM1qA0KvGO9V3vy8fX3ottvA4mJd6x1mglIiIiImqkHLzqoHiFMygLcnw4crI6uAZlyDe1BWhb863RQwFoSryNklhqzsIj62SlaEq8hSQbHyOcaRS+AA5f9GnXbNNk4ESa5Ei1qDt4uGmFJoMybOCfgA6TgOLhoWMqLljDx689C+xdbDDYEL1giNFFZqysR71ji4B6W61DJd6enLrsUMtrUJqVeCsChWbBD73AgHKOjC6uVVltiv1jrbd21XLg6w+CZcUmS/8Zjk1JmwmnzaDUDYzEKPG2uwal7AUufSbYxfjUPuCjaeZjNjyOspQ4yQHKtmODnZeb9lBt/tw7HL1Kx8LbajBw8lP1YzyZQNf/D8hpE/wMfDQNqD4WGrsHuHIZsP3XwMe/ij6f2fvv8qXAma+CyxB8dA8i3xdGAUorGZSqAGWMEu9YkppBmYQSb7P1NFW/HFIGR3UCr46uQamXlWyhxLtJB2DkmuAvONSD0zmeZg3KWF28nXx+UWNRBrWzYwefiYiIiIgaKWZQpoDwGpTNcoIXLqoMykAB5JoTpo+PVeIdPOhZRTMcBcmrKv0WMDuGQYAyUKO/f8jtQzoDAJrneOoyxsKld9oS7zBfk+B6YOGLOVXWlI3AgF5mjEHAR0QFFS0EKC1nUGo+arGCS8rAnlnGTVQXb4MmOcrjmQU/CnXWdVPNS370/dpjKoO2Ztmf4eO1GR07gwmwkEGpDVBaWINS+16wUuIdK4PSmw20udb4tTITzmJsO65um2HzKIcClECw87K2fF/yQBSPBDKb62RQZgbno913gsFYbZDIlwe0vV7/XGbvv5wLgJalweesXA/VqNmVYQalssTbIEAZvn3B+NC5dTLxtFTBpjQr8TY7hiMl3hbpff96MtXZ20a/2GhZCuR1iX2OqAzK0HeSWRdvRxmUeLO8m4iIiIjIEAOUKaDqXF0GJQB4RV1Gz0k0g6RcB1JHXYDS5CLSrMTboyxHM3lLhDONIiXeBhmUGp1bBQNsTTNl1K1BaVDibUQ5LjsBSt2sOaMMSm0QJlaA0qezBmU4iKsNUErWnyugDjIo19DTiuribZBBqeymbfb6FfQChq80PodR0E1VPqoIUDp54W87QGlhDcq4SrxN5k5vjUM7xn0KDCtXB/eSXeJthV6Jt5JeFqK2oUzktsX3hLIhiWQQTDN6jZVLPygzQ/U+g50mB9ecHL0+9piM1sJ0gpXS9TDTLF7F3Jj+8kgxJ0ZryFodT3BQFvYxOFZUBmWMbOmY5wi/Bpo1KA1LvB3OoJQMSrxjNZAiIiIiImrEWOKdAs6eD148Nc0OXrgVer+J3FcrZ0PynzB9fIZcA0DEXoNSb7vsU2dWmgYoNcE33TUo9YKgijI7IYz3sxrQkb2hx1toymBl3cHI9ngyKDUBAG0QV3u88GulPHab64AD/1bvq2z+k68uvTUco2TWJMdiBiUAtLoy+D4QmoZGgLWswBgB67iZvTcB/QCl3TUoqx3IoDQ7XyzZxcH/VMdMwQClNgAfzkxTPUYx7qxi4OyB4G1tKb6RC6cBm2cEbytfEyuBPGWzJ2XWmiqAHG7UJQfXnLRC1bwoiSXesYJ9Zt9NyuBY6xHAzt/r76/8nMbKoLQUwHOwSY6dTtfatTWV59CWeBtlUFoNmlulXINV+Xy5/iQRERFRnS1z6m4HZAB9gY/nAnIget8+c6K3UYPDDMoUcDbURaZJZvAisMh7LHJfjuc8pFqdi3+NDKkGOZnmAUpZ0rmAlLyQVRd4VgKU2jUoQ8GcTj8Axn+hcw7lRWI46BXObFGMyXIWi4D1i2ELnZvDorp4x2qSo1PibXQswDiDcshLwKVPq/c9o3gdzboiy5pjWlmD0koXZb21+gCLAUqdv1DqgzZACSsl3pr3QvURYMNPgTP7Q49JJEDp0O9/DAPqKZRBqfd5VAVmFA14zpuvqat6zPV7gVEfAlmt6rZbyaBs0gkY/h4wbrd6u16Jtx31lUEZ6zOkfA/mdwd6zdbfr/XoYEbuDZ9H36dsoBNrCQ1H16C0kEEZa+3amOfQrEH54bTgWrfhv0u1mYxOfVbHfwkM+28wMBymDH7Gs+wDEREREVEjwQBlCghnUDbJCl4ktfLVZVAWyCctHcMn+ZHjMwlYHF0DoyCC9QxKbXagCAYYw4HK1qOAnLbRj5PMMigVY7J6EWzU+VhP0jMojY6lFxg1W2/zBvNzGbFU4q3p4m1l7TxVea6yvNZC6aVbAUonmuR88Srwye/Ux9AyDVBquqrHFGcWcKxxOE1vDcqYjzFo9OO3GKAEgCYdgRYDNMe1WArd6nIgr7PmsQ4GKJ3OoFQeL9ZnSPk8Wl0ZXAtUjyQFG43ltIm+T1n+rQrcGnyHGMnvHvyz/S3G+ygZZVBmtgIymge/YzKaWTsWALQaanyO8N9n32wAyofU3a/9pY9TS1HktA02/FIdW/NdT0REREREuhigTAFn/cGL0bxwgNJ7PHJfnmRwMX/534Er/xn50SfV6K5BebA2dGG647dAzeno42jWoBRmQQ+99RVFQFG2bPBYVQalpklOXAFKGwGwWFlzysxC7fmtrEFpeJ9eFpJyHT5tV2+D1y5WqaM2I0zvNZS9iWVQqoJuVtaZS5EMSklGzIYqMdf6i6OLt97tRBiOsR6/vmOVeOtRlUMrXgs7AUo9qmw/m6+x6r0cR4A3mU1ylJ+tWMskaD/3BRcBw98Frt9j/XxCsU6n8tzhRl9KZq/ziPeBoW8AF95j7by631GZwYzg4ZXB52Eng7LVFcEu9O0nKo6nyaDU0pZa11sX7xhNw4iIiIiIGjEGKFPAuVCJd26oxLvAW5c1mWsUoMxoAVxwPWpFcAp9Ug3ys6MDGW+duy5YHnnuMHD8o+jjSF5Nd28LJd6qTJ9aewHKSHAxdM64S7yt0nk+ygvGJp3qbms7TlvJoDRqQhGzPFxzbO1rVzQs+GfPB6wfRzIr8baxBiWgyRSzefHuVgallpUmObGyHBNZg9KpoIfRceozg9ITo8Q7nD3XaYpiH00Wbm6H4G1lh/K4xmKhxNtIoiX4qveTwyXeSk27m9+vWi4i9Dq3ukL9fRaL0XdX1FIJMH+dM5sDbcdYX3LAKIMSAAp6R2fMWlE8DMgpqfs5/LnW+4zIvujvese7eCsov2MSLV0nIiIiImrA2CQnBZwJlXjnhQKU4a7cAJAjDAKUoWy888KHbKkamZI/0mRHqVrKAbKKgHNHDBtZqNagNM2gDJV4q9ZKq6270DW64NfLoNQt8bYYL1cFwCTgghuAL18z2llnPIpxZl8A/4A/Y9Wq93G5dk25mBmUGfqZTnKmQaahSbao9lxX/AM4sgpoM8Z8DFZLvBPKoNSONVM/yyosmQHKGz4PZgN/+ofY+0qyJtAaRwal3TUoZc18OCEdSrxL/xzMYCtWrL2n7eI9ah1w7MPgUhAJjUVxXLsNmRINIEtJzKAEgOt2AtVfxw40OhEIF0YBSr2O7A7+U0F3qQUnXkvlL7tMApSe7OgxJDWDUpmxzxJvIiIiIiIjDFCmAGUGpSQBHqkuwOOBX/9BoQuvGuEFUA2fVKMboAwgoy64oNc9184alOELQG2A0lYGpbbEOx6KC9GmPYJBPMMApQ5lgCOzOdDsYlR5DkbvFyu7TvbpB0gslb/GyKDMaAZcYCHTTNt4xyigZnsNSpNMM0+2eYAymSXeue2A4pEWA5Qe9fvMqRJvs2CNlcCR5LEXWEuFAKX2s6DNqPTmAhdcr96mCiQKIKswmGmXKOV72W5XZDnBEm/lGoJOr0EJAPkXArgw9n56GZR2OZVBaZdZBqVTwr8g0vt7xpMTne2ZzM+ScgzazE0iIiKihmbLHLdHQGmMJd4pINzFO9vngUeS4EF08OJcQBMQCF1Q+RG82PNJfhTkRF/41UqKAGWtTlBJ8sKjCFBKVgKHqgtzO2tQBlAXvNLJoLRM8ZiMZtEXoYNeUJ48+uHKAFNGc+PTWMqg1LnIt9RAJEaA0iorJd5xrUGpzH7THNMbo1FOsku8rb5Wkgeq+dct1441x4l08TYKLNr82jUKlNdrBqVHExSzuQalkzwZwLWbgNEf2W86kmiJd4tSxTiSWOIdS6LNfgCTDEoXApTJCPYC+p8RvTJrS2vrOoABSiIiIiIiQwxQpoBwgDIrwwOPLKkyKMNOBvLUG8Il3oFQWbisn0GpClDqNj/wQZIktGueg5wMD7xeC0EFj3YNytCFrq0MSp01KGPJ7xH8s93NQJMuwdsdJiHqbdzmWvPjKAMnZgFKK2tQxp1Bqc3giTMAEFXibRCEs70GpVmJtyYwk9NO/XPnO4J/troq9nniYTWwJMlAZou6n+PJ0kpGkxzV4xPo4m11zT+nxFrPM2p/5bjj+UWEiWZ9geb97T8u0dLowsvrbp8/Zv/xTrESCI/FTol3QhnvGvWRQRlmVOLtFgYoiYiIiIgMscQ7BZw9HwxI5vg88MoSPDolslUiH4VQXBCHLrzOi+AUZkp+5GREX4wFpIy6C0K9DMrQfRU/GYragID8HysZlIoAVUBZ4m1nDco4LnhHvAd8/UEwANnxNuDYOqDtWGDfXzTnUx5bJzCiyqBsZny+RNag1CNM1ttUXUjbyOZRveaywRqUHnX3WEsZnpoGJ0rKDMqh/1JnlQFAn18GAznJClBaDSxJHsCXB4z6MFT+rhMYMS1VR2IZlIYZhHYzKFOgSQ4QfL9H1qG1G4R3OEAZL+2SCHZlKErKj65NfDzxSvR5ANZLvCWvsxmGRl28E6a33rBegNLFRjUMUBIRERERGWIGZQqIlHiHMii9UnTQ60xA22Fak0Ep1UDSuYislTIVJd7GpXs+j4wsn8da0EOZPWVnDUoIxb5xlHhntgiuyyh7geyi4G1JjhHs0xu/MoPSZA27mBmUPv2LfMPgjclzVT4HO8EAVaajZBCgjCODUhVo0xxTGaBue11wbUElT2awcZHZa5sIyyXeode0xQCgWT/9fQLnYxzDQoDSKFhkKYPSwlynwhqUgCaD0kqAsp7KZu2QTd7XVoW7kHe8LfHxxEu2EgiPQRisb9xlqvG5nKD3vkhaibfOP3Hc7KTd/FL3zk1ERERElOKYQZkCwk1yciIl3tEBSj/016A8L8JrUCrKrBVZfaelZjFLvDUbYg9Y9gYv/MJrSgYslngDirHFUeJtSHPBGysApAw6efOi79c9jg67GZRmVBftdjIoNc9VL5gge9SZO9oGJ3pU3a9NMijdYLnE20IASi+rWHUMCwFK2QfU6mQRWwosJlDi7WqA0sJ7SMmRz7kDnOh+fdmLwMH/xF5KIpmSmUF58W+BvAuB9T8Onase/pnQ0Eu8r9sJnD0AFFxU/+cmIiIiIkoTzKBMAZE1KH0eeGRZt8S7RhugDE1duMQ7EqDUBMfOKAOUJiXeEZaa5Pg060qGgjNWMsYCfs15HAhcRGVQxngOyufsMwlQxiJnAK1H6mxP0sW2nljdwMP7eBLJoIyxBmV9sxyYs/BebtY3xiH0ApQS1EFkZadwK128bX7tpmKA0nZAKVUClMou3nEG3nxNgHYT3M3Es7SUQAwlE4J/Nums3u7JAtp9V3H8enif1WeTnJy2yTmXmfwLgaKr6v+8RERERERphBmULgsI4Jw/GJDMDq9BqZNBGRWgDHfxDmVQZkihwJ+cAdSeiex2RmpeF+AIWOjOauViVAoHKP02S7yhaMyQSBdvLbMSb711ySxmUMbqRi1nBC/kvbnAu+PrXoeEs4HiLPEGrDXJsXRcky7ebnYvBuytQRlLs77AsP8Gn+9/h1o/huxVBNsNOoU7FVhMlQClKmvPZgZlqlCV4Nfz6+ckK0sJxNL9/mDgrOWQ6PtUWdb1UKrvRAalXpau8j1b0Be4cBpwwY2Jn4uIiIiIiBzHDEqX+RUxsJwMr2EX71ptlkwoC8sfyqDM9YWDY+rAQa0nJwkZlF79DEorAcpIObiDJd5RY1b8rG3goh1PXufo+yNiBSh9wefR9jogt71iexxrUMbLbD1E5TZlibfPwtqQkkkApOSm4J/KDtn1ye4alLEUXwPkd7d3DKNlBKyUEEsy0KRT8HY4i82MUYZcfQcole9f2xlvqZJB6UCJdypQBYvjzKCUvcG1YrNa6tyn/HskxvegE5yYi6KrdY6r+Px2uDW4vqbe8yUiIiIiItel8RVaA3D+OHqdX4SeWcOx/VwnZHrlYIAS0RmUtUZrUAaCF6d5voBqOwBUB7zweiXzNSijAp9WmuT4EAkCikBdVqSVbsORxgzJLPH2AON2A6f2AIWDdfaXgGs3AjVngaxWgN+gWYSVDMrIMRXP3SgbyGow1k5zEW1zHb35C3ewHr0+mPVnpazdrIt3p9uDwckWA62P00lOrkEZ2dfmV6GqVNigxNss83HkGuDr94PB7VgsNdupZ3Yz3lJmDUoHSrxTQbIDrapGaEkIUF73CXByF7ByXPgkiR+zzRhg6BtAQa+6bQ0h65eIiIiIqJFI4yu09CdvfQg9A69j+YWvo/uO5ZBlCV6DLt41kjZAGcqgRPACLMcbuohUBAJOB3LgkaS6QIluBqU2yGEh60wyWIMyngzKZK1BmdfZPDvSqKuzkq0ApfJCuB5LvLV010wMja35JTaGYNLtWJKDmVducbLEO8xumazq2LL+drMMyqxCoGS8xXMZBTrrOwFe8VlN1+CeE6XRqcCJDErT4ytfmyQEl/O7AXldFadwIAgqSUDbMZptSX6diIiIiIjIMSzxdpF08tPI7ZyM4AWhLEuQdUq8AwaZjgERnMLsyPWkMkCZDVlWZFDW6qxB6T+pOa7FEm/ZToBScczwun2OdvFWjlmyl4FoKsbYjDpdJ6sjbUwGGZTxBJPMuni7zekSbzvH1NtftQalMpuyga1Bqfys2v6MpUoGpUngPZ1YacaUCOX8JiODEtB8PpP0/lB9HplBSURERESUyhigdFNm88jNbF/wYtkrS/DqlHgHokq8g1PXplkTAMCoi8LratVd6J2qzYZX1inxVl6o5VygOa7FEu9IBmWgLivSsBRVqrtQjJSDJ6nE28mgQ34P8/uNSrwTXoMy3gCrSYm3XckOgCQilUu8LR0zTbt4JyRVApQNsMQ72ZmByQpQKuV1Sc5xmUFJRERERJQ2XA9QLliwAB06dEBWVhZKS0uxbt06w339fj8eeeQRdO7cGVlZWejbty9WrFiR0DHdJDLqFuvP8gWnwrhJjiboFbrw6nVBMMjZrVWoCYriYvJ0IBseVQZlKEApycDYj4Pdi7UXhlayziTlGpQWMiiV9yWji3eyApRNuwdfo+b99e83KvF2LYMS+kGXeF4TZcZuqgXCUi2D0uhrVLV8gjLLMk0zKFMlyJgIs+ZP6aReA61JDFCO2Qpc83Zd0yincQ1KIiIiIqK04WqAcunSpSgrK8Ps2bOxYcMG9O3bF6NGjcKRI0d093/wwQfxxz/+EU899RS2b9+Ou+66CzfeeCM2btwY9zFdpeiCnBe6djJqkhPQrkEZmjpJuRZk8EZkj9OBbNxxece6AEcgXOItAU17BrsXa1nKoLTZxVt5X7jEO5JR6XCJt9Pr8hVfAzTtpX9fsjIoEylRd6zEO4UzKJOxBqVTJd6G+ytLTe2eKwWb5NiVKk1y5AZS4t1QMigLeul333aKWQZlva/hSkREREREZlz9F/q8efMwdepUTJkyBT179sSzzz6LnJwcLFq0SHf/v/zlL5g5cybGjBmDTp064e6778aYMWPwu9/9Lu5juiqjWeRmYcYpACYZlNrsj/CFVyTQF90k5/IeHdGpsIl+BqURqxmUcQcotRmUDkhWBqXeMY0ueB1dgzLO10aSzJvk2JHSa1BaHY+dDErJZsBCMrhtZX+bX7tGAdC0yqBMkQBlgynxVn4PJfl51EeJd9KYrUHp4N9BRERERESUMNeu0M6fP4/169djxowZkW2yLGP48OFYvXq17mOqq6uRlZWl2padnY1Vq1bFfczwcaur6zpcV1VVAQiWlPv9fqOHJUzUBhC+zCz0nYDf74dHgm6AUkCd/eGvrQX8fniEBBlAbe15BPx+eEWgrni6zXXw+/2QhQceAIHac5ABCEioMXheHiFHLumEJwdS7ZmoffwBwCt5IAGo8Z+DNxR88NcKwOC4XgT3DwT8wfEGAsHxQkTGG+9rLdXWRt7IArLhczMSPq/R+cOvMQAIyQcpFJD110qACD7Gg7rXrRZeBHSO5RUwfa7hGRaArecQflxtbQCBmgC0+VS1QtIdjxnl+8BfGzCcVyfFmoeI2ujnqKcmICBsjNsreSGJ87pjitoXykUKZMN5jcypJEMSdfvbfY/WvTckSKHPW0BIqE3SvOjNhVfY/6xGxi2E7eecDHKg7jvX7PsqVRh9JiRR95d3TUCy9T63qu4zJpL692AyeVD33a19nbyQIYWqFWI9P8vfTSkoHcdMRERERI2TawHKo0ePora2FkVFRartRUVF+OSTT3QfM2rUKMybNw9XXnklOnfujIqKCrz66quora2N+5gAMHfuXDz88MNR29966y3k5OTYfWqWdfZ/gnDxsHzyMyxfvhwnvpHhLYwu8T727Rmgad3P5f99G34pD/2qD6I9gJ07tmPXnuUY4z8PH4DtvknYta0A+Hg5Ovo/RR8AJ098jaYAamoCWL58ue6YBp07ivCr56/1aFvzAADefuddDDl3Fk0ArPngPVyuGlMT3eNeWxNs83Ps60MoBPDprj349PPluE4RMDAaUyyFtZtwWXjMNQG8GedxysvLdbf3qf4KHUO3awJS5MJ9+Zv/iWS3XXb2BApD23fu+gy7Po8ew3WBWtPnekPoz9pa4/nRE37cJ598gi92V2K05v4dO3dhz157r0mf6gOR5/zeqtU4KX9l6/GJMJqHMK84hbEWjrPuww342ms9c29sQIr6QjSah5HnziE7dPtcdXXktnb/8NwEAojMfdXJU6i0+R4NH0fAAwnBLOTPv/gKW47E9163SjkXI86eRfjb0Or7MzzukydP4p04P5dOuqh6L8Kr7r5V/jZqpGzT/VOF9jPRumYLBoZur17zIY57Tjl+zhsUt+P9bnbbxdUH0S50e+1HG3HUU/fLv+uEsP13T6zvplR05kz0LxmJiIiIiFJRWtW4PfHEE5g6dSq6d+8OSZLQuXNnTJkyJeHy7RkzZqCsrCzyc1VVFUpKSjBy5Ejk5+cnOmxDYvvHwMfB210KvRgzZgyWHv4Isk4GZUGLIqCm7ucRI0YBGQXwfPQ6sA/o1q0LuvYYA+8yD1ADdB0+A12bBC/Fpb0HgfVAfm4GcBLw+jIwZswY3TF5Vj0HHAze9mU1Ac6djNpn2PBR8L7za+DUIQwaeDHwbmhMI0cDPv3Xy/vPLOD8KbRo3hQ4Clx44YXo0nMM5H9IkepPozHFIh3OiozBl5Fl+zh+vx/l5eUYMWIEfL7o3Dx541vA7tDz8GUC/rPB8Y6tC5N53n0aOLwFANCtRx907RY9BvkVOdJvQneML4eO5fHYew6hx3W7bBIubNoTeF19d48evdDtQnuvibyxPPKcrxh6NZDXzdbj4xFrHup2PAm8Fvt4A0tLIYqGWz6/Z1kGUFOt2mY0D95/ZwPBtwGysnKAs8f19w/NjezxArXB7Mz8/DyMGWnzvR46jiR7I8sktGvfERdcEt9nJha9ufD+OyvynC2/P0PjzmuSizGjkzNWO+TNlcCnwdsjR10LeJP3CygnGH0mpK/8wAfB24OHXAnR/FLnT/5y3c14v5vd5vnon8C+4O3SQZdDFF4RuU9+pe6zFOv5Wf5uSkHhihAiIiIiolTnWoCyZcuW8Hg8OHz4sGr74cOHUVxcrPuYwsJCvPbaazh37hyOHTuGNm3a4IEHHkCnTp3iPiYAZGZmIjMzet1An8+X1IuRWk/d+ljNfVXw+Xzwej3wiugMyv4di4FdirFlZAE+H+AJjs8jAR6fD+Fon8+bEbwfAHzBLCEpEAyQSJJk/LwU65lJnizdXXwZOZH9vIpgqi8jG/AaHTeYqyKHnpvH41WNF0D8r7WvLs9TkuW4j2M43x5FYEDx+qj29XgVu+eEnpuWtedqOj96rvsUOLkT3rbDgerjUXd7vJkG4zHhqXtNfb7suvdSPYj5ubOY9eb1Ztobt85afobjUDblVqwRabS/ch/b86s6Tt13hsfjsz+vNqnmQlJvt0OSEvh8O0mue/18Gdmqz3Yqi/pM+Oq+m72+rKR/PlNi7uKh+Ex7M3LUr5OFz61Wsv9NkAzpNl4iIiIiarxca5KTkZGB/v37o6KiIrItEAigoqICgwcPNn1sVlYW2rZti5qaGrzyyiu44YYbEj6mKxQNbQqkEwAAryxBRnQGZceiFuoN4UCFURdvZWONcHOAgM0mObnt9feRvXX7BRRr9llpkiM0XbwdaZ5Rj01yjDrmSi42ycnvCrS9Lnhbr2GG3a7RgPp5plq3Y6tNQex26Y33eVo6j0NftS0GKM6bBk1y8roG/yyZ4OxQnJBqzZ/ssPKdROziTURERESURlz9F3pZWRn+9Kc/4YUXXsCOHTtw99134/Tp05gyZQoA4LbbblM1vFm7di1effVV7N27F++99x5Gjx6NQCCAn//855aPmVrqApH5oQClLEnwStEZlJA1QS/DLt7hYyq7l4YuzMJdvM0CYMoLuuxi4JpKoMMkzT6KLt52A5RRXbwdCFDWaxdvg6CGMtihnav6pPf84+lWrHo+KRbIsTrHdt8LytepeX/g+j1WH2hhF8U+Io73/NjtwNA3gJaX1W2r7wBlPOMesQoYsgTo9ZDz40lUOgeolO/VdO5GnmyqACW7eJPzvvrqK3z/+99HixYtkJ2djd69e+Ojjz6K3C+EwEMPPYTWrVsjOzsbw4cPx65du0yOSERERNR4uXplc/PNN+Prr7/GQw89hEOHDqFfv35YsWJFpMnN/v37IStK8s6dO4cHH3wQe/fuRZMmTTBmzBj85S9/QUFBgeVjphTFBX+OFFwnyitL8AT0ApTai6s4Mihrz4Xus5hBCRkoGgpUbQc++4ticwIBSqEJUMYT9Ig6tmbMTlMGAAwzKBXP3TCD0upzTeDC2akApaTMoEyxAIjVwJLdAJQyENvuO0CTTmYHV9y2Mq8Jvi+b9gj+d3S1YggpltmqJ6sV0P5mt0fR8MgWvpNI88slzevUcRKw6xmgRWn9jokajG+++QZDhgzB1VdfjTfffBOFhYXYtWsXmjVrFtnnN7/5DZ588km88MIL6NixI2bNmoVRo0Zh+/btyMrSX0aHiIiIqLFyPfIwffp0TJ8+Xfe+yspK1c9Dhw7F9u3bEzpmalGsSSgHb3s8Ejw10SXeUUGvSAalJkAZCfiZlHibBsCU2YiaIKjy3NoApSSrg6Jakf3TvMTbKFgnOZhBafY6xnyszvjieU1UzzkNAmF6bGdQxvmcrQTZJbsBTQPKwFQ6lHhTclj5TiL1Lym0gdyLfwcUXgG0HlW/Y6IG49e//jVKSkqwePHiyLaOHTtGbgshMH/+fDz44IORpYj+7//+D0VFRXjttddwyy231PuYiYiIiFIZr2xcVXfB75HCf0rwWCrxNsqgDKjvB+oClOF9TDModYI02nJCSYpegzJWsMQog9IJkk5Q1UnK51Z4OXDyU519LKxB6US2aCx68xBPibYqAzdNvyYSKfG29VidXyhEH9zeWIyo1gat5xLldjcDO38PFPSp3/NSNCtZ3WRe4u3NBjpMrN/xUIPy+uuvY9SoUfjOd76DlStXom3btvjRj36EqVOnAgD27duHQ4cOYfjw4ZHHNG3aFKWlpVi9erVugLK6uhrV1dWRn8Nd2P1+P/x+f5KfUfzCY0vlMTY2nJPUwzlJLZwPmwLJv+7wh87hNzpXA54rOQkvrxMvl1OfEzuPT9PIQwOhCFh5QxmUXlmCRy/gEZVBGQp4WMqg1F7AWizxjgRBdS6Gw+etPR+9j+5xNWtQJpIlGHXsesygbDMm2JCm6UXqfZSNaAwzKOujxFvnsYm+JumaoZVIk5yYzzmRNSUTCFTHHUR1QL/Hgk16ikfW73kpGjMorTHLoCRK0N69e/HMM8+grKwMM2fOxIcffogf//jHyMjIwOTJk3Ho0CEAiFpiqKioKHKf1ty5c/Hwww9HbX/rrbeQk5Pj/JNwWHl5udtDIA3OSerhnKQWzodVfevtTOVf9Na/4/Pl9TaG+tY3CS/vcgdfrkQ/J2fOnLG8L69sXFUXiPRIoRJv2WKTnLBIgDJ8rPAalDoZlJHHWGySo1firQ1Q2s6gDEfPQ8fu8H3gs78CrYaaP9702EkOUGrXe7vgep0xONnF22FxBTAU75HGUuIdd/m0hYCjsJJlaYGbAUpPFtDhe/V7TjJg9gso0hW1jjNRYgKBAAYMGIDHHnsMAHDxxRdj27ZtePbZZzF58uS4jjljxgyUlZVFfq6qqkJJSQlGjhyJ/Px8R8adDH6/H+Xl5RgxYgR8Pn4npQLOSerhnKQWzodNH89N+in8ARnlX/TGiJKt8Mk6104XzYje1kDMTcLLO8OBl8upz0m4IsQK25GLDh064Ac/+AFuv/12tGvXzu7DSUmVQRn80yNL8Eg6H0jDAGW4i3c4gzL8WJ01KOs2GI9JL9inDNz4CtT32Q1QajMoL30muAZY27Hmjzc/uOJmkku8DZvkOLkGpcPPIdES77TN0Eogg9LWa2YlI9Kh8n5ViXeaBo7JAYr3U7ouwVAfhOKXfQzkksNat26Nnj17qrb16NEDr7zyCgCguLgYAHD48GG0bt06ss/hw4fRr18/3WNmZmYiMzP63xA+ny8tLqDTZZyNCeck9XBOUgvnwyK9gGGS+OSAfoCyAc9TIAkvr5MvV6KfEzuPtR0Jue+++/Dqq6+iU6dOGDFiBJYsWaJaL4fs0FmDUpbggU4Gpccg+8Ooi7dZho1pBqVOR2xlgCqjqXq/RNeg9DUBOn4fyGim+zBL6rPE2+giV3Yyg9LB8ncgzteEa1BaZiU7UrlPImuRutokh1KH4j2k7U5NdZSfO2ZQksOGDBmCnTt3qrZ9+umnaN++PYBgw5zi4mJUVFRE7q+qqsLatWsxePDgeh0rERERUTqIK0C5adMmrFu3Dj169MA999yD1q1bY/r06diwYUMyxtiA1V1kypJiDUpbGZSKAKUy8GFW4m2aQRmjxFubQbntkdAhra5Bqe3i7YR6bJJjFAxQBTETXYPSYQmXeNdzMxan2F6D0qt/W3dfu125HZp7N0u8KXUIZlBawgxKSqL7778fa9aswWOPPYbdu3fjxRdfxHPPPYdp06YBACRJwn333Ydf/vKXeP3117F161bcdtttaNOmDcaPH+/u4ImIiIhSUNyRh0suuQRPPvkkDhw4gNmzZ+PPf/4zLr30UvTr1w+LFi2CqI+OxelOkd3hjaxBKesHKI2y8lQZlMrX3KTE27RBTYwS74wC9X16jzMdZ7K7eCc7g9IgGGBlDcr2twb/bBkjc8LJBkJAfK+J02NwQ0JNcpKYQZlIsJIl3gRAnUGZpADlBTcG/2x9bXKOXx+UAcp0/UULpaxLL70Uy5Ytw0svvYRevXrh0Ucfxfz583HrrbdG9vn5z3+Oe+65B3feeScuvfRSnDp1CitWrEBWVpaLIyciIiJKTXFf2fj9fixbtgyLFy9GeXk5Bg0ahDvuuANffvklZs6cif/+97948cUXnRxrA6TIoAyts+CRoV/ibTuDMt41KPUyKJVrUDaN3g8Azul3pKw7ZRIzKOuzSY5RBqWqkY5BKeHAZ4Di4cEu4Ml07Ubg6Frgw7uix9ZQjNkKVO0AVn3XuWPKNjIokUgGZZp28W4IGswvzgy+6500+Hngi/H6TcHShdD5u5TIQddddx2uu87473RJkvDII4/gkUceqcdREREREaUn25GLDRs2YPHixXjppZcgyzJuu+02/P73v0f37t0j+9x444249NJLHR1oQxQIBBAOMXgQI4PSUhdvxeNUJd7aNSgtNsmBTol3OIMy3gYkkQtGJy+qdcbsJEtNciyUeHtzgU63GZ+noDdwYitQ8h37Y1Rq1g/Ial0XoEy0xDsVFfQCmvY038du52zVHKboGpQMUBIA5JQk/xy+fPPvq3Rg9zuAiIiIiIhcYztycemll2LEiBF45plnMH78eN2OPB07dsQtt9ziyAAbspraAMKvnkcGcO4ohp57yiBAaRCMUHbxFg6UeOsFafS6eNsJ4AQfYH0MdqmCsS41ybFS4h3LsArgwJtAu/+J7/FKqqzJeF7rFA9QAhaycG0GAu2sQak6jUtdvJPxXm/oGsLSBQCQ2w64akVizcUaA2ZQEhERERGlDdsByr1790Y6FBrJzc3F4sWL4x5UY1FTW3fxJEEAa6Zg8Nl/2ztIPGtQmpZ4x+riXaA+b2TXGA0I7K5ZaUcqdPFWvkaGTXJiyCp0LmNJFWCLIziWroEcyVu3zqndTMV416BEfa5ByQzKhMRa/zWdtBnl9ghSHwOURERERERpw3aU6MiRI1i7dm3U9rVr1+Kjjz5yZFCNRU2gLmghiQBw+G37B1GtQWm1xNssg1KnI7beGpTat87ojdbGaWUMdukFVZ1kJUAZzmaT5NRY81E1zoay7p4FqmC8zecd7xqU9ZlByRLvxJRMAC57Ebhup9sjofrAEm8iIiIiorRhO5ozbdo0fPHFF1Hbv/rqK0ybNs2RQTUWNbXKi6dAfEE7KxmUkkf9s+UmOZ7obXoZlEXDgIKLrI1Tb3wJS3YGpYXAVXh7vNmTTlOOM641D9M1g1Ix/5mFNh8bZ/CvXWjN0Gb9ou9rMTD4Z3uHlrxgF+/ESBLQYSKQf6HbI6H6wAxKIiIiIqK0YTvVa/v27bjkkkuitl988cXYvn27I4NqLJQl3sEgUoIBSqMMSkkKZpYFqut+NqSTQSnHKPGOVd6t3V87vkQpn4+Tx40c00aJd6oEKBPO4kzXAKUMjHgfqDkFZBfZfKxynmO8fsr33IAngcLL9buzX7Uc+PL14Lqin/0tuM2pJjnJyBYmakgYoCQiIiIiShu2oxiZmZk4fPgwOnXqpNp+8OBBeL0pUNqaRmqVAUoEEF+AMtwkJwDDDEpAHaC0mkGptwZluMRblWlpIUAZ1dAjnTIolQHQGF28422Q4zRVoDaOgFjzix0bSv2SgMLL4ntovOs7enOBzlP078tsYXxfPLgGJZF1DFASEREREaUN2yk4I0eOxIwZM/Dtt99Gtp04cQIzZ87EiBEjHB1cQ6cq8Y53rSyjEm9tlqSqNNRikxy9Eu9wF2/lfh5tEx6TcdZtiP0Yq5LdJEc5N4ZrUKZYBqVKHAHKoquBy/8OjNni/HCcNHp93XsSSCyDVhV0T+YvWxLJoGSJN5F1XIOSiIiIiChd2L4K/3//7//hyiuvRPv27XHxxcEsq02bNqGoqAh/+ctfHB9gQ6ZskgOhWYNS2Y04inZ9SUSXeGtjz6rmIXab5Cj21yvxtpJBmdQSb50xO8lKgDIc0EqVDEqlnHbxPS68tmIqa34JUHITsHdR8OdEmi+lQwMaZQZlVFYyEakwg5KIiIiIKG3YDlC2bdsWW7Zswd/+9jds3rwZ2dnZmDJlCiZOnAifz0KgiiLUTXI0a1BKHpMAJdT7ARYyKBUBStMMSk/0fjVn6rbplXjHswZlOpV4q15XoyY5ofOmUgblyDXAuSNAXme3R5JcksUGULGogn+pmkGZBkFUolQRYICSiIiIiChdxHUVnpubizvvvNPpsTQ6AW0GpTJoJ3sVa0aaUGVQmq1B6TO+T3U8nWBflqIbcricO+EmOckq8U5GBqXidY1V4p1KGZQtS90eQT1RBvYTyaDU6WBv5Zz1iV28iawruho48G+kbdMvIiIiIkpLc+a4PYL0FHea0Pbt27F//36cP39etf36669PeFCNh1mJt8Xgg5Uu3trj2c2gzG0PXPlPILMw+j4gzgzKJJV4J+VCVPm6Ghw/1bp4NyoOZVCqshNTtOEXMyiJrOt2D5DRDCi6yu2REBERERFRDLavwvfu3Ysbb7wRW7duhSRJEKHsMikUuFF3piYzQpXxqFnM3yxAogpkhgMyMbp4q4IZZkE8Wf/2BZrAsyqDMo4mOcnKoExGgNJKA6OM5sE/ldmmVD9UGbRpkEEpEijxZhdvIutkH9B5itujICIiIiIiC2ynG917773o2LEjjhw5gpycHHz88cd49913MWDAAFRWViZhiA2ZIlChV+JtRThIEYixBqXVMmjL+yXYJCdZa1Amg5UAZeuRQOlC4JJ5yR0L6UhCBmWqrkHJEm8iIiIiIiJqgGxfha9evRpvv/02WrZsCVmWIcsyLr/8csydOxc//vGPsXHjxmSMs2FSZVIJGyXesbp46wT/4irxNhlDwmtQJquLdzJKvC0ElGQv0PkHSTg3xSS5sQZlItgkh4iIiIiIiEjJdpSotrYWeXl5AICWLVviwIEDAID27dtj586dzo6uwVOUw2szKK2ugafXxVsvSGO1xDueTEu3u3inQok3uchgWQLbh+EalERERERERERusH0V3qtXL2zevBkdO3ZEaWkpfvOb3yAjIwPPPfccOnXqlIwxNljqBEpNgNIq3QxKnSBNXKXbFvdzew1K1euWhABl057OH5Oc41gGZT0F/xJag9LCLwOIiIiIiIiI0oztAOWDDz6I06dPAwAeeeQRXHfddbjiiivQokULLF261PEBNmiqzDxNlp6w2GwoEkhRNMlxLIMyiSXeyerinYwS78LLgMteBPIvdP7Y5ACn1qBUvqdjfDUmZSkBC1TjSiDQSURERERERJRCbAcoR40aFbndpUsXfPLJJzh+/DiaNWsW6eRNcRCaNShFjcnOOl28Ra0iM8uhNSjNgj2Jlng7+l5JcpMcAOgwMfnnoDi5sQalS991yixPLj1AREREREREDYStyI7f74fX68W2bdtU25s3b87gZDyEZg1K5c8BswClgmoNylDAQi8AaXmdxvrq4p2kDEq3AkfkHqfmX4pjDdi4OFTizQAlERERERERNRC2rsJ9Ph/atWuH2lqL5cdkQwCora770SyDUq/bd8CpDMo4Srw9Lq9ByQBlI6eTUZzwIWNkULa5Ftj5KZDVypnzWaUaF0u8iYiIiIgIwJY51vftY2Nfonpk+2r+F7/4BWbOnInjx48nYzyNilBmQIkAEDiv+NnmGpTKLt5OlXgnNYMyWU1yqNGRnGqSpHhsrDUo+z4GXPoMMOqjOM6TQGBR+ZlMpNkOERERERERUQqxXcf4hz/8Abt370abNm3Qvn175Obmqu7fsGGDY4Nr+BQBBlGrDlDGU+ItTEq8YTHLUPXYZK5B6WSJt0NrEFKaciGD0psDdL3L3jFzSoAzXwBtxsQ/LhWWeBMREREREVHDYDtAOX78+CQMo5FSZkApg5NAjCY5CnpdvB3LoExmF+9kBRIZoGx0klHin4w1KEeuBr78J9DxNmeOxwxKIiIiIiIiaiBsX4XPnj07GeNopBQZULVn1XeZlnjH6OKtl0WoChA63CRHtrAGpVxfAUpqfFzIoIxHTlvgwh85d7zc9s4di4iIiIiIiMhFyWxVSzEpMqC0AUqr9Lp465VmS1YDj/EEKF0u8VYfOEnHpZTl2BqURsdMMSNWAWe+BAoucnskRERERERERI6wHaCUZRmSycU7O3zboCrx9kff3+oq4EglUHS15g6dLt5OZVBaLfFWZVqmUIl3KgeWKEksBtUbisIhbo+AiIiIiIiIyFG2A5TLli1T/ez3+7Fx40a88MILePjhhx0bWOMQYw25y18GPn8J6PA9433Cgb+AMoMykTUoLQZ75EQzKLkGJTkkGRmUREREREREDdGWOW6PgEiX7QDlDTfcELXtf/7nf3DRRRdh6dKluOOOOxwZWKMQq8lFVkug2z3m++hmUCZS4q0MJDq4BmV9lXgzg7IRSsIalERERERERERUbxy7mh80aBAqKiqcOlwjEYi9SyyRgIyNLt6mJd7KQGY6dvGmxocZlERERERERETpzJEA5dmzZ/Hkk0+ibdu2Thyu8YiRQGlIMlqDMhTw1M2gtFjibXk9P8V9KRWgZICq0bGaHUxEREREREREKcl2iXezZs1UTXKEEDh58iRycnLw17/+1dHBNXxOZFAqu3ibZVAqAzdWm+SwizelA2ZQEhEREREREaUz2wHK3//+96oApSzLKCwsRGlpKZo1a+bo4Bq8WGtQGjLIoITFLt6Wm+RYLfG2sgal9pwMJJFDJK5BSURERERERJTObAcob7/99iQMo7GKN0CpEAlQBupKvPUq9y2vQRlPINNCBqV2TMlqZsMmOY0QMyiJiIiIiIiI0pntdKPFixfj5Zdfjtr+8ssv44UXXnBkUI2HgwFKAAjUhLbFKPG2Gni03MXbSom39ljMdCOHcA1KIiIiIiIiorRm+2p+7ty5aNmyZdT2Vq1a4bHHHnNkUI1G3CXeCsqAjKgJb9TZT5lBaTHwmMwu3knLdGQGXeOjLPFOZP753iEiIiIiIiJyg+0A5f79+9GxY8eo7e3bt8f+/fsdGVTjYRKgLOht8jidNSiB0DqUsNDF26zEO45My5TKoGSQqdFRvZ8TeF8V9Ep4KERERERERERkn+01KFu1aoUtW7agQ4cOqu2bN29GixYtnBpXoyDpdfFueRlQchPQfqLJAw0ClAF/5MjRrJbBWtxPKMZupUkO16CkpHEog/KC8cClzwDN+yc8IiIiIiIiIiI3zJnj7H71xXaAcuLEifjxj3+MvLw8XHnllQCAlStX4t5778Utt9zi+AAbNCGiY4nZxUCPn1g/hiqD0mqJt8UmOabZaMoAZTwZlCzxJodYXTc15nEkoOtdCQ+HiIiIiIiIiOyxHaB89NFH8dlnn+Gaa66B1xt8eCAQwG233cY1KG3TKfG2lI1olEEZbpITq8Tbaum2yRqUygxKK128o9agZIk3OcWpNSiJiIiIiIiIyA22A5QZGRlYunQpfvnLX2LTpk3Izs5G79690b59+2SMr4GLN0CpYDWDUnY4gzK83iWQYhmU1Pg4tAYlEREREREREbnCdoAyrGvXrujatauTY2l0JBGIjtNJdqdEcQBhkkFpdW1Jq01ylAFKK1lr2mNxDUpyiuo9y/knIiIiIiIiSje2040mTJiAX//611Hbf/Ob3+A73/mOI4NqPHQyKLWl0LFIUl2AJlLiHWsNSqsBSosl3pbUVxdvanyYQUlERERERESUzmxfzb/77rsYM2ZM1PZrr70W7777riODajz0ApRWpkQTgAwHEs26eKvWoLRY4m21i7cVUWtQskkOOUTiGpRERERERERE6cx2gPLUqVPIyIheJ9Hn86Gqqsr2ABYsWIAOHTogKysLpaWlWLdunen+8+fPR7du3ZCdnY2SkhLcf//9OHfuXOT+OXPmQJIk1X/du3e3Pa764UAGpfIxZiXeljsdW9xPWeJtBbt4U9Iwg5KIiIiIiIgondm+mu/duzeWLl0atX3JkiXo2bOnrWMtXboUZWVlmD17NjZs2IC+ffti1KhROHLkiO7+L774Ih544AHMnj0bO3bswMKFC7F06VLMnDlTtd9FF12EgwcPRv5btWqVrXHVGxFngFKbJZa0DEqzEu8EA5RJ6+JNjQ7XoCQiIiIiIiJKa7ab5MyaNQs33XQT9uzZg2HDhgEAKioq8OKLL+If//iHrWPNmzcPU6dOxZQpUwAAzz77LN544w0sWrQIDzzwQNT+H3zwAYYMGYLvfe97AIAOHTpg4sSJWLt2rfpJeb0oLi62+9TqnWS3xDvnAuDMl0DxcM0doceYdfGOaw1Kk/3yuhjfF+u4wQ32Hm/5PAxQNT7MoCQiIiIiIiJKZ7YDlOPGjcNrr72Gxx57DP/4xz+QnZ2Nvn374u2330bz5s0tH+f8+fNYv349ZsyYEdkmyzKGDx+O1atX6z7msssuw1//+lesW7cOAwcOxN69e7F8+XJMmjRJtd+uXbvQpk0bZGVlYfDgwZg7dy7atWtnOJbq6mpUV1dHfg6Xqvv9fvj9fqOHJU5nHcdaISFgdM6rKyHvX4JAp6mAYh+v5IEEoLbmPDwABCTUaI4hBwTCIcpaIYzPUVsLX+imv7ZWdR6V5ldAvuQpiPyLICy8RlKtUL3Z/DU1xseOQ3jMtYGA8XMzEJ7jpM41xRTvPMiBQOS9HRACtZzHhPEzkRo4D6khnechHcdMRERERI2T7QAlAIwdOxZjx44FEAzmvfTSS/jpT3+K9evXo7bWWunv0aNHUVtbi6KiItX2oqIifPLJJ7qP+d73voejR4/i8ssvhxACNTU1uOuuu1Ql3qWlpXj++efRrVs3HDx4EA8//DCuuOIKbNu2DXl5ebrHnTt3Lh5++OGo7W+99RZycnIsPZ94dKjxR83A3n2fYftXy00e1QvYqw7gjvbXIhPA9o83ozeAqpMnUblcfYwu53fhotDtffs+x8cH9M+RHfgaI0O333nnXZyVd5iMpQRAFQCz8Qa1qN2GyxU/v/veKpySP4/5OKtuCP25Zb+E/Qdjj0dPeXm5Y+Oh+Nmdh47+7egTun34yNdYtzy++ado/EykBs5DakjHeThz5ozbQyAiIiIisiSuACUQ7Oa9cOFCvPLKK2jTpg1uuukmLFiwwMmxRamsrMRjjz2Gp59+GqWlpdi9ezfuvfdePProo5g1axaAYDfxsD59+qC0tBTt27fH3//+d9xxxx26x50xYwbKysoiP1dVVaGkpAQjR45Efn5+0p7PrpfmRG3r1LkrOvSO7pJuxvt6NlB9Ej27XwhsAfLzm2LMCPUx5J07gS3B2x07dUb7vgbnOPMl8Ebw5tXDrgmWlTtA+jofqKz7+corhwL5zjUv8n+zBvLX76JX13vQy2ajIb/fj/LycowYMQI+ny/2Aygp4p0Hec8XwIbg7aKiYowZYu/zQ9H4mUgNnIfUkM7zEE/zQiIiIiIiN9gKUB46dAjPP/88Fi5ciKqqKnz3u99FdXU1XnvtNdsNclq2bAmPx4PDhw+rth8+fNhw/chZs2Zh0qRJ+OEPfwgg2LDn9OnTuPPOO/GLX/wCshy9/lxBQQEuvPBC7N6923AsmZmZyMzMjNru8/mSejEiIbrE2+PxwWP3nHIwIOeRgmtaSpInetzeup89Ho/xOTKyIjd9vkzAqefvU3d+9/kynDs2ALQqBVqVIo4e6BHJnm+yxvY8eOq+xmSPFzLn0DH8TKQGzkNqSMd5SLfxEhEREVHjZbmjxLhx49CtWzds2bIF8+fPx4EDB/DUU0/FfeKMjAz0798fFRUVkW2BQAAVFRUYPHiw7mPOnDkTFYT0eIIhKaHXERvAqVOnsGfPHrRu3TrusSZPnF28jR4T7uKt1yjGapMcWGySY1fU82IzE3KKZHCbiIiIiIiIiNKB5QzKN998Ez/+8Y9x9913o2vXro6cvKysDJMnT8aAAQMwcOBAzJ8/H6dPn4509b7tttvQtm1bzJ07F0AwSDpv3jxcfPHFkRLvWbNmYdy4cZFA5U9/+lOMGzcO7du3x4EDBzB79mx4PB5MnDjRkTE7STeUEk+AMqqLt07wT3lcs07XqqCkk8EezZjYbZucYrXzPBERERERERGlJMsBylWrVmHhwoXo378/evTogUmTJuGWW25J6OQ333wzvv76azz00EM4dOgQ+vXrhxUrVkQa5+zfv1+VMfnggw9CkiQ8+OCD+Oqrr1BYWIhx48bhV7/6VWSfL7/8EhMnTsSxY8dQWFiIyy+/HGvWrEFhYWFCY00OhzMoRahBkW4GpTJwYxbE0c9ETZg2cMRAEjmGGZRERERERERE6cxygHLQoEEYNGgQ5s+fj6VLl2LRokUoKytDIBBAeXk5SkpKDLtkm5k+fTqmT5+ue19lZaV6sF4vZs+ejdmzZxseb8mSJbbH4Ba9NSjjCtxpS7z1gjRWMyiTJep5MZBEDlG+nxn4JiIiIiIiIko7tq/mc3Nz8YMf/ACrVq3C1q1b8ZOf/ASPP/44WrVqheuvvz4ZY2y49NbNjCeDUg5nUIZKvPWCNJbXoExS4DDqeTFASU5hBiURERERERFROkso3ahbt274zW9+gy+//BIvvfSSU2NqRJLUJEc3SGNxnT5Pdt1tb7bxfnaxxJuShWtQEhEREREREaU1R67mPR4Pxo8fj9dff92JwzUakuNrUIYzKGN18TbJMvM1AYYsAS57CfDl2x+LIZZ4U7Iwg5KIiNz1+OOPQ5Ik3HfffZFt586dw7Rp09CiRQs0adIEEyZMwOHDh90bJBEREVEKY7qRi3QDlHFNSbiLd63xMVRrUMY4R/ubgQ6JNUCKPj+7eFOycA1KIiJyz4cffog//vGP6NOnj2r7/fffj3/96194+eWXsXLlShw4cAA33XSTS6MkIiIiSm28mndVkkq8Y3bxToUmOXzrkUMkZlASEZE7Tp06hVtvvRV/+tOf0KxZs8j2b7/9FgsXLsS8efMwbNgw9O/fH4sXL8YHH3yANWvWuDhiIiIiotRkuYs3OU83g1JOJEBZEzmy4T6AO1lm2sArMyjJMVyDkoiI3DFt2jSMHTsWw4cPxy9/+cvI9vXr18Pv92P48OGRbd27d0e7du2wevVqDBo0KOpY1dXVqK6ujvxcVVUFAPD7/fD7/VH7p4rw2FJ5jI0N5yT1cE5SS4Ocj0B6Xwf5Q+P3Gz2PNJsrOU2mw+xldepzYufxDFC6yqES76g1KGOVeKdCBiUDlOQQZlASEZELlixZgg0bNuDDDz+Muu/QoUPIyMhAQUGBantRUREOHTqke7y5c+fi4Ycfjtr+1ltvIScnx5ExJ1N5ebnbQyANzknq4ZykloY1H33dHoAjyr/orX/H58vrdyAJ6psm07Hcwsua6OfkzJkzlvdlgNJFjjXJkS108VYFCN0I57OLNyUL16AkIqL69cUXX+Dee+9FeXk5srKyHDnmjBkzUFZWFvm5qqoKJSUlGDlyJPLznWxc6Cy/34/y8nKMGDECPp/P7eEQOCepiHOSWhrkfHw81+0RJMQfkFH+RW+MKNkKnxyI3uGiGfU/qATMTZPpmGHysjr1OQlXhFjBAKWLktbFOyVLvJlBSUni9vqqRETU6Kxfvx5HjhzBJZdcEtlWW1uLd999F3/4wx/wn//8B+fPn8eJEydUWZSHDx9GcXGx7jEzMzORmZkZtd3n86XFBXS6jLMx4ZykHs5JamlQ86EX1EtDPjmgH6BMs3kKpMl0WHlZE/2c2HksA5SucihAGc5ODFgs8XalSY72eTGQRE5hBiUREdWva665Blu3blVtmzJlCrp3747//d//RUlJCXw+HyoqKjBhwgQAwM6dO7F//34MHjzYjSETERERpTQGKF0kCb0ApQNrUMYq8U6FDEoGksgpXIOSiIjqWV5eHnr16qXalpubixYtWkS233HHHSgrK0Pz5s2Rn5+Pe+65B4MHD9ZtkENERETumDPH2f0ofgxQuipJJd6pmEEZtQYlA0nkFGZQEhFR6vn9738PWZYxYcIEVFdXY9SoUXj66afdHhYRERFRSmKA0kWS5HCA0rRJDtegpAaKa1ASEVEKqKysVP2clZWFBQsWYMGCBe4MiIiIiCiNMN3IRfol3k5kUMbq4p0Ca1Ay040cwwxKIiIiIiIionTGq3lXObUGpaZJjt60MoOSGiyuQUlERERERESUzhigdJGUtDUo06DEm5lu5BSJGZRERERERERE6YxX8y5yPEAZMOniDbfX6WMGJSWL2+9tIiIiIiIiIkoEA5QuSl4GZSqWeGufFwNJ5BBmUBIRERERERGlNV7Nu8qpNSgtdPGWlQFCN5rksMSbkkUZoGTgm4iIiIiIiCjdMErkIkmqxzUolVOdCmtQMoOSHKN8L/ErjYiIiIiIiCjdeN0eQGPmXIm3zS7eqbAGJTPdyCnK4DffV0REREREROSwOXPcHkHDx3QjF+kHKBMo8U6nLt5865FjJIPbRERERERERJQOGCVyUdKa5OhmULpd4i2Z/0wULzbJISIiIiIiIkprLPFONYkEKMNNcmJlUKZEllkqjIEaBFVQku8rIiIiIiJKUVvmWN+3j419iRoAphu5yPEMysgalClY4q3FDEpyDDMoiYiIiIiIiNIZr+ZdIoSAjEDwdqIdtqPWoEzFJjlEycI1KImIiIiIiIjSGQOULhHK5ElVdmMCXbyFWQaly2tQEiUL16AkIiIiIiIiSmu8mndJQAhIUihKmXCA0maJN7PMqEHhGpRERERERERE6YwBSpcEhGINSknRqyiRAGXkeDFKvJllRg0JMyiJiIiIiIiI0hqv5l0SEAJyKKAoVEHJBNagjPwco8SbWWbUoHANSiIiIiIiIqJ0xgClSwJCRDIoJdX6kIlkUEY2WNiHqKFgBiURERERERFROuPVvEvUJd6K4KHsQIAyZhdvEX0/UbpidjARERERERFRWmOA0iXBJjnB28KpLt51G8z3EQxQUkPCDEoiIiIiIiKidMareZeIAIBIibfTa1DGyqAM2D8HUaqSuAYlERERERERUTrzxt6FkiEgBHx6Jd71sQYlMyipQVGu4crfuRARERERUQOwZY7bIyCqV7yad4mySQ7qo0mOaqoZoKQGhBmURERERERERGmNAUqX1AoBSdILUNZHiTcDlNSQcA1KIiIiIiIionTGEm+XCKEMqygDLGySQ2QLMyiJiIiIiIgavDlznN2PUgvTjVwSEAKyXrMaJ0q8Jb0ApXIbA5TUkHANSiIiIiIiIqJ0xqt5lwQEoBsodGQNyljTygAlNSDMoCQiIiIiIiJKawxQuiQQEAYl3k6sQRkjSMMSb2pQuAYlERERERERUTrj1bxLhEBdkxylpHXxVp3d/jmIUpYyQMkMSiIiIiIiIqJ0wwClS4JrUCYpQBkri0zorH1JlK5U73d+pRERERERERGlG17Nu6RWCEiOBSgtdPEmarCYQUlERERERESUzrxuD6CxEkJADpd4SwkGWOyuQelaibfk4rmpwVK93/k7FyIiIiIionRRuRIQEoASYNUqQG8lvMpX631Y5AJezbsk4GSczm4Xb7dKvJndRknBDEoiIiIiIiKidMYApUsCAQeDhGmVQUnkMK5BSURERERERJTWeDXvkqQGKGNOKwOU1JAwg5KIiIiIiIgonTFA6ZKAk2XWdjMohUsBSgaPKCm4BiURERERERFROuPVvEuczaC02cU7u9i5c9vCACUlgfL9zyA4ERERERERUdphF2+XCCezGKMyKA3izle8ChxdA1ww3rlzE7mNXbyJiIiIiIiI0prrV/MLFixAhw4dkJWVhdLSUqxbt850//nz56Nbt27Izs5GSUkJ7r//fpw7dy6hY7ohIGqdO1jUGpQGWWQlNwIX/9o4gJl0zG6jZOAalERERERERETpzNUA5dKlS1FWVobZs2djw4YN6Nu3L0aNGoUjR47o7v/iiy/igQcewOzZs7Fjxw4sXLgQS5cuxcyZM+M+pluEqsQ7waCK1QCl2xg8oqRgBiURERERERFROnP1an7evHmYOnUqpkyZgp49e+LZZ59FTk4OFi1apLv/Bx98gCFDhuB73/seOnTogJEjR2LixImqDEm7x3SLKyXermOAkpKAa1ASERERERERpTXX1qA8f/481q9fjxkzZkS2ybKM4cOHY/Xq1bqPueyyy/DXv/4V69atw8CBA7F3714sX74ckyZNivuYAFBdXY3q6urIz1VVVQAAv98Pv9+f0PM0ct5/PnJboC50F8/5pIBQTWRtIIBAksadCC+khJ5nsoTHkkpjaozinoeaGvjCN2sDEJzHhPEzkRo4D6khnechHcdMRERERI2TawHKo0ePora2FkVFRartRUVF+OSTT3Qf873vfQ9Hjx7F5ZdfDiEEampqcNddd0VKvOM5JgDMnTsXDz/8cNT2t956Czk5OXafmiWffXsWg0Ov/unTZ5AX2r58+XLbx2pe+zGuUPz86ae78Oln9o+TbGNrA5E3XDzPM9nKy8vdHgLB/jxkBY5iVOj2ho2bcHBrcj6zjRE/E6mB85Aa0nEezpw54/YQiIiIiIgsSasu3pWVlXjsscfw9NNPo7S0FLt378a9996LRx99FLNmzYr7uDNmzEBZWVnk56qqKpSUlGDkyJHIz893YuhR1n76GbA5eDs3Nxc4Fbw9ZswY28eSjjYD3qn7+cJu3dGlh/3jJJvnVQ8Q6g0Uz/NMFr/fj/LycowYMQI+ny/2Aygp4p6Hs18B/w7evOSS/hAXpM57K13xM5EaOA+pIZ3nIVwRQkRERESU6lwLULZs2RIejweHDx9WbT98+DCKi4t1HzNr1ixMmjQJP/zhDwEAvXv3xunTp3HnnXfiF7/4RVzHBIDMzExkZmZGbff5fEm7GJHlunXzlMvmxXU+n3rsHo8XnlS8iFKsFZiKF3nJnG+yzvY81NS9/71eH8A5dAw/E6mB85Aa0nEe0m28RERERNR4uRagzMjIQP/+/VFRUYHx48cDAAKBACoqKjB9+nTdx5w5c0YV2AMAjyfYIEYIEdcx3RJojE1y2MCEkkLxvkrV9z4REREREVEjUbnS7RFQOnK1xLusrAyTJ0/GgAEDMHDgQMyfPx+nT5/GlClTAAC33XYb2rZti7lz5wIAxo0bh3nz5uHiiy+OlHjPmjUL48aNiwQqYx0zVQgRcO5g2gAlu2VToyIZ3CYiIiIiIiKidOBqgPLmm2/G119/jYceegiHDh1Cv379sGLFikiTm/3796syJh988EFIkoQHH3wQX331FQoLCzFu3Dj86le/snzMVCFEreKnBIMqUVljqRqkSdVxUVpTvf/5HiMiIiIiIiJKN643yZk+fbph+XVlZaXqZ6/Xi9mzZ2P27NlxHzNViEAjLPFm8IiSgu8rIiIiIiIiCpozx+0RUDxSNZLV4AUaY4k316CkZOD7ioiIiIiIiCitMUDpEpHUJjmpGrBJ1XFRelM2yeF7jIiIiIiIiCjdMEDpkkAgmEEZEBKQaDZlVAZlik4rg0eUDCm7pAERERERERERWeH6GpSNVbiLt4AECQlmUzKDkho1vq+IiIiIiIgaqqtazLG8b+Ux6/tSamHqkUsCjpZ4s4s3NWIpG5AnIiIiIiIiIisYoHRLKIMy4MQUpEsXbwaSKCkkg9tERERERERElA5SNJLV8NWVeCMJa1AySEONSKoG5ImIiIiIiIjIEl7ZuyQQCJd4OxBMTJcMSgZOKSn4viIiIiIiIiJKZ6kayWrwAuEMSiEBTjfJSdmATaqOi9Ib31dERERERERE6YwBSpeoS7wbSRfvVB0XpbeUzRgmIqKGau7cubj00kuRl5eHVq1aYfz48di5c6dqn3PnzmHatGlo0aIFmjRpggkTJuDw4cMujZiIiIgotfHK3iUiFJQUkAEkugaldhpTdVoZoKRk4PuKiIjq18qVKzFt2jSsWbMG5eXl8Pv9GDlyJE6fPh3Z5/7778e//vUvvPzyy1i5ciUOHDiAm266ycVRExEREaUur9sDaKyaZoWzHpOxBmWqBmxSdVyU1iR28SYiovq1YsUK1c/PP/88WrVqhfXr1+PKK6/Et99+i4ULF+LFF1/EsGHDAACLFy9Gjx49sGbNGgwaNMiNYRMRERGlLAYoXTL0wkJgJ+CV4XyJd6oGaVI2cErpje8rIiJy17fffgsAaN68OQBg/fr18Pv9GD58eGSf7t27o127dli9erVugLK6uhrV1dWRn6uqqgAAfr8ffr8/mcNPSHhsqTzGxoZzkno4J6nF1fkIpGq1o7OEzUs0EaoKFQ4s3yXL/JxZZfYRcOpzYufxDFC6RSi7eDu9BmWqfukxkERJkLLvdyIiagwCgQDuu+8+DBkyBL169QIAHDp0CBkZGSgoKFDtW1RUhEOHDukeZ+7cuXj44Yejtr/11lvIyclxfNxOKy8vd3sIpME5ST2ck9Tiznz0deGcLiiJ72EnL+id8Kn7lixP+BiNxXILL1Win5MzZ85Y3pcBSteE16CUAJHoGpRpkkFJlBR8vxMRkXumTZuGbdu2YdWqVQkdZ8aMGSgrK4v8XFVVhZKSEowcORL5+fmJDjNp/H4/ysvLMWLECPh8PreHQ+CcpCLOSWpxdT4+nlu/53OJ3b8ShSTj5AW9kfflVkgJxkfeOz4jocc3JjNMXiqnPifhihArGKB0S+RDJ0HkXQjp3IH4jyVJUGdipmjAhplulAxcOoCIiFwyffp0/Pvf/8a7776LCy64ILK9uLgY58+fx4kTJ1RZlIcPH0ZxcbHusTIzM5GZmRm13efzpUVAI13G2ZhwTlIP5yS1uDIfcoLJSWlCirNIVBKBhAOUgQA/Y1ZZefsn+jmx81gGKF1TF0ysHbgI8rYHgW4/jv9wkgyI2rrbKYmBJEoyBiuJiKgeCCFwzz33YNmyZaisrETHjh1V9/fv3x8+nw8VFRWYMGECAGDnzp3Yv38/Bg8e7MaQiYjIqi1zrO/bx8a+RGSKAUrXiMj/pZwLgCF/S+xwkqcuQMlAIBEREVHSTJs2DS+++CL++c9/Ii8vL7KuZNOmTZGdnY2mTZvijjvuQFlZGZo3b478/Hzcc889GDx4MDt4ExEREelggNItkSY5DmU7KtehTNksslQdFxEREZF1zzzzDADgqquuUm1fvHgxbr/9dgDA73//e8iyjAkTJqC6uhqjRo3C008/Xc8jJSIiIkoPDFC6JriuQoL9u+uoGuWkaIl3ygZOiYiIiKwTIva/4LKysrBgwQIsWLCgHkZERERElN5SNJLVCIT/YetU0E4ZoPTmOHNMxzFASUREREREREREagxQusbhjtvKxjjeJs4c03EMUBIRERERERERkRoDlK5xrLg7SJlB6ctz9thOYYk3JR3fY0RERERERETphgFKt4jwGpRJaJLDDEoiIiIiIiIiIkoTDFC6hhmUREREREREREREDFC6JdQkRziVVShq6m6nagZlbie3R0ANXaoG54mIiIiIiIjIkNftATReDjfJqTlTdztVA5SlfwY23A9cOM3tkVBD0/8J4OQuoOVlbo+EiIiIiIiIiGxigNItTmdQ1ioClHKKTmtOG+DypW6Pghqibj92ewREREREREREFCeWeLsmEPrTqRLvQOx9iIiIiIiIiIiIUgwDlG4RDjfJISIiIiIiIiIiSkMpWgvcGDhc4k1ERERERERE1Ihd1WKOrf0rj9nbn5KHGZSuCWdQcgqIiIiIiIiIiKjxYnTMLVwzkoiIiIiIiIiIiAFK9ySpxNub6+zxiIiIiIiIiIiIkohrULol0iTH4QClJ9vZ4xERERERERFRtC1z3B4BUYPBDErXJCmD0pPj7PGIiIiIiIiIiIiSiAFKt0TWoHS6xJsZlERERERERERElD5Y4u0aEXuXeDCDkoiIiIiIiIgcVrnS7RFQQ8YMStckq0kOA5RERERERERERJQ+GKB0S7hJjuRwgDK3k7PHIyIiIiIiIiIiSiKWeLvG4QzKof8C9vwZuGSeM8cjIiIiIiIiogaNZduUKhigdIvTTXLaXhf8j4iIiIiIiIiIKI2wxNs1SWqSQ0RERERERERElEYYoHRLeA1Kp5vkEBERERERERERpREGKF0TXoOSU0BERERERERERI0Xo2NuiaxBSURERERERERE1HgxQOkah7t4ExERERERERERpSF28XYN16AkIiIiIiIiisuWOdb37WNjXyJyBTMo3SKYQUlERERERERERMQApWvCa1AyQElERERERERERI0XA5RuESL2PkRERERERERERA0c16B0DUu8iYiIiIiIqAHbMsfe/lwrkqjRSokMygULFqBDhw7IyspCaWkp1q1bZ7jvVVddBUmSov4bO3ZsZJ/bb7896v7Ro0fXx1OxgU1yiIiIiIiIiIiIXM+gXLp0KcrKyvDss8+itLQU8+fPx6hRo7Bz5060atUqav9XX30V58+fj/x87Ngx9O3bF9/5zndU+40ePRqLFy+O/JyZmZm8JxEPNskhIiIiIiIiIiJyP4Ny3rx5mDp1KqZMmYKePXvi2WefRU5ODhYtWqS7f/PmzVFcXBz5r7y8HDk5OVEByszMTNV+zZo1q4+nY51gkxwiIiIiIiIiIiJXMyjPnz+P9evXY8aMGZFtsixj+PDhWL16taVjLFy4ELfccgtyc3NV2ysrK9GqVSs0a9YMw4YNwy9/+Uu0aNFC9xjV1dWorq6O/FxVVQUA8Pv98Pv9dp+WJVKtP/LiJ+scZE349ec8uIvzkDo4F6mB85Aa0nke0nHMRERERNQ4uRqgPHr0KGpra1FUVKTaXlRUhE8++STm49etW4dt27Zh4cKFqu2jR4/GTTfdhI4dO2LPnj2YOXMmrr32WqxevRoejyfqOHPnzsXDDz8ctf2tt95CTk6OzWdlTduajRgAAJBQXl6elHOQPZyH1MB5SB2ci9TAeUgN6TgPZ86ccXsIRERERESWuL4GZSIWLlyI3r17Y+DAgartt9xyS+R279690adPH3Tu3BmVlZW45pproo4zY8YMlJWVRX6uqqpCSUkJRo4cifz8/KSMXfr8BLAuuAbliBEj4PP5knIeis3v96O8vJzz4DLOQ+rgXKQGzkNqSOd5CFeEEBERERGlOlcDlC1btoTH48Hhw4dV2w8fPozi4mLTx54+fRpLlizBI488EvM8nTp1QsuWLbF7927dAGVmZqZuEx2fz5e8ixFPaPlPSUruecgyzkNq4DykDs5FauA8pIZ0nId0Gy8RERG2zHF7BETkEleb5GRkZKB///6oqKiIbAsEAqioqMDgwYNNH/vyyy+juroa3//+92Oe58svv8SxY8fQunXrhMfsGHbxJiIiIiIiIiIicr+Ld1lZGf70pz/hhRdewI4dO3D33Xfj9OnTmDJlCgDgtttuUzXRCVu4cCHGjx8f1fjm1KlT+NnPfoY1a9bgs88+Q0VFBW644QZ06dIFo0aNqpfnZI0I/ckAJRERERERERERNV6ur0F588034+uvv8ZDDz2EQ4cOoV+/flixYkWkcc7+/fshy+o46s6dO7Fq1Sq89dZbUcfzeDzYsmULXnjhBZw4cQJt2rTByJEj8eijj+qWcbuHGZRERERERERERESuBygBYPr06Zg+fbrufZWVlVHbunXrBiFE9M4AsrOz8Z///MfJ4SWHCIRuMEBJRERERERERESNl+sl3o2XfoCViIiIiIiIiIioMWGA0i1skkNERERERERERJQaJd6NE5vkEBEREREREZHzKle6PQIie5hB6RZfPkSTLjgnNXd7JERERERERERERK5hgNIt7W9GzbXbsSXzLrdHQkRERERERERE5BqWeBMRERERERGRNVvmuD2CRotl29SQMYOSiIiIiIiIiIiIXMMAJREREREREREREbmGJd5ERERERERETtkyR/1zQAbQF/h4LiAH1Pf10exr99hm7BzbznHThLIcuvLVutuyDPTtC8ydCwRC0zFnTnLPT0SxMYOSiIiIiIiIiIiIXMMAJREREREREREREbmGAUoiIiIiIiIiIiJyDdegJCIiIiKihmvLHOv72l0PkCiVbZnj9ghSxlUt5kRuC0lGFfriiuZzIYngIpSVT7k0MCKKYAYlERERERERERERuYYBSiIiIiIiIiIiInINS7yJiIiIiJJkwYIF+O1vf4tDhw6hb9++eOqppzBw4EC3h6W2ZY71fZNZAr3FxrGTNQ47Y7ChciVQeSz2seck5/R1ttg8QbqVvG+ZY29/O8/P7rHdPm4SVa50/phXDXX+mESUXphBSURERESUBEuXLkVZWRlmz56NDRs2oG/fvhg1ahSOHDni9tCIiIiIUgoDlERERERESTBv3jxMnToVU6ZMQc+ePfHss88iJycHixYtcntoRERERCmFJd46hBAAgKqqqqSex+/348yZM6iqqoLP50vqucgY5yE1cB5SB+ciNXAeUkM6z0P43zHhf9dQ/Tp//jzWr1+PGTNmRLbJsozhw4dj9erVUftXV1ejuro68vO3334LADh+/Dj8fn9yB1t13vq+x46pfgx/Ro4dO5b4ZySBcTh23CQ5dQ44fz72mO08LSOmc2L3tXBiQPUpmc8vgfeRPyAH56TqPHxyIO7jpIpT55w/5rEkXHobjVNIwfmQz52PdPEmd7k5J1a+mxsqs69Ap/5+P3nyJABr/x6VBP/VGuXLL79ESUmJ28MgIiIiStgXX3yBCy64wO1hNDoHDhxA27Zt8cEHH2Dw4MGR7T//+c+xcuVKrF27VrX/nDlz8PDDD9f3MImIiIiSzsq/R5lBqaNNmzb44osvkJeXB0mSknaeqqoqlJSU4IsvvkB+fn7SzkPmOA+pgfOQOjgXqYHzkBrSeR6EEDh58iTatGnj9lDIghkzZqCsrCzycyAQwPHjx9GiRYuk/ns0Uen8GWmoOCeph3OSWjgfqYdzknqcmhM7/x5lgFKHLMv1mmmQn5/PD2EK4DykBs5D6uBcpAbOQ2pI13lo2rSp20NotFq2bAmPx4PDhw+rth8+fBjFxcVR+2dmZiIzM1O1raCgIJlDdFS6fkYaMs5J6uGcpBbOR+rhnKQeJ+bE6r9H2SSHiIiIiMhhGRkZ6N+/PyoqKiLbAoEAKioqVCXfRERERMQMSiIiIiKipCgrK8PkyZMxYMAADBw4EPPnz8fp06cxZcoUt4dGRERElFIYoHRRZmYmZs+eHVXOQ/WL85AaOA+pg3ORGjgPqYHzQIm4+eab8fXXX+Ohhx7CoUOH0K9fP6xYsQJFRUVuD80x/IykHs5J6uGcpBbOR+rhnKQeN+aEXbyJiIiIiIiIiIjINVyDkoiIiIiIiIiIiFzDACURERERERERERG5hgFKIiIiIiIiIiIicg0DlEREREREREREROQaBihdsmDBAnTo0AFZWVkoLS3FunXr3B5Sg/Luu+9i3LhxaNOmDSRJwmuvvaa6XwiBhx56CK1bt0Z2djaGDx+OXbt2qfY5fvw4br31VuTn56OgoAB33HEHTp06VY/PIv3NnTsXl156KfLy8tCqVSuMHz8eO3fuVO1z7tw5TJs2DS1atECTJk0wYcIEHD58WLXP/v37MXbsWOTk5KBVq1b42c9+hpqamvp8KmnvmWeeQZ8+fZCfn4/8/HwMHjwYb775ZuR+zoM7Hn/8cUiShPvuuy+yjXORfHPmzIEkSar/unfvHrmfc0CNnZ1/p1511VVRnydJkjB27NjIPrfffnvU/aNHj66Pp9Ig2L1umD9/Prp164bs7GyUlJTg/vvvx7lz5xI6Jqk5PSex/l6i2OzMid/vxyOPPILOnTsjKysLffv2xYoVKxI6Jqk5PR/8jCQmVnxET2VlJS655BJkZmaiS5cueP7556P2cfwzIqjeLVmyRGRkZIhFixaJjz/+WEydOlUUFBSIw4cPuz20BmP58uXiF7/4hXj11VcFALFs2TLV/Y8//rho2rSpeO2118TmzZvF9ddfLzp27CjOnj0b2Wf06NGib9++Ys2aNeK9994TXbp0ERMnTqznZ5LeRo0aJRYvXiy2bdsmNm3aJMaMGSPatWsnTp06FdnnrrvuEiUlJaKiokJ89NFHYtCgQeKyyy6L3F9TUyN69eolhg8fLjZu3CiWL18uWrZsKWbMmOHGU0pbr7/+unjjjTfEp59+Knbu3ClmzpwpfD6f2LZtmxCC8+CGdevWiQ4dOog+ffqIe++9N7Kdc5F8s2fPFhdddJE4ePBg5L+vv/46cj/ngBozu/9OPXbsmOqztG3bNuHxeMTixYsj+0yePFmMHj1atd/x48fr6RmlN7vz8be//U1kZmaKv/3tb2Lfvn3iP//5j2jdurW4//774z4mqSVjTmL9vUTm7M7Jz3/+c9GmTRvxxhtviD179oinn35aZGVliQ0bNsR9TKqTjPngZyQxseIjWnv37hU5OTmirKxMbN++XTz11FPC4/GIFStWRPZJxmeEAUoXDBw4UEybNi3yc21trWjTpo2YO3eui6NquLQfwEAgIIqLi8Vvf/vbyLYTJ06IzMxM8dJLLwkhhNi+fbsAID788MPIPm+++aaQJEl89dVX9Tb2hubIkSMCgFi5cqUQIvi6+3w+8fLLL0f22bFjhwAgVq9eLYQIfpnKsiwOHToU2eeZZ54R+fn5orq6un6fQAPTrFkz8ec//5nz4IKTJ0+Krl27ivLycjF06NBIgJJzUT9mz54t+vbtq3sf54Aau0T/nfr73/9e5OXlqX4ZOXnyZHHDDTc4PdRGwe58TJs2TQwbNky1raysTAwZMiTuY5JaMubE7O8lis3unLRu3Vr84Q9/UG276aabxK233hr3MalOMuaDnxHnWAlQ/vznPxcXXXSRatvNN98sRo0aFfk5GZ8RlnjXs/Pnz2P9+vUYPnx4ZJssyxg+fDhWr17t4sgaj3379uHQoUOqOWjatClKS0sjc7B69WoUFBRgwIABkX2GDx8OWZaxdu3aeh9zQ/Htt98CAJo3bw4AWL9+Pfx+v2ouunfvjnbt2qnmonfv3igqKorsM2rUKFRVVeHjjz+ux9E3HLW1tViyZAlOnz6NwYMHcx5cMG3aNIwdO1b1mgP8TNSnXbt2oU2bNujUqRNuvfVW7N+/HwDngBo3J/6dunDhQtxyyy3Izc1Vba+srESrVq3QrVs33H333Th27JijY2+I4pmPyy67DOvXr4+U2e3duxfLly/HmDFj4j4m1UnGnIQZ/b1E5uKZk+rqamRlZam2ZWdnY9WqVXEfk4KSMR9h/IzUn9WrV0ddp4waNSoyh8n6jHjjfiTF5ejRo6itrVVd1ABAUVERPvnkE5dG1bgcOnQIAHTnIHzfoUOH0KpVK9X9Xq8XzZs3j+xD9gQCAdx3330YMmQIevXqBSD4OmdkZKCgoEC1r3Yu9OYqfB9Zt3XrVgwePBjnzp1DkyZNsGzZMvTs2RObNm3iPNSjJUuWYMOGDfjwww+j7uNnon6Ulpbi+eefR7du3XDw4EE8/PDDuOKKK7Bt2zbOATVqif47dd26ddi2bRsWLlyo2j569GjcdNNN6NixI/bs2YOZM2fi2muvxerVq+HxeBx9Dg1JPPPxve99D0ePHsXll18OIQRqampw1113YebMmXEfk+okY04A87+X8vLykvqc0l08czJq1CjMmzcPV155JTp37oyKigq8+uqrqK2tjfuYFJSM+QD4GalvRv/WraqqwtmzZ/HNN98k5TPCACUR1Ytp06Zh27ZtUb8Jo/rTrVs3bNq0Cd9++y3+8Y9/YPLkyVi5cqXbw2pUvvjiC9x7770oLy+P+k0x1Z9rr702crtPnz4oLS1F+/bt8fe//x3Z2dkujowovS1cuBC9e/fGwIEDVdtvueWWyO3evXujT58+6Ny5MyorK3HNNdfU9zAbtMrKSjz22GN4+umnUVpait27d+Pee+/Fo48+ilmzZrk9vEbJypyY/b10xx13uDX0BuuJJ57A1KlT0b17d0iShM6dO2PKlClYtGiR20NrlKzMBz8jjQNLvOtZy5Yt4fF4orqBHj58GMXFxS6NqnEJv85mc1BcXIwjR46o7q+pqcHx48c5T3GYPn06/v3vf+Odd97BBRdcENleXFyM8+fP48SJE6r9tXOhN1fh+8i6jIwMdOnSBf3798fcuXPRt29fPPHEE5yHerR+/XocOXIEl1xyCbxeL7xeL1auXIknn3wSXq8XRUVFnAsXFBQU4MILL8Tu3bv5eaBGLZF/p54+fRpLliyxdKHYqVMntGzZErt3705ovA1dPPMxa9YsTJo0CT/84Q/Ru3dv3HjjjXjssccwd+5cBAIBXoskKBlzokf59xKZi2dOCgsL8dprr+H06dP4/PPP8cknn6BJkybo1KlT3MekoGTMhx5+RpLL6N+6+fn5yM7OTtpnhAHKepaRkYH+/fujoqIisi0QCKCiogKDBw92cWSNR8eOHVFcXKyag6qqKqxduzYyB4MHD8aJEyewfv36yD5vv/02AoEASktL633M6UoIgenTp2PZsmV4++230bFjR9X9/fv3h8/nU83Fzp07sX//ftVcbN26VRUwLi8vR35+Pnr27Fk/T6SBCgQCqK6u5jzUo2uuuQZbt27Fpk2bIv8NGDAAt956a+Q256L+nTp1Cnv27EHr1q35eaBGLZF/p7788suorq7G97///Zjn+fLLL3Hs2DG0bt064TE3ZPHMx5kzZyDL6ku8cBm9EILXIglKxpzoUf69ROYSeU9nZWWhbdu2qKmpwSuvvIIbbrgh4WM2dsmYDz38jCTX4MGDVXMIBP+tG57DpH1G4m6vQ3FbsmSJyMzMFM8//7zYvn27uPPOO0VBQYGqGygl5uTJk2Ljxo1i48aNAoCYN2+e2Lhxo/j888+FEEI8/vjjoqCgQPzzn/8UW7ZsETfccIPo2LGjOHv2bOQYo0ePFhdffLFYu3atWLVqlejatauYOHGiW08pLd19992iadOmorKyUhw8eDDy35kzZyL73HXXXaJdu3bi7bffFh999JEYPHiwGDx4cOT+mpoa0atXLzFy5EixadMmsWLFClFYWChmzJjhxlNKWw888IBYuXKl2Ldvn9iyZYt44IEHhCRJ4q233hJCcB7cpOziLQTnoj785Cc/EZWVlWLfvn3i/fffF8OHDxctW7YUR44cEUJwDqhxi/Xv1EmTJokHHngg6nGXX365uPnmm6O2nzx5Uvz0pz8Vq1evFvv27RP//e9/xSWXXCK6du0qzp07l/Tnk+7szsfs2bNFXl6eeOmll8TevXvFW2+9JTp37iy++93vWj4mmUvGnMT6e4nM2Z2TNWvWiFdeeUXs2bNHvPvuu2LYsGGiY8eO4ptvvrF8TDKWjPngZyQxseIjDzzwgJg0aVJk/71794qcnBzxs5/9TOzYsUMsWLBAeDwesWLFisg+yfiMMEDpkqeeekq0a9dOZGRkiIEDB4o1a9a4PaQG5Z133hEAov6bPHmyEEKIQCAgZs2aJYqKikRmZqa45pprxM6dO1XHOHbsmJg4caJo0qSJyM/PF1OmTBEnT5504dmkL705ACAWL14c2efs2bPiRz/6kWjWrJnIyckRN954ozh48KDqOJ999pm49tprRXZ2tmjZsqX4yU9+Ivx+fz0/m/T2gx/8QLRv315kZGSIwsJCcc0110SCk0JwHtykDVByLpLv5ptvFq1btxYZGRmibdu24uabbxa7d++O3M85oMbO7N+pQ4cOjfx7KuyTTz4RAFR/r4SdOXNGjBw5UhQWFgqfzyfat28vpk6dyot8G+zMh9/vF3PmzBGdO3cWWVlZoqSkRPzoRz9SXejHOibF5vScxPp7iWKzMyeVlZWiR48eIjMzU7Ro0UJMmjRJfPXVV7aOSeacng9+RhITKz4yefJkMXTo0KjH9OvXT2RkZIhOnTqpruHDnP6MSEIY5JUTERERERERERERJRnXoCQiIiIiIiIiIiLXMEBJRERERERERERErmGAkoiIiIiIiIiIiFzDACURERERERERERG5hgFKIiIiIiIiIiIicg0DlEREREREREREROQaBiiJiIiIiIiIiIjINQxQEhERERERERERkWsYoCQiipMkSXjttddi7jdr1izceeedjp67srISkiThxIkTjh43lu3bt+OCCy7A6dOn6/W8RERERERE1HAxQElEaen222+HJElR/40ePdrtoakcOnQITzzxBH7xi19Etn399de4++670a5dO2RmZqK4uBijRo3C+++/7+JIo1111VW47777VNt69uyJQYMGYd68ee4MioiIiKgBW716NTweD8aOHev2UIiI6hUDlESUtkaPHo2DBw+q/nvppZfcHpbKn//8Z1x22WVo3759ZNuECROwceNGvPDCC/j000/x+uuv46qrrsKxY8dcHKl1U6ZMwTPPPIOamhq3h0JERETUoCxcuBD33HMP3n33XRw4cMC1cZw/f961cxNR48QAJRGlrXD2ofK/Zs2aAQiWXz/zzDO49tprkZ2djU6dOuEf//iH6vFbt27FsGHDkJ2djRYtWuDOO+/EqVOnVPssWrQIF110ETIzM9G6dWtMnz5ddf/Ro0dx4403IicnB127dsXrr7+uun/JkiUYN25c5OcTJ07gvffew69//WtcffXVaN++PQYOHIgZM2bg+uuvBwB89tlnkCQJmzZtUj1OkiRUVlaqjv/++++jT58+yMrKwqBBg7Bt27bIfZ9//jnGjRuHZs2aITc3FxdddBGWL18euX/btm249tpr0aRJExQVFWHSpEk4evQogGCG6sqVK/HEE09EslM/++wzAMCIESNw/PhxrFy5MtYUEREREZFFp06dwtKlS3H33Xdj7NixeP7551X3/+tf/8Kll16KrKwstGzZEjfeeGPkvurqavzv//4vSkpKkJmZiS5dumDhwoUAgOeffx4FBQWqY7322muQJCny85w5c9CvXz/8+c9/RseOHZGVlQUAWLFiBS6//HIUFBSgRYsWuO6667Bnzx7Vsb788ktMnDgRzZs3R25uLgYMGIC1a9fis88+gyzL+Oijj1T7z58/H+3bt0cgEEj0JSOiBoQBSiJqsGbNmoUJEyZg8+bNuPXWW3HLLbdgx44dAIDTp09j1KhRaNasGT788EO8/PLL+O9//6sKQD7zzDOYNm0a7rzzTmzduhWvv/46unTpojrHww8/jO9+97vYsmULxowZg1tvvRXHjx8HABw/fhzbt2/HgAEDIvs3adIETZo0wWuvvYbq6uqEn+PPfvYz/O53v8OHH36IwsJCjBs3Dn6/HwAwbdo0VFdX491338XWrVvx61//Gk2aNAEQDHgOGzYMF198MT766COsWLEChw8fxne/+10AwBNPPIHBgwdj6tSpkezUkpISAEBGRgb69euH9957L+HxExEREVHQ3//+d3Tv3h3dunXD97//fSxatAhCCADAG2+8gRtvvBFjxozBxo0bUVFRgYEDB0Yee9ttt+Gll17Ck08+iR07duCPf/xj5N99Vu3evRuvvPIKXn311cgvyk+fPo2ysjJ89NFHqKiogCzLuPHGGyPBxVOnTmHo0KH46quv8Prrr2Pz5s34+c9/jkAggA4dOmD48OFYvHix6jyLFy/G7bffDllmOIKIFAQRURqaPHmy8Hg8Ijc3V/Xfr371KyGEEADEXXfdpXpMaWmpuPvuu4UQQjz33HOiWbNm4tSpU5H733jjDSHLsjh06JAQQog2bdqIX/ziF4ZjACAefPDByM+nTp0SAMSbb74phBBi48aNAoDYv3+/6nH/+Mc/RLNmzURWVpa47LLLxIwZM8TmzZsj9+/bt08AEBs3boxs++abbwQA8c477wghhHjnnXcEALFkyZLIPseOHRPZ2dli6dKlQgghevfuLebMmaM79kcffVSMHDlSte2LL74QAMTOnTuFEEIMHTpU3HvvvbqPv/HGG8Xtt99u+NoQERERkT2XXXaZmD9/vhBCCL/fL1q2bBn5t9/gwYPFrbfeqvu4nTt3CgCivLxc9/7FixeLpk2bqrYtW7ZMKMMBs2fPFj6fTxw5csR0jF9//bUAILZu3SqEEOKPf/yjyMvLE8eOHdPdf+nSpaJZs2bi3LlzQggh1q9fLyRJEvv27TM9DxE1PvyVBRGlrauvvhqbNm1S/XfXXXdF7h88eLBq/8GDB0cyKHfs2IG+ffsiNzc3cv+QIUMQCASwc+dOHDlyBAcOHMA111xjOoY+ffpEbufm5iI/Px9HjhwBAJw9exYAIiUyYRMmTMCBAwfw+uuvY/To0aisrMQll1wSVcZjhfI5Nm/eHN26dYs8xx//+Mf45S9/iSFDhmD27NnYsmVLZN/NmzfjnXfeiWR0NmnSBN27dweAqLIdPdnZ2Thz5ozt8RIRERFRtJ07d2LdunWYOHEiAMDr9eLmm2+OlGlv2rTJ8N+lmzZtgsfjwdChQxMaQ/v27VFYWKjatmvXLkycOBGdOnVCfn4+OnToAADYv39/5NwXX3wxmjdvrnvM8ePHw+PxYNmyZQCC5eZXX3115DhERGEMUBJR2srNzUWXLl1U/xn948iu7OxsS/v5fD7Vz5IkRUpeWrZsCQD45ptvoh6XlZWFESNGYNasWfjggw9w++23Y/bs2QAQKXcRoZIeAJGybTt++MMfYu/evZg0aRK2bt2KAQMG4KmnngIQLMcZN25cVIB3165duPLKK2Me+/jx41H/gCUiIiKi+CxcuBA1NTVo06YNvF4vvF4vnnnmGbzyyiv49ttvTf9tGuvfrbIsq/5dCej/21L5i/uwcePG4fjx4/jTn/6EtWvXYu3atQDqmujEOndGRgZuu+02LF68GOfPn8eLL76IH/zgB6aPIaLGiQFKImqw1qxZE/Vzjx49AAA9evTA5s2bcfr06cj977//PmRZRrdu3ZCXl4cOHTqgoqIi7vN37twZ+fn52L59e8x9e/bsGRlLOPB38ODByP3KhjlKyuf4zTff4NNPP408RwAoKSnBXXfdhVdffRU/+clP8Kc//QkAcMkll+Djjz9Ghw4dooK84X+cZmRkoLa2Vve827Ztw8UXXxzzeRERERGRuZqaGvzf//0ffve736l+cbx582a0adMGL730Evr06WP479LevXsjEAgYNjAsLCzEyZMnVf/uNfq3pdKxY8ewc+dOPPjgg7jmmmvQo0ePqF+89+nTB5s2bYqswa7nhz/8If773//i6aefRk1NDW666aaY5yaixocBSiJKW9XV1Th06JDqv3AXagB4+eWXsWjRInz66aeYPXs21q1bF2mCc+uttyIrKwuTJ0/Gtm3b8M477+Cee+7BpEmTUFRUBCDYzfB3v/sdnnzySezatQsbNmyIZCBaIcsyhg8fjlWrVkW2HTt2DMOGDcNf//pXbNmyBfv27cPLL7+M3/zmN7jhhhsABH8TPWjQIDz++OPYsWMHVq5ciQcffFD3HI888ggqKiqwbds23H777WjZsiXGjx8PALjvvvvwn//8B/v27cOGDRvwzjvvRIKX06ZNw/HjxzFx4kR8+OGH2LNnD/7zn/9gypQpkaBkhw4dIh0Yjx49GskM/eyzz/DVV19h+PDhll8LIiIiItL373//G9988w3uuOMO9OrVS/XfhAkTsHDhQsyePRsvvfQSZs+ejR07dkQaIALBf7NNnjwZP/jBD/Daa69h3759qKysxN///ncAQGlpKXJycjBz5kzs2bMHL774oqWlhZo1a4YWLVrgueeew+7du/H222+jrKxMtc/EiRNRXFyM8ePH4/3338fevXvxyiuvYPXq1ZF9evTogUGDBuF///d/MXHiRMuVSkTUyLi9CCYRUTwmT54sAET9161bNyFEsIHNggULxIgRI0RmZqbo0KFDpHlM2JYtW8TVV18tsrKyRPPmzcXUqVPFyZMnVfs8++yzolu3bsLn84nWrVuLe+65J3IfALFs2TLV/k2bNhWLFy+O/Lx8+XLRtm1bUVtbK4QQ4ty5c+KBBx4Ql1xyiWjatKnIyckR3bp1Ew8++KA4c+ZM5HHbt28XgwcPFtnZ2aJfv37irbfe0m2S869//UtcdNFFIiMjQwwcOFDVbGf69Omic+fOIjMzUxQWFopJkyaJo0ePRu7/9NNPxY033igKCgpEdna26N69u7jvvvtEIBAQQgQXXB80aJDIzs4WACKLmT/22GNi1KhRNmaLiIiIiIxcd911YsyYMbr3rV27VgAQmzdvFq+88oro16+fyMjIEC1bthQ33XRTZL+zZ8+K+++/X7Ru3VpkZGSILl26iEWLFkXuX7ZsmejSpYvIzs4W1113nXjuueeimuT07ds36vzl5eWiR48eIjMzU/Tp00dUVlZG/Rv4s88+ExMmTBD5+fkiJydHDBgwQKxdu1Z1nIULFwoAYt26dXG+SkTU0ElCaBajICJqACRJwrJlyyLZhG4RQqC0tBT3339/ZNHzdHb+/Hl07doVL774IoYMGeL2cIiIiIgoDTz66KN4+eWXVU0biYiUWOJNRJREkiThueeeQ01NjdtDccT+/fsxc+ZMBieJiIiIKKZTp05h27Zt+MMf/oB77rnH7eEQUQpjBiURNUipkkFJRERERNRY3X777XjppZcwfvx4vPjii/B4PG4PiYhSFAOURERERERERERE5BqWeBMREREREREREZFrGKAkIiIiIiIiIiIi1zBASURERERERERERK5hgJKIiIiIiIiIiIhcwwAlERERERERERERuYYBSiIiIiIiIiIiInINA5RERERERERERETkGgYoiYiIiIiIiIiIyDX/P2+y3PGVMb4AAAAAAElFTkSuQmCC",
+ "image/png": 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",
"text/plain": [
""
]
@@ -10978,7 +11414,7 @@
},
{
"data": {
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",
+ "image/png": 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",
"text/plain": [
""
]
@@ -10988,7 +11424,7 @@
},
{
"data": {
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",
+ "image/png": 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",
"text/plain": [
""
]
@@ -10998,7 +11434,7 @@
},
{
"data": {
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+ "image/png": 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",
"text/plain": [
""
]
@@ -11008,7 +11444,7 @@
},
{
"data": {
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",
+ "image/png": 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"text/plain": [
""
]
@@ -11018,7 +11454,7 @@
},
{
"data": {
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"text/plain": [
""
]
@@ -11185,14 +11621,18 @@
" avg_loss_chunks = chunked_average(history[\"val_loss\"], chunk_size)\n",
"\n",
" # Find the chunk with the highest average accuracy\n",
- " max_acc_chunk_index = np.argmax(avg_accuracy_chunks)\n",
- " max_acc_value = avg_accuracy_chunks[max_acc_chunk_index]\n",
+ " Acc_max_acc_chunk_index = np.argmax(avg_accuracy_chunks)\n",
+ " max_acc_value = avg_accuracy_chunks[Acc_max_acc_chunk_index]\n",
+ "\n",
+ " # Find the chunk with the lowest average loss\n",
+ " Loss_max_acc_chunk_index = np.argmin(avg_loss_chunks)\n",
+ " min_loss_value = avg_loss_chunks[Loss_max_acc_chunk_index]\n",
"\n",
" # Create a pile plot for accuracy\n",
" plt.figure(figsize=(10, 6))\n",
" plt.bar(range(len(avg_accuracy_chunks)), avg_accuracy_chunks, label=\"Average Accuracy\")\n",
" plt.bar(\n",
- " max_acc_chunk_index,\n",
+ " Acc_max_acc_chunk_index,\n",
" max_acc_value,\n",
" color=\"red\",\n",
" label=\"Highest Average Accuracy\",\n",
@@ -11210,6 +11650,12 @@
" color=\"green\",\n",
" label=\"Average Loss\",\n",
" )\n",
+ " plt.bar(\n",
+ " Loss_max_acc_chunk_index,\n",
+ " min_loss_value,\n",
+ " color=\"red\",\n",
+ " label=\"Lowest Average Loss\",\n",
+ " )\n",
" plt.xlabel(\"Chunk\")\n",
" plt.ylabel(\"Average Loss\")\n",
" plt.title(\"Average Validation Loss per Chunk\")\n",
@@ -11234,8 +11680,7 @@
" # Create an index for each epoch\n",
" epoch_indices = np.arange(len(avg_accuracy_epochs))\n",
"\n",
- " # Plot accuracy and loss as bars\n",
- " plt.bar(\n",
+ " bars1 = plt.bar(\n",
" epoch_indices - 0.2,\n",
" avg_accuracy_epochs,\n",
" width=0.4,\n",
@@ -11243,7 +11688,7 @@
" color=\"blue\",\n",
" alpha=0.6,\n",
" )\n",
- " plt.bar(\n",
+ " bars2 = plt.bar(\n",
" epoch_indices + 0.2,\n",
" avg_loss_epochs,\n",
" width=0.4,\n",
@@ -11257,8 +11702,17 @@
" plt.ylabel(\"Average Value\")\n",
" plt.title(\"Average Validation Accuracy and Loss for Each Epoch Across Chunks (Ignoring First Chunk)\")\n",
" plt.xticks(epoch_indices, [f\"Epoch {i + 1}\" for i in epoch_indices]) # Set x-tick labels to epoch numbers\n",
- " plt.legend()\n",
"\n",
+ " # Adding the numbers on the bars\n",
+ " for bar in bars1:\n",
+ " yval = bar.get_height()\n",
+ " plt.text(bar.get_x() + bar.get_width() / 5.0, yval, round(yval, 4), va=\"bottom\") # va: vertical alignment\n",
+ "\n",
+ " for bar in bars2:\n",
+ " yval = bar.get_height()\n",
+ " plt.text(bar.get_x() + bar.get_width() / 5.0, yval, round(yval, 4), va=\"bottom\")\n",
+ "\n",
+ " plt.legend()\n",
" plt.tight_layout()\n",
" plt.show()\n",
"\n",
@@ -11275,127 +11729,13 @@
},
{
"cell_type": "code",
- "execution_count": 31,
+ "execution_count": null,
"metadata": {
"notebookRunGroups": {
"groupValue": ""
}
},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "1/1 [==============================] - 1s 595ms/step\n",
- "20/20 [==============================] - 0s 17ms/step\n",
- "Val data acc:\n",
- "+-----------+-----------+\n",
- "| Metric | Value |\n",
- "+-----------+-----------+\n",
- "| Accuracy | 93.75 |\n",
- "| Precision | 94.444444 |\n",
- "| F1 Score | 93.72549 |\n",
- "| Recall | 93.75 |\n",
- "+-----------+-----------+\n",
- "Test data acc:\n",
- "+-----------+-----------+\n",
- "| Metric | Value |\n",
- "+-----------+-----------+\n",
- "| Accuracy | 96.474359 |\n",
- "| Precision | 96.393732 |\n",
- "| F1 Score | 96.226249 |\n",
- "| Recall | 96.068376 |\n",
- "+-----------+-----------+\n"
- ]
- },
- {
- "data": {
- "image/png": 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H4p577om3vOUtsbS0VPub73Upulpj/PSnP31ZqPLOzk5MTU1d9Dn7jHd2dh7xHoUK5VR0tuhsoeuLis5ePZ0tVOhqUNHZ4mcLXV9UdLb42WuVyva9y6Q777wznvnMZ0a73Y4TJ07EZ3zGZ0SzWcf02u123HzzzbXP7rrrrtjY2Ijjx4+Pve/Zs2cjIuLee++NiIhnPOMZtb8fO3YslpeXH3ZstF4+97nPvfwJPcFjfDiamZmJvb29iz7f3d1Nfy9U6NFS0dmis4WuLyo6e/V0tlChq0FFZ4ufLXR9UdHZ4mevVSqg1GXSi1/84vS2gkvR1NTURYo9HA7j+PHj8f73v3/sd66Fk/mf7DGePHkyPvShD0VVVTXE+tSpUxERceONN17V5xf680lFZ68eFZ0tdDWo6GyhQtcXFZ29elT8bKGrQUVnC12rVECpq0xPe9rT4pd/+Zfjr/yVv/KwVY3bbrstIg5R3qc+9anp83Pnzl30xoBxz4iI+NjHPhaveMUrLnndpVofn4gxPhw9//nPj/e+973xJ3/yJ/Gc5zwnff67v/u76e+FCj1RVHT2kanobKFriYrOFip0fVHR2Uem4mcLXUtUdLbQ1aZyptRVpq/+6q+OwWAQ3/M933PR3/r9fqyvr0fE4R7fiYmJ+JEf+ZGoqipd8853vvMRn/GCF7wgbr/99njnO9+Z7gf5XrOzsxERF11ztcZ4ua/Q/PIv//KYmJiIH/3RH62N+z3veU/cdNNN8ZKXvOQR71Go0JWiorNFZwtdX1R09vJeVV2o0LVCRWeLny10fVHR2eJnrzaVTqmrTC972cviDW94Q3z/939//MEf/EG88pWvjImJibjrrrviZ37mZ+KHf/iH4yu/8ivj2LFj8W3f9m3x/d///fElX/Il8epXvzo+8pGPxP/8n/8zVlZWHvYZzWYz3v3ud8eXfumXxvOf//x4/etfHydPnoxPfOIT8cd//MfxwQ9+MCIiXvjCF0ZExDd/8zfHF33RF0Wr1YrXvOY1V22Ml/sKzZtvvjne8pa3xA/+4A/GwcFBfM7nfE783M/9XPzGb/xGvP/9749Wq/UYOF+o0GOjorNFZwtdX1R09vJeVf2+970v7r333uj1ehER8eEPfzje8Y53RETE3/7bfztVjwsVutpUdLb42ULXFxWdLX72qtMT+q6/65B4heb/+T//52Gve+1rX1vNzs5e8u8/9mM/Vr3whS+sZmZmqvn5+eozP/Mzq7e+9a3VQw89lK4ZDAbVd3/3d1cnT56sZmZmqpe//OXVxz72seq222572FdoQr/5m79ZfeEXfmE1Pz9fzc7OVs973vOqH/mRH0l/7/f71Td90zdVx44dqxqNxkWv07ySY6yqR/cKzcFgUH3f931fddttt1WTk5PVHXfcUf2n//SfLuu7hQqZis4WnS10fVHR2SdGZ1/2spdVETH2J59noUIPR0Vni58tdH1R0dniZ691alSV+tYKFSpUqFChQoUKFSpUqFChQoUKFXoCqJwpVahQoUKFChUqVKhQoUKFChUqVOgJpwJKFSpUqFChQoUKFSpUqFChQoUKFXrCqYBShQoVKlSoUKFChQoVKlSoUKFChZ5wKqBUoUKFChUqVKhQoUKFChUqVKhQoSecCihVqFChQoUKFSpUqFChQoUKFSpU6AmnAkoVKlSoUKFChQoVKlSoUKFChQoVesKpgFKFChUqVKhQoUKFChUqVKhQoUKFnnBqX+6FjUbjkn9rtVoxHA6j3W5Hq9VKnzUajZicnIxGoxGDwSBarVb6vN/vx+TkZDSbzdjZ2YlWqxXN5iFGNjExEfPz83HzzTfH/Px8NJvNmJqaimazGcPhMGZmZmJmZiYmJiZiamoqGo1GVFUVw+EwIiKazWY0Go30s7e3F41GI9rtdgwGg7jxxhvjMz/zM6PT6USr1YrJycno9Xqxu7sb/X4/ms1mTE5OxuzsbOzu7sbq6mrcfffd0e/3Y2VlJRqNRmxubka73Y7Z2dmYnp6OmZmZGA6HaV79fj8Gg0FMTk7G/Px8PPjgg9FoNGJ6ejra7XZMTU1FRMRgMIjt7e04ffp0HBwcRETE/Px8DIfDaDQa0Wq1YmJiInq9XiwsLERVVbGwsBCdTqfG//39/dpzd3Z20vfa7XY0Go04ODiIycnJODg4SPfe3d2NwWAQVVXF/v5+DIfDqKoqGo1GNJvN2jxYx8FgEPv7+9Hv96Oqqtjd3Y3p6eloNpuxuroajUYjtra2Es/39vai3+9Hv9+Pvb29GA6Hsb+/Hw8++GAcHBzExMREzMzMxOzsbEREuufe3l6SPfgxOTkZExMT0Ww2Y39/Pw4ODmJqaio6nU6SJ9YZWTw4OEhzioiYnp6O2dnZGAwGccMNN0RExP7+fvR6vaiqKprNZhwcHCTeI69TU1MxOTkZW1tbsbi4GHfddVf6jtdrf38/yeRgMEhz3tnZSf8eHBzE3t5e4s3u7m7s7OzE/v5+TbeqqrpcFb2Ims1mVFUVr33ta2NjYyPOnz8fZ8+ejW63m/6+v7+f5sv4kQH+3d3djampqdjd3U28r6oqyWtVVenHfECuIiLdix8IPj0SsXZex0t9b2pqKhYXF2N+fj5mZ2eTvZicnIypqamYn5+vyUxVVckOsN7cu91uR7PZTPLZbDZjcXEx5ubmIiKi3+8nnd7e3o7d3d1kR+bn59P94d/+/n7s7e0luT84OEj8Z3xLS0sxHA5jb28v2bf9/f3odDoxGAzi4OAgDg4Okr2pqir6/X7SE9ar1WrF+vp63HvvvUnet7a2kj1gLGtra9Hv9+Pg4CA2Nzdjc3Mz+v1+rK2txdbWVmxvb9fWDbuKvb0cQiZsl7Ex/N5ut5PuVlUV7XY7yRMy1Gq1otPpRLfbTXNHHqF2u53s+N7eXjSbzThy5Eh0Op244YYbYnJyMpaXl6PZbCbbOzMzE71eL9nofr8fEYc60mq1YmpqKvmlfr8fn/jEJ5JNZ3ytVisODg5ibW3tsvmSE/xAzuF7/hk8hV8veclL4gUveEH8m3/zb5IttgzDaz5/05veFM9+9rPj677u6+LEiRPxhje8Ib75m7857rzzzvie7/meOHLkSPT7/eTXl5aWkt7Mzc3F7OxsdDqdWFlZibm5uZicnIy5ubkk09PT07G8vBwzMzM128C6Mo9+vx/7+/vJZk5PT8fk5GQMh8OLbAYy32w2Y2JiItkY5JB7Ms/BYJB0t9lsxu7ubvq+741f29raSn6O8e7u7tbWd3d3N/kceIo9hTxefvCj+IKqqmJ7ezt6vV7s7e3FYDBIz8FXs37WvZwv6GE+F8bPGvrvxGn48ohI38HXEVsNBoOkZ9zH11t/PX+ek9voXBa4ttVqpevxgcgV8czOzk7Nt+Nfcz9kHjF+fK2J+K+qqtja2nrMOvv5n//5MT8/n+zD3Nxc7O/vx+LiYop5scf7+/sxPT0dOzs7SS6JN0+cOBE33nhjbG1t1XgNj7kW+e10OrG/vx8PPfRQnD17Nvkx4h50zTxBrriH14013dvbi4ceeijW1taSPHS73bjvvvvi9OnTsbOzk+TG1Gg0YmZmJk6ePBnHjh2L2267LV784hfHyspK8q3EvvzO/Pg+Nr7RaCQZxCdbvxm7ZYj7RMRFvsn2HJsxMTEREZH4xX0d8znm5R7oFDzjeY1GI3Z2duKTn/xk7O3tJf+6vb0d3W43ut1u0u2Dg4NYXV2N1dXVuOeee+L8+fOXLW+tViump6cTb8hVjhw5EkeOHKnZaWxy7nftY7FVyA6x+sTERAyHwxgMBnHixIk4ceJEksWZmZkaf5BV8h/WttVqxf333x/nzp1L9hSfCi8mJiZSjIGN3tjYSDnA7u5ubG1tRa/Xi263m9b7nnvuuWye5fQFX/AFaS4RkWyF5e/jH/94fOZnfmb85E/+ZHz+539+HD9+PCLqsSi0v78fL37xi+NNb3pT/M2/+Tfj2LFj8cIXvjC+/Mu/PF71qlfFX/krfyV2d3eTrPB9++TBYFDTV54FfxkX48xjhIhI8en8/HxaT3wldr/VasXMzEycOHEilpaWkh602+2YnJys2SvGxbPQs6qqkp+GyNMdl+KrPSd+z22VdZr4DT8SEclHEicOh8PkH/zM2dnZ6Ha76RqvM88zJkHey3z5DuN3PkdObHtDzIKN6Pf7tRyf5/JdYgDW3vkt+kaOYPtMzkgezP0YF9/J8yzHI/bn5JsHBwexvr4ep0+fruEXtosmx9zILGMxvkDsGDGyvz/xEz/xiLp52aBUTnligYDZ0fB5etj/P3j/3mw2Y3p6Oikdf4uINCnfy//H2OZJiYWQsSIUOF2CAgASno+weNEBeBzYssAE2CieAwcEudvtJv6g9MxvMBjUDHUeMHtuCApjxqjPzMwk52hnimITkHAPHAFOE0XnXxQU52tnDf/MZ5yXlcHgI2tK8EjCbSDTwS3PRy5sBLiW3wHHbMThqxMglJ4feABP4DV8tJw5wGZsubFzQsBYkSvLJeSgMw/4rxRVVRV33HFHCu53dnYS4NHr9RIQYrmBTwQpl5Jx+Mb18IL1tuxat5w4+LpHAjjsFB+JTxh+7EhVVTExMRGzs7MpYCJ5jjh0KhGRACLkwo7SoMre3l66D47MSeTU1FQCgQjuNjc30xxJfHnWYDCI6enplLA4cEYe4R+JWUQkh8L1OEPG1uv1Ym1tLWZmZuLg4CCBVQ68CGh3dnaSHYIXBinztXg0sponDNwXcjAAkMBz0Ls8gMbpYxv4PA+8md/8/HwsLy/H0tJSzM3NRbfbTckVcjs/P5/4isyQXCwuLsbRo0djbW0tVldXY3t7Ozlg/ARjfqIIPlZVFc973vPita99bfybf/NvanLGdf7XwDHUarXi4x//ePzkT/5ksg8EniTaExMTMTExkYKwRqMRvV6vJjNTU1O1IHJvby+NkTHAc6831zjItu7hKxyQ28/k1yIL6Bd+m+ebdnd3U/DuoM0+3HxiLMQA+BIXcZBLAzZO3ohB8OP4UK6dmJhI9tUAv+/p/5snEaOChPljcAJbzjwNBvN/xym5f3KcwjWsr5NO+wvIAThziIgaz0jc2+12TE9P164HyIQvuQ92XArxPcdJtt2PhwyM9vv92NzcjIWFhVRUQMbwM06GOp1OKkpsb2/X5NA/Lnbhj7vdbszOzsaJEydSUcRJO+vrpM0Jl+N19BBeHjlyJAaDQWxubsbU1FTMzMzE6upqAip3d3cvsnXY6JmZmdjZ2YkLFy7EH/7hH8Ydd9wRJ06cSHy3PLEGyAlyAy+RS0As5NZgkedK/Mo8x611nshHjJJV5xTOIXL5cqwLPzc2NuLMmTM1HeQe09PTaR7D4TC2t7fTeszMzFy2rJGbYH+Rh+np6RQ7TExMxOLiYrLTJPEUa9AzF6uxN46BWI/jx4/H4uJiyj/wu8iU7bdzEWRqdXU1xenkHRRw8K+sIzpjOeXH8eUTQfabuSxc6tqHI9u6/PPLpccSX1zqO482jrse6HLW4Ure89E872rx+s/bGj4qUIoFMPCCEwFZ5ToSnoi6MhpUwPgT3Pv7VVVFr9eLI0eOJGOLYcWZ0T1l45U7DAMT/Lu1tRXnzp2L5eXl5Nz5brPZjE6nk5z86upqnDt3LhnYra2tVH0ngEAocBYeD1VQAnqcBAnl6upqXLhwIQUnEKAQwRT3mpubS84jIlLy0Gg0UqDgABzekkiTiBFE2Qk5EM+DXlep7eRxXjs7O2ktnSga8CMo3tnZifX19ZicnEyBGnzLAT07J9YfAlg4ODiIXq93EajHv3yf9YWnKysryRmzNq5a+zskqABye3t7qZOO9YkYBZxOZlhvxptXqQmixlUfHw8tLS3Fb//2b8e3fdu3RavViqWlpaRLW1tbsbW1lZIskh/GY0cMSAOvDMoRQBAkuuMRvlj3nYh4nS6HLtf4DgaDVJV0NdFJDXKfywXyYjnkflVVxfT0dJp7p9OpdTxim9wV6uQ6YtS1AiDe6/WSzhDQcS+CSDpPHLzBD4N7EXUAaH19PQaDQczNzcXa2losLi7GYDCIXq8Xg8EgpqamanpLZyXjxAZdDmiYk+XHSaJ/+BuJBzY1ryJ5vozFsklnBWvoIJmu0hMnTsTi4mJ0Op10zcbGRq0w0mgcVrqZ//T0dLoeHdnZ2Yk/+7M/Sx2o2IWZmZmLuhwfCz1SAHQpHTg4OIgLFy4kPtCxN45ImNfX11Oitre3F/fcc0984hOfiE6nk7pX6Sql2or/sm7gW7DFdBQir9gEEsb9/f3EO2TMVVnIxQbbcXTYvh/7RdUT0BJ/aMCD7zE+yxvXAgzAT8bG93d2dpINY20svxGRdNkVYPz09vZ24o2B/YiR/aXLGP+Zg3HIguOePBn3vPkb/skxGP8yR8BFxyCsH9e48xEyqAV5PB6rddnxGjxwhwpFBa7v9XoptvDzGY/5g31D9gC7rNePh7BBVVXV4riqqi7yO8QSxAPYYXdN20eSvLvqzGfYazqD4RHd79zL98POM2cXHCMixSdzc3Opsxvw4ciRI7G2tpbA2HH2Li8a7+3txd133x2nTp2K22+/PU6ePHlRFyC+0gCVZQQQ0vrXaDQSgJTrD8/PO0+wEfgbeOmOPj63fObdmJYX1oI4no4fYil2dNDZ6+/TUdfpdB7WtptywIjiLbZ6YWEhpqenU25iu408uXPFMZjzpP39/djd3Y1jx47F0tJSzbdiV/NYDnvD3NmBAnA/NTWVQEGKFQCydEu5oEBsQmzgOPtKUs577An8MfieX2tZ9WfjnpHnVZd6/qXG6Ps82vmNo3HA6yPd43L5f6k5PdJcx/1tHJh3qXHn63A5432ka/z8/NqHA/zy3y0DkNfV1zwaGnf9OD5f6r55jH2p7+TxTt5Y4efmOMLDPT+nywalbLidpBqFtwEhqcERkHh5i4wDG7piPHG2lhDkeiwRkaoMTiwxcrkByyszGxsbsbW1FUePHr2oUt/tdhPQQWDPNifmjPHGsOKgDVJQefSWABz67u5ubGxsxOrqas0psA2PeeIYcUAGh1gTgpnp6ek4ODiInZ2dqKoq5ubmao6Dji+cBFuQcPhun+d3ACnGDIgEIEhw7goXfPHWMJzW3t5eapVfWlqqBQgRdcATAA0nSkLkyqa7PHg+11lxHPgeHBykdntkDxkhuXKQbJDN93RyZuCMIA6ZZ73cAk7Cgaw7iL5S9Nf/+l+PV7/61XHrrbfG3NxcnDx5Ms6cOZOCY/51Ik8iANhiGeD/zA9Aytc5sbHOwicnEo/W0V0uERz1er0EFtA55Y4i2y3GS0WT+eXgOTrIluOIEdDuJJZgF5CH4NCBNzx2skLgFjFKTr1FBVuDjQHo9HiHw2Gsrq6mbUXovbdtkuxSqUTOkc/BYJBa5h8NKGXnaseUAwJc524dy4TlMb+Xt2F5qwWf0T26tLRUS9ampqYSiO+tQPgeklWSPUCDqampOHLkSGxsbMSnPvWpFEQ7scFGPl5ZHheA8XnO5+HwcDsEttodfy996UvjH/2jfxTr6+tJ5prNZvzqr/5q/NRP/VQMh8N41ateFe9973uj0+nE/fffH3feeWeyh/Pz8wmcQk+mpqZqxSeDQsggCS68pvvO3S6sF/KLbc2TP8sJNgd+I5PIAN/PQU9AXSdMjhGsYwBa/r7Hgz8D2CDpbrfb0el0UkJFMkh3pG381tZW2h7LmvAc+yj4684Q22qDT3wHfvFM/HIeSMJT+E0xbTgcJrtAxyW84bvuVLhU0Gk9NjgFPdycmY/nRocQMQ9bore3t2vX22djOzwu20cAt8sFAx6O9vf3U7eLA3V3PNGtSpeJY1jGu7+/H2fPno3jx48nYCmivr3MIDBz293drcUa6Gyrdbh9m7gWHTavrCfoI9vIDQwy1gsXLkS3273obxBgkTv/uPb06dOxsbERT33qU2tHVORxv+2KY3bmjs/mOXlclidI6AExj3XHRTnHMq1WK+kCdt3dZNiBbrcbW1tb0e12E9DC/RkL/oXxw7fJyck4cuRInD17Nm3pfDhqtQ63XTF/7M7CwkLaskUnKzKQA8/E0/ALn4ccwS9vAcRuIlPOFfDTEVHTNwBgeMrRF8Qe7uCkGMy9tre3a6ArRRNyqytBxFS2YcgjgPHx48fj5MmTqZCbb9vKyQk68Rqdxpubm7U8ybLv4i2yxt/t/5Bv7DbxpIsmjq8cb+ZxGLrcaDRSjOSmENac2Aad8DVuJOEzd8nZtufFGcuJsQC+S7HKtogfF0W41nrmOBPesC75GhKHW66cyyKfPNNxLPowrmBE8Zq/u0hg4Dv3UcilO+/ztfNcuJ/B9nFAOvfm5+DgIOnn7u5unD9/PvkOd2jbhqOfPHtc/usCo/P03Mc/HD0qUAqhcHBgxB4h4G8wAgOJsWQCBD0Ro9ZtGEnQQMV3fn6+Jgz8OOhC0DCyVl5X6yYnJ6Pb7capU6dSVxTzooqJcBGII6yMnesJUAHOSPK41v86kbhw4UJsbW2lAJ7n0UGB4ed3ggUH0gg1wgVwhOMm2QRkInFFofPEgGfDM/gPr70tqNlspoSfIJ9qn4MA5gDIt7W1FYPBIHXt8BwnhCiiASLLG7zCaNjwUd2zU7aRtzJTvUJuCboJqJAZAEfA1aqq0vlAnAsRESkx5cdrbuNoBWd+l5v0Pxra3d2NxcXF2NzcjKo6rOLOz8/HjTfeGKurq9FsNtM2JmQZUAZ9wrg4iIuItE4GBeCdk2obUjseyEH8lSBkjrVGn7E/yAkJQkQkIBg5dtLs7XQkR6zv7u5uzM3NxcLCQu1sOwd8jIn5W37NV3iE7XDC0Ol0altP8mvgH0EvgFy73U6AreWUdQQsRm8si2tra9HtdlMwluvbI1EOJEFOuOEV8+AH8M8JtBNugghANSceJEWLi4vpPDGuZ922trbS1gbsFvbRNoCgf3FxMfb29uITn/hEAhndAer5Pl49tq6Yzw5G+P15z3tevOxlL4sf/dEfrQFiVVXF5uZm3H///ekMMdaes/6Q9VOnTsXi4mJ8+MMfjt///d+P48ePJ94Q6OBnSQIdmMFzZG9nZyed5+BzlyKipg/W0TxgsZ4YXPEZMFyPz2i327XOMMaUd2W44GC9ZjxeR/6PLcdG4F/cEUCnI5202HgXqPb391PByMUVgkkH65Z7xmvZMOBhntgG8f1cnpzcOJaxzhGnWXd9vzz55285MOWAfZyMO0ny3yJGoBmyaiCJoJ8zvhhTDmgwXnel0ZHKWubAyqMl1rbT6ST/0e120zmgc3NzNRA0YlTY9TibzWZcuHAhbrjhhmTnLNPYYdYdfuNHOO/OW0iRUYNM6J8BBWKXiJHszs3Nxe7ubmxubqbY4OjRo3HhwoUE/OfnS9H5Oz8/n85TctzYarXivvvui9nZ2Th69GgsLS2lsxJd+LackUs4NrMuMyeubzRGxy1wP8eZ6Lh1jnvyt7ywjK3ne1tbW+mcsxzQtr2hyEQHG7HW4uJitNvtBEgBaufnnkHNZjN1fbPu7fbhVsmjR4/G8ePHEy8dq9HxaH1EfuBnRKSiLj4PoBr5BkQl/uUzd+Ohm6xHq9WKtbW1aLfbsbGxUevWgqd5zAzf2KmCf+/1ejVg5vGSu/xyewEwhax/2Zd9Wfzpn/5pvPKVr4yzZ8+mNbJdxobv7e3FmTNnotPpxO7ubhw/fjyOHj2a+OUCfW4/PZaIkb4TE+GTXeDEhjF2zk51F7Bzd/vdfr8fq6urERHp/DvzgTXPAbM8DyZXYx62x/an+VjsLxgXQNTe3l7SddbKNs9xGnG6ASfyGOMV3vIN6AMvKAq4GQOfASBlHUCfiHlcJIAv8Ng2CDDdQJ154f/bjtgn2+4ZTAMjsM9Ar+AX1zMuOuzJGwDjKXZjSwxOIaf2nY71WQe2eht8vBx6VNv3SOrGMdgAEQEpgzV66wAX8t/dbs69t7a2Ym5urqZMZoKZZYNop+TEBQe1traWtrVw4ChBCwvGwiAoNmAEqj4XIiISsso4vMVve3s77f2HCHKZB9+x04Tf5iuHrWOsAPDMO3hGhRuFZZ3yBJh1RjBd1bMjxIhERDK0gGrb29vJKDKHzc3N6Ha7MRgcbifCeXpN8uTViaYTKwIpI/cRkQIDHybLOL12jcaoKoQR5+BI7utOIJyCtzLBs9nZ2VQxyA+szSt3jBdAgHV2QHglyUHI9vZ2LC4upgMOGT9JOQbPAR6GhACT77DWli9k0rLLvdyR5USFMVqnHu98898BVTudTgJqXFHgOnTXW4vytd7Z2amdVeXtbp1OJx2c604mOuKoYNqmmA/IOZ8DdtsmABxGjM7vQeZJiDiwf2trK209Q/cJmkh48goLwfXW1latW8sO0jaXv+cJp/UWucgDHQgeOUGKGFXakEUDGwCMThxYr4WFhQRK0ZU7MTERy8vL6cBUHD8JBbqL3UTekZutra245557UoCNbfC5eMh4Dro+WsoDVQMM5nFExOd+7ufG2972tnj3u98ds7Ozsby8nGTpD/7gD+LNb37zRfcneW232/Fbv/Vb8c/+2T9Lh5TffvvtNZnAr2CnmR/JTr7lut/vpy4KulF5IQe2kfERWDtwdaDrmMC+3skknxt8ykFPg4QOZvkeoAYvKzB/DYIgY/kZULOzs7WqJgcAY2siRmDx+vp6bduwfS4JyzgQivn4Wsbp4NkFvYhRdxn3dFXZdp5rKZCxPk4wx3UosEZ8n/Xw2mAHHIexLi785DYGogPcxwLwwg3iGH64P/+O4x8xGwU0x1iPlYhPOJsM20TssbW1lV7YAyDmsWHvsYWMN49hWEufCzgcDlNCROxqu+j4xQCh1z4HJvlup9OJI0eOpM7UpaWlOHHiRNx8883xe7/3e3H69OmYnp5OXV10Ndxwww2xsrKSOuJ5SQixY8ShX15bW4uNjY04duxY2lrtboc8tkeWsLGWFyc/li8n7tzHPsvxLXPPr+OZ/f7hWZXkCgAlGxsbETHaTYKfd5ydx45TU1OxubmZ1nN2djZmZ2djfX09Njc3a/JlQAobiI976lOfGjfccEM6N9HnSBk08PfIoSiGE0uQmFourMNsM+Q+2AzrwMHBQQKuIiJtyVteXq4l/qwXwCZ21S+L8doZhBnnEx8t2Wbb5rprjGLmM5/5zPiTP/mT+NIv/dL4S3/pL8W///f/PhXj0Zvf+Z3fiR/90R+N5zznOXH//fdHq9WK3/3d340v/dIvjZmZmVhfX4/FxcX0fMf7jqXgLS/ncc4MH5zTWEd4iY8Bb+JBA855HL6xsVHLufndNsRxukE19D636TzPdjBiVFDFTnIN8sJcvT7EojzToI/BcQNR/M2+iO+jj5Zx7u/uWfjIHJkLuT/3JpYxYETszjOYn+0vz/F1edNJ7ptcODJIy7/wDp6aXy66Eu9z7izfd3HAtsNxluM1iLHnwJRB/SsOSqEMZioL4mA1IlJHlB0DBhADi2NFuakouFPDycPGxkYsLy/XwJv9/f0UHDsZghEOzsxgFpotdD4Dw2grSRrjd3XcABSCQhWEeaB0BCwIAB0IBEhGgC2oeWAHkomhmpycTOfaoAiM1ckECSiC48oHgohSsn5GqBuNw8ofvJ6eno5jx46l5NXCmAc2Ozs7yZlXVZXe3JSDUA5OHYjwfBJXB7IOTvwWBp5Nwp4H2PCDbRRLS0u1bZHwh+cxHtbA1aG8RdGJuB2ADRH/8p0rAciMI4KnpaWl2N/fT/NdXl5O1RuCz9XV1RoKTiICgAbPHVCwNgQbzNMVWiofOS899xzYeKyUA1zoQK/Xi9XV1WRbsDkki3Y4dhZs8WKO6DTbdpAvgD1enoDe29YYpLNthKfoEUBwo9FIbxGBj9hgHAPzoDrCm0LR5/Pnz8fs7GwKNt2lyv+xWVNTUylJOHPmTC1Qd6DqxILKc362iHXA4AIBR0TdJqOjyBbP5TpfY+APm4IdXFhYiPn5+dob3yYmJtLh/j7Xg3ljNwksPB/4/+lPfzoefPDBWuCKvNvfXCkZHke+N7//2q/9WrzlLW+J4XAYH/7wh+P++++vyRwArLvkuD9A1Pz8fJw8eTLZTgf//g6few3RMfQGHe/1esnPkHi4wg+fsQ326XkQmQdnri5ijw0ys6YE4jwvtzMOzBxAOjm3D3Swh4y6sk8nNz8Uq9Cxzc3NVDBykm2AYJzvcwDNvXkm8Ri+lGfmspTb13HABHbE/3eR0UEtf89BKI8xBxJdhMiD1DyRsQxArVYrVaT5nUS91WpdJHOeG8/lM7awGSh9PARIhozhL4lpkTNATWwG3RTMdzg87Pw6ffp0LC4u1nidJwLIiQHHiNGB2mfOnKltieLv2NnhcLT9Dtkxz9GLVqsVKysrCcBvNptx4sSJePGLXxx/9Ed/FKdOnYpGoxHHjx+PycnJmJmZiRtuuCEBJOi9j7DItxACxLTb7VhZWYnl5eXUZclYsDX5+lonrFfWIceo/Jv74DzZ5t7W+4ODg7SFDL3j7ErLG3wjQSX+nZ+fj6qqEhBOIWlhYSEWFhai2+2mc7s4n4p4wYl9u92OI0eOxNOf/vR4+tOfnoB/zpZiPZ2vOfHHFyND2PNmc/QiJ2QFuaB4C7hmUNsy6K427ge/LfOOCZBrv2gF4DhPpj2Px0PObTyHRuOw++U5z3lOvP/970855ic/+cnY3NyMj3/84/HX/tpfu8hu7+zsxHOe85y4++6742/8jb8Rx48fT0c8MG/7qBxE73Q6CYhy40YOODDO3Ca4sO6/sQWTmId549tYe3IFzuAEmKCBgGIT+uFt6vaVnpvjP55pn0UMhRzYpzs2Y50cB/B8bCbygl5Zxm3LkE2DkeT+7vrhOsaXfx/eMVfH53zP8mXd4zkGGyMijd92LM8j85iGZznvzX01/4dv2PLz58/HAw88cFFcwXONf/gaxo5dzu3IOEDKvHgkelTb92xIMDQYLf6OoOboMxN1JZnrYZzvRWsawrq5uZmML0YaI2VHyzNIxFgMB1YseFVVceHChZieno6lpaV0PpH3GqNsdoZVVaVk1ovo5BanwP02NzfTq9fhgasNGC/G6So8hrKqRgdpggzDq+Xl5Tg4OEhniCCUrijSDYAiEpQZcNvd3Y2FhYUU4NqBGIzb2tpKLd2sL3yvqsMzu+Ab2yFJ2Ak4HBSRQHodDdjY6Rtww8D4UFu+w1pTRRiXaNEZwmG+vDWN7znxIpBD+ZGHdrudXiePgXOA5+/CJzsbK+2VBKdwalQTMHjI4MrKSgqcvFXAhzZjnJBNd4TYYbgb0IHfOKPsRMXy6EDysfABw8gac49W67CLcH19PSXI/f7orZoYXwcFzBcgFieNPtNVYNDE5/vY2SB33W433Qs5YiyMkySGLcY4BXhv8G9vby+mp6fTAdfYBM55w1FjQ9A562mjcVid3NzcjPX19cQjbI+7MADmmI+TSwd5XnPW2utiPcSJ4vRJqtAPfAS2hO6vwWCQwLHp6em46aabotPppHM5AEOWlpbiwoULsb+/H3Nzc+mMCmQUUAQ/ggOem5uLVqsV58+fj7NnzyabbVDEgAFrZxl+LJSD8eMIffnTP/3T+NM//dOYn5+Pj370o7GzsxN/7+/9vXjve9+bQGXk0+vmbkEHFnkF1vrkwMRBr5MKg1nY1u3t7Wg0Rgdns3bIFfxELgziG4jKPzcwhh4h38yZvztIYq3sz7A96Bn3BFzAB3Jdbrt8NhxzA/A+ODiItbW1GkCQdx2xVr6v1x9/wfNcFMg7nlgDA1n+O7wz+IXf85lkPI/CmX2x4yCDYpZb+znPJU/4beftC2w3vM0RoA9+O/mCtw6kc11yEXViYuKSLwN4NAQf+BkMBrGwsJB+ByQHmHI84U7aZnP0Fr4jR47UtpU6zmR9rQ/208TOw+Gw1gHorfbou9c5YlQsQLd5Yy0ADNtPp6am0javZz7zmXHHHXfE1NRUKm7ZzzFu4kQD28yZQuHZs2djdXU1nQe4sLBQ08k8cTQwatCK361r7l5Gjuy3fW9iMx+NYTAacMr+ycUK5BW74zP1fNYSIN9weNjtvLGxEXNzc3Hs2LGYmpqK7e3t2NjYSP56YmIijh07Fs961rPiWc96Vq17yXx2MY3xuEubsaC/2BHnNMgB9s0xA/xBpqpqtIWKe9lmopc0JNBF54InazUxMZFAEK+FQcTHS5YL20h8zf/7f/8vTp48GTfffHO85CUvie/4ju9I+RDnQ1kGsZl7e3spHkPWDGLiV9CRubm5FGugG4wpLyTmAKILQhTmDHTYZiCPEZHOerTeExOtra3F7u5uOtyedYuonwFo+WE8Bp/sc2ynDSZh+91hSNclRy7QOcmxLJ6bixhskSXn9nZCiDORkX/iVsbmwpzjT3TY/p+dDOPk3DLlmMaxjv0hMUre4GBQyePgMxdS8SWsBXLnRhnOU93f30/FZ3INN6Z4vRzn29ahK6yfc+29vb1YXFxMvoJ843LpskEpB0gAT7TrEtw7ETX6CWBgRwgz8oTdwmrB6ff7sb29XauqsngogIXJAQ/3zAUnItLre6ns8BmL68DIi8PYMdjMnYoCFVKqpAgcypZvz0PIvG3GwAlA1MzMTHIiAAFsIbjppptSWzTnpfT7/dQJxjOoIuOISVxxmLz612s3NzcX7XY78Z+ODD6LiHSmBgaX6jCOkUoRsmSkGsNtoKrVGr1+lnXlc6+5g+z9/f0UIGBo7Oxs4CMODSZvVmOuDowB8gAbkV8Dp3kwbZ0h4faaOljw9VcSkIqINL5Wq5V0B9kDHDxx4kTcf//96RXQVPGcPPGWNnQRAAv9z7cx4XQMgMCjvJLgdlDr9Th+5Nflf3cii1xwv3b78FyDwWAQN954YywuLtYCXXcHMhd3JVl/uJa3t3kfN46PrUsEqoyl0WikzirkED4BDLNOOD0Doawn8rO5uZn4z7ZbgLXp6el0nhj2w0AujoJtbWfPno2Ieou3HY5bqwlmnGQYpHQw7AQVh8k6Wd/4vtvPCQZwuBGRDmHt9/sxNzcXi4uLSZf4G/J/3333paCGs7XgO/PCzu/u7sbs7GzMzc1Fs9mM06dPx7333puCL+wBNtBbuQgYcsDh0RJ8y8GHnJrNZjqElvX4oi/6ovjar/3aBEqxjmypABjix3Nnfk4eDTrBX+YLCOD158c+GpAQu+HtHlzD7waUkA/bVAdOjhFYH78d1TxEby1X+AEDBMg0/rHfH70R0wcs4/fw/1RikV+6JinI0BmdP5v5uPrv5IhnGcBxoQ6fZTCEa/i/bRf3d+cJMQtdLV6Xqhqd55OvjX23E7Q8dsvX2n9H572e/I1/sYtTU1MxPT2d1oYuEwpeBMyu1jo5YpwGiNwZ9niJeAzfQeyFLSLRs34wzvn5+bS+JA3EItZvkgcXiPDD9p3InuNr1sCJVZ6Im3fwrNPppPgbOZ2ZmYljx46lbdG33HJLPOUpT0nFL+5JcRafg31wXO1YjoR1MBjExsZGkmFiTIrSeZLoWApZckcPvIFnBqwM+kWMOkjwf+5EwA5QLKKQZTvDfFgn7G5VVem8LXzPxMREnDhxIprNZnqrIXb1Mz7jM9KLinq9XnS73XRG6MmTJ+Po0aPpbZTMAx1w4ktuZr+C7mIPvaOAtcP2E78a9KWIR+zD8wD62u12bG5u1mw9vOB++B8XcsgV/AIgkn/W5ErFyGtra7Xzt6wb2J2jR4+mM6IiDgvxAIi+Ni/sOEZ1gwNxIEUA4kN0IQeIbbucr2DnvKYRFx/k7VzJABU+gxjTugrPecGYz1rNgXF3tzvPQ07QLdY9B3bgOzEKvKJJAlk0v+17+Tt5BUVf7zYwD+C9ddpbTSNGW6m5No/DbHMYvwFcA7zEmY7p7QtdUMJf2KfmMZB9J8/n3D9AcmJrHyEC0EeMdHBwEPfdd1/SYcdMjIeGkxxopEDX7XZr9sJrSh7iGIxml8uhRwVKNZvNVHkhOcPpIHhGDgFGLqUgVIkcGLKQOKeIUYVsc3Mz5ubmUiBosMLC7sVDGGzUEAgE2Khno9FIAY8rhrRicy+jvVSNFhYWEsDS7/fTdr28q8mGnutRZAsrSRC0sLCQgJqISHuU2SJHNwDzgQ8I6Pz8fJoXTnN+fj4F1XY2rJWBHLbMEESxfY9EG+PFYbc+PBHEO9+z7AqVnXpEHUXG+MB3riNw4O8Okm1UjKrzuQMZlAgZYpwGMg0mEnh43zKBkM8Zs8OwgiMHlvmrAUp5X7r5vrm5GUeOHEn70KlgPfjgg+kge3jNXPxiA+YJLwgu4LcDXHjI35042bYYnPRYsQEkJjgOWs1t6CPqbcNOmJvNw618p06diqqq4tixY0l++/1+qt7ROWlZNEBDcOkDtJk/8sN2EgAinAUH5DNeeG0Hap45cXNSjP3DEaO/OKaIqL0NyFVeZB37tL6+Hmtra7VgiM4OeM/6Mw6cdsRoq53fkIOcOEDClsBPJ81O6rBB1uFms5kOH+Xg4IWFhVheXo7JycnY2tqKtbW1FLC50or9JjDMx00S6K1P29vb8alPfSoFW4wVf+I1ZcxXgnIQapxNgHeA6az3fffdF2fPnk26iV/BfsGHnZ2dOHfuXNx66601UN339zoZrDJglYMOuc5GjA6BRu+Rn4iLDxc2kHKpBMSJdM6zXq9Xkx0HdQY/7fOdVPjNWATsrrjmgJCLG+gUSSvgPnrg+fJDEcqggAtozA0e5YAOn1tO8oo793O1ns/dsRIx6ui1X/W620973b0G9sHw3rqeAwBeZydfvif/YuuRZzo6OdOHsfo4A+YMv9FfA6iuUD8Wso32GBuNRgIkiQkYH3o3OzubbIeLt06cWCsDo8S+tk3WCewp8aLlxbEN30H2AAzcQdhsNtORC7w4ot0+3BbMdml4C5AyGAxqb0/23KyPBpUYL9dxODYFXmJh1s0FUeui9cf+A30k6SSeNzDKD7bd/tYxB/Es60xcaICZnIj1rKrDnQ4rKyu1zqrt7e1otVqxuLgYt912W23r4sTERNoK1m63Y2FhIckMxUMOwUa/8NcuShBbEK/hH1hfcgrGTlzjHINnGShl62qz2UxFaXIi1pHP/KZ0ujZs8ycnJ2vbRMkv/BbEx1v0MZHT5LFXRCTQsdPpxMc+9rF4+ctfnnjpoqpjT2w5+Ri6hM4jy/DbQCJ2xP7CvpfvuBMH4jvOr5HdPMb2ddZ57Az6SPzU7/fj6NGjtcPTAYac644Dm/hxUTPnf6Mxeis8a+C3K1u/HA+jWwY3JyYmEujCPH0WoQElYwe2ocRI3BNg30UAbAL5Tw4s8i9j5Md8Yf7E4+SR3IvvuKjmOTgmYN4Gl5A/YxYcOXL//fcnu0ycjx9EltFVxjkcDtM6eezwDp7mQL9zCfv2h6PLBqVIBn3AqRPdiFEnCs7FiYWTKiuyE0g7KIwiAsFCbGxspIrrcDisdWy4qsgzUCIrsQWnqg4rGBsbG6nlmvHwfaPd7XY7IezwgH23KO729nasrq6mjiucj4MXFvXg4CC1vbLACMi4hY84DM7oOqIqx7Yi8xIjikEjCZ6enk5vYfLZKrypqdvt1oJVggrWjMOCOezRZ+swFnebsV4oOXMxkIWBzJ14q9VKbZ0Qhs/AG8GVuyR4DooE75AHt1czBmSCz9jOxxqQQBjcI8AwoMI9MFZ8DyNjGWRcV4N8RsDy8nJap263m3SagCAi0sGmDmRyYA1+OBC2rjuQy7sTIupnPlhXDcw5ODQgxT0YO50YTpS5Jq88YL+qqoq1tbW0vYtKAlVe5oU9QlbtVOFZp9OJ7e3tJKMYbtqNZ2dnY2FhIXVXmQfIQKPRSHYA+YQPBAyuOu7v76dtaAbt6CAgYHVbriuc8HowOHyL0MbGRkqeCERY08FgkM4kgI9uEWdMjcbhljcCSwO0yIYr+tzHeuH1Zr4clMrWSsB//n7hwoXY3NxMfN7e3k5naaGTJJ2cjYA/IbhnbpOTkwm4P3PmTE0nHWwBAlrnzYvHQ5cbdD//+c+P7/iO74gPfehD8WM/9mPxvOc9L97xjnfEU57ylPjKr/zK+Omf/umLktz9/cO3TL70pS+N173udbGwsBBf/dVfHb/0S78Ui4uLaZ0i6mCYE7Z8zZ2Y8bkTYCfW3ooaMTrM3qBFnlzy/Zw/Bqfwwwa+HJz7vk7Sbb+cbNjPUtDxnJFRgnIS3WazmbqjAZ8NutjmGSwxn124ymMWZA9d4X7EID6k3Em3k6ncd/JDAO+jCuBvxOitW/g8xmT7anvmOeXyYD9iwMk6xryQSW9l89hmZ2fTK+ThSV5Jd1yY+xh3ljxWcmJi0B87RtUZ+8ZbW1mPfn+0TSXiUMZPnToVy8vLtSTJBwojq/bF7XY7vUiHRM6xLDKV2zJsWG5/czudF1bZYYCO4FMASYhN+ZwOYuYcUe8GceKJfnjLGJ2u3oZGJ6PlLS+GAMo6tsMv+GB1A4D5WPBp8AA/jOz6eSR7kItKLuwwHvSWw97Zdo9tGw6HNf3O4wgX3pDrXF4iRh3GxIDIJuvjHAPA18CK42jkBl46xnVBErvC2uQFaMsycQ5JOok09sO6+3hpMBikl9TY3vR6vfjsz/7s+B//43/EH/3RH8XLXvayePnLXx7/9b/+1/jIRz4Sf/Nv/s2YnZ29yBeyHrfffnv83M/9XHzDN3xDHD16tAZYWC6dlzjOhA+2fdbffBeC729QCV7nthCdd0zMd8lT0a1+v59AQsdpxHkAhhGRtqdyDb/nviCPH7Bvji8oHPj/3NN/d3OB9QLbAlACP/CJESO7Mzk5mc5ug+Ahtg7+Aaza5vrf3CfzHcuX8yVkPN9C6LgI2ff8+T/3IfYmzgM8w8b1+/3Uvf3JT36yVgzm/uZ3RNT0zv7CuQl+gOusS7YnrPHlbpW/bFCKQI6FxBC6is5CuALnyhXf9+K4gh0xSlLdaeIgYm9vL7a2ttLbHPx3/9+BkAEWmG5klzZwADcrNvdylZjtHY1GIx1wur+/H+vr63H+/PnUgUA3mZWa+7kihBJ5m5eVFOHx62dJMBgn++IJ/LnOCQEO0wrgDiEEamZmJlXh3TLO+g2Hw/S6Vhux9fX1pAzMc3p6OmZnZ2syxH3cbcYYcJbICGOOiFpygPwYQMDBN5ujAxZtFLgPABRdE/CD4IOKRH4NwQTjwFi6Im6gYFzA7Yq1E8DHm8yOIyqrBF6AJ1Q8V1dX48SJE+nVtxxyyPlLBFOu6OegD87VCY8rCcwPPsAvAmAo12GcAdsMcb5cy7YTACCCIdaBgJhxjausnjlzJvr9fhw7dqx2HgL8QsYIvHim7RvgjwFit7byhh7Pxw6VDp3cuOMoCBYMFjmhRf94NkECFTsSE7fo82aZXq+XxseceW23A2bmh+ygp4zN8t5ut2NxcTHZFtaUe9oRYlex/VVV1aox+A5s2OLiYurObbVaaawLCwtRVVV6uyfgE2u0sLAQq6urqfDAobqu8LVarVRsWF9fT9uTHfwD8ObdChB28vFQDkRcipApVwF//dd/PX7hF36hdtCnA0SA0r29vfi93/u9WFtbS0CoQV3G4cKSkzvbaHdOWX6RC77nrhW+ZzAKn5W3uBt8Yt72Q2ynJOHDD8AfvuvkyG9azEEOdC1i1DGIzGPv2eaJr3HXQ6/XSyBJxOjMFMcSPNOd2LaF8BG+Wu8texMTE6lThe7lcV3DXk/W0LLmQmAOPhp09Jqhk15jg05+ruXYibNjjksBV4ybuAYf6yLR7OxsbG1t1cA6g2PWUyfP3AfQ57GSK/PoBvLeaIwO+jeAuru7m2JHOj+5fn9/P7rdbjrzEX/q+AWb6QIxMU/EYazlrWWA7vDG5yQSTxqUihh1zjmhQ8as58imk+R+v592ENA5b5AM0MOJd67jjlENuliu6bjhby6WMD70jBjYRdDNzc2UUMNPwB94ure3F51OJ80RWbOMYyvcPY49Yy7cE72h249ii3XDcRCxQkR9q51jGr7rnIk4ia25rB1JIoVsg+P+voFS9B97hwyRBBsEwTbA91xmhsNhehvy9PR02g7pXAiQH1vD964UKOUOKeSJZ//RH/1RPPvZz46qOnyBxR/8wR/EHXfcEc3mqFuJQr3P4PrEJz4Rr371q6PRaMTS0lJNj3gGv7O2zjNZL9s++1X0E11kDrZfBnscp4/z5waC0QvytW63m8Bfv2GaeJ9zqZArv3TG4JRj1Rx8ZE3t5/Cj/Mu9bJciRr6aOUOMybmkr0HvuY7jLtx5mPssx3TwyjkjnzsHdBEFwr+5e4kiEHy1LOCncv+KXfFLBeAjPhH+AfCur6/HfffdV+uK81zdael8mvvjYyIiba/Fv9gfMy74Yp/MTpRHossGpVAUDCGDzBFDfkdYnXDxd28Rg0kIrBnsoMIAwPb2dszNzaWF48DfccGRqxh5QAtYgzGqqirt1UaAXYmxQSDoXFtbSzza3t6O9fX1dC0G3AtrgfMBguYj48GxR4zOqmEdEGoCVX4A7oymIyAHBwfpdecItBNaxobxweEwXipF6+vrqUOFtl5eQ4+BxmktLS0l3roy40qRE1MbTIgEIpcj5mr5c5WG+TuY4ft2dozBSbM7fgBO+XEljHEC2hitR0e4T47G2yk5kLpSNBwO09jRIVB0zlRCd1yZOXnyZExOTsaFCxdSxW44HNZaYSNGB9wZVLRB89zQcVcS0GkHURH19nwnvtyfa+2Q8y6MvCpF0ood4Fmbm5sxGAzilltuSWevMU/GgLwSMADGeWve7u5uAs9w6PCAZ/CGO5xwVR2e1ca2N5JcnBNBAvKDLLsTAlkjKIC3TlSmp6dTEOguzs3NzVpXkwFVt5Qzd+sNTtcgAPLGd+fm5mpnWnGdAUa+Q6DAelkveYa7p44dO1YLLgCP3cY8Pz8f6+vrte6o/f399LpyeMM8jx49Gvfee2/cfffd6TPuC6+9LS4iUiKIrD1eUOpygu6qquIP//AP42u/9msj4lDWP/KRj8Q3fdM3RUSkRDcPeqkO/+Iv/mL8yq/8SioaACLmQI1BdQg9NrBqYCUPirkH1xGwubJoEDQHNWw7mAuBl8ER7CsBct49YeAJf2tbYB/i5xJ88Xz038khsQD6lFcEHRvZ1hsQhf/MYxz/IiKBcAsLC7G4uBjLy8vJbhMcRozeLhUx6rZ0DOTqM9/HZzL/cQGxwRfGxN9zYCkP3P05vsB2wTLA9f6efT/fJX7hvCkXR7AfBlXyRNdA6mMl9IFY0T4C/9Dr9VIXJjxEhuyXkBfWBPDIcoleV1WVdgggNxMTE+ksGK4jxiWp5l98Kok2CUyuC/bt6BgJlWM9fmfurIPfmOqkBZsBD13gyP0A6721tRXN5uF5ehGjXQjoNGsB2IhM5MCki7E8zx097nLkzWkc9O6uPLoL6SzIu7O9Ro6boHa7XYu37UtbrVZtmyAFM/gGj80nYg62WToGc4JoUMLdIxTenEw3m81UsLEfRk4ofjiu4vvYQuQVG4eO4L/9meP4PEm+UqAU65UDOBGRdIHPASx4GZI73nL+Uygjf4D8jJwPxHPwyEUJx9H+P7pr2fA1uW22DcnvwRqbH5yNjL9kFxGxFIARcRXfdzGI+1r2I+pAdm734Eej0Ugdl+iVz8/jfo4jsfe26QBT5Ak8i/PYzCvnpOYbf3cXk4tXrIH1yCAj9sRd9b6XsQt+uA7ZyYEyyH7asXq73U6x8NraWnzqU59KsgV57uTzjuHy8Ri3gR/NZjOti8Fd5JtrNzY2En8eiS4blEJACOIxiO6EGg6HtS02EaNuGDNhHOPzKiWTMbgDU4fDw21oCFav14t2u117LTMVXRsMmIYDQHmqqkqvy+z1einZRJAwUhGR5kcnkZ9HAOJuDAdJdiwYYhyPW3wJ3h00s+gYCZwoFQ8qpVR2ePsg86VzCoOEoQeQIrhrNBq11xS7NRfDQJcW9+N1ovmB9vCR9XVy44AXhXaAPC7I9YF5ESPgiUCM529vbycZwnnbEWAMMQw44aqqEkBDIsRau202Ii56uyMyPS4w5zveGpEb0yuR0OYE4OH9wXt7ezE3Nxfb29spWB4MBrG0tBRVVaVAE10+e/ZsetsIOupuFvTLHS25XgPYYS+ckLgiyDNwIsi/t9ZE1PdMIyu8AAAi8DYAAn+9nZhkZWNjI8ks1a+8LZ0tYQThjBN95q0fEZEMNTKDXhOco3M+3BOwHUeKzeV3DjVEvggSsCGWQzr+pqamYn19PYGTOzs7cf78+TRfOmXs6JF/utR4kYLBHSfB6CbBFVVcnCMdOgaVWSM7L8YSMerCM6jC8xcWFpJcERBPTk4mniMr2E2SBwOc2AYCrunp6fj0pz8dd911V80WIq8Q252RTTtvbNgTQQ5A3QnBGjgg4Hr42mg0UoeNr/F9rWt5wMm/7rixnEdETeYtr4Al8Da3g8zHgWwOXuQdagQ7gO4GdhxvUKxxMmrgxIl93lbfaDRSMYckfjgcvSWXbXv9fj+9IdKFC9sSx0f2TYBc8C+i3mU6OTkZs7OzMT8/n95KRic2zxkMBmk7MuuTJ6185qQ2T1xZB3TGXYUGe7geXvonlyd3YtkHwhP7T3yF199Aj5MpZIot1I57Go3RW6KRK+xvrhuPlZAzXkSDjGEP6GKfn5+PiFGxkblubW2ls5icAHEP1gC+8ju6MDk5Gb1eL8kgB9fDbwolTjrzbYvtdjudG8URFADy6DExK4CJO47w/+6i4feISNurm83mRS8NwH8gTx4nMYmLkoBEzebhWVckfYwF3hAno9Ns7wZUAnhwxwHrCKjAmACknFRyrbtVXXwG9LcdwgbAV+QFXXVC7fyBPIt7ea50Q2H/kQ8DFjzDcRfxv2NXgFUXwT1++0TWb2pqKrrdbk2XsUkGR60vExMTsbGxkcbh3Ghtbe0icCPPFx8v2aczJ+uygRtyKxdJDajwXb6Tg6u+r+1W3kkTUe84tCzbr+egge2G4yWey/9zW28Qp9Fo1F4qxbjRO95AbP+6tLR00XpwT+bosUWM7Brrm++WQZ5cBMIGcD/LjPlIPOd8kbl0u93aji3m6SJcXvRAb2wvjSk4dsWX5p9xD45Ese3OC1Hwz/zgOeanbYQLZR4r8nPmzJm4//77a/aR71m/uN7ylefhyCdjAgegOOliI/NxTOy45+HoskEpVz6MFGMMncRDCCjgjgNcn00AIxESb+/BaNshIlRbW1vpjRzu+HAlwAz2whpptpDyClaSHh/uBmhBEspcbKy5r6tyKJa3peAox1UG3DXkMfPdiKglzoyB7XRbW1tJqX3ottuA7dyo2BlM8bMIrnu93kXGlTnQueaOGwf6jJXvE/BaKeFVjoY7OEZuCMZWVlaSYqDsPNsgkANz7o0CIV8kacybs7MIwKiQE4ChFwZVeHYOxjoZtzHAyF8pZ2vifCEMIgaCeSCvbv+mpbrX66W380VEAgTQAfhsGcBBs44GluGj7QM6g4xymCc6D3+RWxtEO2sH+A7wnJD6rZU4ZnSQ7/K2SKqXyC/t9ZzJgAwhK+g655tgH/JqHHLNVmHmAqCATBNkdjqdBL6RKPtNmVNTU7G0tJTsBW8s8mHwyOPu7m5sbGzEAw88kLqy0CWCStanqg47uAB74TWybkdj+xkx2jbCvfyaeeQC4B67ap6iS15r5AgbDYhncMnbGPNtVVRzWHsO8WTrJGdL3X///bUuUgoN/hf76QCfeaFbTwRhTyH71oj6dqhxCcXq6mp6HbUP1sX3eqs1a8k94bMBFQdTDs4gJyXwEN9tUDQHSA3YYIM9Z/M8B3hYH+INZMuvZ0c/LG/YZAfTBmogbA3bZg0acK+80AAfHKM4KXQy425L3grJWz2JT+imRN8pSOWxDTrFj/2fee/vcV3ExQfLO3Eap7MmB9cGH/13z99xIbyIGL0RCH9seSdZmJ6eTm9FZd2QX2wOBSXrzeMh7APzRy4YJ9t8dnd3E2BJTINNgveOLYjxnMjBPxfxDOCie/gxfN3CwkINEPa/Tjg4J9FdEradjm+QH98Df2EZMvDk2NBdOszLz2B8LnwQD7gjgu/Y3mMv0EfkwvmI7bz1HeAs4jB24VzDiEh+2HbVnUK53DoxJ84dDofp/ErGY3ttW0kMbzAQYBwfTWKegwAAc06urTPELo1GI8mlv49tcSGHv+Vrxxohpy5gj9Pv/f3DlzLRDIBvhe/Yevys7YJtx2Ol/F55QYnclljG+QO85LuOKYm1uN4+M5d/64jtsv2Z1wtZdnzN3xgDOpAD99g/r5NBCoNwBqyI6clnyNn6/X6sr69Hp9NJxUvHaciXdYLYA9752Av7Qm/lxL4BYMF/7Cc+HFmEP4yDvBDeeIs/ubZ9hX2xwVXbZP7vWIPxOx9nrIyN+N/NDDx3XEOC9cV2wX7AgJR/Jy7B5ht8tvwj8y76Oy4aB0xhLy1veec093Nx73LpskEpCyeG0N0MRiu5nkXxgbt2rkb9Go36GQskv/Pz8ymx6Xa76WBtOgaq6rCDgUO3O51OrR3UZ58wLv5GMs3CuSrkdjvmwZxzJNvJHERAikFHsIxIYvBJXv02L4wA1T/zjQ4xwBmSLowK/HJ1k/nRTeADU21gaVPFofT7h4fdEQgbyca5IoQ4NmTB3UR5gA+fndA4oYyIWmcDThdlpmoM8LK6upreRBYxOquMZIE1weh6TSJGyDGGknsQ7LBGvO7SyQ73Zs2dKNvx5ICtjdqVcLQ5HTt2LHV2TE1N1ba12IBFRDpbgXHOzc2lLqqbbropzp07F6urqyko9Llm/G6HCe8dGCFzOAbWlATDr8i10UaGCOr4cdDN7/n1DgDReQcHVVWlFvKIQ/lfXV1N58YAgLC+vEnT+u6DbW0DSUpcpYccDAACREQCR32uFTwmcMI+2XlTsSWJ6HQ66XDDvb292NjYiLNnz9YCQMAuQDvkmsCXAz2d4ESMzmNAP9y2HBE1PfI6+1XP9gfMAefnLd0GfbHPgOhU7ghwADlcpeXthvCcQIWAgw6FT3/602kLpINz5JB72l95HR3IPdHUarVSV1wOQkXUk0/sUcSoZbvRaNTejOSkygk01xo8tI93kOhA3kAZ+optBCBiHdEfyxK+LvdXzMVjiqj7+Bwwwx65g8GdMx6bk1vsDQBEs3lYfeUcMwfaACS2T8QkDujsBy1HzJ1OT7pYWAMOZsWfzczMxO7ubmxtbSUZdmBMfJMHpdhDx0r8znx91hH+It/aa3/KXJyM+MfxCITOw0OvJfNAdrAT1nHiOOIdbBn2jfGh85aZccnAoyXbMvytE0NkBr8D3/mO4wKf8QPoyLUutrClFL3wGHh7tLtv8Nuu1ue6HhHJpzNW7CNdvY5FmbsTG/ODRJc54ntJmCJG20XGAY38DgjDddhvxyHkDgYX3OEVEckfzs7OxtmzZ9P6u6PZiR/6Cl+RM+bkpJm4kgIL43eCOxweFko5R4rxst7OI9wlAoC2uLgYc3NzNWCOvwNS8zz+n4NltpUGRlg/xs78fCwAemSwg8/xy+ZdxAhUA9gkxt/f30+7TQC/BoNB2m7JvQwK2H49XsJucs4Nfgf5QW/dYQK/sSHuUgFw4Fqvt/UNvtvGOel3x42BIlPe3WbfZqAKHfE6+VmOWa3TuT/lM6+9ddkgHLrhgodtBOS5m7eMz80czJlxw1/4RIEyYmSPyNts88cBLdZxeGQf5RgU/+O5ERPwXeQVv4r/ZK55A4Ln5DFwvXWPuMT+2nESP91uNxVm2Ha5vr6ezo+F/7YxxC4G25h/no/lMuq42SCrY+lH42svO4IGkPIZHDzICmXE10bTk7Gz5D4sZL/fT62+y8vLqWvBiL8NFkkiQBWJCYvKD87YQpALlo0yQaWRVwwVXQZGz6mG+eAyO30HRX7bgKv9DmDsZB18k9xRNUQoSS4nJydjZmam5ph4NgrC58wbBeK53W43BXUHBwcJnPBcSDrhIYmROwjMWwumE02ei2GZmpqKhYWFdDaN93HDB+ba7/fj7Nmzcfr06SRTedXL3WF5IkNS4HO7LIfIKoEmcmTe0VrualTu3OG9x2F5ulrdFefPn4/l5eXU2k8wYEPf7/dje3s7rSfnHwBEwoMjR45EVVWxvr6ekiSqXAAU3rpm54eM2gn6QERkFSAEfcuBHOsuHVXe24/tQaewR/1+P3WAGXS03eF5zWYznRtBNQ/gk/m5vZ/tIjnwSDDtjsqI0flxBJboYrPZTLKeJ1kG621X6Fy0zaFbi8C/2+0mwBaH6sDZYN9wePhGJYPbtPG7kMC8nBjiH7BjHA7L2R+MFx1zMoYNckKQV+wY787OTgrMDfziEww2O8gBXOz1euklGdjDBx54IC5cuJBAEif2BJxcazljHq4EXamg+XKJdaIiaBr3/xxw8lkCnEXD+nM9OhsRtTW2T4XnOd9ta7HLrupzXd6FlieITg5tRy8V8HKNu665J5X6PGDiOd5KhE/0ixDwKWxbN1/gl/0sSb6LD4AHDsqRK+Td4By+JyLSGNg63Ov1ko4zf+bN/PgecwAw4/55wufkzF3f7lbk+4yXoN3r4A4CA1MOWL2uDoitU56Dxwjo4BgQsALZ8fw8BscJj4fgi4F4PkMuIkYxpc8PxLYwD4At/KDHh+zaLpJ84QPoMnaSBu+Ju6yX1lv4lPvcZvOwYMSYkGtemDEO6Mvv6+QpYrSFPgdBuRZeEQ97Kyzb5Dkf1QkdY2Ht4V/eJUEns4FO5wO9Xi8VcXNC/53QE2Ogpz6fiuexJvwNGbFPRtfsg1dWVlJcADkBtu7mSa4BDeuV7bHBP+7pDme+ZzDF62o/gIz2+6Pt/gYgsbvwjGdtbm6mnINxYWsce+SA9mMl7G/E4Y6IVquVzqB0jBoRtQYIeEVemst1noAjXwb/3QHl2NBrAr/ggbvV4J2PTzDQBKGDxGfW0UuBTowj5xXjQe64l20WTQnWe75vkM55q5/LHJxz+/mAzxSU3OWEDKLf2A/zwueB2jZbFvO1y0FY85kxGLTiWfYv5rd1051kkGOiHLzy/VgH29B+vx9ra2vp3FriGPCAiYmJWFtbq8W2BjV9f/6GPpt8DXNmTMQLzMX/Xi49qjOlEGwmaGPlAIQtECxCrqAIghWw0WjE4uJiLC4upkoCDmh7ezsh2wTR3BuklgW2kOBgjJ46SYbxMNRbQAyoWfEtXAiyUd48CGNuOCYUgaQIw+LAFyFmHgilFxcHBU9RBBS20WikbYgGYiYnJ1NnB8kma8LWNLqpIkYHSO/v78fRo0drYBHVWa5lnMhAXmVyoIJSYYSOHj0aKysrsbKyEjfddFN69S9zYywcJEuyjZHDoVnmUCgCszwYJhiAJxEjQ841Ozs7tQTaXRysvYm55b/niZevz43wlaJ77703nv3sZ6e5siUt39sMcEKwh2xxbhtjpJWZt8+QCDA3qtLeuuiqks/1IXBB3nnrhysP6Hqz2awBKayZ7VFE3fjlQDgBHk7GskdHEPzAmK+vrycAeHFxsZacN5uHFfkcSEKOMNquBJNwAhwjywCBrmrhRJmLk10CPmyMW2j5frfbjc3NzTh//nzqLMJuOxm1/eRwVEBFtlO6ujMcDpPd4Cw2QJk82GAOBs7p3KIjzBUx2ws7RM99d3c3yaVlDnlkPNhtV8nyTtipqam4cOFC3HfffSlgMaDuAoTtPrriYArZudKgVB4UOJhEp7zdOU++I+pVyTyowlYTrCwuLo6153wvB4sgnsu98wA7T2YiIgH+1hHfi3kbuPc8c5DQft7FiRw0GwxG5zKiW8zVBTNvm+G+XMPLDJBTrneBy99hfugDxRDPm2TfsYSrjnnVfG5uLra2tuLUqVPpzZB8F7/nJJV4w9cZuLL8mg9VNTp/h7/BV3wLfLDc5ACTk2f+9nB+0b7aPtj6YDsPf9FtdNTxBjwl5kI2Hg/BW7prnUBTZG21Wqm4hVy588a6YzsfMdpGha+liGaQg+9yjmdeRc/Xmw4sx7Wej/UdeSJeYt3n5uZieXk5Dg4OXz5EXIA8eP1ZB4M5ljvHCS7usE6AjcgJwDJd0NYR4hvuiZ8wQBURyd9iF7Cj7rDIOzCc2BqgJmZCtpwToIv55+aTY0bW+NixY6lQcClQJpcZ2xDzBXIMbJ2FB8RU7AhhdwrPpUMG/8h4baudnPb7/fQGRvxoozHaXsp62Lbl8k+seKUAqYhDnXrKU54SP/MzPxO33nprvOhFL4r/+B//Y3zO53xOnDhxIi5cuFADDeCX80c6VvmMQkEOTiP75r/J9s0yZwKwNEBlH2jbbH+TA1Emg9S2s77WsTVr7JyHa5vN0VlxPkoFnbefRSZtX5gT47KeAMDZl+cyHTEC4JvNZopJjQVY3r39lHvYP/DjuMX8Zk3Qb9/HfLZt5zv5v/lc7DN9rYsSzJdrut1unD17NsU1BvSxT2ArvPyHe0A5iObCuG25czxfj51DDm2THLc+Ej0qUCoPUN3uOA75dEDqSRpZpJrO3tS5ubno9/tx/vz52NzcjJmZmfQqcFft19fX0z0Bk2gjdIDI8w2MuXLE736Ll/dh8l0UCPDKSR6CRYBtY+2FPDg4SGCLKwIkc1S7HJAbYWfRSdDpTKIVmPNB6CAa5/hwmoPBIL0pwo6XtaGi4U4l7n/06NGYmZmJ1dXVGsqaI6X8S9Kd858E/ciRI3HzzTfHTTfdVHvbGGch0erb7XbTFgW3TObJpB1oxCG4Mc4x5w7aiW9etYN/eQANKAgP+Z3xGCgzGJInAVc6mY2IuP/+++PYsWOxsrIS58+fj7Nnz6Z50jmC8eCn2+3G0tJSTExMpDe0zc/Px+LiYmxsbMRNN90UFy5cSEAlXXVeB3hnJ0ZAy1yRO9aIs9qQJYMIdh6A0+a7D1n3GmNvJicnk1wxf/ifO1ivOWDVuXPnotvtxvLyciwsLFy0rW9hYSEZec+XtcZmME6fCxURCVQy0Olxkbxgi3Z3d5NMcx2B42Bw2L57/vz5ODg4SNVgB/3wBP6h4wsLC+m+/B35wAnZITF+eMu6INfMlUQGANDJgJNfkh07dQcsw+HoTVGNRiOdSUEFHz4PBoPUXesg0OckYW/PnDmTbBZJDsE0vIqob8+DJ65UX2lACv5YlwjWWFPeEovvNc9yEMCf+/7MbW9vLy5cuBD7+/uxvLwc8/PzNT1xImaw0ck04+RZ8CZPgGwDARBdCPK4DcpwHwdtBDz4ZNbFgSjzdaJtfuCL/UYyf8ct+HkHc0QkOwEgQBzg+Idxsg3ewbJ9EXbFMmXwMyJS8rOxsRGnTp1KADX89jzzLlzHZ8yHAJ7veb55MOoigIEpbAR/Rx8tH5aJPDnLZdj84dl8l3nwbOKpRqNxUdcrIDy+aWdnp5YIPdrzLsaRYzB0w/+SdDNPrw8xIddTBMu7ESkUYpvp4KmqKnVCcxwEemtdcgyMPruo4+QL/hmk4HvE2HxOMaPT6aTzDTc3N6PX69WSKSfH3Ase2MYhSzlw6rOq+CFG9XwBiuAtv1MQQa+Q1/ycrXHAhxPRiEjdbcgf48a/86/9mcEM89fxfbN5uF1yaWkpxSy5DjvRsz46UUeH4beLCPbNjtdy8IEucbYGI4fsWHFs5jVjvKwP9pTiSb/fTy/ZyW02McS5c+fS2NDPKwlIQchvRMQf/MEfxFvf+tb46Ec/GrfeemvccMMNKSG37Dp+zG0WczaARi7qszYBz+E3+Rx881q6EGpfjJ47fmKtAcHQU773cOAz488BNGQLf0b+iFyzbtbZXq9X2yUQMZJ3x3zMj7F4vPhl51aODW0bXCSx74evnhPzgodeK8d72Czrb8QoFud5XOPx5TEM43UeyPVeizwW8PdYf+fybEfe3t6O9fX1i2JV7CZ2kgYYCjd7e3sJdGaN7C/QW8ch2BByH8cnvkezWT/HdVzh6VL0qA46hzEQyYEX1crov6FMs7OzMTs7mzojOp1OLC4uptel33PPPSnoRkBJSqiut9vtlITBNLqpAKlc7UU5EEAni1ZMX4dxQRBwNA7E3PWEANnJWQgNyvkefC8HKAi0fW8baroCcLydTqf22lbWwEgl1WN45jEQmHNmF4AVlRoOVnRb8tTUVOog8YGFGJIczcfg8vn8/HycOHEiTp48mZIgB4k7OzuxtbWVXmXvKjJGjypdRP0NfwaVWFP+TrXH62CeRIzavV0RAxyxcbYTQUExAE5qvO78HQfuJOBK0nA4jIceeijm5uaSw3eA4eSSSm7EYTuztx2wz5/1X15eToHLxsZG3HvvvamjhCSgqqra26pIEFgLHAgOrdPppHOvfK35zO+AIDgOn7mBnhDw0ZFAa7HtCvLB+uGkCLypsGJgNzc30zh8NoY7cNCzqhodCO+EmkCY5Ahd4lncBzlxhwh66u40zrQZDAaxsbGRdGV1dTVtD+K8Ke6NE+I5rCm2Ap4AJmMnXZjo9/tpiwPb6XD+3tpMMArwxrqhy3SL8AwCHQOWrC3z5Fp4Bj/RXyc5BGcG+emAuu+++2qdtei+AYU8ARjXmpzb7sdL8OO5z31ufN7nfV685z3viUajEc961rPiJS95Sfy7f/fv4ujRo6nDM0/+x4FUOdlPwK+qqpI8HRwcxMLCQjpMN6IeqLlKx3cdzEDjwAUnqxEXHzqLn8vH7cQz57eDbgfxTrbs+52kR4y2qbg70XOjC5KOJHwC9m0wGKQ3Z9KRia1FLxqNw25CugW9jZ84x8Us2yLGwMsPtre3E/DsBAe+We75nHF47fwdg4x5UM2625bBB+I+AwyM33GGbb5tXL6mBlXHAaz5v+gl98G3z8/P154HoG9+28Y+VsIuuSoOoIS9GQ6H6awnbCkyAhDl5B4ZZM2QY+w59/d5jTMzM7Vt1S4E5naJGMR+y/qGnDBWdz+Mi+koMDYah8UCwCnO7PH4/Qx3QDvJ9LjxLeN4w7XIE/xFl6vq8O2GW1tbCZSyL3TMh0/ley7oGjzyfHi2fZntJH/rdDrR7XYTn/OCPsVegPFxcWVubyPq28XyZJ8xumvU68zccuAKPmxtbaVxASCZ1wAqxP3Ww4hI4Cmyyhh5huM65ut58DcD3J774yXGMDExEZ/1WZ8Vr3zlK1OybTDKeZ95By9tb5FhxwfEXOS1zWYzvTgHGwBIynqRjzBfYkqvle2NgaiIkf0FhGDcXmfmlNsJ20zW0dfl83e+wxjYSj03N5eeTW7gZzMW7PLS0tJF8prP2/EEf7c+MX7sL3OzDNm+mX/EjwZR7FMt+8wXvlimLaPIcM531oi52Sfio3OfzD12dnbiwoULya/nRXDu2+l0kuxxP/QWmz0/P5+6XDmOxIVgeG7+QcT7XouIUfHO/t3y83B02aAURt6LiCN28u1kkQCNCnmn04mFhYX0OuqJiYno9Xpx4cKFOH/+fGIayRwVQYMNGH4fAB4RCUjhehSSpAOFyB22Fypi1KXA9jQfDm0Ayr8jKE6iIXhVVaO3DThBdRBDIs29rLSM1UFrVVXpXB2qGGy/IfitqiptEer3+6mNFsfTaDRia2srms1m7RWtPpvLCSLnsgBaRRx2StCpxvzt7OE3c5yeno6FhYU4fvx4qvYDavFGscFgEFtbW9Htdmv35V4cik23D2BWrpgRdWDKhoBAA5AvYlSFdqJs9NeOFN54fSJGLbQQjhhwinva8F4NmpiYiBe84AXxjGc8I2ZmZuIXf/EX09+qqqodyso2tWbz8HA83uKEU+l0OrG+vh6zs7MJoG21WunvBMbcm3sRfMNXAxbwHOMIPwlUHQAbcMBpAXbl1RrumTuevFMmYnRIJ+vNWjgYtt3b399Pb8+jsr22thaLi4vpuYAlDixwctgkxuPtNGyN9Zl5ltWdnZ0EGpDYYqM2NzdjdXU1zQugCP2gkg+fsNErKysxGAxS9xbPdIDIGqNTdjjoBNutIfPaOsSB3FT3Hcxhnw06ex15TTnVn1br8ByIg4ODBMgDqvPmTGwmvCaJP3fuXGxsbNQORM4TFR+gaR/hRCBPlq9EwIxtf8YznhH/9J/+03jPe94TERHPfe5z41u/9Vvjfe97X6ysrKSOGr/unXE4oOGevrc/sz4gqxcuXEgytri4GPPz8xdt93HQ6IQnBxG4t22dbagDf38H3TbIlScJnqPnnQd02F3iirzyGzE6U9CAMPppveX/dEw2Go2YnZ1Nh097/E5O8e0GRNzlgY/le9hO5oyeb21tpdgn7+h1MO1g1wE1susEHv6Zd9gej8lrZFki8IyIms64G4PkxXZoXELtQBab5HXPr2Oe3p7orZRc4+Q34uIY7rESa8MYnJhRgGWtzZuqGhUu/AbSiEigwOTkZDqXj5jWemNAikIJdvVS6853nXDkSZCBagAFFxUMMvEM/DBvLeNFRaurq7Wk1eNvNpvJljs2gw9O+OA134uon+WWx5wRkc5DHQ5H3crIjbdl833LBp9TAGY8OTjCuo4DCRg76872fmzI5ORk7Q1m2CjHH5BBA/SCmBK9AryDJ/hynpn7K/t1jx9ZIaewnfELjCDfy7tAWGd0g5jIMYZlw0CsATLL6pUifMGtt94anU4n/tf/+l/x0pe+NNl120L47xgA8k4Agz8AJdgg9Io15G3JxDrElBTA3X2UdwOyFnRjEsPkvhmggRjcdtdzywsO9qPIpIETx8v2H7YDjUYjbaEGB3Cc4ufjX9yIAW9d0Ges2Kl83B4T96MwwDMdL/G5sQz7cMuvQRbP30XMPMZx/mL7gr31j+Nk21XLK7tTNjY2YmtrKxX0kbV8jfw7Omg+YT/ZHo6udbvd2otw8sIhvztOy//GnN0BeDl02aDUwcFB7TXmVgwYyAQ5bPH48eOxuLgYCwsLsbS0lJwrB3OyLQaBMJpJYpJvzeF53kLjVmdQO5BaEqGIqHU7+aBFLx7351yrg4ODFJxbAfndhpnqqZUFJ51XlnOB5n6u9O3u7tYAFYJOV94s0HROsIWFdcO5ELD0er2ak4mIlNSxRk444S8t5PzO/AE0jIbaSBAUdDqdmJmZiRMnTqRE10k3gNTc3FycOXMmzp8/n94C6K0BjBEldEKKcjgYxsijmMgSwAb8Y644d9YJmXCrKQaHFkUbLAfR44Jtnm+HdTVoZ2cn3vve99aSqV/5lV9Jc11cXIzNzc0UFLNGdEoh/7zdstlspvVgPj4sNCISQIgc06nE3HGi6DzgDQkd3R84JwIsJ4nMJTfwBqgcOBP84ZD5Dgef033EgdEOgvJqsbsC3Plo0A2QBHm0rvE2uKqq0hlejcbosHKSKhILgNONjY10f86zATjkYEODWvACneJ8Oew4wbYPWOWtZM1ms7aND/uYB7/z8/MxPz+fnoudou2fYGpc1wk2j85X3sTD2tHuzvyxOcgf68maVtXoEH5sOz4FPcO+7uzsxJkzZ2pdeba/jNH+AR44OM4DOj6/UrS3txfnzp1L/9/Y2IhbbrklvuZrviZ++Zd/OXWWOnjyGHJfxd88H/hk38Tf9/b24vz586mQsbS0lAIgeMb11jfbT/u9HODgWsabA0x5IuIkm89sn9FLJ4joqAEV1tzgE29sw58hN7TI+xwf/InvRdFnYmIinXEYUQfHI+pnfhDjUFCxDrn4xRbpXq+XAmqf2zcOnDO/82TZQTI6antqmXAADr+tv3lyQ5XVHVMuFBn04P7oeB4jUdTM5TUP1D1PZA4fRZGOz+2nXUR9PGS/zljgF+vkLUrEro6RDJgAzhsU5/7ElyShdFvYr8GfvKBLbOItji6g5XGw5dTr7M+t9zybfyla4VM3NzdrXZA8j/E7fjao4vOgbDsMYiA/dOTmwHXeacLao8vwxN25JNI5AGF7z/3MZ2TAnY/Mj10FzWYz+U7WPU/8xtlL8hz0kKNLfOQI3VbWSd87Tx7t/3JgCN+JjhIz8V1AK77HPPhBv3gzOgAJMY/HaUDRIAL250r62MXFxTh27Fjs7e3F/fffHy95yUvi277t2+LFL35xLC4u1uL9iNGZaLY1uW1y0s1ngM7osXdQYA+xxRQZyaGRe+TLuxCQK3wIsmG7bAAI++HiqMcTMdrynMub5814na/b5o27NmL0ciDnX4zL/LAu4TvQnYhRB2B+LAVyhi/NbZbzMn/ms5FYBwMtyIA7mngufLRvxdYQayO7eb5v/zvOjyJDbl7odruxtraWYg/bqkajUStsIC+Oh1gjfLS7pyKidnxBu92OxcXFmp0hBicPYJ2RKfPR8Z1zw8uhywalnOiR5NGmzu8oEsZ2bm4uocgHBwdx+vTp2NnZifPnz6dFg1FGegn8LPQIRMTIIDj4YJsIjLOgsAi0w1vw8vk1Go2U+MzPz8fKykoKVF2laTab6ZBF5ofTdbCFYUfhDIw4sLSh8HhsNLgOvvHax8XFxdje3q4F7Ainz8CIGBlLDhseDoexvLycBByDaDTZ5+EQyGAAADVYx6oadckQ8JD8Hj16NCU1OCQ6siYnJ+Po0aMxPT0d6+vrce7cudSmjuLjzDnDA+Vn+w7ngcFvB935Vgk7YOTDnQAGy1BAJ/s8n21YeUDhgM+G2M6e622kryRRKfi5n/u52N/fj2/5lm+J3/3d3421tbUUXB09ejRtjWRu8HRxcbEGlLjTjzddevsTAAaOFT1Glw3oYOzYtgeQ3Ww20xYVdBu9gP/uCHCFw2dMGDzB4RkAZkzoRKfTSc6Gv1t2XOEyeG77Zh3jbX95BQO5mp6eTm8uypNFJ9V0ajabzdjc3IyNjY0UePP9xcXFmr66suuKCHOPiFS99xYCrvOaOgHiM2QL8MlnaLCeDjKo4DNGKjPIAbbSiRfnL/h8EL5LEueCBOtgkMEB2NTUVOoIvffee5M9Rx9zYCfiMEHBcTvZczXficiVBKS4J0ERW7RnZmbiG77hG+Jnf/ZnU0ejAQCvcW7r8nszb/udPDACoDx79mz0+4dv4QS4RCYMGhsgypMc7me+Ofjl+Q7inCg78GEdnLS7Ymy7xXX2+chqRKSOFOwKFUN3nOzt7cXCwkIt+TCQhM2EN44DIBJyb4fIz2QxcE5BjK3DJIW2Q+YXYzJf7fOcpBCH5HKBTsB/eJ77sDzI99r6c5KI3M44SMbG5m+5yrtm8kQut3G2ORH1s8uckFnO8iT9sRD35ocOYHwpiTvgrjtZ3C1ve0VXiTvnWc88+KfwyN/dgevise0B6+hEzIkT98Kn8j13PjvpMQCSg8msL9cDsuLX3LmRyxh2ncIksgGRoDrZczGXH3ID7mcwi4SLopS35RL/GWQ28Tf46OKXec642SXghNZynuuqAcDhcJjOkGHNndhikwEpWNO8i4rvcW/Git7mBT3sVD53ZBv5J/4bDAbpeBX412q1EpBgMMrzIO6xDxrXkXUl6IUvfGEqKH/WZ31WfPu3f3t89md/djzzmc9ML/3IdQNfyVrmtpTfAely++ktU63W6LgMeGHAE7kwiEvcxA/Ag2WaQqeL7faf8M/rb9nBrqMrzlc9F/tzy5+fge7Bp8FgkI5acb5pWeN5ANkGpsxPvgvxXe9+AWSxrKEHjBf75bwWnSFutf55zbneoBk5gH2N7ad9tcfNdZZv9IquJb9cxSAjcZiPcoD3/X4/FhYWaoCpfaD9OuPgfhSwWQ8KaPjptbW1lKMZDMTvMEYXCy6HLhuUOn78eMzNzaWzoKanp9NrYql22xmSeG9sbMTm5mYy3k5sETYSWgdQXiQqXXTI+KBHkg8qlAcHB7G+vh5LS0sxOztbM25mfO64vN0GZ9nr9WJrayvm5ubSAccYlunp6XTgd7PZjK2trXjwwQfjzJkztX30rqA4KW82m4lfJNtWYAf4FsCIUffG9vZ2reWfAMYVR56VJ/kg+JOTk7GwsJCux7FQ7XXQ6s6Q4XAYa2trF1WyDK7Res7+ae5BBwcGlW6OVqsVDz30UJw+fTrOnz+fqsN2+hGHYBxriTEmUfVbFxiHwSHGiDGqqiqBBxGRgh+cAK/55X7IHb/nDn4cGm8iOHf16mrRYHDYrfT1X//10e1246u+6qtiaWkp1tfXE78wrBzAyyGFgLjw3CANAVpVHZ7XgMFxVxFy4TPIkE1Xnegy4LDX4XBYe+mA7QlrTUDLeAwSw387Opy2g3GcHHMzIOTgkKTTASdJBwkAgbYdF05wcXExOQkcH7zlvhGjZJBOHua/sbGRQBaCec7eYvsgjhD7TDLdaDTi3LlzMT8/H41Go8afubm59Cz0BmAQx073h4NHbDUHzzpharVa6dBx+Eowy7gMZkRErcuSNZ6dnU1VGmTG9+GHcWJz8AsENPgWuss6nU6cOXOmVmlqt9vJFzAH1pa5IQtOpPFNtntXGpTivhGHh2nfcMMNsbm5Gd/3fd+Xzm1xwgPlgSKfGSRxcJWDVw68nQzTYYssTE1NJQCUwA+dRPfhMevoRMpBf8QI7MuDOM8lr0YzNq9b7usNXpB0GpDNgRlkh2cazHbngJNpftjGfqkqKVVGeMz88a3IMXzCPvBmXsbn9WbeTobxRYzbAaFl1TzMCyjwNQeMGYOLMSQxuR/M9cfFISfs2GB8EjJrcNLrZLl3UO3nDYfDmp0cJ/MGbh4PMQYnRMg9SQ6+hLkRB1gu3dXHGnANMTTyyXW+V8ToDBj+b7tlsCMHNX0v/g7og89HdvkhXuRfA5YGkPH7AHL4dseYllPbbvIE+1b+TvKNLAL+MX8XUohZ7Z8ajUYqpLgLyOCyk0/ugx7nfsKgOz4dO+qzPQF0WF8DgT4Pl8/cQU0XM/qd5zF5AQCZc1zK/607zr1sF5gf34PXrD86lB934qTfdq/ZHL3UyTkN3dJ5npPr55Xwsx/96Efjcz/3c6Pdbsf/+T//J77gC74gnvOc58Tq6motpkTODIpA2DhyLOTXoAH5BfEPfGm327Xt3wYNbR95NvNuNBpptwuFqk6nU+uacUHdOm5irPbb7jry8+wP8o5Mg0qOB+3bsX22+Ry1gw4bNAYId7HM43ZuGzECTm23IkZxPXJo3czjt/w6ZNZ5kOMAg1HO+ZBzbBW8g0fWN9+Pv5nXNFuwXd84AvN3/MRYWRvHBOwIMRCV56cGL5kvz8MGka8PBoM4efJk9Hq9eOihh9IaIEfcD/2lweRy6LJBqRe96EW1AJQFB1HjrBUO2gUV9yIwOCbubRYsSC7kuXKB1jl4Q7lw3MPhMFZXV2N3dzcWFhZqKDaC4qQFoSG5c4IyHA5jY2MjdnZ20mHcMzMz6UwPo+DPec5zotPpxMc//vEkyA6qnOSTWIJ8+twaFtWBJNUgo+dc2+8fHiDvVmOUxYJhPviNWThC7oVz8xYFlJX70FrPK9o58JD5TE1NpeTSgA7BF6DU1NRUzM/Px/7+fpw6dSruuuuuOHfuXO2NAQQu3BPDZadm5UfJkTPPw23c5n0eKJPwGmFH0VyV4DPkyUYZ55B3cFl5bZSuNDGflZWVWgBv5+FqgCsVrh4CRFPlAMQD0DAwiM6wzjg55uuuGoNJdsoOCFxJwz64smcbY1DMlWg7BI/JnxlQslPDQeGALCMOwB2gMs7Jycm0hc+gGwk+SRNrBTiCzAPIOjgm4XeHqAEnv52O+Q+Hw3TmCMFMRKSKB/y0vfLhmLbR1oF2u50KBRQp7HgMyHOgIs90YsMa8K8LFRzAiL10ckx1FoeIT2CtpqamYnNzM3U8VVWVzppCfvKk3Z+5c8E21wlxHkBeiYA5J4oG7XY7PvGJT8Qv/MIvxNOe9rQ4d+5crSAQMfJd1i/G6r/nSQifOZHJbakDXN6uxRtZ8YG274BSHgeVNsAd85QxOKmzLPE3B1xOnqybDu4NkjioJUninvgT5MqdILbnTjThFfEOCSM2NO8CwXcaEPPWd+7vLnQHzg4m4Y/nCi8sDw7EbbcsB/YNDlqdmJmfxGHoCD7QSYGTLe7jTjTGyvXu8HRiyno6mfCa8yzLsH0R6+ptRrkuPx7K1xd5q6rRFm1X5LH/VLC9RdrxFjEYiQbApMEiv1XW/BkH4rGOXiPzwHwFVCYGIo4ySBMRKQZeWFiodchCObjKelNEpohBLIxtZ1z4NeuL44S86wH/AQhJQkwBEV67KMWZTjmwhpyjRxTEmA9+3sAEoAz3npqaiuXl5doaU0wwaJnbW/hGToLu8PyI+suniNmx7+N0x3qfg7esjRNV65FzieFw9AZy1oS4w/FvRP1A+kajEdvb2yneZzzEX+ipZdM7Cq6kfyU+g9+f+7mfG//23/7buP322+P222+vdaR4bew3LXuM0TloRCS5Gg6HySciz4yD+buD1mvLc5yH8LyIqG0xp2hMlwu+mevt4y1rtp++lh/Lau5HWRd3NTrG5pkU/A3cIefueseXQIwXG2ifYaAa287/xxW54Lm7S53burjOM/N40XNFtvncuyUg+wfbacuT5QcfeXBwEL1e76ItlnkuypzcMOIYyjxCnyMi+fA8/vK4+ZuPUfLaLi4uRqfTifvvv7/mq5BZN8LkYPul6LJBKZA2b2UikOJznIkrO3YYJBoYNCeT/M3VtjwpwhnZUMF8HA6OnMC52Wymqj73c/AQMQqAXA1xVRd0kMQR5cEQc5bJjTfeGDfccEN86lOfSjyxMDkAtAIRPKGkrkQwXgSRcdsQsR6Mn0SXZ3jbkwWfINLXOInEeboVEaV1NZm1JsifmZlJYJQDGQt7ozECDDY2NmJ1dTXuuuuuWF1drSVFBLMRo0Mt4Q9/43eCNnd18ByDJvDUDtZBG2P22rEeBJokH3mSZ0CA+WOwbHC5jvtfDWJ+g8EgXv/618fBwUHcfffdcfTo0dqaDAaD2NzcjEZjBFbmySUBH0aJAAnDaJsAiOwuqaoavTKdTgOeY6DJiQ3bYwGz2f5F8IsBhMe543Gi5WegZ3SHGaxiLRxMYFuGw9G5RoAhEVE7K299fT29aAB7QWcVjjkiYnNzM7WJM2Y6lfgxeGugjTl0u920lXVqaiq1+boa7UAbnaB7IE/Wsa22CYwbnvp8HebDVm1AL4MX6A5OHD1ykop+MFcScj/bawxAxXr6fClsH28cnJmZiUbjsDPs7NmzCSjAB8EjAiMOfIQfedXXgTn8u9KErT04OIinP/3p8cY3vjFOnDgRw+Ew3vOe98Ts7Gx6E2SehCJLBk7GgRQ8x9/LA9b8c9YV20VnGoCUZdXVNfxSq3XYBTc3Nxc7OzvpoHlsipNjJ+iMFZ57Pvwf2+rk1cCzbS2gU0SkzhPW1vxxYBcRSe7d9cLYLa++F/fBJzlBcOcT/zeoxljwX+MSSuZPFRNf6LVm/E4oWB8nXXkQmweyXgPsMPxwIGxyR8vExETaQuvxmd/YTccLTq69zdzrbz3I1xE/BS/xVfzdifxjIXea4w+Jw7Bx7uKEV5ZDbC4xg+1zxKgbgG07xGc802AWyR9rzhrYVuWAnPnIeFh77yJgHMzB4AQxPON1Msbz8kIccYbjSmI1x4HuDsiLiHxG/Oe43rG+bQP8x27hQ8xrfBTj9iHMPkCZs4Dc6dZqtdIxJnlc6PXCdrIm+CTO2nXcjWzkwIB5ZJvE9/OOkdy+5vLDWJADj3lnZyfFiE7ssRt8LweoGAfnSvFcfy/vLHROxDOuFCjl+DVi/HEG5qfzxdx+2dfm222tz9geirrWS98b++HdCcikdQO99bN4HgXNTqeTznUbNwe+k8sTlMc5OUCX2w2PF5lyTs38mLOL/8YNmBugD/ewj4SMCRhItC6yK8njYH6MP+cDxJyJY7kHc3CeYTDR+AbX5rEI12IjKNixdd/5o2WOZxlnce7Ds7jendbwD7kkTrd/hzfOl7Bh/pxc4tZbb43V1dU4c+ZMLbfku+j/5dBlg1KnT5++iLk2ZCyqk0A7CK5zEpILsrei2ZHZMRitc/cCwkESwrVbW1tRVVWqyNgQ5A7Y82Jx8yAeg0xyw2HQ/B1ntLGxkQwrThTDkI854uJtYzbGeULtYMYVOJJ2krPBYJAcAPPCwDqhHwwGKbHmnlSWEE6COBBTjDrPHw6H6Vwe3qhH0IRxpevGAXW3243z58/H6dOno9vtJiVBTvKAOSJqSp0bZRTcjoI50mHiDggbQysbz+I61gOAxLzi2Q6Y80TA/1qOrQdXmljjv/t3/2687W1vi/e9731x1113xdTUVJw4cSK63W6SJZIFDo0nwGQtfEZPxAgkIKnDkcJny6erQegDeuWgx4CW70EwMzMzk5xtxCiQJ5ix84oYdRF5HfO99owLcIM19CHbVBOazWbaCsf6TU5OxtLSUkRE2i587ty5qKpD0IhOnenp6eh2u7G1tRWNRiO9CryqDsE63paHfKBnBNsEuNgzuimQK86Xm52dTVsJ4TN6AdDnV8RimxxAmq+tVitdOxwOY35+vnZuFPOfmppK59pZF+GhkwzrJmuEXbKe4PBYK4P3/q7PCqDbwlsbB4PDSvzq6mqtQss48mTfAZXBAifc1ukrrcNOFpvNZhw9ejRe8YpXxNraWrz1rW+NG264IZ2DZD9iO+j/O8By8OIAyn/z9xygWpcajcPCyIULF2JzczOmpqZicXExbZsHRLXvihjZA+QdWSRByH2/k+aIOrBCDGBbyzNym819HdjjxxkTcm+7xL3wXQSAzA874ACY7sact/YVVTXqmPB6MG/AX2wSfMzjL64nuWB94IflmL97PMiJE1Pu4Q50yHbdiaSDUOsjfDHgjlxbnz0eAmi2zgMgWEbzZNVdIJZtz8Vvm42IWlL9eOgXfuEX4sKFCym2wRa+9a1vjaWlpbTWTmYdx+XyDlDupAVwCV7jc+G319Gy7uIMfMhlDv7xt7wrAp7ZPuc2kwIwRUF3RIx7vnMAx9v2F6xPs9lM/tD3hGfOG/jXXc4Gf3P/4c4Lyyy8wXcQyzsOYksNcQv3XFhYSIVsYmfGRlxju43sc5Yt55Ta3jo2te/Jf8bFygZX8uudF4yzA3S+oIOX2mIK34ixGUsOTDimgs/kT97ixZgch19Jmp6ejpMnT0a/34/bb7899vf34/Tp0/EZn/EZabzwyMVVA1HojIEkdAcZtT9wfEd81G63ay+scm4UEQlUwn4SNzKGvEAfMXrBAfoDaEtByMfa+FkGR8bxHvk26MT3xvls5IG1RE+d/zAPx9R0NuYxl2MRiqH2G+iMcxR2ghgIYwx5vJE/zzqUrw/+xX4P+5bHg6y7x+9nkKdh78kx3RmHfllXzTfH1o578hiKYgY20kVpA2CWLftnyzd+h5ia80ZPnz4da2trCXxlhw24wCPRZYNSgBt2ojhOJg6Dmbzf7GS00Y6Ne5KIYvwsBDaQOCYnz+7GsWHlet4Uh2LbOPjezIWxOECj88OJ0NraWtx4442xvLycDjmfmJiIW265JXVNIFwGn3iuO8swHHaKdt52HsPhMC04oACCRpKLgSJ5sNPhbzjVVquVDq0mMOAsG1dfHTTZUbHey8vLNQOFMXdywnwGg8PunJ2dnfQWRnfRGJizA4R4jtcbWQEg4vt5l5YTTZ6D7M7NzV0UbOM0AdmQHd/P6+Pn2VDjKFwNtBG80oQsP/OZz4zNzc34pm/6puh0OvGpT30qgRzoohOgZrOZKn95SzBnarH+6Azyh+zZGeTBD9UkznFA5uEbOo6cou8c6N/r9dK2UZ4L0BMRNTlHlwkS2f4J/33GjNuHrZs4v4hIh/YD6JGo8rZO5JDzn3hWt9uN7e3txFfAW/gAoIvsAnivrKzE5ORksl3on7s1er1ezM3NpTdvApwPh8P0sgnLqrvg3CUyGAwSbx10UIniO4BbExMTsbS0lM67ouKLjedZfOaEh+c1Go3EQ9YAX8G5UdgZ7sd2YXwRjpGA2aDE/v5+OhuQeRqQ5/7M1d1h6Lkds5MC9NvB+ZUktoK/9a1vjQceeCD+3t/7e7XXyzuYhGcO8sYFQfap+e8Obrg2T3i8PugX17PllPX2lpW8Msx2NwAs1i6iXmXn+wYZGJsTB9bL68A6uxCATYqIml7wPewEPo319TjgMQGewSa/8j0PhtFvEm4Hql5L7KkrjCS+llUnAsi7/aDX37JJHMYa+jt5ldoJLXNkDfm/wSV+XMDgPEnuHTEqGHAfxzbN5iG47niOmIt5Mw/f0wkK34Wf+Bj+78Tq8ertZ33WZ8UrX/nKWFpaiuPHj8d3f/d318BYYiyKBD6E1/GC4ySKjByKzbxt55BfgyoGCJApdxEgE15PeIKtxhY4YeGZeVecwWZiOic96LcTM+ub/YD11wUDJ2KWEWI9fCZ8tr9A1/muO9VIphgPfOE76KlBFroYHKPji+AT96OYFTHaxsb4PT9kgN0PdJdyf/scj5V5+TPLsuPS/Hv83cV5YiW/aZxYg+uJBfndOySw+ZabqhodSwA446643D4z5hyouNQcHivddttt8da3vjV2dnbizjvvjL/1t/5W/NAP/VDceeed8RVf8RVpC5+LnI5H+Zd19xsP3UAQEamAl+dSESNdIybDNiG7MzMzqdO+1Tp8m+Xy8nLiN2SbZn9un5bntNhUZJt/eZ71NfcxEfUzk53H+O95nOQX3ETUz+Vyhw8+D/lEJlgTYjuDfRGj87Rs53Ig1fqNrch1y7iD55zn5AbCmQ/r4u+bn/hHx8H8kNd4nH72OGLuyBfntJJbVFV10dEXBsohF0Ly+MTrxfjhaQ4+Hzt2LMXdR44ciao6LFoQGz4SXTYoBbrtxR+3aB5kq9VKCQ7CD3ONNuIoIqL2mZnBtjz/DcNm5+UtRTZsfE4lPQd+uA/JEQlRDnZQzeC8qoODgwToOGg6evRotNvtdMg7joyk33uW3TniRIngiiTdCQIHHoMoI2QIIXTDDTfEqVOn0rwMFlAp2tnZic3NzWTUWDNvcRkMBqlajAK7m4W3LwIAYiQRYgdKOCheZ8/rQm2wmS/zg5wAYtD4LgbLzi4HNj02+I9BRH74P+tJZcKGJ2JkBBxQ2Wi5HRWDYADgagJSUKvVite//vXx/d///fHGN74xfvqnfzp2d3fjz/7sz+L222+vvTDAxhs5AqTs9Xrpb8zLwB7krgj4THKHU4PXgByM086awIgtdoAtvHAAmQWU5X6NRiOdn8SZFQBrDhx4jivAANisi4NK5JFrO51OWmcnE4uLi7UOQnd1+eUCEZHOd0JWXA2enJyMY8eOxdLSUg10s11jXgT+x44dS9v3GD9vaAP08tZKAy+MAefB9qper5f02t+n6kanKPNyMoOdZu1JqPLOKZ+bw7h4mQb2iO14fpaDEV7MgOPzmw/Zl28Ay2tKgEYXWcTFh5nbOaPLBgigcUDQoyXuiU1tNBoxOzubQGE+43n+PZfvcbbPZFDHf8+BLgIO9IXnuFp8cDB6A+3BwUHaNp9/l4DGtpmzY0hsGIMJnhswIQYwqELwbR+Bz4wYvaRiYmIigejIJACZEyP8AmAvHY5O+pBviireKmBwKWLUocO4uIfBdIBab6cG/HUA7WTBCbVl0bGO192xmoE97oOd5l5OormH4zaDFfg9v0TECZefC09YT+Rlbm4u3YeAmXtbbx3/GVDxlhUDhAY9Xfx4rNTr9eJXfuVXks9pt9vxwz/8w/G93/u98fa3vz2OHTuW4icSfeIwy3MeY+SAGdvGuQb7xtr4fBDLhHXHcuD7WNa9zuPsgYEcxjcYDFIXL3rIOuCHDYDxLGIpbAJ+k2fmMmvAB33Ngd08gSTe5PnEHci5eWb7TiyJfLujgVjaBa2qqlKsT6LtM6zcuY0cE1/ngJnl22P0/Aza+57jyPptfjFXxmEQj3/dUYHvjIg0V5+dx33QOeTF4AOxhufQbrdrdhUbebVi5Pvvvz++4Au+ID3/2LFj8Yd/+Ifx5je/OW699da0HvwdOWW7J7LjDjlsJoAxcmH9znMWyzTyzP9pFiC/JObmuW6S8BpzL6+j8xIDKranxM8+KoF7WdYsb5ZT1pXCo22QwVpkinu528u5ccRom7vfgpwXrHmW/Si88Dl3gGh+rn0H9+NvjN+FGvPQ8/PfbOPs3+BtDtTxjP39/dob9PJrGMO42IF1dAdYRL3Ax5gj4qJ8u9/vpxjT/Pf9zDdkHX9K1y+yfvPNNydePvjgg7Ut7o9El+2NSXZsgGyg+MwHYjlRMbrpCWGQ7BQtsD6/xAufAw6uHoEQEiyT1JLY2DkxdgQgVxCCoa2trTh79mwCl7yNZX5+PiXrdP9gUDgAnPNeMFSuOA4Gg9RyyVwwdgBCGHY7RTpFSBIRLiq9ExMTsb29nZypjSwCYgCHYJ1qiP9uQ0SCT6LtoJNn+3qScdam0TjcnrC2tpZa9B2kOtBHOVAmH/DM/R3cOInkh3lwvZNxkhuewfNZZ+TTSLGDdXhuY4Qs8H8UF2M/br/81aLBYBA33XRT/ON//I/j9OnTsbKyEu985zvjgQceiFtvvTUWFhbS2/gsk5zn5FZyjKoDSZwx4JFB4tw5OWHirZWsWUS9AuXkhvUi4SQY5A1kbjudmpqKhYWFWFhYqL2VhG1CdPkRmPIcdB3b4zP0xgV/EZEcCQA28ra4uBiDwSA2NjYuOuC80TgEl7BrnjfXzM7Oxvz8fBw5cqS2vc7Vd2SIChg2ANnD1tqmGKg5ODhIgboT8ePHj0er1Yqtra04d+5cAv1IOAjK3HGGrWAOduzYX4KKmZmZ2pZP7AEVNCe/VFjb7XasrKxERMT58+fT1mJvy+V+VGMduPMabeSWNWec7p5BVuGNq4PogefqxI97Px5CjjyPqqpSB0J+PqKTRXcxWa/GBUm2V1AOUDhh5v/WR3TCcQBnInDuHofaAtjQuZcnpI4FnCzZ3yMfrnTm4KEDY65zYklgj1weHBxcVMjBZhNDUHji+05WkRNiDAfOfjY85S00yBXBId3OzAldd4zDmW2Qkz0nsE42WDd/nq+919pbobgeebGdJv4zuGydc/K0t7eXbJTXjmudMDgRoCDimM3yzO+5rPO7wUrrO76EOPTxglJ8f2FhIfb39+NXf/VXIyLiBS94QRrDYHDY+TQzM5O6wOCHQRjGSRLT6/Vifn4+rRtJhrfW+03J1k34Al8NNEC2H/6/fY7JiY/11Ik7tihi1DWLz/M4ct22PXWC54SKmIJEiDjDMVrEaCuY5brRaCRwmUIj8pHHjzlIZ95GRPKBEaNONG9btQ11go9u8B1kku+RB+TJrZPUXI/4u3MDjz/3T75XDopgOw2McC3/Z57EXgbdeU0880efyRsBn4hNeD5NA54HcmHZu1IEr1kjPrPP9xoydvyziyx5oYb7Wm7go4tprJGBQGxAnkcQH/F/g4SMz7pv3w05N3JRB92B7563gRvIOZlBTuuK5d4y7uaU3H+5YGBfbrllPru7u8mG0ghhsM42yb6GfC0Hc/LczTaSWN33cS4wLoayLDOPcXaPa4jH3eFq/ci/A0+4n+0Xf7PselcB/4LB2H46R6dwQuzm57mphnGBe8D3VqsVCwsL8Zmf+Zlx4cKFuHDhwmXp5mV7YwNHDpgcgDkQtLDmzPLhjnbEfA/l5vskTWY8htFt/9zfAgDzSKJIUPjMLY18H+WgK8qJCWPe2NiIU6dO1V7P7jfQoTQIBEaAM2VQDNozSQ7dWWEhYmwIpTssDOCwrZHk0R0Y8AMDBM/dJUO3AULMeszNzcXCwkItycbYwDO+A3hQVVXaboIhRAE5yNHrngMQzNlyYl7w4+DbiYqRc56dB8XcC96wVqxBHqzwf4y1/0aABDl4cuJlQ3wlHW1OjUYjdeqtrKzEu971rnjTm94U733ve6Pb7cb9998fz3jGM5LcEThEjF6XTPAG4OHtKoBX7FfHeNF95ZZkJ4b82BEgv4AnDsDhpY20t+8ayHUlywmIu5ecqLCOrA/ryfi8Fbbf7yd+ICf8y72pFtAJ2Gwevgq61+vF2tpa0tfhcJjm2ul0YnJyMubm5qLRaMT8/HwKeB1Ess0PMBBnjOxxCClJCkEU44mImi7Av9zhtFqtOHbsWCwvL8fBwUFsbGykgMW8oFONtc5BEZ8Jwhq6Kmyw3IGEO1ecrAL2nT9/vpbkcQYNsuKz7FjD7e3tlCAzz9zp53vquY5/8wDEf4ceL8iMPLkFO+LwFdYf+9jH4mu/9mvjJ37iJ9KbuwjGxiXXBpnyRD5P6g0GOlB30ppXLPOiFPxAT+i6QybZesCZK+afEyMnV77G9hY9AhjmewbFnVxjc/iez1vjudYlnm/f6aDUr3PHFzoJQ4YAP/ICS96l52Sg2Wym7hKvS+5zuRdjY86sFWO1D4UM8mA/7Tttm/O1ycGCccCVC2vmpe29E2jHbO6ia7fbqXCSB+x+Xp5sMzbbdPycY4V+v5/OvHisxHPN86qq4ty5c7VxsM3bILjtHF126HRVHYLjbAtvNuudgL6P423sK7FonpTxu/lmmcqvhdfjkjLLu5Np7g8YCTCRx9uMIY8lkQ/LFtfmia7tnAsH7mRotUZnI3EtftkgTH5/kjTHpsh2ntT77FjHdQbp6IYwz7Ax+Erk1DLt381z23I/z37J+pv7McsMa53HMozL+QdxnmM58iXHwwZqfMYRz6dRwGfowO/cruS+9vGQwRODfDyPNWTtAOvdDULMYxm3H3DSD89zwNLxh0Ee2yfsgm02z2F90RXLL3NBbp3PMV83bgAY8WzLBzLiMeYyxt/MR8ss1zUajZQrc1+eg+3A5li37TOxfRSU7P+JK2mesK4St9hWeR0cE3oOHr//NVZhvcrjGMu1c1MKeI7leC73ydeC+3rs3lKL7Nn2kcNhh+G5z/J0161jYT/f6+s5Ome33d3c3IzhcBi33HLLI6lkossGpebn59Mis9B2QAzI5zCweF4cKzUMQ+hIWgiSXP2wAUaYWRgU0JV/yIFKu91O57hsbGzE7u5uOu+A7yEorkQBOBGUY2zOnz9fO6TY7b3MncSx3W7H0tJStNuHB38xLwA3J3S8ypxqIQ4KY8L1KCjVaTohfKjdzs5OHDlyJAk/gFzuzLg/PMAQVlUVnU4nFhYWYmVlJTqdThJkvx6X7guMdLt9eA5QxChJ3NraSgCBZYXAyYmqDaOdYY6uIys2Rlxng4yiRowq894y4ADMz+Uz1jJiZCgIpOElOkCi4uo3n+eJloONK02NRiN+9Ed/NAXz73jHO5LeNRqNOH36dBw7diwWFxdrgNPMzEwyZCRvrBu8ZYtKv99PLcZ0xHW73cTbTqeTEHTvrc/XBtnzVlYHt06IIkYAovXGzrbRGAG/ecJn0MOVMb6XB2OsITaIZBMgFifqSjE8BZimAgqoy3h87gbJOjYAGTWf6Nz0m5dcjc9l18mG7bKdXp5oQvALwNGBD/bUVbaI0bZuwBV/DxtBYtJqtWpdZtzXAQn3ZRsv9pZ7EnDkW2Hm5+fTNsSI+kGRuaPlO8g5c2T9c/sA3+DzldRfj83027/923HjjTfGD/7gD8Z73vOeWFpaSoGFEwOvt3nKva1TEHNHxgz+8K8DP7d38yyfgwKP0BGKHBsbG3HhwoVYWVmJo0ePpgokttv3yoPb/DyMiEhvwjTQGzECmPLCDQkTsmm9dQzi4N9b7tgybx7n4JD1AVvCmBuNRtr2iz/h+cQVjUYjddOwTRM7wvYwZBP75uDacZL1wXzhhxjO/pJ7OeF3rIBfy/UTHjBvFyTgU26HmVezOTrDkC1qljvO8aOwwP1yW+bveG2ss04cncg8XnrKU56S+La4uBj9fj++5mu+JlZWVlKyx1ktrjL7BTLIqn1ev9+P8+fPp+0P6AEgCHbVhV78U54A5UCOeWSZto0n1jGf8gTMsZiTbGJbrzcxg/2HATAXIx2fMkbsDPPME+NxNg+Q34VtjzmiDtJaJwBoXLSam5urfQ8+MXbu4Y5M/iVmdseztxtX1Wh3iI8DcdLudcr1HfvlvzPOiNFLIHg+z3T3FsVF21t4iw7aJzYajdja2ko5gwv56DbjIZcxnwEY6GbP5e1qgFKWIY/HeuGjDvAD5DbEiLalBiew5QZbyXfsf/OYy80f/N26mPspfnLA0R3+rAX3dczn2AzZYN7esYQMkuPlumP5RM541riCmQET88KxC9chH3leT1wAAGWddwMAttKg27gcmLkwLhdUPUbHfvaB1ktkgXgdkNPA2LgOJPPBQCbP8Brm/LQ9ZQ1yAMu2mPg5366ZP8cyg4ziX5zbYvOQEfuie++9N1784hfH5dBlg1InTpyoBQjuUEIobKicuJPM+iwFJ4hGmd1Kj8PmfrST5QbKjPXfxqGerpTu7e2l11LnjsYdECg0SbcR/fPnz8fU1FTcfPPNaQHcoogiEzjQlYRDYPsAThNhbbVa0e12k4KxwGyrwkmCOJP0t9vt2tkDExMTCRiwIQEAhBcYWYwIb/+Zm5uLpaWl2vkxDuQnJiZStQOhjIiUbBoNR2DzgNhGJGIEkFnYcyOC0KPY4xQaGbChNQqPIfc2JztygxCcNcS65QbNVYJcWVFoknTL6ZV0tDk1Go14+9vfHsvLy9FsNmNlZSXe/v+fcYE+nTp1KhqNw+4cJ5bwiQDGFT7ON2EriR3xkSNHYmNjI22XarVGB3rjpOERPOb/rtLh1JEbHAy87/V6aQ5U4AGA+K7lwHYld/i5EUeGDSqifx4rh7+7WsP4qPAbnOx0Omn+fqmBqy3IEXpiPY2I6Ha7ae4QzyaI2N7ejvn5+Vq1DyDH23OwSegP+uhDqFkP9A9dYQzIA8EB46TTZzAYJNCcdcBXYBtY77zS5kDeQAfAN+OkI4CO04mJwzPQsJ/YCBypgVHLCfyxPWGNxgFF8N5J0OMlZMDdtWzfabVaceHChVhcXEy23Gvjn9xGOQnD3/KTB2b593Lba5mFRwbFmIcDbBISeLm3t1d7ZXo+nzzIRicYi+/n4NNgA0EsFX0nCxH1pM7JngN97H+e5DtoZszolgFTOho9P+Q5L1pwv+FwmL4DCIXtAaz2WiAb9jO5bzJ5jXJgB4DE8xgXNDNOy4tt3zhw2wkGMSMg3szMTK3zzkEzW67osPaasSa5P83jPnjlMwyxXY8XlJqeno4777wzdWwePXo0Pv3pT8cnP/nJOHLkSBoDHfT4AbbjRoy2PVveGddwOIz19fVk34i3GL9lFb3I15s1wEeYR5b9XDdy++Z1zvltkMjbm3i+bTHj4tB3V/jt/yyjfMcFWctSPmfWmfsACuCnOKoCvvnefGZ/gfzkHYDmidea7yP3+EFvY2RNc75TBM/XkN9tw/O1s+332kbU7XLEKL72/Wz/iPsoANpXNhojMMqNAk5m8zNhydWQbyfNfJ8ixdWMjw0+OVG3rW02m+kQe+sP68UaIuuskWXR9zIobF9tgMy20+CI7+1Y2XET40Nn8AURUfN7AMP5m7P5HvYDuea7uc8nb8x9hHmIH/Za5r7VfDLZT+Fb8fXwy6CwfQa8Q0991jF2k2usTzyD8TmPz+2dAanch+eUxwjO47vdbnpxUl5scmzjHIjnWG6MUdiXOjexz+ZvgEy2Y/DYfhyZYr6MM/cTjNF6vLe3F//9v//3i/gyjh7VQecOCryVhcnyQ4LIAKlmgoLjhCJGig9D/dpAt8nyTBbCnQJuxzPZ4DgJhuFsExoMBkko2FLT7XbTqzp5th0xQra/vx8PPvhgDIeH57G4HRKn0mg0UqWVA3xJOjjkO2J0sDKHC1dVlcAkQDSECJDEh0iToMNfzgpifhYq7stznVT2+/00zoWFhej3++mtXCSz5gnz9en9EfVKNq29rAcAAuNGMTCSdqquvtpgIz9WuLyFvt1u1w6td1W+3W7XDtilm8yKxViRDXdk2PgQVDqQZNzMi/G5o+tqUlVVaZsPRn95ebnm4NbX1+PYsWO1wAu55+0/rLmdnudrxxcRtVchNxqNxGPkh7F5ncwPO3J34PnsI2QJnQeYZYwYcjtU+O8AxAEoW4FYH1cHHLAAipBwDofD1BXoYAFbQCCBzHP+Fskp1xg4MECCbdvf309b6biH6eDgILa3txPQBK8JrKjywWOSdgeAdsKWX/4+GAxie3u7BggYtPPWS4JVA0IuIESM9Jh/cx3DpzAnxs5WUyf5gKreRuqiCHLn4NBBdN4BZHuZJ0b8e6V1mHW+9dZb46u/+qtjfn4+XvGKV8TP/dzPxXA4jNtuuy1e+cpXxgc/+MFYXFysAUJ58pGDS+P+7+fmOmGgyWBDHoDm5EDd9+Xfvb29uHDhQmxtbaVzF/PvOJBy8BkxKqhEjDqTvF6ukmIPrPdONvG95kEODACuOinLkxLOyPP5FpbtcUFtu92ObrcbEaMzEr2tkHnmxRQfku24JweEHLCPC5rzKm2j0Ui+OKIO0rvqaj+bgxNOFvK/4y9ymwpg4CQ+D7wjDrv1KQR4zbkuT9DHgWZUzK0zloHHSk996lMTIL68vBxf8RVfEU972tNq8V3ebcfaAKIDPiNr3lJJxyfJA+eYEtfYV+VyQLGXOAgew7e8+m6+8pl9jW2z9d92wuf1MA58mYEfgDr8KfrsrdbWy9xuW5/QOxcYndhtbW1FRP0cOPIAZD4HDpx7GPCOGMUntpuOjw2gc8aeeWh/jG4wZnSCOIc1zm2U75fLu9eSufrMV+dEuS5SjCG3wq5xLAFvs/VZZsRdjtGJm4nhDEhhQ7xVPf9hfFfaz2KnzC9k13EsuakL23yf+APeWf5z34j9dt7r6yk+2Ccxf+JG9CWibtvtky0X1mvbS5/n5Q4vxgXP4Q3/on/oKtd6S7R1AZ7luXcOlOH74Jt3KtFJaOCu1+vV4h5sBmPzWlmHPQYDUtjAiKj5Buuc1yP35fzNPjD3YzzbcmF/sbi4mM6e5i3aOThlnWDejteRLft020uDW45fsG3E0hQ9mJdzIWMEjIfY2zm4YzS/UfBy6LJBqT/+4z8eW3E2mIGDdBeME0FXBLxgJO0HBwfJcRDoUU21sYdZDoDcWYCSYVzdVhsR6dA4Mxg0j6CHBNUIN/fKq7NVVcUDDzwQg8Egjh07loAio9qNxugQeBxZuz3aTuhkjGDVwonScIYK7f7wEcPOswEMmYMR2NxZNxqN9FanvJPJnVEOylEoAq1Go1G7/97eXkrYASOtCK4+8Lzt7e3EX/PW96aja2dnJ30/VwYUE4CR79s526hNTEykgBfH2e12o9lsJiXlHpY9G2r+dRXQ/OB7TnicKFwNQjbgteXIBpR19gHWEaNWYsBSzmDjbWgEk9wP8JOzj6yT6DOGj+5AOvJyB8M6GYhCVrkG2WY97MgcDKPLyAXjtRNkfTCwfGaHhYy5WoAO2XkaIEFXGNv29naSF96+FzEC/ZFnJ7zIkYHWcVUVAkeCSNusiYmJtOXLwRL6jf0hGSJZwO4C7FDtzbdAsX6sEYcyc1YEcyRIRTcZ4zgwgcCYcTpp7fV6CQjEB+FD6DYdDA47urhfnszYBsJf5on8OPm23lwtQn5nZmbi/Pnz8R/+w3+IW265JSYmJuKTn/xkvP/974+VlZVa8JqDSdC4/0OurnIPkhMHzKxNfi/W3sGek2GDewYFIEBq9BjdMfDlsfkeJDTovjuTeSbygN1hC/E4IM0gihNofEYOgKDT7pzgTXF8j99zoIT/O7lFr7jGYIOLKTzTgTYdFfYrzMn6mMs883L1m3XL/++xYqdsjywbDtyZi+/Dd1g/4qKIEcCIjg8GgxTTYGMWFxdjdXW1FhvwvNyfmh9cm4Mxnv9jpeFwGK9//euj0+nEiRMn4p3vfGf82Z/9WTSbzdRtmq83vssJnUElxk4MByAQcWjvtra2UsxH9z1+ljkBQnieju3MC8eETkzxjwZ1mbP1H0J2nfDb1nj7Kb4HX2NwKPcP2CbHhvaVPMfjtq2niM53sBnWcQN5vl8eqyGn2Cv7MMsjn+FDDFbgn6qqStt0mTvj91Y4x7Umy7h9nP9vm5BvaXYMy/paXui8wF7zVl5sjju/rO/whzESRyDLjumZL/fz56xfXoB7vOTE3LyxzvCvgRu+k/s6N0/Ae9bcz8t3iExNTaW4OF+rvNjkdUZ20B/IYJnzNY8hImqxKv96rRwDOWZst9vpuJqIereOCxbwpNFo1Jo/iF0NonhNms1m7QD9fIdLzkvzw/4dP+nOd+dw8BM7Ymwhz5Eso753bmfMj4jRGYO+Ji+uWecA5WdnZxOQA0Dk4hA8IAa3jScG5vn2pfZBOSCLL9rd3U0FZ+Jn7CX3cXwTMSq2AFJHjM7OM28uN9+9bFDqgQceqDkbBN9O3RN0QtFu19+0h0DacPmtBE7w19fX02vdjXCCvAPo2IhxfweD+aIiLBGRvs9CoxgEigAh7Xa71vJopL/dbsd9990XW1tbMTc3F/Pz8zUhOTg4iPn5+RRcsMBTU1Oxvb0d29vb6c08gC4IBFtl3CXCvBy4cGBnv99PAk4SSOCHo8Pw4PQIApgrysJcDfqgXLu7u+l8LPiLgdjc3Kxt4XKLtg+EhAwgwSOSdGQiF2pkxwCjA0wDawQhbl1mTvC73W6nrTF0pvkV3MwtT2wdeDgxcaUJsjHLE7UrTflzXB2xoeIQ2RtuuKEWKPOWGgITB8vdbjedibKwsFDroPKB/ciGOwBYi9yJENz59dHoMc6XRNPtxoAODsKwDVQoSW4sh3TRoC+MD94hWxBjyfXfoJmTdWTWjhFgysEQcov9ccDtpA6n7gSM9cmDDb7rRA9wOCJStwWOyOOhS9MVfq7Jq32sN0A/AC5rj/1zIuoKMc8E7CQQYc0B8rEHDqhJjpEHklgnWRH1TgkXRmxfGAdrbtk0qOxE7moQ/PzEJz4Rb3rTm9L4l5eX46677opv/dZvjYmJiTh+/HhaAwM/TiidIDhocuLk7xmAzYNwyP7dfp/rHaiPA8ucVMFj7DPfNzDlxM8Jsn2C7TNjj4iaPqLzHg8/Lly40u9uw9zOu8vC98JXYmfdfcfY+v1+OjYAX+5Ygg4J7AbAPUUj9AX7aFDWfsg+MwebnLj6b7kMWAcAEfP4IF9X/u7rsB0eK3zkJQSAfAZssPf4qvn5+dT1kh8GnYNu/h15MwDuxOnx0O7ubvzO7/xONBqNWFlZiW63G//iX/yL+K7v+q609ZBnu2DACwBsS/AXzMdgDH+DZ2xpzBPYZnNUzGG+eYLl9cmTLtbe9s6+3+vtsec2CH3I5YskxmeS4j+JQS2bHhOyS9Ka/514Dh9BfGAQkuvt8ywrBmb8XecX5hX+hiQMnW42mymux+fZ9zFe7Aw5kOXZXZHmK2tpWfd65ICjwcQc8MltMjY3IlIsQHGHH8jxOXERawIvHeMxZ3jqjhA+d3H5avlZ4r0cjENmeXOz7XrE6O24lm3/nscSuQzzmeMz+zvLmNc854f1Kfc/fE6c/XA/OfCVxwQRoy3d5JTwxYUQb283iMgB9x6rx+yiifMqy6+3+HLvvPEFueGaXAYtf87bmLPl2jG8c6c8Z8v9KOtke2s++745GZuoqtELPubm5uLg4CB6vV5NVyIivRHYn7Xbo11UOUhv+WSMXOf/Y1/BHZxfM058GDG7813HBF6Py9XlywalEEgDQRH1Nx2RsBjdt+MwOoehzBWCfwm8AGFIPhAQmEQXgO/DM8yEcU533HdsRNwJxLw4xNkVBxwN27940xCGjVe8c9Al/w6Hh6fTnzt3LlUfoO3t7TS2brdbA87gL8oK73u9Xho3a8P3lpaWYmtrK4F7CBOBkVuu4ROG204HAeMtfSQU3s60t7eXgDaDASgYQTaOCSDMe495LjzBABhwQKHsAHLjbIduQ859kFMCYGRvZmamBrD48EYHMHmV2gbPwXfe/cUYrjbBNxwgib75vLm5GTMzM9HtdtMbnwxGwQc6UHAUzHtubq7Wcsw8CahckSBRdMBrI+aEjnWB9wRKNqwRkeTXsgoweuHChaSjeaLe6/Via2srhsNhOqzdjtjryz0tP0543aZs4J6qj9uCsaOAtXzHCaIBL+xcs3n4ds1utxvT09OxsbGREh5scg4WmG+sGetcVVWSe+STeU9OTsYNN9xQS4DRfxJH7k9iBcDlZ/sFEQQ3BqPRE1eRkU+2WSDH2EMn8tvb27G4uJhkhuc7kQVQszw6ycIOGZBg7U3jAv4rTea/CwQAp+iaAbZxfiwPLPN55MEp3+PfSwWnlwKj8iAd+4J9diCXB8MeIwEu37WvBBzl7x6vkzJ8GOvsc9PyeXFGZrM52ipsANlyy/fYAuYkEn1wJdjbzJhDxCHwtL6+Hr1e7yLgzhVNOnaJIWyr4CH3x544IYAPBmUcvNreGCQ2fxiTq82+Lzo0Tm4caw2Hw6TX+RZe6yQBOffBznrrDy+nMQCQB91OxD0Od9leKR/M/dly9t3f/d3x9re/PX7gB34gvuVbviWWl5fTM1kjur/gA8UIxg4vmQffZd6sQb/fT/4AnpFA+oUdeSw8DtAZF5fkcbOTLX+P/9s+GNwhlqNDyOCykzU6b/i+cwX7XecWzA8/DDhi8IT/061jH83bb929j7wxV+cT/j/2qNEYnZ/kN7xubGykrebeOjczM5OSSsePlin8JFt8L2XPvTa5b8I2jLvGz3Rsluse9g75ht/8GAzIcz7W3V0bHOXR7XZrnSC+lrFerRj5BS94Qfz+7//+Rdu8sDWsVUT9TXfwKv/cHXPj8shcByMu7lxrNuvbKw1eudCIXbBM+m+2g9ZF5z0Ro45f+x7HBox/cnIybRe2fXdHk8+LJI5EptBL+yHHNREjGSbOddGCeeZzupS9Is71uhrQx5e5wOo5I4eWYZ7rnNS5QW4fvW7mZbM5/iVu7kDyHOFXp9NJALHjVmxUnts6v4IH2EOe4RieGJ2XDcBDYiKeQ8OCj8hwp7qbBbwmrVYrXvnKV8bl0GWDUrlg2DCP6w5Asd2J02w2U/cP/8eB5IEZAs0zfOCwkzQcHQeHsWBGCp1QIihcg0C5gsI4HEw7AXOgHFFHzyMitd0tLCzE/Px8HDlyJAFUCAv7sjc2NmJ9fb12lg3V0TyY91sxqCaxFYpnDwaD9KZEvjc/Px9ra2sJyMoDDXevOSkCkXY1ByNkA0Q3xMTERHS73TQvJw8ks41GIwUGbFNxIICCch3JPMF+7ngdxHqtMCA5ks39q2p0iCzzZX4Ro1feNxqN1ILP9230jTAbvHBSZlCPezuJvFqEHDtAJyGwzGOIGDOHmKMr3W43FhcX03r6rVE++BO95u/WJztD7AB/swHHlqALJB58HyfP+RjopDuE2BZ45syZ6PV6MTc3V+smApDa2NhILf3INa/sJni2vWPtWEdALOwbY3aA1mw2U0UY0JUExPNibk42cCROPEk+c2fvbjF4TyLImtNhsLq6mmyT7Th2dXNzM63p0aNHo9Uavc3O+sk4fdgp+sN8fM4ctgD9wDnzXDtH5sj13gJoPvFKb4jrXLlj7uMKJe4EhWd8nvugcYH91SDLWp5Y54FdnqgYDBiXSOXX5dc76Myf58Aq3+rigJjvXSpJGpdYoUdOjokhkCVvnTdIYt+NzRgOhwmw5f7IIbrkLplOp5OCNPu2iKgl/O12u3Z4uW2KQU2DW8wdWfIZZx6TbZ8TAGyuX18Pb3iW9di21eMbt+asLc9GB/09F4YcFDt2cnLhgN8yyN8NqrDO7qLAnrZao+MS0HHWlhc30HHG+ByMezxO1nie+XIlCBDzP//n/xzz8/PxAz/wA3HkyJF0lqiLUo6d8uSSXQDc08muk0bGTpHh6NGjqSueeMZJWb7u9hvYYZ+fZn7n8uRYnf9HjN5OxnOdlDlOwjcQx7vI7KMZLCf5GtuXEB/7jZXwLz/0F/2x7/ZZajm47rlYZihQO8mF8PmWRXcEsRODIxHwpX6eC208J5+H19ZxPeOxbeR7zMHJvPM23wubBX8mJyej2+3WeOMuE9shZNw+m651AHeeSyf0zs5OLX5mbFea/vJf/svxP/7H/4inPvWp0WiMimUU0uCt+eZdFpZtf2bZc6dKRP2wccsU+u03+tnWogfkuOaN/Z7102O3zR/no/nMhTnWD0AEgJT420V32yTHrDQ/TE9Pp6MWiGHJGT1Gz8PxA7phntiP5OCS7YP9mX2aczODhybnTTzbhQRkNz8uJMdJ0HnbHuSNcduHWzdt/5xbOoa1LwBQMqhnwJe4GCDRcRXdqzSbDAaDpI/ELOYJ+QVYBPe13WPsm5ub8ZVf+ZWX0MY6XTYo5aSShzmIdeKxv78fJ06ciJe97GWxtbWVOk96vV588IMfvAh59jNIVri30UQWFoLhPHtiYiKBHN6yZTCLcbsDIe+8MRLNfk0cGx00LBBCZmXA0e3t7aVgF4MTEWlv9vr6ekqM+R5nsDjgzR0PgoWizs7O1oyOjQXdGoPBIHX9uGqMM5+dna11CvEMACSCRf7Gwek4FN5ksru7myogeVK+vr6etiHkiDXBBOvYaIwOyLZceAsSBp2xsY7wKE+I/Ls7QCxrzMnb9qhUU7HiPnlwHRE1/ufGiO9d7aTWY6RC4ATABo8kjPOIABkMVEZEba1WV1ej3++nNdvb20sO0/rD/0kAXRny2zCwLYCt3q5DQMQ2PAdCCwsLCejBiGJINzY20rlD6LCTWwwwtoP2dG/pstOEn66eGMhgPfPuGnjIvfIOQQOjOEV4kydtyDTgNnoPTy1vPANnQkC4ubkZ+/v76ey+5eXlmJiYSPfc3t6OTqcT29vbcfbs2djf30+H3RtsY66cLea3guUVJTrZSJbtYAnkSAw4DLaqRqCxgV+29qC7AHvIAMFtRH3bYZ5Y+aB85B57kgOREfVtD1eTHLQamIiIlBTkgFSeKEaMdNsym19n+5gHrA4y+Zd7IS/wJQ90eX6e2F0q0YSs8+N44O43fHyePOQBJ74Xu5ffz74e22VfhP3x3ByA4gOcXBnQdvzBPZBTB9p8n0SdezNuAsjBYJD0KCJSsM+z3LUIH+yn8kTLABBzYyuu7aq3IHuNIetYHtv5uawbxQv70YgRmGzgCNnHvvO7z/Gyv7dNdsLhGM8+xnHtY6G77rorzRPZ+MAHPhBvfvOb4xu/8RvjXe96Vzo3Blno9XoJ7CT2Qsds852QO7nNwQbWiHuaxoGG/h7rwt/za/w5lIPlzNsAZ0T9DZd0zQLI5AfqIuv4FN4g68Sdtbf+mRfwF13CJzEuznA1wI59MHBtueAeFJ24f344d0TUiss+n5B4wuAiOzC2t7ej3++n+eYxouNUj898z9c2By7yZNfravliDW0rrfdzc3Oxv79fe+N2RKRudOJNb1t27MsaoYeO6R0zwDfsw5WmH/mRH4lbb701IkZ+lQIIcSL8xudGjF7MZZ9h+2F/7Pnb79h3e40i6ucbQjnABA/N11znrX95HGGwzPITEalr2C8pctOFC9D4RcfJblqwnBO3omOMyaCymwscczj2omDJmN3VDE/yPDYvQuYAqv2zZS3XG9sS5874JcssepYDNBGR7B94BT4aHXOHtbuGuS//uqOcvMYvF2O+HiOAMM8COPT8kQnnfp1Op9Y0Q/7iDkjuZ5vPc5eWluJ1r3tdnDlzJh6JHhUo5SSd/1uI3Aly8803xxd/8RfHT/7kT0ZVVXHkyJH4qq/6qtje3o7f+73fS0wwQIVjItG0cESM9mDn1XgchBUF5jgZtcC68yZH//wdmG3ByANcyMriDg62PVFl2NzcjN3d3VhfX0/tcOZvo9FI5+pERA2oyt/GgZLizNzp4WDEyDZ8tgANBoPa+QY4FZ/3Af8RbhQYUIqkkjeE2YEDfKAwnB1QVVUCsHjjH8gtier29nbif949ZsPlhBiDZ1DTQTjy4nt6WyR7eJmzExgnySieFZHx+zooDyquJtnpOVgz6MLcAS85gLzT6dRAIbdz2pn5MFICDPTFDjd39Pyf+8JjAAy3z8I35B0ZPjg4SFUY1psuKXRhbm4una8BoMw5Dw4YIyJt/eI7TsK5FoDNSafBctsLO5a8m49teCTADnzpIMzl1+ArPMReIqcEE9g4b5fgRQIEuVtbWzE9PR1Hjx6NiKidfYGtOHfuXGxubqbtyNY/eO0gP2K0lduJDHNhm4QBNO6ZgyC2pQBsbhW238EHYIvghwMUgx4k2Q7K7Vwj6u3Z1terqbt5cAm5cu7AlvHkwbD9sgERX2uf5spvRL2dne8jUw5MxwXk1pU8afV8IAfo9sU8zwUE1jR/tm0/z0c28y5EB3jcn24bxuWKLmSwku/yOc9Ab3iWwbN2+/AFJiShDqbRF2TXfENXsGPNZrP25k7bzIhD+9VsNmvFLo+f3/OE3HIREcn3wzNkCD5ZXxz0E0NZjtjuyLo6UTCYxbwAGwxaOyln/HNzc+m4AICZiYmJdB4GY+WHz/wWvscLSv3Df/gP05jwSbu7u/G2t70tdVvjUw3GG2AlSUFO8qTTa2AAp91up5ffGHjKk1H7IwO3Tvyd3PJMx1W5bbDthI+O553gcT/O6WQ3gZ/rNUYf6CQCHDBogl92Z5YBHSfPyJDlj3vYnvGvE2fH2vhdA8d5UkaMa/8dMeqGwK7ZN/Z6vRowZd46OfVWmnFg1LjkNV9T22g+537jOsIiIsUOrdbhywacA3A/+Gow1TbBW//y88QiYmwcdbV8Lc0L+DMXLll3y4R9l/+PbBmkMvjj8dt3u+vKMaS7Z7wueczj8YzjkceQ62sOWDF+b2XEntDRYxkknjQYExG1z8YVZQBxDBZzLV2/njPyaLs3roPdOmrdt+8iriYXd+cX17qryrYrxwRyvGJ6ejrpgmNGruFfx8Tow+7ubu3YEHgAX/kOY7V+2zbnMgD/GLv56XwmIlLuz9zBJ4yTDIfDmJmZSTmSfZDBqlx/sdV03F0OPSpv7EAOhjjpzQXtz/7sz+K7v/u74+TJk7GyshLPfOYz44u/+IvjN3/zN1P7tZ2lAxsnJXzGoqDsPM/C2O12a2i3gzIcnx1oRNSqbFyXo5de5HzRvfeccU9PT8fy8nJC4Okw4XBwOgIYD3PgYDg7WgIo9m/SeUaQi1L5sHgHj1RmfYiyA3v4QMcDwY1R73GoOGtCV9HExERsbm7W2hsRWJwdAm6D1m63Y2NjIxqNwwN9AUZc7c7BEHhjBbDSQjybNbOR9NpGjBKRtbW12nYCZN5OymCB5Q+eOenGgPgcBQcrV4sMosGvcVUCjBRb3Zg319nYAnqwjZWXEEQcOnpAVmTZVbaqGm3DNSiKvvtMJGQDWUfGI0bnQ6yvr9dem+wAsdVqpbcAAm6SqG1ubtZez8x4aKv1+XkO3G3kkSnboLySgEwh6wTADhgcALnyQfCJrKC7jAV9pNWc8QK4wk/WECfjatVgcHhg7tTUVAqGWcO5ubnY3NxM24xxnraZdIWgP56bbaEBbAdIzAv7iIz5FeEuLgCOE9TyDGTSgLG74AweYrN4g6mD+Vwn8wDfnz0R5CASXpp/4wAsf54nHx67E1YnqPzNdtZzR86c8OdBee5LPa5xwXEedDtJBXRlK5638WNTvbWF9c2TTHwPMgEoybPRGQfU5jlzZf7457y45UTKMQSyy3kq2DIn/9Z/B7x5xwZgEgA78YbPKUKnfBi4ZYf7OEHOK+gONJFBdzJY5hwk54CF1xieR0QtjmRNASBYV+xZo9FIgLa3Y2G70HmKge12OzY3N2v21XLbaDRq3RyPhz70oQ8lnnD/Xq8XH/jAB6LRaMRtt92WEiHrE7z1S0GQV/MQ3jpm4X5zc3Nx5MiRWkzsbSgu2hGbeK0ZtxMm1j63e36+1x4/yb/mAzYZvXUsSJxnENf2hO2qjAVdIt6wHcmL2wbUnKPY7jnhM4BjX2/AAP/kfMTf9f2QRfMW3XEcbj4BIk9OTtYO2radZG3dgZ/7I39ODJFvTfL3+Jf4wXE0skYySm4yPz+fzo+1DGGrnJdFjABK+Oa4nvtzQLPHf7V8LfJKrGdAKmLkc20n/XMpuYKPtrn+HrwZ52sjIvGAmBfyNTl/cjA2t7/+f/5cPqNDCp9nwNbygBwYdEU3vL2ezyJGBQB4wEulGIf9pvnmLhzLBes3DoSH/74f3/V5o47r7beIjZFNxxM+6N2yAZ9oZrBuO59xPMDn2H/470JFrgueY0SkGDkiavw1uG+/7MKZ7ZUByaWlpdQ0kuetee7r+JnPsddeQ5qY8pjgUvSozpSCOQRwVk4jpgQVt956a/ydv/N34iUveUl0u934+Z//+VhfX08HXuMYcVIkx7nyWBg9nvw6GM71CBDBrasTCBWGlDGjTCwwoAnPsnFhcdxyS/C0sLCQDlCcnp6O9fX1OH/+fG1OdEMwNhSHMVJZ8St/OSCR4I0D6Hx4oxM6J8o8IyLSthajncPh4SGiAF3ckzX2esPfqqpSgI8DxAg1GqPD4u3AOXMIRUOxOIgd3pEcHxwcxPr6eu0eDh743eAmyhBRP0gb5Z2ZmalthcFpbm9vR7fbTUbEBnYcEIWhMMDi9kt0I09YnggC9DWNSxgdrLuKgJEhkQJcmJubi4WFhWRIuQaZdDeieeLg0YErQTLBJsbceu8WYoNWVCbRYa5jKwMOCFCDw/cxxg5McuCVdm5kw7pPwIe+wreZmZn0NkM7dDtZ5DBi9OZO5tvr9VJSiuMA3HP1iXPt3NWU88xOxZV4knuu2djYqL2VkPui35wNaJ5HjN5g464SnpknqHbO7uaamppKyQmyYKcJ3+l6gffoE2Aez8+7r1yttuygo7mzz4O8PMl+JDD5ch3vpcj+LwcQ8s/y4JZ/baPzcVmHnAjyO9caeDUv0FN3LOfPzMfKvVhP7pf7UicC6C8HkeNjp6eno9vtpkIISQ26zPpgZwB2mD+gE2Pq9XrJjlk/c/DY3ZvYF54JWeYNgEaMkjFkz2+yoyvTMu0t8+a/7Svt87wBlwO1kXsORnXyab/NWJmr/SfXWzfw864i+/5ebycDTqbhh7tNnODZVjsx2NjYiLm5ucTn2dnZ9GIXOmI5z7Lf76ft9o4ZDHpYBx6vznr8BoboYGLNHKCzhiTnyJe776zfLvgy5tnZ2VhaWkqyi11nS4h5ja54DfI1HGczxyXSJnyGZcb38Bp6G0luT22HHBsCTMEr7mWg1gWTvMOBNXau4Rgo/91JFv/HnznJNYDlQqvtoP0l4+c6isT8Tm5ioIa43wewwyPWOs+XvFY8G3vntRwXh5rP8Org4CCdL2oADZ/v7bbw0DE46wEgStwEP7HdfnGO18PreSWJfBP+Mvc87zTP+RwbB8+QhUv5bPtOz88+1vksMu2X2NhX5rqW2zj/3/f279jkHMiwz2ddc51xIQj5MhjlZg7bFq61LsArPrcdzQsguYzlAAhrwXjhP8+Cl74H5HiTHQCAWGAAvFm60+mkc5vd5IKceFuy/U0OkrEe6IF1zHaQ70SMmlgsi7bNFGX8kiAXghir/ZTtKfk630FvyQHhsfNZYnp8Ww4YMsc8F70UXTYoRdsdzDSCaPQ7n/wtt9wS/X4/Pu/zPi/e9ra3xQc/+MH48i//8oveKGHBd1BjA4hg22EQeObCPBweHobJW+/smLmWtk2j1Bh6J/RefCc4NjRW4vn5+bTdhVZuKkIcbkjLnhMjFIski7+5K4FnUkVEMQhEIAAshBNBaTZHreLNZjNVWgmgAGX8DFpUEXj+jjMh8Op2u6lFjwCE4Bl5gIeuOFdVlTpstra2Epjn9no7ZYwc5MB/XDIMPxwstdujt64gM4PBIM6dO1c7b8aOZdyP7513BTnwcteGt4RcbXJA4kqWwV47UQ4ZxVi5C2B/fz/m5uZieXk53a/f79cOqaRDJX8bEM+083WgB+EICBRsbGdmZmqH+CH/2ACunZg4PAOMc654HnqIzrZarXQuE3rNPGjnNm8YR3498kHCfnBwkA765wypiEPbxAsMuJfBUxv3qqpSN+Tk5GRq7x8MBrWtvawRv5twLn5jKOATZ7oAPPd6vVhYWEjrFBGJBzkAREUXncxBxlzebSddibNMYN+xUdh6gE4frg+v3XESEcnGGlC23tM9Atn+5jQuGXkkajQacfvttz/idY90D/514uwEms+tv5dKHvNx+x6+Bl7wuwHKPICCv77edtd+MwcsHKDnNhy702qNzlsDtBwMDre44J82NjZqAZB5gf1F9rmnK4d0//rNjIwZn+Q5osfuVnK84sSBbh4H6PYjDowdZzjgYy5O7KwX7saEf4uLi6lg5bMXrb8eO+vIGSkUDhz0jgMrGZ//9TVO0rwW3A/+O/l1/EQSwJiGw2GqJsNPgzqtViuOHz+etvD5vI48nnORgqTPfHwslNsP7JTBb363DYK3DvYdU5hfeWA/Pz8fS0tLKV4E0MBv8BILKAffDL543VgXJ76XAu+ciHmtWQPmTmKTnx3krnLHdP4/Ma/jLZ6dx8juBsoTcvwBcUueyOf203rCvdzRa355K5/11YcDu2jmeBFg2WsB/9i5MDc3V4vvG41GiqkBt/O8hN8Zv9eUv/n/8Nv8aTab0e12U5yA/nB9p9NJcX+ek8EHg1Twx+MFrMxjZhcwrga5+EEcaR1zvMzvjl3y6/N8g5hmXIwNuaDhGNJywPj8HK7HP1jWbePz2Ibf6TjlfF/ncpYZnsV6Rlz84gv8EH6P5+Cvh8NhklVsILzLAT/LLvN1Ack5uHUWH2A/nNsLr0nun2yjyGd8TIhtMAX4RqORcmbnx8TrefGb5/voI9vgS+WMzr88X3hkOeSavAhoIMvFY/jF3+Ex+bx9EM/g++zMGgwGCctwAwzjfLgY+1L0qEApCKFjwq4I4KgmJyfjnnvuiW/91m+NW265Jd74xjfGwcFB3HjjjbWAyoKYB8yeUKPRqBlLI4h+Nt9vtw/32nNOkZFLL4oNO4EKyo6zzx1bHoyy2BERs7OzMT8/nzp+IiIdLsx4qf7kymUFdecGiSx7VxEO7mEAgLH3+4f70xFuKxMGhECm2WwmcIkuIVfmEFyqGQTyjAVFYAuekw74ExHJQDE3Eg4rnB0EPOU6ZDAPrmz0ucav8M4NXVVVaeuZq9R0uDho955x7mH5Nxhl5cv/bz5cToJ7JQijbgPtxDJiJLcASxgWzl/CAbNuMzMzMTU1FZubmymZ2NnZqZ1HVlVVrfXXelxVVVpztpTh6AjO8wSTuZCo+g2eTnQxkgCOGFRAMeQ3Ii7aEuQ1xFYAWCPfDpiQJR8s2+8fHv6+tLQUVTU6N4F25bwd2t/j+awHvF5eXk7gCwESgSwgkceYg68Royrlzs5O6jZgm+Lq6mq6hjFyT64h6ARAI+kGNM4BXIL2PIDiGq5ztdJAFbLJmLe2tmovaEDG0EuScLYqob88y2cdGAS7VPDiRAn7Oe66XNeGw2G86lWvukztvDwiETB/IQcy9iMRF1dHxyVd3N+/47vzqq6DJQISJyjj7p0HWQ5ELRv5GPBtBEv9fj9tu/XWW4MZJPf4HftqfBvPI8inQoid8/fsA5Bpr0OeIOT2nSTUcZPBCCd//pz/5wAVsQC8tFy6i9QFHOyYtzzm9zCAnCdXrAP64PW1PXewjP00uGfdQ2ZI5B0/5b7SdgU7TVEBe4X/phCyvLwcW1tbKWkmhvN4G41GAhhyOb8SlHcKcJ4UHXLoGDEZsV3EKLY2qG67im6S3LjLBp+Knc913n7Ya21wzFV1f9fEeuTdb36+u+OxGb43cofPGgwG6SUb9g9ca/vhTpuIqO0KuJRNh7eWWY8PnfH38wSXMTkfQb8NwOQ2Gj/JmgI28HeDNHlMwH2Hw2EqojpH8BmsLrjktglb4OKdgUfsooECH5uxt7eXYjzm5JjYifc4kLrRGL0ZjK5n1iwv5trHXy3CtzBnyHKR84otdeZtDpY4l8tz1dxvcF/AqDxGgs/Oa+0v8VkR9cKDddu8xAZzrAXP8Fydz0A+0N+/u3uf2J05tVqtBFpG1Aum42yRu4objdFOghz0Nt9cfPGWXjAJx8COSfg/c6XzKyJq58E53vf33e3KvP2Dbm9ubtbi1Nze5nrjcRn4jBgdMUQXJb4eGXae5AYeivz5s3OZt85OTU0lPriYbFkiVzFYx99YL7CFh7PJ4+iyQaler5f27hvldvCeKwMK0+l04uMf/3h8z/d8T3zFV3xFzcjDlEsZIwuD24+NXhoYiRh1StCl5IQVYea7dhQ4Do/dQoMzMdjiwMnb9paXl+Po0aNx+vTp9EY6K+ju7m7MzMwk541SInx+pS9GAuFzsEgFt90+PF8KsAXDcezYsVhfX0/zJQhywg5f8uolTh6gyHvL/TnBEd/jOv6WO0tXQeH55ORkLC4uxokTJ2JhYSGNl+0VMzMztXO4LBsRo+okhmZcAmeAE3Bga2srFhcXY3Z2Ns6cOZMUnnUy6oxxciLowMZJjJMGG5o8gbmaxLra2ePI8mDWXTp0pTDvycnJVFGhaw+5BSBxgmQDjD7ZOSNzPvCQHxINeJYbUAcS+XZfnjHOSfAqYr5PFxKf22nyPKqv6AaJk7tzsDvmNWd95IE+zhvd9hkfDsQBmunwxPEBKAHmOZBxNc2JnhNc1hybMTc3F0ePHo1ut5scLFuK2V4wNzdXa99vNpupG9IJp5N3g2sRdUDC64ytxLaib7wJsd1ux+rqaqytrdWSGNbGYwCo5O8kC3mQ6IACuUHOrN95YvlIxDWf+tSnLkMzL01+vT0Fknyt7Tf5gRzcMC4HNzm4kAfILgo4WMkTRD6DV3zf9/TveSKYz4Hf8U3u0N3d3U3nmjnIy5NQJ5GODdBJVx7NCweBBlrRI85XyP1ZHszhP/OOXRNzs82Aj3kcFTHaImtQCrAYYKPT6dRezuCk3cG8g20H3CQ+kEH3XD7w7+M6GfJEdBzohf+x3wGg4xoHvKxbxCgwx95QFMNnHT16NN2bt9v1er2YmJhICYKf6a1RV5IMNDjxhnesP36WjnYKHO6UhyfuwqmqKr2Uwm8Gg/ck/vPz8+kzfCF20WsVUe9uze2DdRgbmncluYBAMuIOYBca4IfXwp1T7mZDnu03nGw6FrStttzn8ZZl0JTP1bE/PoqYOQdkrFcUnRlrzlPG0O8fvuk4B4uZN0UxvxmYrimDGSSidKnn68XY8wTY8/ZuEgBpiukRI9DCxTs6Ew0aUNCy/SGe4GB7v2beYEQOJFzNOJmO8Yj6eTjEB7bLw+Gw1rXO9+w/nFPZpzgezkEpPuP3XD58n/wcNce74/IK7pWDIa1Wq9YBj7zg91lP2y5y6HE+l/V1gYXfnd8ybh/bk4PS7rqKuDhmoGBK4cWxZp5z8Lu7uniWx0Iu45jPvs1+GR4wBxo1yBGYNwVlihA8H/4wNvvn3Hbla+wmFtYZXl8KaMaGsp2Q3BjKi7yMkbzMfKMgTd4GL8iJ/BIWx0nGSi5Xny8blCJ5w2kiWK4c2LhgtFqtVrzoRS+Kubm5+PVf//X4y3/5L8fa2lpta00O8LAgRgtZGASXZ9o446Qx2hhyEmELL8aTxWAxDSQAXOTBXI4ycu3KykosLS3F/Px8OnxyfX09tfKxYP1+P1V8qQLjDKxsOZqMws3NzcX09HT6Xr/fT10f8I/96LwB0MGXjT88wChERG3eGGK6zTAIPKvdbifnxN9QROQDoafjiYMeeSZJeKfTiU6nU+vSge/MIzdUecLoOdq5uOLkjpD19f+PuTPZkSxLzrO5e8w+xZSZ1V3NJpsUIQiQQEjQQk+gl9HLCHoWrbjSUgAX3FEUSKpZPVRlZmRE+ByDe7hrEfjMv2vpWZXdXZlZBwhEhA/3nsGG336zc+4o+zWZTBoOxIQB97LhqOy+Ayb3rxrsjwlyf6xGf2wcbORs2Kh4grywwaMUHVlDTplHxoesREQGDeiHDT/gDocAiDXwcXaRhqM3GLUDRuZdscn68z7nT5iMYo5sWxxYMW981utPYERQyP+QP4yTLEfE9vDHiC3gs31B3rk/5/hAWhhAOsCJ2FZGIbecn0ZGlTl9eHjIfuJs2e7M2vnpK4zNDpk1MMlJAE1/mC9KnbHP1mnG1G6301ZW0AOpZ102GGaNCHBq8GHg5jWtAIH3DeJ/iJzi+3/7t3/7UXr5ofbXf/3XGYjc3t4mGOUetF1Bo9+rwK76aMsa18U/VVDEPFfyhLWJiMY8Y3crqVH76u8Y8NOPiMhAF19pQtg2nusbeDlor3iBfu6y77bx9Ic++nB/E3T4Ya7nuTGWOTo6alQw+0EB2EfWgO8zBq7L/JCsOTk5iW632yC5mRt0hjHZLuySA5NsvF/lqlZxWTYN+Gl8lqSG7bXHbZmx/jnQc2CyWq2SfNpsNvHq1avodDpZIf8Xf/EX6Yt+/etfZ6VVRDSSc5/CH1uWkElwj0krAD/9ARdblhxw4Xd42jHXJDhwhrzf7zeSrCSQbMeRFfvhalcsA8Z+Xju21i4Wi50+HXkHR5C05HPICfPgANJrZF9kQvP7ZLo2vl+J4yr3xo2Od1ydYDKP/2vCDxxAMgk9nc1mGdA5UMXmIKtgd+61Xq8bFWWOX8AZ1b+ZSGB81i8TLfxdZYXPbTab1CXbKcuOyQmq5zxH1ZbTH+sM1/0UzfNW407WnvnodLbHPEDYoQMQrKyx59d+1X6h6pf9l/sX0axgqtXdvof9vJsrtvBn+JNK3ti3u4LIFXMVT9BX+gYxwfeIFxgn/oj+sKPFeJ/x0LD72Er7c9bDa0d/kGV02IQ2Nm+XD+c11tYcA79J4hqDugIb8g+dR5aqHnq9/fqH9BT76/8Zb8T7VV3MaT3awgledLrGQJYdHkzH0R1V5kw4cR/iHtbBhNj3tY8mpdbrdQatdqhMDhPIYH/729/G//yf/zMz7998803M5/P4+c9/Hn//938ff/M3f/NewOfFY2FrEMEEWinb7XY6aBhtC8zd3V0cHBxEt9ttHDBqh1MNCJPp0ngbS7OKVEa8ePEier1eGjDOsCF4I1uwXC5jPp/H4+NjbkvD+PR6vQYb6wNPKQPnAFNIACsr5BTCixA5e2uyy2Qb5zZEbA81JSjtdrsxm80yA+TvuoQRwUO4a7kh38GYMB6AJYeTMi4Cau4zn88ba2/lZVyVbUdumEvAGhlFQF5EZEDuZkBvUsXEGcbOAUFENIw5cvwpQPCHGgbB7LoBEGD56ekpK+0YD0EiZAaAGZlqtVoJQiMidQ3i0SW42AV0CwIyIjITaLBugGxwQpWVdRc7YXITsMocAA6RDQ5uR14ggPw6tg4Cje/WM1hwrM70oEcGrFQfRWwzRiaqHXzakRtAsS4RzzKNPaM6EedDNhLnyDo442Iimko4Auflctk4b45gwYAfIEIwYdsESWbSnmu7CrSCZ0jD9XodNzc372VSyQZzX+RuV4DL+GqFSw1E+L5f8/Wr0/1QM1D7Y9t/+2//LQ4PD+Mf/uEf4r//9/8eX3/9deOayKvvtYtw4rd9M3bXr/s9g9wKFP1928NdILv+zb0cPFXghP82cH98fIzZbJal5N7q4XtWsmyz2VYLQRSje/jKCqidZKnzix5hLyHjkSkHN/zYhrFm2EWuga2hP65Swwe6ag0750wtOkHDdjCvJnG95tjEKh+uIGRsthkeo69VgySTdCb3sa1eK4IZgLVl1PbZ2AzyDgIGWTk6Oorz8/P41a9+lRXj19fXMZvN4ttvv825mEwmeTC85+/HaB6bATx4kj77nLJagcx8OjHGHOIvSCpY5lgzcFe3221UFCBDVb532Ub+dwBYt+3iJxaLRW6lsX/2eVcR2208EKXYVeNk8DwNW24/WOMFX8s+ivtVeUM+0Su/X/0D9zNBzbWrPfRuAwdntr2sHQcn8x0Cb/puPMQ4XLXS7XYb9sH9NjFV19X4037cfXf8Y+Lc9tb43iQZn2MOnPhDb8F8xsxey09NStneVzLcto11J1GOnUYvSNA6sHfMapmtttMJuSp3YDHrWk3Q7iKnaJYJj8+21DbGcQp4zufhodvoDK3idvtixlDJC2JLbJG3jTKuiGjoG77Mtsi4z1jU36m+3oQSRBI4nut5LpFRk+Qm1/FBPJTr5OQkP7vLR9IPr59lpjaPlb+rbFWc58b6WNYsTybVuD54h79ZQ7gcJ7+JD5AV2wAnryKiMR/f1z6alFqtng8NxVm4mscCERFJSv2f//N/4q/+6q9isVjEwcFBvH79Ov7u7/4uzs7OchIMeGwUajaczxAksxAEaWwDwRF68nECHBBcD3Szs0apGA/3claNvrANqNvtRrfbbZyPQjUUWYLb29u4v7/Pw99YeD8OGIBkIOf3EP7JZBKr1fMTZljsu7u7xhPrAM2UEFqRbKD4vgNNhJvyYYCUg2cOQOQ7XJtg2GCRbVPz+Tz29vbyLKd+v984HA/QSlYFQ4WzRgkMYAyevZ6s365gCzA6m81yvRaLRZycnOT6IAsAC75rIFfBp8GRz1SwIn/OZpCGY8IZEKzTmMfRaJR6xTZKwDOyQHk6hi3imVjqdrs5l61WKyti7CxOTk7yWj5M3MSWs0M+EwQHxtiYX8uziR1IEAxnRGQQ6EAJ+zCbzeLh4SG3CQ2Hw5RxAkr0ynYLA04Fnh0sj06O2DpO9xubQZDC/wCfiEhgaAfl6jQOmKciE8Drx8HTT8r/OX+FOadiqtXaHnTI2CGpcFL0GycDoKF/EEesEZWIEdvKWttdO2Y+z7r4oGJn7JhLAlT7iaen7VNFHBjSGAN6YHvvfiBn9fvf1/6Qz+5qv/3tb+Pg4CDevHkTx8fHDVmpYNnEzC7Cx+9XkqjaxA8BGwch9MPVD7Uvta+7+ujPE4ByTdYB+25w/CF7WoE9awY5axmswZmBPf11tZHlh/va39h3Omvrg0Dx9RBkTpogp8ybM5hUnTJv+F/Pr8m6WhXKHOwK4g0gHfDzvufVgHiz2TR8bwXb/p+AwP1hPrBT+FnbaxOUvi6ywVkiYDCfm/Ff/st/ieVyGV9//XWMRqN4+fJlPD09xeXlZbx58yZtHU9v5P8fu9UxG+PyUBf8BuME+5j4QS4cDLkKx8FFXQceTBKxlUcO92eda6C0a90jIklVB8nEBIvFIslPJz+5r5N+YBCSYOg+NttPDkT2WXtjU1dUss3e9/WY6ho42c080DcH0A6uwL+slXUfWwp+Z26YD+uPibDBYJDvowtc18GnbR34F7t0eHiYFccm01utVuP8H9tW1tMkhXF0xJY8rNU5yAZyS/8dPznQ5bVWa0vGQYLU5K1l70/1oz/UnGzx3HJvx6eQf/aBFcOxXnwfXGEMzm++j91BZ2zX6Z/Xyd91v+s9do2HNUQXrcfGiPYnrE/1B94eDh4zjqJ/vEaS0T4Gn0PVa8UUfIZxc2+TPV4P6wt/4wut/yZJrecmsBwLm0irGBNs2ul0MmaIiMTLftBBXR/bpl14zONzsz32OvO/15If5AqdNdHkOfV8mqjmmszL09NTJux34SP67vFUMvP72keTUpAbTDIBAYuFANCJ/f39ePHiRRrHTue5BPI//af/lMDGSuggDCFhglgEG1iEA2dpg2zWne8ySTDeDlhYBJSARYNcqECSIJVqD5/xYCJjOp3GfD6Pd+/exbt373JM1bFbsKzgzIWF2qQHFSsoHFuwKJlEcQhoPS7KviEcMJA+bJoxr9frvBcgmj5wYPl0Oo3ZbJZZM5wn80drt7eHqgPCZrNZDAaD3NbAfZw1jog844ankZm8snGpSo4R2Ww2eY4NFWtshZxOp41MFWvI9whs2DJZgRv3Y7w2nv78p3a2bgbylnEHWzgGBxysPYYE9t/bdz3uKtf39/cxmUzi66+/zgwaYLPb7WYfcPoOCuzg0Q1XHTnrzw9zjx4SHB4fHzfOX2B8yDgE2u3tbcoqukRQhKwDpAiCILgBogBqOwWANbrifmDHyIYxT/zvOTARhQ3cbDYpwz6kfrPZ5KHfrBN68vj4GLe3t9Hv9/NhDMPhMNcA4heQ2+v1sozb1YOWbchNbAZVLQbHgCuDVh9KXXXIQUpNBtAc8Br8ujrD36nJButmdeQObvze52j/43/8j4h4thNnZ2dps90M7gxUI3Y/JriuRQVH9mset38cBNYz33b5rNoH+mFw6uvZbrs6mMDOgR6y7mvWceLn0HW2s9e1dfUVPhhZwrdGxHtbnxxsIWsmrZFf+uHqgpOTk5jNZrFerxsPbkCeeZBARDN4xB9GNLd1RGyDOgfOnieDbY+hkmrWsSpz1iFe8+9doLoSZNUXgsXwB/P5PLclQoJgJ5gjEgzGRT//+c+j2+3GxcVF/OM//mPin6+++ioD/t/85jeNoxLAL1W/foxmwG794ExBPuPxR2yrbRx88Dnrg4kE20EHJ268bxmiD/apfj1iG3T74QfoFYSUZcb6SF+dBOJvCNq9vb302xHbs36QGx+7Acm7SxcYs+0Z/Tc5uOtzyKMDYMu4SQMT9GBh5p+xsk7oP/iG9We98ZvML0lc7zYwUWQ7zG4LJ4R2PdyHxJTjF88Nn7X8OBDHT5tURaaZT9tKk+IO4MEuxC2Mi898boxsXMI9qx8EUw6Hwxyng/dKXiBrrFElQo1bndTzPNtPV4zrPrhVMs0kgWNG3gPve/x8nvWyreb7Ji+M5ezfvIuG5LRjOe6D3LJrAnxrAoT41Q9RQq7oq+fCOzOs83Xs9nuu4KzVnruIW8+1ZdexjysDva2zJtLsU6ue8xnzHVV+jVns13l/l73zfRij59D40L4FObUtY058nrT1x+NkPj6mfbQ3ZkHJ5FhY6bzZSgceDnj4fp1gO0cbLO7tfqCoBHr0xwaDyXEQRPXHZrNpZMpwDDgVXnMQjGDjSMjo8V2AFMHi7e1tvH79OiaTSW5Jw8F5niIig38/gcWOAgfHXPl8KgwI82EGG4BsI9LtdjPb5rJcOyiTQWbO6RPBMNddLBaxWCyyMoxtgMgBfTfRUQNvyDSq6qbTaZyfn2emD0cOwcE6sH3M96kOlMC60+nE+fl5kmE4B4Jun70BeYZRw4lgUH1tG230omal7KgdTHzKZuNrPXVAxRq7hDViC/QMCL39DGBsgBkRKf9U7uBwsAPcYxeQsTFkHg2EbSNwdhUkoI8mWngf8gdbNJlM4urqKkajUXQ6zS1EAAsIXcbPwYYEj1SOMQ8eI6QWW9LIXM5ms/fIAIME5sWOB72D7MWmkShABiGkTEjS34hIoEBQ3G63YzAYNKrWsNu9Xi/JLbbHcC2ui01w5hS5N4HhdbezNNjnNYIPtlyjZzyFFPkz6LBj5br0wTpNtQkBksGF5a7q5+fQ14jIw4kjmlm/iN3Z1ur8a1BiAsh6gi/j//odZ2ZZHwePvi/XBYy5Px/qL7rqYBfZQN4MgAGoBssmmDwOZM4+iuYqPACjr2McgW+NaD7J1IGDg0JIaZ+74HuTFaZSEV2kIjAi0pa6asREHvpmMMp63t/fp35WH1PXwnrh9yMi7fou2fD47ecsW/a/zIGzqP6874HvwD/v7z+fDwihiC8Cf3HdiIgXL17E8fFx/O///b8TM0wmk7T3p6en8Rd/8Rfx7bffxtu3b6PTea7oZOvBj93sD+27rFe2OTz4JiIaco9dw25hC03082O5ZF69DiZ0qn+pa2E5gRiiT/f391lVbHtfsQZj82HZ+PSjo6Os8jGBYTyFzfEcgBvdTxPJNMbIXDIe+wK/bjKX92sCzzLLPfFLDw8PMZ1Osx9+YA52hnWDKMJWMT88Xa/6x0pScd1qb50cQr5qZaVxcY3ZKv4yUcV1TFzwt7/P61RumuRw1TPj47vcl/8/NoD9Y5t9Dn1n3lygcHZ2lnNqzMa81F02XNsyyrhMIjjG4ruWyYj3HxzCa5Zjx6YR0cA0u2INzzny4RjXc28cusv+I8/G6sbrfGc6nSax5OIHZAoM4LOmHAM7aW65qJjMcmTZ93VsQx3fer7t103E8eAZ4yevkXEDfh55cfLLuNWYgj7Y1nh+WT/PEfLk9TGpWH1zlS3LApjMfeC7PiuMuUWG/bqbMRY69THtDzpT6v7+Pm5vb+Ply5eNTOVms0kiwoxoHZhZXy+2HZknoBovGkagLggL5T2/zn4QlPI+ZccoBo7EZZVeuM1mk+QT13RGn20zb968ibdv38ZkMslzWUx6Ebxxv1ZrW9JNQMj88nn6j2PiSYgGqT7jhiwggrZarRLk2kBgSGwYHMTuAswmcDgbi6eu+PwsA3Y7QCsy4+N76/U6ptNpjMfjmE6n8fLlyzxni+8yF2xTZCthlTnLFkE0RBb/s26czeN72Kk4COA+u0hLG0obG/ftczVk1A4LmYiIhg4ZnNqROevA/GHU0a+6xQqdoALu4uKiUR1YHQ+BIv8bdKMrNevvqjZew9lTvecqJYPc1WoV19fX8fbt25jNZjlfNTiFhMKgUkm12WwJ+tPT09xOwlYhxoa+2KYwT/TV5Gy1NYyvGnP6xueY/4eHh3zKJ8Gbgabv8fT0FOPxOF6/fp3BcgW1EVsij23DrVYry5MraWBAx5iZb+6N3NSnRtGQR2wjWziwLSaKcY4QggbIJlMM3g24HByiJxX0fA6A7GYywMDhQ+SA/672xcEIzXbR163gyvcz0es1c+BWt9Xuuo7tp/XVpKTPQHKSABvgwMWgGL135Qt+DBmgSoU1R175MQYxmPNhr7ZT2BbwgM/Rs++wftNndIeEDmfFUPnIfENqe9742xiK+XKCkMoJJyGY2wp+nbTwmtjGRmwDVloNemqzXvlelkHrGNfDJ9/d3cVkMol+v58PqMC2AP7pV6vVivF4nNvzwTnYnJ/97GexXC7jzZs3KbsQK5+imTzFZtVkDPPjKkDO/zD2YJy+5i4c6YSf8ZUr9oytjaG9BiSR/PABkobz+bzhf4yBKqFRExRU34JjWWvuj/+h0i1ii4E9RmTLY/HnGBsYntedXLWs25cx/w7mLJt8/vb2NiuWwP3MpY9voArfgRy6xev0h6rwvb3nbYnYE+QHG+iKcu7nbbVcwyQ1euYYzD7FVS4OaPmMX6vy6XiI9115zlqwRozJiSx/91PpJM3EfCUjkVmqpD7kk2vCqAb1ThqwBtY3yzK/vaZ837KPjPr/GmN/SG7ti7mOj1ow3kGvPCbGYbngt327fYeTh8S4rDn+De6g+gfrbMTW9/j/GqNZjiyv4HO+59ieZE7ElntAp+yrPZf8j47ZJvGDjXeFkecfe08ime/V+9k3V2zl+ef+PgvMJLbX0LrKOiCXJmVZD2MqksM1TsG/mMdxzPlJSCmcH4GaJ8tsmwds0G9jZANv5+KgyVVFVSBMIHjiALoskCfT24Ta7e2jzQF+3opo4MnEc4iyS/lwDlQSXF9fx3fffZdVDPUAuYjt48pdweMMikE2/cAwcG+fM+MMBsJMlpGtdHa8Fg6Ey0DUAMbbEQzkIRIWi0WjkopGQIERMfHAtfykFTtU5uD6+jru7+/j9PQ0zwShb6yh5cWll6yZZefw8DD766o2rlkBnwH3crnMSgGMCdetDtUgvDLhNXD8lK06LObJzhBZMIhF7kwEPT4+5hraoWMX9vaeD90muMKw3d/fZ1aU9TFQJevtA6wN/CKajzLn9ZrZJDMB2cH5TOggcj6bzeLt27fx7t27xnlVEc2nxFnHkS10A5n2lmMeMFBJD8ZLEI3dYRyQOzjqXbKBLh4cHDSqMLB3BMwEpQb82BX6gw0kgB+Px7mmZAWRcV7f39/Pc+AqSeNgyUEP16nyiMMkA27ghNxsNs9kt4ELTy1jmzDfqbK9KzCyTfFhmayvbXMFRp+72Ybues1BhIGg3zNosd3x+/a//o7Bjb/rxAU2xVUffM5BYO2v7baBK4Ha3d1dVv5hY6vP2GXT2u12Vi67z2AP5sn3xz6gQwTBPhORPvu8E8s+8oqf5PP2UU6K8b5BopNEJtrAE/TVQaQD8YjtmSRco4LJKj807C06YF+6S1ZqkOp1qEE8vsRz7mC2Bkz1WoyXZNHDw0PagFZr+4RPYwy24+OnOWfTZ+P1er3o9/sZ8C8Wi/gUrQZFrC19raQgvoMAzXjYNtf4jeuZyHI1EjoEEWuf4GMVCGocPLtqmL5y1IIDTeuln/BkDOAq3BpIkRChf+iTkwn002RMu93OymN8nklakx+eZweHHn/9HK8ZZ/M692bHwOPjY1bsY/+M+7FpbEUCx4JpqajwGjNGjrlgTh1o+nPIA/PqpJe/4zlx4z3G4GuxvujzLh9j++Dgl365As62xjjbPx8bwP6x7V//9V/jb/7mb2IymSQhYtu6t7cX5+fnDWLCcuF5s556TowHI5qJsDqHrtbhdyWkKt7hmrviuvo+5IllyDFbXYMqZ762dYRrVB1y/8EOyIETznyvbl/kGpY/bA36aJtKfzwX9Hk2m+VWV67hdSRu4XV8/mazLdyAJDam9Nzusj/2+U5AewcHmIMHunh9q7w4JrLPxt8zZuv4Lj9tOXbiybKAH3HcQIyCbWYM9IfxQ+zXrcO//vWv35PPXe0POugccDafz2M4HOaie8KcufbEfUipKkGFEUXYLUBmQ6tAeLKdLbSiUd2AE7HS4DzrgXuMbTgcxmAwyP957DwVBO12O8bjcVZfADCoGEKZajaMsUCK4cABzR4rykhm1gSSyYJ2u50BpI2Fsy7V2DHnruSK2BIHVnbWyyV79IXtcA6MaXzW2zhbrVbj0bK175xRtb+/H2dnZ3F+ft4AbRHNg+Wc1UB+WP9aAcZa8EQ3HDL3dgm4syeuynI/WE+P2yWbdlCfszk4MQOOU8EwudlgQrhAPtkIIyfOuNiQ06zbu7JGnKlgI2yiz46+AgTWlnNI7HytA7PZLL777rvMcNrIsz6eJ75r8skVT84EOcCtDsSGnfWgOSCoJA06zdo42OR/B9wOSOkbsm0isALAxWKRT7o7ODiI09PTRjCCTvb7/caWQQgrHHYF705U2I6Z8PFcMRdkz5AtrwOyVQPkSiSg35ZVJxhYF35Xv1R14XM1gyUHEfTDIIAqskrYVjDrYMcAx/bfusRvg2xnBG3fLNfMv4m+XfcwiYT83N3d5RZu+xdvRWWNCb6xS+5DJXYJyp2Qsv3zfED+mAjBDmLz0Td0D19kXTGYtOzZ97iKgWpCkjT4W64Nsez5BQdEvL/dxURfRPPJRF7nXbJmP1VJQAcBVV7cjGt2geFdMmH5RJbcJ4j3+/v7mM/ncXFxkfIPzkOuWOvhcBjj8TifGrTZPJ8penl5+V7Fz6dongPm30kfgh/6b7tlkG/cxXUrIVQDLSdDTGaaJNxlhyMiK6Tse0xIWQ99j0p8kMSyrJioBN/yXZNfXH86nTYwLWfCemurE0j013bKPoh7eDuJ4xL+Nt5wwIos8vQ4MDCHtjM3rA3fc6JkNpvF09NTHm3g40fwf07IQ2Bxb8aCb7PO2K5HNM9BqjGY5anKjG0Za1ftAtdg/fw6/WHMJPGwc7viiYrXP2VrtVqNWM/Be6vVyvOCwbzWLXAFc2HcxlrYJnE/fqoP5nuOXUza2F+5n1zb36v6hk20XfS5ZuwQ8tmk4EjiQMhfY7u63uAq26ZWa1v5x+ccQzAOk6DEn/ZFNXbiO9V3GKvbznjLGbqM/tvPIw9gT1eII7ueg6OjowbOBGeADZyQso9m3cC32DXPg22278n3LQuuNLScuWrJc2Q77wIcfwbMhyzQfxfvRMR7nzG+tl/Dfn9M+2hSqjKPvV6vIZB0zsaGv5mcahC9SFYwA+8KZmi7nJAX0wFxRLx3IDAHkkEuRUTuGUUJmfRut5sHAgMqIiJubm7i7u4uA7/xeNx4hLWdFqRUxDazySPUCaghY2qlBuNmO5KfUMLck/Ej8+pD3zAilc3092sJH0bJJAbEGE8RpMqIKhLPNUpSK1oQUrYo2Aj6N4KPkSKL/vj4GK9evcqDplmv4+PjPHzTQbgDeNYeYgl58BlRGCsH2CiXswLIeB2XjbeDwU9djryrVWdbjdIuEOuxMWc4EqqlIFSpfvKhvHZKBA2cl+EgzUaz0+nkEzoimgc38j46iq2wXNGXbrebZ4gg84xtPp/Hzc1NyoedBdfGKXibEMEz1zF4ZF1tvwwUsUeWwRpE2PkaCHislQDyGtIX3tvb28vzPkyWcg3bZ9Y1InLr4dXVVUQ8B3Q4XXTclWgEc9gN+sc8mXx3QFCrWQESFUzzN4DWfoAnGo7H45wD66KdckQzmcH8GeR4jaqDtg59jlbHX9/z2Pjt6gLbJwPnap8q2OF/A0ADbPtTy7GvYx01UK4gisypE0qWV+w3gJjvcA2CZuyHddj2ztUFyBD3tY/nM2z5JZgiW+qsJtjCgQo65a1BBsVcy8AbwM44T05OGoQcpBv6QzLNAe5ms8nq7V3zZDtb5Yt1pNWg3HPjIJH+cm3/cB3LWtUb+yDjECcQd8k8DQxwf38fl5eX+aAZCL1WqxXffPNN/OxnP4tXr15FRMRf/uVfxr/8y78kKXV6epryOplMPlm1VA3ajEvsg42XkR3jL/tvbOrT01PKhq9BoOn5xof6Pq7i8Pwid8i3MQDXN6atyRuCD1cqRTQPIXZA2ul0YrFYxGg0yopB+rtaPT/dr9vtJu7gfJrhcJjHOjiwQsft/03KcE9j4V3Ev7GDyWT8cz1DhS1AVFYZZxNngInwtY+Pj3F1dZVnSREH2I/ab1tvIuK9xHbFp7ZPtkWsOfPCZ13lVq9j3awEBK973SppusvnVFtWfz5l++u//uu0GR4T+kPRBXbUyX8TgeAjxlSxNp+tuM6JYJOjvG98Z/380NxYl71uR0dH+VRsmv00ONo7ZRgndr7qOWPZZds9Hssg33UFHnpGYUC7vX14Gv2z/NFP4zHmEd10DMP6MBb0ln5g05gTdLoWutivW15arVYmtx8fn58myxZz8IltjWWERqXo4eFhkoWOMSpGM7+Bzrui1MkG+siP54h1QuYtz45luA5yDslJ303KVhtxfHwcs9ks5/Lf/Jt/857c7mp/UKUUwsXBfjjHasg9OBxrNfyeRH+Oe1jRI5pbFRwMci0Hd7VKi+s8PT01DhlkYSzsfIYF6/f7MRgM8v4EaDc3NzGZTPJeo9EoptNpbhfygjFOtgt2u92IiHT+Ziwp20dJuT4OC8DqJ5JBpLAOvGcBRJjtLFEa7zuvRGDEVvgQvHoGk0kr7uUslbN/yAZK4iDFBj4icg8yyuDyy1evXuVWSvre7XZjvV7n04xYfxsZjAjVWii1jZ0BoA0RBs0gqG7PdADIunttPmejH8i1jZl1gDXy+rvPq9UqptNprknEs2wQSEHUIksYU4PDXWSoHd3e3vPZRd5WYFLbINJnW0FgukqKMaFP4/E4RqNR3N7eNjJ2zIOJRII/Z5YIAJARkxqV4KsgjnnkOw4cI7ZP0aqZH68P9hHQ7+08BuE44NFolAC4npniNbZNwGbd3t4mYfWzn/0snaUdJdtg5/N5Xod5MBhFzyKiQU6ZVDCpZjLRZ9zhFzjvjvGTca6BmefPgNi2sOor/1tvqpP9HK2CEMbhv/EZ9nc1mWAQxHvWc8uWZcKgyXNQ59HXtV9BL2vf6aureCB37u/v0z9gT4wl6KerN7ytnEA6YrvtH4wSEQ2csmtevX3IftEZauuLM4H0G1lnDCZmsaH4FOMC5otz8CAknE1mLV1lQ2KN8bvilXX2lg0TJLZV9ve2UyZQ6IMTk9Yty0DVKcuZ9clA1jLn902i1u+h+71eL7766qsYj8fx4sWLJBbBWpwlwnl06/X2iUiQIJ9Ktz3nFf+AcxyYOrNs7GQ87CDRiUkTJ6wPnzdBSsO2+rMRkX6OeUafWq1Wo1oAWa6Btwl+24jaL84hPT4+zrNdI7Yy6CpF8CG4HJ/gIJrrQy47aHYMgs7zuv/n/pZFy7KrGNxX5hn/ZDtjcg/589ms+N6IbWLIh7vbj0VE42Eq1da47dpBYpnk+9a/SqRX/faY/R0HwrYT/l6rtU32QwB43ukb9/jUDRvMHBn/9/v91DefL8bY7fesK3zf47JeW4782ocI/kriMTdeS96r9jjiWVaoxmOdjE2rf4dUJQbGn+BnTFZZN6uM8XrF+/a9dS5YC2QEnMx1kXXje2QY/2c/x71NjPI9vlu/w71MpHqevUPHZ6LyWSfTuBZ6zvuOzV3BDL7xLoRKSlXMgv2t5PSHfKrnsvpT+lflmea40A8f4r2IJqHFtYyXsHM/1P6gp+/x++HhIcbjcZyfn2fnfT5L3e6FMDmQ8yQ7QLCA2gF6YUyQcW07Ghtp7uXKBu7HpGEgvf1mf//5iVm9Xi8dQavVislkEq9fv46bm5uGILDNzPtOcfCMjUWhr94OsV6vG3tKHaiZRCLjSuUJ8+GAfrVaRa/Xa2S4zKYaXFM5hOIAkJ+enrcs+FwrAlcUjfVlXSCWrKz+zWcRXmd3+Rxz57OAIO8wWldXV7Fer+P09DTOz88zC8V1AAg2QD6Eluoetotxbx/ezGsOuEygWPENxA1afI369+do9A05MVkYEY1xQLw4g8qYqCywwTk4OIj5fN4glKmmcWBg+aaazdfnN+tssGsSKKJ5Fo3Xxf02+F8ulzEajeL6+jor7ZAp5oeGvfA2H1fwcMaGgwGCXsutg0g7Yd/D9giCxQ4eW0MlEwGvqwrZamxQzRlye3t7SaRPp9PsAySsq8wMuugj67q/vx8vXrzI8TNeCMTNZpNnXO0KHO3ovGY09NIJCMZjAsLgmblEXrCHBtXct5IPda12AbkalFQ5+aH2p+p3vVcFq/TXVRIGTxXQGiQ7q+hA1981CLIvJYNJVTD3qERNtTHuN7aEM1UgYhyQmogwcHNpvMlqtrKv1+skHwhcPU4CJfs520fsh88K5HvGFpBF9Xwt+m+gZpLGQYyzxQbc2AIn4LymnhPWdblcxnQ6TR01ucsPOu5zHr3u9L3qgeUKrGU/WwPh2vz9Kte1DxU4f+haNIIlxjQYDGI2m+VWPcvQ4+Nj3N7eJtnnTD6Jvk/VqkwbrPN/xHYrWw2AI5qJD9vSeh/7Z+s/8maiAjkztrG+RWzxtb8PHrSM2q6bMKvVlawXT510hZqDThNKq9V2Kzc2aLl8Plx8f3+/sWPDxJxjDebTes74waR1Duv6OGnEGKuN5ByVdvu5st/xDJ/HP/sAedaLLcJUS3l7MmOxDfAcI2dVJ6uPsA9xM9np+9iWgL8rmWSfwrx7nrmWk1W+vs+8ZCyfutEf9xM8xtM7mWPmhN/2bdblXb4aGWBe7Es999VP0sddc/Kh132NdrvdIFH8eXyh/TSv+x6OPfHdxrdUOLH2PMmUKnrjWlfy13gQOTY2rP3FxiA/1X9HbG2oP8/38e3uF+8hfxHNJ1xaVn0/fC2fY76ZK9bUiW/uyZxx7qGJqVar1Tjw3XrGmH19+/eKNZhf2wXb/ipPHr9ljDiAe67Xz6Q6Z92xVsgHa1ZjTOOoH2ofTUrZiD09PeUhfy5BJ6NBMxNZFcMTxWRgBDy5NTCoE2+H40neFXDQ92qULcAEXBgnO4Db29t48+ZNjMfjhuMjG+eAkT5xL7I8Fjpv9dkFFBBcZ259uDmCgeOAvLm7u4t+v99gblut7eMvnXWjjNEC60CEfjLPnLzv+aVSg8B9s9nknnsbJOaXKisACOtINvPu7i7nxJkhxrBer+Pm5iYD0pcvX+aBphB3ZshtrAEOACRA/K5sIt+roLmSpn6/ypsNmeX4czQbbP43GHN1VAVhJnkI7CzTgMga1LVazxVU/X6/4Wxq5t8ABZCLLHAtO1AMdkQ0qgIAcMgG91mtVjGbzWI0GuWh2awBxJifFome9/v9dDB2pABJz5mdtp+YFRGN6qWa1eX7DnZNTkW8fz4PJCpzyfU4M8dEGITN4+NjbhHudDoxn89jMpmk3BOs2rmaqJ9Op3FwcBBnZ2eph9jPdrsd3W73PT0ziDZBAYCzvrPWEdt9/K6YhEj2GXM+dwjbyBkVJgIMeOgL9zVY+pjA+mN1dlcw/WO0XX7QPoPP2C5HNAEGa2aA5coMPlevxf8QOpx5WNebe+Fjqu2l79Ylfk5OTvJ8H5Mutk0mcNwAm1QOOdNqGwJ5QRUkctvpPG8fIqPMeB1wODFzfHzcqECiD27Wp0qAc22wE0Feu91OQgU9iNhWeTF24x37RO7R6XQaT6njs9glP9GwypWDaJO4/NQgxuBzl8z6+nWO6vdrn+p79XPI63q9juvr61iv1/H69es4Pj6OwWCQ38HW9Xq9mEwmjQQm80WV3adqxgfL5TJxmskfxkO/fcCyA7nqmzw/zHlNDBp/uDngjGieP8T38F/2L8i+zyt0wGh9BV/y5G6wmc8m4drYfZOgFYfYp0yn0yT52GpussEBmDEO4+NeVYbBdLYHtrXGLvxPX/FX3sJnAoLx8zrYgXnHv5n0pu/EC/TJ+IG22TSfTlZ3nNhvmDBxAM3nbbd9P2O4+j+yYTKgyq777Pd/yKb8mM3xJXOxt7cXZ2dnaZuZS2NU5sXzaZmqPmGXjqLfrsRnXSOaZFf187aTNONy63L1/yZnaryDbnmtXVxhf2r8YIIsIvLsUfB13QnkIhVsnONLZKRiM+bEds7y6/E6+esYolYw8ds2iLXB/2LLGKttTJV321rrlBO/FHugL04UOLaoPniXr7ZefUjG6/vmT3bJkHEFtt5xHn1ZLpfR7XbzwWF83/ENnATnd3vHxve1P+jpe26AwOrsnFUxwcHi2jlHNDMS7Xb7vdcZrFud0Mr0WUjou0kQs40IBsAVpSKoZDKvrq7i6uoqtwDZaROw+SkOBFk+m4hxUHrHwZ2QfNPpNL9/dHQUg8Egt6IdHx83zpRCmB3oMn4LOD/ey+49/6yR//a2toeHhxgMBilQPmcDkI4Tfnp6yuzO27dvcz4xMIxtf38/KywAZxhv1sdVZa6oYtx1i96rV6+yEqfX6+X8YxC53nq9zvOkWP/vy0hUZhr5g9xy4LMLREdsiVA+97mag0z3h/+R3QoQIA8c9GCsuE7VV57Odnp62iht9XciokGEmDClGcz6MakRWzJqs9lkMEt1FfcCaE4mk5hOp40KI67Bo2GppoAU6vf7jWyLHQ19d9ksjgs9R7+oIgLQY/88TgfFJuz5/Hq9TtDv+WJ+AFFPT8/ltDUIns/n79nB09PT2N/fj+vr68aaExhD6nK+F1lt+oHuYQsI9trtdh5OayDFmgDA0EWDa2wzxLirU0yCsS5cj9cMjAAtDtK9hv5tQOJ+0Ow7PgYks37/9b/+1x/87Pc166ztDX2C1OF1ABxyYeBGAO7+V5BpEFQJgfoe1QrMuTP1DkZZwwqauLe3brbbz9vaB4NBBmj2ZayB54F+4KMMmrh3BUf4m36/n8ATfeG72BIH5xHbwzzr4c/2r98XXDmg24VveB8i2QEf887Y2VZhPbNNvr+/b1RyOaAyqf8hmWYcDkosfw5ePNa6RhXw+u96712f3fUd662Jg81mk08+Pj09zYrO5XKZj3T/D//hP8T/+l//KyIizzqsff5UrWIE2xbk8+npqfGUPPxcPXOT71XSwP6Zz9XEhv+vyQFfz983KUV/LRu7AnX0A3w7mUwaZEy1bYzX/ULuOaIBHG19p18+j9IBoNfVelrnh1Zljt/WVeavElLsSnBSvQbYNTDFnjN27udqq0oiVaxqkod1MX62XNX+V8xhufT6VDvFPSr57jiH97CzTgI5cGdO3L9PrY+0Gl/2+/3odrtpYx3UYy/4DRmBTFe9rjEfcsDrrnLfJauWiV195bVdc4ifdfEBMZSJZsuk7UO1wdVO+Z4QT1wP3GaZJdnCGMAlJAOMXTzvVR/rsTyuNDSxZdlF5jjM3TGHcb71wfplbO75NJ5kXLWSrNpTJ6pJ8Dk54Qrquu6WQx9x4qSg58s+wlVKvlYltkzk82N8hNy32+0cS6fTifF43MAM1hcwjWOTH2p/8PY9GqSUwag/6wCRw7x4z0K+S5EdaNpxGJQwkQ6uHLDVQNfOyItvsEiANxgM8n7L5TLevXsXV1dXMZ1O4+HhIbcg2GjRZ4IzBMeMMRU67Xa7wR4SEE4mk3h6esp99vf3940DEiHLEEzIFhYdQM/Bp/v7+9Hv9+P29jYiIssrvWYAVJST/00aodjO6FABBcCzcWP7FkQV5I2DU48P4wTpVgMN1sFVO51OJw+Xm06ncXJyEpeXl8nMs32RdedpfzzlycakOhYbRWTK2TE7GDsj5m9XtcvnJKNoHpMNTa2W89YBg62ISGIvYvvY1gr8mLNut9t4IlR9ytBwOEySscpMq9XKaiFvw6mZCGTy+Pg4zw2hj67S8yOakam6nZWMGIE068T3CO6YEwyxz3Pj2uiIt9AauOO0I5ql0na0nhfkHNmLaG77w+h7HbEB2ByXU6Mz2BGIZw5+Zzsg59mhXzy4odVqRa/Xy/PwuDfzZhKRPvo91gH9dBCFkyZ7xrU4Fwjd99NhCF4qSK8BEvLuflWf40qAChCr3/tQw+987GGO33cd/22SCrvjg0ArKcd79m8GRwaL+GWDWvtXzyXyxsMtut3uThDpANnf5f3Dw8N8GIGD7ohoEJL01RlW66a3CFQSh7nBTiEr6/U6bm9vE5dwvYhIHdlFqhiAuppj1zpFNM/rofG+CX/bHMA6JO9sNkvf7+3M3tJbA02TtlR/YavAR1QUQ9zQfxOdtk9e+w8Fi1VHKji2f6wk7y59qyQAr9f7OFiGABmPxzEej+Mf//Ef4z//nYSkGwABAABJREFU5/8c//E//sf4/e9/H7/85S9TlqiuHY/HDRv6Y7eKDegzcsQWEFe1VVtmvMz3jXMjmiSDyVgHJsZ4tRKx3jdi69uMm33fipP4jV2BYCaT3m63EzOTvPGRH8iniSsCa8ca+GD6jG+B9GDrleMB982+wRjQQX8NJm0XsSteX5LSBODr9Tr6/X6OAZwNNuCp3BDFfI7PcG1wDePfbDaZ9DW5vauvJo4qGWh5se/7Ppmt88DcOZBF5lypx3WMmT3H+A/ju8/RbOeJry4uLpIIbbWaFbLoT9VT5qXOoWNTjxM5NaatPt59rHjAPyZHPB6ua/K27h6p8hHRJKMZF7jUdof1tz7ZJuC3wdUegwksV3PaHxIbWzZ8f77j6/szVRe8LtYXCkKcBLBseC7wkZZz4xLHM+YVbF/RC5M2TtbWZrtfsa3tvMdf8dou2831/LsS44wZOxqxTYhhh1g37DzXcf/dl4/1s3+0N6a6x5lbC6rLHhHECni9iBY2lIkJqCwgn6sOxqye71MztzYwGKXVapUECcCfCime2gWgcXUSSmtyyQYflnY2m2UAaDIqIrLcHifsoN9n4BC0rlaruLm5iW6329jvyedMMkD4YGSrAeF6nU4nMzOsH8baSsR8Hh0d5XkegH6XmR8dHcVsNmuAZogDDiS3oPrgQwCMjVslzZBBMsOj0Si63W70er1YrZ7P1OJpfCiiDZxlxMbUzsXOpq6ZyQPLpOXCzrYas8/RPG6D1oj3jZAdjiuSIqIhg3zX16FKkCqEiGaGzZUVli3LOnq8v7+felJJZmwNe7IJDk1IzWazxtPncLwQGp57SFXAoKvvIrYya+KVw9QhfviuHYAdgzO8BJ7OGpr0c3DONTH+fo97ONvlylT0nM+x7382m+WYCK5ns1kGEFREuWrJhylT1gyBxZpGbKtXAM8VAHNNgm5sncfAnLIGXj8yXSYY6Jt9iUEWc8JcmVD2Ni90oAZmf4ieIqN/+7d/+9Hf+b72IQIgogm2rFfoLBV8jAm7bOLCpeImHAx0kIFafm+ShFbnnmv5uswTfeFabKO1HPswYwe7lgn8h8Er94D4dJJgvV7HbDbLp3TSsA91W17EdhuEbanXwRWlBqDYVgfHDmyxBZS381lfi0pjX5f3fGBqRNP3YsPBIj7Dy2SgK7AcDHl82Knvk8sKgr0ODur4sbzZJxoffp8MMcaq9+g0PuXFixfZ9/F4HBcXF3F7e5ukFHL8sRncP6Z5PJZlr5U/Y0xZK1wYJ3rpoNZzV20ZNrKSMvhmByrGpw5O65hYN2w3uszZja5iNCHCnNveR2yTHJA6xgYO2l1Zb/yJX62yZLtjmauknX13XTfLCDaPeUM/Li4ukjTDlvl+xj2z2SyPwYBE9jECYAx8LWsNDvOaezy77BZjcYBo27SLEHJs8CE59vdJHvpajpE8V7aNlu3PTUxVEncwGORaGPvaXvN5J9tYf/tfEygOyLG3lk+TMxHNZFK1vyYd3OxbkGN8pftPXG1sZSLUckrjM44bSVLST8dOyB3ywPW4JsS0dZp56nQ6WThgDGKMZ07A2GYXEUVfSJS7Oto7mCyTXMNHdTAGx6zMKzG5SUfHI06eMp9OalXSrhJDtlvGAI4rjdls2/x98BCve2zmX6y3zBlryefMT2w2m4y1iWdc7MLcMO8f0/5oUgrHyWHYFhJPLAGQA5NKDlQWzeDWTpNgq4IXGzIbCJqJAldJEGyhJGR/ud90Oo3xeNzINBDA2WhBWDnQdEZ3sVjkGVwOxrkuDYcGQcacsT0H5eZ6GEauZWeGMJgctPKbGIhonv+FIlEVQYbWZ/dgkJhTgzyUmqcJ7HKIbEGMiAQrrLsBF0QEVVQIP4bI1+73+/l43VarFcPhMAMe5tbkF7ISEY2zupgDP6LYymwC0IrOWBx8+L3P3UzQMk8eB+tUS4kjtttAed/kgw3s4eFhdLvd1B2+CxFwfHych58z955DB5DeY26njsxCRvnJeBj11WoV4/E4JpNJY2smh5s/PDzklrZ2u51b/zg01XrnIJ8xEgTSWq1WVgNigB1UAkJMhNkw10COsbiiiB9nHh24+xo86ODw8LARYLPGPJEPmSe7CzGFPeF/voscULnUbrfj8vKyUZHDQxc6nedz93xujQGqdcHrFhGNrZCQVsfHx5lVBrgbnFDthexUZx2xPa+hBoIGwTU48TX+EIC82Wzin//5nz/687ua72mwUskDO3z7EIMzj6v6W4PoXdf1PBtU1GC0glyvAe8Z9GJTIKWwtbaX9V74KoOoTqeTBKhBUg0u+f/p6SkPFt1sNrmlwHNtwuxDwa1/Gwxat2pAx7i5F2Rsp9NJH4uN4tpsQa/EnM+5o9W15Z5PT09xc3MTBwcH8erVqzznj/uxbsyTgaZ1oNPpvLeGlZyrhIADKZMeu1p9/UPYzmPd9Tlscb/fj6Ojo/j7v//76Ha78dVXX8Xvfve7mM1m2V8qbSHvPnVz4MbcOPAwlmJtkSfWinkF89V5cGBaiSiTFDUo5BrGxyakjJ2tM1zLxLKfpGcbTDCDHHG2G74eP4Dv9PlqJFbxT7vOtMJG0R8T81W21+t1VqnVIG6XLGJj6BvjcgUYFVrYLgf26DrjZScE92YHhW2L/T/Xwxa6WhK5MJZmrrChvm71LfaJDv6Nq/k88+X5tl3mmtg3B8Telm0yzBjcffnUjXlbr9dxdnYWFxcXiSF2+X3WquIC++iIZlBvMtb/e5wfupbl1dcxzuR+1k+vQ51nVyX7vYitLFd7bdxmG4aNQUdN5IAXIyK3qXENVxmizz6/yb93+V18jQtdvG7o9WazLWBg7rm3t805yektmSa7kGViIfrtmMrxihNAHquJNsaCTldyyvLnNbXseH6Ml/w9xxt8rup6jbWMfyLef0CT+RA+f3Z2FuPxOOfHc+RjOD6m/Ul1yzgAg1eDQCYOZcBAEVhRDVOBRwV1vqZfrxOFcph99mFr/I+wmmwYDoe5raDT6cRoNIrpdNooDwQkmtiwAScA5h5PT0+5jQgldcWEAS3Ox3NB9o8nxPDZ0WiUpA8HjnEtsivMMXNGH/i8FQjmGHbX5AA/ddz8zxwaONmQzefzfNoK5JWr0bx/m+Df82DDzGcYD6QD74/H4+j3+8mMY2Rge+mbAZfBGGOzfFjOragO6qrCVdBuUPS524fu6W0oBjae81odZUNm8pZKCsgU9Kjdft7WilNCrx042lhjvCB7ALDe7gUpZQO/Xq9jPB7H1dVVbjtFRtgCNp1OY7FY5LlR3jrjOQHsMmbkB8fp/dEVhHFNZMzy5IAAXTAocUAPEIdcdfaFLbQO8F0Vtlwu4/Xr17G/vx+Xl5epL34aJ9u29vb2snLJRDdB2uHhYQyHw7RD7XY7bm5uYr1+fvJlr9fLfh8cHOQ92GNuXcEGIyutVvPcMAMvgDdy5mDeQQbzyT1sdyqAA9j79V32perxH9p+DB3fFWyakDF4qSS+M6v4MusYts1zyZzsIqb4H11FjmuAaqKK/z0nDgjte7k/1+TeBtL4FoOmiO1ZT6yxA1vPnQE8us/cEEAbvzBeg0HPf10TN+t1RLMqg/+Zf9bSTzTzvKH39kXezuw1wsa4T5AEbMs9Pz/P/vf7/Qb54L7VoNRnUGIzK1A23quA2TK9K2CynDgo8md36aXv326381zOwWAQrVYr/vmf/zl+9rOfZfVst9uN8/Pz2Gyen9Y3nU7zyIRP1TyfDuqsr6wnth/SloCFz+NnIprbvvG7zBX3rX6W9/FNrrrx+lmGuT9+yGQ0SWkIKWPAiG3FL3KM3DjRiE9A18HGyD0+xokY8DC6zPhsywiGbI+s19jBGvxZnlxNQB9sf/k+/cVvEiSDsahYJLDl/E2C2b29vazStg2LaD40IWL7IAmwEeOnWt3zRHMAyXW8TpZNv+8qD9sEfpyIrnNVyfF6XxM8jNfj/NSN+52cnMSrV68SH4CXbP8sN8Z31RfQbCOZXzf7410+pvoUk1nVnkS8/0ATr1XE+2fD8b/HWBMn9tMmJZx8oCLStgvCnFjx4eEher1eo1ouImKxWGTS3z4O+1DxmfvJWHfFVZBPtpv87Zgb+2fbFtFM9rHDwK9xfh3Ygb6Ds/gePt1xCv23n2ZdSZox97vkwrGr5bHiHcudZdj9Yf4quVSxJw3+hDnmNdb88PAwHxJQeZr9/f08Mudj2p+8md6TRPMB0Agdh+h6Emp5XGXpvNAsCpMFoYIzZmF3gXY+X40zP5xz4eu59NhbYyroxpkSYKGoCCIK4TnabLZPscMpkcVFIb01qNvtNsganBxn7wBkAPBUr9A3g28Y3FpCiNGyAWbeeN0kjcfUarXy8Oinp6esbqB/bBtiPufzeQyHw7i/v8/HWaPoVNVwfpadGUpVzxpAtubzeW4pxZhCdFUA7rEBOPgOylyDHIw9gZ8DBH+P/tZArTqbz9UqADE4wVjTKhPuDJ2BHtex0UUOkS8/1YiMCWvogBS9Jfjx35ZJH95o50NF42w2a5RU393dJdkC6YrhR0YhgAaDQeO+bA3j/BU+t7+/n0GP54CA147GJBVz5EAEGWIdsEXMv6uRWC8HMev1uqFrgI3RaJRj5KBYgDDEnhtP6BuPxzne6XSaVY7e9stTlJAFtuKahLy/v89ts9W54rQ3m03jUGfma39/P8+Kg1Rkzvb29tK3GEgh49Xp2V7z2/pYicHPBYj/kGZ7YhCCjJuUqbabIIrmuXAF3C5Q43sZxJmYqqDRAYkBke0eMmtAxOteP1cb47vQB9tl98XjdKKnJgharWei+ejoKPXQc0Df6j1qNtJkoIEen0WmPf7NZpOA3UDcZB8kPnbGpBzrznZb7GIlw9hKy+vYQPQVcrgSZlx/l6800HTz/Hqs1d9ZXioZsOv1GmB5fPVzzBNJiE6nE4PBIP7pn/4p7u7ucrzMG8kNqqc+ZdsVkBvzsA4RW7nhfYI0Y1YHEdUGej4tx7vmjObrmdQyCYWeoJeQUD5AuZIMvg5j4IgMAlf33U+RBddafx1U2t9YPhx40QewCf1Ebxl3RFO+bcs8J9UuGrdQ1Vtxiqud+Z6rhLFR3mJEYOdjAZxkN2FAv8EtJLNsg22fHd+YiKwJgoitHXTy1X7G+IZ5tUx7fVkDE6HGSp+7gWu+/vrrrFwzRqPVNXclSLVHxjsmECK2D7io1/WPX98VwNtHWt92+V9fF5yAfLsww37F96yyYTKa7zgW55p8F3lfr7cJfSc7wdbgRQgqj8n67y1zfKYmYSKikbT1lsmIeA+32M+hcxHbBxrxObYJI//mA9AB96tuua88Bn2pBJWv68RP5R6qnd3lE6sf3iVPu/yJZQz8wnXhGNjOR9LB+n96ehq3t7cN2TcG/Zj2J5FSCI4n3wpSDb+3iEQ0t2rY0EZsWVkLvsE2i+gy9rpQ3NfsMK9bIXu9XlbvtNvtrHjiUOyIyIMLAT1UPj09PWUwDJlEZQXjN+FG5gTn3mq1EqTjUHzOC8YdIGnyx9VCvHZwcBA3Nze5Fuv1OsErvyO2GVcU104KAol+LxaLDEDJjFnBecoZSm4mnrWr1WmwqzaakHqdTief+sWWA7YbMW9+7eHhIbPC9/f3jUoXSvR5HLEdtdl0/q+ETQV6vO5AgjEaVH+Mk/kczQbM2RtXBSAnNQuI7EZEo6LNgAq9wTARdAFMbPj8mGRkwMSvs54OqDGOECsQFBHPGZfr6+uYTqcNkmu5XMZ0Os1DVYfDYfbT5Gqr1Yp+v5+PZ18ulzGZTDJYcTbw4eEhHU6/33+vhN6Oq4JmrmUw52wbzSQ19svGn/n09hv6x/dpd3d3MZvN4uDgIObzeW5ZPD8/z21+JrV7vV4cHBzEYrFIHSbTRXVaRCSxzKHRjIstd8gL2TCCQebFNpB5iNhu48VWIB+slcEuc2eAYVDtIMV+xNsq7KMqoVwD5C/d6AsgoSYVIpoZcVcRRDSf6ljJwgow/QPZajLUQS/yRl+4R+03fgV/ZcKGdcYmmIxCzt3nXZVT/Bhsuy/uM4QNFcYG0/THdtxE0y47X7Os1lvPqWWQ/uFHTC7SFweEyH5E5LZVB4KsO+sEkW7Sw/4NXOBtr5YTPuP5rEEUn6mtguQa6NqvWnaq3lVC0+voPpv4R2awb+v1OjP1rNPd3V1mdmezWZ7p9SnaLjvCGrOuXnNep4EL0QcIHstaDVYsW8wZ17L8Wt4qeer+s15gLAhOX9ekKq+78pijJWx3rJv4PJ9JyBY+1hYiwRge/Eer9sd+1rLj+3o9KiHtebW99PW5DolRbBR49eTkJLfrcV2fTWnfRpX4bDZrYGofcWCcYxngWui236v23XLyIX9nu1sDauIR99/xA+M0IbJLb/ncLtn71O34+DgrRumP/ZplxXJhUtl+LeL9ChO+4xiwzsGH7Co23t9B9ite2XXNuvbuk+MS+xLLUa32Ml41HnXs7kIMY2J+vEXd/Sf+NBZzMQpz6HtWEgX9MPmGf3Piyf6cMVt+0Z2IbRGHfa31grEYd9PnSprxWu1zrRzls34giWXsQ6366brWu/wx81CvUeXFCXTuQb+NyalsZUeFfdPH4uk/uVKqgiuCVc41sSIDknxIp42rFcUK4GoWgFdE89HGLK4VmHtWEE1fcaxkOtjWxtY9grYaEKxWqzzQMSKSqKGvBHrORvrJZSwywokiMTeci8M9nT1hu4uDN66FUjkrYSKB9WK+UDjmmn5BKjB3XMvgCDCwWCzyTC0Hyc6SGDjT97u7u1RCgJYV3k/eYkwuTeazbAfkXpSSs8YEBgA65oB1tON0H2uf+B7GkPmy0fT3DbgqCPvcrQZRfp2x1/JMB2hUkZGNcwVb1XGTPiaB2EbL3PAZZCsi8nw69oUjR+329gwoZLrVeiaI0VXALLJDlYCrDLmmt/AsFosYj8cREVmh4zJk5oLtaxERk8kkIiKrq5z9g6RhTGRZIORMNjFflVhnO4D/d0CP7tdSffTk7OwsSdj6Pc62Ozs7S1BGZRRgttPpxGQySSfPmVsmC7FHq9UqXr16lVVxBqhv3rzJfh4cHORWTusR8+KtKbUajvM2mK+I7RYJxs1c2fExdmfc0GcDCuvqrut8zlbPVzAYdmWLA1oDEWyeAamzXSY2I5qBP9fYBZ491w5+bANrYoPv7spoYgNsCyCLnHU1MWPQin2mbyal+I4BqgNQvk9fHZDbJjqQdWD89PSU8l4zygSbJOCMbdxv++RKStWgOKJ5Fgf2Af8N9rDf4VrHx8f5UBLsAPrpQIg+4icrcPX/lrn6vvXGvvVDJNSu/31ty9LH6CWJLbaQY2/4n3UgWQFh/7ka47AcmOQwhuDz1WbxuYimr/Z3fD8+710FbsZ+u9aVAGk+nzf8D9dFh7zOyBT64K0eVFubRHVVI/belTcRkTgTe0YcYWLAskQg5cSXP199p+2Zf2gO9j0H+JjFYhFv377NNWT83W43dXQ+n0ev18sHJjFXYB37SM8182aMX0kMYold8lL/pm/Mkz9jksr2wTgH3I3PMbFme1l9el33L4WJj46O4tWrVzlnzOMuTGXyw77UsRXzbsLImMh4zTaee9S/iZOMd+2X6W9d512+Azkz8cL17bsqKUTf+Y2sEDvVNcamcaSE+0aMwfhqvGFMQXPiDZ0zSc14jO/oW0Tk+Xbgd+IX+j6dThsxMhiUuMc+sY7lQzG1sZDtovGHx813nDziPT/lmzWtvtT4zXrL/xXD1c9Y7uxr/F3jrxrbsnb0++npKfr9fqzXz086Zi24xse0H4WUQhAqWeQFBViw1YTgzErkwA6A68PNmQAGagNSFzuiWTJJcOeFBbQdHx83HuvKocgswGKxyHNbHh8fM+hlywzBricdpeS6VAbYmaBUkE0QV5Q04oQA6Rwktl5vHz2LM6aSBeWzM/bjrlG4uo/XGW8IK4w1xnG5fH5CWafTyaCfrC1j9yPpTeC46qXVamW1GUG8BR4BxtEjU0dHR1nN4QzE3t5eOn++RzWGz2dg/A6EKviw7FbnXjMU3LeSkrval3TANcBg3TGYOFr0rTq2WkbvwM4Ob7lcZmUaekSVDCy6DaLlBlkiUEBGI6KxnRP98eO/sTFkRpDbo6OjPEgYopLtA5vNJs8gubm5yUNI7UwcNNZ+UwXk4Huz2TQOdccRU65MtRDjRdYrYW/nZLlFhtBHdJt1IVjudrsxHo8bQJb1hFBnDgeDQT4mPmL7tA2epDibzVJfq5M2kG+32zEYDNLu9Pv9rKZiPgk8sO1u2HTWuNPppCxi3w0GGb/nr4IJf68mKHgfu8NcV5D3uZufwlQPBbVcuDweotiABTnGp+0KNPnbpMSuYKQGbBW0UN1kQFODa/toB8HWWUCkKyhtm60LBDnc0yQucwY5jPy5Iht7QX9MQrlKyYFjJUsAxPQJXad/uwAizcCevjFvtsv4Mr5Df/E9yAr+1tXn9AcCi4DfB0fXubVMMCYHru4HWM065GYS2ESg14nx1O9XwO97f59+gs+4hrc4LpfLuL29jeFwGHd3d/HmzZuYTCZJBH+K5vVnHjjTL2I7R2zZtMwaK5m0Qb6s73y+BrzWGebE1zaRQEM3ITHBuj670DjKgQpBzP7+fj7h0qQpvy0XxlrYBDBzfWgJZy95XTmewbaKhi4zXsbmRBD3rYSUkyPGPpylhf4aQxETOJBbLpdxfHwcs9ksvzscDvN6EFRUJnM/zojCPpr0r2QQa43dJ0n39PSUmGKXHfVWP1eiICuOVZgTyH7ipl2JCMuGbaoDXcvlrkB5V/uxfPPLly/j9PQ04zwT8SYX7HtdCeQYwUeVgHMYjwsl0Ev7Tq5v/8LrjpmqrfZ81MSSk1XVJvg+JqLBR5UkN9Frf++Gn+LzYDcXAfC6cbX9Q0Q0EsHsNsKGY1NYH37XrfCMg34yB09PTzGZTNKeEtdbj3hgDziEiinzBuYh6I99mhPxYAPvjHDSzDrA3JoEbLe3x2HUOXfju17zeh0+Z+LPeu5WbSZzzXhM4NWYmEQATyO9ubnJh1p8bPLnTyalGJSFAkGr1TLtdjsziASBNmK7ggneM7NOAOcy5g8ZKxsDBx30AeOEE8EJUzHx8PAQk8kk5vN5CjPZuOVymeQT/Tdgq4GmhdekkcEBxt77cFFIkwnMr8k9gm3Pldnmh4eHxrkcBHHcF1bZAJ8fAAn9Wa1W71VtuPrFGR7IQeaY+y4Wi6xOM/uMYhE4MK9k6nq9XpJTEdvtXS5ZRh4gKOp1/eNAxRl65q82G3v2o7vaqhoHO4IvRUy5zyYzXX1B/5xJt4Os6wz56C18vIdemGRhywyG2k6LOQOQQux0Os9ng7C1leqm0WgU19fXeeYQfUEn0Bfkr9PZHlI4n89jNpvFZDLJg27RMT9RD3vGa9bt9XrdAKCcheTPMUebzSYdLGM0mLTjcuamyhH2h7HhrFyFRqUZQQ7BBffyNsjxeJzb81zhwfxhl9hOQGUjugWAHo/HGTSRJWm32zEcDjO7xfh99lUFVJYf/2/SH6BBQAzg5fMRWzvj/7kO9zRA9DybSPkS7fr6Otfx4uIiX8eGmbSP2PpVdBT/6O2vtk2+lm2gX99FTnEtPrcrsMTXmKiJiIZtsaxTCUs1Xg30nLnlvg4UuLb77nP+SDgZ0CE3R0dHDaIcmd21hSGiKUPoBHKMH0KHqPRwoMP/+G3On/E9fE7Per2tqnR1BMmWOhcQTZw3aT9rkgNChLHWQL76g+qzjM3qGtvH7QK6ztJX2atg2q/VgNXfqfpqwmA4HCYBRRUs7eHhIV6/fh3j8ThevnwZn7I58PbviMikgYlQY1avM80Yz/jXtg258tpXn7LLhnJ9kigciQDGqnoR0XxAwf7+fvR6vca5n7YRDup9JirEDLJ/dHSUidrJZJJYjDNWsWnL5TJevHjRCLSwMdzLuNxBO//XOfRnPfesEbiX10kkm6RptVqp5ySKwfQE7ff393Fzc5NnOtJnEkSQ6Jx/RkziLXy7yAzkrc69/QQYotVqNRIankf/b39i+cE+VfwesU2sEMdADLt9X+y2q/1Yvvni4qIRo1bdsO802RDRfMAGhK1jU8aEjnj7eyWQPAfVrtF2+dL6OvdxNY53mRjvew5r5Rf+GBlBLrBT9ne+jsljE5Bu2BX8NK8x98YJjM++xhWWvrcJMK5pOaNfs9ms0QfuxfU4nsMxAz63YkxsUSXxvCb03/Puz5vksRyYZKYPrGW1T27MoX1x1WnwOHK+q2q7YnDruvGC7Y8/x/d5qMj19XXGdR/TfhRSigmwc4pont/B55ggnBeVFA6aDEQqw+4FtOOt5BP3Y+JtvFutVnS73SQ3eH+1WuVWoIeHhxiNRjGbzfJsGjsixkYfMUieD/cBBhjiCBBp5eIxxQRxBL5ss0FIUU6fnUTZpIUYUEAA4G1XzB1O1qAVJUeBAAsVSNvZ1YDJIBTwQGWJDZsPSoNUQhZcXsr/nF+1Wq0ymDYDbwPLeBwkVQezywm3Wq1GxUIFJ1yXsduhM3au7WCwyufnbjUA4G/WiYDKTyDBCHnN7u/vk6ybTqdZxg3Q5DBd6xuVUJSMAxwtK8g1MnhychKt1vMTUgDQy+UyK6TIPCKPfjKfnRn/Pzw8xM3NTUwmk8aBofUAVa8vATPgzdckK+ZMDUA0IjLwjdgCNNsx5hD5jYiGXrEuEE/YMtYAkMD3kOvBYJAVhvymb/QJezeZTGI0GkW3243hcJhrCcnW6/XSPkREQ+7p18PDQ4zH46xow7axv3w0GmW1hvUzopkts7+odqquzS4d2uU3eN3fcUBXyZkfC/T+sa3f70fEdouUAZgz41TXOBg1oRLRPBOPSkHbJI/VADni/ac++fPYWtsHGnLu7/g63kLH2poE3rUF0LbZvysos4w5GPU2ICqImLt+v5/VNZyJBrGxXq+zehk9draT8fK3z1ix/0RXsHO0XXPL+LCXNXuNDDA/+FaSX9XGY0u8Jd/JCGyu59PkrW24QbbHUEkX37+CVtou4Gv9c4Dr1z8UvPl7BO/dbjf29/fzwQ+QVSTW2GZydXUVn7q5/55LzxH2rQJ/272Ipm3w+PENrqTlnu6DA4wajG82m9QHEggeQ9VBro/84YPBw44HsMsQ0BBezAfEy2azyXNVW61WnvfInDnhCeHT7XYbR1rYDtI83/TbGHcXJsQW8brPs7M/OTo6ipOTkxgMBvmQj3r+E58jKYRfBc/wns8DZI191AbXdaU547KtsC2B6CKxVKtw/Lh7k+nME3PkJ2kjt6yN54U+O2nN666utDxZtz9129vby6QIY6vEpDGq9QR7PpvNUn5dnQS2s67X+DPifULuQ39zTWOm+l0SLbUC09+nmfyuBAW40L7L93Yi2f08OTlJPeWaHjM2rJJa9p/cG1toXM14jN0YS0RkvGwyCszvCirer2SSx2I74CMCGIuTU/QDTGCuouJQ389cAfEHGJ8qUMYMIeyiFq9nnWv3lz54Xb3mxm8VYxgLGkdhI5ArH5PAGtFviCns1se0H4WUYmK8mA5GAUVMAtvjFotFPmHGgunvexuPldEs5t7eXmPfp0GgjSFCA8mDMADMyNzM5/N4+/ZtjEajzFziCOykcIwOqFyWB0jGWFABArOOogOiWGCD9FarlfPU7/fTaTEPPkOJgNQKauLETtQHVRq0sg7Mpx9XzXUgbBijiSoH0KwZzpdsCWvGEwcdMBh4cF9kAWcGQXh3dxcXFxeNrV0R28CZ641Go4ZDRFa9TYT1rE7RAB05RwlNsPrcG2TBhmUXcP9crToQv14BAQbEIM0sP+vM3JIp8rkJ3W63UfqOPDpoQz6ddWKtuQ7VUZ7X8Xgck8kkQZZLqQ3K7XgPDw/zvIfr6+vsv0urkUXuC1luILper/MpO8gmT7djfjebTW634Ls4JtaBKibmlf8dzBpcGPgA8ly2bIAb8Uxu93q9aLVajS2U6PB8Pm8EOmydWi6XcXp6mk8iZS739vbi7Ows7u7u0m5zVk1E5Fpgx6zXw+GwoWeWL29LwN4RZDCftlEOql094u0hVIYYCJqQsm+xDuzSiy/RSDR8CKDaVtl3MT77TYMmA4Nd4N/+1yDHFWYR7z/90fPse1Wgw9/oM2uCHQc8mpR1wOK5oA/+/+TkJLOcACiCMW/1qX6Ge+FTOajfW1lMijKnxhPYHmwUBKETFiaQwSuuFjTZGBENzIENgCjm+j6nx0RHRDS2raMX+Hiv/YeCJQeYXpddevGh1/xjPGhSscqQZemHfGbVU+uJ/QxE1GazicVikWCZBCH241M093GXzWEuwC7YT17DVxlfVj/OdaptoFVZNSnLD7rorUgmC+2jHbyj0/jTXbiTsVJ5xXEYrdbzYeZeQ9trY3eueXx8nDqN35nNZu/5y4hoVOHXgI3+VVLHa+XvMf8Omn0tSM7j4+O4vb1NH0gMwhN+IZo5MD4iGj647gRwP7FJJGLtN+m35cj2Ch12dYefqgte4nsRTQLC1ZXcyxVA/HbQauLKc8hY/F4lDD91A2u4oMEkAq/Zl6KP9/f3SUghA7bl1hHrXcT726l2kQL1837ftpIGIVx3uKATlbw0ybMrNmAdGZM/z9qadEUuamEE1ZaOLdFn5hQZpG/GxY5Z7Ud3kXmWH+R4l5+2j/M9K8GCzFf5NUHkdfYOG+MjXnNszX35jLEB98KmuqqL97km12V+vJ7+bQK6klmeQ9+/ypjXw+viGAo/Rbz08PAQR0dH8eLFi/fu/X3tRyOl3PGIaBg2gxvIkLu7u5hMJvlUO5wvRgIn5GvZUADKfHhaBSleQK5xeHgYw+GwUZGFMI7H43j9+nXc3NzEbDbL4LPd3h46BiFipxURjX6QNfEj6DebTVYhUInE1icLK8AUcsqP76RqygQLjpd+bjbbJ5Ig1GaHXdUFWCcwdiWYA975fN5gfH0/Z5kgrAjEI6IBvk1oRUQG9CgegYnBEobB1XSMBQBH4EsfvUUBA4nhdCbNcuKAwQaChuxVcN5qPVfyeL+sDd0PAevP2RwEWF7MbpsdB9xihP0dG+1Wq9XI0rMf24STHVYt9UXvzcgjM1wDmaayxyQ3OuY+sl5UOlEByWH9EdEoy3VAyPY3ALLJWIIaMm0Ei8hXxNaw1ycU1eoJA5JdAb7nwJ8zMe9qSb4POOZhAsvlsnGoKcEH32+3n7dVX19fx8PDQ5ydncXp6WkcHx/HdDrNewGu1+vteWH0jfv73CzAhc+swqZBaJmAj9iWNEc821pkk6w9WycMlDx3fB55NmCvDtdBV7UJP5VGP1lXZ6scQCBPDkgZL8QfAJHrVBKgBloR20eHY0+dEcNWI/91Pt0n+g9BjX748OQaBO8iHRxQQbyQnUUuwAX4WWyLyWGuA3CyLqLfy+X28ccGuZ5vBzUEjWxTAQvVCin7F+a9Bvv2xVSusGaHh4eNpwIxXkhKbC9r4qovxsz965mf9v+7qpkYt+0Q3/PfBqB1LZlDZ9S9vrUhL8ZJfL7qrJNCT09PmcCo/hk8VoOFT9F2ybDXHgzEfELM2/Y7sLLv4D1sAfavBkbc22SE++PEhO1qDUbokzGVE1jotysfsT+ucufg+Xa7nfLpQ/hZQ+RzPp/ntj18FuNhfalE4v5u9o+7fqwDNUAzlqsy7HVAL9FVE4hg1fV6+xAfY3uuzfmk2C3OozTx4+QZAXGtoqvBKPYAG4nvtt2z3bKMOgAnCYQNIB4x1kE+iQVMIoAJvDsD+/c5G/bVQbWTj65MsZ0jbvVYmD9XslQ9dexq3fNv67ntMO/Rqn81Icx33CfjJWTFOuKkEz6M142hjK3xvfh2Y2rkBJ1ATo+OjvKMUsZt+1v9Cn4TfG+M4fftd/xTiSlv8zNBZt+GTOBTWWfiVmIZZLbuwqm+0FWP9IP53dVX6513oPj6liXLhfHfLh+8y19W3+TvMRe+hpOGHp/jasg7ZO/k5CS+/vrr+Nj2o5JSDCSiqaAGsgidn+ZBpQAgqTpdgyOE3cEyk4XRrhlh7nl0dBRnZ2cZoCF04/E4rq+v89HyKIKf4kV/vCjtdju63W46HB88aueFgWaLgAMMB4aA4/X6+ZGKLDAZYBv9iMjtBmyxwTHw+FkMFdc5OTnJYJI5ImtrVt0KYCPVaj1ve6RvKLSztS5ZtqLx/3q9fawva8W4MJ4YBQMiDJgbn+HR9AYCBjaQhHYmZALdPkQM2OAzD1wbQsUG08QsfTG7/KUaBtXGrTpgZ9aQY8sAhqoGGtPpNB0kQARj5ZLdmhVANw1s6Qc6wrV4ChyEbbvdzvMr7Dz5vVwu89yoiEg9MclUy53tIKnm4XqUvptYQr8dDNrm2dlXgEoAjc5g4L0GrVYrH2/OvW3buA46xVZDzregioq59IH8XJt7R0Se+RcRcXp62iDnVqvnc1qOj4/z6Xw8SYh5hJRiHIwV4guin/GxlpAbDm4MvqmQrCX3tvHML+tZCW6DIf9t//Kl24cCH0gHdLIGWQS21in0nUoRJwccBHMfB52+Dvarzrez8vTNAVTEFmhDSvMZ7IAz/fSL7xkwMRYDwl3AnffcBz8MAJlkjE7OYFv4HHrMXHl8Bpp8luv6ASgeS/VLDkYYI3oIJjA5xb1Xq1VWWeAvITc4F4/r1q19m80mq1S8jg7G7e+ZA+SoZqr9GfppUL6rwqqSxQa7NK9/JQT8mueP12azWXz33XcxGAyi1+vlPZDFiEjb3+v1covzp2z00XIV0XwqmWXJQTx40WOo2BYMgk/hPZ/jgZ1grZCv+pQ3WiVjWHtXZZhYdJKC5MN8Pk/8gMzie20/kC18gNfWtubh4SEfpsI5VPhEki8R8Z7eMv7qIyq56ver76i4yYFtxLaym+3zg8Eg4wdIUfrDmG1X2+12PkBlvV6/F8Db9rnv6L1jE1dugKeZT/teyG1XAzE3rC/Xsf1Cho6OjnLnB/LqeMhn4laSwGtse8B4awD9YzbHi+Ap9MNVXw66IaSqLbOf5nvWcd5zq/Lk1+vf9TNcj7UlbrRtdsWjEz6su20JvqomT7DLJrVoPjbFsSav8T1/Bt8E5rTN4Br0yfGGcTlj8FEQ/jwy6nE4Vrcdi9g+hIEYiHllfCZVK49xd3eXvhO7yrWYC+u2cZ3XttrYWpBj4tT2wIRnjS39uRpXeZzGaRVzMc/GMR6/18h6tLe3l2uMjzk6OoqvvvoqPqb96KQUrSquXwM8TiaTuL29jV/+8peNck478Iit48NgmJRi8h3MGfShfJTP4rR4VDtkFAGvn4LHgeMYYgJyFpVrYgRQcBMz6/U6lYHXMRgRkQRSt9vNM3TY4geBBqFkcsRbm+jH1dVVVk45u+GgHYflLDBzhAJ67ycZdoMoC7SrxgguAcA4Ix82i9C6WoxtP3weGfGP2XfGz5lkVLl96HvtdjurqUajUa4f1/OWCI+bzzmYQw4NuAH9BH+Mk/4gF1+y2cnv+s37gHXWgsogyJPVapUkgbMUOKCILZA2OH56espgiPepOozYOncfVo++G7gzn3Urjo00htcl8jiKD5U0I7/1ccyMc7PZJOmE/NbDH90P9KVW3dFfV0cwLpNdvoZBDv0BLEVsAYpLjyO2W3gAhOi7z63DeUBW42x57eXLl3n2CnIxGAzS7mFHceKMw4EKoIH1p6/eBmlghxx6OwT6aELAY4dUcDOQqWvt4MC6/VNoNdhGlxwM7Qpg+aznE3+IHqOvuwAzr7N2rHfE+4fQ87rv76CI97imD88niMRfeJuOAz2uQxDkMwddWeTgln44aWDCvRJSVRf5Lq87aCSZ8fT01KhwrkS+56ESLp7jOt9e83quHz4dwOdrkFzCTtPvCiZZfwheGjrhdeM3fWCOTDJZPrlnJY7sW6yD3NPBbyWWeZ3vfChQrfdcrVbx+9//PitWeAocPv7p6bnikuMQZrPZe9f8sRvjWq/XmWRkbPgP66VJD+bd4wSHMeaqQ/ZD6Iwr9/n+dDrNalhjZ/rM+uBHTEjh8+17selsy4VYAQe64jUiGg8AoVL++Pg49dCBoUly+ua5A9uCffn8rsDtQ5jMMmZbZH2N2BLEdY663W6cnZ3FZDJJvASmgKhyhSfBHL681dqeXTmfz2O5XOZ5WSR/0Jtd8ZIPWneCDPvEd7DByIJ1ftcPc+r5g7SxrPI58BXrQD9cOedrV3vyqZttjdfPiWTby/F4nLFaJQDsS0zOWGZ8Pd9311grYcHfxGy74l5/D2zlJ9m6CozXWBt0xsR09fskdsBui8Ui+08C0YSwcQhjILZATyGVTbYyT3ye+Teu9RrabxvbdTqdJExrzOZEmJN9TqS6wsnJa9aBfnpN+THWd/94v66h+x3xfoUaa2R5q0SdbZbjXu5rP8r6eD7533oasU2m2z8bH1LcYRxed8UQ632sXv+opNQu0IAgGIRgyNiba9baimxQRBAVsWUPmYB6LwM5lGYwGOQjHwEp33zzTfzmN7+JiK2RJeNCn9jz7iofyBAMLwf6ETDiHHBYOOKrq6uGsSZLtbe3l5VjKDdEyXg8zmv2+/0EBTYGGMAXL17kPDJG38vCC/tvA9tqtWI8Huf1Kc2lUgNjwtzbmBPoIsTMNY7aCsjWir29vTzQ0gG318PryFj29vby+rTl8vkQ9NVqFYPBIE5PTxMQMLbLy8tYr9dxfX3duL6vUxW1gnW2ddBHAqaTk5OYTqc5Jx6T5wkZ/dytElGMuRpG/sYQMe+uzmHMjKM6G+acsnyvlQMYA2j6hHxwuDkNR8lZRnbQOEw7/Ol0GqPRKOWWdWDLBvbEW2IcWLFVD1vkqkYO83YWjUomiB6Pl6DBAN4G2pUWljleo7laypkinDfzjL04OTmJo6Oj+O677+LNmzdpPz2XlgEH99imh4eHOD8/T2fqrVER20oNzmdxdoxxQIoxpl3VO5YtAxHILOTPiQeDMXwM73s+DSTtVD+kJz8VcirifRtE3z1HrLlJWT67WCw++CjeCp7rnBi8GJCgDw6gd/l9B0voC7KFTzSYsb4zdnTHZ8ABOq1jZLIdvNueeMy1UsjnrTn5xG8CcifF6kNDmPdq2z0v2E36XMfNdw1esa307fHxMfr9fq6xz/BYrVZJsODDq583YVy3cRn4VxKoBo3+qbaOe5i8MhFlGa7v+x7GjtVHVJl1f9H30WiU2VkTh/g1/HYNMj9Fs6zwP3gJ28lrrkSuc4ouWf9sC20TLV9O4DCPrgy0Ha7HOJgA5vv4GGNxDkffbDaZeK3EWK1SmM1mmeDAD4LpIWIcWGH7u91uwz4xjx6H/SxywdzY5+2S8Sr/nrcafFoHTk9P87gA+gkROpvN0obUIyaIAfb29jJJi10jSAafEEPZrlknLB8mNfmbaxjfVazqggD6UZOE4BDmhc+baAYvcw9kqQbPu+z/p2z00SSS40t0DRxIZbjjVOw182lZZ0z2cbZ7tIoFbY8jmnJbbaNttu0reAmiyUeqoLN1/V14YR/G2tVER0QkIQE2gzzF1uzCwcgkY3FC12MhWYrPdCWfSWknZiO2lfqQULyP7eOYGleH8l2vGXaI/uLvqaqtcZ512TpU48m6vvx2PGQczHhZG6+z/dauuNzzvstfstZVd6ts1Ri4EoSOoW3jKWTA1n1s3Pujnym1qzFQKyAKMpvNYjwex+XlZS5ERFNAnMl0gI/QOuiM2AZ5GFbOSNlsNrmX/be//W38/ve/b2ztIRjlWgi9DxdngdjrTiBWz2zhOpxlA9FkoSV7TJBNVs+KCmkH8YWT4zHGgOeHh4es7GIfeq/Xi9lslv/DVuJkUCj+ZrwIHkqGo2N8df84a8yYqOSggolHz6Mg7DMdDoexv78fg8EgFZ7PGlwgW/TDa+/gxATg4+NjnJycxNnZWT5hcbVaxenpaT5mOKJ5VhQK6NcxWq42szx7u8cuubcDqSTDl2jVMH0oEGTcBn4GYzZYAH2A7t3dXR7wH/H+U28ITDGgBuoOtmx8mXMIZqpiaraPteUsERtLAreahfU8oHtsU6NP6Ke3q5ER8LlxNbiyY6UfnldndpgrBxnWU65XSRyvYbfbjYeHhzg8PEyi+PHxMSsC7u7uYrFY5FPekHMCYP5mfFTZdLvdDATQAc7QMOHgNcUOYxMYNxkWGplwn5lkQA6hhwy4IsXnkTEeEzmW0RpM18B6F7nyuZt1MyLe0z9kC7tfbaQBlp+i5dJ4xrqLIDH4sD002Weba712M3CpVR22705keGwO+mj4TwdFlgfGvNlszzrYFXBie46OjnKL0XA4zKfusZ2vJsqYM2MQPmfCg34BxHbhH+wKeu5qEMsCwRJV1vy2DcEOUaXCb38Om0tAW5/UVMmPSohUv+g19/eqXvl9k1W2+bvsZg22KqjeJXO+PzbbW31dneTkJX7qczTWmPNCbEuRIXwf2Nc4jIoj6xN/Y7NNInk+XY0D1uV77p/11n7YSVe+g/6u1+tYLBZZnVHPFsXPQrwQxIJvGT9BNclZn/2zWCwyIQw+JrFCX7ybgPmpOMeEFLrxoXXib8+xsa/tKHblq6++iv39/bi5ucnt86y5cat3OvhJm5B5rDNy4qp1/JlJQftXbG6tOqExrzXwreO3PjF+46fNZpMVcBHRCESNEcEqjs2cgPa6sG6fstnngHkgMrj/fD5P+fQam5DwNWoc4QCeNWQObRv5nP/33/YdxgImTUw0Mv+2+fTXulBxquMby7/j8ip3LijB7yCfi8UiE6OVeATPudrI2IU5t10gjrXf82d5zYkpYmIIRjAtWMH+xAQXfePeTvRR0Yn9YAeFZQdZYb2RD+N+y3v1+ybiKwbkHughGNl2yrLD2LAv7idzw5xVH0zCn0IDy5KrUlutVn4m4tnegMG49hchpb6vecCemPv7+7i5uYlXr14l6IQl5XsR2ywiAlLJEDOLvmev18uSWbJMv//97+Obb76J2WyWi4qgWomp2Gi1Wo0KKspvEbr9/f3cO75eP1eAjUaj3Ffvx01bCfmuDQ/sM4c6YuQRIISM4LvX6+W+fYJGhM/sJJUnJpVYi8VikUAVY+E9wyYTqNbAiHQ6nWSdvVfZTy5bLBYJMJi3u7u76Pf7DfKr2+1mVZYNKwQTh+I7kDdAxqkuFou4ubmJ09PTBpFwd3cXJycneYCzs4rImYkjKxGvVbLOIO7g4CCBnh0U/fspBb0RTZ2spFoN/nE2Hjvzxuuch7BYLBpbx5hTgisHTC6LpbmCiTXAAKJDyBqZCwDvzc1NHoROX00M8T9BC+9tNs/ZmYuLi+j1ekniYlec2YzYlk1bhiAusWMGezhqOyUHchFbR814eQ/7UmUdm8Z8UraNowDM+8l5vV4vdXA+n8dgMGjMATq12WyJaLZg7O3tJZllEOB7ofsAKM5CwT7S94jtmXAOhJE17JjJJoMj7s2hzbvIKMbha/Iec0QzKPiSbZd9wOkDPiK2wNaAie8zBzzlthJNBsLWf+u1wQ2//beDsV0EAp9zlRR65EfN7yInDHAIhCsgw/d6LR0IOKAmUYNM8Rr2Ghlmu4y3ODFWxok9ZCzoCW293m7P2mVTvRYmZ7gf7yHXrtRqt9tZiVLJH7ZT4POpQjw5OcmA0HrAmLFLYBv7M8ZTbRSg3RU1lpt6jSoTlkPmtfrGihONHd0qSeVrUH1iu2tym6e2YdM+VXNA53FbB5GbSjZREYyccyYL5AsY0QEIerzLrzMPxrn0hXvu2kpIn5jXVquV1f0+Qw1ZYvsQsuYtMFQzQEwdHh7m2V6sDcTwfD5PAvXg4CAuLi7y6ArWzgG1D/l3n5l/+1hjGzfm0UQstsBVNCZTfA0qRsAhft1P3iXoBONHROP4h5ocQl6pHgOnQ2x5K2S18bbtrCnXsC01PtxFHpF8Ml44PT3N83Wwe8ydfb4fsFB9SNWVT924D9V4zA9kqgsLKsmJ/Y/YkjTgPJO4NJOX9leWM9vNug6+nvGZ/RH9NBFlneS7rF1ENJIc9gH0CZmuCQn67ofscHA9uBqbBVGLHahzgf/yVmCwjjGb49VdNs1zyFzYP2IvGVe1Bcyz17Tau1arlcfoEIfwGrbauMb+sPop3jdRS/+qDjvpzW/HRh/ytdwP+TTO9ryZoK2YZL1eN45AsG8nPkcG3H8+y28IxY9pn42Uimjuk7fDMzsbsSWYLEQu8fP5FCaUaiYIp0UWsdvtxrt37+I3v/lNZnXIjkZEGieCPQeUFi6AtsEZGWnKlskGR2wXDHKqZiD8RCLvLz84OMjycjJre3vbrTLc20FLv99P54xwzGazhuFFyCGnut1ukimAWEqDz8/P4927d2lgIK8A9PP5PEkkxmuDwvhYNztinC0EQsT2seE+DM9PIYQoY8yWLe6NoX3z5k20Wq3o9XpZ6TGbzeLly5exWCxiOp02gFwNkJBJ5KuCWoNMqtwWi0UDIHK9n0rbFbRXIxXRPIjRmSIHrMheBccEuLVaypU4BE4Aq1r54Tnz02hsPCMinQPEbESkznA95BRnA6lFn3u9XvT7/dyWdnx8nAGqgb1BO86CABLHhNMEADggtFOOiLQrdi4GyciV18g2zoebes0sozxaGlKLNaIylAwndoHqEduup6enzBhif3w/bCOO30S/s8PYUq7L3zh0bAh2ape8IXNUXyELnkN/xxVS1mv/7/n+KTV8INlx5IHEAvNOlRvfwVaTLEDGLMfMAa95niK2vroSobZplld/l4bu8DAOP20PvfAaWD4N7qwTyFLEFqDTGNPx8XEmk3hCpis8jB2oyHCFigMOgOeuINTgzMCVRuYYW+EAISJS/o13SAyZlCFoJUClD/hX2wDmhzkbjUbps7H37itrbEBpgOmKTdtlB7z87yCWvlegavmr9+C9iGj0ibU2UK8+Yldrt9sxmUzi7OwsiUKP4/7+Pp8y+rkafa7bVnjNW+xMCDjL7WSIk2RV/3iv3hvciJyYrKRPvOf1sR3mvCjOBIUw43p+QhsyjD46eOz1ejEcDuPo6ChOT0+TTAUf9nq9rJw6PT3NpDX+yhVmJlptp3YR7MiB37NeO5Cvc2AbsYtQbbe3D1V68+ZNziXBmXUY+z4YDDJxZJvL9YknwOrWcdtscL5tO/dyQszXZX2Mr+xHjf+wQcQOxELMs7Gy7ZqvZfzJ3Nvnf59eVxLrj23Mmc8JNXEGNnWcavtv+8b/TpLX2MQxLP87IWs7ahvH3Pu61ZaaTEGvrb+2o8ZALrQAY5vwYk1MmtIPZI/YznjX80J/2A3kpD2FCiYEI7aFGuy4gOiKiPdkNGK7fdk+yGQ/8+ddEcThdV0tgyaN+N8xhHkGfrsyyOQ48+YiD8cy1k1jIlex0tAj9Mu2zvrGj+2T581ysetv4yzWxbpp/13tLa/xnY/x17TPSkpFvJ95A0CTzYxoVj05KEOAI7aA0QwzABblB3BGbAPAd+/exWg0ysffRmyNTLvdbmQWaa1Wq5Fp9daj+/v73HL28PAQi8UilRsgTqWDCTi+b0CwXC7j7Owsz3I5PDxMJ3N2dpaEBwZktVpllslnSACC5/N5Vh7xXe5jo4XALBaLGA6HqQzedrNer6PX62VWzFl7DDlrQ3BK9RN/o7TL5TIuLi4y24WxYOsFwszaUjWGUfOByTgTDBrOm/n47rvvYn9/P/r9fpyenqas8GQU7rkrG0/Q7znASNm5opze4mljZ/b+SzeDAQI1EyUR0Qj8dxk4xob+2Ti3Wq0Elsgacrleb/dm8330vILtiKaDxgbULF273U7d8xlFrmDCmUwmk4ZzPj8/z7PXOI8C+UJ/0UV0rdvtxunpaQOEYGcAmIBSssX0o5JbNMsSwQfOntddMWRgjm2pVQ/tdjvPhGu32wl63759m0RBt9vNa67X6wS7dSsRtnaz2VZNEcgB5LCNjB99hhDALjvrz7rYpgJuXClnvWT+0EU78Iit7bGDZb79OX/vY53l52gVnEY0A3LbXXQT8AbQW61Wea6J9cAZWsuWAVjdnmI5xYbX4NegyiCJILeSIfbVyDWfw+96HQ2yDYbpu4Mb2yv3m++bdKHvnOV4fn6en41oblUw2POcGSsYi/C/32OsrnJBl12Jib00MMXWUKnAePG99b6sOfP38PDQOGvGmKmW/u+SRfyt7Ss2x37bAZXnEvmrARK/d+lhrWLZpae79IXXW61W4rx+v9+oqrVdqJn3H7O5f8iRAzsHt3wOvFFtPvYTzOP58RpZDrxWEdHQJ3wN37MMQFLYNkdEVr7zZGpv0/Ih5mwZdjICAgNyhYQQeN1kihM3yL+Jc+t8Pd+N+ahzUQNV262ILQGFXFRCbxfZwGvIL/1k+zwJLj5nHcevcrQHDzai0gzyj34zTifhIfgdpNZqMa+xx/n0tD3Sw4RKxYP2PyZBmRfkixjHzaQd5EIlrGsg/n3tx9JV5onqfYoJ2DXiB1d5vF5/V0yB2fjb8lN9lP82tmHu7GNNYPIdmuNHfGMlpGxbuI7jUPR7VyxszOqqWO924DVXaxIHmZRkrpyoIaamWMNJbo+V8Zj8qHEZ88S8LxaL9wgl2wDknWva/37IzrhyH1/M9RzjVPvL37bDJs4tT9WfMXe+lj/nOajxK/23H/F75iEc11mu4AWIw3nffox7eE4tdyaxP6Z9dlIqYqtMdB7jC5sKKeHBma1HcMmgoAAGWJAobFfhMebX19eNskycAoLJ49MROgOs/f39fAoDnyWQhehhUeyEW63nMmQW2UYCh+vqL67Htgf25aLwDuo3m01Mp9P42c9+FsvlMiaTSfT7/TxLinNx9vb2siqC+TKB4gDPgocwmZQwwGE9VqvnpylgDCKa5zaw7QDB5SBIyEjGDxEQsQWPKD+H593f36cDRDl5zcoCQPj222/j5OQk/uZv/iYGg0Hc3NzE+fl5TKfTxvYWK7DnxgrsdcM4O8j2vPA68sp1vmSrhskNmTABZ8eEkYdsqkDGwS3VNVTf2Km4Mi6iWVbPmjqDwg86hn1AByeTSVbyRGyDMj+9wwElW2k5nw3ZMUlKNSAkT6/Xi9PT0/yMt1FB3lW5dyWEDwaP2O43Rzcd3PIdb6e1c3SlJGvKnJD1QvZZS0Ay97i7u8tD25nn6+vrBGhUj0AAU23lc/HQV8AmW5+8rXA8HjeqWVkDB7QmWgymatAMMHe1A46edTYBxdxYrmjVgf+UmoN1qlwYh+1NRDSA8Hq9Th9qf2PCwP40It4DGZXYiNh91oev68DM37EPdRm/yQsnHyzPyBSksAOWaket1/hdiBLmCBCLPpv8rOdIubKBZvvE/UwOOMtc7aGBrnXdPpx+07AdyD+YhIoU9NuYoxJmDmw855YZ9IXqx11Pj60BbV1n+mIfaXm17FT9dp/rehLA1kDJ/drlx7gG9+JIhHqPiO1jrz9VM1lS8Z/7v4vsjWhuMQO3kZCI2AatDoRNHlR/TibfZL9lB6KE/llfSW76CdXe5oSdR/dYP0gSHtRDEOox2vdh1zkqAvtv3EClQ8T28fTgZtt7H3/BPNSEFXNmOUSm7U92VWlYDlkD3qcKjLk6OjpKUo6+kBQzVuZhLjwRnHVm7FQ9eosU/zN+ZNoytku+nOQHz/E964x1cH9/P2OJSmrjn2twXu1DlfdKAHyoWU7+lIb8ITMPDw9ZXMC5wzTrreMC4zLLp/Es79kfOA5i7HWN7JNtpz1f1YebjOIz6LfntNoM94HrVfm2X3QSmYbM0W/IPsbO05T5vrG99cb3rglqxgZxy33RA/yq3zMhtl5vt5xzvA665ISByTT7LGydz06i6MM6gH12PO1qd8dW9d5cxxVS2GDmwLpkf+i5tBxUn2Mc40SRsaHjfa4xmUwy+c73OffOVfSee2PVj8XZX6RSKqL5lBDIlNPT04jYAmILh4UYJw7g4H8AHqwnT6ojYBqNRjGZTBpb1VxdULOTrVYrM88A/clkksyhF569/gQDzkivVqvo9Xq5oFQQWCi8lY3X2U/vw5UxoCcnJ8k0t9vtuL29jc1mE5eXl+kwCCoZQ6/Xi4iIyWQSg8EggcT+/n7Oy/39fWavIiLBBO8jWIA5jNFq9fz0o+l0mk/U4zUqWQ4ODpI8u7m5aZBvKCLBgSvT6gHMPEmxZuZQSGdx6Me3334bl5eX8W//7b9NhzQYDGI6nTYqQwxyDTbsNDFCGF8qFUxE2AEjvyjnl27V6DuorOAAPePzANHqMHHCNr4ALLL7ELAYKuuZq45MdEVEZrEMtpFtSGI7UQfDkK3tdju39HBtHMzJyUnqWr/fz74i1xCm/X4/twb6/DvPQ8T7B8c6KNwV9OPwbH+QdV/TFVV2Rs7wWIe4NnNxcHAQZ2dncXNzE5vNJs9qg8zATkAsMU/1rLTpdBqPj49pJwiSI7YH3zrDho3hfVdH2VkCGFi3iGiAG5w9oOPk5KQRqFhXK2lRCU6DxJ9Sq44bXWBO0UdeNzhGN2ezWcxms50EgsFGDVR2ZaHRbWcceX0XAVPlzltqkE8CSF5HfyB27OcczNfgEbvKNZ1JpYLI/sRgFRnEDvEasm4Cqc6T58/+hmCszh12MSIaWy2xZRVc8jnbKoho7KBBH9fw0QAV6EdE+kpsAcExyS8+7605DkprBtR+kmwo/9c+1sy9189ybrDsDK9tZ/VXtv1eI3wy+IP7mOTkHMRP1eq6WgeRWZMeNK+byQB0yrJB2xXccC1wENez72WdfG8+B9FkgpgtP/bFNSl3fHycZNHx8XESLegYlSj0GTtvXMU9IGT9tECCW8aJfakkkWXFWGCXXtdzbSyrtndVhjebTaM63HILPmBstt30G//PXHMPYgZwNNVnJvSMw0ggYbfQL/tmrs3cG2NYNmxzsY0RzYoP7Plms2nYV1elOkHG+tTdJn+ID/7YwPaHmkmW8Xic21Hxocwtc2AZd6WKcR/zYKyDjvh6vqZjFY9xlwxbLiOaZJ8JMN/f29T8OTAsONdnQtGQLQoH+HtXnIBvcCUvPo9rgmGNDcF0nmvLp22afa4TrsYFxAUeo+fbhQSr1Sp3GaBnXh+fvYxfxH6ZhLd/BjfXOXRitfosE8J8Hn/uBEE946yu1S7caztnv4NOW689dl6LaG7d5ygd7AzYnrU1abwrUfcx7YtUSkW8v53AgMaBMJNmkOtFxShWlv3k5CS3jiE8VOe49B9ltHFEkDudTp6BMZvNGmW4Blv87O3tZaaahbTgr9frfBKcs6woD46FiqP7+/s4OjqK8/PzJLxsIF0e3el08hA2nCvb8XyC/tPTU1YbIUj0jyDTikIAcXx8nMoOcGdLIIYAsBIRSSa5pJfy2MFgkO/7/pALAPLBYJBb9xzYtNvtPAPo3bt375VqOlOBQtzf38dvf/vb+LM/+7PodJ5LjS8vL+P6+jrm83mDNMJgOdhjfQzMTXowN/1+/72Dtu3kfyrNOlQrjGzIcGQYTQd+6Ii3i/EagBT9MzmD3JtA4TMRkU+ZsUE2GGA7CrqJDngM9JfrIi8E7YyRvkM8ueoGwhUgiJxz9hT6g3w7qxuxfbomJJeDAeapZtUimk/raLW253BUUoBxVvuB7QHc008cLTYGPeZsjouLizg+Po63b98mmGS+vZ0DfY2ImE6n8fT0lOTj09NTktLMB5kl5MngjvH68Osqp1SmAipqAOQKP16jmcCwU3RAAbjxvH6ptgvQRLy/3dBgjHGwnuPxeOfnHFhZv5EzExwmHBzgGBBvNtuKRpNW+HBXLdsPAJSRKXwHABW/YRvsvtMMen0+W6fTybnAztRAzSAeGZ/P59HtdtMuRETjqbO2kxUA2ncwZ9hG2zHsFLriPlSw6nM0IrYE29PTU86RCXgTk9Z/X2+9fj7bEl9fiW9sJOA1onnuJ8CTecT/fUh++YxlqsoyvoG54fU6d6yt54lWSQPWZLPZ5HmDBwcHuYXb9p9qlU/ZDPRtp6rdQV9M1lr+TO6iG8ijCZUawCKvBGV1G3WVPT7vbfG21/gUcDu6hb0nmcraci8Hku4TiQ0HYyZV8G/0qdvt5nZUJ0LsF5lDdKPKnW0e42Ku+I7xtm2qCVf0wQnlKttv375N+et2u7n9vdV6PqTcZ0HRL8h2Kqwspz5jFVny8SZgJHAQhKZtPfdnnCav7Ru5PutM38Bp6/Xzg5k4o5XXHA9Yn+kD8uB4yrryKRtyP5/PYzwe5xZuE/KuUjMRgPw6RmXtkAuuYf9sm8rcWw6Rd/te5sO+3iQN94+IXGPsNH11Vb5JD36js06mmrAkHjbJz/yZVEPWIppbEbmWCy/sq42XjR8Yu+Mw+uZ7I/eWWxMiJkDRTR9Ib78JdkDnrQfck/vXp/A5PmEtbcdtl7kec8dPja88h/AVzAtzYX1i7ivx7vvRdmHeXT4JGTK5GvEczzNnHAXC+8yFCat6/w+1z05KGWhXRWNC/KQOvmPQhTNk0SsQ2dvbno7PQkHMcJAvQZABtSeSLT2uwqmOP2IL5FhIH5IOGAcM7u/vx3Q6zetgROgzQS9zcnh4mBmI4XDYOAQSsBfRPFzMCkRllg0/hsGOCKOBovkpfhgRgxJAHsbO5YxHR0eNJ94ASAHH9/f3uTY1u824Ueo3b95ksAxTz/gIONiSxPpa+BkzfV0sFjEej+Pi4iIrtwA2nhvuQX+Q1drscCO2pcysA/PDtX4KrQJPB6cR758PQ+N9qsSQcfTFW9iQJ+uVKxYjtgGWjT2GHSfIHEKkUjbO9gHeg+DEuUKYmpByUBqxPRSSSkJINJzSev38ZD8qBCG5Z7NZ7O/vx8XFRY6Tz0dst7cwJmeCbMMimtvubMi5jh20txQ5OEc3mTNsm0Eq5Np6vc5zK6bTaZyfnze2OAJeqRbjPWxQt9vNOZpOp/mI7oeHh6y+xBbz48NorYvYQwBDv9+P+/v7xpl8zNEux0tAzn2YO2SIuXPWms85+LBO/BTarqAesIKdgtzwWSztdjvu7+/j9va28YRUj9HzGrGt4PQccl8DJK7hQMx+xkDUFZEeB/IIuGHbJzIGSGSLqLPt3MP+HF9lOUG3TPhUfbLNYltkRKTNsC8zkKOPBpyQGgbCNeG0i8hD/rAZ6Djza6IPf/T09JRJKcaHPjpjC9lQ7S9BPdd7fHzMc32QqYjtg05I1nn+dxFBbjWx4/vV4BdyBJ9pmxbRTEZ6ff2//VYFvA66J5NJbsGuBDT9+FTN/aAtl8tcQ3CiA1POAjQBR78hbzyOqocfCkKMtfm/BsDMCWtHZpx+OkjDVyJ7VM+CO7mWAzZwoOUAfXcFEFv8jo6O4vr6Ov05ASPzg43wWVvYRNt55Iy+GC/zGfyRg3WTTSalGI9l0HJOPw4PD+Ps7Cw2m+ftL1Q9mag4Pz+PdrudxwkQm5DgZt7QTZ/hSDOBx3voPHaBRFQl62jMiWXR9p7+kmhjLfl710NSjo6OYjqdNmIN39fVKdaXT93ATVdXVw1yrZJp9hmWAWTQyUbLTE2K2zbbpyI/1lX7Vn7qHJm44T41YW4Ckrnns8gs9t/+1fFPRNNvsf678K71g/8d4xLfMiYTpsZ4jmOxK5vNprFt2XNj0rDOo8eCreEePCkcfY7YEu703YSXiTJ+IwfoqXfR+D1wEWNkzMw96+PYwGPg/jzZ03NecZxf97p4zmj2I7ZrFVf7deYF3EGBAEeeGFPwHUi/j2mfnZSyoETEe0YwYptRNGHhxiJCYPF9AqAaZJuc4XtmSiO21QkPDw9xe3sbt7e3+cQunC99ptTPmR/6acKHbWeMyecz0QfujdJT4szTsebzeYPFR8EhvPx43F6vlwJ/dHTUINNwGmTRCeZxCmSs7YwithkZP9Ldj3alnZyc5Gcg6ggcmS/3lfvj8P1kCm+nA1Df3d3FxcVFIxPAml9cXMTj42NMJpPGwY/M/WbzzF5TfeYybQgyjJOzEozP5BLzyThxwPQd4OjqLV63AfhSDV3x74htNh5DG/H+9j3rpPXXMoZesY0VkGqw574A0PmfgJUSf9YRh0a1Hdtoac78ItNHR0fR7XZzi6WzxARk6Mp8Po/r6+sYjUYNYpJAGoDJekOKsU3B1Vo4HBPkDkAMEAgqDSAMbAmy7aRNrJmQwz6gy3yO0nSIuE7n+THO3W43Tk5O4ubmJq/Xbrfj1atX8fj4GFdXV5nFXq+fKyz89B9sE9t/TN5BimATfPYUoKBuWUT+dv1dAy0DCQPs6lT5rgkq5r1mq6wfP4WG7XTwbF9TwR02EHu3K7Po8e4KZg1adpEQyLRl1Wvzoe8DXgzurN/48lpe735bL9AjAq2IbWUi/sy2DLnD1tTKKYJbg3aXqXOdiOYTTE34OJh1dccuLOO/66HtPpvDpA0/EAV8hnvaP2EX6AdYxAQC88uBzB6rz5/0fSoZh4x5+wfrYzl2YIdsOKi33tl/mwTwvNUAzdeuDaw0GAzeSy6t1+uYzWY7v/djtQrySYDYv7riAFlkPo3fvFXaJES1BVzL84pNJjhD3izzbNPDX1WdNOGxv7+f5yahLyRO6SPHLDhZgI7ix0ejUT59D3LGtno0GiVu7Xa7sdlscs3QFcs/pEkNynf5A+TP/jhii3VMLvO6ty9W0tTBOPJ4eHgYg8Eg8Qpb5ZHLyWQSnU4n5wq7g44SuDNOjvNgHcEBtpPIkXHAbDaLXq8XJycnWXVlGWN+vP0fOfC8EQ8Rixi/sWbo7HK5TCIRjGDi3UTI5/S70+k0fvvb3zbsJTJiTGXbbyyP7XScadIpYnv+lW244wCvVbWLDuYroQAuwI+CPY0xWQ+Tr96Z4/UFmxsHOGbhOsb89BU94D3j8P39/dydg4y6GorrPD01z/VjLZz4Nk7x3NEf5or+4J9s3xyPsTvGVVTeloedwldGbM+uc7LM/YKgt++CbDfBay7A5D5zil03ZuAe/X4/np6eMnnrwhQnhZABzwP3sx3ku1WWHefaH2G3+Z59x8PDQ8xms4z/2RaPDDhR+X3tix10jrGK2DL0zjhYESO2ZWp2wCi7HQKv2VmvVqvcsnN8fNw4G8mZgLu7u3j37l06QDPLJiZ8JoGzpxhnDG4t49u1bx4gOBgMsoyX70CijEajRoBiAw7p5kAOJaDiwaQTAXhEvFcy72oWC6G3B1TSptPp5Hae4XAYt7e3abTv7u7S8VCRRMWLFQTQQP9RJB/o6mwnpYL+3t7eXpydneX2LIAQFVYRkaDJjpwzdiDw7CABUjYw9BFwx/o5W93tdmM8Hr/HQNsJfalmvbIs2UmYnGO8jMVGHsOJQcL4AFBNsjJ+iEmTW/4N4GLLlrP99/f3MZ1OYzweN4IgwJ3lFQBvecLRoI88Gefbb7+NyWQSEc/kapUdACTOCmAOKIyIJFvb7e02RRvziC0xvV6/nw2nugUCD8eAgXemgTlGVlkXssVv377Nvjozxdp1Os/bkkejUfziF79oBDfcY39/P4bDYb6G7Ft/sD0+0wt957BQ5sj6PJvNGo7fVY7IjR0u/QYsj0ajlCmDmV16ZYBl2UfGq2586WZAaT+GjTF5zHgAp1T14Vcc4DpQrcEqc1GDZ+5jOTVArAFQJZAimkFNxJYMcsayAuXq+7kHcofeR0SekYRPMDAH7HkbGBlS7IZ9MXrtAMLbRg3GHag7eIDcAYBDDNX1Wy6X6R8dtIAv6D+voUvGIjTmxFle5B19xfZ4/bjXeDzOBx6gk3t7e41sqGWu+kfbYeMpE4MVt+wil/jt6zhQquSCr+Nr2LfRb8bCGE36ueLkU7Sqa5U8A2MQ0Jkojdge1M/4KzHtpOkusp3P+JqMHSzrBAcybx1gDNgnEjok9cC/DvAJnhgPyRH0zE/vY0vb/v5+nJ2dJTk7mUyyQohEF3PFuhIMYQPsq9G9iGjok5OWNMsb/ohgyvgPm1txuHWVz2NbT05O4uuvv46rq6uGzYnYPjSHSn52E4Dhqcam31wXPO3qKw6j9w4H5mG1WuXREsQubBs0Acx1W61WBqD2N9XOO35j7o0ZuXeN0zxfzKu/9ynbb37zmwbx6GQ/Y0CeseHIiOM6+85KLDF2Xvc2uojmE9j8Hf6v5LIJCp8bxVw66WnZrkRVxJbIso03eYE/N9lbyS7bdEgdrm+Cxv7W84keMCb6BdbD9ngstvG2gb4O/Xd/LXPGN/7bVbPWB8dGxMcmZn3diK2NN7Hja7nSDJ3G5/O+SS/GYP9snaq41na6xpzgkBqbuvjAnIdjGMdxJuW5BnpDPMb60b+P9bNfhJTyhBswV2UwoPaCmZlEafi+P0cGkoPsMIA4gYhnhZ3NZjGdTnNLSj3LIKL5pAJnS5h8AiQf/Es5rq+DI4VJZow4V4OzTqcTg8EgBoNBnJ6eNrI/7uNsNmtsiXNGLGIbKAI4AMs2rHYQrANbGRBYQCwKC8hBQQigbai4Hk/Yo7IEgBERjaes+HvOBDw+PsZ8Po+rq6s4PT2NFy9eNMrEWftOp5MAGxCOUeBcLPbaVxmyEUA2uH+VWTsj1h85xWkwx9Vh/VQaY/6QMzTQZA4BRdaF8/PzRhkzc4IeEFBV0IwRRG4ODw/zHDjrwuPjY+rnfD5PkIwuuXKAJ04iG1zHhPZm8/zESp42hw6v1+u4vb1NHfLBopvNM2nd7/ezOsPO9vHxMbcDQdBFNA/7MxmLDXFWhLlx9jdi+2RJ5t6VhNU+QZJFbCvRmP/1ep2Hg6/X65hOp/HNN9/E/v5+/PKXv8zXXIXB3FGtGBG5vYC5sWPlPKn1ep32b7PZvJclnk6n2Ufr0WazaWw/o8oNGwJptVqtUlYqeKsEcM0eGUT+lPQxouknKrlpW83ZRwa8ZP9MCkTEe0SLbaXvuasPNQnkdbJ8OBA2EK9kg+fcugN5YuKp9qMS5R77dDrN4A3dZJ0BRk5gObsPUUXf0DtvN/TWSfpegSv2zT7RREoNeJlHz3ndkowNc7beuuC5YN3RSQf9DipMYEVEHlHA9lsqzsEPVT6QMScNHAw5SOV/A3TPV5UPA3het37X4IpmebEc8338hoMsvmfS9FM1Bxcmiu7u7uL09LSRlPWcObvcbm/PbeT71aeaWHCA6LPR8D/4EjCRs+o07IgDHw4tJwAhuIdkIbkwn88zYUEiYj6f5xObqPrhN0lU8D94AEzOma4EdVRE0U+2zjhY9tzZPzBnjNcyYNtmssVBu68bsa2GMQZijbBL2JzxeJx4wXpie8FWv3a7nVvkHfRDjtDHfr+fPhq9t83jc07yIkv0vyaLSTJ6Pq2vJKSQa55ExrywVq6O8twxdtaj6u+nbBAj2CITacwt+MmkRUQ08IXnljFVksDbRpmTXa3Gw5VciogGkWhihbU2Ro9okqvYBghQV/JCINm/bTbb5JH7A0ZF/vyefQFz22q18pgV9I8xMD/0m9dMbGPjwIWWQZp12XbTMZxjYB+b48pH65e5COSaecWXmMzxGnptmBfGT6sJPNsnksn4cid40DXsLK36QGTZiRjbS+MsbDmvIRP03/MHtnEi24Qo17m9vc2Eiqtff6h90YPOaV4Mk0wIOqXCzgBHNI2DM8g87r1uGdhsNumgyS6Mx+N0Eggz93cgHrHNWNWS5r295yfUHR0dxWAwyGoAFst9MxDjmoeHh3F+fh6z2SwfuRuxPfC53+8nCOTMl4jIMQAGOMCUx9sjgIBPHCvvWwAraF6tVvlkrqen57Obrq+vcy4JDhkP2az1eh3j8TharVZup8OIuepjOBxm/2kEJKwRYNlBVqfTyfU6OzuL09PTRkaauR0MBgmMGDdPmaCawGDDjLCdk0GGAwgHI8guGfXNZhNnZ2cJ9HAgnzoj+4e0SkSZaKNhrDDMJnyd9bejdXYRwgjyICIaTgfyB+CEw8HQAj7n83m8fv069aHV2lYccf9+v58B6dPTUxIZ3BPnc319nQAZOTBo4PPMCTqHjlGKD7kCuMZw1y26tmfOUjuIwGlAxDPPdoZ8n6DRxACv89re3l7ahV6v18h8AD7Zmz6ZTOLo6Chubm5iOBw2tj8zv8wTugeh7krXk5OTODg4yCpMxsZWARNkR0dHcXFxEbe3t40KRZw8djui+eQpsknYEW9tcLWVAw4DRmyVg1vrwk+BpHISw8G4wQ92ZjAYZH85sNV2GFtWM9WeE89FlclKNNEv20N/x/4Dn1z9p7PMEVvih4wt/TEw9XXRNxp2h+1pVEu4uoNkjYNE5AfAzOuDwSD29/ezPL7eE32rANFJCohwg07mCwIc/WbNDfwrUWVyjcAIQiEiGtXHXmMC+3Z7m0zyuuOTOFOPylGTjB63CRX01PdERk22mdQ0MeMgic/UDG6dB/eBVoG4A0Lb8Nlslg94qPJaA78fs+0K6JHtzWaTB167Dw6WkTO+b9tf+20yhL+pGiZ44P7gvvl8nljVPto4GJlBzipeYt3cL/B2RCSujYjcms9W74uLi2i1Wg1Sut/vp0yQYJpOp7n9kqDNQa6rXMCtxmZVhpgvywmvWZctb9X2+ZpOILiCgcZn69mYbHesthqZhTTkgUck2h10e959tIjHjc1iTVutVtzd3eVn/WRi+2HOlyUpxnqD6ek/azuZTBqJTNt16yMEov2H7d2nbsybiTrrHDESn8X+0jeTnzXZAfaoySBv0aprYrlkfSoW6XQ6mdy3TKKTYGInDR3fcc96JqvXxj74+7DULiLRNo55NUa23/Z1GRPyan0waez1YlzIqYkff8cYBbn0eEn6IM/MKTsvfP4a8Sn+3gkj44tq66uMmyhknSK2GAa8wjwTM3tuLB++p/XI/tkxgP0x81qTgdZVxsFnja1MNDlWZn5ms1luQ3Ys833ti5BSLJKZVw+WwMmOwRONQNtAIKCcj2BHhaNwRmI8HmeFlAUwIhpECBNMwMm9IiIrchycOSBgjFTsEDQ700OAyDh8ZsD19XUGXATkXGdvby8rpAySGSeOjy159/f3ja1BOHUMsskAO0fWBoMAIeXyyojIYBxg4y0/jAdHhIH2OVJPT0+Z6ec8LAgBO0Lm10/6oCKDPvL5k5OTaLfbSUIQ9Nftl4wLp2TywI7SSs5ryBifh0hgbWvm96cQ+NKqbjkgRabMfjMHGEpXBEAmorfeT062FD1BPll7wJTfB5jf3NzEzc1N6h4VcBGRWwCOj4/zPLXpdJrXtHzN5/O4vb3N8ngMPesCyULQCHGNo1mv13nmw+3tbZyfnzcyi8gWcuYA0HoFoHh6ej5nKWK7p977zJ+eto9vt5EHALH9h6otACCySx8IsCn/R/Yh7Ql0X79+HTc3N3F4eBgvX77MTAngxUEUfTJoi4gEbX46CzYZu4HNPjw8zLNIkD3sQk0G2AbZUXr7M3LItZBvdM3Bhgkp5M3g6ks2SCUTIZwfErH1f/yNTR+Pxw3QW4kA5s8EqbcP0Jh35gn5j2hWTrFGgCUDImTYIMrrCWHN+SkOaumD+2owg5wATll79IIqWIgWxhqxPXOKcfOUMMuZtxZYXhgD4zXoZ6w188tYnZ2sJJ6Df/sF5sjkg0ElvrfdbucB7egmJDL+kbmhWtxrvbe3lxXE3obrCshKTtofsyY0A9tKIjnRYxmqCQ6TU/YRTjrt8qF+zeCacdhv0Z+ILRH8KRvjMHHB2uMfwJLeEv74+JhV8AR4DjCwgcijdY/r4bf5Hr5lMpnkWYPcn6AWcoeKBubIa+nkDX7e9oCDr2lgu263m+QM1TVPT0/x+vXrODo6SlxL/w8PD3NbPfJDVRD412fLouOOM1gDy2YNfC1zFRdZB5nLigW5npPX/O9tSWdnZ40qyNXq+bzTdrudZz7t7e3lE7LBJLzO/Jv8IwHYaj1vZe52u0n00VewSSVAnAAyQc8c4X9cBMAP+rRarZL8NCauhIsTjthFX/dzNWTW/bWtA6OydvwQjyAvJjJMetinfYhkM04xJjGxELHVNY534TWuC8nnBLzjmBr7Ok50vGaCwbgBzPX0tD18f7lc5rnHNJNyjAFZAjvU5D8JIpN5+PYaM3kXjv2C147P+cBxx7OQ9OBg+ug18jZqxu41RVepZmJnBNfHl9lOYxeNqyo+c19d8GEfyRp+aJ2M0ZAl20bmFDlgLfELxnJ8jmv5LGlzNhFbHTZhxdgjIq6urjJh/UPti5BSu0CvAzw+U0G2lapun2m32xmcMmlmSzHK4/E4A10eD8w9/OhbK2zEdovRYDDIgA5BMehGYHz+E8aNwNbkENlZnAlCslgs4quvvsqxogAsfrvdjvPz8xwDignwdAUCBBXXdymgK75MvJj4s4H05w1IIAtYD6qZAB9cZz6f5yGLgCqqZQBYLk1lbQAo9A/A9rvf/S4Gg0F89dVXjfLmiEgizpVUMLYcWhexraRhPSvTzmsoqo29QYkBwHq9jl6vF4+Pj8nE+xpfutVAkn7ZONvxIefOEGGA0JmI7dMrHPxxDzt5+tDpdBL8uoR4s9nEmzdv4rvvvsvAlbVABumfASFr5a2rbP0jY2vSLKL5SNmIZxLAgRoOhbPTbDMMPiG17ZhcAWSiFtnBCdu42345M+IqBwfBdW385CwDfwjq1WqVQHO5XMbvfve7LMUnuD07O0vwTxCBbB8fH8doNIrFYpHbnpB9iDaf8cU5YZY77DVl5AbX2EYHqMiNq1wINGvywgG0CQVkmn7wHt//KbSrq6tYr9cxGAziV7/6VYzH44jYOn2Te6z71dVVbseI2IJY/jZQto5Y/+sWg4im3+V/5smgifvZVxgs1eovZJQHiXB/fAlrZMLbdhb7X5+UZ5CLbtJPE9SunCTxwb19wC2ElQMsfrs6EnnE3/J5/Cv/sxberlDnrAb7JmwiIvWQ63krOkEvldXoZUTkQw3wfwBVbJUPQ6cvtom2s6yjx8b6WQ5q4I/8OHAyzkIumcNKoBmP2X8Zg/m+lsPNZpPb1FyN+fDwEKPR6MMK+QkaeLdWGngc2F5IF1e3giPtJzxu5oHEH3MEPl0sFrlNm+9BcBq/4jtYa3xMRPOsOQgpV1xBCh8dHcXJyUleywnciN1nhb19+zY6nU58/fXXWcHT6/XycHNkbD6f57wgOyRSHMCaCLauuSKt+g+TE+gh/Yawts9wIpP+OdiF5OD/s7OzePv2be4kOD4+jpOTk+j1enF6eprnS9FYGzCzSQSPC90hViEmoHoGP2A9Xq/XSWBZdjw/rsBizSuRxHcgGbkPdg6ywLJa7eMuTPopmm00/akkhOMZE1jYdc7J3UXGRGy3Z9l/VoLKrzFeyy1zApnieAtCZD6fZ9VbRHOHQ60soiLGZI7vHdHcCucYljkiXjOpyD2Q+9vb2+wL13RyC7mwjhCn2haAqW1ruA4HaXMNsIZJNCdl+N79/X0+eGez2WSyGVnkwT32fcaPtpfVF/J9vldJI+sLfTcGqzgHH45N82Hr7CYyEeS5xWaYTLVPt00z6WbcjbxZb9BbcAx2w0kPJ0k+dAzAh9oX276HsLTb7WTnzQb6qUMR20CPRSQDhDD2+/04PT3NxYOYYYIAH2zfoQ9mCxEsB5QIwnA4jNPT04jY7ifFAQDm6AvKgRBERD7Nj4bBXi6XMRwOc/sfW99evXoVp6en8fS0fVoLZzExnuFwGD//+c/j6uqqsZ1oOBzG3d1dw5EcHh5mZoktcwAeSqshUCAKHh4eGo+ppfzdQrdcLhN0EPST7ScgJaBst9u5JY/1xHmSpTUxhvPF6Duzb4d2c3MTm80mzs/Pc65YR+77s5/9rBEMRGwPnsZZR2wfa22D5P+dZYVMcJUVQT99Jmvme/wUmp3nrn4hzzZyzpYi/zWgB/xDMrmCDZl3STPnU9Tg5ObmJt68edOoksBRIndUDEY8n9PAQal2GNPpNG5ubhIMQcZgG5CxTqeTT3EkaKUKi0CXMQO27WSYH9snyDHLUnUeBin0h/m2I4aAJyvGfLhigXtgB+x0/BnOo+F8guPj45jNZhlcv3nzJu7v7/Ogc/ed6kMABIFxRCRIQtatN1T1sD2Q+5+dncX9/X3jaUrYOYNi/AW2IyJyTZ2dZ42qzlWihfnjvep8/9j2ofMiPraRVNnf34/pdNogkACFEPmAh8Vi0QBvDjgcADhojdhWXSFzleyK2NqGagsitiST78PrlQRzxRRnj0EAGXg52GNtDXYJ3rg+AZO3gmCX+BzbuCMicQOYgzkzUe2AlLHX7Cn63ev1MrAxMVWrKaqNdGDLvBp3MF77TvpEcOr+YPPw5/hB7BtjRHZIlHjLHeNwvxxgYYtsp1nTmnm37Hg9P0Sg0D9AtPXVslvJBV+fv01ict9W6/k4Ac62mE6nOY8fC5b/lOZxOJiJaD5JlP9NPjow5Bqr1apR5c04ubYr8HmPcfPQHwco4KlOp5NPzGVd6nzati+Xy9wCCKm7Wq3yqbebzSb70ev1YjQaJQnih3scHx/n2YkRz1j09va2kdjs9/sp26vVKs+jQqepKMLWYN9MvliuTEjZz+A3eL3aOJMFXK8mj6zX4BxIAf4fDAaZvDk4OIh+v592iTnh3uiEcShbbcE1JycnqZ8m0vr9fgwGg8TwvF8D/s1mk2ev7e/vZyWzA+CaMGB+ncBlPtrtbaLWOJ7XPLf2xZ+jWY9MRjrRZYLE80k/d/layxLzxM6YiGa1sZuTPNVnm+ikD8y/t98aCzq5a7LAcsprlQAzLsWnuMLORA/+w8cn8Hn6gK/BT5Pcte56nFQIMq/0yclLY1rGQkyAzQArWvdNhDE37Lhg/sCz4HHsLOfyHRwcNJ5+x/19XAv9ND617qCn6B56Y0zlsUe8f46VY6/qL319y/uHYj5k3uSafZKLdRzXmZg0FrCPqLrzQ+2LHXROp+1ULWQMkkXCIUOCOLPLY2kBe63W81MjEITRaBQ3Nzcxn8+zbA9yxcGPBaDdbsdgMMgyYrbBuLQa4YYx50Bm7h0R+fQRxkCJq596QiCHsz08PMxsEUEgB9uenZ2lYmCU6CPzgoATZJKNoU8IN4DVwBRFRVk4hJbqCgSO9Tg9PW0ES/xwELkBZkRkpRkOC4OBgwNs4zCtmGbJTWBuNps8tPrFixeNg64fHx+zJJotWZBvVKxVRtyG2AbQmYiI5tkMNu4EFDjzzWaTBzv/FJoDDuuYQRq6iAEy0DKAI7Bh7SA6NptNnjPE+U7Mkbe3IbO8TuXOb3/728wCuFoC/b+8vGwYQJwPIO/4+Dgmk0nc3t42DCJ6y9ltJg+Hw2Hs7e3FaDSK2WyWFVxs+Ts5OYnBYJBybcdjkt3zYdAasbVxOAGTRXy22kG+zzoxT76nA/+a/UWWIXYZ9/39ffT7/firv/qr+N3vfhfX19fpIK+uruK7776LV69e5ZpjL6l0vLy8TGLKWTjsKf2aTCZxd3cXFxcX8fLlywbI29vbi5cvX+a1HfBDngNUIOQgMQzQnXGzjFfHzG8AtMfmz/yx7Q9xvruaz5hptVq5BYZ+YZeZA6peapWF/ZmrVxy0VztmcsDgyHPi7zB3lmN/1lt4IZ3u7+8T0KEnBjQ1eGe8PE0TcthkVAWwyAZ+1VXJEduDmJ3oMEmCXGLz8f+MHTKcZI4Ds5p95NqsIbbKlZ/MoQ+VtT/xeRsAZm879Lzh8wj46Rtjx6+6qhhdRI5YX95DP+wbTR4yXsuC8YZljf5aL+0XahWX1+VDumlgbh23vEZsz13j6Z1gFhNpn6K5LwB1AnX8JHqAHyMAo7GGu4IWZJS1trxCcD08PMRkMkkcUm0FBCakJcSBt96wjrYXVDw60OQJu1X32H5HX9lCOBwOc+1IGkHIkBR9enqKs7OzODo6in6/n4SwSQ1XS6EvVFPaPpggMPatQa6D9yrLtpdOlltOve5gBWwHa8XRGsg+a868mnB38Mp5uMa6BNWuOvU4wMEOqLFHXJf5YVuhZYH3mUvk7OTkpFEpjQ5jRyG0nCDH1li2PP+fujmOoJ/8b2ITgtBbnFhDy0lE88lvyJoTcw7iGbsr+nwNGrJDssSYDiLI5BJrxG9sDb+NiSpJy/0rRiDuNj5l/WnEkhHbnQfc10SUj1zADxoTR2wfrOXz8/D7NGJoyHNsJ0UV2BLGRmUX/ak2gD54x5Z9HNdgHTm72ZXZxv/Yd65ZE1Qmq7AjjNH9QQaYez+4Cbtq8si6WYl2x9LoOX6/xhpVZv2/Y0NeY47oG3E11yeJ/zHti5BSOFKqJAxo1+tt+ZsBNYaToBLQcnx8HOfn5zlpgK67u7uYTqdxe3ubASiKRgAXsd12xfUwst7aQ1943wDT+9wfHh7y6RdmjQGWlItTGWIDh/NBuBFQwAlB2XK5jLOzs+wTmSUy7D4scj6fx97eXj6Gl4oIjA6ZltVq1ahAQJlxNgQFZOcApswRVQ/dbjcJCJMJXIOnqLRarRgOhzEcDuPo6Cgmk0kGnE9PT3nmD8oe0cwCGiB7LXgk+osXL5Jg6Pf7edgzBBUGg8DCRhSj5vUz6UQATKCBweAHgAiJYMBjQPSlG/NXg0iDOz6DQccQQojiiGpFDjqL0YaYA3AzR6enp43Kpojnx9q/fv06CQoaOnR0dJTgarncHgbq6qvVapVECISTKwtcBs1DEU5PT/OAVa6BYWW8ZBBduVjJDZNeDvD5cfBmJ49ceCy2VZx3xRiwGzjZiMhMKXNMCbJtLDbE25nW63VmbdnGB5l3d3eX5BxBAFWYOEW2EU+n0+wz9wK4bjabuLm5if39/Xj58mUDmA+Hw3h6eorvvvsudb+CN+wnARbzx/9kyFwVUit6mBvGXMFJDbT/WL36U5rPAnKlq2WFOeVcFQMQxlvtjGXCQSXyVLO5JrS4ZiUXHBib2Ge+8e/Y9bu7uxiNRkksuh8Gx7ZN6C92iC1AyAX2udPp5BlVJnsimofattvtrLzodDpp7yHPN5tN4yBTtqM6CK3kE9fHH+JH3A9XO9mH8L9tA/MCuMbP0E+AK5UltkPYKjAWwSgVQvhmE5V1bfF7Ji0c2DiA9ed5rcoefTFZXt+vv7kO8+Tg+vvAbQ2yTCis1+t49+5dQ5f+VH39QxrjcHCEvuMDmSP7T3TA22awn/gK5iniOagj6cpr2GcT+T7LivNqSAZgH01wrFar3NLO97im9dQk9Gw2SzKVpC5EEzr98PAQvV4vD/Lm3BrwvSs6OTcNjOoqDvAAx2v4wHMnSyAAHXcgB3ze9tTfQQ9t8+p37GeIK3x/1ujw8DAuLy/zvNjFYpFxC3HEfD6PbrebRNxoNIrNZpOkEYl2CC3O30JuHAjjF/v9ftpjbI13mIANfBZORDT8Eba/YsFut5vbYYlfTF6Y1DIu9vv+/akatsy2xYQU6woOdSUq64i+RGwrSSOa27yxm04KVaLT461kRUQ0MCzXhczcNYZ6TdvuiO0RLMTVtaqX5moY+oXeQjSgF/WBGvRnPp/nNlL0H/1hy+hwOMx+mhyzLcEPVx9cCXia9dHXZZ2YQzCGq7ZZv5q4IUHopILfJ55lJ4arruuxN9hXbPr3JXJ2kZ687vXjNX/Xc1ATZSQN0HOKV0xYUziCj6LvHovjZdtK/p9MJjm3H9O+GCnF5JgVZJL9vo0TDoiJa7Va+ThdiJGDg4O4ubmJb7/9Nt69e9fI/NXMIiQMbCvGYL1e5yHDEc39sDyFArLn7u4uHYPPqIDlxZhAAB0eHsZkMomTk5MYDofpDCK2h+WR/ZjNZo1KMowSgYi/t16vMyhdrVap6BgUytUZ1/39fe5dtyJxXYIKjAz/uwqE+fSZVygHWw95ChhPDMSQLhaL7BuVU8ynjZGDD4gA1hMlMah+fHyM169fx3K5jPPz87i4uIjBYJBGgDUgQ+KnihAMIltcm4DXRJMBOWTUriDE9zOj/aWbQVPENqsSsWW+7Rishy5ZNQnlYI3gycy6A87hcJjrwrw/Pj7Gd999l+c4QQRFbJ96B8CDXIWE4L6r1Sqm02luc+U73BtwDwHFkwHpL9vSML7j8TgBSLfbzYoNP1XEJHnE7oM7CfoBAs6m8Lc/bzDpTAeOkHngml6TXq8Xy+Uygw5klnnwmRQEMBcXF7FYLOL6+jody3K5zCoxtvIBRBwsHx0dxatXr6Lb7cb19XVusWBuACvcc39/P375y1/G4eFhVs6wjZAsLmvO3DB+7/WPaJJWdoz2HTUIRa+x1diRn0KzTUe2GRNr6PNfbm9vG2dJGVThX00U2LZZz7Gv9oEmprmeQVX105Z5ZBTdZBy7qnjcT4BSq7WtckBGTU5hvwjcsO8++NkBDt8jw+wqWIPG5XKZ1V3oC3PDtfBp+Bvjikrcm6xzcOb1YC2xt3ze10J3DFyxQ+gjfoi5xrdCCqLD/hz+DSDpINKkFLiA9aH/XMNBu8k1xmKZY92x2R/SPcZrYpS26zvWd+u/X68JAa/tp2oOpCK2a2uCmNctSxAwzCs2jrFhv+2DfE/kcbFY5FmK1g1IHvTCcmOdB6/d3d1lsiVim7WnUr8GPegxPrnV2m7/xp+DmUgW0h/uPZ/PG1jdcgiBjH2gP5DO7r+3VVuuTDSZUGLuTKLwG5/MOrnag89XUstrbL2gSo4HtNgmkUSYTqcxGo3i5OQk55HPHBxsn3gLnvJ7rvDknqwPZ56ChbG5jje8rk5CeC6x9aylbZrtlknIWlFcscynbtgx5gesZCKHSiTkzeO3jvA9yJqIaMSp2Dyu5fcimmeQGqeZGDY+r9uj7KNNkvGZmlhwdRg+nOtgS7gGcmj5hwx1v1zJ73iAGNAVgHt722Nf7C8itrjHuklzRRt9ZZ6M8bA7NaZHv0wee56RVfCV/RjxiAkg2ykaHADJeNsfrmViiYZM2GbYplv/7BO8Jt56Z3xWx20cjLy7uIP3HZ8gXyapjLF4nSQZfaZPJAs+pn2xM6Uimqf1m4XF0KNYbL3yohBQArq63W7uWf/mm2/i17/+dTw9PeWj4hE2H+bH2TDcD/AWsX0SSg3IYRRHo1HuiwcUR2wNDMB7FzCK2CqnCR4C5PV6nWXWT09Pec4KbC5gl/cMZH0ODMZ9f38/Tk9PG9t2Wq1WXF9fJyCBJSWLdXFxkeOi7z683UA4YlvmR+YPwLFYLKLb7cbZ2VlcXFzE7373uyzp4zoEuwZf7H+30eDazCdEhh3xZvPMwH/77bcxGAwalSbMCQQeQY0NDfNox4xMMPdWeggn1soBGtlsG4qfQqvgK+J9kioi3jNcrJfJR3Sykm/OSKzX60ZWBV1lTvr9fjw8PMSvf/3ruLq6yiwrjo6tdfXMBAP1iMjKHshcwHLE9iwZdGO5XDa2A+HMeEwzegFwj4jMWNYyW5MvjB+SxWcq+OwsnA565DLcugWIObftwwkgYycnJ6nDACBXqRnwmtiClGXLxGAwiOvr6wxUydoC3C4vLxsVUzhDyDrO5cLW0mds63w+j9/+9rfRbrfjL/7iL1IW2RqJzSHYBxA5wOH6VKQxh4zdNqmSyLbDyJcD2C/dDLpMXNRgY29vL66vrxsZboCgwY3HaHCGjhq4ANCxlw5saSaRDE4syxAp/B/xDFoc0EICkUGzzTT5xNjQsfV6W8nB3652MnivZAk+p1aEAvKZexMDJnYBwcim7+e5gCR1ssOg1GDeVZH0yWOnz9gWy/nBwUES0NiYil/AVev1c5UFxLoBPf01GK14xllUg1z/mJTc5Tvcqk+sn+czrG3dAlH9lMlQ39Ovm1Q1efK5mu0TOoje8jp4xcEdSU7PTSWeaciaAxZ0m2QL2Lfb7TZkHF9jkgrSF0KKIAX7vre3l2eVMr/IF/50s9nE5eVlEsv0GR9kfOatrX64D+fDMTb7WwfAHB1ge8n7Dvz9Y6KB+Tdp7YDZhKDtowN34yPuZ7LGpBXy9/DwkFVnrP3FxUUmsTmcfjabZTWZbQOxxnw+zwPge71eYhx+sBPYIT+1ExlwHyHGbKMclNNfGhV3YDjOh+R7xFnIsIkf7v85msdjIgrbYJyPPNJnEhaV6Kk2yaRKJVeQdxNcVbYitmcv0zevC/pK9TCv2WeTZKC/xKUmZNw/n/npeMykB3pLwni1WiUBQ3+dsORz9sUQP3XOIrZb6Bi/sUK17fb3xt8ma2xbHes6jvPWQ3Ar/1esYCKWMTgxgP2z/2csNX7ydy03lQQ1QYTsIHtUzTn2MvFkDGjyHHzm5JlxIXPHGmPPsU98x3E6/zMGzoq1Dv1Q+2JnSgGa6vY9FpLJwHmZrQSMnZ6epqM9PT2Nd+/exd/93d81nkhHFoHrYmB9thMBMCSDqzRYSJwymVbOx4B0QGFcgWPHH7EFJdyL7BVk1P39fZyfnzecubdxMB47cJMCkHPOcBgAdzrbA8zr1o+zs7NYrVbR7/cbyoVTYk5d2mvWFQPBdi4blcfHxzxklDUjkGarJoE43+92u7ktk7WYz+fvMbUEaCgWBuzi4iJWq1VcXV3F5eVlPgUGJps+mqEGoDA25JD1Yky7DIbnmGuxDj+lSoyI98E6r0U0n77nMQF8mCODN2SCOSNTCbB1JSJ6TTUC63d7e5sGzAau2+3GcDjMajU/8h3Dj75wDRwOAbsr/HC4lmMIX/oFmcW9kC8TYbuyTABTAgh0OGL7ZCnkF31lLeyI0GvrlUlA/vYc4SAIhgFO7jfghgNj6QeEVETkfHQ6z+dKoQtsncMmmZhz1dXZ2VnOq7cw8j1s12QyyTOt2PbM04eoZAFERTzbOqo6/Wh7Ewy7AA5+wMEOcs/6IZM1e/UlmoMZAwMH751OJ7fG1AAp4v0zUfy/yStsuwOpmtFDNmtpu0Gy5TUiGjrHa5y1aHknY27QaLBo8tbZQL4bsd3WC5nG61U26XOtcIiIBrFXgy98qAE010ZGI7ZVzLafNZOM3bQ++zv2QRW4Mk7rPkk1tvY7o08QWnUCvISNw3Z5qy1rYACOHyP5gE/DxoN7wCnIKcDZ46jbDZDLOi80B+rGip676l8/RFztIgQ+tW+u12esBBn4xfv7+0Z2ndf39/czycf3IyLXOGJLwGOX8dsmEZBlP7UanUB/kGuSraPRqFE57DVClufzeUyn0zg6OkpfDb5/8eJFw+/7fBH0gmohEij4Cnww+MEEDkSKiWTky5USzImTy1UuHXw5oDdBY9vq+baeWMas0/4u/cR2oDMEy9iybrebRBRV2be3txkz0QcwP//bz3JsCYegYw8cU0VEIxHAuIxnPAfG/JWYw94Ri+Fnqi4Yr3ue3Dxvn6Ih98YEJqFc4bFcLvOIARcCOICnzyaQGOsurF3xMLpn/2Gf4/M6jeeQ5c1m+3AdkyHG4mBX9y9iG3O50p9ts8wLvpq+u0rI/tFjxFZUDMo12O7pvrTb2wIF5sYPG3LCyPNjLG7bYhxEY96Rb8Zu8hVOwDpCQhTZBF8wFxW7cS/rFfrjhHZNEKGbtSDG5BTyRxK9ftbjtT7bNuG7XSWFvbBvR4aRf/qAzzABxnvMB3EG8/0x7YtVSjk7R6ezUwKOgBs7aoico6Oj3HN9d3cX//zP/5zZFQSBbWKQLVRQsKAsJlUMLCzCwsGYVAVUUM9na8CD0FG6SBDONjqXyZqtZawnJyf5mG+UwU8pjNg+HTAiksEkk+JHr9rgeZvSYDDIbAZGgOvAqPd6vSTCHMRauCszulo9n+kzHo9jsVjEwcFBjEaj6PV6cX19nQdjdrvdePnyZZyeniYB5qB8f38/WefpdNpgs1FyztRywMD15vN5QxEw0j54kacnTSaTVDhnteiH2WQHLNzfwNpgBxl21vun1AyyDD5wdtWhmvxw1aFBdUQ0ruHAhQwgT9vhrCQef/z0tH0s+/n5eVa0QSZ6/lgbytuR8xrY4HTI+CwWi3zAAU+9tF5AfGBkJ5NJtFqtPNSfSp39/f3GIbUG68xnxFY3eQ/9AlTgLCDYeB8SyM7YoAQ7gyNutVpJNA8GgyS90FcAlR9AQPUZW2fRn7dv30ar9bxPHuDLVgEOO+/3+xnkAK5Zu729vRgMBjEej5MoxOYCrE16AqoYnysjsS2cH4UThzRst5/PyUCnXcZcQQGyzFq58mMXiP7crYJzg0ns9/7+foxGoyRX6zaLSkjZNzko4n/8kYF2DaoMquzn/D/3R9ewj+PxOHXASYaISDlHtxgLZDDfATjbRlGhhy3yuEwA1R/erw15idgCQGTHwJvr2g+aQNpstk8Kwv6ZIHZw5/HU/21DndwweVYbdgx9Bx+4/6vVKsbjcVaBMh7IR6+37+WAwoCdz+BrWT/GYftkMrO+ZvxVWyUaaL5W1SHe45qVWPA1PnVzoAGhyN/4N1dE2b6h88aNYCT6j7zzg8ziN9vt561irkJijVh3EojT6TTPKeL7y+XzOYQcl4EO2++aYGQcPJXP64GP4p5gMhrbemr1CWQ884X8Us3HeCPiPbk3SVB96q6gNaKZpEAHWS/Le/Ul3M8+39uD0Wu+s7e3l8kYkr/ItLEGRxG4MoIzjzgYHsKRatJWq5VJaAjB+sAI9LzVauU6MBZj3hpUmsBi/pDR4+PjxOxVFkh+cH4kuo2esCafsvkMQfrvMWK3SGqwVdoEO+tYSQnmtBIFxiARzW3NNfFhv+uqKK5tH7TLt7mf9tP0y7YWWWUcu+z3roonH17N/UmKomeuuKLf2Avus8t/Vh+GTvH56gN2+QETxxFb8tlVmuAdEzDYRxqJAReKVHKPQouI7XED2Gnsk/XEY2cd/LkqC/abzIOLYrzGlZyCeCOmdfEGfQEzU1ln3XblGPJI/5AzrmeMc3t724g9fvLb9whmIppnfTibuVptD0M34Oz1enF5eZn7Urvdbvy///f/YjweZ1BbhbTT6eQeajK2EdEIkAjM1ut1Zn+ozgFk46AjtuQE12EhrYQRzZI8BJFqDyqqyBQhgD7AfTabpfPmKQb9fj+dE4JPkA5IJ7glOAD0z+fzJGYwEgR9OFWDJLJm9K1m4JhfZ+5ZSz8qlKfekMEZj8cxnU7zIFaCL5eC393dZaDC1kSqYkxIYSxOT0/TCdJPSDUfknt/fx93d3dxeXkZ/X4/gxqyzlWxHeB4K6CV1cRYq9XKSpGq5D/F5nHULBBO0WMEbNg5sobIDZ/H6AEUIVsMTmazWcrj5eVlZlA4DJEKHsr40bO7u7uYTCaNqgvWv9vtNs6iwej6N+CEp+pBzFIViX1AP5Ej73tH1myYHeQ7mKUhQ5QLGxjajhBkGkD7uw4oIdzJntzf3793Lgmfw7G6MgawT+XSbDbL7CrrzRwfHh5Gr9dL58oa+fpk6dbrdZL6g8EgfvGLX+Qjqo+OjjKpQMUWZ0t1Op18WlDENlFg4IETdcWNgzUHCQafvO8kw09BN2sfmF/6bn2JaJ5X4nF5bHzGiRSaCQXm1gRBJQ+cEf1QcO/zomw/eN+Zcusr491sthXLflQzYAq/Yl1BzpDzCmQZO0kOfBw2wEDYAQVZWwfI6DdjZjz0hc8a5DKX3qbOerlCxEGiS+UZ02q1ajzFjOQVttV2zWX+JrMsLyYB0XPsDgEtpEZdNwf2ZJkNkmkObh1AuV98rsqUm4lHg3C3XUGtwbyDgA+RVJ+iOSABCxrYM/8kCZENsCdY2TjEOlrHjN5zHewxFf4OQpFFDtlG57AVh4eHWa1kfPD09PxEPA4v7/f7ecYq8onucZ8a/EZEkhWQSmDG0WiU6+ZKRB/KboIH0s1EqvWMeTK+2UWsMEaTuLuIpoqJfP16PdbAgdx8Po/ZbJbj7XSet6P3er3URcaILlPRzRmQfIYkG/jaNpRdHv1+v3GOl2Oq6hsdvGMLneh20gdbi7xgCxzvEPSD8fHp+Abu8Tl0MSLim2++yaf+sq7YcOwzevb0tH1KO4SDfSsygZ00IVpJ54j3q+e4lv+nsf6uiIQYcD8jmjsMPkRG2Sejw3zfVT+8hu9D73jPT761rSH+5nNPT09JHNvnss4kGfHfvrcxjG0P8lt9Zo2xHKcim8yvY1fLubGg9RmSnv93zaO3uLvvEJoQcSbk6KfHTX9qkoqxGNtUvfU6207zXesi19jbe96CjQ3eRT5bRqgs59583rocEbFYLHJdLy4u4h//8R8/pI6N9sVIKQIbhMuLxGBNIpgQYOsdRvXdu3fxzTffJAmAc0chCOrMFmI8mFSCm4eHh7i+vs5F4p4EXtyzCrlfRwBx7jQHAAjOavV8CCR77judTh4YzOLf39/nE/cQKA4TxlBQ0bDZbJLoQZh9OPjj42MaVbOaKF51VjhNO3KCQBwnzpBrYNxQHjI1ZAg5t4r7TSaTmEwmMRqNotPpxNnZWXz99dc5ryYVaskqZAQHx3tvOp+HEDMR2Wq1smIOkMNYnA1jzqsxwwCzntyT/139YuPxU2k2/BXgO3Cp2RlkiODKLD0kMk6Uz6Fr7XY7Tk9Ps+KFtWq32zEYDGI2m0Wv18vqD6rusA8OQGezWVbCQYJhL3CMZHMJbnmNe0Mc3d3d5bxAUALA+F6/38+n2rTb7XS09B85pTm4RX7YIucnXRIoo48+S4uANGILcLwfHhDPvENEGzDyuXZ7+5RRnKtBAOevcaj5aDSK6+vrzKjyXQjxVqsV4/E4bShEnUEaVZsXFxcNgL5cLmM6neYaDAaD7NfJyUmSZdiSk5OTtMdHR0d5KCxVXjy9qNvtxnw+b1RVVOBSAQ/24UtXSNFqdp7fJg5ms1mOOWK7FY21wBZvNs3zYgxcsF/4Qj5vUtFkqsG6wTb3tG1FNzgnxVlH3xdCijVA1pAfE0HYHcZDMGQQxthcBYb+mKipZLq37yAL1klkxyQWesZcVuzC3ybF8Dvuk2XUc2TQzFxwLZN4rVYrXr58mZW+2C2TTSYCGCv2mUQSSQACK+MaxuJkE+OsWe6I96vQrGfOSlc5Z36/r+HvkZU65w7CbN88z3WuPxcp5XVFHh4fH2MwGORnTAAaQ0FIgi9N+no8DqgqGcLa81RlbPX9/X0+rRafiZ45KN5sNukr0aHhcNg4V9NbgGyvGBN4NiJiPB5n/4fDYfz85z9v+BZvq0V3sB8Q1GBZ+0VI8aor6I/xGjJsIhl9rwSm7XGV+UpwVlIAnWB+IiKfdIvvm0wmucuA+0NYV/IHn//VV19lAt1VSU5GQOCBi7A/q9Wq8STESqYZa0dsK2SIBbx1noCW+eVJ39yTmMBnRXp+PmQLa/uxdJUnPvI0Tts2J7oYO1X9vV6vseW92nqwh7FgRPM8uYpFPCYn1PBlrDt2E1tsHbPMck1fn6Qp9sU+iPuBzdw/7BB/29dyLcbO+iN72Dj7PxK3vq+PkcEvMxfcj9exZcyD8S1yv+u4FJNg/r6Jecshn8fueacM2IvPICfuuxOAzI+PP2F8xjseJ33jPebe5JIryT1nrlh2Jd16vU3mVZ2zXHmenUxCN7gnfaba3fKJvhCjmN/5ofbFzpSyAfMeXRQe8Eeww+CofPBkA9IJ2lAAiAacrZUcA+IzKthqYmWkv1RfsZAoasSWPTw5OYmIbSAfsQW5lCST4SaIpD8EYwglW+JwsCw2joeAj4ASgmW9XjeeqsFBqBwIOxgMYjqdNoQf4QVcuBKDx2wzHp40aCFbrVb5uHruN5vNkuwhc+ayx/V6nQQGwQbjfvPmTUwmk3j16lW8fPkyxw4wPzg4yKqc6XSajtegJCIyAMHYQkz1er0Ednzn4uIiK8+sxBHNR0czbwYxvAfgciBlI/FDgPtLNgN3O0YHuM4E2bAyh5BCVEKZuIFM4vGwgGsq046Pj+MXv/hFLBaLrFg0McPPcrmM29vbBMdUB7HlDgPos5VYL7anISsEZt4ai94ReCNz3W43y+eRG5MdZHLJcqNH/I+cM79cAxny2UkOBJg35MoBvMlSAylIM2wU8oeNAOQTkNOH/f39ePHiRVxdXeXTBiOiYYvPzs7ykds3NzcZUEyn0xgMBgk8CTCPj4/j9PQ0ZYBgiPmpQMD9oTKLv7HvJvWp4PSZC3aaVbYrMeOkwfeB4c/VsL04dgJw3oMwrSAIH+mgwgCI9WB+HIQ4WeJt2iYcDH59bwf1yBVgieoKV1ryQ6W0q8AcAKKH6AjryW9sgUkodAW7VQGXz9dwtjBiCz4BY05QQFDVfjInJmmoOHNiw6QiiSOCTPsP+uJxe+s48w+5jY1Fj25vbxtVKNgkxoM9BntxL6qnHcCzftgtrlMJD9a8JuAc1DmBw3V2tV3BxK7G2rla17LpufT1HPQ6mPrUem88Yf1EVtjSzHusPz4Sm0D/PW6P1ddFfgh0WBt0Gtnm3CKOVKj6hX4ao3H2H4QJNh2bZbIaHE1icjabxWg0Sgw3HA7j+Pg4H6eOLI1Go0yGtlqt3CpuMo1Esbd52z+D7Zkb5tbYl/myrDAmdM+BpskefntNLOO2i/V/7CZxgXHEu3fvUrfBHK5i9W+S9ByzgT/ExkREoxIU3wi+clUPskL/KxlVEwrcw0Ewx42A9U0COAjGl2HXTFJ8XzOp96e0X/3qV5n0MkHDmOwbINba7XZcXFw0CGHjOGx7xcZguSoH9TeN69YqfyfPvJYuuLD99v2xl4yZuUdfbddNUpIgtA2zrDihw2vWMbb92ee6wgqdRK/Bs9WO+290HZyOTNp/VDzRbrcTYzMW2wS+4y131nNjVc8714toVrHa9zIu5gH7aJxfkwy2VcyJcZqrFnfJEPJs7BWxPcrE+Ic1QUeZJzCKk3vc37ocsSX7mJPpdJprEhHxu9/9Lv7sz/7sg/ro9kVIKTIANjAsvEGe2UCC3r29vdzSgaLf3d0ledJuP5831el0kowyQ8cWOTJubCUj24Bi44CdHXDGmMYCERC8ePEiDg4O4vr6OoUc4Wi32/nUsYhnY0eQRwDYarXi5uamQQ5RpYGwkslG0AlYCcadteK8l4itch4dHTWy11RrkCHmaSqAUQNjDi5D2HkKCACB7zBHrB2EHeehcLBxp9PJwB0SkWzd27dvY7FYxMXFRVZ+2fhyJtVm81wdBslnA4vRZZ2+++67ePHiRfzlX/5lAiue1tbr9TLjZIILgw6YswLiYOkX9zJAtzz/VCoy3DBoyDuGBsNjB2HDiS5ylpSrgtAvAxgHKQQ8EJ8ALAfXy+UyS18Jxplb2HnOggMI0W+DUtYF4tc6fn9/36jeOjk5yaAPgLxer+P09LRBJkGeoBvclwP8uT86DymG/tUAz3vU0WW2TVi27KRZs4hozDvzRNCADbBcR2zBpu3cwcFB9Pv9+PM///PM3qLfBB2AG57Mt1gs4vj4OEkrqjcBNAA/PgPhEBFJAnPWBUQDtt5yAxihqo4MW6fTyQoqO3ecdiW+LPe7gtQv2Wrpu6uH2u12+ilkPWIr77vIHVdOYLPwS3wW+YVEotkuRGxBofWA+1fCwXqKr8CfI9f0j+uhS/heA0+2LAKUbX8ZH2Szt/oYMB4eHubTM31vzzXy5884YKoEC7aAPuOzTR5jQyK2ySgHdbzH+OsWClejGXibVNnff37CLnPOmBmTbSQgl/tgB9FfE2r45vl83sha248xH87AmxjhdQf4uwIDv/9DjbWh6pQfZHCXrfR3+f0hguzHbsxbDc75/fT0lJW7DvL29vbykGXbKJP6lWCDIDE57d0C2JLHx8cYj8dxe3ubMulsvPWLreCDwaARJBM0Q5TzP3YZHZ5MJim7l5eX+V18RERkghEbCAlF8giZNZ7H5zEmCGSTrugZOxk8Z1zDRDCvoZe2rw5srcP42u8LFLEfDkYtD17Pm5ubaLVaicchsHhgD/fC7pi8BCtFbCssIThcYQhW4FiEbrfb0FnjQFfM2CZWoo0YgvMkvZb0jbn1GnmOvk8nud6f2pzEQiew/ayz9QfihCcPM3YS8vgzXgMfe12Zu11kpckYqr7BtuiQExMUUhgTc1/HiPgnfJR/GCf40gQPsfVischD8u3zWSvkH1lEvsDxbNWkX+zaIfZzfI8s2P9VnfQcW058dIQxoN83YeZ5x87wPvpwf3+fZHrtEwUtNHSDH9sM5t5b7PmM/bcxnfGPeRHkCX/uOajXNL7wa8g+Ddlz4s+2jXsyTgpe0F9jmL29vYwL6D/y9Otf//qjdPOLkFIHBweZPbdARmydd8Q220aAgvFAOQlwOI8GYMKZKiyiM4tMFHvnyRAxyRgf+kL1BQ4RpcAwuyqKbUR3d3eZMV6vnw9KZjwux+z3+/Hzn/88ut1uXF5eNpQCogSQibJSIbTZPD/1pNfrxd7eXszn8xgMBnF0dJRP6YjY7tfHOEVEBugEN5zfErHN3kHIEEwDTgEHCOJ0Ok3ltqF3Fpm5B9wC3vv9fhp1s/fM5+PjY1xfX8fbt2/j9PQ0Xrx40dg+ZeA/HA7znCjWnYaxxknyHbYOcSj0YDCIm5ubnDNne5k/nAYZOpe6A4zMoNuBW86/dHPAWQN4DK/BLmteM3ZkTdGPvb29PCuIbC56yn2RrYhnneAJbJPJJNrtdmYHIUEpa3eViLfo1IofB7gYe9aTcaDDXIfzrCIiwRmPVj47O8tMLWvqzEFENIJZ5snEMURcp9PJw//RKYgy5MzVMpxlAPFpp4Aco1tsYYAgBtDYqeAgsBmAfPqM83zx4kVEPJ+9wLZa1n02myWBT2YFoNDpdN4DHN42eXR01DiU3JVArNNwOIzN5rnyA2BGOTBb+VxFsFgs4vb2NgGGSWIHHsisSXXalyajaAYLzgwiz9jKCmA4s8J7/Xmv/kRsQVrNSpqoQo8MrlxlvIukcrbQ8ofvIIBDLiK25zFyMOb+/n5jC6CrL5bLZforbATzYwLOAQ+2niRERDT8vcdgAsifcZAHSHUCzQG6CTjIAwNrryuvGdhhi3mtkqwR72dBvZWYyhTG4G0ADoqZL8Zyf38f19fX8eLFixwbiQd/D313NQ2BF8GBg1sHFh6fAwfr6h/asK+sda2EqXPt+34OUop5cyUijfmB2GGdHQRBVkE2mZjdRbwZq9YqbZJqnP2ELpk0dEKOe5+dnaXuR2wfEe4+IA/4a2w1W9QIViMit9Bzv/F4HIeHh+mTiA9cycF9sAPYxPV6ndv+fDwFNqASz/gtY5uIZnWTv1t9vclEk4p8zzjKdrgS2k5GQdhxSDh2iipGH01gAgpfBnG32TzvnMDeYj/a7XbiD2+BxueS1OUcTubDFXb44hrk8j9j39vbS/LS9tWEKGtYiUbHDJ+yOWgHP9ag3bY2YrudsY4bHUJWTHzwu1Yv0kw20B8eKuM+sV5PT095XqjP8bQcO+7guhBRrpYiFmV9jO2x6yZSkD1wsxO8JniYG2TbZ0pxbe5prF4JFuTWyebqB5lXrgeGttzzecuy9bwmMOwTIfJZU7Aoto/4hr4gJ8g7cg1+8A4rr43X2LEX46txFzjF2+kqGUYsZDLTNp45AKcwTpKT1lXW1vNW7Rx69ObNmwZJyr0+Vq+/WKWUg1L+xkixlYztJi4PRbjb7XZuSXt6emqcicCEAn659nK5jJubmwYR5eoNGkIFSIjYlvSyMAiklWaz2cTV1VVm8wnMz87OkjRj60u73Y7z8/OG0LKdrdPpxFdffRVv375NJ8H2RAwO5wLgaMnIUo3BNkS2SOCsyXhGRAIOnM3x8XHc3d3l9jYqYJg7wCdjtWEwUUPwYSBs5Wb+/LQsZ9VNamG4yOb94he/iMvLy1REBzsc6gxLC2HEWvL3crmMf/3Xf43BYBAvX75MMHR8fBz9fj/P0bJxt1GyU0IuCVxYD1cPQPL9lJrXz0y5jaYNCvLvIIPAiOzXcrnMA8MNwpgTPmNdhWQCkDHvVBTu7T1vt5tMJnFzc5PzvNlssrrQWaNW6/lxyshEv99PUttnplVjD0ka8Wyfut1uVnHt7+8n8CCw8DYl+u5Mgx0zlR4mbnCcXJN1wAbgpKkoRQcZA2vj6zpbiR6YzCCbW8/0Qj6Rb4AaxBRZ7tVqled8sTbtdjszaRw6TyCLXFHSz7gMrHnd5CKJAbaTcC8CYrY787hmr+ne3l6SnHU7EHpag5GfCiFVm+UFMpNK1RpM1+BoFzBFZ5l/2zATTdh4/KKBHfLBa/QtIhq+lj6xrYQgFRnDllDFd3Nzk9trTf7a9mBLuLZ9L0RIxPvBAxWVrs5mXkwscV0Hb+gCoNLbKQhwDWhXq1UMh8OI2D5103bVVV4EGDQHdSYnua7vx9oxl+gM1+VeTuIZ0DtIIsABcE8mk7i8vGzMM/MGPqFPDsSQAfqGb3CAx3xXzPWH+EcH+jRXE1iGamLlQ4TBp27coxK8Hjc4BTy2t7fd4uoqDst8DTpMDDs4Zk04zBys4kQpZzzxP5WFFSc7+HTV63w+j8lkkhXGs9ksz0lF1iErqMyhnyQaWL+vvvoq/ZbJTs8ZZBe+uvoB+2fG5bWuhLwDYQfANdCrBK//j2geXMzcOyB2wOx7M4/4y8VikT57sVjkE6sjmmfDmSyEfOCJpvhUcBvzSRxFXyHGbm5uotfr5dPKWWsT1PP5vDF2k2zYG6pNptNpVntxT1fCOoawnnzqZszB38b69o2MlXjJhJRJOmO2SkIit/aZJleqr6I/JtGJdyGlTAzWmMQYnTUxHojY+umI7bZIk2T4eXABthz74IQTiU4nB9jhQBzqPnJ9dNSFEBHbKnt8P/piuxaxTeLxvquA+Dz+y/bOPoHfNdlscs52FTtFYpm+M/fMi301smFyh7FxfQ59Z058P2SmJpqJcavdsu2yrDphZNmE5Oda2BrWCfvA/4yTH3zU27dvG1uIGT9xy8e0L0ZKeQ89imPWnBJVFv3paftYSoIVgLWZvojtIxmZeM6pIbvvPbJ2RASc6/U6SQwWIqL5+GxXQwyHw4aRYfHZxsO+b8i2s7OzBKdkIV3GeH5+nk6FrXwox2KxyMdnE4AxTpwZgoczOjw8zEwURgYnD8vLvXHwvBYRKawAX7b/oTStViuri/wEAq6JkPLkI+YH4wRYWq/XGeCipM5A7+09H2o/m83iq6++yid1YTwgM3q9XiNgZbsg68N6/9//+3/j4uIi/v2///e5hfOrr76KyWSS48FI0l/+9npjNEzORGwNfKfTSSfyU2rVIUZsDSrvG0yzJi7lNIDloGnOc/P23M1mk4RVrSoCcEVsMwWtViu31lIOimF2KfFisUhnbUNNmbvHZEIZYoOx8jRK1vDh4SGurq6yssel0ciBs0cYZch0B/YEiLzvoIz30As7R5wG4IlqLjs7gkTrFTJYq0Io1fdTISHucOiMCxJvf38/fvWrX8U//dM/5eHkBAL0g8oW5mA2m8Xj42OeE2JnydrxA4kNScGcIXM4SCql+Dxz1el08nwx2yk7wOqsmTPmypmmL91McDC/9J95cBDigIDEg4Glg3DPPzIB6GEumaca3JqMrve2TpBBpaKJJInBm89DYAsRW/MIsrgm5KS3qPveyBBA1EQr/s66wrx6/Aar+CkIZe7FdSxbdTss2KWCf4JKcIrBeSWYwAX4dBM+fAZ7YAIKPxuxfaIwmIL5gVjDV3oc9vmTySQODw+zYspjs5zWDLH9K32vFWxVvpgPqp2+r1UidlcDB3J+WCVwLQeW5U/ZrC8OhnjNJCc/ECvGwk5oOLCIaJ4bRILE9wEHUxGDLoEZIyLOzs4Sc4NLIaNoriAAt41GoyRRsL9ch21/vV4v+/r4+JgP1uD76CGV6/ifbrebJBp42vpkHYd0rrK6K6A12U6rBKHnFhvJ35Ydy7/XycQWts5kPtiRz4CvIfCpAB6Pxzk2SGOqqfr9flZx7+/vZzzAEzrB0ySA0cFamUK8QHL59evXmVB3pUpEZGLdgarjN1eYnpycZOIRPG/yxInqz9mc3KQhH9ZXbC7Ve66a4XP4NZNVTuQwP/YH9vPIKqSffS4yMp1OM6axXppYxPaBg3nNhIRJSq7F3x67K4YjIv0ieu8ts058OUnjLXHYPWJ3nydpgrsmQegzmN1zxj1ddRexrQJ3MQDXcIUXa2E/wfjMDZh8weeZMGM+8anMt3dNIDf0LSIa9/FcmoSKiHxQAf3FX7ZarcbRJl4rZM3YB1mulfaWQZ6EjRzt7e3lQ9UYa02E+fq3t7fZT8ZoP/Ux7YuQUhYo/rfDZvLMLiI0Bm00yKTVapVn0AC0FotFVkd5D7X3WLNFD6UywMUg0ah2IvAGwLKNzULPljBXBfAIdJzs3t5eEib04eTkJJXMFUsY/YgtIYBTfnp6yqAxYisMEFq9Xi8Fl+w04AdgTx84v8mPnkbJqF5jjjudThI4sOiez4jIYJz+4nC9v9lPT7FCIfT9fj8NF9d+9epVPpUQA4A89Hq9uLm5iZubm3z8rMEAffuXf/mX+Mu//Ms4OjrK7WP+HME7xCjyYOMVsQ0gnV238/khwP0lmrN8bnYwDvrqnCBf/X4/P0MGFCfIPJHhI/jBKVGVxpyjTwSqzqA9Pm4fJmD7MBwOs6ppMBikrLP9LmJLdkVEksLYhKenp7i6uorJZBJHR0dxcXGRhCT97HQ6KcPoJvLqTFpEZCDOHDlIxVYB/nid6+JofPA5QNcggiDdNsoBF+uB7XBAj724vb1NnSaIo3SfalLs2atXr/L8OO7HewAgzgri+qvVqnFgPcAauSA4IpCAFOOMwG63mw90gCjs9XqNp7V4Xk1y2245c+VAGDlinX4KzQEOtgiyjfnyOtMMojw29NiBWUQ0QBh6wRwYYGNXsV/IkAkvy3DElsDnN9lfyCX8xHr9/Nj3yWSS/SfZYdBIkskHvPsQYO7FmpPld7Ur/QNQcW0yhCQ+PO/VPvKa54JkBJ/zXDE/Bpi8B7mKDTN55uws/sTzbrKK5Jm3ABjAU33MHGJHI7bncGBLmcO9vb0MfLFpldR3eb7lFdnikGPLFTYIGfec+rUfapY91rE2xo2tZV2qflgnPmWrRHElOOkP2MFyTQDgbaAEUxFbnErmnmvZHnIINrJgvQWnHBwcxHA4TOwH1nKAwucfHx/zCbhUpuNrsPveZo4N6Pf7aROoprq9vc0dAQ6+sAH1PXCCyQAnwtBf7JFJeNs41qWuPzJhn+x181o5ELUdrcSj++NrgS1NJK5WqxgMBnl8Bck+znqN2FZhXl9fx8XFReIexueAuN1u5/yRaGf8TvoiK2BzEsUcm0HfI7ZbfmwT+Zv32L5p4soEO/3w/O/Co5+igUOrDtpWGdt3Op2s3necSkWJ5ZY5sP0ndmPNHfNGPPsDnuznMwHv7+/zwT74TubQa1ZtKLbZlUP2k3zH/gVyCtnn2m7YJ2/Xti0zYWEfCs6o1/VnXCGMT/WYwA3E0lQWWVe9PdeN61tWGQ99pf/L5TKPh4HQBc/ip8Hovhbj4TrMqZMyrEP1XayJOQ8+9/j4GGdnZw1fzHfAO3XOTaZVW4dPwVb6KBFIqM3m+ZgOZJuYYzweZ3ECfAG+1gkPz4Pjm49pn52UOjw8jJcvX6ajwElCEBEEm7FFabyNjANDZ7NZBnIEkgjLu3fv4vr6umH8ut1uBkFMFtllX384HOZ2NAdOLnO0QYCsidhmFVutVhJMOM3z8/NcICp9Xr58mcL/+PiY++oBEZAqkFtUc+GEUEpXNxC4UdnCtZyFZhwRW8fpgI7zWiIihsNhjMfjBhOMwnF9xu01WK/X+XQQjCjbGsns7u3tNSq0UBgCZWf0cY53d3fx+9//PiIi/vzP/zyJDGfh7VQclDDm5XIZ3377bbx+/Tr+3b/7d3FzcxOj0SguLi5iPp/H7e3te8bEgZgDHjPpNvpU8/whTPHnal57B6cmOSK2gbLPPYBk8JY3yF6MMeATwwaoYg29JQbQ8vDwEKPRKKtt6qGAACUflkwF0ddffx17e3v5NE6cLFvv6N/j42NcXV3F69ev32P0TQYPBoM4Pz/PMXEfBwwmYyOiUeVHdoHXuQeO0JVMOGGqlFgTyBaAuINBiDhsn7Nizui4GsPzDVEAkDC4r+dAcdA7QIFtGujDyclJXF5e5tY5qt/QO2exATCsD2Dt6Ogorq+vcxsJdhWbiI2EQAZAmPymP71eL+UFHXbwYBtmXfixdOpP/T72gwoDtu4ZQEe8D2qYTyd5GH8FTyaaeY31qoAXH8z3DC65Tq2Gwp5zWD8yR8ZwOp3GdDp9j6QAKEVEbhVB9iK2gQtgiENhCdR5n0pIg0ATdOAOB4eQo54vMrHYcfrGZ6iUZB2QMWyqfSXrw9ygE+j+Lh1m/vHFvocDutVqFaPRKE5OTuLi4iJ1FV3zGnAuBtdDZiC/p9Np9Hq9HKvJHe7NXPG/A23Ps+WT9avz9EM6YRn+WBLp6ekp7aPPcjJwt3//VM1EMHYdHwuupQrIMkXlhElIJxgcfFimHbRSuUolMvc3mYj+sJ5ORrG+rVYr7TqEyHw+j9ls1khQMB78pc8+ooId0iUiEh/6wO2jo6PcmseDR5zAQAYJiIgd0JdKPjkhZX3kbxpzaBmzrFbyKWL7cAJXSpjEMq42sWz8jX3BNvIdbCO43ccQsPOCKjJiFSee6Iv764eumJhhvPP5vHH232w2i/V6HV999VWcnp6mLeUJ3yZ17A/W6+cdH6PRKG0L4yPwhWCz365B/qdqnn8nUfBFtsMR26RmJbEoLsBeV1vL9e3TkCP6gUxRtYj+rFarLKiIaFZuWfd95g/NfWdujQVolnXeqzbcCSIns8AEzIfJHY/T1TSeA2SMebDtQl8ocvDnjN3qjitXmEZEQ7e8prvIT68Z/UUeTEJz5hLrhY+N2BZSYK/QSVecmtwyUeTxMR6257Xb7RgOh41dKl4jxue40wQp74HPsAHgOBccgKFGo1HKyGKxyDMIWSd+sOmj0aiRXOO7Fb/9UPvspBQlphZqBIuAj0kiC8mE10obDOb+/n70er1k+GezWXz33XdJWEVsy0QddNqxY0jM7J+dnWVQSuAW8f6jtnGUzroyLpwmwID/qdoaDAZxeXmZ2/UQHhyNtyUR/B0cPD9OlwOIXbIMOcbfEFM+GyriWVgADVZUAj+AMp9fr9d5jhXBO+QRpBvfIQhZr58rZK6vr/N1glPGhmKg1MydHUC9Lsax3W7H1dVVPDw8NA5Bn81mWeo6HA5jb28vQTjZYQcW//AP/xB//ud/ns7y/Pw8lcqZb/rE/xgQqlmc/bFz/ikSUrVhHB1c8Zuni/C5GsibeYdIdPWiz/ICfPN6xHbLzXL5fKgnW9W8RQUyazqdZp8hni4vLxPwwPJD3JrQ2Ww28e2338ZkMklnjn5ERKPC4ujoKM7Pz2M4HCbYJ5uALakZZOQAp25Hi91BThyYODhEx9jGQT99z4jt+SO9Xi+dE9dotbZP5cGOmjyzo4BgBAxRuTYYDNKWYDv+P3f/sSTXlqRnwx4idYZKhQRwRInupug20mgke0AOOeGYN9ADmvFeyG/KEa+AF8AxRxzSaM02tlV1VdcRUKkzQ6aOiH+Q/+Px7IUEjqgCDqq3GQxAZsTea6/l4vXXffliHnZ3d5ME5h7j8TjJZp4znT5Ub6JrODvkx/phuwKZB1iez+fZV4ygHkBA8GPywQShAa9JZObpsZ//Ptfv+30DSWfcqIpxT5XHvssc8MefNSlqEAIQc1m/dZzvuFKGd3WQWAI+ZNefBWQOBoMYDAYREW9th5/NZmm/qczjIjCjB5wBMHoHmLT/YMwm1rze/Lsk0sAg+EZXS4JFTB6buGUMzAW2Ah0G1/D9clumfRDvYiKHPpTOXkMeDIfDaDQauc2WLfS8R0QkDmFNTdSgkw5+WJ+ymo9AGTnBBwLGrWe8G98tSZLHLv+uvMf3ucBSkFMOKj8mKQWGNAGIbCJPXuOVlZV4+vRpyhTzxfqbaCfhggzix87OznL7BWsP0dtoNLL5v+XQwSd2lp6OPmTCxA5jG41GMZs9VG2RkKXxuKu+7u/vY2dnJ/uY4qvoZQbJ7TVDZozDqJwmaYIdM9HuQM1ku+2cbRhrVcqt78l7sw4mtPg+PtD20j7ZmIsxsnsC3WEHAbbVlZFLS0vppy8vL+Po6KiyBRP7C3ZDz0g8gbMINpFN7DSx1d3dQ68Y1sP39XYw5oax8y7IAyemR0TGGcx9qScf+rLN43nIlHUT+0B/Hew8eJLxci++Y4LZRBL4NyIqsscaOZEHEWBMgG+xXHm7V+k3sMeusLJNtxw6TnGcCIaKWPhfLv7NvJmksp3is46f/DtknHEhZ+gsOjKfzysnrpqgNhlvghgZLMkSP5O1wHZwX2wPuJ9/m5xkNwVjpTUIXMD9/X0m2Y1RkBP7Aa+fMT3Yd2trK/G4cR59dI2FsfEUqdjX+g+HKRnDTafTTDyDOZn7Mjk+m80ypjLxyd+2id/n+uikFAro/dUIkLfmGSggnPSJMXtpA3B/fx+Hh4dxenqa95zP51m1Y+Nu5Y9YsPXtdjt7MhA4w0TamHiLoNlNyi3ZkkDgHRF5qtTa2lqcnZ0l2OTkvOPj4xSmwWCQWSkaxO7s7MRoNMoeVmxfYGtDrVaLnZ2dePPmTcxmD/s7CV4hgmj4ipPA8SCIgEwUEOPgknIYaissawBp1uv1stElSjcajTLbXIIbFMZzbkePPKAkrPf9/X28efMmzs7O4osvvohGo5HEBu/U6XRifX09zs/P8+dkIwg6BoNB7O/vV8oa7Qw8Bo/pMaCNPE2n08px4J/aZUbeARH/j1g0/jWbzuVKGQNBAoDr6+vK8cWtVqsiR3YcVARSpo4cRiyIQYhW1peqx0ajkd+NWPST8Xa309PTNJz1ej1P2PMWD2Q34oGgAvAyN2xFw9BHVJspmpCybTEY5v15Z7YIQlKjW9goHLmr/CDfIhYBF/aT9yNA4b5kJSMig/2IyC09y8vLSchTjba8vBztdjvOzs6yKg7Cd2trK1qtVlbwXF9fJ+j0li9stYlq9AE9u76+jtFoFGtra0kis5UW52sCEPBHZYmJKduFx4Ldx4gI1uhTII+3trZSTkiqINu2Sb5sg5y1dzAUUfWXkDXYKQd85X1ZT4NLPodeRkTqMDYFOTa4u729jX6/n0CfgzdYa1fzQpTyTuvr66mjfB6ZQE6o+IDQRSbwYdgDCBbe2ZXT3BsynLnEHjjgQsYMznkH5tC2syRJ+Ty+kDln7viuyS0ueneZbCi3BF9dXUWr1Yqtra3choCsY3siIm0n70CpPqQFiRywhokB+2n8OPrPOjghVF7fp1LqsX+/72dcBsjuRcrPPpbeW5fsF1yB70DIgQq6FLHwuRHVnpWu8kN2+v1+YkDsZK22aIGB/fXWDxNm6J9tBNiYPn48s2ycTgAV8ZA8ur+/j+3t7TwEgF5DyL0DPpI/4E/kq9frRa1Wy1M62c4PWRJRPcCAe5r4Yc1NIDkg8+ddhct6lbqLL3IygPXGZxkbsG6eb/SEdiDY0Pv7+3j69GmcnJzk88HuzBsBcURk1QJygXxZvk2YEzxTBcp8QGCAoZjXi4uLePbsWZIAa2tr2SsVP4++8V7r6+vZzyoiMhajt6Wrtd6nx3/oi+IA/KnX08E2ckg1FPJhEgqb6jiwJCmINUryDt/huUTXSbAyFnTRW7K5p2MU4h7kl/W0Hvu9+Zv3LsfH5aQidpN7gikYD9gzIiq+C7kjzuN3jMnxvHXRxQGOvUxwlWRLiZf4jPUjopr0cSKFdyJGwLY52YR98vy4j2pEVPCAY112MxnLWIYYm9ceHERxB+OLWCSZkNuSDDL2NvnqOB27x7r2+/1YWlqKbrebv+O+yNf5+XnyD1yWR+xdicXfdX1UUspOz8KCAGCcAZ0mq9bX12Nvby9JnIgFKLu+vo5+vx+vX7+O4+PjdOaPVQvYqSBQs9ksS5hR4LL8DiF3kGTlu79fnB6HwyRojlgAEEAgzpT9nASDk8kkGyu7Uqper8fFxUXs7+9XyDaCT8iuu7uHE9DcL6fb7Vb6ccCoRiyy2RgZhN2AJCIq1RKsWcSDIrrpJAaRTO7FxUWcn59HrVaL3d3dVEKOcKdSy9v5NjY2cg25FwbCYzV7fXNzE3/zN38T+/v7sbu7m2QaIH1lZSU6nU5cXFzkXuTZbJZbgyaTSW6HvL29jc3NzTg5OcnGwjYyOHackmWB3+Pobeg/1cvZzpIAYQ0wjvyMbXk7OzuVUyPo1cac8+5ra2tJULlRNcHCaDSKfr+fculSXioMAdCAKbYyQHAigyabrq+vs18U2WiAGsCaMaFDW1tbSVoB+AhWsSHMCTrBvBBwOAsVsSjZNZmCnHBZJpEtV1KZkKNa5P7+vnJSITIPuYW+llUr2Aps7v39fdomqk0Hg0F0u93Y3d3NDDg2qdlsZm89gl8DEsaA3by9vY3hcJgEPzYYp2iwwxaD0WhUAfNk3CnH3tzcjOFwmM102f5sO4Yd4r2tpyYTPzYoftf13/7bf4v19fX4X//rf8V//I//Mf71v/7XSTYwdgelEdXMp4kY3tNBl8n1iAVIQo4NXAw+eR6EMr6E53o8rloiAOI+FxcX2YPQ446IClkAEJvNHrLSVEr6974HwT334H3INAMUsUf4DWMNzxX2iW1FBnroOAQA8o0fAwf4WRGRPttEiUm9MvvNuAhcwB62Nc7YNpvNSjNaSG22unY6ncqWYogmkmzYYuwU96HPHz93Egs8Z2BPZtakuOeg/Bts4qsEuAbq/ll5PUZC+2/wI+B+Pp9XCLkPdbFmrkZB7/x7fga5il8z1mCOsV/gSs/pZDLJdgv0EeJzJDTBT8gg8gc+5KACSLFWq5U2lnVnW7WrmKnAur6+jt3d3Tx9uqzMgCi9vr7OnqERi55kjAmfiU6THAI/t1qtnA8Hnp5XMKR1jp+ZEItY7ISwv4co4J48A313AG8b+1gC0z83idNoPDR+926Bu7uHFhMk6mj5gZ80eUbQPJvNcusfcmMZIV4Aa62ursaTJ09iMBik3QL/uzcldg6iybGA/Tf6fH9/H8+ePYvBYJCJMyqewRvYQ9vgj3GRkMfnMYf2f/gfMEvEgnTDpm1ubla2h0FeMScRix5otpGWi4hIW4kdLgkbnovM8zv7vNLHGwdAwtm3Yqd5lvUTfbKfKf2CsQA+ykl9k2LMneN87zBxFTY/x++AI3g+/7ZeY+eYBydeygM+nHiyfSh9sQsbbFt4JnEOdgkZYZeHSUMSOtgZ7BnzzvNte4zhkCXrNfEPusN2W//MHEiJfx2rE3OU6zkYDCo9vJAVYgZaMLB+xiyWQVdTftf1UUmplZWVePLkSYXxxCkwORGLEz4Ag7XaQwUTi4HizucP1QvHx8dxfHycWRwm1aV2ZiD5HZlXhJNSfEgWbwHCcUVExegCsF0+jcIDMAiUGV+v14vZ7KHnlQN4qqMQxqurq9jb24vJZBLz+TwbB7bb7Tg6Oorl5eXsIUFwWKvVkkihHwkOBqdnJY6IDAq73W4Gm+PxOCIWR2DjwHyqIQ0uCf4IDldWVlJ5rKyXl5dJ1H322Wf5M+bYQSiVXfybeXelCaQZxOT9/X18/fXXERHx7NmzNJKMBaPAPHvdyPJvbW1lME7pskkA1vf29jYDe5ypSRQTL59CBcZ3XTiHMjA30GWtcdIYJQgimlA70MMgAVzRCZPC/X4/RqNR6iGBA/ru7Eev10swA2DHiJuUubu7i8FgkFnViEWGEuCFnvkeBE/OYGOf3DMH2eS7vKerqHAEzmoBzjD6AFuAg50/tssAhgwpZIuJAWet2N4MgDFBAZgoMyuM1VVmbKN6/vx5VltR5YnNJNAlmKEJZJl5clYUsOC5AVQ3Go3Y2trKbUgAI/QJu255Rf8dNPOO2D8HBiYF+H8ZGP8U17//9/8+Aca//Jf/MknI0slbJ0zSlyDQ78uaAQr9e9/PPhOg9ph+8HtsAv7R9/SYhsNhAm/7YT/f+kP1Q8Rim0D5f8sE/+fCdjtgjYjKu/A95ojeiOhcxMKumwCzTXfFFnqNLvE8Z6it08YkDmQN3rE/JrmZW96NyoPl5eUYDocJ+rF1g8GgchCEiVmq0SC1SpsQEfl7vmPS0TbadsbzjX4jvyaDf2wwal1wcMfvHruMN6nO3Nzc/FHP/76Xg12TMvhFZMLkKnjM23Vt05Ah3gkyCd3hRLuSBNzY2EgcyBy50uf29jYuLy/zBGLWCjzI/0kIU3UFsddut5P8ZP3H43HKA7bMFTbtdjsxIjJFchpf66odfAkJJlfyOThFty2L6AN+AV1mbcqACgzIfdBL9NzfI2BmTVhX3qVMaDkgx0a4egcs/otf/CL7pZoM4pRMAl4TBm4RQiUZmBW7iv4RVHOqN8lUqvzR0dlsFufn57G/v592d319PQ954n7M22w2S0zNScbgwxKvWFfed71Lr3/oBVHhky15Pmvk9ylJSQfzZbIoYnEya8TbVWDMJXoL2edELPcz0ehnmMxwIop/Q1b5XVkTMGhEpL7wXFdi2b4iwyY3mAv7SsZmQoYkrHWF8ZT3KvE0c29f4nWKWCRw8FFgfOugiUKwPHbC+sy6lwQy84zdYuyMkYpqzxu40+9mMht7wfwgg/aTxm+sHT0hIRqNDxw32L8a5xiLeM6YVxNIYJHxeFw5DCriobqaHQ3Mb8nt2JZ+3+ujklJUUhgAU4KGM4b9M+PJiXUEWQg21VEw/ARzkDhMKMCL/6+vr2c/CpeyNpvNbKxIFQWlxRGLvhh8x6SLwShKCZEzmUzSGfC5ZrMZ7Xa7QsRBHDm4Oz8/T6fDO9FLJ2Lh/Cj/JQNxf38fnU4nZrNZZrNsPCC5MHasDRUHriZiCyFA5O5u0Rvo5OQkCUSAMI4YUoyqC46ePzo6SoKS9WTrENk29x4wM84cAi4Ya8TixLEXL15EvV6PJ0+eZBaYQHZ5eTn7BAG+Ia/cxwawxXMwBowD8O6gCuXFcPH9T/my8bVsOHPlYITPNRqNSgNZ9IS5pAohImJ/fz9P3THwm81mcXx8nL3hMLoGlBjYra2tdCT9fj+m02kSpOgGATxkMceTYsTJjmC8Gcd0Oo1Wq5WkZbvdzu05BK61Wi3X2yX02AwcVsRii6mbxOL4CNCwD2R+ISIcEDoQpAcDz0V/LV8mSefzeVZceouAg3acGk57Ol00X0f+AV4vXryIdrsd29vbERFJ1CIXbL0bj8dJfHvbF/N8cXERp6en2bOLDKKDKlfltdvtBNfYSuwbTfYBDJDWvIezf3bUlvf3/f+nuHgn/ADBAbppv4gOlYG+/zxGfBDUlXrNfBHYOnNn0sgkosdlosP/vrm5yWrUkvyyPySpAzEKWDIB5Awia4qdwVe5NQDjRx95Fu9ZEm4G9waezAHfgVAm4EXXHZhiCx2IluNHNktAzO8MVLEVDtYd+LBlurRXBH739w/NSO/u7mJ3d7ey/dVbLelPERFJ8HutmS9sGfOEzWZeTKYQlDgA+S7Aan9Uft7ErPXWQUsZXLAOvjenWn2Mi3lhbrw9HPliPTY3NxOfkWilcT/vVRKbrF9JJs5ms2i1WtnLFTk2uUQyFvJyPp8nPgXPIJPIODq1tLQUz58/r2AGcB9b0gjK8Huu3qHSiUCae0BEsW74S9YOYiri7Ub6/Ix5x6aZuHmMwCKG4PuWNdu0kjzw+vqZjIEWGozNpITlwzIdEZk0B+PSIJ7qRwJmAlOqSqfTaVYo3d/fZwwC0dVqtdKG8V1kylv92ZKPbDJ3rmZhCzb2L2KB39kxcn5+nu9NPMbY2Kb2fYLXP5R/ns1m2c/2MaLDNtpBOxiN98NPObgHZ+Iny/v6HYjtykQS/gedxw/aJ/N5y719TMSi9Q16grwQDzueRhZ80iVjJs50sv3+/j7xv9tAMGfovOfW4+UzPKPEI/wb3wPuRZchV4yDrPMRC3IQX8q7uorLfob/u6qN919eXk57AeHEu+Kn7U9Lv+QYoYyvTCjxPiarbXPco5dxg12QT6+dE0yeM//usRYgJBvX19czhqd9Bz28fR/0ivk0djQe+K7ro5JSGHxAp7OGvAz/ZwJ3d3fjyZMnlS17jUYjjo6O4le/+lUaUhaMiiMWNSJysZaXl3NbDmzw+vp6dDqduL+/T2fMcwB2lDG7RJ/gFafu3yNMJsrYntZoPPQ86nQ60W6307GSTYK0ubu7i52dnUqWx4wx80eDQoJFnJ/3Mbfb7cyM4YypRCGYY78+DscKWK/Xs+SdNaIJWqvVyuA/IioEE1sJXSFhh8b2QwLhvb29PIUPp8XfCDiEHKQC1VooNM/56quvYjabxZMnT9IQeR7ZyudjjFkvE6UmB8zcY+ggrExy2Sl8CoHu97kMCOw4IiIzorwXFX8meyBRMdhkGDnOmcDGNuDly5dxdnaW6+cAi8+zvdVBkY8axqjSNJ+eVC5J9v06nU5W6xEoYzAnk0k2TWctDTQhpFhT3petaxC1GGpnY3gvE0MA10ajUdm2iMOE8J7NZpV+MAaCyCDbJZA1ACjzBlFEAI9NbDabFZthsHN399DXiHuOx+MMHrCtnP5Tqz30COr1enF5eZmHMNBomfJf1gd99ZZeV5gsLy/H/v5+bl3A7nBUOXrG37zru7JHDiJ4PwPgMoD9qS5nEi3btj8Rb4/XZArrWWZoTWQZnJogYV2weSbACBAJdJ3VNFERUW1izx9XSpYViawlOo+e4E9dpQg4XVtbi1arFbXaw0lVPAObMZsttgA6++dqW+SEcfCO+HWTZt6WgMyiv8xrmXHHtpSEFLpooMy98ZGsJ2PhPqw3pADJFggMb7931hqS6vLyMl6/fh29Xi9PRuX5PgKay4CTxCF2wdlfsBHry/v7MyUxbAKlvN6nj2Xg8ti/yyDXsmwb/DH1HtkjsIp4qPKkWgsdomq9DBhZX9svkqLgPbb7EsCBU5EFcBXbsEjs8T3mjcCd/oX1ej1/jxz0er28t0lQDgsBH5kI5r3u7++TfCNwNvkL9sMWjMfjrA4yOYz8l6SjcZjtVUmyc39jbWwB/3cyDXlx4pS1ZZ7eZ5+5FzYBuYxY7EzA5qOTbKekkrPb7UZEvJXEAueCE/jZbLY4nbrT6eSWM1ekeW6azWY2TWfumA8IF9sJ4h0H4cYbfI4qaq8fcvtYlcqHuhgX8sT4HyN2wSTezld+DjvClm7Wlne0HJhMBruYFMRve2u4fSxzhMxaLu1nPC7k2ImEiEWVkX0dVXb4GPyeyVxjA8Zqct1JG3y754tnorfoqROxnnu/H//mOyYBncRhayj34XN8zzGjZc42BByP3K+srGTszJqQDHX8w9yBEbBZ9j3lmpikgtBFv022YR/Bw6yB4zTrIjiM+zC+x0ht4wt+xljR/6urqyzaQX5NChoT8n/jie+6Piop5aoSHImJI36HMV5bW0uHcXNzExsbG7GxsRGvXr2Kb775JokEQBwZmM3Nzej3++nQWq1W9Hq9iIhKTwcMI4y3WVcCVpeYYhwAFs50sq+SIA/HbUDaaDQy6zGZTPLEkOvr6zg+Po6IyNOvarVaBvqrq6vZk2V1dTW3n1F5RNCKINlJunqCbNjy8qJJecTi6GzmByUC7JspR0nv7h5OSnOJL/17yIjjhFkf+gbQn8aCPp/Pk3ldW1uLXq8XNzc3cX5+niDHJZn0zGI8rIuBzzfffBN3d3ext7eXxpX7NBqN2N7ezmzNYDDIajBkqnQEDuIwbtyPvkYOTJDrTyHYfdfFOtmJRSwa7JmpZy7IpNVqtewhRT8ujDOnNGxtbaWRIri8urqKo6OjOD09TbLFQQykIc1QYfGduSKLzHqxj3s2m8Xa2lpsbGzkelD2DmjAgQHcACUQaGQbGUuj0UiSB8fnzBZ6Z7KmJPZs13gPb2l0YMFnXf7LGvDd2WzREBZylrkFELjaw/23mGd0DpDhraoELFRM3NzcxGg0SjIaEsrAg0pCCHeCCL6L3kF2cZDD06dPo91uV3wB42i322ljyNqsrKzE6elpOnz63S0tLWVQ7Yo4V0CW2RqDlk9BVy0/DnjKcZWyha3yVpMyWDeYjVj0WDBwdrWaq8wc4GEH0GmIJoMT2w4SJxFRAXOMD0DrCzDmIM0NmamertUeGh9Pp9O03656KMvTuZgnk+F+d/TG2Uo+b5vpLYIec61Wy/HyfJOKyCVz4qoF/CvzD/5wc14IKN6T8QFMOe0HG4O9iVg0nEU3jUMglvhDhasJMaqKrS/MBz6aOeUz2Bd+91gw8NhlUuld/37ssi97130/ts4bsEdEbqdypcTm5mZWDdJrycG/fQ1rSoDCz4ypNjc3KwSr5YCqKJKq6C1+Ax1huye6t76+Hpubm5VgDQLiMXtKdRT35HvHx8eVihKwYsSi6tHYu9/vx/LyclxcXESn04mnT5/me7niwzbIpAhzhKw+FigxZuMdB3ieI8bpQBR7yRozN81mM30t7+jgsEzK1Wq12NzcrFR3kozHj1P15EQB96vVatHtdjNe4Nj2+/v7OD09jYuLi9je3o7d3d3EcMYfvPvTp0+zBQY4yAmhiCpWBI+w7pADnU4nbm9v4/T0NOeaRD1z8106/Ye8nOihwgyi1ERmmazBRo7H48SYrowBs1GpHbEgVkqCjmcgX+w2sA5FLLZz2c9HLCqb7YcYrxPm7l9krG8CwqSjkzgkhCMWhCk6xBqbNPPP/Z5OuIAXmTuTMn6/MqFQ6rc5A/AEz2c+eA7jL/GA9cV+2iQY72+iGHl/rJITXYyICjEFzjEJSvzDHCFL2NN6fbGTwgk85oE4CxkjmeV3Y36Mh+0jHN9FRMowuuD+giapLIPmVOzbuaer/b/r+mik1NLSUlYkmWmdTqcZ0CLwZpufPHkSV1dXsb6+Hk+ePIk3b97E//k//yfBmcEm32c7DVvIXIJHIIeyAUBR7OFwWKm0YPGZfJwbztykS0Skcyd7hMPkFBuqEyIeForS1lqtls9GQTCSvV4ve+64/8H19XXs7OzEdDqN09PTPN2PDAnGBaVhLIzV2WgU0NvvarVaZs/a7XaWDKO0VKfM5/P8HYabC/Lo+Pg4Wq1Wbp2jlxNCa1AEUIFgxKiwJm5sDbGJkWBucTYvX76M6XQaz58/z2y3WfPPP/88f47RxuhwfwdygCcDCsAbFz8vA+BP+Sqz4xh0jGHEArBxQhryub29ncAGwLm+vp6n7VBJRrD05s2bODg4SPLS+64jIo02vQ0ajUal8oH1ILDiSGNkyBVukFUmF535iXg4FZOtrgAHE22Qac44mTygMT+Et7c/mUyLWJT3uoKEgNCZBcaPHi4tLSUwjFgQyYA+2yF0n+oSgllAAzpPrw5XVdgWogPMAd+HjGYc3W431wWAsrKyEq1WK66urjJAjoisnqKcfDwex8HBQTSbzeh0Oql/rgAbjUZZjQaRhX1dX19P4L68vDhB0D0g6vV6nhxoktVz8SkQUhFVAEqVAuOMWJBLdvDIl9/Dvgq5IigxAOSeDgixA7aXfhb/dwUUNh0Qgz2hdx/jcrDp06Pu7xe9FfDbjPn29rZypDF+imoQPoeeM3bsDr6A9/FVBpc8AyIcHWNtANEGck5EoTv8G4LJa8AYsZWMEZIf/8o9DFqxKcyL18yBgbGOt9/Z9xOwRERWvEBQuKFyGcDYZjgYNtkIBrJfQW78eQfwvh4joPy35+ddV6nPHpt//0OyuD/2QvYJWC1XyAvb0Tc3NzPodSWJP0vgh37aniFv3mbOuuEfTk5O4vDwsJLs80EXbNuKiPQJ9I0EOxFogJHcAoFnI0+NxsNWubOzs6xOb7fbsbW1lS0zZrPFllRsBvKNnKIv4D7Lme1eGSSXa86/+T/4z2S5k5GuorLMop8m6vH56A2kmv0v8Q9rw5YbyzTrAv7p9Xpxf3+fp5f6HdgKOhqNkgDk/pBJ+GzGTz9e8D0YB/8AziV245RdJ9Pu7++TVMXOcGGrlpeXKzEBmAX7XdqAj+GH7e/m84fWD/gvV2yxtpCps9kscantsom4Ug6ocIxYVErxb55vPXa84ftbfuihyRoZY5og5nNUqpdksclWvhsRGaMzH6xRWeWHnnIh98YkzCP+gqR2iSW4aJWCztlW+juPPdfEFX9DPnorvecXv2i/6ao5r4MTT2DmkhAHa7j3K4Q0Pf2cUC2TqMxHo/Gwy4kTb/kZtpY1dtxa+gJjBna2OI7Bj9MapEy6g01ubm5yO3B5WY/wT5ZxdMC24X3XRyOlMIyQGRgrFtLltwRB9DYYj8d5AtSvfvWrnESqJSKq3fKpjOK4cgQEp8d3eD4VOaenp5WSXZdZI7goqrO2AG1Atfsk4YxxunzX++P53vn5eY7Pzubo6Cg2NzdzKwzzicBR7YEyEHw5MKPkns9HLJhMv8dkMklgzYlnq6uruU0HVhbFZgse4AcwXzK3GxsbcXx8XDnNiG1UNGK0c2cOIKbItp2cnCRgQk7IShAYe/vTbDaLV69eRa1Wi2fPnlUA1MrKSmxubmaWm2o3Ki9M1qGMNlwEIs6mGQQ5G/6pXzZeEQv5ilgAL0gCDDVzidODaGDt2Io5m80ywKFvBXKIAazX63maG4CJ9acSzZkMKhpoZm5nzpyzlqwfTfWRN5wdIL7dbke32412u10JBj0n2AvbAtuLiMWpVs44Qaa6vNZlugSaJrj5HE7Sxh4bxrw4g8O4nCW6vb2tELysiQNFqiOoOHUgi2ywRvROo68YhD5bpGmQPps9bKHt9/vZLBfHSADO9t9G42F7JfJAMPb06dO00fV6PftsYKOwPdg3wApkJ3JBcFDOzffN4HyMy1tATHCUQL0E8ialeC9A52Pf9T0eC9ys+8hBGehFLIJE5AV9vb6+rpR5GwjjHwmSAbAANWw7GTr0yPfBtwJcuSe/p8TeQZ2zt3ze+mnilucZFDsxwX1ZMxPR2ATsI37AVQjlZ7FnvCO4xcGCqzDK6i/msFarRafTSX+2tLSUFee8P9u1IAapUGabF5dJNI/boB9/DZ6zncKHlnpm+SmBvy/LYymjj5EM/vm77uO5N4j/GJexDUEIeAXf2Wg0otVqZWIG++uEgUl73sPvvby8nIfpUC3He9NrkZP5lpeXYzAYVLA32NHVklTTOTOOLZ7NZrmdE3/qvia3t7cxGAxyvjmJl8QCclOSufgkMvsQesahXMZqDoRNsPidbAOtl5ZL1oN3sq9HFpl7bIZ1ljXHBmCf+L6JcP5Gj233WWP872w2S3xE9YZjKiqPaLkBGUD/NGQK29vv9+Py8jK63W5uG8WuW8+3t7dz7jxfpRyW9gnbSCW1T7s08Wj9/tAXz2ENnLhwxRTywDteX1/HxcVFVqh53JY32xr8YondLBcR1cpl6yP/9inUkB9UsxN/sK7EkfxxErSsxiKpD3Fh8gsdZOzIDLJffg/5sH3z80goRVSTAcwda8OzmQuTSCab+K7nlHvbX1m37TdZc+Mn/D3fcdLcuBt9s0wZC0UsDuBhHGy3A+vM5/Mk8E0omZDjQDbWjvdjjvDRfr4r5yIiYzS+i2x7XYjf7ffZgTYYDN4qvnjsMh/D3Ho+vs/1UUkpAKsdrhl3b+drtVqxv78fk8kkG+y+ePEihsNhJdNNwAmDTwM/SB+ALwpjAHV3dxdHR0dxfn5e6cfCeCMiM+8IBwrvEw1cmk4lVL1ezy1ABMBUZxmAMqajo6PMCLfb7TQuCAdKw7iZQ5odlwaQiqjhcBibm5sxmUzyu2Svl5cXDdQJgGwo3AiVsTxWDophRMit3LxHu92Ow8PDOD8/z6qx4XAYEQvHjfKxljZUOHx6X8FYY6htIEo2vNFoxOvXr6PRaMSzZ8/ylDgqyyIisz0oZ9mzh3GZPMBIGYzZQX0ssPv7XiUwt0Hn94AzO63RaJQ9mgCnGFkDaxNQbO+MiCRkms2HHgnOljq7g4HGmWBs+ZnJMGTC4C5iAfYZT632kJne2tqK/f39bBJMU22IUBw9xh4nDrFph23nAFnElgVnbCKqzt1O2nYH2cNhkcnkO8hlxGJbMgEBdhZbwxh4F+bPAIM1YQw4FG/r4Vl8lnL8ra2ttAVUTQKgVlZWYmtrq0J607cAOzQcDuPVq1extLRUOXUKIoux81nGyVh5JycF7LBZP4Pqkoj5lPQVEtNjNLAyScLPS2IKXxpRrYQsySju4e87sDPxhPzwpySr6K8wm82yqs1krU89wsbzHsibEzToBDrHe/iz6Cjva2K6JK4YL59xJSDgle/4Z9bTu7uHXnkGbyZi/F0CQgJwdBodsC6SWGFuyuQZPtiVxPhn5p37saas12g0SpvG3PA+8/k8SbBer1cJSPH9XiewDJiBNUG+HNRatvw7/GOZtfb1Pn18LHjl8+Xv/H9/piR2P+RlwiFicfLU/f19bkluNBp5Gtjy8nK2bUBWTAJGRMWfIEOsF0lTEzEXFxdxdHRUSYTgK1zNAv6kIbYTviZxsK8E8GzvoMLz/v7hMIqdnZ3Y3t7ObDun+u3u7ib5BNECCUUAx3cgpO7v72N/fz/nBjtFooL5sT00+WP77ws9KgNg9NefNzFlcgs5B7Mw7xELItv2x4kRLuN8AlqT5pyQ589ZlpABWp3c3NzEyclJpdE684R/RicHg0FcX19Hr9fLOAq8i++HFPP7lmQrOJj3AYtYxsFpJl28Zh/6ciUItgo5N9b0O11dXSWBG7FI2NhHIl9OiOALTIRFPFTKQjbwc2IqdB27fnd3l7GXq+0di7EW/By7EREZu1mPGT86jL8yDvUhXmBJZC0iUgadvCgTaZPJpHI4DfdBbkjqmJQybkcvsTMmoEyk+9/GCXzP93Syyckex7DGyLyLC1m8rsRI6AzJH/gJ8LvXhnknnmenCRc+lneyrURWebar3sr4Df1yxXQZIzv+9/pOpw+7jXxa6/e90B1kzwTe+66PRkoh1C41LJniMkNOULK3txeDwSB7BLlMeGNjIyIidnZ2otls5nYzyCEaOZq1v7+/j8PDw+j3+wn8S2Blw8BiuMGZS6Fx/IydTBOBKEG6q53YfuOyQnrZsG2I8aJcCCzBO6CWsm+zp5PJJLfJMGZYTyql7HDLoBfBJNDkcwbVrgJyJRrbaBjPyspK7O7uxtnZWZyensb9/cOJG3d3d7kVjB5eq6ur6bQISprNRQ+h2WyWx8WzbSticUqJg3hnK5aWlnKb0NOnT/PkPwx6q9XK9+T0RQwXVVGACFdlGWjaMOKYbJw/1cuyHlGtujAgjlic8Ib8d7vdfGdI5el0msCG0xTn83kcHBxkrzcMNsdBQ176qFMcCn0HqGiDWGXLnisa0T0a4pNZpYS2VqvF/v5+7O/vZ2UQgTKAnpP3CHLtGHDS6Ct6UVZNofcYd8sQRBXBcK1WSzk2kcB84yiwT1QbYgPtfJl/Z1mxQ2RQTfg6K4bzdPbI2xogm7wFuN/vx8nJSdze3sbOzk4lOGetJpNJtFqt2Nvbi7Ozs6y6ZM6Zz+FwGBcXF2m7LXf7+/vx4sWLbGjtfgHYG4NEZM7klMGL5duB6k+tqxAnVPb5KrOB5b+5vJ7erlYScHzXIKwEZhELYAiowFfaZqCf/I4xsH02orr1xuNysMZhBfy8JE0ZAyDMRLXxBPKLfrJlAF0pSXdIH+swgaD1ARmDqHVAxveowjX5h4/n+w7qqYIh8eFAFfn1XJfygi5DOkVEbpkFbM7n88oJpNge1mRpaSkPIdja2sp7OXvP50188z5gCeTM8mQ9K33L+6736eP30VPL+7t+5mDxQ188i2DAMoxdxb9g20jsRETiP3yGEwXWO8s/zxsOh3F0dJTJCzA0FRXD4bBSvUSQaBzn9cR3RiwIttXV1ej3+9Hv95N8Itk3HA7Tvy4vL8fe3l5WCPT7/Urghx5GRCYT3W+UqkdjbkgX427mqLTzEYvAtKySsi1ykOc1KWXHNsrz5NjB21dMTPF/xsH/0Uv+kAgHd7AdnTngucwjn4UEODo6SqzElmJvoVteXo719fVsIbK1tZVtGZgvxo+M8HPsAP/H5kAwMn8maLCtbuTsef3Ql2XDzzS5Z3uFHxuPx5mAc1KP7ztRADYxecm6k6jnc9ZdPgPGRUeRKQhbVz3xO+wyz+K+thWOS2w3kHHuy9jOz89jY2MjNjc3M5FonQGL4VOwWRDUxKfr6+uZ1DYOYO7cM9EEn0kj3sv6w894N2wCa2RsXOoYcm3587yVegVOIB51YoDvlusJNrL/LIsW7Bexb/hyf86JMu+2epdNKQk5iCKTyvhukhI+hG48HmfbkB/jJ0uC7/tcH4WUgh33ligE0xlfFt/ba1CGfr+fhgEhYKK3t7fzdw4ocZRuknpycpKBkYM3hCdicYwuAoQDRDhQNm/LczUW9+71elGv1zMYbrVaWcZ8d3eX21cganAAbMcjSCd7QbPl8XgcrVYrBcWCCSmEsqM0OLa1tbWYTCbR6/UqRCHG2IbJJYxkud1UzSx7eSKWWWMU48mTJ1mV5gozQG3EwxG0JycnsbW1lScrQVA4wIHkOz09jclkUjEcgDkDeozSmzdvYmVlJQNob7sg+02mjwDGWzWZb5dMW464ygD5U79scNBRgB3ziVw5QIRQAkThgKg8urt7OH68VqslQVWrPVQpbW9vp6NDfsjW44TIDF1fX6eB5LPO8EQssvAANweONDFfX1+vyD6VAThPKg9xEHbkdkg4FQhe3h85IHvmCg/IKMAKIJf3Rl7InqNLBAjc09tkTCYxtwS3yB7PMlljUPUYsVWr1XLOIx6A+fr6epLJPtZ7MpnEq1ev4uzsLD7//PNot9s5vlptcRhAp9OJ/f392NraiuPj4zg9Pa1UzDQajXjz5k1m6JG92WwWm5ubSRyzbZA+e4yN7C5ZetbN2b2ScLH8/9SEVESkr2D7Gz0HIqoBNO9gEsU/R1bwA6V++362a3wO/8xnIZjwrxGLAMS2HlKt3+/n9hHIEZojA/r57tLSUlxdXSVhDVA0yc/zHKjZX/l9fG98nk/wNHBmS0tEdbsjOgqAc7bW82hfUIJmKnLxc2VVxHQ6rTSLtv5HVHs+cjnhxbq4WgZd2N/fz0MC8JlUmxCUus8NJLC3wkMOlEQjuuqAzKDdwT3z42pNLr77Pr2z3L7r3++7ys89do+PRUoZa3oe/HvjEchTk0wmIEzsGqtFVGX5/Pw83rx5k3pDBZK32VFZs7W1lf4W/A3ZQ3CDPwL3XVxcZH/Rs7Oz1Pfr6+s4PDyMXq9X2dq9t7eXdh+Zsm0maDs5OclEENuA2VqPzPLO3AvcwrzZ9nnekU1X9EUsglqvSUS1bxCy7PtGLJIg/qx1x3bWxD73dzBeriG25/7+oXUBeuo2B67Kxgfgz/f29uL29uGwpNFolNiF3RyuLLm7u4uTk5OIeEj2k/i378GvgI2RV0gLsFu9Xk8bNxqN0g6X/qUkhz70ZTxl/S9jUeYFTAFW4d+uKGEesZFlryHWhLYhzJ/9LgkPVxuWiW4+T1GAfSF4MCKyyAC5KYsO+Dx2HHuCrPlvk0fMj5M2rtYCN1u38D3oJVVBJnadZEWfkUfrj2Nx6wdjKpMhEYuT9vhcqav2s/wMXeUqfb13QLB2jJV55Xv2k+6XBeHoAprNzc1ot9tv2Xpsru2dK/PsIyzLfi/+RCxsnWWdHRrI+GAwyHiA2M6Y44dc31e3Pwoptbz8cIoLQXzEoiphOp0msQP429jYiJ2dnQR0EZFVMUw6gSaCgHEtXxzFPzk5ydNmms1mbmmLqAqNt7DN54vqIQTeGY/ZbFGq65JV3hnj4Ow3gNh7RNvtdmxsbKSTqNUWp2TBmjqr5cCZjDogGEPIe+GwqDaZzWZ5ogsGq8zaRSwAEkEocwA4IEjh/Xh3gA7394kjBMwXFxeZxcexUsaIIaJX0P7+fu7fhogiyJ3NZvH8+fOYTCaVJmx+JwcpGLDhcBjn5+fx+eefV4IfMkQE4ry3s+Hcj3t6vgjqyyzLH9vlQAWjDlESEdmgGKIZGUUmaMZPtqxWq2WTzOn0oQlqt9uN+Xyep7Khc1Q9ldv2qHQyiDCIRa6Xlpbi/Py84nxpuk52gxP2INlotDqfLzKGOGSIJRwl9gMnXYLPiIXuEMgiQ3yGIMMg0+AV54dzR88NIhgrP4eMgbCKWGyH8nYCyH4CB1elOsNme3p2dlapimD+0bHxeBzT6cMpa6PRKPb39/PkHwAL1ZGsNRWS1n8qXH/zm99EvV6PL774Ip01ckBQzhYqbCJAnSawnNzIHCPXBvyey48VmH7XZVBTZvaQIS4DLP8sYqEbVO0YpJlMiIgK6EXeS+BKZQQAGIDkcnCApZsZU7lMLzJkHBAPec2JrD5R0nJMEoNgl+1mZcl7xAO2oC8ga2zy2kQsnyFw4vuuhCabaPIPOXTloUGzM5pgCj7nyhguV1a5WtKBm22F18fbtZATgD+BA72D3FOTebWNd3KA+fUWZeaWKj4H7TwTEoyAgnEx9pLMet9VkkiPEarlVQZe7yKifK8PfTlYtW5ubGxUSIy7u7vcMmccWlY8lriU+fUWlfl8Hq9fv47T09P0I96ijn7t7u7G7u5upWoDPYX0wN/UarU8IXo6nabuMoeuhiBxCxlxfX2dWBf9AF9GRG6bbzQaMR6PEys3Gg/9aDmhFTsAvvC2IttD/nY1lO3qY0GnSdXy5ySoiVPKZABrYzvNmPCxYHMuxmIfhbwg3/iEjY2NrKSkcury8jJPJSSG4PNgcRrrN5vN2NnZic3NzSQSWRuwG5VU9Xo9ezhC6ptcY3wkHXhXfme553cbGxsxGo2yR62xufH1x7h4nnXfCXjGjq0eDAYpa6wVcuV4MGLRr9AyaqKDHn88B9s5nT5sofTBNSas7K+xzWVVFJftDDgWLESyisQLvysTQKwPxRJ8zglM1sxbv0hCRET2lbVcIItO9OAP3QuLsSFz9ndcxgTMI3Nl32h5tRzbLpSkG2tjGQW3eD5dDYc8QDgxHmwHdtRb8pE51gws1Gq1Yn19vcJruNDisSo8bAM4xMUljlWxZ+4hFRG5i8XFJhxKxgmexgsf4voopJQDCWf9CSZQVgIQHFWtVstKJx8pjuOiEqLZXDS7diA4Go3i4uKickIXGX6MOJNvIOatAAY1GBcCcZTWzg5hpW9St9vNoJdteRik0WgUk8kk35UADeB3fn6ez8KxMJeeW4TSwe7l5WWST1S5MH9UUwFY2bNM1QuCSZCBoDtzzPf5PesH8KnX65mFA9Q3m83Y3d2N09PTGI/HqZzemudtgNfX1/HVV1/FaDSKnZ2d/IwJNJzdyspKVsABtp2NwcBzwte3334bjUYjfvnLX+a96vX6W1uLIhbkFIasNGoONMrPfRfo/pQuDKqNVkRUyuXdUB+y09+bzWaxu7ublUrIHWQOwIn7lwE1hu7m5iarIyljLisjykCR5trNZjO2t7eTzDRwevr0aTpkdKrf72cGa21tLT/r5o2My9mdUv8h0ameiqhuq4AgcKbIW2mp9LSjxpbwfQMD1gGyjAAT3cVesY0Q58R7AEQM2tFp5GF5eTlP/2QLRsQDGOj3+2mP+/1+6uXr16/j8PAwdnZ2MuPj94aApiEzVYno7P39fbx+/Tp6vV5la+fz58/T/tzf3+eBDvy/JL15H7YJ4KgBm8icyeWf+jLpb0LiXZdBvEkk/g8o9sXnIqoJGQccZYWwEzLlM/GngEICHMAgyRIDTPzneDxOsMN3fdIMiSeDRIJviHHPgxMHfj8q6Vh73suBHsEEW+ydfQS7OFikCsvglqpeqpkgliGG8a/4PLZhlIQ2PgWd9VZ5k9dUmeH3SXxRaUmfSmwC24F5LgGKE0tUymDvDbhNTjM/XlcTKU6OMZ8mit5FKpWXSaby8yYD3qUb/ozvVX7uY11lIGF7b30kILCt4rK+3N/fZzWLA6OLi4s4OztLP3lzc5Ny0Gw2o91ux97eXmI11tzBrYlEKlM5wGA2m+WWT8gM5Jog1Vt5vOUc2aH6nxNdr6+vo9vtpt1ATtvtdmxublbmyIEtiSMuZNRyxnchh4z7jBFZo4iFruFPyuQT72Gbih54nU38lzbUhJrJKZ6Ff5/PF0lyMP7R0VEMBoPY3d2NiMj5dDyDT2Udl5aWYnd3NwaDQYxGozx4Blu1vr6e8cv9/UOrk/39/Wi1WhX9XVlZyfWxv8ImIEs82/MDqcbW+zJR8qEv7JjJMI/TxOb9/X325HW1iW0c33FcwtqxvmBD1oe1r9freUo5MS3z6WocxgtWNA521SAygg2x7y6rBfE3XI5vmCfHBMSB+HfmxMkIyzr6TnUQPtaVS7Y5pR5GLCrBIhb2iLn1u4AxbCN4Z97HJJP1lLVwfMHnKAJxD9R6fXHIA2uOr2WtSp/Jd1kryD1iaOMSKlUpuCFmMvFrjMaz6BvGXDP/Jr2wM+DwiHjrZ8hivV6P3d3dytjgZVxp+4e8Ptr2PQSUwMnOFwHlxVl0MicYsaWlpaxo4AhPBIgFjnhQUow1QkAzSQCaM+8lIMCBR1RPyQB0o/QIbaPRqGw3WVtbi06nE6urqxkYA/wajUaSbowBJUcoMCbz+UPpda/Xy4ohKlY4aWoymSTzTqPIy8vLFGJAPu/J+7EVhvVh7szmj8fjfJb75zhIZw1dClir1fKZzE9EJOm0tbWVbDC9ApxJqNcXJwThxJvNZqXCi+8w5ysrK/HkyZNK5cZj5IHBzOHhYayvr2fFFIpoUgvWOWJxyoPLSTEuVMuZnPpQTPIf+jJYcGWQg4+NjY08GQ15g1igQT3ygYzjhGHjOUETEGKABvAmILq6ukoi2pkK9MK2w9/v9Xqxs7OTesAaYF/KflV2FBDVBnbD4TD6/X4sLS3l+yPfkFZ2asgMRArkkJ0JwSjfMalCX7WIBXHE53h3Z3a8JcYOGnknW81craysZNDt7UHYB5dpc8jD8vJyVppB4OHs3JOPOUGvIyI2NzczuGAcyBUnd/7yl7+MwWAQ/X4/35VsOqeDmVwj8PaJjgQHnU4n7fxwOKwkFuyYHbB+bED8vgs58FgMeD1uAy3WETl/jHTi9yZ4vB74Um/XcfDkwK+068jDeDzO6qj5fF7JCrvhZ6PRyL4zyJ9lGdtBAotxeKsqOg/RY1IkIlKfeUfsMyDcGWLGBPDyEehOLtjuO5g0OY1/AMtAGPO7iEgyH1+DbAJ8mVeAp32Pn+01tb1mfSMiSX1khN5B6JGrMpkXbFS9Xs/KnVptUcHNGJh3k3cG26xbKUvflax5H2Fl/XjXv/l/+T3mxTL1MS6Pi3VADufzeco6c4cP4sIvl+Qf98Of8bvJZJJtHk5PTzO5Q6IBm47sIQclweKE23A4TOzMmpPEAZcia6urq7G/v5/vSbDkpIsPzOCe9/f3cXZ2lhVXEZGEACQ0eJBgGL9qmQGnuuoTHWX9nVwskwDGfA6yTXLZF7MeDhAjFocJuNqhtOHWH/tjtljyfNal2WxmNTr3Ojg4iPPz82i1WtHpdLIiLeLBB9/d3WXDeOaFLb7n5+fZu4ikMHaJhORwOMytvybp2VppnbKvMu7gYCMSbe5pZf/1MS7Wkst21OvncZX2AjvLetmmOCYygQWxant9fX0d/X4/P2e5snziwxwnlkSY4yPHa/ho7LareSOqfc1cOGLfgz2ish17wfuYOOG7jt9IBqO7+E3jL/tgk0rMCXPk9i9eU++yAJ/g6/BN/AETurrUpJ3XwkSfd4BEROVERHQObEFizn2awCAm23gu64ctBJ9ERCUBxvqANUjy8W4uHjGXYcxBeyH6MxOHIEvT6TS5h6Ojo4rsQ5SVW/L/ENcHJ6WojnBwCLFjwsBZDyaKPi8oFMb15uamEgRHLLYKnZycxJs3b5JoIpDjHiZNIiJLMt1jIWKxt5fxAhZ6vV5+nnfgPRFcCBR+DhCo1+vpGAiC3bBub28v5vN5VlCRUTVbTaDK/an2cPCPMN/e3ka73Y6IByWhSoigPyIqmTMCdisOlUc2Qvf39xmMRiwMGobpMUIPgwVZcHZ2luSE+1cgI1Z4toOQ3aO5NsDCcrO3txdra2txcnKS2xohCDjymvmezWbxzTffxNraWjx58iRqtVq8efMmARvEGGsascgcuMwUGWYMGNo/lssBrp0q7w1gRm/phQa4hQgExPC9+XyxZ54SYB/z7qbhyD0ExPn5eZKsOCBObmN+nYHq9XppZFkLqrXINtBzCBAI4MIQ0x8DAgWbgR7hkAjasT/cw84Scgc7YQLAc4vMELQaLEBukcFmOxT20kEIlysqRqNRVkwQYBucsEWJoBknz1gg8ieTSRJFy8vLMRqN4vz8vLI2Nzc3MRwO0zZhp169ehW9Xi+63W46UuYDUFqv12NzczPtEsdSQ4bzu0ajkZVQ6CVEGvaQE4Rsy6nII2A36PSa/CEugNDve2E7Hcz4d9/nwv95ixvyYkDLZ223HDABdBwwRSx6IrGed3d3mfFFLiBkHaTd3t7mNgWDWAe72BsDURNu2F0ANIQKfqRWW/Tc8FZUZ2Rt6yIWW+jIFpY4xUQf7896l1sImWuPl2wyZJBJLe7r7cu8O/dCfxz8lBlOg1zGGFFtjg2QZV4Yh7erc1+2BIPhvJ7YC2Mry56r0MuA7vvoidfox1zWk5K48vx9jMuJA9sfZMgnoUHuY+Pdy8MyjLwYs0VEnliH/EAckVDa2dlJH47vMwHGc8fjcZycnMTFxUV+fjweJwZAnp10Zgz0jGI8vBtJqU6nk1jagbKxHI2gXSlmn+/+W9iCiCqJb/tu0tY+0zJmHFSSAyauXIVl+8R3S4LBBH5Jkj1mY3mW8Y5twHw+T2KK4PTi4iKTYmdnZ9Fut6PdblcqJ/CZxA29Xi+2t7fj8PAwLi4uYj6f52E2bttRq9Xi9PQ09vb28j62LQ64TaYQePs9iYW8fuU6fOjLsuExmNixP8SOmWzAXzB25pb7sE74EVfQ8LPb29s4Pz9/a1wlecq9bTtcGMBOBFduUcDhUxyR2zKhQgxYVuZa/lgvbBJrSrWyCVl8f8TisC7Wn91AvrfjbidWmC/Ic+YTgol7Ovno5JljBRdemLhDp4hxeSbjcbWaCZvShvBd7uldEBGReLccM2tgAtKxLXYXDsS2mjkwSWwyGzzEPYmhScIRZ7x8+TLHRZJhPl+0Pnnx4kXOB+/bbrcrO4v+UNdHI6XKLXFkVmzknckAxKHsS0tL0e/3M7NKw1WE4PDwMM7OzlIwS6PHvQmkzFJGRAbeBLH0miEwM7BlW1JEZN8lhM3Bl8c/m83i4uKiEtCyBaHRaES3260EUSz+5uZmGhc+S0k3gk0vnel0muXUBGsXFxdJZtkZ2IiyJi77jViU4NMfhEwbgDMi8thZyC4INwiHiGp2GdZ4b28vXr58mcYAQ8z6MH8GPnd3d0lQGfRgAJ2N29vbywzQdDrNY+ZNuOFc/t//+39Rr9ej1+ulEWC+6ZeAkXZA4Ooe5MwBhh3Ip3yVGSMDuEajkYGfS27ZekZvH2SU30VEOjl0xEHsdDrNSr2IyD4V9/cPjf9d0YAukF3FKVAZs7u7m8SFm0Mi671eLyuEIEy452AwSCKj0WhEp9PJYB7n22q1coso9ywzqAThkFsGpmQSAYZUjTBHkEbICoQKDgdZdaNQHAfPRHcAJwSQOFTuR4UpOoV9c+WDCZ2IyHUGFC0tLcXW1lasrKzEixcv8lkEWK6SazQetpFMp9M8ahziEpuJnYEI9pYttrAgB9hPsmH8TVWXExX2JwaMZeBhcvn3vbAJv8/3Da4cxJjQfBeR5p9hg+jVFbEgC9Bv3tkEiANcfJgBrf+wJtzDJA2yyb3q9Xpup3eDT4Msk3HIv20q1+bmZh7+EREZNBtMY7NcZh+xqMLyz0wKu8qY9XBfDf42vii3QGAb0LWISLKbObeOOvB2UOdgogTijNsBvbc6Q/ISwLO9YDabZQU6+Mw6Y7s2my22bfGOtVot5QLCge+CSfx3uXb2n+/zj+/Tx+/zO5OO5efLz3zsy3KNjSdRYALZmXOTNw6eSYCAO0nsENSiF5ubm7Gzs5PY12QrzwBDXlxcxHg8zm0kDproE+ptwVQIREQeGLK8vByTySQ6nU4GoyQG6SmJr7RO8v5gAvQInGtb5iC0JLBMQPN+6DgX2Jc5RR6tDyVpwrxy4atKcgtM6/njc6w1OmY9wZ44UEen/f16vZ5VzySZsRF3d3cxGo0q9hs/CFbB16+treX2PJJeYCywNrii3+/Hzs5O+gKSixDdjm3sGyAiGo2H/mA+bRhC3Pj5Q1/WH3wNa21Syp+PqJLY9lX2hbwzsQyyB0GIvt/d3cXh4WGlosr3NmnjZ/MMyEhiJfwo/sa+gsNhSEQSA9JfzlvvWAt0wicwsnslYrFV1Otv28TY7HeNZZyEwia5ApL59xzyc+5lAotnokO8D/Ll5zM/fMbxcElU2g5jZ5xodtUnRD86hhygd9zTRTDGOcw9OBl7N5vNslAF3AOn4viU3VnYCpJKrVYr55dCHwp/+DlrbOzw7NmzfJ4Pc4F3+BDx7QcnpcyW8+I27hYslJrP8W8WC4bfQgVRcXx8HBGRC+isgNlbxkL2iOoBrm63m1vxEDoTKjhI7jmfz7NPDoG6Bc1ZX8aOgFxeXuYWw/X19bi6ukrW3NsGXDnR6XRia2srTk9PY3Nzs5JZZXsLBpagE6MzGo0qCrC2tpbbK/w8O2kq0Mh+u++AtyfB1lJdhMEDZLB1kcw9+9Zp0sicwiYjJxh85GAymeSWRSowIhbbIezU9/b28jhLZ3e8PmQ8/vqv/zr+4i/+Ip48eZKljE+fPo2rq6vsXUNAYQCCXJUZEgfHfyyXgZznKGJRObi3t5fOARliXby1FgIiImI4HFYCNxh2njEcDmM4HFb6RmFQvYcckgMZbbVaeRqPe7YgNxEL546Br9VqcX5+Hv1+v5ItoRKPZwAKNzc3Y2Nj4y2C0VksA3tnU3F2rnaBOHVVhoENzRGxifTriIgkp9BF/h+xcMieC/TUv2OcbkZrh23H7fkBGPCOEB1/8id/kpWpGxsbudURIEQjX44j//zzzyt2hffEee7v7+cYmCueTRUWVaE4dapNIxYEGvdlflzJWAYVyMi7iJ6PednvmJAqP2OfVZLK/KwMrJhXkwKlL+Zvvo/cQDD5Hg6mHchwiADEE7J2fn4e5+fn6aPwaz79jnEha86y1mq1TGQZFDqwdHN9voM8zOfzSgPziGrZvIG4E0qeb8A/vT9arVZiCkgs7tlsNhP0sSbe4uDArlw3AmyvLQEfn3F2v7Q5yDeNqOlr6bWzfJlwYg3ASw56saGQYY8F+g5EIN3K7crMk59Ryvj79NE2qvz5Y7pQfu9d3/nQF2vksdObBfxDApR5xv45Yeskh4Njb78g0FxdXY2f//znmfxg3fkc97q8vMxTMOlnagIcLFer1bJHK7iXhBPb61jP7e3tSnsK/972qdvtVrZv9/v9uL9/OARoMpnE1tZWBsHYI+YzYmE7It5eY+sXcUZJdJu44h6sgfuuIJcmEBwL8F2Tfo4f0FO+VxIOfgfWhUCSIN9blur1h4oysPy3336b/R3Lwx4goKmmAEdgM7a3t2NnZyfOz88T87LWnU4nZXU0GiWp+JhsM25kkf5RnjNsYllh+TH9L7bLZJ8LDx4biytFvD4mB7DxrDVrBA4DJx8fH1fIfvta5Af8a3vIHLugAH1yQQDPJvFDQo81j1j0rqRyzr7x9vbhtEYqLPG7XGA4nkMCBn24urqq4FziJ35WYg30zfGrkz2sjX2di1xc0cTPwIcmHYnJ0Qt2JERUm3cbDxhPkriBfwAbRSwOFyoxHONnHWx/wPm8M7YZIjhikQy7vb2N4+PjjEuMY5hH5Gs8HuccUjyC/Sh1E3mBLCN2p23Lz372s/jVr36VdujZs2extrZWeW8u1ubHXh+NlDLLizN1yZ0/z6LwohhPCykky4sXL2I0GuUksLXPQYgDLUqMUQg32+YZJssiqiXwCDGC6FJ4xo3QXV9fx/n5ecxmszx6fjQaVSqVzs/PUynIJFNxtL6+nkF3vV6P7e3tLPlkf73BP0x8xCIra4fq/ad8xqSPK6ZgUbk/84ESRUQFXNArwFsUeLYrLAgMOp1O9h9BsSHPAB/OvDsAAugDYjiVpSQHVldXo9VqxeXlZfaLIhDGWMxms6yWiYj4t//238ba2lqSlRhB5hjDxjNQZAdlJYHxqV8lgDMZGhF58k232015uL6+jl6vl+SRM+PoMNk4nsG9ydJTFUVGln4VEZHZHztlsgaAduQ14qFyAqIUx8y2s16vF83mw/a/k5OTXHd0GeeKDTBZgkM1mISkqdfrleyB5QonHlElEZzlms/n6Xj4DLbB1SzOsqKfkFcAGMhhqhp90iH6yXvyvIjIzBO2AIdGAOLqI/Sbd4J83dnZidvb29RH7EbEQz8bQMjJyUnc3NzE06dPK01TG42Hnnxra2tZJcU7ExiRWcQmADoA3pCjvAe2rtySZLKtJALKwOCnuBhTGSQ9dpXjNgDiwmahTw7C+D168JgfjlhUqLlSis/4GcivARdB7OnpaSZcnE01seKMsYFjo9HIo+l5rn0zAJhnsubooG01PsYEpe/nSjwHLE5UIa/eCu7KKPAB20gdtHhdsSPMo9fewUYZKJvIhnCr1WqVnm2sOWObTCZ58tXu7m7KDvYDIg57wTyRrML+EFxBnuArPU6vqd/BgUGpe+/TBcvk+/7/2Hce+93HJKHK55sQilhs30NOx+NxynoZYDKXro7D/uFT+v1+BhX39/fZw7Neryf2REZsC05PT+PNmzd58nCj8VA17EDFW/3YRoLeEvhsb29nZQUY0rLSaDTy4AOqN0wsYS94f3Aw8+BA3JgiYrGFBuIJ+Xb1lIlm5h//YD0sSdGSAC/llud5PdEDE8BcJqx8H+wCCRfjXvtgglawc8Ti1Dcuqt2oFm02H3pDdbvdPIAJ/04l5dLSUp7QNxwOU7ZI2s1mszg9PU0shk2iGhUdJzaiX41JBPAR8xrx9vH1H/qyTzBByjy/i3Czr+V9iStMlLp4Yj6f52nv+JzBYJAVh2BkfsecuK8csk/s6kSmxwWeilhU4eOzjI+9PZOfEbtZHzqdTvoot+JwbOY4yFvVwLB8nvkymcN7G1dERMUmlvaa8eG3WEsnfxkf81ViPv8buS6fhc66sOWxxuFOPoNZmAf78YgFH+LqVhJgtD5wrA0GKS+SuKy/STbkjTXj2SsrK9mnFyLM7QT4rONeTgDkhFa2VP/Jn/xJ1Ov1+N//+3/nWpZJ1B9LTH2URudWaKoEIuItx8zL0ODcJ7Eh3DC5h4eH8e2332agQoBoBwVoQ2gw4hAjnAwFmMTYu5yexSEDi9BCHEVEAgL6RZH5RSGazWacnZ1lkIQB2djYiPX19ZjP50ms+b7u2cI4INFwwDhKA2kDaxwiFSARC+JhaWmpUlZL4IJyUn21srKS90boIKEwalRAsc0PQwYRR/NxhHdp6aFpfbfbjaOjo7xXGVBAwBEUOJCZTqdxfHwcw+Ewnj59mnvsIT5YT/pYmTzB8a+ursbFxUXc39/Hmzdv4s2bN/EXf/EXcXJyEtfX17G7u5tVPAaGGAqvJ4CJIO2P6bLeONhlHRxgAVYjFltQkE2MLvcpyQQ7KwAMQNoGDRDFXFMls7u7mw4Jo00vKOvwxcVFVgeQ7YFoISuMnAOUe71eBps4PIAyoMBEUEQk+YLjAlQ7E+SMEuDeoNRBMd915sNBBO9BcIr+RUSW6/N5CDAcG/qHvrJ2EJC2T+jHyspK9vLjD/PgUu6nT5/mvPi4Z8iFtbW1GI1GcXJykuvK+iwtPTSR39zcTIIMW2Lgy3szTwBhMkZkxEzEICvYZM/3HwN5XJJT/L8EWgaltpP83sCKz/lyUOYLP2OdRl5YWxNJBDfIX8RDpeRgMMiA2tl+vseYnFwg6OZZJlIAi7yHn1mCe291BQQa2GNf2AL8WEU2pI0DLoJ++wV8CtV8nnfWxH2ZbANMnKLnvAfPxdcDIE20QIhhO3gvKifpYXl+fp4VLMgN78szNjY2sqom4sG+DQaDmM/nCUx5JpgMH8C6YCtNgpsMiVhkeB+Te+becm1S4V3y+th9yuc/Fnh+6Au9Ae9GVBN7jMsHvDCP1ltvlSr9NeuNbSdYdp8qH5gxmUzixYsXcXJyUpGx+/v7ODo6ypOuITwhxLDf6Pze3l7FphL4RizIKXSd73HyJTJGr1Bk6fr6OrfPRywqSRgPssfFuJAp+wzm2HJgn+qfR7xNTpgMKIMuB7R85jECygk/8BCf5fflfYzLaIPiaiSCSt6bA4w4TAlMhA3ntFP6ZLZardRT5hiSamtrK/r9fmVrEgTKq1ev4he/+EUlBmEuiGHAASQIsQVgMM/xx04IkXAjmcEYjO0fu+xzTYZEROJlYlawJKdJYvun02klRuK7XCRz+L1jHuQBuS0JHcbOHLviyHgS2WN3Dd/lXujE+vp6JgrBUciePxexKB7x/Hm3E+8CGWdsYfKIxKrnPKJapeZYkniSeXTcjY5iQ/mu/QK2ymvqhNW7fA04wJgLXcG+8T6MwUUvJt3gRNzahPUox4PfHgwGlZ1ltmlUVxpPuDLq/v4+hsNhvq8PMICIvrq6yrh7eXk51tbWEuf3er08bI1xegzInuXAFZ7vuz44KWUnCih0eZuViZPq2M7VarViOBzmdwFdL1++jG+//bayZYqFoKLFxA0LQsaVQNILgHGlxA1CBEGdTCYJQiMegDYZoPl8nv9H2NkTaiGlabkrrixoLChBLL2svC0QIwpwYDsDQBgGlm12zI0NGEGlx8ncrqysZOmqj5LneYBOGzkHf8vLy9HpdBJs0LiY9wLck3WlKScZHRSHz/HOzBFGkOcS6B8dHcV0Oo3Nzc2sIDNbTpN31onfHRwcVLaJvXr1Kv78z/88yTVOl4PYMOPtzJAN/ace6D52lQCI+QEgOvi8u7vLPk0EPKxJp9NJPXYAa8M6GAxiMpnE2dlZLC0txZdffhk3NzcxHo+zqo3tWUtLS7G9vZ1OikDJxCTjJhhCFr/++uuo1+vx/Pnz2NnZSZ2j1BfgAPERschI3d3dZZNtfm7SxgC50WhUjDoEsCsEmQfmF3vjgNXrgH4CAiKiYit8ShekHw7X2VrIqIhIcEjg6tLrksCv1Wq5ncN2E/L58vIyienLy8uYTCaxvb0d/+gf/aN49epVnJ2dVcqFTe4dHR3FeDyOP/3TP80+IRGLyk3kxLbXpIQzhq5gxBFzQYLQv4LP/hQB6fe9HgM/P+R75XtZJgh0nAnz80ycINv+WQmmuD/BKn6I6sNarZb9aQA+yCJr7oSIM3qA8rIiqlZbHFLiqmt6HpSVSYwdX09FJfey/DswwQe6spb+TNg7ZzFJfjDHbi7rwJjPYkdcOo+M428trx6fQbLtCHrKll90zusOPjg5OYlutxtbW1sRsQhm8MHz+Tyrv6lkjVhkj/GhJMBcHV1mbktgX/7797m+rx4/Ruz+FBdr5S2lrvjl3/SypNUD26AtT65cxK866PTWOogJfMXl5WUcHR3FN998k2tNwIK9pt8gsjubzWJnZyexIKQGckLQUasttl7f399nNZ17yDoh7C3rkAJg2Xq9niQWzzWBw/OYS/SZywSRdchEqT/Dvex/jHvw5fbZ2BzWt3y2A27mCXK5JKQYJ3bIZERZGcPfS0tLSS5xnZ2dZVN54w12Drx+/ToPKyLwdC89CIvt7e08cZe1QX7H43FsbW3lvDQajay0i1hspbe/gbAyMeu46WNeECguQngfKWV5MFmE7EZEJVEREVlFxqFL4CwXUJSJNj5HrIbOlqTl+8h5ExHgXseGxHWOWWynnVBgnCZA8IuMkaQQ/57P55VqSnSBeTEOK3WW+TU+JmY03ude4EAXklgneR+/JxfP8e+t87YNfLfEK9iGsoK51F3blfL54CviCBJPJJZKnELBycbGRh5mZhtmTMa6wAmQlHZyGtkwrqYoBh+yvb0d9/cPp6N2u91KtZ3X913y+H2vD0pKmRTKBzYXeyTdKNFVShERnU4nS3/JpF9dXcWvf/3rODs7qzzHgYpLFlkMSochEJhIsjz8/P7+Pqtp3Iw3YlEJEfEQKJL5rdVqubfS/+fdUGQaQeLANzY28t6MjYbALnX29iVIMECglc3HpROMeWzu8wIo4fsQax4TTspZK7IL7CvFqUZEVi0AGlB0nD9scAmQIbBYFwyOnTTlwzCzJqxYO6rUnj9/nqQUTDEEWavViru7u2y6PRwOKxm8iIijo6MYDAZJrNCw1wbJxB6ginHZGP2xXawT7+byYfqXsXY4Hkha3pk5j1jojEtDR6NRVs7Z4ELmPHnyJKbTaTa4RlbZjgpINklNaTlyenx8HKPRKCIetnaSPY54ILXoe+QjsXl/B8ZkZCOqTRGRu4iF3aKEuwS3yC467ioxHAvVJSasXd0A6JvP51lyi66ZdHZFF87PlRoEA9gJng/RfH19XelfghO9u3s4VY0gh6AX/cSRjsfjWFtbi62trUoG3PYKWSKjC8mFY2MLJUHb7e1tZpyYI78b/qXZbCapRc8MyDMDOROa/P0pEVSPjeWHBO6PfR8wVxIb5f0NxviegRXzXmbuvF2GeyDT2GwAkElPAmV0ii0hJGCcVPG9TdiyvcekKu+EL+X9AFGALft03tFAE2IZf8Oc8N6NRiP9KQGYqycYN8G09bGcd75jsgsfarvM/ah4eWybgN+h2Vz0lrHtxGdeX1/H9vZ2JdCy33VCyraQLWGQUgS/vBfr7SCX+5pMMDnwvutd8vrY798FjN91v491lUEPeontdjIBu8uhESZEkH+q95ErTm3GBpanXiM/19fX8eLFi3j9+nUFxyDz3pqFHkHmEgSRPAQvu5qLShDjaOTIOJ/ngGHpU8U8IKfcCyw9HA4rSZeNjY3EKRFvnyZsohXZdIBskrmULWyCSX3e0QSg19dz6e9hDxxUu9LCOJNnO9hzhUcpR41GI1t90C+z0+lksOx1Zus8hwb94he/yNNymWc+u7q6Gjs7O3F4eJjrRjIcUoogmWpoJ8nAM8QSrVYrbm9vc4eIA3XHiR/6MlGG/EEAveuyPkQsCCxXrrE+To41m82Mn5hDvuNKJs+DZYjxRixwqHEd48C/lTIM/sWuuOoR7Mx78Y4kgVlbV/WRUHKi0NW76KJ3y3Bf5twkLsUMyDeYnHd3Qsd6bR1kTu1b+b1j0MfIJsfu1k/+b8KONTMxWK4D8eBkMsneyWBXMD0+nMscBNjCvZaJkUmOQWwvLS1VbL7tAXrn0/iIRYjTsEsRi1gGe8nuBRcSsaNrc3MzT2Xl8DnssVs4cP0Q3f4o2/cM5OwYyvK6ev2hIRynoJHhXl1djdPT0/j666/j9PS0Apr5G4NgYojyyVqtlqeIUH0UsTAy9BviWRGRP0Pg+RkED9kFtp9QKo1wkLWMWPQ8oiEwwR2gl73bNogw7mZmG42HbUYEXBZwxs94qQKxIEMQABam02mWZvq7VH4Mh8NsHN9oVLfrRDwIGs4FA9hsPvTuYX0JMA1EG41G7OzsxPLycpyfn+fvDg4OKuB7Pl9s48PBEciSQWPbDg79/Pw8Wq1WOl6+58Zts9lDfxvKF0sQcHx8HH/5l3+Zp8C5OSCf5b2sbCQErkQAAQAASURBVGXw+8d2IR8AUgI+iFDYc4ya52Y2e2gMT8UCZJUzMmTvXNVCfzCeAVECCQbAAdA6Q8S90A8aKeNo0XVIFdaLqjkcOYDKWQr0oAymSqdUry+2LfqPAaQrMwFi/j2ZYoNMZ1AICk0241AMdk0YcD+qMdFhk0WMk6yStyhDGpiUN+nD+xhYoacRkcdR39zcVA4bYMsy7/369evY3d2N/f39SpNq3tOHSjSbD6f/XF1dJci1bWYdhsNhRCx6Bbj3D+tYXp8KMYWDp1KBy2vGOvsyODKIKn/vNbLd9jMISMrfAdzxd+5tYzlot9txfX0dp6en6XcNhHkusoY/xDdQjWTSH7CGbwMI40MBbdgJv4PBYhkUcKEX+EVjlYioEMPoFkGcm79a5w2a8RMkgPBxEYvqJOTTRBQ2lrEwfuYI+Tdod7BNsuixAz+QlxcvXsTGxkaW5nMf7Ay2ATtzeXkZ3W43g122CRMQlQQSQVcZeGLvHiOISln1u/lvv28ps6X8P6YXEdWTAT/k5eAFIsrBGf+3PyFBY3mfz+eJLZEFtiJdXl5Gu92O9fX1Si9QvndxcRGvXr2Ker0eOzs72WsMW8t48Gus7+7ubmxtbSX5BRHLeqJbV1dXWYEDzubkYxKay8vLaaPv7u7yhOjT09OIeLDlW1tb6aNpco48gnlNApQxhnXIBE7pJ03EWi6c4Lau8rsySH4X0Yx9AgtTrcB9jAdKQuRdWJJ3cWLAhMQvfvGL2N7ejt/97neJe46OjqLX68Xnn3+e8QC26+DgIE5PT+Pzzz/Pg4NYUzDQzs5OfP3114m1b25uYjgcpnyAl0r8g01zSxOT9H7nj42ZTUx5TKUdiYi37Iaro8bjcfR6vUqFlX2Y4wYSimAxbCvPJ3laJmQYE6Sg7bdJKSeM+IwTIcaNxplUIhFv0SSbGNckOrEQfY0Yh9fPpAhrDE6IePsgJeuPcW1Z0WiSzlgIvbaus2beXmYC3qQU32PcPMt+kHHbrjLHvA/vCBFEJRIxEQkcYgK+Y5/GPHNPfAJbYo3pwULT6TR5BxIa6Kblt9Fo5POJ4cAxvV4vOY5GoxG7u7sV7DwajWI2myURTdJvMplU5smxDdcPwdcflJRyttH7mrkAPAQgOEKqZ1DY4+Pj+Nu//dt0niiZDQWLAZgHREECbW1tJePLs13SxgJx2hrVGVRAMMalpaVYX1+PTqeThA0sOIaEhr04X+7nALNer+fpc0tLSzEYDPJ9ms1mdLvd3MZgFpLKMSslDSzduNx9ngA2NCnlOWSLUcSIyH3+BNAYWgJQmhbyLoPBIEkMWFUCCYAo74QgU93SaDzsgV9ZWUly7Ouvv877O+DBODHf3jbmk4UiHqqdZrNZVsLwbhgdKndMmDhLdXx8HFdXV7G5uRmj0Si2t7fj4OAgj8u1fJtUxVm8q/z3U74Mypwx47q6usrjpKlA6XQ6Kc+9Xi/1FkfmZrlkQSF7HYQC1iIi9vb2IiLyZEqMGeSv9+Gvrq7GeDyOwWCQGTpkx048YnFEfbP50KSVLSnIeNmsMmIBCp2VwNG6d01EtWEnYM/ZpYioOAz0xw4PXcVh4nyQTYhhB68lKQBx6wpAnu+SXYMw2wEHvwYa6DLzhoONiKxm4t+cSDabzeLJkyfR7/crBAXzSLBFxoWgi3VqNBoJALwVwICNuWFO+CwVOvV6PWXLfqMkeT6VCxlGz5xs8N/lVQbj5edms+rJPwZdfP8xIspgyb4aMFYGbxGRGTn8N1W5DsABRfatDv4eCxgB3mUiweQxugBhg63HP1lfeQ/fg/njXfC/BHze4sJ7GIQBVNEx39fAvtzWYPKbOeR3BtXoKkSyyR6IBZ7lgBZbAqGMzSVgBkfs7u5WSDK2UaOb2HIqJwHMAFawBbYdghW89Rhp9Ye+SmLgsZ/zb8vlx7gIfiD0SIgY1NumO/nI/EJamVTmfuicM+DYydPT09wyD4lLMhVchdzT77PX60XEwn6gS5C4bPszweoglt0H6O5gMIiLi4u0cWzTffnyZZJZ0+nDaXwkHk3AMF7LvINRxsr97dMc6JY4rpQJk1u8S0lslj9713o76YuuvevZDvCdcDLBYcKBOUemsGsrKyvx2WefRavVirOzs+j3+3F9fR1nZ2fRarWi2+3mISQQYi9fvox6vZ4n7UF2gm13d3ez9xhjgKB2UgC/jr3ADvvkRx8kwVp8TD9cVtXM54tdMe8ah2UQYoYegnd3d3myIe/IDhlwD2SmSVNOquXnxjTGiJC+xn/YaD5rYhObywWm8q4OyExwOklh+8aIxe4e94vCL0Gi2b7j97iPq5LLpBgkFxd+w6SS7aL1wsQc78NnII94b3TDOAW9xKYaAzuuc1ED8Qp6Ufo2nuuY3XMOqU6rgYgH3I+dM1awbeD7PtCHOQSHX15eRqvVSr1lfbHv6CFrzMFpEE5LS0vx5s2b1OWtra1KRfb+/n4cHh7G1dVVXFxcxO3tbfR6vSwUQm+Ymx97fVBvjJKUPWYQutKZtNvteP78eZydncXGxkb0er04OTmJX/3qV0kutdvtODk5iYioTESz2cxgiK0pOIHLy8sEZWYQIa/G43FcXFxk+bNZfLPnNhg4UD5PBYD7qFxcXOSxmAa+DvoIcAn019bWMgPFFj4qPm5ubjKoh/Sy0HLqVuk0yZgaDNEPiGcRTCK49fpDqZ7JHwAJPZioaqF0HyBOgF+v1ytbJ6mKMWjFwHv/65s3b7J6zI34qH5hmyGyRd8fk0+Hh4dxf38fT548yXWbzWZxdnYW4/E42u12zgfvy5zNZrPsmTMcDrNPFSRJWTrrQAoH/ikFut/nsswAhAG1y8vLsbm5mTqII6aMtNPpxO7ubjLsyCpZeohZg1qMJsa/3W5Hp9OpVC8hy+vr61k1wtxDbBwdHcXZ2Vmub1lqTPYA4gS5JbPoTAdg++rqKrcuAgLQN5NRJlDIWuAgXOnowCFiQWB5O0ZZKYEu8VnuYfAKULUji1hspyuDcoh3bAYBLEEZdhFb5y05zDk2h8Mo0GMSBltbW5kNYgslFZv9fj/H5Qq76+vrePnyZUwmk3j27FluO/G7sUbMP4kOfhexaNKKHDoR4mawj2XWPpWLIDAiEvA5aDF5VAZDZXDjC93DD0RUm/my5hGL0/Z4joNCbB6yjfy7+ohGu8hTWe3G9uoyw8haGZSzhZ93Mjls0tUELeOPWOgcdt0n8jFXBPkGj3yHQMLbK/iDHGI7LLMEOWUgjP1yIGI/zTP9POOliKhk3ll3xoPdM3AG67i6hKAKnajVFr2jer1eVne7AgI7wVbqiEUQwXYey5WDGNbZgVkps74s5z+EOP4hJBfyyrt8jMsYo0yWRSwSEx4jmM0ZdS7uY98LEUAfqoiIk5OTOD4+rvRE4pk00IZY3N7ejr29vVhbW0t5xRcyJnSZ93GFXcSDbwM/cgLbdDqNVquVVdAQTxAmrsy2nSHBw9j5uYPCxwJT5gd9Anu6Mhn9NOFjTG15Qr+YU5Pz1sHHLn/HhJlJdmywSXCvb0moE88w3yWxjf/+7LPP4smTJ3F2dpYkO7s93PS+VqvFixcv4tmzZ7Gzs5PzeH9/nyT006dP4+zsLO3bZDJJn3V/vzgsImKRBENmIh4SiexmoHqzTIB9rAsZA+9HVA9WKG2OSVE3/I6IR2MZyAa3RUCfG42HimLk0kkIZHg6nWayFfxi20nchg8ymVnGrKwjGMpV48g4+Mk/X11djX6/n6RJRBW3UkEMHrCMEge5chlbge0A7zv+8xo8xhfwfJNF9sVcxglOfHG54pEkK7FHxNvkNBfYmbll/f1dnm9Zgsh3rIlNQY9d/e04B/seEVlw476VJP+Hw2Hs7u5WYtuNjY0Yj8e57vhiMAm+HdzG7ykyGY/H8S/+xb+IP/uzP4vRaBQvX77MHs47Ozvx29/+tuKzyrn7oddHqZQySxhRPRbR2R+yJufn50lkkUHhfq4agJnf3NzMz3s/s5lQO6/19fWYTqdxdHQU5+fnmYGHTMLhsj0hYnG8JsSXGWYUx2V6d3d3ecrF5eVl7OzspHEB8OEkmSOInIjFaTiAeDO+sJu8R0T19KGypL9ef6hwarVaFUDMmN0/h/9fXFzk3NnZm+BCeAlAIiImk0kCDAL1Xq8Xm5ub2RhzMBikUbHjpx8N643R5eL9WAuACrLlyifWt1arxZMnT3JvrZn4ra2tXH/uzZhRVmeSzdwbjJhUeKx08Y/hcqAasaiSoAqOjC7ZVZzv/f19nuREdZwZeche9AUSFOPXarWi3W4noQqIvbm5yV5CZFvRxXq9Hqenp3FwcBBXV1fZHJL1bbVaCcghpSF5MMSQmQBnnKQzTEtLS2mYHRhEVE/WRP5cyYXs2DAbuAKK+UPAiDNyxi1iUWVVlvVyIcONxuKYcG/H87O8BZBxWXfu7+8T0Fr/0TEcW7/fT9IQsMY7Ywcg6TmaHKdtQhB9Pj8/j2azGc+fP095cBKgXq9nTyzbOOwca8e8YOtub28z4OLnEVGZg0/lsu34PgE2awoY/T5AgM/bPwAaAUkEbuiF58rVA2VAGBEVsDSfzzMQghRyvwrWD39L1WJEtSF4+VwDd/y9E1QA4jJzjz5Yh7gnn7WdwU9aXwlymRswgxvcMsaSAHPigjli7iGjeRZYBvDKHPKuBrUlwW2ij+QAv+N72AlXTEZE+mdOPXPwi57f3t5mZTa9/uidwXj4N/fwmBl3KfO/71Xey3LxLvLrY14mxhkPpC0+w7aapKR1jJ8j566SghRwxcTJyUkSCdjiiEhSARn57LPPMvjktGISUWBI7H1JwmLPCc7Ozs7i/Pw8iQp0KOLBr0KEUQFNxR0+eH19Pfr9fiacwLw817gP3IzeRCzWHTyGLkUsthb5so/mb/wi/0eHuH9EtUl6abs9FhMFtrPlz23rTEqBOW2zTTpEVJNKjJ/Ee61Wyx5TnIQNEWCCeWVlJY6OjuL6+jo+++yz/Dn4pNFoxLNnz+Ls7CzXHNnDjjNWbAPxnclSWmO4CvRjXo4ZIDYesxVeU4in09PTrIri4l4UJ+BXSpKReUGny+opknTsJCEmY90tC5Ydxmw7axni+/4sz4bQIQbis+B8Hx5GMhri2BiR7/Jv8CA2n8Qyfty2A5/hhL6rnhx/MtdeHxN1tqv4Z2N63s9V6fhdk8TMj8cQscAXzK3vaeyAjbLMwQGQUGVNuYh9wdSWx+l0mtgIsp4efGBtbLbtyXw+r8RuxiCWGWJ/x9202Pk3/+bfxNdffx21Wi3+w3/4D/Ff/+t/jW63G6enpzk26/Hv41c/yul7dqI4RAAn2biNjY14/vx5DIfDWFlZiZ2dnXj9+nU2Y+SlUYzZbJZ9S1BCBIzqCO9XtiM7Pj6O4+PjNJgGeRGRCuUyZIxBaRT4m4qK6XSaARWBGcoGI0yVxt3dXW4T5DkIGwEWQJIggWCTf29sbOSWOgNuCz7kGoE9zc6418XFRfZ/8fvg5FFCG3FnfhzMo6AIPsfAQ3JByHU6ndyTzNjX1tZid3f3LSBgkgvSz1UbGCsbcAgrjs3kuHnKy3FEvV4vj89FhgiMUGBAkw27gaXBt5n4P8YLw+Kj2AG7t7e3sbm5WakGWl9fj62trQqxSnXO6elpjEajSoDIGkVEHjdrYhN5QmbZFgaRNZlMYjweZ08KTp6gZNb9rpCFlZWVlG9vL8A2oSusq3vRuaE7OgeAdrYWuaGKDJ0BnJgsd+DhrIlBC4SM5QzdxKFEVBvMUxKM/q2uruapkVSmRETaDBMO2CW2NHp+kIuyN8Dd3V1WP3a73ZzLu7u7bJ5MtWjEosH5+fl5EhQ4fOTs7Owsrq6u4tmzZ3nylMextPRwVPVkMkkdhozE9tVqtTy2vqxa/L7EzU91mQT8rut9AXj5c3/ea4rMe46dPXS2D9lzUOcMJ9Vw4/E47Sb+iy0AJtEiIm0s8kU2sQRzJmasN+iEDxdBRyMi7YrlzO8IIDfRZSIIYB8RlX9HLPrl2RfiC9Bp7mXQ5uC4JK6wJ/V6PX20CWsH5ryPqw3KbLgrRJw08RhNJoBRSAS4rxlrwTtbrsBWrI1Jblciczmj/UMuB1Xv+4wJnHd99rHA80NfzDX41zYf38m4TbaW1dwmSAlyqcpAT29vb+Ps7CzOzs7Sp0RE4iaSN71eL78LYUFQxAluYFPsbbPZzCp23uX6+jrtPdgW3EWw5tOk8R+DwSAiHmxzt9ut9Aqt1Wqxt7dX8Z8RUQmU8VmuZkCnHNjaxiGzJa53wsKxAeMz0YVs2VY43nmMcIKoYE6wD3wXvcBv4ZvRWQfKTsryzthssBmfqdVq2c7DCXLbXualXq/nidj7+/tZ2RYRGTNQ6eSkMViLueYda7Va9rGBcAErcX3s5BDjs70u/adlYXNzM7744ot4/fp1/szyVtob1tuVKSTk8IXIETINxgVDlaRVRLWK2f7PFTv2X9hi1sK+n++4whx/Q5/Her3+1o6c+XyesVu9vmiPgEy65Qxygy9wMuexRBi4mTl14YJjK8Zj/83lwgSSXfh7Y4+SoCt9y2NEnrGx7RHxApXFcAasQZlQK8kgk5fT6TTG43FsbGxkNWlJQGJj2u126iE42AdCgRnW19crB8eVB0/xud3d3Tg8PEyCi3iduO/6+jp2dnbi4uIiPvvss8qaWH+sDz/0+qCkFErFokRERTgMzjh+djgcRrfbjfPz8/j1r3+dn7HyPXnyJIM2CBI3abQTQhGvr6/j/Pw8JpPJW+XnLlHmPgaYs9ksM0gsHlVSBmmUyUPu8Bz2kBpwEOCjIOzNxmDhXAjkILvIUm5ubqZxMGvO1j4EkgDd2V4TYGylYT5xhoAYPgtRg6LjVAjQARFmu5lPG7PXr18nICCLA8HEszh68vz8POr1xUk/VLVwsgyONiIqfaZsCGezWfz93/99tNvtePLkSa6PK1K4N3JKg04MNFs0bVSQYTsh5OGP/cJQIkMATk6SAAzOZrN49uxZrK6uJsnBn6Ojo/j222+TRKQKYD6fR7fbzeo1gp+yIbdL9q+vr6Pf76fxZFtfRKRMkql3sIzMu3zYgJgtouhNp9OpNCL2NgKcqQkQ2zJXehiI4BAJDLEBBm5UPLpCBOCBgymrkCIiSS+q0CDtIVAB8ug9cxyxOC0TZ8Z4ONEQ3XIwxDh8GhLrcXx8nP0AcfxkZtH9wWCQpB/+gO2SBMP0IHrx4kVsbW1lFR5zzBx+8cUX8ebNm8zaMScmp2my7v52zjo6SPnUL4+1BPBl0O13Km3VY78v5wEg6iDLnzEZgx4A8NFxAxICXHwGa4lOsnXIW8fLMQL68O1UJV5dXaX+A5AMxHkXbBg+CbvgaiV01nqKjysJqpIow584YPR9jSdKQsFAHFk3CcjzsGcEtYzdZCw2lAs7sLa2lkkpYzLWDz01wQ6I7Xa7aWcsI/btEZFYgHkwdmMdeT5XSfQ9dr2LYPo+xO37rp+CnLYOWmdMnFpeOFYd+1jiD+SYigPwWbP50O/z9evX6Q+w68gd1R7YRmSRylInYtAdPkt/IILSi4uLuLy8zF5+JJaRBb5LXzdkia17Duio5kFefLouJHiZXPBz7F+dxDER6p0TXPh15tafQfbQb55ZBmEOILlnxCJQ5nfotckQ21Nffk/b5Nlslr7YlUz1+uIQIC50PCJy655JNmIBiCu2+wyHw/j8888Tg0P80/4D28+7Mxf2t/wMGwMG472wZR/zIq4r5cIXfmBzczOurq7i66+/jojFLhbm3e/tn/NuEYtKdh8sxXdqtYeWKPSytRyVRBff4ZngRMbL8xxr4j8tN6w58ST3uLu7q/hH2nVwXxd3TKfTmEwmaXvA3FdXV7mlcz5fHHiA3IP7sf34JhPOfoZJXnMG6ISLJng/E4PMBetu0ghdZp7QA/sc5MRrwPqa12Cs2NDr6+tMUDMO7Afz7CIVYi1ISYghOADss3HG6upq5TATDiBBT8tKLzAYNpsTeJEfSLBXr16lX+JUTwiq2WwWz58/z+361in/+8f65492+p6dnsEcLPRnn32WoGlrayt+85vfxMnJSWxsbOSCE7Ts7e3Fmzdvkq01MLKzbzQaMRgMcuJRKAeLEVVgZMAI+MP4Y8AAuQgWIJhyTgLtiCrrSrUGASInLTn7RLkzxIsBPex1ROQJd4B8tqfxLJfUWihZB1cgeD+6K5eWlpYqpBf3JdhlGyD3b7VaaaR4NgQRoIM5hRR0MEpJ98rKSp6ex/qsrKzEkydPYjAYJJkAUVBmwQkkarWHk7hoXt9sNuPLL7/MuQQg+8h5G3wDRBtG/ji4NVj5Y74AggQdrgYCKCPvm5ubeSLP3d1dNj4/PT2NFy9eJNA4OTlJINJut6Pb7UatVkv5tzPCaSEvp6encXx8nNlQwFdEtcdMxCILzRpCJGPwITIjIh2jHT9VjThXVyG4+s5AkjW3HkD24IwhjJD9iGqTchyeM+jYR2wK5BvO3KDZgZ5Ba5kF9LY9/vaWYW/l4V2c7WQunSHCOeO4ObGTyi3mGl2q1+vZn4vn48RZ84iFDcU/NBqNzLpTZmxZZT0hp8rAwGtVZp4+NWKqdObvG+P7bA7vbJtmsOb5YU3LLB6/Rx8iFqXpBrpOwjCv9GXj/waHfB5SyYDTAboJDfso2yLLJTaa93DiisDI2488h4B65qskk+z7eQf0ykS9ZQ3fRRDvihWe40wplwPTksQ23mFcJuRMClH1yZiwTawpdsTBPVtH6vWH7P7JyUl0Op30k9gt3o+qGCcfsUkO1k2AOtCCdHmXjH8XuH3f7x2wMefv+t3HvBxsecsT8olfcMCOXHuNSwISX3h1dRWHh4dxfn6e2BO539zczP6NkGLGNt7CwVZ61hncxjZBTrsFI89ms9jc3Mykqas0dnZ2Kj7ZSd319fU4OTnJ5CDywU4IsJ4/H7GoDOE7yHNpx0rSxDrKVQazXOipE+OW4fJiHokxbL/tc/i344+StOVCT7j4HGNlPq1nGxsbmZihLyi9PfkcJxv6gKd+vx8Ri2q0X//61/HP/tk/i+3t7Zzb2ezhFC5X5hm7MAd+b94X+0nsVBLpH+MimWbb54v1o7K7Vqtln1TbErCh5Q8shL1EztfX1yuxFjI5Ho/j6OgoCSknG8rYOWJBcEVUK06JgWwz8E9loob3x14Yx9m3IEv45FKmvQXOfsiHH5TEkH11SerxWf5vMukx4tP+usTz/qyTMXzG71zGfMZOPN9j5B0tu8w/uwf8LCepmDPu7Z1U19fXGb/aJ15dXaUvhyS0PXQvUhLjtg+2C2dnZ/merhzHrzebzdjf38/DEHj/3/zmN3FxcRG//OUvo9frxZs3byqHUTEHxlM/Blt/UFLKBteO1/1SYCfX1tbi4OAgew69fv06JpNJReloeGyCh6CU7BCOlj3tBpA8E0DtrYQE4QgNf/MMB58mcwzC+Bz9FmjK3el0YjabZcBEIGilA7yurq5Gt9uN1dXV6HQ6aVC8lWc2myU76gZupUNivpkXgzDANQbn7u4uGx8yH2TeyIYxlzghxo0R9u9YYzOmPJdG6TZsVDcsLT00wLRhdCkzpxJOJpP8jkkyb6MkKEbxXr58GY1GI7a3t/PIZDL03W43BoNBsvSsn7eCYJDKwIU5/GO/kBcHqK4gwgHbIEYsjDOk4ldffZXyBiEDicXask2AbA3rjT7f3T30paBBqjMRXJSVI2P+3MbGRmxtbaX8uKz9/v4+txmVp2a6UhLnD8GLHpoMQo6xF8yNQQT2z8223VMAWULGy6xWs/mw5ZBm4ibLHFhGLPSF3zM+5JjvRFQbrvNebNXgnbCn9KcyOLAcoOv39/eZQVleXo5utxv9fj/nkHmgCoDgCLvpLd1UUf3yl7+MbrebVadsI3n27Fnc3d1Fv9/PCimORYcgMSHlwCLi4/eT+T5XGeQYMHEZGFnu+d1j9zNYK8m4dwE5E5h8Dtlx5WDEgtAE2Lj6tQwCWR+ej81xRRI2n4qtiEib4qCnBLeeF2TCIJAMrIMh6yG6T5PnMsMI+OX90HkHG9Z9bBk6S7UAmWgDbVeZMS8Riz4Vnn+vu8EtgaF9X6PReOuUNANuzyf6gd3h38gBdoA1J4HgZADy4/VinWzr3iXzlvPHfu85Mrn1Pn1+TC/eRSp86Mv+ypUkJoWxlxHVXqqMvfQXkLvIAI3Nuf9gMIi1tbV48uRJ3NzcZCUMvgY9oQrEhyzwbHYZNBqNSpUxlSSQWdwDn9tsNnOrIFWt4/E4dQy/TN9RCOvLy8vcWojsYF+QIwd7zJ1/b+zppJFtKn7xfYQlc439sN6aRHJc44pp9K2UXdaX8RFIMi4nFfiMg0x8nHEy32W8kD+1Wq1y6BTjwqfO5w+n67Ldnt0M9Xo9vvrqq2i1Wrm2rEWr1aqMtwx0+TlyCR4h2H2sYu1jXG5274u5pA3E2dlZ2t2SAOJv/5x3cbwWEXloFTLRbDZzB0C/36+0S+FvbDH/x546UYidMIFl0ot3Msld2hMIaBKyyIYxsHef8N7NZjP9gSuG+Cyk1/X1dRIq/K5MxrhnnN+PeTBOj6jKt32adzJEVNsOWG95DvdyfG/bio22beZZZZLaSV+I4tIOoePIkyusGS/PBQNfXl5Gs9mM8XgcnU4nY2BsBbga2+jWPaV8U+DCe7fb7dwJNBwOMzbrdrvx+eef53iazWb8+Z//ecYg7ESgz6B9U4klfyjh/FG275XOtLx4ydlslqTUxcVF9kpyM2Sc4nw+z6a3LMrS0lKcnJzE69evM2sTsTD6Zf8LJpuKEDKDKKJBIBkkBImtdNyDah+yjGwlImvEe/KOBL/z+TwNPey6CSFIOwBrRFSO9SYLzHvxN6WDJk5MOqBA3Pvu7i5P2ODezqiQTcE42BAhgNwbhTO4xthS4QBAxpHiIAeDQVZKTacPPUpMLKJ47XY7M+1811tz3IfKhv7NmzcJzhgLSok8sc6z2SxBtzMIvDNy4Xn/Y76YJ97JzcZp1g+QXV5ezq1V6G6j0Yhvv/02/898LS0txe7ubrTb7QzQqB6AwGBr4HA4jDdv3mQmh/XEyDabzQqh7BMdGT8lrYAk7sO/Ly4uUlYMHkrCgnU3ScW/CSqR+4hq9tIVfJCb1lEcdgls0amIaoNRgxn02eDPhIDJhPLnpbNijCXAbzQaFdvircx2/tgYlx1DFBMMR0Qe+ADAiFj0zaB3FL3dJpNJZW/93d1d/NN/+k+zKS4EFoCIbb28N4kL9Jp5LYkZz8Mf6nI2+8dcduplJc9j12Mk07vuy8X9sIkOcvh5xAKguVoJG+yAeT5/aERPBRD+EPlDjgl2+ayre5xQKccDjjA4RtcZJ5/x9nX02RW9vD9BGAQoeoZvpNLPW9YMLPkOQb23HjJ2yHaDa3SmzCaiLyaImJuIRVNWJw0sA04koL/2rRAEln1sGvaDz5eZXHRnPB7nlg7mPSIqgQGf9zY+3gMfwd/IBu0DHqsQ+X2vxwgt/+5jB8MR1b5SDppIMpZJPuTGW7hdCRGxSIrWarW4uLiI09PTTEaSMKL/iH1XxOIIb7Z8kTyhCibiAU8NBoNs0cDz6GVFgIZtx7c5Wz8ejzOQAVfRL4qgnOpKkhYOctH5MghychjfUJJ4JvK8HRxdLoma0haZVGetTBSVtornliSB/Y91rLxM5vvivRwf+B2x6cwplRdgJmSCMRP41moPDeypYmeLj4nKV69exc9+9rOcb97TdtX2cmlpKXs/QnwwF04g/xSXyT5jAQiS9fX1JKQiqj3GjJHAZBAu5bZ3yDjvomHe+/1+Jl0hRnkW/sr40PLgamJsBD7MmMCEFTYe3WXtkVEIYmJZ3heZI/FfFkBERCaoeIar9GlJ42RH6YcYO+sQsbBpPMvfs+6hc/gmV356pw4X+lFWEPM5x5PmDSIi5buslmSsjA0ugGQB8uHErnGLf24y0z7x7u4uzs/PYz6fx5MnTyq4z3bAmAZszpzCX/h0dG//Y+3ATMzF1dVV/I//8T/i7u4u/uqv/ip3n+E7jFHfRVJ93+ujNDqPWJw6Y4IEcLi7u5sBUK1Wi6Ojozg5OUlSgX2SGDcIodlsloEHzcsjIoXA4J4JthGiMTAEiJvw4XAdqFHRhQN2hoYqAwwTit1qtWJrayuOj49zCyD3pGJgOBxmth/HzDtGLE7WM8vNXPB8jBJz7YwFoAcSql6v59ZHgshut5tzx7isiAgqDmw+n1cYWbPCGxsbmSEGVHgdIuItYwkwYB5psHl5eZlNx/2ujUYj9vb2skwdQ8m6konHWKDwt7e38fXXX8d8Po+tra08fh6S7PPPP6+QnxHx1ikbrDsgHoPyUwDcP/Rl48Y2K6roLG/1ej06nU5EPMjb9vZ2zOeLY0/JvHDqGn0KYPjtJHFab968iYODg9RfgCMACEIDwwoB5SrAtbW13EaIzaDJprPCy8vLsbGxUTmhA73ExkCoAJzQa4NNOyQMs/XQhJKzvOiKqxjt6EwA8Tz0Gt3nnZF7ghUcCmR9+dky4HTw4MbTEVHpJ8C683mPFedtIp8j5j1nJqL5Hpn0y8vLzNa4V+D19XW8fv06NjY2cs2QjS+//DK/w/ZvxgzQ5hTUj6Gjf0iQXQZIXAYj7wtsHgMH6JGr+BzQcv+yesnZYoNF1tP9E9FX/Ki3FPX7/QSmrrJDhrl3Gdj5WRFV/wEo4p7IIn8uLy9TXqz/1kPejX9HRG7xNghn7vic8Q3+BnyADBrA83kCCVcmEUwzfwBK1sHr6ZNkbSscbDNfThI5IIWciIjcqoXPdvAP9qHlAHYdu2lSyoGCbR5r4moO5rMkjt512Tchp9/n8vP8s/J+H+tywIHfYY4IFggA3WuT/owOjl3NtrKyEuPxOLfUUQFO9ZG3wDuZhJygdyQX7u4e+i5SFeUAs15/2BpKg/SIh8plB74cWkRPErb7gS0iFlVK3W43+03x7iSqqIjGtoDH8B32t6yliSITCuid44Dy99giy4gDUGTW+uj7oW98Dz1wrxqea9tme1badX5vOwSetj/lD7+7u7vLJBynYDvwjlhUT3AY0f7+flxcXKQccNr28fFxrKysxGeffVZJcpWktAkzyKeIxZY5KuHoWftDfOaH0FXfE5njyHsuy5ZtP+vsZILlbD5/2LYHmUHcyA4NfAXrRwWXcRpyAunjBAr2H78WsSBM7LvLvlDGYo4LeZ4rNUsyrJwXk5H4LOQdG8Z33XLG8al1GP9DDM98moBxjGDS2GQx9hQZ8/vxjhGLZHLpSxkP97adZl6Ye8e6xL74ztLHMZfIv7GQY6jSLkU8+OmDg4MYj8fx5Zdf5mFBJoVKXI5sWJ/fvHmTPtlkIM/e3NyMFy9exD/+x/84arVaHB8fx3A4jE6nE9fX13F6ehrD4bASe4EpePaPvT7a9r2IyADUgHZ1dTVPiVpbW8sXns/nGYSYIGBfLos1Go3it7/9bWWrBgJvobSC8TdB7nQ6zf4lGHICona7nd+noopMvJ0328nK/dIAMR/fCKAELGMw6vWHE3cosWWM/X4/q3icVbQxcCbYPTNMQtngLC0tZfUVhgXl4n0nk0kq/sbGRu5Nj1iQUDCyZLeYd7YLYCAwRlZo9sc6oEC52YrDdiJnDhw89Hq9JDJZazuH0vgw9hcvXkRExN7eXmxubuZWUSrcrq+vs5cNimpAwLvb+fxDIaVcrYMxvrq6ir29vQSCjUYj+0rc3NzkSVEmk5aXl6PX6+X2gohFVQ8VcRsbG3F7exuvXr2K4XBYCYaQSeyEWXtIVchb9NPZ4YgqqbK0tBTb29vRarUqFQI8DxKLnm5lNiSiuoUzorqHmp9TxePAFPDhoNoVRdyXsSNvtpUEgegRTSjttJ05455kOk3MuxqUcRpI82dpaXF8N1VJzCV6wrthO1ydxYmJ29vbsbm5GaPRKJ031VE4Q5y47RLzd3R0FJ1OJzqdTuzs7FQqWpE53t9ABZlmDQyAS7LnU7wsdwZl2KF3fcbAgM/zGYM2ZC5iUY2Izhssey4JEJEz1gsSm3t4zh3clkGbqyqQW9bS4JzflVlJ/m+yxL4OX0Ng3Gg08nRG3qEEvw5qywqmxwLXMrAE5DLXzmrzLr4Pc4tNwiYYB3BAA+MlgCkBKWtSEtvYTvytAxdsiSsivf3IPpxDL0rf6sNmShnAlnlOeGfWsKyUcoBuOfe8/xA98nyW9/vYF+8GWVRuEWFdOciBBCnryJp73hqNhx6q/X4/6vV6dLvd2NnZqVRm4XOoKERWSE7O5/MYjUaZDCQJib0mgMW+j8fjmEwmSaSRFKBBL7gQAqzZbOZnqd7j/bx9h21hyJkJ5+l0mkQJc2Ii57E/zLntYES1+T6fBdO+i7g0yVzipccILf/OAarJX79DxMK/mkDge8gOY7QNMDlt8oQqN4gpqozdosDVNs+fP4/19fU4ODiIwWCQa3lwcBCrq6vx/PnzXBe+b5m2f2JOfIgKf5NA+76Xkye/z+V7YIMoODg+Pn4rqGaNbOediGDeXQzRbDZzi+rd3aKBODqPzEHM4hOID8v+U8Qb3haP3jI+sHBElRzFL9fr9SSvsNnohaumSDagl/gqN8V2ghJMF7GQQe8w4LP4C9sTx+aucjSmZb085/4bv8dnmCvkG9kmjrAfMEmNHvi57l1p/oD5Lv0Uv/cuDJ5nkovx83ljDC7rOfN2e3sbg8Egvv322/jZz35WIaYosGDH1ft8/dHRUZ6SDU5DNugvt7W1lU3W/9W/+lfRaDTif/7P/xlHR0dvzRXv5Pfl3X7I9VEanXsRDLrcjwXBuLy8jMFgEKurq7G5uZl7j7vdbjrCev2hOdzr16/ztIiy0oFeUM4kIAQICguek/H/F1ozooBBHGnEwrmRIXaDOhaf4+5N4mAQcOyAAz7fbDYzgwR5FxEVxphMFEyxDQMKPxgM0olTygjpRhAAGGB+6/V6Vo5R+ktQ0mw2K6eq8DuMAIKPkYGZd4DoXk8oibPxMLY8A2NwdXUV5+fnOX56FEAkolCdTidOT08rAa2NlTMaEQ+Z5hcvXuSRyARVBOGTySSur69jZWUlRqPRo8CAd41YlNf+Q7icpYhYgLDxeJxZT4yXKwbRwYjIxqrT6bRySgNzRNP+g4OD+Oabb7Kipt1uZ3XMfD5P+fW21vn84QQ/9MvBEToLCUYJO+SMt81GRCWDAvmFI8XRQDxjp1xdYJvDPJgY8P9dUcfzcQbOzPDHvbbcN8TZMUA/Dhhilzl0c3cDSO6PTBuEm8xgLtE7qpTcz20+n+e2DfflA5QNBoP4+uuv49mzZ7G3t5d6HRFJ6PNe9NO7urrK7dv39w/bml+/fh2dTidt5Xg8jlarFc+fP89qKWckyQDjjHknbNXvk835WNe7iDMHXCbg3/VO5e8BMSVJwD1NRiDDnkODLE5PREf5TMSDXcSWOojxPfi8K3ws44y3VntIdlBujm1wZtkA2rLOWNz3wf6T72PPsCtukg655fvNZrPKtlKTgfztgNs4yLYEchh5NcjGLtlG2KeZJJ/P51klY//MNmeCQL5vYrHReDhkBtKfzzFv6Pn9/X0eHGK/TxXjY4F5KT/8zkHJd+nBD/k5l3XnMYLh+9zjQ1yWEa83gYETb75MHEQstuCSPAMTtVqtTL6gFyYp8BNUp1LZjKyQpKNCmiQQ+BS5IBnrCkXkBF8L9uW0ZaqqSSrZFlnn8b3G6a5aL+26/a7tIvPMv3kOeuTLdrJM0nAf9KYMNN9lp7GfJtUYv4mpUlZZYwe9xhkmoGxHLeckDyeTSfrZq6ur2N7ejna7HRGRRDWkDAnljY2N2N/fj9PT04qNOTg4SD/MRcW6q1+YR8bj6hETaT+kUsrbA3+fy8lE4rvDw8NMrpU2wX6mjCUcS0LkGGuZFCbmc/LRMaOrGZFPfAP66e8Qx9ke2HeboOKd8IFcyI77NtVqi9YM2HgSHcRqkFA+CMSJXJ7tNhCNxqLfsP0Pn7f8whMwRuu4iTxspr9brhHfQ9asg7Y/JJUdI5RFB05iQX6VOJ/1M3Fjm8E8EhvzLqwV72z9dgIO2Xj16lWeKo8dclKcd/M7sE53d3fx6tWrJJ143ueffx5nZ2dxf38fv/3tb2N/fz9ev34dv/vd7+L29ja2t7fj5OSkUvnIWrpyygS83+u7rg9OSnmAVtzHhGx1dTX6/X7M5/Po9XrZeBqSgox/v9+PFy9epGEjeBsOhxVAS+CEIFpIKA9ut9uVo98t2BERGxsbSYjR64Q+Rj6ZzuMjo0mPFCuK52BjYyOFbmVlJQOyjY2NPL2Ei+yQty0BEhCoer0ew+Gw0ucCI8vfzBUMO0D18vKysrXKp5NFLJqolspsAIDh4Z0hBvm9AyYHwwbtGBvmkxMAUAKTRxBYKAWVO4AgG2+ezTxFPDTc/eqrr+LP/uzPYm1tLba2thI0UPkzm83i4uKiQkoYOJXy8sd8lYGrs/nIysXFRXQ6nQS87i8AEfHLX/4yptNpDAaDBBEYS/T87u4ufvvb38bBwUE+l7WD4Ci36ZbVV8giTtbBrwlRb3mwrkII3d/fZzNusrfsB49YABKTMc5wIk/IOgcWOHNTkk4R1eNpsVuWVZyJQbC3wJgsQsdYE5w1R3sbALNm7uuGDgKOWH+cJjLgxpYEwJeXlxWCAFmA3CRbSAZya2urUl3Ku9v2QDgCmJGLr776Kra2tuLZs2dpj7Gb5+fn6RgZvys4kUXr76d8mSjib9tcB/1+nzJIccbRwMR2CzkyCcU6c7H+LsHneciXATQBUBl4O8DjgkRBXyOiorvYYm8FNQnMO9pHlVv0vN2lPErdc7O2tpYVyYyNyoyIeCeGYSz4TWwnB5x43pmjZrOZdsbbcFg7g2tXN/rZflat9vaWGUiF4XBYwSlODrqKwX3nuLf752GD2Abp4N3g3H/b7tkH827vCuitByWJWc6/dcGy/Sn6Z/QA24Y+ud9eRJUEKZMK7hGGjpBE5XCdRqNROWyEe41Go+j3+zEcDlMWSV5GRPYWgtjHp3U6nRgMBnF+fp5yzVZ35Hpvby/XFBu+tLQUz549S/0lMHeghyzd3d3lVkBkx+RSxEI2rSPlfNmfouPIcykz3DOiSgiRYImoVkhxmcCyHPMM4wR+Z0LJvzPZXeoLF2PgD+trmXLM5eSR/fnLly9zq5obUNdqtUxMz2azjEVev34dw+EwNjY2chtmq9WqjBP7yxhMivN7Pmti5qfwxb1eL05OTmJ9fT329vbit7/9bczn1Qb/XCYUWacSQ/AZbCQ7XiaTSfqTiIiLi4tKrzaeaVvLH/qBlRXL2GwwbClDxvCuJCptJva/JIsiIn0CcTRYDl0wAUn/JOaBv0sMwrxRSOLKSOYX+eC9nZiNqJ48WJJCxs32OxRnRMRbMmlMyDhtM3w/+2avifXZpJ4TZcYY3N/6iQ12H8t3VSGVmOzk5CT29/crh5vhmyEU8deOw7HNJycnsbGxkfwCp9yPx+P45ptv4ptvvont7e0s0iAONP7jsh0z9prNZvHFF1+8WyF1fRRSCsFgMSBC+AMDy2J2u90MMCjtRRBfvHiRHeARZgIxDGbEAsQxKfxhSx7l+648sgNcX1/PDGjEwohaOKwUGPOIxRal6XRRpokQs7+YTCOBe7PZjG63WzFYBIeuIlpbW8tM7t7eXvT7/QQFVJhRycQcAHxRCgijiOrR9Ajw6upqgnYCDRQFQ+StSCgJW6UAF86aQHwRnLiclPnjsjGjeuz8/DxOT0/j7u4uer1epXqN5zebzSSW2CqCMXRlHu+yvLwc/X4/jo6O4p//838enU4nK8JKx27DjsyVwd8/hMuBBSfjOeCkB9dkMnkrOFpaWsoTHM/OzpKcYD7pCXF+fh7ffvttyqRJDAgoHOLd3V10Op3shUBgC3nhSiMqbGjy774ZDpJNqtB49eDgIO1Tu93OjLDLob31ziXJOHiywwCKVqsVEW9X0VluDJAfyxTzB9tCxSDjsdNiPAYv2FtnUZB/3sEVRZBLDvDQWTvZiAfdZZsj8+4+KGyVwg7e3NzE+fl5bG5uxs7OTmxubiYgm88XpejYT07IjFgA3slkEqenp7G/v5/rQPZ8Y2Mj+5bRR8zbAktgZrDzx3hZPkpg73c0SCo/4387sCoJMOs4ekz21AGyK1lLn2nwjA7yPD8LP22Zs48y8eTADsCLLjhhhHw52+ttq64ggWjlQv95hrehR1SP5baslXJmctpkksmFstcP9/M2ZN7N/nw+n2e1qU9E5PMmAm5ubhL/YJ/x0awFa2B8xrixJ+PxuJIMgoB2gMuzGfNjROv7glLP4w+9HvteSSr8VJcDHFc/sfYEMuX7O2iyr8BmgoMjqmSf8djFxUW8fv26gut6vV76L2/jiIhMEiBjjJeDUDY2NhK/kmREZiFBW61WRef8bgSg+CtjUwi7iMhE4WNJHmyKCWrm2c9izpB1rwcy6u1EpZ5x35JYsh6iPw7QSjtsW4dv49/YuMfIBK6yehUZYEzYSO4HtudkrVqtFmdnZ/H111/Hl19+Gb1eL/Ex30XO2K7He9Tr9Tg+Po4nT55kuwsnGz33jMWE3nw+zzYAP9X1//1//1/81V/9Vezt7cXvfve799oEk5Qmf3gn/JKbg7thP2s0GAxSTyMWzcqJSZBtYkn8Y8QiQVNW/jIm9wJlfMhIiX0iFrguoupDjdNL/7+8vDhgiKSwCQ7jWONSxopcE5daR0oSCIKPhKIr3u3/mJuyusgEqeepJGusk6V+Od4rMbz1k3ki8VXiSttr/KcJeeuxd1aVJNtjMgfJfHh4GF988UWlTUG9Xs8Y2ePnnaieBhdh59H/u7u7GI1GcXJyUolXXr16lbreaDRyp1ipM+Vc/vf//t/fpWLV+fpen/qRF5PtbKaFFuEjeIF8okk1QS5Ey7fffpukAU7WRt/PW1payqCXQIkG4mQKy9MguKd7XOBEzV7iJDnSk2MRvTUIxwsTimDzN8pGc/alpaXMWiC4ZLjISKBINISk/40bsmPMAKg4e5o2Uy1FpRGCzr5U3qsEjSXBxPoY7JDxnc/nSSxEVE9XgiVHgQ2qAGYEvl4PttFB4m1vb2fGnoAFosw9cJyxKg0J33v9+nX8k3/yTzL7gJKtr69n9Z1BZAl6yqzJH/OFY2Ft0SeTjYCX8oQNZC0iUq+9j/7+/j6Ojo7i66+/rtyP59n5zecPlUDtdjv1F2fONjzLCveHHMK4opd3d3dxfHycBpf3KLe5QBjPZg8VPYwrYmFckUvu5eqAMgBF9iw72AjI+bJCygCYDBKXsz4uMXYAy3sToPJ99CUi8h4RkSQjVZd+NiQVYydA5TnYV+bT1R4msFmD2eyh8nA8Hsfz58+T5DTA4b0ajUbs7OzEZDLJCtqVlZU4Pj6Oq6urJKWooHOmcn19PQ/BwJ6WQcg/hOt9hNS7fud/mxwyMOVyZShr7eD5MZKBhtgORkxG2bZ7vOh1mYn2uEgGePze8uZTag3QrH/lvBm8U11IZpj3w8/gp9CN6XRxmqRJtIio+DhnKJlXA3JANDYVm2G/awLP4+L3Jqv5fwlomR/6DoFZXAkNPnAg4i3C2MuIqBzuwlyur6+nncBmg80sD5abd5FOZWDuq7xH+e/H7vnYZ38M4fWHuJAByHjm12QFpIzxbmnzIx7sOdXuTpqxDvgREkJUmWI7J5NJrmXEYl3RVfSS6lZs+u7ublZYIC+WG2NnZAFZ4t+8Cxe9ZSMitzuR5HDV72PEcCnz4AHe1dts7XORSd/HBKzv6cpkxo7OlkSUg3MTiyYLGEuJIfiuK3/9zmVwanKhlO1Wq5VVOoPBIOd+NBrFr371q/jyyy9jb2+vYnfwnbe3t7G+vh5PnjzJZONs9lBt1+v10i4Sd0C0M2aT+KyP5cHkxce6/st/+S+xv78fv/vd7yr27LHLOIw4w+SACWJkv9vtVqqcjo+Po16vV06L5t7gK0hl4q9arZYVLI+RCiagjDsZY8RCZvDLrtIxORqxIHCx28gTB8hQDRmxqPLHd6L3kE3oPDKMr+YzjIOYz8SRdcl+vaxO4h2wMchTRPUkbGM/7ut54d1LzEFcwhww78wN/oznMpe24VQyohNed57lnULsRMCGkJxgzkt/CKa+ubmJg4OD2N/frySX4DDsWxqNh3Yo/X4/5+vw8LCi/1z4+xcvXkSn04nz8/OIeOjFfHl5GWtra1mJ/VgcbFn9T//pP8Vf//Vfv1fXIj4CKRXxdraLBQXgUu7obQFkXAgu/vZv/zYBD4oMiLSxdpBH/wlK71lA+jhhzBEISAkW0JPM0aYw2nd3DyeTXF5eZnYYcI8SILwAWfZhlxlNFBEggGCShb69vc0tMFRP8R3eN2LRCwgnQnB+fX0d3W43G5cD4mezWW5hBGysrq7mqVkE9pzM4y0+AAXWs9vtpjGCfUVoMUplXwwMpTNoEAs2TO7zdHl5mcfEE6SWJFaz2Yxerxdra2t5XLyrungWckgpb6/Xy8Cc6jucbGm8fT0W/P0xXiYj0KvSOcxms9xSi0NwZQ3zheyhF5eXl3FwcBCvXr1KW+CtfdbBlZWV1BUHuBCw3NNggL42a2trsbm5GREPOntychKj0Sgd4MbGRvZMwFnQF67ZbOax585SRcRboIDttQRx6KMPVuCABpNC3MsZGFcRlMEyc85YcCo82xkeg1Jn1CIWmReT0VSOYUvpIwKRx3o4yCZB4CwVDpitj+ibSX9OQSTYmE6n8c0338TTp09jd3c3IiIbLRuo0VsQQEdPkuXl5Wyczrj4PkAAoFNW2ZTA41O93jW2x/yqicwycPHfDo74uQGZSSiTVXwWgGGyl6QSvoqqOAde2JOIReWOK/2QI0CtCSs+j747qGEO8BseS1lt4Orhm5ubCjHLNoEyOHWwZ0LI5BT3N2HAZ3g32yr8nAGg7QHzjQ4wD7YPXk/PRQlcwTLohpNq9IeMiEqFE2NmXCQMI6oZ4un04eADJ6UA1siJ78U74V8tHyb2fdnuWc49B5Zj/9sB+ruIp5/SBtjm0wrCMkQC1diINXKVOH5nOp1muwPWnfknKXN0dJRzj7w0m82sDMR3guE46AfCcjabpU1mu4hJq4iofB7CslarxenpaX4H38COBPA+9p8ttjc3N3kyNHJY+jyvrwlqvmP54rKuo5vWe96DZ5XVadgccCiBfClnfoZ9u8mZxy7bXNaqDN55j4jI+IIm86W94l3G43GMx+PcHk988M0338TJyUn8/Oc/z15T2PdarZaVx51OJ46Pj6PVamUjfNtO5sjbokzgc7CJq9p+Cv37v//3/yZp+l0Xdop5ZO7BHcQoJE+3t7eziqRWe9i1EREVu48ddjUPODYiotPpZOWVE+4817GOZYu5dELDcZVlFJnC7ziBx7PAik4wuHLeFUrgP+wEsQFj9mf528Qany+TuBGRsaO/4/dyVTDxo4lV4km/O7bWOmbb5Vje/ZmQCf4mpi6Tq+7PXBJexNKQPtgt+1rGAq6xTXN84p0ch4eH8ezZs9w9wJjX19cr7YZoWeQE+cuXL/MwofX19TyMajabZeHN+vp6FsnMZrNotVqxtraWO7XA906gIO9/8zd/8526FvGBSakyWDd4Q/hZLDcPrtVqWRbc7/fj7/7u7+L6+roykSiJDS9ZHxqusmgYAAArThIHRzNkl55CZiBwBLaATSqZqBLAgMBGR0Sl9JiGdGbcyWyVQQCEGkSO5wUFa7VaGTxzsh9zY2Z6MplEo9HI8c/n86yKouTUZaaMIyIq8+ptGQbgKLt7AVE1gUGzUhnEO/gsnSz3h6QwGcG7HRwcxO3tbTx58qRi6HGANOo7Pz9PgwGrzhjoLXZxcRGvXr2KP/3TP801gaBAXp0BKJn5j53p+VAX78gJLRGRxCtVesj35eVldDqdBJQEaqzzzs5O9lV78+ZNvHz5MoNVZ8xNgrDHH0KKMmFkLWIRrNl+lID45OQk3rx5kzqGzltGSgKU76PH7omGLuO8nJ10NUAJcpFhZ7NKJ1k66YhFPylXbZpogXSyTDpYdIDKs/x/PouDg8zm525ey+XtTuVc8Xw+t7y8HOPxOK6urvIAhXq9nqQ8tu7ly5dxfX0dz58/z+2YzO9kMomLi4tYX1+Pra2t6Pf7+f7j8Tg6nU4SZAAiyorp5cA4bT8d3H9q17uCZ//ewRGXZeixf/szBlQOvPwZLoBv+X2DTOZ/NptFv9/Pbdroju+JT0Cu/fMSVKMfvK/7VgDmTGzwXW8b4vP4MAcUfN7v72ohByMOXkw48X18IGNhvhx4IXsAUPdT83vw/jzLOMdzyIUvwsYw1pKA5XPoNb4Qwhhfy+cNfL3G+E8CLLbxISdk1dF9r0H5Ln7Wd13vI5ccrPyxXMyDiUOfZIv/IahAxknW8l0n7/g/wdja2lr0+/149epVHkNfq9WyihSSiOo2b6nH3kcsGox3Op08RAM9BiNZLl2lQGIDgi3igcAk+bm7u5vvcnl5GUdHR5VE0Wg0qgTkrDH41RgSufQW0tK28TNXcxiTWIZMqvPZiHjr/6V/5/1L/87clLr6WMVTOQZwTmn/rRdgKWMkxuFdG/xNnEXC/cWLF/HFF19Ep9OpVMJATG1sbES73a7EVASpyKCfV1akMHZ2Prhq7mNf+JPvczkxUhIjyCC6QFsCPn9+fh7tdjsTAyZOwFroW0QkjnEimPkjTo6Iil9mvbHN4EXWH/lx/GVCgp+ZXDE+xMdZ1uyDLPPG06U/I+406cO9uR9+1PjYz2VOTEoxBpPNJV6xLeD76JOr/P05J9JM8DrBxOceI60Y33w+z4Q9c8/neD//DuKIdYDY9RbP0h+SnDg4OIjPP/8841vWBbvtrdkRi0rxq6ur6Pf7sb29HbPZLO2CT5DEBuCDqG6fzWbx5MmTePHiRWIF5MiJ7u9zfZTtew4GvHgQKmba5vN5Vjednp7Gr3/96ww8UHQ3/OalMbBlA2OznmwFQpjMUgNyx+NxOueIqFT3RCwySGyDM3lUsrZUANXr9TTy9JKyAJpVr9cXp25BdPFZjAKBBME24G9tbS33hqI4VBA0m83sPxWxcGR3d3d5mhVgBFIMQ8u7AjaZF6qcGCMXSuZtVyZxMEBmx3l3y4fLJOkhRYYbJ0gDdNhhG2EMfavVitlsltU0Jiy3t7fz+WdnZ/Hs2bPodrsxn8/j9PQ0eyF4vLwDcvZTZXs+xGXARkUU4I95ZWvmwcFBtFqtrF4sT1xkK+RXX30VX3/9dQaLgBJ0EDmDhSfDAFAjoMSJQbBaPiMe1uTi4iIrgCIW+nt1dZVVhg7iqPrjsmP1dgGcAs4Mwswybf00qVQGYBHVk1tcsRmxyETZ+QL45vN5pVKJRtIQLwS7dszIJ2CAZ7qPF5VNPLvRaOQpeci+QRNAytkntpDwLLZhQOpRgm1b2Wg04uDgIKbTafzsZz+Lzc3NBM+12uJYZOw+DXexwREPNpkeOTzLmVyDiU89eDX4xdY4SDG4Q0beR0KVgN+A613klEley4tBIjYCXZ7P53F2dhbD4bBSJVOSaMgFgMtBif/m/U2AU2EM+HJW1fbAOkOA5mACEAwp5G2u9hs82/YdPcXHOsHBnD+WvGAMlsOIank+8s662ZexBo/dg3HV6/XKUeN+X28Noa8elaf4RUgM7C0X9oltF/h+HyEO0KWfUavVislkkv0/kQXkyXIYsTjF9bHKhceIqMdA+Y+5fkpyGltuG4qcOGD0dgxsPf7WFYqsGbgaHHt2dpb9bCIik06sZ6PRyGTB1dVV9nFlTZaWlio9yBqNRuUgD+5pe+3DdrzlEz9zcHCQh/LMZrPY3t5OPMs7IC9UwxI0Y3P47PX1deUgAicR7Y9LwsfBZRm0sj62kZYTxsY7lVXNtn0mdiz/XKXccw/smz/HuGzzS+zNmpFAHI1GcXl5mf1s8aVgefAN9vtv//Zv45e//GU8e/Ysq8qpqK7X6/Hll1/maYyQh51OJ+/lQNhVy9gRJwSxkx/7+iE6jyz5u07+m2SIqG5J5XAXJ8vLRufgrY2NjYo/dDxmm4kMuS9TWSmEnjFWY2jjPZO4kDwmErAFrFtZaY/NMTmODLL2jL18F/Ao5Lt7mqE7xNfMkRMtvK+JL5NKpV/2n5K08r0eI3v5P3NXYknLgeNh77iyPcSGEZOYxPM4uB/9Ual2cnzkseEXjo6OYn9/v4LpwWns4jBWwf7c3t7Gmzdv0rayFZUYifhrfX09sYoxOrjOfAX+zEUc77s+eKUUg/P/7TCazWYyy9PpNMHMcDiM3/zmN6nABEAoBoaeQIUJMsFElo7Pk9FhG4m3iuDADbYgayIWe9sxGigRDppg3caB94YIwiBYKBEaDAzEWmkceC+DRRw0AooBQCEABgiHq1xc+QQ4sbKZtfaWGzJkgAJAE1unxuNxxWhAjFG95uCX55OlJdPAHNv4usINhwBIoT/GF198kaXHlDBj2JaWluLo6CjndD6fR7fbTZYX+Tk4OIjt7e0cO+PnyHkHh4zxMSD9x3o9BrwgATA4EYsjgJEpjBP6BFAeDofR7/fTaVGFgO4jG650Y3tAmann9zgmwM/l5WUlm+ry34iFs2KtAE4Epu49MxgMYm1tLdrtdspqrVZLIDWfz7MvAD3gXD3oYN4EMzrm4KOcZwcjdo4EA8yD58Pyh0MiaCZQmU6nWYrvykh6rxkI2FabHAaAYOtYG9sK7ABZeGy2T/bgQp95xuHhYcxms3j27FlsbW2l02OuydDQPJ4xQm7iVwjkkQu/h4Hdp0pMoUNUlxp0/D6EmsET/4+oVqb63v6c5dAA/fLyMuebwIfv4pvsl6moLIkj9AO5AvTiF0lEID/0qzFxhy2BTMeeeJs36+9j6CMWQaWzorw7+g+gd+DpAJTvoU/Mm+9tkMt8O3Ndbv8pA2XexRjA4za+Qv9YY2weJDz4iHsBmL2+nU4nbaMz5awrtoFxQcDxXmRSx+NxrhHr6oRgGfT5KoMMy+6Pucr7sEY/1WWsenFxETs7OxXi0ckO5C1ikemHACJRgF2m7cVvf/vbOD4+TuwbUT0SnmATnEQQuL6+Ht1uNzqdTgW7cX/bVCdJbWdZ26urqxiNRpWt1jybilof6mO7T8adJCnYAdkBR7jiIqIa3JVJQ2TKgXTE2yeHWV+xRX4v5sGkcnmVz+VzzJHJahPQDjrts0q8aR1CLvjb9m53dzfjk36/n2Q0yToq4y8uLmI2m2WbhS+//DLJfhL/EQ/JXGzyZDJJ3B3xsAWUkxlNSEPMeN4+RR9cXk5OeN2tn9jX7e3tSqXe5eVlYmV2DZQyQYUq/zaRgS23zNqv2UegD7bV9jvovRPsll8IE/TGP4fAYrsm8oBftN/medgjt/vAfpgDsP9w4hSZZi7BFI7hyss/5/t8nvn23xHVpubMDfGLCTLub0xTYqOSXMa/YM88LhJt+EnbALf6wKdiXyGmuJeThTxzPB7H0dFRPHv2LN8LfuXs7CzXk3EyJ/V6PQ8TevbsWepsxEOiaTweZ8yNr+AecAO1Wi25CtbM/Tm/6/oop+/ZkUUsJjpiQd4MBoMMYO7u7uLVq1e5kA58IqLiQCl1jlgANL6DwsD6IRwXFxdZOkolRMQiq2njA9Akm4hAwl6yAAiwhZ/Pu1SfQM0kE5kInsux9hGLLBACS68nvueF5vmuQLHD5o8D31qtlr2mSiE1YHHZPdkrenYZAPjezAWEI2DJ1UVU0zBnGE/mq8w8ObAyEBoOh3FwcBBPnjxJcstr2mw+9JliDskO+prNFk2Y3dC33W7nCQSeXy6P6Y/9KoN2ghtO33FlAmuJI4uIdCzX19dxeHiY5fnX19fZG2o2myX5SgXh/f19dDqdBL+AdUifev3hlKfpdFHNiA7jxNkiO5vN0na43xPySmXEZDKpVO6QjTg/P88MEQSpS15xTAZc1i8DYztYg2kuk3nosqsnWBNnYPy3T0tC97C1nJzF+9nx40A9foADc8rPqICr1+tJ9LhKha2XNMRst9tZvckzqIilhB1b4a0fr169itPT0/jTP/3T2N/fj3q9nt+Zz+fRbrdje3s7bbLvxXi93dIAEp19DJR8ShcnI/Fulh2u7zt2gzNky7bKdhQbX5ZYm/yPqCYz+P319XUMh8P8jIM5ZNlbd/gcwRf3IkEBecVYS0LR+sW/XXWFznksBsyQMfadjAO8gPygTw6kSvyCT/BnjT+4L37YYNtAGwLZNhiS2e/JfRgPuoxeY0+95gaxJjfRQwc83NNbvByI8T1khaDi6OgoIiIDVPrWYBNNtJT20PNSyrDH7OtdIPexz/p35ecItH+KywkT7Dg2j75RfA4sxxojY8ZbxkxUSXF/5tm+g6Rjv9+Pra2tPNmYY+whEk2+3t/fV7ZlgoXBi1Q0opNHR0cxHo+TWCLxQ8/Ju7u7ODs7y6p0cIeDWgfg+LCbm5vEnw5ASxl3RVNpC0tCyLjVv/ffXH6OK/BLss72pyTKyu9wX35mkhsbUpIR/HEi+7H7LC0tRbfbrVTFkETiHvTKnM/n8fr166jVavH8+fNKr6D19fW0R1dXVzEcDmNvby/tB3gAmfPaOJnLGH/I9Zd/+ZfvJLA/xGXs9S4SBNwEEW9ZYM04qMUxsPGPkx/otnsRmZCGJKSIopR9Vyoz38TITqgQgyKfboXgXsW20dyH8VuGSXTil3gWtoR3c0zMOP0c9N84g/nnmfzM+ujfm3fwVn/0hDmz7hszWBetq6w572YcXdphYyASQayBfSA/K7dtltVF/IyEg+2inw2+OTk5iU6nU9maGBFpC2xby7iaMb9+/Trm83kSUPR2Bttvb29Hu92Oi4uLlHvH98gjcvt9rg9KStmhMkA7BaqkKA1lz+PLly/j8PDwre+gxGtra7G1tVUhdlBiFNuKieEdj8cxHA6z6oWmxy4x5pn0MYqIdLQus7ZSsYARiyPkEXyDUognDD9g8/b2NvfXk7lAqQgWMBiXl5e5Hc1G0oC/Xq9nA0OfnoPwuiKKLUrcj98zn97qFFENajCK7Ctlux5O2hlcZ7+ZR969DDgA8wZfZGmoxkK+MK612sPJMvP5PPb39ysN5XlPKt8Gg0EFABmQR0S8fPky/uzP/iz7NqyvrydpZ9LSwOcfysU6G4zakVK2Xa/X4+nTp1mNQ7DRaDwcEXpychInJyepn0+ePIla7aHpIz2CAJ7tdjvJYQOqMhC+vLyM4XAYx8fHcX19nZVK6IiDV5cnO3DkM2QAvMXWDowKDebCBtZVko8FdO5hhXNlbi3rEe8+DcSVS9gB7IgBIeOJqAbpVFd522MJXkyQ8z0TFCX49dYp7InLucnu4rxcmcq2LRwqJwDxLOzX9fV1/N3f/V3c39/H3t5eygLABhKM8XqbCPNogqF02MzTp3odHh4++vMSOJQ/f9+/TdY9dr+yiscBGhcy754WjcZDX8R+v5+6UgZcyKerHgmITM66sgpd5bIuOGtdgjveAxl0NR+/c6DJdw20IXNd8WOQzAVJwD1MYJXvhP9ibrELpb5yX+a7lFcTU7Z1jIOtEdzXlaNlNrUkaFn7srKMClQODnGWmPkHI/ggGObR743tcpBnH/8uUspBiH/u/z/mj7+vb3bD+x9z2Uf+mIu1ZNsNssE8uzLO713OgfEyDa273W42Wua+vic2fGVlJXq9XvR6vfz5fD7PZAxBCcGQSYyISAxPgpUerfSYM85y0omkig8Iiaj6fCogWU+2DoJRIxZbX01+lgGd/ZkJVq+hbR+64gpn/9x66O9wX/tqyyLPKmXbNqS0ow6YTYg/RrI5YVBiDfrgttvtrHQdjUZZieyE+Ww2y5OSP/vss+h0Orl2tVotq7EhPfgupBTywPuV8+gqkO/rk//zf/7PFTv8oS+P23jMhAx+kb5RvNtwOExZo3LMW53sP8AqzFnpk5gfiBr+UK3muWWNbGfBeNwLfEzsyne9mwbZ5v/cg3Gif/gd7uekjolJ/IUJF/vCkoTieaX9x+fwM8eX/N6+25VoToiVuubkiLeV2hY7juA+HqdjRObO+Jx5LCuZywISfh6xiMngC2q1Wh5OViYHuD9jffnyZfR6vRzPbDbLwzBOTk5SP83RwB9cXl7G2dlZzt/d3V0luQzR6Pe1bavX65Wigu97ffDte2Yc+T9KyVGXEAb1ej0Gg0HlqEKcCwxhq9VKZ2XlgLmNWAA6qiE4JQ9HyaTD6hoU1GqL42xxlmRsYI8JjAi4TKzg8AHXzAPjdMNl/6HCgwwZc+YtLsvLy7l9hvckSOT/CBW9CkwyOShgXjGSKAZHRpd/SiUsA2hKtDHQEDpk372PNaJaMumTFx2AcKFsMMkAGIA3ykoWbjqdxu7ubpJxJpQajUZ89tlnCZ7Ld4uI+Pu///vY2dmJjY2NGI1GuW6WXc/Lpxzk/pALuUQWTQazTnyOygeX/AJmLi4uot/vV7a+RUR89tlnMRwOEzxSqeQKHesEP7+7u4vRaBQXFxcxmUwqQBH9xDHgbL1vn6oI1gqWv1arJVFsXbm8vMzqPbYtov/Itwlq/gac4bQMniMWwU/ZcwFgYyIO+Wbc/MyZUxyZAYfXkXsQ7JtQZQ0iovIuzAWOH9JxPp/nqU4m8LCN9HxzoIBtubt7OKl0NBqlrOGAnb3Gbk+n0/jqq69iPp/HkydPsrmqCUiDgNlsFoPBICtf0dPpdJpNmPkT8WlXNhqAlcFuaXMeC+L9WcuDg/byOcgS8xZRPdHFvgvZ5R6TySS3GbqCCBKLHo+2nQZQlgHWCPl14IZusP4mRE3ulHNlcgp/jazhp/EnDv4NVk2UW6esl+XYH6sac2CJ/pswt4315eeX9zSQLH2nK7/9O2wKQY2TCthw5qNeX1TCmVj0s7EPt7e3MRgMsnm2ATD2lHko3/27ru/S18f0oSSpfuy9v+v6fQip+by6bQV/4eCHbdcEfNhyr7WTeMPhMI6OjipJX3QULA3p026388TaUo8goBnj1dVVTCaTTDBgr5F5Vy1PJpPE9rVarVJ9AdlJkINu0xN1Op3mlhYS0aPRKLa2tiqkkhM2VJW48bnnOGJB6iIn1pl3XaW827fwf69DaZv9t/Xf936X3LpSBZthPGEfbx2yfeU+BP2bm5sZEJPUOzw8zJ0g+HSP99tvv41WqxWtVivnl7XvdDpxdnYW19fX2QDdfoq1ddII/ObPft/r3/27f5f//j524/e98BURb596xu+xl91uN0m/iEgymG2SEYt2JsaUJjnAs45JwW8uKmBOKcRgLiBinGiMqJKAxI38nHck7o2IiqzxeTC5v1euNz1ImSfwA9ghYuEvkUtjUxPFzC/fwZbjpyFC7ddYJwg/V5I5nrbc2cczLyZUmQ/bC2xlic+YYyekIhaYH+LOOJ+YhfXHNq+trVWwGe/Az9yuwHNk3UVGbEtI6vI59JGx8je9J7ELnMA5n89Thm9ubmI8HldaIRnbPUasf9f1wUgpQIdf1MTN6upqnqjEEYSHh4dxcnISGxsbcXFxkUHm0tJDs2pIJCYZ4WRSWWAmcDgcJmg24cQ2HzPHvp+DVSa4rOLgns4YOsOKQJE9jFiUtRtkI8gmsajSQsCcfSA4uLq6yqbAZp0dmBHoujTUmQkH/lY2Ey+wnRGRRCDjLp07hmsymeS+V6oleDfmkWPDrSw2vDyb+zgYY+8qAN7Z3Vqtlpn7Z8+eJUOOMeJ0Rvod8V0Dupubm+j3+/H5559nRY17MvD+OI1PNcD9oZeNrbPsNog46OfPnyc5fHd3l6TyeDzO+aeacDZ7qDbr9XrRbrfj9PQ0Go1GdDqdfCaOHbklw3BxcRHn5+cJrKmURA4ZkwNIO0XsEODBWWOe52ONcazn5+exs7OTwA35Qp68ZdHz4mDVumY7Y7tj0tjOymS7SQWILmS5BLrYFX6HfvG+jNU6FbEACtZ/5pWfGQS4fwz2lm3Fzpyhe/yM9yCo4dhvLpMhb968ia2trTzEAJuArTaRPRwOM+mAXXHQ6/+/Lwj5qS+vtQGf/7yL4PF3H7tnxOMBePk9z5HHElHtZcOcG5zW6/XUE2eV+Q5kJn4a0BoRCYBM/pRb6rkH9za5A4AmACcBZMITuaO6F9/p+Ua3TFR7XhgD/t3bbwGlyGipU4Db8ufYhjKAZiysnbOPrkLodDoxHo/zHgT8EQtwyLhtLw0aTRAzr+jzzc1NnJ6eRq/Xy+oYZCFi0Sy53+/nFvr7+/usMja2sv45iHcGnKsMHkq5fFdA/0Oun9IeIF/Mgwlf3rvZbFaqXkmYQhxCyOC3+/1+Bc+SpAHLuGff06dPUxYt81wE0yZxkUGCKXQIXY14wGSc2su64bsIzCA57u8XBwe4cgO9xad5Sz5BZ8QiqYLcmixDTpAL+zvPNVdJJjmwNMFT2ln/nzEY2/s72CvjKi5XGvD80h7zGeuvL1d8IBe0yeBn4LNmsxlPnjzJBD5kIbKEHR0Oh9kEHzzEM9bX1yt9we7vH1peeMsSz7WN5x4/5Pp9COAfc9l3OOBGX7wrx3PCGqEXJHAbjUbGXNhpEwlgFfTEJKR94GNrb7zo6i7PnYk8xtdsLno9QsagX6W8Mm5kHPzHH28n5v3wX/gTbAdjASOA/U36Ms8RiyIMJ4bQL+MS421j3YhFtVfEooLc8awTSuYv+F3Eoke1iz2MHWzLIRmxlfi3Uk6oJnVl0Ww2q/AUzDdyAcFJDOZY2vbv5OQkT6ifzxenPXLqvGUCX8TuMC5sOe8J30Gs5ySc5Yu5I87+PtcHP32vDMoiFqeDLC0txWAwiP39/bi7u4uTk5Ps58Nn2fLhrRulAKBAbNPjuFn2SkcsypS91c/BYcTbW0n4Ds7IpAgEjcvzeF8As4M27oPQ+28DeIJgjP3S0lJlGxpC6WNIceaMgcwbggqgQKGZNzPp3ltsIgKBs7FxQIBSQ4bxO0g91oQx0JiesTojgIGnio3eQXbKBlvMh4NnFGc8HserV68yoAVIEdzyN5VVGCC2BpIZXl5ejrOzswy4J5NJBTD+QyGkuJAl1skZCsDv1tZWJbMbEakXo9EoP4ezW11dzYamjUYj9vb20ghTjYjjZ2452vTi4iIiqtte6vV69qOYzWbZwBN9Mwh25scgADnCmfnzBLWupnQAifxBdDxWpWQnEbGoorIjY64BILZVdpQO7NE56zhAnbWwQzIZbMdsAg+dN3jnfrYrOF/WiffmPugMBCU9um5ubmJlZSVarVYG8RDW6+vr6diYB/SYtXVA7yQA8skcet6QQd7X27N5nw+hux8ywDUwKi/7WJON/m4ZGJWBvQMZwBvyRw84LubcwVdEZOWjt2BHVEkWgmTrAPdDrux3LYfldjAuBw34GnAE8owe4Qd4V0AyF7pq0pd3NpltucTvMUb0iTkwKex78g4mc5kjbJeTPibgIGX5PYEPc+Gm0D7WGZ9t8pz5Bshbl7hubm7i7OwsIiL7yqCTJi3H43E2UsZvgs+ccMAWez3/UHr5XeTUjwmIP9RFdRF6xHqAIbGf9gd+P74fEXF6epqkFPKysrKSeKrb7cbW1la0Wq3ESa1Wq0LaIzPILBe2mXXHTkwmkySbCXg4+ZQKKLZ8OGHFZ9AX49PLy8tMPjEXo9Eo+0qyywJbYVl3ErQkMSOqzY4tc8YOfIf5KMktbE2pIw5kscOQ1J5bk/gO9h3X+B6l7+JCXkxsEPhaPz3H3o3BszqdTnS73Tg4OKhUXiwtLeWhLy9fvqwcVOFtfD5UhCSlx4jPZpzsyPgY1U6/78W4LQtOjpHcwBazdpDAVJPjl4g9IYRcVGDSwYlP5ovP8Ae7UCap+H+J4/BhJeHjJIl3FnCKZ0RU+kyVpCqklONexkD8Sl9adMJJEhMq3u5GzG4Cy3qILkYsKuc8D+iY3xmZR47B1o6BTTQyztLX2U7wfNsK5N48Be/tZ7p4g/fmeWB7J9pL/MFWu3I9XACC3eZ37Dqz7TL2MxZHlm2LJ5NJbG1tZZUl2MUVvNhWyOnvi40/SqPziAXgpUqq2+3GaDSKRqMRa2tr8eLFizg4OHgYlMqONzc3M+vG4hicE2gSwNze3ibbz7OdpfPFJCIUFiIrurcX4UidfXeAxpYFFhHyqN1uVz5L6bQJIzKQ5XwBKB0Am3nF+fA95pB3RDAjIokjKwjGy07CwYNJGObcJBJgk9/Z0VjQGS/vvby8nCwy4ya4ZKwcNcvckoXH0WI8bEiYz9FoFGdnZ/H8+fNs4ImTiIg8sp6tXpQwN5vNGA6H6UwgsZwh8vUPiZgyccoao39s2dvZ2UlSjzXh9KBXr16l86LEE6eyubmZxpa5x5iWIPf4+DiGw2HOu/tDsRUAp48OIzsOEA3eDYgiIvdHz+cPPZDQDbIENrJlsI2cA0xMdhm0cPHeBPGuwsD5RyxAJjqKI4hYbMfB3pl45rnOlBmoMEesBXPCnDtz4zGSuTYI4N0gfiwjd3d3WXnmeaOxLcGpD58g+ICYY544ifX169fx2Wef5XYO5sxZRU75MmjESbM+AEIDlj/09YcKqL/r9+X4Lefo0rsu7JcDhcdsvC8CPy5OxCsDQTc09TxzP1cDMk7u4+/ggx0IMGbkG/8bUQ0gCRLQTW9VRW9MHJUgmrHazqNDzAU66gSadcTzxvvbTz/2XAfEJrqZJxPLvC/3N8lk8oq/IeKwbQbXruwC9zC32EDm4u7uoSn1dDrNVgrYJfzucDis9L2kv5wDq3LdTIL+WB0q1+F9V0nO/lQXa8dYyoSrL8aJbXfwFhF5SIcTMKxzp9OJp0+fpk13hSJrR5C8vr6eSUXWiSpUPk+P1ZubmxgOhymL7idF1QU6OBgMss8OunN5eRkbGxspiySGCGZWV1ez6uby8jI2NzfTZuC7sT3okN+tJJwiqhl8B+OPYQavk22QSTSTLiXx74DZwXKJDcrnMC7riW2iyUlXl3J56xH39eE9rB/+nt0Az549i263G0dHR2/hv6urq3jx4kXFljj5wDvS8oB3AK84tnAVK3P4KV/2Q2Azxr++vh7b29sV2wzWGo1G+W9izPl8XsE6EQuyxfpTkhyORfkO8s6FfOBriPvsgxmPdxrhU60r2A6+y1rzPXyO22dMp4s+V+A/39NbQ5E97mVSBgzI/y3fJmX4vQsl8NUkm7Cx9mncOyIqa+GY3pf13aSRSWOvgf/YxjiJ57g4YtHSwu+KnYuI3AKJvfP8cOIlyT7HQIyVeW80GtHtdmMwGKQ8Q0QiD+BxzxGVkpubm9kO6eLiIg9fM7HqyqjvwqPl9dEqpbgQ7na7Hefn59HtdqNer8fZ2Vk6wuXl5djf349+v18p741YMPsYaEiG+XyeBAMMMwpkI2/hQUmsEI85mdJBIBBmLyGfOLWL59Bc24HXcDjMvj1UXFEyGREZZEUslJVnsuAEdy7Ft4GnMVlZVQRZBqDw53xalqu5Go1GGjfmrHTmJsG8zQdF537j8TifBxFg2bBBgJxEuXycrDNsKL8NUsQD6UDF3LNnzypl3ZAOAOvDw8Po9/sVJ+25N4go2fB/KJcdhU9Y4W+2hDjggCCi5xOlpPQgcMBhB4Xs0n+EdTo7O8tjiXEyJqQIfACpyJXJEfSJn0FkQRbzTmwptIOCsEJP2epgwOqsrAMFABhzaaNsAIaOeMugnZgJJc+ff+ZnIaN2guhFxGIvPTLNWtTr1d5zXIyRTA5NbLElzAP2gedBFEZEVhyaAIPE4v4uB8Zu4pgZ99LSUh5OwSlgJbkPyEN2mRfICObPVZefOpFs+bKMPEb0PPY9/87BEDabi/n0Z5kj/7/MZgL2nPFlvvm+A/4y0Ham0IEXF6DUpAq/J3D1WqI3gDETUdgd/CoyWAYF6AH/xl+UJ9SY4I2obquwfroiyJWOrCPvZ/0u36kcp4mCUmcBgtZndIzAtdlsZkKhPL3HJLyBseWJsZEdNUjmnbG9q6urMZlMMpnB2CwbPMfVWu+6Stm3Hr/re5+6nkcsMuwkGk2e2JaXyVJXENzc3MTJyUlELAgJ1nZzczM6nU6srKxkawVv7eSzrAUH71D9wNhIIOGDqbDCnzmYNmnSbDYziHGVMViN7Xqcgri+vl5pGA05RdUt/rfRWFTLmmA36cS/kWX8nwlg3t/zUF62hw72WAfbAAfHvjffNw5gLlx98Zgv9s+cRGI8rpjyuyJT4Ij19fVcR+wkLUawd2tra/H06dPEWCaMr66u4vDwMH72s5/lPDpecXWpSTSIcHAL3/1j0E8ur5vHTVKR+cfmY6dIzrJ9MiLeit3wKWyz9fdJ7CHz7ovEnDr+Ml63P+Md0GeTEN61E7E4ed0YCv+NP/F2Pe9WmUwmiQPq9YdeWxELOxcRSRgZyyErxsOuTLZvjViQUeC9kgQhxjNhXW5rtG806cQfZNm6xb89DubWc1xyDZCzjlFtJ/kd+sb7M24qHCHtmRd0m3YoZfNzqkrBaY5rSUCg38iKk23M02g0yu2EzWYzTk9PM8nrPmjG4J6f73t9cFLKWXwGTIOs+Xwe3W43jo+P4/DwMAmPtbW1ZJ5L5WWxIaOYDByWgyQDbFdSRCy2HyA4/A5BYOuRgVC3241ut1tpyE21EEDP2af5fJ6O1oEFJ+xBwOEIGTPCZ8eJwpqBZL7cYwqwiaNHae2sIMPsnE3soAgYHubQJZAEDawPz8LAMY/O+KJoOCgADoDUTfQwdID/VquVbC0BJsSCmWGcgrPh/X4/vv3229jb24v19fXMsKFQZPSYY4AZZeaMnQu5cXb6H8plgHd/f58EK4EpW0cjIqufbm9v4/z8PF69epUyD6uPjGOEMaJsPWF758XFRYxGowRQEYteGhA4OPd6/aHXGIQujmU6nWalRq32UFbONp1Go5EBKVvyKDE3ycuas+WT+6LnDl7RCVcN8p7MI/PnYI/vuSwX+2YHbKdIHw2IFWyoA1tnuubzeaX02vqK/hrAmIT3OEo7Cijhb55tkowx06eCU0VtIxgPIHk8Hsfm5mZsb29nPxIqX2ezWRwfH0er1cojbllz5tiJA2e7bNvKhManeJVBkQHS+0gorjJrx8U8lESD15bPGLRFLIgrZNil+gYvJi4dGJq8NGh/rErWFVBUxoEbGAe6aYImolodjB1gLlzpZR+FvNiO817Yi4iFjUCOnAzxfNo2GIS7uhH9x66ura3lZ00+oXt8F1tUPtuBt/EH783YXTFOQA/W4R3sq00IlqQIPrzUR+MrV7ibKDP5V5JT3+cq9eNd5FQZ4Je//xQuB5ZgpnKu+YyTAhEL3ZpOHw6aOTg4eIs8Xl5ejlarFZubmxWs5qDB1S6Xl5fZwzEiUkdp1kwF69raWm7Zury8TIKS5IdJdONB1hgcQRVuq9VKvIFctNvtit3C/7Xb7ay6ddK5JA3woyaMWX/HBSbFS//LHxO3jxGjfJexcxHQW7cYD/+3HtsO87uSjLW+l/bHSVTsnHtJoZfIDZ+DWMPu4GsHg0GMRqO0tc1mM05OTqLdbsfOzk5ur2csllHsOGPl3p5j5uNTvWxTLWNgWBJp/nxEVHweu2Rms0U/ZOw5OoqN9zyX+CticfIc8+cEDPEga2t8B2414WP/VCZITET6vSGf7ZMZq/WEucA2bW5uJint5ExEvKVXZZLKvhnbZSzJz4xlmXsT5Pb5fIZ7mx+w7SqJJt7XhBrPfAwzlQm9x2xLmVj1mLiIZweDQSUOYv4iIrflEZMQPzMuH1pExTQxgp/L+EvfeXl5GQcHB7G7uxs7Ozu5s40dEozZnMWPuT7a6XsswMrKSnS73bi4uIiNjY1YX1+Pb7/9NkvA2f8e8XA85dnZWb5ko9HIk2D6/X5mZ9lKQDWPMwgR8RZZZeVFKFA6gPHGxkaloTJCwedhCw1QDQYsuI1GIx0412g0ina7nc6abKKBGYGug0ADORSNTMd4PM7AnK0sHkezuTgBD2LBBtLKivEjGEaQvf2H+WKOIeYwsg5mHNQyDw5myRAAbAheDB7a7XbMZrPo9/t5uhsGGVAUUT3lCHk4ODiI6XQan3/+eZ4yUa/XkwxZX19PY4fB5t0M4P6hkVDlhfxfX19XKqIw0oBGqt44EYcyTvqgEXSS4UEPCKwiHnTg8PAwjo+PIyKyTNXED5k+fg4xdnl5mdsr/Tuq65AbdIRAimovnHmz2cweKJZ1E1OMn/9DRiPf1n1v73OgCvDD1tiBQ975WRGLbRoG0hHV6knGgi4RFDAXdv6eCweCJcjmD6R8xGL7kvWXoNOgotxiFPHgzPr9fupZr9fL7QPtdjsDC+w7oBiSsFZ7yNAeHR3lXnhvZZxOp9HtdjNQNqlOUO1S5E+VkOIqwdpjv3vfd8vvPAb6S/KrJAYMLL1tEkIYshOy1yQLeluOwWDUZCzjQP8gkMoAh7/RI96Ve7E9AoDqJIzv4y11JrgclFmHa7XFibzemuCx850SWPNe1huvIe8APjCGYDyufJnNZnmIioOLkki0LvO+BEHoallFXhLT2FT7RNaRZMJ0Oo2tra1K5Q2VMdjZq6urigyZeMSXoP/vk9WSgCrl633f88/K7/zUQbHnlT+Q8fzfMus1xS6fnZ3lsfNUKNdqtWi1WtHr9RLb0OvLR7kjc+PxOEajUZyfn6f8XlxcJLai3ym4j50JbNfudruZLCHps7S0lMkF9ACMwY4JV/zSe4pKa6ox8Kv2fWA4dNpETxkku0LRtglMyvM9x44XyqDT5AT/t0765yWJHBEVPcOGmUAzfrBul4QCv8e3+/AD9Ax9pSfcfL44aITvExAzr2W7AqqrwEaDwSA6nU7lHd41RyWxgr31O3yql30QeoddZH6wa5YB6xtYpfSvvDftZpjDspcg68t6mazw/FPVblxnopV3sJyXPgs94nnonLG7SRufqO4Cg4gqAeOqK35mObePdjVT6TP5LHMO1rBO8Fz0nPliXRgnmAXdse6Z8OIqCUrPK/pMAt4xN0ky8LTtOokCx72uoopYJAbMOdze3uYuojI+Wl9fj8FgUIlnGZNtEXwMBRk87/T0NLa3tx9NEk2n0zwsin6/PB+5xZ49lhz9PtcHI6WsdAh+xMPEsf8R8uL169cptD7ZhX4yCD4VFTTUJeNKgIoCo0yz2SxP7KjX67m4HiMZQ0Awz8T5IsjOrvo5zmQZBC8tLWU/HZ4PS87WPUChgQZEGErpDDFzWQbMGBRnolBcvzNbq3A8EQtnCjCwcTRQBPTb4CHgPBslc5WXA3NXGwGQme/Ly8sYDoepvBh91on/r66uZlNGwI3nG7Bkw8SYT05OYnV1Nfb396PRaFSOX221WhERFSNLOavHzX1tUP8hXMyRq4bm8wV7T3URgJfggjnmgAJ6+7jJuANaAPfZ2Vm8fPkygxevNTLHuuIIeZ4bJUPmmph0NtgOlN5GOF30Ht0t98wj89Y9b4ctM0muqDIA4f4GaOg18uM5MgFt0FBmUk0MO7Dme4+d3uJ/G+wi4+g172s7x9gdNBmMMeaIqFTCYLsPDw+j2+3G5uZmbG5u5jy5SpL17Xa70ev1smJxPp/HaDSKfr+fWXLe/ebmJra3t+P8/DwuLy8jIpKs9zYzr9WnfpnYK23Mu8DaY4QUn/f3LDuWpZLU4F7INDLlppplcIyMGZhZbjwOyCd8Gv6w3MblhFD5WT+DzzEm+ymDcuwK4MnzVG4zs15BTrkymD8kREy6YYPW1tZyKxtYgXuaLLJf4bkOrD13DlJdzQv57sv6XDZ1N2FGEIsvN1CPWFTh2T+en5/HbDarBKhgHfzz1dVVVl1gO5aWliqnrPFujwHhcg6+6/o++v0p2QATDVzMC+N0MItNZh1vb2+zPwgYj5OH6fUBZi2TI/hXDgeC3EI+eYYxNXJAFQ6+tFarJUmFvhOQRkSlohXfS7UePQc52Kj0PciVk6JgYFf6lQSObaX1i38b2xG0lTbB/rRcDzAwzzPB5ItxQRIxp8xlSYjxHctHaQOwScb8YB5sK4nlq6urCuFoApjxQO55LpaWlmJ/fz+Oj49jMBhkm5HJZBIHBwext7eX7+T5x54QfJvcZg0f0/VP6TLe85o6CRJR1UmvO7LtnnuuJmetyoQZ/3fik++gjy6uMLEIBkVXTdy4YqdM7JT40piW75MENtFRJhT8Ny1WrG/GiI6l7X/Qd5NwrvIi/ohYxJG+SpxT2gXHbtYBfJ//Rp9LAtr217Eu784OCc+f8Y/JIT6PvLjqmHlDhtA/3gdiyoUrNJT3VmdjCngTcDc4mvFdXV1V2h+UOrG6uhrHx8cVe2g9cLXdj7k+WqPziEWQhzNsNBoxHA7j8PAwbm9v49mzZxUFiojodDoxmUzi/Pw8jo6OkshgYTjhA0UCeM3n86yMQODdUNUZWYLV0lF5Ys1uogQsngFvuU2HzxNI03vK2/04ZhUHi4DheN0LZXl5OXZ3d1MhCKrN7KLo7u1k8gzwQgYKB+HPXV1dvUUmOqA24cj8uoSvVDjPi7/HvGH82epDtZyb20MsbGxsVEpih8PhWwaStbJczGazODg4iHq9HltbWxUDOZ1OY3t7O3ubWW69jv7ZYwHgH/PFmt3e3ub8mlhqNpuxu7sba2tr2StkdXU1zs/Po9VqpSGGDMCAloz96elpHB4epj5yId/eNooxjVhk2JaWlvJEzk6nUykfZYuniVtA5crKSh6c4N9TJRcRFXITx2QnTbYIQMl3cJTMlzNNDv5dQUj5rAN2g8WSNLCOQbhTpcLnqJQsmwqjEwb1JSBnLVxNZbngnXieHRn6aVtskMHPLy4uolarxbNnz2Jvby/tDfYX8M/PyPqwPfvw8DDX0SeqsQY+npatQ+PxOJ2xT+X7FK8SYJXE92OBlonGEgjY5pb/N5GFbJQEAQkaV3A4G4u8Ip/ov7OTBr74fUiriAX4t+zb36AvBpH+nt/HpFBENfCLWJwgVlZaMfYy4OD5fMdjMXAzDrDeMWfufVOuR/m3gzU/z3LgSkiew3uyZdmA1sSH+8iUBBl6zhphiw2E+TzvMhgM4v7+Prrd7luJNhMhzBHr4/d8jBgtdeAxPXjX9a7fW2++z30+xuX3c7BUyrx/xvciFr36XGm/srIS29vblee4MoB1J7kwGo0qdpbsPoHL2tpaVtvY32Gn8bEmoDiNt91ux+bmZp7SBoajP6UTBxGLhr4cPIPfpRr/+vo6dnd3U44cSHteTErzc2Nz5tTf4Sr10N+zDDnQLZNGfMb3Rlf9fRM2pd8ticpSVyMW+AN8UN4f3bu/v88Efq1Wi06nk98hEUx8hs/GB+zs7MR8/rBVs1arxXA4jKurq+h2u7llqNzi7HvZzvqkvk/5clxiMsaYziSm41r6lxKf+mCXiLf7GVuO0c2yctR+rPThjNHV/o6Bymo9k8xuI+H71Gq1HCPPMxYo56OsIMZH8W/eibGVpGRpi5EPf9bvzXxY50y+lzGqdd7kcYkLHys+QW95TqnTxmWe46WlpdQ5k8jEOT5My/0ESztlHQLjUHxBQsh+odfrxWQyqexC8LwzFnR/MplUxn95eZky4HdFvss+XsybY5cfe31wUipiwQrjhHzqHj1H6PVDBZUF8fr6Ok5OTpKlw7m5rxQBSsRCYGEXyR5CavCH4BSlZKIRSoIsfuaMocvm7OANdCMeStzm83mcn5/nVhRnI2hctrKyEicnJ/H8+fOKkyH4ImBnzASdzCcsvAkqG7oS2FgBeSfmzmWQZI5N8HAaoLM8bJEx6+6qEp7NH1c6+N4RD+w3p1bQ94dKKbJqm5ublWodjKeJNu7LO7BWv/vd7+Ly8jK63W5W95C12NjYSCDE/GCwHfxBsJRg5o/9wsGwPvf390mgknUrs+gYMOaQXixUBuLkbm5u4vj4OA4ODirlrY9V3HHh1Km8YlzIF9lagq3l5eW0JRA+ZGVXV1dzyzC6gQ4zZmc5kFPKoiMWGUQfZhCxIE/tGAyWywoDA2gDNMveu4gqg1WCS9YMe0jQbGDKHDmzg+5iw7CZdsjYEgJzV1QgFwS+AFNsA7bXvcKGw2GOZXd3N4Ex47Lzxt5AdpDp/eKLLyrVVTjik5OTSkNe23TG8ynrbBl4PxaQ21ZzAWbfF1yVgf9j/3aghN6S5EHvkRuDTtYAoFTeHwLJW1Jd/cT4fF9k2pVO5XwYEJJhNLg3SOZyJaPH6AorsATy4m3IBqgm3Mt3jqj2UQRY8jmvkcdpfSw/U8qJiST0FFtkIOvPQ8i7ogrsYEBOz79y+4lljbWbTCaJ78qEU61Wy1PWqIp+F9nid/S7/lidfUwf/LvHnvdTXSUpZXLKemS9oW2D9ZIG5OAjfAWYCOLftvn4+DguLi7yd/hMkjl8lgy7e3lyT7ZPExjf3d1Fr9eLbrdb2QIIjrP/No5zsOhebNj0Xq9X0Tlk0GNxMtakc+mXy+D+MbtQ+svS7tpf+fJ3eEZZXWESp9QD2zUnqExcsaaudMYumVgiieeEPcn9jY2NyhYiv59lhuB3MBjE2dlZLC0txcnJSfz85z+vEM7MtRN03lboefvUL8sIvogYo6yqY+3QGeMv3pXYjV5qJvF5BnMYsYhlHVeadLE84rNL0oVxs8aMtfTh+BEnUPB7EZE7f6yj2Bb03wkUY8UyyWQSyZ/hnZ38NN71fBp3Wlc8n9YlkzrgwjLJahti8tfbOPn5Y/pocto6RaLItpj3YVxeZzA9ds92Az/N94mNWVM+Q2sV5tu7TvgZdth2sVarxevXr9POWr7X19djOBw+6o9tp39slVTER+gpxSQj4BsbGwlQVldX4+XLl/myV1dXOblsv+j3+wl4mKDxeJyVPA40UOiSxWMBCEqtmNyTTAwXAQ3kj8EdGaHpdJrBKuNg4QHVKI0DNAJ4gN/GxkYq+mAwiIhIQaY5HPfjswATCBWABGADRfc2BdhgBBHjyRgBizZyzDEN6Pluo7E4zQrwABBiyw2GkLUhMEHp7FgN1lESylG9LWtpadH0nB5aHBNcZvB5Ju9DYDoej+N3v/td/PznP49ut5v9tebzeTbuximYeXfQ9/sA5U/xKgMj6w6Gm8oiVye6QTxzhHNBn2hy+OrVq/8fe+8WIvu23fWPqr5XVV/Wda+99tnn7HCSkJAgwduD4Ml5SBRFg6BIHgKJEBKJeRBEJRJIkChogogXjvgUDajBgBAkEkQ4hH9UJCFoQhRP4kmyz76se1+qq/pWVf+H5jPr8xs9a61ee6+9Vq99fgOa7q76/eZvzjHH5TvGHHP+4v79+wU0kXBAlgDRGFKezzlCnOlCH70FlGCMrWG8VZE2aIezFJyAyQ7GDu/s7KxRcQlYJ1Ec0TwkkufB04h5iTG2ITtR9APZJTlmZ5dXk91X2o2YH4TZ6XSKjZrNZo1VDwAigQUrp9YVJ3INBAiaGQM8Ojg4aOgfti+viHH/0tL5CwhWVs7PFcG2MC7kaDablS2XZ2dncXh4GA8ePIjNzc1yH/24efNmvPvuu7G7u1vaQAa9GnaVAtFF9Ly2ZREQyMkMB2EGIfl6ZM2LEsiO7SsEmAMEO5ihnbxai/81ePaLCvAX4IEaf2jPgSlADcpA3p8bOzB+ZNDVSHxOYMlzXNXtfmVb6sDW/cD3OYCtJe280OW5QtccULsKzHPNXDggsd6zsAPoBVdQPcOc2oa57UePHsXS0vnrpsEBvLn0+Pg4BoNBOSCbFeI8JlckZHqaTpjvmUf5umdd87LJONlbeSKalX7YcHzD0tJSWWxFLtfW1uLmzZsFm3JOI3PK565CcNVcxDz4Rh+NHR2oETy6CgSdZdHh+vXrJRHZ6ZyfcUX1P1s4IyL6/X6xNexA8EKP7Uiv12voV0Rz27k/c7Bvfvta9A2fY32yv7Bt9AJKTsLbNnl+M7bKbdk25zFNp/PKIz/Xdt/41BgG27G+vl6qwt0ffogHkCvreUSUA5Z/93d/t7T54MGDuHbtWly7dq1cS5VcTjRjxzOfrzI5RiQ2oYgAW+WtcMZYxnVOIoCfjU8joqFTzIl1EruN/tO+Fx+4zgu9YGrkCX8SMa+yi2jKjxee7ad9kDqxITEYb9ImznWCDH9GHsD+mH6az9lXO1YE49qXZjxMmxkToS88A3nMySzik3wP/8M/J+GYQ/rJfHHsDH3zkTrGNlm3eZbjdGPY6XRadgHcunWrFHAQd9tOGguAbTKucNwynU7j8PAwer1eOauQt6JyRMYifXFi7qPo9yeelILRuWKCQHd3d7ccdnh2dhZ37twpIPjRo0fx8OHDWF1djRs3bsSDBw+KUuAAPXivDkQ0twBQgsxzrbAIubf3RUTj4DgLPcYCBcHx04aNgg8powQaR0zfeO0q33ticS4kczBk0+m0rHAg/CgIqyIR8+w5yS36hXHyqiqrbjZyzKErWiBXKhC4d7vdUrmSwTKKvb6+3ghWnZ3Ojow5AOzwnZUUPrLCnBNiGNvpdFqU+vj4ON59993GGQbIFBVUEVFWiy1zrxrEfpIEv5xYtXFn7N4aZzlwZp62fu/3fq8kLQDTDpzscO34cWLIK29zW15eLsmIwWAQW1tbBSiwPRajTMLUeoGsc/6GE452+NgKB0+1A5jRHxK2ERfP4MDQ8x1BBLrF2A1EI5plsOZvvt42z/vt0Vk7Z95aSF+8Emv74qSZwasdm0FTDobpp1cDAUGz2XkS/N133y1v3ev1emUO6DMJsE6nUw7B3d3djXfffTdu374dm5ubMZudnzd17dq1IjfY4dXV1WJvDXQ+qsP8pMl9cjDzrOtyZQ20KOnk/2vBEzJBwh5gDdhkDvET2AzPPfKE74Dwu7TL/5wvCLEIgSzaPzgA4G/sP+3WeJUDTNqwHtt+GfQTLPgadLcW5KE/mecRTWDroNt9xi8aeMM/fjznOclkvwv2ms1mJcHkgCMHwqyQ40/H43Gpas9Jc2wJ+GFzc7MBrvPYc6Dk+fCqfU2WFyWfFtFlrr8KdiAnLS0fviaiGcgg7yws4PO4BjsI5vWWIXYaRETjUHr0jsA5B8QsOLjfLCqwAGU8S58nk0kcHh6WPoGpBoNB4wUp2J5cLcJnJFsioiEz5lct0WP9QacckOfraD/LELrl5C14IWJeTe1g27qS9SYHjA7Urcd85i1U9AE84diG/0kQc+C8q8NZ5IbAQ46BeFM3FXP9fr/41KOjo9jb24ubN28W/rgalkQGfPdB0NBV0L8aWX48Bw7q+Q2hN8yB7yHOAyPSvt9m7YUcJ+/AdTkZanzkqid4bP3NSSKuc3LNmNWLlV6EtU4gJ06y8Rk+Bh4ZN0fMcQvY3s8n/uB57rMTRNgRxyg5QeT7eSaxMPqGzKN7jMfFCbYbGSdl++GYwjENWzmNmVwNxTx562vE/DwxnuOFMN5UTVIQLITOUknrmJpYmrHa3jJXjx8/bhyxsba2Fnt7e0/VV2MyEnDPS59oUsrKg9HGWHoFiDeC8B3b9R4+fNgo4WWfrieKv32oMefCuISSYDIHiThgOw8bGSe2DOYIbumvDS1JEyY8l+phCCLOHQPBuvu2vLzcOKgYpSGBhuBY2BD+iChnRnmll+/5zf2ABFcxcY4Eist8+bOI5uuEyaganDOmvKru+UfIGXsGJxFRDtCkv6zYE6jSl4g5aGOlFyWhgos5Pjw8jHv37sU777xTtoROp9PY2dkpb/pjrgyuLdOfFjIY8wq9jaADMXjCllscsLf3LS8vx/vvvx9/8Ad/EGdnZ+UVz05+oCMR80P0qJaMOHd2PgeBCksSTABaqpciouGEnVBCBnimgR92gGeyUuvDHd2uZTWiuf8d+fIqJ87UwSD6xnUOMq0TDhwBJtYrg1B0yk4YnjtAjYhSQm5eof8O2OkPPLDd9fNJVPosBbaHoPMOtAksDg4O4vj4ON5+++1ythT8Yt6xhTzv/v37ce/evbh+/Xqsr6/H3t5eSSg/ePDgQkWHAd1VplpS6mnX8H+tSiqifnh0/sz/OyDigG7mm2pGv4USefNZXk5EApJrzyfxgi/Cl6Bvvs+J2KwjNb3hOfm6iGbVBNfVfiLmwWoeL9/nBJ39nnnqdh2Ycp35EREX/EsOjmjX9siykJNNUA6WzCMHDg5OsLe9Xi9Go1FZdHIVxmx2Xo15eHjYeDMX+guesV2p8Zu5X0Tus/nyUegq2QLrnZO7+CL66qTtbDYryQF87/b2duEnCQh0DAzFoqBfAsQce1EVHMCCMdiLfkVEeWHF5uZmeRujk8yz2ay8jIQ+469rCSljZ4J4fAo6Zt9NAEfCxDqU59d6B+W/M/6vfc9zkP+cKLBO8J2rTvJ8+3qeY5uOzFv2bcv8ve9Bjqhk5q1ZnEOJD0bn8Nmz2axUvnhbPsHs3bt34/333y928PHjx/HWW2/F5uZmI+B3sm5paakseDuop69XkYz57JcimhWdtpPYOcsn8QqxIEfQuBoH/AVP0b+I5m4NbGpElHacYOJe+zxkwQkivrPtNcakehLbwRgdG1AR6RjUxSfYCHYo5CMu3E9+u4qPGMJ64d85zvQYwI/ol5N1kPXO+uO4wLkL+mQdRI98rTFRzjN4HkgoOi7NuMG8yJVstnNHR0cxHA4bZ+bmeC5ivlvIvDw7Oys72OADfRgOhw37sAhnmqfZRnleLkOfWFLKwgIzSZAguBHzpAyO8uDgIB4+fBgPHjwoE4wjJFAEFHm1g0xkv9+PwWAQ29vbjTfleAUOx2Xg6cQG+9xJgHGtM5+sFrHy45VitqTQLs4/76vGgPM2msnk/HWL169fL9uabLyd3cUJ2ylGRDnrxm9AycrjA4IhlJDxA1ac9YyYr17zuY0RhhYlsELRrrP/VKY5eMwgFYCD4h4fH5ftm0tLS+VNXA7iqUrDMOAQvF2L8X/wwQexubkZb731Vtku2e12yyGhyBd9tRO6qs70o1AG++gdVXYcPM93VLEwl64iw5k8fPgw3n333WJoc6KUeUBmXCFFQgOdWlpail6vF1tbW7G5uVn0g3l0ctRVVugdcsd12AxWoLyKxOcEyj5ThbknULAO2nFZrg12zR+vKMJ7J3v8P7LnZIvPb0IuSfRAGQg7ye35Jsi0fQPgZGeY+0v72CXm1xUW9JXv6DPt7O/vx9e+9rW4c+dOSV4ig/CJoGcwGJQ3RY3H47h27VqjjPn3f//3Y39/v5HU99jhxVWmWjIhf56BxiIyOGDuc9AUEY1573Tmh7Yic/gvZJI2eDkFvsVA2c/ONhMd86oz7QImfV4a/V+UbMJWZ2BuYOkx2keht/Z5jAH5gzIotvx7rAa8OVlsIG2gji01OHRfM7iDVxFN3cwBU04+wR/7Mn6cPHbQxFmOVExxEDV4gefu7e2VF2XAr+Xl5Tg6OoqNjY04ODgofKMvrvR8VrIo8yD7Ln/u+brsd6+CXPkScfFFMPjA7e3tRrBFALy5uVmSPfjUiCiV+chfxNy3s0rPc6mcmc1mxY4zN66wMa5cXV0tOxB4gzFVV5ubmyVh5GMk+v3+hYCY9rP/ZjGZCmn8rCu1wILwLfPPtsP2yPKJjKIDtimZst3IvtvtZnvi68EJ4CD66oRwtjUZexr3mI/4c/t7eAcWQ4c7nc6Filjmgq2We3t7ZS57vV589rOfja997WsxnZ5XAA2Hw7h582YZFwUE5o2T5U/j71UhbyP3ogE2Cl4yZyxiOmZDR0ejUcGp+D3PLW35TE945YUdx2M+doTr+N9JM9sR7x5Bj7Et+Hg+oyraSXL64m29VP+YH5ubmyUG5Rw5MLcxM7Jp38wYSHgtWkTyuGr+wBgkYwHj+Fzt5BgVHaUtbCZt0M88HmP0HKfTNs9DFuxLPVY+ByMZQ9AO5zAjW046u5jHix3m0dbWVjx58uQCRhsOh7GxsRHdbrcsYj+NiPVrC+OX1fVPvFLKjsHOEmKSYf57770XBwcHhYHOXKIMOCX2l/d6vej3+7Gzs1POCPL+69rKq0EbSQuMBsEUgNgA0QbCSowAn52dlRVmVhUJkFjVstNASPhN8IYC81wUi5UveIMDQVi5t9/vl6QXyjedTstWQyd9GCNCxDkQdvCep+Xl8wPY4VGugiKBRf/yPPqZDqrhgwF6zTkfHR2VgxYjogAx5jNi/vphnKbPu6FNlP/+/ftx586dxiF+gG/z+XWptvio5KTJbHa+zYPtAPBqPB7HYDCITqdTZMzVeASmBwcH8f7775fElrdZAjBx0OgrRpwXF7DawhbL69evl62VGGkAsLeS2fH6oFRXUjH/AO8cyEXEBZ13oOykVHaaLqenHQd8yFS2STUjbt2nLeyN9dIJ+uwAPR76nEuGnXRywtBbZhmneeZEFqCI9lkhd2LQyTg/Zzqdlrd43bx5M27cuFGcKclowNry8nLs7OzE7u5uDIfDuHXrVgEMvBjCWzwiFm/numrkvuWApAbCmMOnjcm2PrfjBIllzAASQOND47mfz10hYR/pgAnZjYiG/qNbeetWrZ+0aT21zuQ2/Hz/NtA0cDfYRKa8WmzfVbs/ovn22WwfbEfgswPh2pgcyBqMQk6G+V6DZCfQbC8MbPM9TtQTHPitwWCe4+PjUtWCLvssN88FiwnmhXlp3FGjy+juomDl47T5SZL5A5h30GLZphLZ55J2u924efNm4SWYjGsfPXpUzmWl+gW/urS0VJKEBB/g7LzFylUCm5ubcf369dja2ir4G6xAosT+FpnDrtBPxknS08F+RJTF15wgYG5dweVjBGo6koNLjy3LQA5sLVOeGyeR/Wy+4/tMtOE5tt5l3Ovgms/4nnGyqJcXfOi/Zct4CXwLdjs7O4snT57EaDSKW7dulSQi/QOrbW1tlWqQg4ODMjZ8NIQNQRZIpsCHq0idTqcqc/YxtlkUA1iuJpNJ7O7uxunpaSMpw7zw1kInJ+2vvBhjf1xLbHQ6nVLBDE7KSVJkwLKWF1LAWfSTufMRCIyXaqrMq4go+M/+2N+hM97hRJUm43EizJRxQU5IWU+s7xk/OAntsfoZrlJzv5gH+EzcaKzL/Rxs7+15xFmuHkZGLEPgKyfuPA/MIwUy7EhxVRrz6WpTn3mGfDupadyR/dHTKMcgfu5l6RM/UypiXgbJlrdut1vADQ7w6OgoHj9+HO+++275jsHBLCZ2c3Mz+v1+bG9vx+3bt8vh5azaZLBbWzXlf6o06CNK68PQmUhPHECNibIj4UDA4XAYo9GolLWSVKKSyW8rIbu4sbERGxsb5Qwo9vh7G4tXbnHsOD54jHOhXwgfyTLmxZUgBIpUIOWAxM4OB5gDJgwWSkeiAVCUy0czGM/BN0roYAfjSfJye3u7rMrlTDBVFd62R/sYlr29vXj48GG89dZbBazRP/g0HA4bynUZBX0diflnnJah6XRaKiLYZkeChorHbrcbT548iffee6+xmonhQxZWV1fLgfXICwYeh8YzlpeXC6DG2JKcdhADKODlA8gMNsAJGpIt7h8rXU4MGyT7+uPj48be8OwscECW8YjmqjfXZTvpJJYDY4+JPrscFyfjBDDO0mCXINsg1QCGVXQSShHz1wSjNwYt2AP6zPOZN8blZFxtJWo6ncb+/n4cHh7GaDSKz3zmM7G8vFwqpAAz6Ch76eHByclJXLt2rTHvBudQDcxcFar1qQa8oGdVSS0KrOwbIy6+0Q/d8quMLQvIvQ/tpJI5L1IgX07E4vMMxr26a3m0/6WPDhZ8bQ6A+N4BmsdZ44/5wf3WXwM2Pw8yRrD/AoQjw151tY2w3NoWZ9vg77BTroh2gIz9JkFAe9gA+0c+w8aSvKdfzPHS0lIDSzjZNhwOY3t7uzHHfhGLbYDnxDJp8jifRZe5Js//q6asw7XEgoMMeMnfKysrje1uEecvGsGe7u3tFR3mDE2OPiCwnE7PK10Hg0F0u+cH1w+Hw5Lg39raiq2trdje3o7r16/H9evXy/a9iLnNYLHQAbXlw/Jtvc9BF7JmTGdfC0aHJ34Bh+U/J52yDaB96yuU9cifu93s430dPOA7621OsOfqJttqJxJ4hoPfiChVqsiCcTtts53KfcCfgzVGo1E8evQo9vb24u7du+WQY8c7169fj8ePH8fp6Wl5o6555kQOc4RteR59fhWU7ZMxJElXaDabNfAksgA+iZjb/5zM8rw7iQPG8u6imo8wr8E9EXMszLMj5lWFxpCODTkX2PqFj+A3lH0sskZSmnuM67me2CwnLhkb8u2kiSsJ+e17LMvZ1/M3tsf+O8+ZK8i4J/tzVwHZTpMcB9NiW52Qoi+Odbge/EQ/WfC1jbTMZTtHO8PhMK5fv96wEfgLEtAsGtEuL2Cr6UHm5bPIu0o+StL5E01KZQGAAevr6zEYDMrrvTud8+qJDz/8sDhATpKPaB6+t729Xd6uRXLKBsOAKQNuC5Ozpc5COyC1MMBkhJBrAeQYE79xhmotG6yc5MF4sC3R4JLyR/qCQGUHXyZT1Q8YtIgo2UpWUQxu4JedHWNEeOkjyjQajcp5PvDIh+IxpwYlbH/zPMAH5ggFtMLxuQ0bCnJychL7+/uFxyTEANk8c3t7OyLOtwjl1QPaf//99+ONN96Ifr8fm5ubxUngyEmAcR/g/9NEBsEAo4goBms2m5XznW7fvl0MYbfbLYdU7+3txXvvvRdPnjwp8+mVF4ITZJPkDrS8vBxbW1ul6hEjjSNFr3PAlYEn5G2uEfNkFADcztIrD+ihA11v83Mgjh3hftsP+BrRPNPCJcB2fBC2ISfV+R/Ztt45oHdgZ+BCux4zz2OcBu3ovHXQrwmOiLIiz1u66KPPnDHA4n/4Bm/9ZsXf+73fi6Ojo7h7924B0CTksVfXrl0rlVTYmG63W14AkavEXkYSqhZQPw9ZZhyA5aQU83zZ5HgOyvyDrNp24w+Pj48bZ5BEzPXNixmujDOQ8TPgD4kR65sTy35G1kfLaE5SuQKnNn7z0rJhjOI+2h9ZLxxU2w5xj5/vZLb10/11JarHxf3u/6Ig28/w6rn7k59rPeUzB5aZt06GRcwDYPtBklsREbu7uyXQyuA6JyHd3/y5x/k0yrpymeudEHiVlPFPTs4ZzxE42vYvLy8XrEbgNx6PY3d3txx30el0Ynd3NyaTSTnzy8Frt9ttVC1HnFdGr66uxhtvvBFvv/12vPHGG2WxyC/9gNeuorXMggMcNNqX00a217YVyBb8cQDO8QycgZfbsiw4+QrloA/Kul/DsFnPavfaDljmHFdArlb2wlYeA/bf9oB2WBBi4YBdDcgOC+cOfDmImfkdDofx5MmTOD09jc985jNlWz2JLQ5XPjg4aCTZ6RuLdrZZ+Hrz4iqSkyaWYcsjOMYH+jOe0WhUKhCRz4hmEQS+0zEPbdjWoydgJ8sL12Ef2JHDtciS40dXxETMZd8LhjkOcFzq5/tNeiSmnJSmXzzTsWynMz8/znLixS/iOsfrPJ+ijYhm1aATV/TdW48d4/KZY2B0AqolrZ3gYizGMLQzHo+L7nFEkbE+806/nYzM+QsneS1TOWn28OHDePPNNxu7HHzgODz2gjWVph8Xv0ZEWShhbK7Mvgy9lLfv2YBGRJmc09PT2NjYiP39/XjvvfdKwEuShvtXVlZiZ2cnBoNBXL9+Pfr9ftkil5XSARqUn59XBRD8iGiszKCIGAWcPmNzGaJXiygjphwaoeN7rzwC6FHQiOY5ETaIDuCzkwf4EShgGEweM8R2QK+485uSTeaDvlOSSBKN84bgq40t/CVTm8nbfwwm7OxxrDhV2oV/BwcHcXp6Gjs7O3H9+vUyLjL/y8vL5QBQtgiZv91uN4bDYTx8+DC+5Vu+pawK9fv9stLhV3FeVUf6cYlkhgNiJyMmk0njMHuv9FMZ+P/+3/+L+/fvN8BuRNPJo284WuS61+vFzs5OXLt2rWzDMui2s/RWSvrqJJJBXa74ccWGA2N02oGgHYETVP4cZ0LilzachKI9xt/pzLdZ+Hsb7xzMefWG50EOLPidK74Ag4ACAwm34SCC8eEEuYYtlgYkrrzKThU75zOEuB77Y2A+Go3iq1/9asxms3jrrbeK/fAz2d6HrhKk3bp1q7y22rJg+qT0+OO26coTg7GciMiLIoso+xH66P+Zb4NNAgrzFrvJvPkcRldIOQmK7CET+TOe7/us2/kn62NEPbh1EOf7DETdjitMcp98D7jE82KsAeCzP8NWEmDkecx9ykmVrCvMFc/OASky7/azrfP4vUDgIMl2knaxSeYxoNaAGVkYjUZl8YrPqdZm0c3zQDBLIsV8zbwx/2tJAdPTvs9HSrxssnzYBpsIhtFR+ytkkqQCeOnx48flRRPYTc4R4uwoB5tgNRZEV1ZW4nOf+1xcv369vOmUA4uz7Fjf7T+zrtBXB7sR88Aq38vYbZ8cH/haVy9br2r66v9ttxhLlinaoF3jG7fHtW4zJ43s55ygyomJiGjglowT7MO5l/n3Io9xB5UbLCTxJl4vNHkxJOI8MXnv3r1ynpT71Ov1yvNHo1HB3+Buxuw4reaPrxphL22bXLXjuGt7e7uxADcajcqiLMfBwHMvcjB38AQ8TXKA5/M3cuLqcsfIlm/ssp+VZcpVa4zHmM36GzGvTPTCIvGydcZVUuB6t539uCsk/Tz7d2ydx0WxhRd08k4c+1D+BovCGyd9zUPLsLe7GVP6b2xoRJRYFV3En5HE47xaHyHieIGiluXl5caZtuB1ZIGqPS9gR0Q8fvy48UKak5OTEqMhdz6L7/T0NFZXV0vM60QYPPsoOmQduCx9okkpiEkH1A4Gg1Jeura2Fvfu3YsHDx40yto5/Hx5eTmuX79eVmi2t7fLag6JFwTWiZ6Ii6WwEXPnbuOCsOM0LWgW3lw1hTGnDxZaJ7oMFnkeAs/q1HQ6PySYZJ2DUgNc7ltbWyuH0RmwGmRgkPjbK2k4ptlsXmpoZ2cQ6gQc8+lD+dbW1mJ9fb1hQOETMoBRRtD9JkUbBgJizxtt5bNi7IC5lio75gSF3draKtlrAyOe++DBg/iO7/iOWF5eLkZkdXU1Hj9+3AAVOUD8tJD5TdDJfCLLnDNlx0Bl44MHD+LDDz+8UNWGHPnVt3ZEnc55YvONN96Ia9euFb1HVpzgMOjy9xkQ07YTYF5NQYb4nPnELuSgy6DTQBwb5NVoAzq35cDU3/GTdZRnwK8Mtu3QHIT6GlZouHcymZTKl7x6ZxlAv7E/XnXjFdEG7Xam6KlX7DwewAw2x6tv3vI7mUzKmxvv3LnT2OrEIgEytbW1FQ8ePIiTk5NGhR3PNAj6JOmjOG8TPMdPRVw8WDdifjD908iJG89VTvT4eq4lQHXJN9/z4g5AJwDLupfBoec/98tjzNd6HE5mOYGUqwwts+ieg4GcUK4FoQBX+zF0gD45CZsDRsbN7+zL6JOTPMYWXjhyYon7c3KLzx0gOCihH/Q322fm2743Yh6AZL47+cRZQsgseGI6ncaTJ0/KAcnYMbAN11NBmRcNL0O1hFT+37JUoxexOvxxyQkG5sY4LCLKkQqM2ZgWjDadnp//cv/+/djd3Y2IKNv7qBQfDocR0XxLsee21+uVRaEbN26U+bKuO9iLqJ/jZH9ljM71JmNbfGV+BkGc9ReZMT4gqHewbV1xuxmnWjfRQ2NE4hiTsaFtqm02tsN6k+2xF9Lok32xEwc124Xdtl8gPlhbW4vNzc3odOZvyWZBh+NTkAtkhHk4OzsrO1k+85nPlGeQCNne3i7JGPxCTszh90mUORF3VclJEWTffmVtbS22t7cb20rPzs7i4cOHETHf1gcWmU7nL9FxdSo+j4Sv5zei6U/BVk6I0Ed4j9/mre5eVKRfWd6NXe030Flwrv8naUKcYD/rdlhQNAY2lsvJIyemfE0+1D8iGouUjAfyuI0BvUWRftiuZN8K1nRinHYimkUvGXdMJpNSHMEcObnps2ndDn3j5RO8LAis7P7axsJ7jqwhIch4bMNYnMA2nZ2dxc7OTjx8+PCCfDyvnjr/UCuQeRp94ged21GReFlamm/3Go1G8d5770XEvFQQJnNWENVRvKbU1UsIsZUWgMMPAm3BzJ9FzMG4HRHjQKEtPBkcR8z3U2JYUOC8hQll5SysbrdbwB2n3BNw+Q0m9Nv8dbDs4DQHHfAW/jImgAmBzmw2Kytl5g8OzGOmneXl5djb27uQ6c/BB4GMHXMNFDpINQhDecx/+Hl4eFjOleGtfLkqCsc8Go0aQINrXF57fHwcg8Gg8RY4B1GfRsrBlZ1CxMWqOb4/Pj6O3d3dhkxRFcMrn9FP5mRtbS1u3LgR165dKz84ZZ8ZlQPXiLgAEOy0ciBpAJjHmOWP652k4hoMNfLL365MBBRwvRM6tk8kaXOQ7CQclSgek/XRNgZd8xstCTrY9sa1zK1XZ5x0gDfZ6TkgYo79LHjHavzR0VEcHBwU0IId9dkFbBNDtnhTFH0Yj8fxta99LSaTSdy9e7cRoNuBAxIclMAjJzANvK4iDQaD8ndN7pkHJw4Wkf1d/jwTc2efhl9l3pAVVtLwxfzkRR6e78QszzcQc1CGXGYdzPPnZET2wQ5os4/0/fQ7y777DyEvxgEOTnNwnvXU19IuoBRwm+9z3+3r4F2ex5yQzkk0A1fmxIsQtqX4Wes9MpFxz2w2r4Bh/ofDYam0Pjw8bNhg/IEXCO1vavKZx+D+XIYWtbsIf7xMMo+n0/kh8Rlr5K1pBMb8j21/9OhRvPfee2UBAr3ER7HgdP/+/TIvm5ubce3atVIVBc7zYm9Oitg/5GAs21f0zbIPdjSOZ6x877mpLcxwnxMg2K2MHeyLrVc1mbLMP83WcD1t5qRtlk/6Dv719/TDtsWJbfiSq1J9L21TLecDqq9fvx5HR0exv78fo9GovAKexSFXybELgvk9Ozsrb+B78803YzqdH3g+mUxiZ2enXMdZo5PJ+TY/qvXMz6teLWV/6H67en91dTWuXbtWkj9gQfAguIhFQXAQuNhJ3l6vV3TVmBvdyjtrsM25UtGJff9vmeJ+x0Z+nv0l8SmxJfrPodw8G5vgpFq2D/7bRB+tn47NrFf2PSQDweO50tcVYeaR55Pvwcr8b13KY8FmOQ51gop+un2SifmZPpvZWwxtl1jA5WyqbJscW0TMK8eploKPR0dHsb6+XhJcjsXoE7uerJs1rPEsYgEKcv+eRS/loHML1Pb2dtmiMJvN4sMPPyxvbvBKwebmZty4caMko3jlLYkcFN+rRBhjv5XEWUnKDw18rcSubEKxLDwogcGznSzJGWedXWkCqEAAu91uOZg9A1UcLQFWPp/KmXdANQ6o5thxCnaIGSgTfDhg5a1pOckHaHVgy/P5nH6jYF4NRdk41B6lsfPKwJigk22Unk/mnf27N27ciMFgUIJeZBAHwFveDPx5jSl8Ozk5ie3t7eK0c3D4aaM8thwARkTjDT3I0Gw2i93d3eJsKQsl+ACQugR2fX093nzzzbh9+3Z5ZSyywipMxPw8OfcFXc3OIqJpa3LgkgFzDqCxIw7O/Fz6kYMYjK+Dy/w7B7mZ7+gH/XIAaqDiUmVX03gs1mGDbe6x8wI0sZXGJcgc+I+eGKTBH/TRgI3+TKfTAn4jogCw2WxWVmU3NzdLO67Mor9898EHH8RgMIgbN24UZ+pVsgymfH/EPFFx1fXXINH2xuRVz6eRZS7LoP/Pn9veO5CNiPKCDkBpTnpl+XA1s2Uo95Nr3I6Tk77fYNV9N4CtBY+u1Mi6DJ6wr8DWOKkJGagZbNn3Gpv4O+OT2Wy+DQHZN+7Adma55neW5wz+st1xAsh2zrbFbWTe0v/MS8YBz/j89PQ09vb2Gm/0pdLdlQDPoqwPz5OYelb7zwu4PwlyIOSkC3OM3PMmL3xN3g41m83i3r17xSZHRAMrssVteXk5RqNRrK+vx+3bt+POnTtx8+bN2NnZKavnEfPg3Ftn7Fds77OsZVvu6knrs3U6j7sWmHK/24+Ihm3he/u7LMuLfnNNjbK9cV+ZR55pnJ754oWYWhIde1PbjlTDPn4W9sL+mu2a4IzHjx/H/fv3y8uYqJTi7FSCU2SLSpEPPvggVlZW4vbt26VfVD/ylut+v1/8+dbWVuzu7hYe5fkB81w1yr4NfmK3bDuJ+brdbjx+/Lghy8g97XAGFYv3VMO4osX4DHnKWA4y7rFOOomFrbdv40BuJ0kZt9uDD543x84k43wOEtczDhZFbaNoJ9sR2xOuNQY2GZd7kcX6kbc62sca02cbAW/tz4n/7S+tb94u6xwAY8Efun2e70Scdd45Edu6jN1yziEiSvIJf+xzkvH1LBgtwpp8/lG2uGMHSVRfBrNGvKTtewjJyspKORiv0+mUc3ycSex2u7GzsxM3b96M9fX1cugxb+MiA+xEjbf9IYRcx4Ta8eFkrURsw8MIIATOXrr6wZliKzT/8xzeCuekGP0FKLBlj6wpyjCbzcoB0lY6Z0YRwJyZhe8G9pR3W2lRCkCx3+BCogcDZKUnEWYl4nlkrd1/b90zfzhfbDablYokGy94xd/j8bhRmjibzbcK8PzDw8Ni3AFYGA8cBCtJ8JpVf86csnEx+IhoGqtPG9lwz2azxhsWj4+P49q1a2WuCDSGw2EMh8NYXp7vr19ZWSkHxrOCQTBz/fr1uHv3bty5c6esDgGULZfMETJmJ5adZQ6OMwC2TvG9dTIHaE4Uc6+DKO7nu7zVh366fDg7QHSOcTspjrxjN3MQbLBj0II9IWlDH7GTBgTWX1bzfIguB4zDS76zs8VmYxc5MJE+OIHoZ7L1GF+AzGW+cv3x8XG89957sb29Haurq42VKRYD+Nz8chLc4N2BxFUi96kGFKhyuGzfM4hdBAINar1Aw7Yr20MDdoNY2svBZg44DbwNtJxgzQkgjyeDM3+ex2e/abm1nnoMXmjCz1NRsLS01Eg8I6e+JwfR5in88wriZDIp+uL7vGCWgaf9qIODDNx9P3y3z/dnOVnoQKAmk9hn5o3nMw7Op9jf32+c+wblxQ1v48vJBj878zl/X6On6cpVsQHIOHx1Et2LhBHz7SS+LyLKNqvj4+NGpYTftDSbnVefDgaD8ia9z372s+UNa7W32lofbcfhXfazNfsAWT/sF7jfv01Zp/xcB7zWxYxlajbKAav7mNvgb+uUfasTALSHXnqO7evoL5RxuZMfmRfuK+PgPusoScy8TXgwGMS1a9fK+UePHz8uuJrKHxIoyBWY4t69e7G1tVXkhDcwdjrzHQjdbre8EAl58Bz5LKJXQWtra08958bVgdaFTqdTqodIvhmjDYfDxkHfrkwnGU/sSHLKMW1Es9rPyRLPuxcXanpouYeclPKWQmTViYe8XRTZxG4zRv4GL/vHMZnHYP3Kcmk+W088HmNddIyxeZHUhSo+g856Qh9YeMI3Hx8fF/67qhH/nXlHmxx3AH43jmV+jaegGoZyTE3FEzsPPE/MLWPzdtLd3d3GEUN5gY0XWYDdl5bOt5Hav+BHnpfgC2T//zR6Kdv3CEK2t7fLwKfTabz33ntl8AgOCal+vx/9fj+uXbtWqom8t9WG1xVMCFAGpa6AQMCtoAhmXoXI2138bDthxogwY7Bd2eWDzJ1dtyPyFjz3n2cbKM5ms3LGkg+fRAn8Sk/G5zacROMMGBIIXlnLDh0eGDi7r1Q8TCaT8maOXq9XKmfgmQ3MbDYrFRs2NA5gptNpOQyfMuM8z1TWHB0dlVULViEi5oHP+vp67OzsxHA4jMlkEteuXYuIKK9QJjlI5ZTBcAYbnybyXDvwYT42NzcL6OWax48fl3O6tra2GgadtlgtuXnzZrz55ptx/fr1RhUgW1Xpg18dnhNQtT7XVpMy+MUZOPnlBAbgwwGewZ4BItc6aVmzL+gBDoF2XQHmpC/fZ6fnhBhjdb8gJ4M8B2wTsgM1yHdCyHyr8dBJfuYvV0Dy3Fu3bkWnM184MGgl6c+1GxsbZdzwx8HIcDiM999/P958883GQgc86PV6BQg7oGNurkoAehnKiQB+M4eXJesC/1un8rMizgE7VXK+nwpVtpLnFWO37aSw/RJtOZmbEzluB91G5tDJnDDJ43LgiZ4ivxFNeUAe3U8nrTNgzwk1B1YOZGjHNsl6jr/H13o8XunO/fWzjA3om23Oont9Ld9h091HrrMtx5Z5Ac/4ies5Z3I2m8X+/n4JXs/OzhrnT9quMgfYq6clKD6NxHg9j3xuPM15euA1Fj9OTk5iMBjE4eFhWfBjKzU89/mN29vbpULKL6GIaCZ4CDJzoin31zjL/aWNjNeyz+M6JzAsp7ZbyDXBKffZJuSkcQ5uvXXX9+fEVO6X7Q1xgrfn0I4xPnyi74wbPfNWrGwPc3LBvM643/iZvtnOOuHV6/XKG6eHw2Fsbm7G7u5uCX5JzDMWtuN9+OGH8bnPfa74Cvw3fp5583kytOGzgV6VHj8NC3S73cZipeeA8aytrcW1a9eK/BgDOUHvI1D4H99Cgifi4tZtZMKVPm6be+xLGZc/y+0Yu/E57fI3OMP+lFiSuIgx2yZA9MtnmfKMfB33ugIJGV5aWir4rmZnaBtflxdzyQc41nU8z/zihy3DPMtv/MRe8AzG4DNZfe6Tz1Tlx0meHBfUFmS98JWTQ17wZt7Qfdrc39+PGzdulPGcnp7G9vZ27O7uNhJtEedV8P1+P27evFnOI/w4+uk3Dk4mk4Ipn0WfWFLKwRMCurm5GYeHh7GyshKPHz+Oe/fuFaN5dnYWt27dip2dndjc3Ix+vx/b29tlz7zLJhGomkKSHMqgN/cnVwrUMtUR8wQOY7ISoRBObvE9gWbevkCfvKfXZ9IwJoJ0g3IcBIkonu9DvmkPI2nFRQlowxljVjMBPFZchNnggfvhFYAIgwY5wCSg8asxScL5nKHJZFLO1eI7DAQ0mZwfuEwZLPx2dpnE1GQyKW/f47m+bm1trTjeo6OjODw8jDfffDP29/djOBxGv98vzoX+fBopr/KZXxHnjrjf78d4PG7wg8q02WxWKtOePHlSrqON27dvx9tvvx3Xrl0rjp7VOBKgOQHDPGGw+TwDGstrxMUKDOQjA+ecoOE7f56BNrzJz/H1taDbdsU2Al1ibK4ktF5n0IszNT/8XPiWA3ZKeOmvS39xmq784j5XsrDyZOAdEeX1yNikfr9fSneHw2HZwry3t3ehkoP+srWWMWKjJ5NJ3L9/P7a3t+P69etxdnZ+MCOLAVTm0R9kD3Cdg4+rSLlflhvz5TL0LNDvQMaBDTppWfKiBv/bf9BeTh458eV+1wLVmg4anOVEku9xsOr7GZOTZxn4+3u3y2+fe+ZFI+u++5D74USNF2TgNbqYA4jMKyeJ8jzWwD4YxTYn98fyYRtgcJx5YwyWx+9AyIHJ3t5eDIfD2NnZKTLytAPNff5mJn9mXGVePeu7fM2rpuy/zGv7j4jmtg/m5/T0tGyh7/f78fjx48ZCLvjt5s2bcefOnbJtvtfrFf/rqtaIub+Dshxm+42e8rcrAn0f4zMOZ6zMRw527cOsjzkJDfZ08sX+2vgi89HjrAXQ7od5z+cklukX/fC46C/y7SMwcrIty2b289m2egzGqd56yTVOKPOGtIODg/Iyqb29vdjb24v79++XoJugPCLiww8/jFu3bsX169dLn1ZXV2M0GpW+2X7YbhM006dXQbU3gUPMZfZF/p8FCx+ZkuMi5piiByel+Nsy5N9gF+ywExq1/tIONr/WZh4H1zuOth+y/WQhKqJZqe/xui9gOT5DdryY6UVPJ4vMZ1OtytiFEb7HdpPYMfOuFj/AbxL+tONCBP62TSB2YSHFySmwM7/BzF50sU3FDoMDvDBBdR1vdDTmQKaRQ+aWohWeZXsQEY0XF3W75y8EM48+qn/MyfPL6vonWimFYC4vL5etVqenp9Hv9+PRo0dl4thTe+vWrfL39vZ2CXApPzUz7Wh4lkGRs69MOH/DaATcv2uONq+2AKgi5mV+TkBxrR0RiRPa4tA7tpy4corPcbKU4NKmAwQrjT/zm/HgO7yhwsiGyImIDHhIaEXMz/hxuSPfMybGQZ9IWrFi5619KAN89NyYj9zrVTNn15krlI9nsB0Rx2sjP5vN4saNG+WMKa82rq+vx97eXtl24KoTO/dPG2UDhHx5W8ZwOIxerxfdbrdUmuGE+/1+nJ6exuHhYYzH41IhdePGjbhz505sbGwUh+TD+71f2wDNQTNz5mswdAa86JAduauAfI+dgp1x/t79Ma982KcdPv1Ev31WE7pjp0z7Xi22veDZBneAF/rH995+QbLVADEHfE6wu428ymQbi0wA7GmLalCcJlWSEecJq62treJcHzx4EAcHBw2ni+3hnB0HHyRAHz58GJubm7G+vh6DwaA4cM42yEmDzOPXgRxoYN/Nj8tQbcwGGXnV0c+2rzTYZfuB/ah/rEeWsfwsyzH35cDPMpsTSbYJ1n3f688i6ufM8Tl99DMi4sL5bAaY3JsTweiEn+Mg1JT9Sb7XNqsWqHuM9M/gP68eYw+d8PY82W5hk/nciYQchOSERMQ8aGDR58mTJ2WBJyJKdYZ9fU58ZF7l+asB5vzZVUg6XYYYk1fhvShnOSEgjIiGv2WRbXV1tVQvc47UW2+9FXfu3CkvFeE4jIxXnTwxZR/IPcgE1+S2IqIh01kPsS+0STvomvUPv2Mb7+ATH0VbYO18nfthO1sLzO1buQefY3uW4xH44iR97qMXsj32jDFrATd89bXoOjg930t/0H9jNxZ4Dw8PY3t7u2xFe/jwYXnxERi80+nEkydP4u233y7PJfEJlqcvm5ubcXBwcAFPXWW9dKKT2NPn+iKzXnT0kSbYWFeEkohiuxRyULO/xqDGsRmjRjR9q2WN66yf1vUaBs26gpx6dwHP5HPrTvYByBa402NDx3Pc6WucmIPA264G9pitr/SFz7zgg690kjsn9PBVbMvzXNk+8h08BK/lsVIswQKzY1EXc2Qd73a7perYODfPLfG3Xy706NGjuHXrVmNeXcE5Ho+LnFJhm6s2PyqRu3AO4Vn0iSWlHDgSjAyHw7h27VpMp9O4f/9+Mai8dYstXpubm+UsJpc7wkwHL9l48GwrWk5K0ZYZnx2ujQLPWgS2GadL8AF2vs8ZTCuiK4cionFelbcEItQGqowd5WdrlZUcxfb5WCS5SHzBs+l0WvpBCSvX8B1JKYAR+3gt7BHRALTejsPqQkSUijAfaG6+Epyenp7GwcFBHBwclGdxHf1hvvKq+snJSTx69CiWls5LZuE928m63fPKHifQuJc38GXH8WmkDPqZLxJLzJX3TQNYKAuF/zdv3ixbH1lRQ6cxejik8XhcdAiZMwA1+X8ny3KJerYN1hcH25Z3B53Wa/+dgbvlwkF1TmJnQIq+1saSg3O3TWKAM9V8vwPGDCrgnQE7/aQ/2CXOZVpeXm7YDGwK/PKql21ztrM4YOa+0+kUO7+3txePHj26kJyirzh5xjSZTOLBgwcxGAziW7/1WwsPeBYAYzqd7+930PM6EjbpeSs0LT/5cygnUm3b+NsvhSCI9YJTXv20bixK4C/SHweIPMP6g2xk8MtY8tjRX/eFv62PNbCcz8lwW9ZV+9ockDtgcdWKeWz8EdE84NZ9reEOryw78DWP4ZnbMe8YD0FqthF+phejvEDkJEbNJqyvrze26s5m80Up8+pZsmu59N/52pwkyP9fRXKCISdJ+B4bT4Kg2z1fgHz06FHhWa/Xi9u3b8fXvva1ssD29ttvx+c+97nY3t4uOJsKDijj5VpSJM+3Fw2y3ED2wxEXdx/Q3iJMb/3NvtHXORDzc6xbxgH+zGScR3uuJrbvdPIp88o2zLrpPppH+X/blFqSynbGY8svUsj3wWN8Is+hCmN9fb0ki3lD8vvvv192HRCvnJyclPPJaJezIvlsOp3GYDBoLFxHzLHIVdRJ4054hc0jORURBQtZR31+DzaOcXJmMPNif5kXm/Lcck/2mf4u43f6b9/g6kff43H6es+R/ZT13voZ0TwbjWvcPgtcTmzZN/tZJMmM/3xQt3XEdsB9ywtFtGN7a3tmmWR+jTdcgYxc21+7cAM9ZIzHx8eNt13CD/tdEr8s2tew0tLS+YukrOd5kZg+PH78ON54441iqzhKaDQalefD3/39/eh2u/Hmm2+WLXxZNp+HeBYJwMvQJ5aUckZ+bW2tlIfeunUr9vf3Y39/v2TR7ty5U4IszpBiNTYrvYUJBbMTzY6C31YuO9Cs6LmM0D92pgA4b7ujbR9kjmCgtM4YWvEAbrXxUF1CEmhpaakEpiihhd/8sjBY2ZwxpxrLjhaecB3PZjz5TBwDbWdxM1i2EgOoeJ4PYqayC0fA6thwOGwoiTPgBOxUrwBSIs7fMvD48eOSMZ7NZnHt2rVSSRIxfwPTcDgsr8T1tsVsyD6N5OAHOSZDv7W1VWQWOWOFljlCJtfW1srWqlu3bkW/34/V1dWyZcAyZFthHUW20FUHqFxjx+ekVMRFJ+rkE79rASmyxvOZ97x6mlclqC7gu2xvfIA0/UEHfI9tht9yxjU4rOxEraf00/ww0HLiDbKN5HsHBR6LAyb4hH5DgDB/1u/3y0srNjY2SsUTbwJy+bmTWz7Q8fT0NN577734/Oc/3wA58MtnIwBWsA98flX1txZwe2v081AtyPdPLZEAeI6IItfYAVfguao1zxVtet4to07E1Prgv9FN+7Mc6NXad9LGFYA5iPOKoYG/zyXLgWgG/rXgxNf6cPQcmBqIZ33MASl2yaDTtozf6KLnAZ5BrjT3MzMozzrtZ3mObRNpzwkncAyLZmCE9fX1AoTpSw6G/Nz8WQ7QXmey77VvorLCuIfgaHl5ubwR02crkXRaXV2Nb/zGb4y33nqrHIvhV9HXkrGQZT/rj32r7826kRNS9qnWGQey1g3aqAXptOekQU5sWR8cuNFOTiZxj+Wx5v8cV7hK2nroRSDax4YaS9gvmU8Zy1j2a0mH2rw5GM/PyHymz/6cYgESVu+//37BbisrK3FwcBA3b968kMSAN05YkGDAZnhL1FUiy4/n3QtlXpx3HAX+sp+iWMFJhjzP2X/lZFHWw5xoxJflcdiHWma80JP9r30Lz8+LLlznOc92AXm3PkXMCz3syyKa1bjW+ZycdXLD8mT949lQXqgBD4IN7f8958RB8IB22LrpxK7nwMkwZCOiiYksO14ky3zMuQjmiap1+sj4bFuxTfgQrmNBmPFyrBK0vr4e77zzTtnN8HGIhZFXWimVBdRb17rdbjx48KBUy1y/fj36/X4sLZ2/qW5jY6NRIYVgACxdZeSf7EAxChEXHSafWQB9YF0GulwfUc9gI6hkcMmQ5nI8J2bc1mw2r3LCiGFMGIMFBiGDn5wR5eSNjYsTehzkhvPhPCCqj5aWzktOCYSoLHI5LrxB4FFuJ21QdhtD7ue6g4ODcqi4S7IpWXeSwSDNwm3Z4G9WEpE/2uNwt1u3bsXW1lYMBoPypobBYFACv4cPH5YknQ2jDemnlawT8PP4+LgcgAefvW2t0+kUuYJH3W437ty5U4Cxz47yirkrLwz2HPRmQ11zgNnBGmBbdjJIw3E5OLWjRY/9fXZ4WVc9Bn/GPdn5wAN4678JHKxXPNPbdNw+39vO8L/tnnlFu/SZBGMubTZ/rNfwnsQcbQ0Gg8bWWLYOr6ysNErbV1dXiz3wwfmz2fk5ZUdHRyVRjB2husoVnbmyxuN7nZLJ5i1+JQd8z6LsIzP4zQGT/Z5BuLdhLWoLf53BM997XLmN3I+s08hx3mpU63tE8+02tTnP8s9n2T5Yrp0sQfdqvPB4Mz5BDxyAO2hzG3mxi2v4LPtV/x3R1GnG6UruWrUK17o99JNksBeQcl/AH8ZEBGk8czwex9bWVnk+59ewagvVklJ5Dm2DLkNZTq4S2V4ZP+VrIubYC5u6t7dXElLGYJ/97GdjbW0t3nnnndja2irb+rJvyj51Edn/8n+uiuDzHFjncVj3re81P+h2+Z3b9eIIv30tbVmP6Kdl2gnVbMOs/xmX1vTUPh6fX4tFbNf9TM95vtbj9nO9C4B7M3YwrvVCmOWPs+CGw2F0u924du1aaffhw4fR6/XKuTtPnjyJnZ2d0o6TcWyrt83wWJ4XS9fs/ydBTj5Zxr0bxzgOoiJ0eXm5FFfAZ+yj27ef8rhypZbl29fnPtsPZBmrXe8EUu07yy/98xEJ1oEcl9OOcUG+BizHOEnouXDDvjn3xbLLZ5wpHRENX12Lj514Ioamn+aD2/PfNb/JPTybxT1sjcfuBVcXVkwmk8Zbf5Enz2+32y1vuM0xMbwjjvY2Rcdv9H1/f78cl/To0aO4fv16KZL5uOQ45TL0iZ4pFXGuXL1er2TLTk5O4vHjx7G0tFReS8qh5gBfEjMuR+ONaxlgYRgs+DzXWcXsgO00ud+KHxEXhDMrm/tmBbNj4NqI+cqAEzzZMHtFAcfAvRFREnxMNA4kIkpASJBGdpLzvEjWdLvzvbLw2udRObNPX1GKyWR+sDUVVhgeV0Eh/DmjTiLDhpWD2BxQzmbn2/xOTk7i8PCwZKU3NjbKPW7LST8OWXZSk/7t7e3FzZs3Y3t7O7rdblF8guf79+83zm6xgtupf9rJ4ycxAwiB36PRKE5PT0uQwRlCzNP29nZsbGyUN/+QkCL5SrLKoC0nWZysIqmSD6HMwdciZ2ywaxtgQ2/9hQ8kV53E4f9Op3nGQMQcUCCDUE4uof+2TRmQ25mTWHLf7ODo68nJSSkjd8CAjcwgNQMsAyYnim0PI5oA0cE7f5NsQvfG43Gcnp7GaDSKjY2NWF9fL6BtZWUltre34+DgILrdbjx8+LDI18rKSmxtbTW2sF2/fj1Go1FxpsgWDtcr4JaJWiB+lSgnGpjPiGYwYdBZG4cDyFrCIst99n1UAZNIhKfYXfuTvPqfx5OBsvvmvw1Efb39qOU9t1/7P4/JfMv88Wc1v+9kF995Xvg/L14YsKMb+EcnIwy8bcOcbPcYbQfyHJt/vsZJRvptvmBj/D9jzDiFfnE9SascxBmIe1Gr0+k0trN43rnXC3sel+my/nhRwHZVyIkBJ2oimvps3zAajeLw8LAsHnHd5uZm3L59u1Gd7KpzB6SQeZLtpANJ+92anXH/87XIobcR+flZ9yyLlj/PoXEe7eQKDevmogA688FyucjeLi0tNY4zyEm1iHky3bjF+ph5nTFnxinZPvK3E/hZpyxD5ocPWs/88Gvop9PzY1ZYTHL1yO7ubty4ceNCX7AJrvY2n9yf56FP2m9n/+RxMYcR86rdiPk5cFQgcjZytzt/c12Wx4zj/PysW+5XjqMW2bKaL1ikl0/Tc/pt/2SMy3jMM8t+LYm1qL9Z1r2om+XeCVn7YFcUg2v9EgL7QfMXmTYGsL1FV/KCMG3lY3BcnZ/tA7JjbILOOK8AD4nFLXP85txaY7ja2armnfMjnU6nxAxLS0sxGo1K9S1vy/645NzNs+gTP1OKSonRaBTb29txfHwco9Eotra2ymGL/X4/BoNBAb/OrjIggzy+y9nmiGgovlcmPJEOcB34RjSdMP/nZ/mQspzRteI7202ig344YO105q+VpG+AjM3NzaJQ7M9EaKfT+StXfVZFpzN/40PEXGhr50itr6/H4eFh+ZytWigBPPP9JCCOj4/Lax799rRud35wNfOJ4pKU4PmdzvmbC0h2GNyMx+MYj8fle1dFOFlg44JiYxAMTrrdbtnzDi/X1tYa+9w5FNRJEgcQVzGQfdFkUEGQT8YdQNntdkv5Z7c7f/NDxLkskGxGvznoPm+XwUAiV06cRMwTwzwXUGAgnIHlov8tX4zR20R5LsA1B38Gq/yfDT6g34A5ommUHfg50YQjybbBAR7J2VoQHBHFziD/Br0OatzvPFb+tyPPSTLzyqtHmU/ML8Ccw3h5kybbTGpg6N69e2WOut1uSYrz98nJSezv78fNmzdLtajH1+12G2/+fN3o9PS08AvKwd/TyHNcC3j8G1/S6/XKlkt8XUQTHFoO7EP9fda1PL8GRJDbYP7sd32fbVRuxzJPe/n5efw5YZF9dQ2cetXVPs4rutxvsJoXUuhz1q0cQHouPd5acOqkRm4z28BOZ7691dfn4DgvMOV+AcwduGFH8A9LS0sleQLO4YzBbLM5NynPpeWsRrXrFwVuV4UcjCBD4FbbWgjb9t577zUW4mazWezs7MSdO3fKGY75rdAmAs1a4tMBG2TZ8/c1u1LD0vb1iwJLy5mD2XyAP/qDDcoJXcs648xjyLi9Zkt8nxd0+N9HdeR+0Xf780X8qdkybEVNXrKc068cy3gunQxzfxir+YZuRsxtWL/fj8997nNxcHAQe3t7pb3xeFzevIsddFv8ULHhGOF5MPXL8ONZDpAvYhkIXTW/lpeXy/mpLLr5xTLGUbW41fLhZGFN19wW97jfXqzw9/7MFSwZE9rPZn2xDNGOF2DBjtZxV3nlWI/+2idm22O8m30Rn0c0345n/iFr3uZsrGtcnCviIqKxC4BKfvroakswG5WsXsyyLXB8he0gprW++DxnYnAwFwUdxNXeYebcBMUqYBb3nWv29/dLzPvo0aPy7I9Lnc688OQy9IkmpRyQHB4extLSUlkJ39raahysR2mZSz9ns1kjgeFVt+l02jgU2c/MGVsLOobBySoLVsS8KiA7Fh9SboOSy3QNOAGtkNvlb4THoJGJnE6n0ev1YjqdlqQdB6E58KIywkDPB751u/PqqLW1tXKOA8/wFkCMK69vp28oDwbX23Kc9cUgGbCj0LxFz1uiSBQ52ehEQVZm+ENijM/8HQaCfrsC7+joKO7duxd37twpiRYSVVkm+J65zuDw00Q5ILHsW36Yl729vej3+3F4eFjkcXl5uZwLxwqRqxoj5ltCPN8OSL3anoFsDl4c7BmQZaNr52fDz3deOQXget4N6vjfMm/bRYBvvXebBmX0B95622oOTD0+A2OvCpmXDlZIluHQkWvz14DPQCVv++Me260cMDmIpT0n1LEb4/G4YSNsr6fTaTx+/LhUSi4vn7/FkZVXKh+Hw2H0+/2GDej1erGyslJeUZ39wvMA4eehHKg/L8FH/M3Ttu1lGTflcRqs5qSC59ZbSTngnIop5jQn/2i/ljCqPd/2BX2pyUtNl3Mw5jZrAaT5Zh9tfa4lsfy35cY84z7PUe2ZEVHdDo5eoZPZ9mb+Gcijr5lHBuuLeAaZ9+Avn7Xhe/P43Wfziso6DjEHSywvL5eK8v39/Ubic2dnJ/b29hqLY7ZLT9PV2vfmkakW9F8lsg/CR7ly1/MNbx8+fNjYJbC1tRV37tyJra2tkmT2FuyIi8ErcmPK9jJ/lrFCxMXKQ8tkfo7b9G/f6z47sZRl0wvWOblmmwDfHNRm+cl2p8Yz2sRGmi9+Ls+znuTE7yJ/ZJ76s6zL/O+4w/zJGCGP2X6A+cmJNRaqqXgnXlheXo7hcBgnJyfx8OHDuHv3bnkGb9nlGbTlRTpk+CpRp9MpcSW8MV98nX2N48qtra1i8xyI55jCCVe+tyx5Lhfh2ix3/ty+wP3033nLfZYNL8742ZYlMH5E86zCrKf2mXlnkxdRzQu3lcdJX83LPC+1xR3ro/mRcwXcQ96BmHo2m5XzEdnBZXsC5gY/Gc86qe326SfFKM41MLZ8Vqx3nAwGgzIneesu2wEtF+YPce9oNCoVtTzvRWHl6XTaKFx4Gn0iSSlPpMv7u93z/Ypra2uxvb1dDkAng+dtZGxbYD9mTqxk58j3NnzZ8OfA1ZloX48B4noEyoftcr0DvVzexzUR86DUq6QIIUEiwSz8ItGEYBPUHR8fl4RSxHwlhuAWY0/gy+olwgivLfQkEXJJI+1mAEuigQytDZZXrZyVHgwGjb663JH9slRTMV7e8MGqA/w/PT2N4XBY3sZHv+0AI6KcZeTKsel0Gnt7e+VNNEtLS7G7u9vYisYcIgcZJH1aCSOfAw5WiUhcIruuSDs7OytvVfOqEnK9tbVVzhTyds2a07PeOmCzznEdv5/mbBxw5eQKcpeBNnwwUObvfGi5ecW46CdBqyu+zEPLZQ7UHbAzngz+acegArvi1TIS0JZpO24qKt0u4+dvO3/4bcBLmx6TwZdXiWw72ILX6ZxXa25tbRX56nQ6ZcvwjRs3otfrxf7+fsxmszg4OIiHDx/Gzs5OqeCczWZx8+bN+NrXvnYB1H3S9HFthGXFla+LaBFgyIDOn/s+flvH8LPHx8eN6gTLQkQUQOTtQNaRRWA7B378nwOl3O8MOLnW+ppBOLq0CNQ7oKB97kc3ud6AOyIu6JAT1k5OOYhdNCcZkON/nDDMAUUOwK1/+bNaksb8cPDq620PvQXR+u+xotdUCDAObAs6b3/i8x/Nv+yD8vzV5IFr/NvyU5OBq0I5yI2Y9xXe52p3qsgHg0FERLzxxhuxvb1dFuLQT+xwzZ/yHJ7Nb8sjv62/tXvcZsQcYzuAzfJlfPU0mwGPnDThc+yUKet2Hqf5XPvM48qBqn2obaL1wwstmbJO+3n+zhiltlCWeZftSx7LoqDffiD3cWlpqSQ3vU0UuTo4OIj9/f2SlALXd7vdci3zwI4OMH6Nv6+SrGMRzQQGsuwkL31fXl4usQTb9rxLY5Gtph3HrFln+Ny/+dt+raZb+V7724iLuKiGk2r2yPpf292QbbATJRHNSionUowRc3/db+tC9rXGz/ZrtUXijN3zXBn78AziGQo52HVjX8abpt0XfCPjyFVq2RYSr1BpfHZ2VqqKidstm/1+v+BpJ6mpdjRuy/bdMYL5sbxc30L/vMR4LkOf2EHnBGVkiikjPjw8jK2trfJ/v98vByATRO3t7ZVMocvqslL7f08uQbIzlA6AHWzZ6NNOdpj5ed7WYgW1AaIyyAeWZsBHIoWzGPLKEH0BxNGGwa/7ypgxmjZwOYkHWOx0OgX8bmxslHNfKBVEQX2wKXzknCHeBIjS2bB43jqdTmxubpZMMkEn8kFCirmfzWZF+f02pIj5AYpra2vloFQDcANdwEHEPEiezWbx4MGD+MxnPlPOrppMJrG5udmYL54Fz83/TxsZ+NmYkszIidD19fV4//33iwx0Op3Y3t5uHHqMMcJZoxfWP4NBJ2wyQPb/ThblYBcHRJs29E4CW17s7LAPXBcx30JswGCA6EQ2fYpoHrqcr8v66HEif9gtr3TgKJzEws74DTAGJvDFyQP4wriwG8y9x+uqGI/T8+F7HAy5hNnXuQ8+0w7HSz93d3fj3r17sbKyEoPBIFZWVmJzczMODg7K69Bv374dN2/eLGeskFw2cHAlwlUlQLyDr1qw8izK48yBeC2ApIKPoMIBRURzpZG59NY9VxgboPKZ+5ADLz6zrzJwzn65lljIfrtmN3LfMn/538niWh8z8OP/DKTzdr7MS2xeTop77j0W651xiCkDYds48z0nxLExtp30Mcsi8w0fc4IaoI7tAAtyD9jCbyXy3JKUylST4xx4P+v62py+arI8RTRxKPYfGw3O+vDDD6PTOV8xPzo6im/4hm+IwWBQ8I6PTqgFujlQzgkg+pED3Nr/3O/7+Cw/l88tm74+B6LWCcumfT735TjB/iyTA7ocnPKMHLjV+JD7RL9JSLldJ49sM2q8s1xaf7Os5ISU76vJvp+Xddxz5udGRMH5riTa3t6O8XgcZ2dn8e6778abb75ZdNm7XpxEwB9fRRxNv8F/LlzgzB1Xv8G/fLYiOJY2M7aLaCb9PDcRzQO1LYvoAdcs8itua1Ef+D/Li/Fqtq/WE+71dmO3577U4kE/BwzrYy6yPvH5+vp6eVFa5kv2xe4Pz8tJ+WxnbG/5nz7iw9wOc81xC+x2YKHVffWiMDjKL0AxlrcPd7KXZ2T/76IenuHzqFwgkvEXczocDssb/az7L4Iu62c/kaQUk0VFjc9gYjKoYPGKGkyimsLO1JUCFrYstK6igKkGzjzbwpvLKLPht8F2ljLi4iHmCAb3UbbuJIuf6copb3Mi60qZez7B3yXdJIjglcv6UKrZ7HzfN4JZA7wOWrMym2d28jbEvHWCw8NzQioHhSgffSSxBW+olHNml/sxXigczyWR6UPaa6BhMpnE/v5+qdzjVfIk55g3G06vjn1ayfPj5I2D5ZzwACTzRkMSghnAkXDg+uwYmCu/GcL9gLKxzLJR021sSA1E0ob10PNcc2ROovAbGfd5aTb6HoNXryPmlVcEaQ4su91uowyY79EnxubKQ/QyO58cWDoZlgNj2rWtMiipgQ8HBgYq5q+DUQNh7BY2mpJkkp74BVaMJpNJeSvfaDQqfR2PxwUQwGsDqasIhiEn/61DH5UWtVPTz5WVlbJFm0pln0dj++9Kv4ho+CQnMaBFiSXLYy0Bmvue57G20hkxB8HWYb7LCe0c/CMfBv/+7UCacXpF0W1avt1X65zxioOAiPlbQbmHYMWJwCzTWb4daBjPeB48B7SBL86J9WxnvajHywi80gsB6CeTSfG3PpzbgYdlZhG5P/7fY87352Dr4+rXi6SMRcF2x8fHjYAYuecA6tnsfAvkjRs3iv5wvas18Gu1xc+ajtnf+busx/nHem19NY7mO+uasSr3uD/0t4ZDM9a0X+52u40K1Oyv/DcL2NbfzAc+z/LHczlOw37HY13ET/MpY0w/P2OTy+DR3H9+2ybk9qzjnk+fD9fr9WJ7ezuePHkSDx48iLfeeqthFz3vLEY7IL9s/18WIYMRzSMTlpeXy/m5Z2dn5UxLdDIT9+KHWADwgpzjuYiL2135PuuMk6h5jjyOjPvst2o4qCbrGSNnGXKb6Fi25TWfBlEc4diTv/Et2DTLj7fSmVfwwXid7/OYM6bJ828cUtOJzGvmGrlgcXV/f7/xxnHbRHQFf2nsRNGGdy+QS/DOAtqEV/BpdXW1bCXlOu9E8nwzxrOzs3IURqczP+v6slVOJtuX5/GzLzwphQIjUAT3/X6/vNKb4JSElB0HjhaAbCFfpBQ81+A5Yr7yy+8MxMysXAFjp0SyIpcYIiw82w61FvDyfITPk21jxTY1v9HBzhieZt4QrEXMy+4MeCeTSQn44cvR0VExDPTDyo6CYIipTjo6OirKHzFf+aSvTmJYISKah827ryiFldEHUROskgDzHK+trcXjx4/jyZMnFzL3lk2fGzUajeIP/uAP4p133ik8j4jy1rjhcFh4YDB/lZzoJ0HIGQEEMuVVo06nU2QAeXFyD9mnMiobdfOb9khiZwfjgDZXKnF/Tr5g6J0giZgnQpExt8fY6Quy64pNyPoaEeXsneyM0S2PI2Ke3I6Yn3HF+JDfWnDuQNzJAdowr7wykh2QAU22qZ4/nB3yYLuZg5PM5wxUPP/oEXy0/RyNRkXfefX09vZ2PHz4MIbDYcxm50nNzc3NYq+omoqIkpzb3NyMDz/8sMyrE3lXlTLY4bPn7XP2c24/6wp2F7voM8/Q06wjGYjngCnLl+Us+3PmJl9rH7TIl9aAd40XxhiWhUXBXQ5csQMRza2OtlVOGDjg8txlYLxojnLAkQMW+6MM/nOVV8Yifo7JADw/k3E5ye+2XDXFdyQ1c5CF33Db+NtaMsNU49OzyDKS6Sr5cq+iWyZsw12JBlbq9/tx+/bt6Ha70ev1yjaiWnVULelhObOcZLnKn3me/FnNp7gd/2T5rrXn5zpAd3/zQrTtU02vMz8WfV9rI89P7jdn5GZdy/Y8f+bf+VrbwyyzeT5r1+S5832+37bV/Mk2zPpNUurw8DBOTk7K2755wRWJPuP+jBdeNvV6vRiNRtXvsvwaZzm5YZ8AXsN/Zjl2NY0TphB2PNu+RXph+TZGy/3P8p1xuPUp+93sf9xn5ITrHHNBXhSNiAbedZ/dVsbsGSM4qem5cezpMVvu6VNO8tX0Z5ENQwZchMLiwXR6ftYa503lBVef10ibyIPj7slkUmJP/LiPyHEyiqosxpwX3H3+Kv7CsUvmVUSUPA3tMN6P6it5BkndZ9Entn0PYaDSZ2lpqax0R0SpkiJ4iohyYDKf56DMW1R4hqugaNtGwaWR3oqTJ8OOMa94ejuRDSoJHmeuPdEEnhZwV4igYATANjQ2Yhg6gjEUlOfacOEA6IOVnXJuKqZow0LsRBa8QukPDw9L8Mu2GJ7Bm7HYW82ZJN6iiDIg5O6njZQNjyvtkAdKETmnYnV1tbw1iuQUFVbMEXPmSgTe7PfkyZO4c+dOqb7o9/tlDhzA5FXGTyPlAAu+w++jo6OyCossHB8fx/b2dqmCIrtuY57/pyKDecGh48gcqOZEYAaMODLLu3Ui6xttYE/y2JFZr2R1OvPqqIh6AOc+c40dsw09em2n6YCZPtKfPB/wnna4l98GJX62E8EGIW4bGwXfmQMH9gZhtaDaFRfeIuQ5pA9s2XXgwWpzr9crDnVlZSXu379fElMk4YfDYTkQfXNzM9599904OzuLN998s/xNX7FZVykYNWU5XwSaLkO1IJw5zAEV/sCVN7VAynPuuXeS0TLsYMdgLAe1BsUZhC/y01m3fG32izkIzEDX39cCQgM5wBUyiH7ybK8EG0ijM66GQSexj0762FYZdJsv2CUHD+aLfZflwnPs+ctygc910jsHKXyez9HkbCnOhjSW4Hn0bWtrK548edKYK9pfJNv2UxlYw5+cXMzX1HTkVVGWXezs+vr6hYoML96++eabsbGxUfTYC73wwPbYMmiq6Z+xfE0vfE2+13YmB0C5YiHbPeu5ZRnKOMwy4K0tliX6lfW91ten2QN46JeSeCycSVSz2dkG+Trjl5z0zff7J8cCvtb/Z39vHcuBao6tjAWMpViM3tzcjA8++CAePnwY77zzTiOug1/5Tdzo/6ugn/mZn4kf+ZEfuZAYy/iLCinHLOAZPiNezXqSk0xOSuTkrmNdJ6jMc/jOfRl3ek6znPE761pOhmVd4ZnZjlqPcz+JkRediejkZLfbbSST6Id3G9APFrht13IM4eNjkNFakirj2YjmS63A5rTvRBhysUhf+AxcQEw8mcwPMecaV88xBi/knpycxHA4bMw9xGK55xX7v7+/X0368bfPfYPQ9eFw2MAo2LnLni2VbR8y8fM///OXuv8TP+h8eXm5VM0cHByUTCLXwHhedY6CRjRX+ay0rpix8OQklp1xNvzZ4bD6lEFNBuYYIguCBZ8EDPe5ogmyIbBSUJESMT9YmtVqlDGfjYTyw2+uo8oFwfU9OVNNP9j7yhzCV9pme5wPYCPpSNILXrkShm05uZLBc8W5CE5G2RABihkzjgI5oqKCnwcPHhQDRelnxDzw5xyp2WwWjx49irt375akS6/XK9cy3q8HsrxbnwxkOp3zN/xEzFdwqZ5bWVmJg4ODRlIiJ2Uj5o6f9u2UvP026yK67oqdRWDWnxno8Z0rppxgyVVddqgGU3zP9bYL2RHwXFcTGKTb6eNQuQb+0TY6y3Oxm/DGztJBr9+uhQ3J4Mk2hf4g/zmZwX0R0QhGmcOIeSJ8NpvvmzcI81hpx7xENnzo9vb2duOV8qurq7GyshLj8TgODg7KWVKTySRu3rwZ6+vrsb+/HxHR4OtVJeyeK/lqwUmWzdr3OUjM/0fMwSv2k/LuvG3M97lPGdhkPUcmclv8jQ3wD/1y0FTTbb7L/LC/tn/LoM73+rfvI8A1AHYCztt4s5/IAD+vHGfAnO+xHY6YrzDDN297yPOex5rHlm2Uv/fc5sDVfMuVz54/eMUbfdy+t2djy7IccP0infX9mTyfeQ5q97+Ig1xfBDEn+CRjTOwqNvTg4CD6/X5Mp9PY2dlpLNQZB9eCSdqr0dPsRu0a9y/zvcb7WrBszOFnuC3LpNvK/ON3rha0rucqTz/TfjnbWP+2zzY/Fi32RFx8IUrmR+ZJphxcOrjGZ+D7bQc8F7X7zVcCefSOnzyXLBaTDOv3+3Ht2rV48uRJfO5znyvxAf2KmL9oxfjiVS3y/tzP/VxEXORzPiokormABm/Aj/y2/eIe8525MY7KOrTo/5pPpe2aL67hV2Qh89tHStgX1JJJJvxdTZedqMryZSwMbiUWqPlD2uFZxrSOHbjHOxAoWjBlzAsGNTav+cIaNnAimOcvLS2VJKUTa/ZxThbZ1kdE2SEE7iVB5B/kyeek0pecc+E5jmsyL8xnxlhb6Lqsn1yEr//JP/kn8ef//J9/5v0vPCllEOqzojqdTozH41LRYiadnJzE7u5uKTWLaJ5twP9MHiDRQmojgpBnIO2AtKagebXBzOVaJ38M7Pw3Riw7BQy1waXP30HBCJDt+JwJdsCPgcwrGj4PydURCPHR0VEpM8yvmvT2QQ7wOz09LWctEbzSLxJxJKUw1H4FfHZI/CZ7TLkiz19eXi4Bt8/xckKR65xk63bnW0M//PDDwi/6jMKOx+PC39FoFEdHR6VCCnAH7zzXGVR8Gok5s/EnAOOQafPm+vXr0ev1ytuAXJ02m80PnY+4uE+fBKT1xAayFtDSv1xJkFd9Ipp7ze2AGaf7g3FfBHbtUH2tV11wCtm5QR4L9+WyY3jh5IyfA0/z29F4fn4OOomzc8Wot1pTcoxe5vPvGIuBfc3Rra6uNhLCXGfn6OQj/bG8YXN4i0nE/Py97e3t2N3dLWOgn/fu3SvnmtlesQ3YYPiqEqvs3hYAZYD4NHtkgGtwm4M/rjWfOcuP139zH7JtHTV4zYA499t+OaKZnM66kvtvm2SMwT2ZF7kv3JuBpD+PiIZu1wBpRFwAyMg116A/tjfoMu27vdxX5t8+0sGhdTwHPjmwoc1sW/Ic0W/f56DC/HAw4ufnIAqcZB2NiNja2irXcBRBbd6dbF8k97UAzHKZ7/F1fM6b61414Vv8ltScyPfZSIPBoLwEBv21zHrM+Tm2C5bDbCtoy5SvqV1f47Plqma33I7nLieRbMe4zrbB3/n52f7lwNv65D5kPeGzXHGGfczX5nmwH/d4bc/4XRtHTed4PrKRrzPv8hzxfy3J5r550Q3chjxubGzE7du342tf+1pZAGas9IMxc/RGzRe9LPrv//2/R8RFbEYMSixBPx3Tej6cAOZeY8mI+WLbIh0xX91ubTHVvzPWXaTzNV+YfbZlns9rb0jMPt9zaB/P/4wvY2/HtdnPu2rbckys610BPCdXTdNf5itjWCd085jMJ1+f27fOMIckLcGe9BnfTyIJ7O7jTeyjeXPf0tL52VSbm5vx6NGjMie0MZvNyjEWjqk2NzdjOByWgh/wPwUljAUfY18fMX9jOp87Ds825Wlkmfov/+W/XOqeTyQphaIiLBb87e3txv7c6XRaVrI9YO71xEecM6uWNOE6EjoGNNznagOEsmaYs3Nw8OlxuuyPvlshCRgnk0kBDCiXn+v9wGRZHWR7hRanw7X0GXAHPxBYl9+jGFzL5zzfATxjnM1mje1zfN7pdEpFmKulZrPz7Q0oms/mwThQPUYyjDMmvLJCn7xqT3/pE8kjlAeFZOteRMT9+/cb/OMVyjZSnU4nHjx4UM5lwJA44ebV8a8HYq6ygx0MBqV6D1m9du1aTCaTODg4uBB0YpiZV+bZZ404CKmB0hx82YHRT+TXwNBJXZ6T283Jb/chIhrJyZpzcjVZXn1xCbH7xP3Yh5WVlcbhgx4z/cex0X9X8NkZuk22UfJc9ylXwtQAhecxg/4Mer0o0Ok0971nJw4P8sKCV5IioiSTmNd+vx9LS/OD8DlAkgOUHzx4EHfv3o1erxePHj0q92WZvMrk1agMLhcFGJl8Xw667OOcEMJfIWej0agsMqBHOXmEDHt+I5pA3XJhecHGZpCex4TNzf7HbdUCNFYNLZ+23zmQs20w+M42w/1xO/DBATR+zGX9DjawDbnKsja/DnAc5HjsHm8OPGzXuA87YP/qNnOiDt10IOXA3M8kgTKdTuPo6KiBcdbX14te+77MZ+RkkZwvkoU8l1kf/Dv//arJvOVsIgeVXlnf3NxsBNHYRycS7UMs1zkpZXmPqCei+F3TVfuLzHNTTha6D9lX89s+w21yLzrkzzOGt12gD1mGbRs8htxf+pN5nQPv3BbPz58t4mdefMv66XnL43Ggm/tiDGv9dbuu0LbfgMATfuZgMIjBYBCHh4cRESUGy4sJDnZfFZ6u6bxxC3+DU/FpPvfUPi4vZtomeeEwz3XGttxre53554KARRiNdphz49ecjM1Yz7FrpzNfIMl+K8tf1k0vBOZFFsfZng8wBTF6fjZjzokUcK3xTR4XlBeeHGf7upxcsw7nGN5yzji82ApGZXGfsYEb8gItfhn5423We3t7paCC3Qfb29vF/ngnFXG4j6yAv0dHRxd8ZMZqtpXkXcbj8QW9eRpl+3kZeuFJKQBYTfi73XkmmnIwDsezoeceC4nBoCscXJlgoF1LInAfmXo+y8IN2ehEzBlsB2ihtOLlwCui+cpGxo/wcuYCPPL5Cy4t5JnwiLN5ut1u440QjMNv7SOpQ9sOavf390tf6AfbYgBIBtU2luYhz+B+xp0dp52mHZ4TY8gKKy9OXE2n0xiPx2WFEMcxHo8b4+92u/HBBx+UOT86Ompss4CPT548idFoFDdu3Gg4zE6nUw5Rzs7j00iM0auynO8zm83KloGIiGvXrhWZ2d/fj8lkUs6Ki5i/Uc5JYgynS1gjLoLdbCCd9Myg2o7HK1wZHNccM/fZvthhcV9esa45IweOdkg4UdpChtEPAzU7NVdVcJ1X0f0iCHjJCofn08DdwNZOlzGg/670ZJz0y8/K8+YEhpPIfAZfxuPxhQpNxsOYsYsHBwexuroam5ub0emcb4nmVdSz2XkC8PDwMA4ODuLk5CRu374d9+7dK2NFJmyXryrVtgrXHHsOvvP1lpv8eQaPs9msUa3i5Cfz6R/7uPycHMjlgK4GXvltOTYAdL9zsJgBVa2ywNcbU2QAn/uGbLkalzkyKM1+LGOXRZUefo5/G7Aiux6nbaZl2kED7bhd6yjfe34ykLfe2h9im7DJfG+b4Wf4ldfwYm1treHXLQumpyWRs+zX7s/X1q65Ktv3IprHSMBf+pd5jz3kSAyuyTpRs9URzcUdnu1g0brpNt2XRddlG/C076AcoFsO3Y5tzyIblH272802yW1kjGedMP7I+N6L71m3a2N6lt1hzvKzFyXOHEBCT9uyk22n+8ffXlg237mu2+2WRWF4sLm5GQ8fPozPf/7zpZoPP81icK6Mv0oERrGvm06nJUGcFzW4jlgFXAUfj4+PG7KR5bWmqxHR8DnWYS9w+P4ce+Z+2p/yf9abvDiS/bXbQPbB5vgscD19sGzV+u6EMokPJ2scjxAjEzfzDB8NQwzgRJMTwfSjZhONWV3Jn/U6L+jy2/rJ9ySViIn5fmNjo3HIObg+F8wwNuOAbrdbzkOeTCaxu7sbd+7cKUeogOWcYM2Jd4pDHC/lBPHZ2fnLzWyLaP+j0GV1/YUmpey0IqJk7QB3bPsh0TIej2Nvb69kESOagNjZSwTXikNVkL+zYaC9HHzZmDNhXvXg2e6Pg8zaeEn28DkCxTPz4WaujuAz7kV4UHJnlxkPlVCU/9nIHB8fF+Vyhp6SwV6vd6G/KMHR0VFJQLl9r/pmZwfffMA1c2DQmo0ehthVFbXKDu7zeMj0zmbzw57ho43zrVu34uDgIHZ3d0vlRZajTuc88TIej+P69esNIB0RJSl1VR3piyR4h4xaV+AL23xYmX306FFEzN8S6RUI5ofzxzKYzaDKyaCIiwASOUEP7Mz53NdGNLcj+h6vOqOvvs7fe9uS++XnoyNUm1iv8//um7e20ldskp0zz/NWO2ysA5eccEavDCA8pwYZ9Ckn1WwjMoj23zkQtg23PSQB5RcrMGbPL8Err0XHHrFFd2trK8bjcYxGozg9PS2OFnkgcLMcXGV6UbalNk98Xpsj9AJbbVmwrBhUWsfth31d1ml0i3u53rrrpLH/zvLq9jweA2b6ju9AfrgOeXRSiXbps2XSQaaxTg46vJLOWGknb/9zIsA+xu2Zl55X6w68yaAZXarZ0kWJWvtn8xMdRW/NA8sK4+VvzkDCrvBdt9str/cGwGdsVqNFerJI5vP3lo2r5s+n02k5uoAjL8Bz+OCtra3o9/vFH1OJb7llXLZ52b9GXMS0tUA0t+3r8+f2x9YT5Ck/MwevGRPU9K3WTk2GbeOciKnd737nNhxou98EarVte7W+eZw1POHnWzb5OyfdF7Xf7V6sSMpjr1GWH+yJ7QHPwY+D61ZXV6Pf78fh4WHpr4NYMBU4PCfRXjXZN9kfeEEuormLJ9tJv4wi8yzfnxN8tvV5u1lE08fY3nteadOy7mf776xfPMNtuA/uoxcq/LfHThIP/2a/5Pa5xgsd9iG0PZ1OiywZ0zupbvvuhDE8z/Y/2xX6ZT9awxNc43tqWMRjcIWzC3e8CGE++DxLEk1sC8zJ8qWlpcb2bbcN9sZ3OBexyAZ5ziG2G3L/InJ72a5fhl54UgplBby4UofAlECDtyg5cWSQa8NAwMdzKK2MmK/GZYDnNzpZIAzKzTAMEEbY17rCiX6iFDWwTh/JunqLH/f5LYMZaFNRAq94DgGphc5npsB/nAHVKlYwhOrs7KwcQr61tRV7e3uNMbnqym8+YGuQFQoBdLBsQEn1hgPK0WjUOAAOoGvg5HHCY4Jm/j47O4vxeNwoy6S/q6urcffu3RiNRnFwcFCuIQFhefWWFUAy/EV+rnpQ+3Epg9LT09MYDAbR6XTKGyT4fmNjIw4ODkolC8nM7LRI1OTVRP+OuJjQ4O+8vaTmDBxA5r8d/JK0zInVHCRmQ5pBhJ+/yLFn+2InmlctnDSiTYAJFUO0zXhdfVYLIp2stwOzw+TZdrbwjfYzSM06QNveBuBkQh5bngsDKL43EFxfX4+zs7M4ODgoARoJqdPT03jy5EnMZudnw7jCpNPpxBtvvBFf/epXS0L+qtNlHPdlrjEY5p5a8Mg8+M1IEU1Qm5OF3GuZy3rtBIV11Hqc/TX3RMSF50Q0z4XM4DbbALeXfX7mUQ7UuQcf4bf95qAlYr49I/PFyS6ebYA+nc7fXuXErZM9TtRBxigeB8/OW4Xpi/83X7k2kys13b7lIieVadNJfPx3RDTe2uvAC76Q3MzPNRks5wC9phvZzvt3LcB/lYTuUF0Scc6bfr9f8NzR0VFsbm7G+vp6rK6ulhcH5eAZbJblo8bPHICY7zXbwX2L+J2xsD9jTFybA/eMvbk+3599r+/L81sL1nMS1X7bOlL7nz6AIXPfazy6jJzlwLDGz4i5bcuBJdf485xMr81jbs88MkY30U6v14vZbFb8tO8l7vHCMeeO7u3tXbBDr5Lsm9AnYgkvuMAH4rpc4e3xc33Nx9XwrNtwW9n2RkQDi0NP0zvrsZNPfGcZcFLGffYY8AE8F9+F7Sde9BjZ0h0xL1qJaG6jpz/GrcbNWc/4n4Sg30jtGC/Ldv7fMuu5zjbEspD9Jt/lYw2oYqJwg+dSCUWM6SN/8JHmDX0ZDAaxtLRUXk726NGjsmjb6cxzD71erxRwoMvwxdVg2U9AVPoxx5PJ+dsm/VK0Z9Hz6vgLT0pZMFkB29zcbJRE8spCVoGcfbVBsKA7659X/RE8BBEDYEHOjtlAt1Z2ng1HDuR4hoXWh4mxH9Rb0Bifz0nhWWREAXobGxtlqxTJAAJIkj4R8woJB7IkA3Gwh4eH0e12yzY3+kn/ucdvyiIjS9UHPEep/DYvgwAqjHygMQG9K+JwYqPRqCFDGLN8QB3z7i0NfsthxLzckPHzvJ2dnbhz504Mh8PyPECyVyeQQUD06upqSbJFNKv4Ps2EnhH8A2yokELmNzY24g/+4A/i+Pi4rII7API2PgdrkIEmfxvk1Rwy1xqU5q2A2VFjoK3DOZC1PchJJtsk2wgnjuyA0Gc/C/31uWmz2Xz1hj6YJ94+ybNyNYnBgYFPzR5GzM+/c9DMuDJ4t17YMZu/ObiEF/6sZlPRP2/txXbmVUAnBHmb18rKSmxtbcX9+/fL8zY2NmI4HDbePLK9vR3r6+tl9faqU82+5IDjWTYoy6J/8r3Y5fX19Tg+Pm6c80R/agDOumg/5ntrwaF1yX3wM7K85GdmXvj+GkD3inBEs0onB515hdp9Raetr/lcLOQ198d+zwkoj8V6mPWbcTi5lG1qtmd5zrA16KzHbYBtu5d57mSCF9NIsrlvtnEklXd2diIiGj4eW4CtytVRPvYgy0O2RTlZlXU+X3/VbAL4EtzBOPv9fpydnUW/349Op1OSyN4+FdHEq/ZT5sUivUQOoPyd5TwnsKDsE7NsPsu+5e/9f80uWbfts2vPIKD1PfZp+FfG4ISC/azPhCF2YOz2q3kOamPJ/XYCwHamJutPG6t/Y4+8sGodp3/Zz7vfXszFf4NzHPi7asx2zLbN+nyV9I9+2iZmGch4iHtsXx1D5GRU9lGmp9kqvs+ykeNaf28/mvWvlgir+Y78TMbnPjpJ5X6DQf2dY2e/nY4Y1Iu5+BQnyRbxq9YP+oCe5gri7Ff9uf24E9WONew/rTMRc53LPON+nsv99NNFJvCIHVV5oQd+0g9w79LSUqOQhbjfO4Oy/Vgkk36BGTx2MvEy9DTbX6MXfuorRodkDIrp1ZzJZBJPnjxpgCQ7uYhoBE5WKhQegbVBIANIsgRB9kRaiPJEIFQZaDppFjF3vAiKg2g+AzTwGYLhPuRD9ZaWlqLX60W/34+1tbVyPQk+J5v4nUv+ELy8GpkTKjzX1Vt+ax7lgOYH/XbiiVUEOxxWlw8PDxuGp9M5r66BNysrK9Hv98ubBlj181k7zAOrEjg9ZMzJyrOzsxiNRsUIra6uRq/Xi6Wlpdja2oo33nijVPMYRNsgMTaSMAcHB3F0dFRdSf60Ugb21jkDjIiIw8PD8r8NJ6CD5CbtICfoDXNpfTNgyqs0lkM7FYMByzlj4Td2Ieu+AYltERWZ+Zk81wkyPstVWNxrh4b8YR9JZDsBmqsQstMnYWhQbUBl22hnTBseB59lIGbbxv3ZwTh4zyA723b6BrhdWVkpK/7mPzrt6kdsJC9KWFo6P8OCSj5eioDcceZcxPx11Jd1jK+K7Jsyr7MePE97Ec0EvAMo5sQ+NaJ5wHX2w7SDrXTCh3lywFML8LwY5KALWXLAF3ExQDUQzd9nXhpgux/WXXTQ5fMGp7TjszGQxQyiuY9xIJdLS0vFb+UVWcs6/HciLAcgs9ms8aZTrrMNycFH5gH3GOhmO1ML+o19sl3Mz8nzg88lIY2vyFuk6XuWg9xWlolaYuOq633EfO6sV2AqVtRzwOwqWdqIuBhoZp8Q0aykW6SjWVf8v9vn2VlOuaY2fzW5ycGc5TKPxzYkorlYlfvkQJufbHdoN3+W+5fPasm8cn9rSQjrmZMd9sO1hETNt2d+14Ju9zXPZW1esz3B1mFP/D33gOGJVZDdk5OTsogELuT3s7YBvWxyLBcx5zn6lyv+I+a2zDzM9sa6nLGT7WvND2Z5qiV6vBjgdiLqSepsJ/2/24moJ7/42/bD8gCGoN/ZD3ENVTvgB/7udrsN7Os4fpF8W++zn8TPZlmjH8b+XiTOc4WPzjx2/3wf9sm41sUo/O8XdhEDEycjd9xPLLK0tBQbGxsxGAyi1+vF8vJyPHnypIybRFJExM7OzoXnI9fGGjXqdOZHH8BDFxzUcGjN/z4PvdBKqVz9gyOl4wCoBw8elIoXJxRgqINUQOfJyUkMBoMyQTwHBTAQc1CIArvaJ6JZuk8fAJD03waGTC7Jr4j5oehW5HyOFoa6dqhYbpf71tbWot/vR0SUMjmSfPzNgd6z2SyOjo4agp+rOexUut35Qe+dTqdRigcfva2m2+02thB6pYhryZ7ym0yvK9qYG34zZpJLPvjYyoLckIjDGNJ/5tNZ5ohonLGwuroaW1tbMZ1Oy8H6ebWZ5NV4PC4Hza+vr8fu7m4j8LhKTvRFU83AwOudnZ0i9yQrx+NxQ56ZX4ING0frq5+FHPEZ7VhXM0BCL+0c0XH0zWDhMluOrIOuRrJDz4GtbVQ+183AgvZzksXP4dluh7GyzZfPOKPLQIdksLfiebyeY/jq8678mXUK4lqqPA1Qsdde/bRc5P9roCUiLji+7NiZY7Z+A4a3t7fj8PAwRqNRw67s7e0Ve5eDtteBPFeWK+hpY8nX15JB1ssMHHPQyJxY5iGv+OU++ycHQlBOjtSeUeuPx8VnBtfIQg5KPQ77TECfgwgnStBfJ3t9LX32+NEt3oTDMzkjIgNjyAts7qfHyPMYjxfPbBtyv2rBvm1kDnAM/m2rAbYRzYpt664TK5yTRDtZhvJ8Wm4y2XdzreXClIOLHPBfJZvgxLG3usAzBxRU4nvhFcpBXMTFrWGZL5nsv3IQ6Ge5HetpxhM5GM/3OyCvJWUg64F9ODyq2Rg+py34Qxu54jnbs4wVfK2xRI1/TjbV5C7PcR5/DZe5bxlT1e7JmMc2qyYHngfPLX7CY0O/83EqbB1i90K3220k+q/Sdnpj1YiLW+uctABjcl/2X+iuZQM/ZDn1Z7SV9aimT5ZbKOtPJh8fw9z7WdmO1vQ3J00j5tWtGWPmZxv7erHBY7WPdbzKNV58ts2Dx37rurGjk1tZb/iedt0/j6fG+0W20zbC2CGiuUBnG20M7G3XTiJRMFEbN/kUYx7GQ0weMfcLLO56UbHmQy1XjJkXX/nlOItokTw+jV5YUsoTYQPOeUXeK3lwcNB4k4GDTQMzkhw2qFwDowGJDoicNDLIYeJIcvAM+pwDqewUnMzJRpV+MsFkOOEBCRMqINjnaUX1VjkHug7CuZ6zKLrdbjlwmpUH+HN0dBRHR0exsrJS9n3DHyuC34ZBuwgtPPEBfrlyBl7RN8ZgIJ3nzqCUPna73Tg6OmoEq4BeB84YFDsMf+7SRRzI8vJy9Hq9uH37dhweHpbX1sLDjY2NmEwmMRwO4/bt24UHOOEctH1aKYMwzia7fft2+R/Dx6u+mXdvvyAxEDFPQmZjHTHXtezwcjLDhs2rGRFPr+zKLx9w8jY7/dwGlJMmOUhCx/297YntT40HOAaegS566yvtoYckSgminfCGr04Wcy3Ppc3MR4N3+OwkP2PJK7WeO/+dk3cZ7DLHyIiB62w2rwTBduNHOI9ieXk57t69G9PpNO7fv98YC+2srq6WrbhXnWrA4OO0A3luIuqvdY5ovk0yV8FajiMubnfgua4I5BrkPgdSPDMDIQeeNb5kkJgXqbKu+r6sf/hbziVET3weo+XecuvxYB/5mzG6opgFIL6HJ6wa02fap3yevttm1cZuf+vr/D3YJQc8NQCa7XIOmuCnq2gzwHc/OReO77a3t+Po6KjopuXMY6kFzjXK8mFaBI6fFzR/UpT57aMKLFPme65iB385GHCyshZsZjsdcXF7uL9bpIN8Vguyc9uQ5dmUsXdN7hyYmTfGm8as+NRsa9xn89dt02/Lci14rcloLXFmfXPwXeNzLTB3u3y2aL7yM71teJFOGYNb97nex43ga3lTF5gQeXNiID/z4/q5F0VgK/rrAD+/oAd7Bw8i5nNjzOu2oSxvviZjsdq1NVlcdJ/9XN4pk3FbRFPXMi7nc+NieMVvL7KCS32/d1HwLGQ/J1atK8yNsbxxsv24x+N+Zl9Ys3eZh9yTMXGOB2oLLJl3theeI3+Wt/YhX+gZ56Lmz5wrePjwYdy4caORPIXHtg8k5Oib57VGJycnsbGx0ThofmNj48LxO57D/Pdl6YUnpXKHENDRaBTr6+vlTV1UJU0mk6L0TgrlM6gooYSxToggJD6ryQKzyME4K5szm/TfypuvpU0EhD4RCAIqySjOZrPyKkf6QYkez/ZZSlY2j9EH+XJuV7fbjV6vVw4uPzk5KdVVPqh1Njs/n2A2O39d6cnJSeO8K4NJDLMrM/if6iwbFSrj2J6AEtkZElgyB/CAJBjjsTOEz7kyjTb8JiD6Ph6PG+diRZwHBzs7O3Ht2rU4OTkpfOF1r5PJpJxFFRGNld383E8zMV8kUak063Q6pWyT5KH1lr/z1ko7A8tXDuwyCDOw9DV2Ep5z9933LlpJzH1HxxadUUV76GN++YED+OyE7MA9fu5Dh9Av25aaPcIe+nmuEPOLDnwN9taOFB1irBn42IY4QHJA7WCK5DjXOBjKVWzcY9/hBLv7lJMjT548iTfeeCNWV1fjM5/5TEwmk1LKPJvNysGP2ONa8HPV6EX0MQeG+XPrHd8DdvA99AUC+PB5LbDMgaCTswY9GaR7bixfBqs5uWn5gZAhPy/rMfcb0FvuMoD1a5vhl3Uh9z/PH+MwBsBn2se7/awbDlptNzx/DkpzAMy1eQ48z4vspOfA/9vfWp7w0/b7WY68aEAfnTCryUGmRTryNN1xP2qB3lUg5hebSrIy4ykSUvgjy7TP5bOs5ECsNm7biFrwvOj+fG1tzjxGX5MDcsu0dT5Xm9SCTfsc66377CA825ocqHts2Y64P+5r1lUnCRi7f9dsqalmqzJWynqWr/UYLDM1TAs/bB9s49xHjy9XazMHPNP2aTweX7lF3rxVCntEXFbblo599lh9v/mU58P85HParflv06L59me5+i/b41xN675GNOX3aQse2CnHd64CAlvzPMusn1/jlxM88NQymxe0coLXOut+uy9Zb2kT/cCeGPfm/mff6/agPKe2O8YBTt4hg+QVLHPe/n92dlbi1eXl5cZL1NDFjP8W+ehF5NiHOH91dbUkqjI9zXc8jV5oUiq/pYDgyBU0XinLys89BBGUhBJwWQm436tCGYhmJ4lwAzQNXv29BdfOfxFYQ4gJFskQk5BCaRkjxpsfK6YrHlDkwWDQOLw3Yu7UMDwOCqfTaTnct9/vl0okwA7/Mza22tkxZwGycXAQ7nJevxXQb/jzq56ZV1dfrK6ulsPYnRDw9flNivDcKxLZwJKg4/kkEt54443Y398vfez3+0WW/Pra4XDYAA+XVarXmZhfH0S7trZWKu2Yh9FoFMfHx+WNK+ghOuutuTkLb0NsQ828OzCpgV2DvuyouBbjbJ3Jziq3Y1uAbGUwZYBGP+0I8xh5Hg7be+V9rpJXjzD+yGROFhigGwQ6sPRqqKtKnWQ18PRWW5d345CzTYTHkPljh56BrO1UBgQGMNxnO0F/jo+Pi9158uRJ3LhxI3q9XmxsbJS38xkk4kg9rhr4v8qU/c+zqAYIcsCV9Qt7PZlMyhkhuYTebdfmDxlC5nOiZxHf7fNyUOfAptaPWjCcA0Luq211csWzkyX87RV/no1fQ8atg+YpPgzfY/2ogdvs3/mbfthuOXhwQsdBf+aV+ViTJ3/nPuT7bBMgBwi2FU6YePWcZ9SShK7mWpTgsDzUAmVfVwsIsny8akL/IubYz7jD8ouvhT+WLQcfHlvGdk8LnmqJkpq+1bD2or99D7/zXOV5rPmzWiDrcds32tfDuxzo2qc7aMvyZB9mu5b7n8fgMTMPNd7ltvIzuQ6dynq+yD5me2sZyEkp23r44+QF13thnOuxQa7IzvPlN/Tl6p1XSchN1hsnJPjfWLFGlrOcZI+4mIx1DNjpzF+Sk/GVE6h5vhcl7rNM8bljuZo+1HQ0207ut8/GZmU7RFxs3NfpdBrH0NC2q247nU6JZfEJHrP12/oOf11ZZL22Xrnv2Sb6hV6LbKDn1p/V+Gj+R0QDm3JfxufgKKqsnWz0uOkzSSn4SAzMIjUxrvWXPuY593cUjdiXU0SSKfuE59HzF3qmFMJhRmxtbUXE+cGz7733XkOwHVDlBA0VLq4iQoAAh5xdRFbQE+hgywrnYHk2u/gmgQwqMcBM/nQ6LRlCnslzIppvdfMK4OrqamM7Hte4FBCDzrUA3+Xl5bKyQNDPK4N5Ux4JFfpHnw8PDxtbFi2oKysrcXBw0FBUXtnMeE5OTkryiuoo+gtPXP0E7/0cqpLy4WguP7SBISA/Ojoqc2yDnLdDWuBdIcdqI5+trKzExsZGbG1txbVr14pBxGBGnB/cTfLU1QN+099VcaSfJLnCrNfrRcR8+6vfCLG5uRmPHz8uTt2VfsxVRHO7CnrhJEsOkGugGsrGMq8I+XMS2v4+J6Cd2DIoMyimvx4b+khSCXnhPuyYn21d8X2unsCJZ8fudtAx2zM+d3WU+ZVL8WtggPnht+1TBvm2qwYuHovH5yAzJwFJztFPAwkIe0ZSqtPpxOPHj4uN39nZKaB3MBjE/fv3S+WjbW6WoatENXlfFFxctq3a/bZlXpGLmIMhFoic7LANhmr6hP3wPObgKwNdfnv+LVt5AYexWSectAaU2g84eGKM2LJOp1P8HTgitwXRFgnfbFecTGacWYfyHDk44BkONtB3J3XQG+useex+5wDcspDH5f55HDkJzjUZXLtdz0lExN7eXmxubjYWMmwXMtX8gG01z7qMb3afM3C+CgRPjFkWnYsScXErSMav/P20pAWUbe4iH2we175f9L91NN9bC2KsU1nn+e3raosOtcTsIntaa7cWjOfF78wzy7111X3Nn9s/5XmqzYV5k8dhHa/ZXd9vfa/x3/qfMQU7KNBFzmHtdpvn1mIPwT7ZfrwKys/3vBqP4APtY6x7xmKeW1dURcznEl22XFqObMPdR89dbWHP85rl1ePk2U5qmnL1lPGw5SQnT9xP+znvbsnJGxa+vbibfR59Ym7sH/1sMIwp88R8c44g62Ueu3ltctzAc8wLPjNvaDvLRy6yIIYnzl1aOn/xGLjER9R4HGAvV8ryPzqbbellfafjeuSHOWSnRc1OZzv0LHqhlVL8RtBIkDCY4XBY/neZpJ0mgUl2IBYAlzaT7HBSy0KWVxd5tt/K5ACN+yKayo/RMnjvdDolA0nffPgXQStGmYlxpRBgzmfF8PrfwWBQxkX5NgZ/eXm5bLuj8oln+qBx2iYhhrMia9rv90u7BBEkHiKi8BYeWXhR7Fx5xrgi5ivKp6enpb9efYav/pw2l5aWSkLKAm3HbUOQAXS3e17RQ6IPJ7q6uhp3794tByHjUK20HKRs2bNj+LQSc2iZ+cxnPtOQ1wcPHsR0Oi2HyeNweGtjTvbYKVj+/EzI2Xvm3U7LlQ7ZIeeAlLHYINqh5eCP/73aY931PXwX0TyTzfaBtgkKSWQ70RkRReYc0GUni3PJxt0AFLuIk7Ddgl84MfSZsWO3nNR1tYL5nVf8+DuDTWwRSens8LHBPIsESHZi9Ik2lpaWykGLKysr8ejRo1hbW4vNzc3GGQfr6+txenpa3lxaA3tXjWr9ehH9zTqSq3Qi5vrW6/VKwp95R7asn7WAx37SvjQHhtl+18CmbW4GmFn2arY/VwbytwP8nAzxs7xVKgcEPNOJaMt2tiFZb7PdcoCRgw4DQaqE7QPNN+sOz/ZCkeWoFiS7P1xjOwlf4Ek+B8t9sq0xHmPxDP5tbGyUYx08908DsbVxPIuyDNbaugqUk8PwK2IuEyxE+qzP7Nsc4FkmasT3+SdTTmBkeaMt9yVjRj7Lz/f4fF/WmxwbPM025D4b69t32n5Yxz0+Y/gaH31vto2+L/vc3HZuPz8rf5dx6dPsbu0anp8XbBwv2K5CnGvpdlhQd58ODw/LmJw4fJV6x+IVGCxiXhjgHzAux1F4DiMuLnR6ES/i4hllvtdyYczoWDXLfpavPDfYZDCQ7W/GsDmmoq/2oznhYp+Wk2+Ov70IlP0YPHdFo8dof8szHR+DHSloiJifeeyEnzGsZTQiGslRxpKTX9YDPndFU17cMf8yRnZbi/7mh/7YDzrx47HxrF6vV3SY3Ao4IW9LrdlU+9taspLrjo+Py/E/4JFerxcHBwcNW2qazWYlWX0ZemFJKbJxMMuJl9XV1Xj8+HEBIAwwOxE7GyoQDGasdDCAcrQMhu2EYAy/XdLnvkQ0D+OlzdrKYEQ0KnVoE0PX6cxfk8qh5lbkHOjy3Mnk/MyXjY2N6Ha7jbfS1ZwWSu/AF0H0/51Op5TfUWmAgaSSgHtICB0fH5cAhYSOV4sJfDEaKysrpfrIZZ05wGY1msop+L3ooDr4Q5BLe8yLV8JRao/Fh6iurq7G+vp6TKfTuH37dimJ5DndbjeOj48bib5soD/thANBDzFCJFUODw+LbJOMiogS/PNWIL+kwADQBjMHxcxjXjmA8iqlP7NDzYdQ+nr+dlvWy5yIcVLWJcgOItExJ1boC87OiRk/0w6v5qidPMqBLnzj/tlsVt6KyBjQXfqcg30DDxyNX6TgADMn/g2McjDryjJ01M4Wu8FzqcTMNpdnu+IqbymgfY+NVUlXq/l8vqtItUDwo7ThgMg6F3HxTL48b4AYEsc5oWHfaT/shFdeCYSs7zkwcXIny7jllTk3WGTu+Zt+LwJc/G+94jvkZTKZNBKnXO/gwAA4B7fwPQd2fJ6DAuMn20I/w4A3jyVXoVgnM0j2dmHLi/tjDOU2jHf8vQMKMIsT4XkeZrNZOSfJGMiyYMrJs+elF6FbL5Osl9ZTFvpy5T3Jwoho8DknTSHLob/Pf/t6f5bby8FpDnysy9aLnPzhujz+bC/crj9HP2vJXl/rs8+sN+6b26LvPNNnsuQ+5ECbe23/XK3BM2txRm4n/28drD3T/a7xzGPONiX7e+5xIG/9hhwHjUaj6Pf7sbe31/DFr5KM+yOagTnYw/gWG+W40ONFL7mPmCPv4jEGyrKVcZVl2NUwlmljMsgJGu6xz8oY0zg6j8nP9Of20YwrV5FlrEsSnWf5O+JE+p6LFiic6Pf7JUlv+5GTSmASsC/P4hnZPmb55TPriHFUxp/mjflgPJB5lOfNMuc4g9ghx8hOqJKoY9xgaJ4HP9nxtAhPPY3gxXg8brxpczI5P8uVl4dl6nQ6F/TtafRCklLdbvPNBd1uN9bX16Pf7xdg5W0WNVBrwSAr7Td9UcoWMU+GAIQRYtpw1tlKj2FAmel7TUAjmoAV4bBCMtkIDIAe5aPf8IQtd64UQqmdgDs6Ooper1cSPN5Gdnx8XKp+CB7oC2co2VH4OXzmjKrBr89v4TWujJmEFIEk88Bc8h1JNb/pimfhyOmL3+7HnPlV2bUgysmKHCjYYNP+bDYrr4qnTfj3zjvvxFe/+tU4ODgoCs13Phw+4qJh/rQSxtOJRAJUy5GBJIdLU4Hot3ahE7TNvNlo1hxuzTlnAJ0/M5iMaK70QRlw22Egvxho9IixYN/426AXgHt8fNx4KQO2AJ5yv6u/nNSNaL6EwSAuA0PbTAeztGndpO/0hzm1HhHU2JbmRBKO3dWujN0OFRttW09C2WBjNpuVF13MZrPG6uv6+noBhBBt8Dzs5pMnT2Jtba2cocdWAuwkL3246lQLLJ6XHHTUApn84+8s095OaV9CP9HtWjIq98FBnQMff57Hne2vK/74PoNGB03mqUGX/TjX2oYYjOOj3E8HFE4C2Me6QhoAaaBuHXc/eAY65THVFm3cN8+Lddi21GMwMKUtB0DMfSYHsPa9Hours/ysHPiahGTiAAEAAElEQVSYj7YxNZlYlJCqybm/q12bg/SrQg4SvM3bvtC2NaL5EgKP6VmYxXpaq2Dy3zmhUrum5qNq12QfxudZLvK9vt8JypzEyRgCHnGNsW9eQMpB9dN4wrPyeE25DV/r+MK6s4jXmZ6FTfk+J4TzPfDTMmObkYNX62HGivgAjhshPvLb+ogDXGX5cZLNH4Uyv1hQZYzurzGsjzUB+2D77DPz4qkxpG1c3o6dMav9meeI76y7fJYTot5q7zZpyxia+bXN8eIO97nPxgfeKlZL+tI2dox7ptNp4ywykkeurLMcEeu6H94a6djRGDZiXnSBb8y85lkeX7a3eQ7MU+uZF4OyD7MMGk9l25fny881Fo6IUtnnOUUm4Jufa/zvZzzNb4Bv7Pf9hutFduuyfvaFoHQzAYEYDAaxubkZEfOsOecjsUeXCXAiot/vlyqLiLmA2ihGRCPoi7i4ysr13l/ppBgTRfvevkLQxk8G2zaiGFYUv9vtlion98OHi9lBMG4CQowMyScLFIGg927bmCCQ8Nd7ok9PT8u2QAAyik/bnj/+dmDc7XZL20tL56Wt8H8ymcR4PC6Kko0xyaiTk5Pyxr/Dw8Oi/MfHx+VvjLcDZMhVFP7f8megy/hwkszpYDAoByTbkMNzfnwmydcLOcHBNlKC++Fw2ABTERH9fr/h+OAnvGM1HANI0qK22kAy1/Ljn4i5vWEu7QiyDaglVSwj6IMBAzqfq3WwXTlRnat0CBrszNwv9IjP4ae3AXq8jCUDnhrwoU/WWSrZuBZdY9yuZLKz81v0vCpDwtb3OvBH53Do9CMHwdiA0WhU3kp6cnIS4/E4xuNxOT/KIGB1dTUGg0FZuOBg88PDwyKbvE2Tc2s8PicyryI5UFwEDC7T9wySbENr47ft9/xZj3LwFzFPWtjXZf+GLuSVR3+fgVBOBNneLwqichs5IOUz5CHzuwb6aiAt4xHrnQNBJ7h9rpnBbQagDgQcBNjGZbngWX6eA13sjYGk2/X/xj+Mi8/djxr//Ux8svkYMbcdJycnjRVbgsKcHIDwCZmepSeLdCnP9VUjsJf9C74DzOKAw4FzRFN+zUt/Vksim7Le8xntmOiL5XgRXxf1Lbedk638dr+yf0X2a2N+WlK7Jgu5XfPKlYAOjGtjtn7Wglh+48+zfc59zrz0eGv2NX/ne+wP/dtj97P4zljHi8u2CZbbwWDQSPTkRMqrJCeTPGb0jxgox1xcH3ExOWEZz4v/2Vf4eTmJmRfejV2ZD1+fn+3Yhu9oL1dMWae8UGPfbt9iH81vJ0isIxl3Gyc6hnMM7LON8yILFWiM3Ri40+k0qunzPOXkd7YpHmNE83zdnEDMPjrPh9vx3Dihnn2oP4cnljvjNJJBLLo6Z+DiHT7zXOT45nn0kN0YjPPk5KT04Wl2/zL0QiqlDEKcGWS7mF9N7tV4CMCEcBrAObhDyHzwHMLpTHJEs6LGgSZ9YHI9+RaopaWlsoIPgLWAeXXKyp0BJ0mkiCiZX1c0cO3S0lKMRqPY2NiIs7PzN+SZnyT16Pvx8XFsbGw0ti76OhznbDaLjY2NAgTph/ts44Eye14QXGe+cUguEfTKOudHWaG8/YZ5Go1G5Swi8xBDTsUWW+qclGSuUHQnLSw38JF7B4NBSZjcunUrnjx5UvbGkjSzUcmrQ18PBM9saFdWVuLhw4eNMtuIiK2trTg4OChGyatCEfOtIrYTEXMjlcEQ+lBztJbHfD9kZ2pHzj3ezmlAgKxgo9DXHJBTFuxVnJxgyhVR6C73nJycNOxHRJRVDCe80DGeAd+9fYa+2/Zi2/IKONdRTWSQgEOjbScNbT/Qa/MmByQ5sKAvdv7YOfyDA1nskit0SLBxeDnjZjsu+9pJVNnvkIjPCYarRu7boiDnWWOo6Zfb9PxYdg2OvIXPAJS5MaBxsq+W8KoFstZNz0sG47bt9NuVzIwPvfU1xgQ5MeWVVL43BuB/vs+BtMGXK4xpwyut+A/4aizDfR6vk/7mNzKcgXTmbeavA6Y89+5jjffmGTbBPDHIt9wB5KfTaVnVjogiT36TDzbVyXYHQNk31GSrRrn/+XPz5CqR58fndyGT2DvjLXjnBcFa0MTfNX01L2o2pmY7ana/pu9eaKqNteY7fK3lvJYEyn1xtWKee/hqG2bdcP+tExkTusqKz20XLKvc76ol66arN59m8zO/8t++3uTFodqcg82M0/M80YdadTZHcnCtMRI4HvySA9dX7Y/BPl6IZGzr6+vFTnkrlJMEefE82yv7WMuu7abn1fbV9jbiYsUUvMsJGPO0lsjKtjuiuZ00L5hg+3lWrQ+05xdiGR+7T47Lfa+/pz/mq/Gg/S7jxPdaPt1H+xae44Sk8al10r4pJ4Hto+zvHQtl3nsuPX7m3PNm/OPv2cbd6XTKMTXr6+ulPfOEWIPn0CfkxmPw4uEigse8GA2b2u/3YzgcVu+/rJ99IUkpFBImksHj78ePHzfOOjKjI+aGkWw6n0XMhdOBJAy0EvG5X9MMzWazYiCtTDgmV1BlwMr9NsQZzPgzZ4ENTB1EMS4DC5xCxPx8Lq9A2JjRb1ZfaNt7ZUkgDQaDC1uvIs635/F2O0Ckk3+U4fnVkg4Kl5eXy0onINPGmioqKtBms1mphGAco9GowS94zRzixBg/FW3cz3fe8sO8cL2d6+HhYTkj6eTkJIbDYWxtbTX2jDtZuMh4f5rJhnUymZQqG3hD1RPOmiQJb4aIuFhy7HNZDK7MW4I1HImdSQ1Q15IhkEGjA2lTBth85u9y0FYL7niWwUxOmPony2Yt2Od7HGNEM7HnfqA/OGzsSA56CWpxkt5KR1+xGVRQZUDlbYAGHTzbyVvacXUcc0dCLCKKPK2srDRkCD6hsz6vjPZJYrky7fT0NAaDQRwcHJSKyNpCyCLw/6rJdtDzc1nKMr3oc4Nrz3N+NiDF+mh9MrDOQNU6lnU498ltZ6DGtYsAXE6eIqNuk/v87Bx4eZw13sELJwEcaEAksyMugtisU9Zh98uJWOyBP7M+WZZz4M+Yc9I9J7My4M22pdZuBvs5GHeg5qQcti8D5zzPbrfmf2u23//bfmc/ktu5KlTTF2M0B1PGujkZnBPRkOcsf16zEVnPL8OrLFMRF6sVsy+t9dn9dF+yHDoIzJgg98HjqSWsMsb383Pw/azx5me5XdvdPG+2sfm7mi7WeEY/HWRnO2ib5HHQt1plq5NrzEmn0ykvnKKyzy9IcszHYtzy8nLjiI9XScQ52Bq/8ClXi0bMXwZkv8A1VFVhS4lR83W0Y1+afSQYFv7aX0c07ayTFnne/ayMha2HtUo+MGnN1mcZ9bO9tS77Eha7vBsKP0lixbgC/2d9oZ3xeNzA3sbTxi61ZLGTNU5geX6cjIuIBq62na7FGB4zc8p9jC8noOCH5wbecJ2v4bv19fUYDAYNGcsJVh+xAX8y3jZ/nkXs5uJ+fNL6+nqMx+ML11/Wz37spBRMgwnLy8vR7/djMBiU4IMzfehYdkhUJfCbiaSaBSCIMTDYIaCJmGdBfehqbRVzEZhhDNznckqXRtvY5moJTyzP45C2iGgcvubkGkZhOp3GYDAoykSAyFkp8HAymcTR0VGpbsAYnpycRK/XK1l/g2ifw3J4eBjj8bgkHuhTxHwLGwfKUb3mV1/TDoGgDyxmLgiEWSkl2OF7+GrQzfiYD2+XYs6c6DJwR/lwqE52OKHlYHY6ncbW1lZMJpPY39+Pu3fvFr4zvprB+bQSPEbPdnZ2YjgclgSiA3x0Fp3LyWDIxh8ZR4ftUP38vPJjg+2zCHyPDTXkpLXHxmfojnUYsG+w4FXpiPlbKbEvlK+SpLPO2eExZrfL/wA2ksyM0YEH4IQz3kgQkpi1bMN32yUSzhlYM14SSV5tyvxzwth23AFtDpbpC2MkKeaEshPbvV6vPHMyOd8azDZRwIhXdQCTo9Eoer1e4cn6+nocHh5eCDCuKo1Go/I3VbPP21/GaBCbKw8jmnaWBR1v9WR+kFnmtAbGc1CcAzkHMJY7A2EnMIwruD/3GTnz/RmMmxcQ/iH7GydWaN8JcgNDyzqf5eQt44N3xhO07epD+uZx+3/PlRcPuH+R/c3znMdZs7m14KRWPWEe1IKhXFWJLDmAsYzCy9xnBxUel2UpB9BZ/vJ81XDgqyYnWZxszfyIiIIBHZDmwDQnKdATB2o1LGx8lfW69nceQ+6Pr/cc+Hm+3z/5OYuST9YL/EbGDuiJMaVtk8ecE6HIW35+TVfMe88Z2IDPreeZJ04K5bYzL/13nne3a38OH3yP/bbtQcZDtOmqDY8p22VvK1pdXS1VzVeBcn/BGE7+53FNp9PGjhPI8uXg3/fyTI8/x8e2tcbX+CkvXDBfWTbsk7MuWF4995DH5AqpvLBnWbdOuHCCvtAG8aSrsiKinP3pBK4Xbt039w/e0L5tqOcj99+LnPbvXO+FYN9j+5iT7bYpnlPrkp9hjE172bdzHzu3wMnm4crKSnmJnPtELoAcBH3JGCYnwC9L4/E4er1e4dV4PC5vhsUmmD+XoReSlPKZPoAQhNVgJINGFMEHmiO0OGUCPw4C9gTOZvMsvLcPOhizMbEB4V6DSRIpBrtcTwBoxcuZ6hwkdTrnp84bmEZEI2jMBh6giiGiLyRiCLQ8JhJHbPmjvySb7HDX1tZiNBqVbWreFuDtPGStKcN3dQTt8v/GxkZj1cNKSQWXFdPGnuv539lqGxRvpXSgZAMGH/ltpzudTmNzczPOzs5iPB6X9iIirl+/HqPRqFEq74Qoz6gB/k8TORCD/7du3Yrd3d2ywj2dTst2UCdXSORFNN9+Be9sA5wsRH9dxZirADyPdu55hSiPI+JitVtOYGI73G8bT+toDeRDeVXRvKHv6EO+xoG5k98OhmvBf+aTx2F7BE2n08brxc0nEtpO+JI82tjYaJy1ZpDmVZyaXWQFMuI84XJ0dFQqJrHxJLuxN1Qydrvd4mhns1nZstzpdGJjY6P4Bj+LhZBerxdHR0fR7/er4P6q0q/+6q9Gr9eLX/3VX40f/dEfLYmpy9LTHL/9YURzgSgnC/Jqbw4O0WH8VF4s8nUZsBq4ZdAXcXGFMQdU/M1cIl8+z8K6iE0xIDUv3L8MEm3brLt8n4NI95XgxqDPY/D1tMf3LF4R/ABGjZncd9uEXKFkX+hxZ1uQA1T3x77ZdodrDOA9b97mbaBKYt0JdxagHOTbji8iz7X/z9fUZA07d1UIvoKB0UW+I/EeMcc47Epw0AHV/IeTnjW+5mRB5l3N3y4ai++BrDO0la9j7DnRRH98Ld9Zlnyt+5sxZn5+Ds6zfSDYXpTYMg+NNdyeY6K8dcjXgRfsvxbxOSfioez3fK3tu/noAoDsOy03xooeJ9/lrcrIb6fTiYcPH16ZpJSLGPxWMTCVd9PY7sIfHyeTz3WzPBHb1uKW7M8cHzFHfsGUFzGy/Pl/J68yBo6IxhzlxKkXZKwvGd97p5HlIMdy9NcJJ1fzoFs5iUNc4fHTjs9m9WLQdDotR/t4PPTXNt/9s0x7Pqz72WbWbI15j0y5ejr71pyURgdtAxlvxHyHAb5ieXk5NjY2imx4Z5aTek9L1LtPlyXv+KC909PT6PV6sb+//1xtQS/sTCkHNysrK2U1/vDwsAHmSHBYKVZWVhrbhAhUMMhWhlzJBENJXLkioWYU6If3k9tJc+aBk1PZgdtQ50oPAnfup+rC1RjOaDNWqiwcBHAmig0GwSF/A1QJ0hiDldPn0FB1gCD7fCCfOeVs6tnZWWxsbJR5coA4Go0aDmg2u3iw2tLSUrnO4NyBDPdgcGorxySnTk5OGm/HywlHJzqZT94QNx6PS8KOrPzt27fj93//9xtVY06omP+vI2XA87TrIpqrPTs7O3FychI3btyI4XAYBwcHsb6+3qiqwGCSkbds8znOmrniOSQCcxY/J5xyEJvvsSxEzLe72dk4Oevgh77kpDVkUGq5tDN3wOZrzH8nY+GZAaoPb8xAsNOZH666tLRUts52u93yN7YHfXUwOplM4vDw8EK5Mn/nVRPz1YGbk4zYouxU6ZcrJLAjTu4DMvjMgIYxdDqd6Pf75RnYSmwP88xW5dlsFsPhMHZ2dkp/B4NBkZdFAdRVoS996UuxvLwcH3zwwYVA57KUEzN53PY/Dux8pghbhrAFluEM4PI5cTmRYFBbC5xyEJWTGwbvObhyvxwQOMCqjdv6bJn3tQBeeGH/nQNfUwamrAyjm7YRBvLYK+MFJ31tD7PdpH957iMurnDntvjJga3BrPWeNrN95XOPKwe9liEHSD5PzrIDwe/Ma9tJ379IvvLfYLSrQsbFLJw5KHEA6Ap08ytXb5jvTsRkfFvTzxxw+W//7z5kG2sfHtGcS8aWxw9ZRswLPzdjeD8399f4uqa3XJ8/43fefZDHi464b1zjBIIDzhqP/dtJhJosc02urMu8zPbPn9eS+SxYe8Fw0f1UbhNXsKjkWAq+7+7uxtbWVuzu7jZeoPIqqNPpNLYgGV9yDE3E/EgVxuP7c1zqGNQ6x3zlKijLRsTFxVHiLfteJ2DzPRlHe06RC//YznvbPn3N+p5ja+aZ64yFc2LF/zM2nsPb2+A3caXjULCjfT920Mk+x7DWN8cqHle32y3xr3nvWNW+1zpJ/6zftjHgB/fZ/1sGFtlf6zY+gGccHR2V85DBK1TQEpPBV3Izlhvz5zJxYqbDw8PY3NxsbM3vdDqxubnZOF/qsm2/kKQUSYBO5zzpRHCwtrYWDx48KMKwtrbWWD3kWmdLI+aZUIBmdhZWhhx0GsDZUDtLnUE62cVOZ77nOQtiLfC1IUABfKA7fxssMW4LpyscDDhJvlBtxOvmOZQPHk+n03IIuM+y4rk4C6qkMJ75HB+ECh6QpIGvCL1BAHvDDbojomz3Y2yrq6txdHRUgh4qKAxG+Q5e2CGzqru0tBT9fr+R5MJQWY6s3DyLOfK+5U6nUyrPOLsmOx7ooyjsqyaP+1kBLrKaZRcDs7e3F4eHh3Hr1q1i/Eh+RJzzhy2hDmqYW3jqpK6Tk3aSEU1Aht1gvr2tk2fXgLiDtRoQt4PMYMs2wuDX/6Mztg15hdHPB9Tafnk7IoDXlZ+2CQ4eM6Cxw5rNZqXMl+AQB077gCPaxj57m67tJX2zs/eZbw5+cgDkRJftp0Guq1GwZdiXbLexLbRF4B8Rsb+/H9euXStv+bx+/Xq89957cXBwcCE4v2r07/7dvytzur6+XmQrBwNPs0WZ73k127Ljz7l3dXW1JPzQOSeJAUT2z9Y3+ocu5+Qy31uXHJi6b8w5/TbghZB1ywf9c4AY0fQr6J4TAZDtFrYQPXJQ6mAiJ5RqVQgOblh0ynOKTcmJmJyEd2LZ8lCTEQeufIcNyMkBLwZad63TtsWQ/YttCLqMHpvXxl3GApaJZ8k61yy67nXx22AR7KfPCD06OoqNjY1GkDYajWIwGFzwmRH1yjD0NtPT+JODpEXPyM+qjS3i4hv13Jb1ddH1tcSX5d36gn7kID9XCzzL79vH5v45PrBe2q46tkFOjTvsL/1cB/kO5m1z3Hfr66JxORjmsxxfuQ/etQJ5cZ/YiViCtpxkhzgXcmVlJW7duhVnZ2ext7f3TFz6SRLxgWMqxlPTFcsf1zpp6s/9jNrflg1jxJyodWLWCeqMcU15YdQykvtqPbHe2V9wH/GBn4EP8REsOXnmpFptrN4V5L65bb9YK9sF85Pv8LPZP5uXWfbpn5+1tLTUeFFO5ouLFoyPcgWusQz/2597XmuLSMYW3G+eOyaqyRC/V1dXGwvUbu9pNnwRdTqd4osg+rWxsRGHh4fP1e7HTkohUKXB5fNXZxL4+61x3ksKsHHpP8kHK4pL8JgEB7suAcwBGEFWRPO8AwxJxFxwUWzuISvLGLMgevwGeghjXmHIE2+FsXK4XNRg0OAQcLe6ulqqnrgOo09iySCefnLIOYeRwbelpaUYj8elP04gjkajIvQkuJhT3pA3Ho/LNksUdTablQy4g1/Pa54/eI9c5LJH/nb5In0myIDnvEHQc0SCi7cX7u/vx87OTsm4u68YEDvl14W8x9iJOidT4B8HD66vr5es97Vr12I8HpdE4HvvvRdLS0ulUsoJCJKozIuDUDsFyMENht8AzEEpNsY6G9F8g0feemodtzxENCsec2Bno55X8zkHKa9E0zdkt5aIi5i/LcPO2CDIiRtWHjMfOp1OSSzBR3Qe+Scxje6Ox+Nynd8CyJzDQ/jhbUP87/Pj6AdJZicP0BknxwAV2GRstF8EYTALHwEy8M1n3XEvJcs7OzsliYUPYY5v375dEjw876qS+WabZV5COejItAgMZ/Bh+2BfmIMFg9fsl3LyKCc0I5oHX/MsAyf6mReaDByZwwzkGBfksfg7v/3JQV8tiextUzXw6jkwKLUOZKxijOLVVOtRThjRLvrgZJX5g27STwN0zxHz5EUprsmA2LbQfXWfc3KhNoe5wt2YMdu3LBOXoVpwlpMUi/TlVQbFpk6nU7ZK+6yT5eXzoxm8qBgxPwDdAYdllTbxMbXqgNrc5UQU19fsZr7Xz8z+P/cpf2bbs6hftc88DsaKHbJ+RDTP7oHMQ/jodv2Tx2rbme2zr0f/sOlO+jrQ9H3W5Wzjcn98nXmU54U+m+fGOr4+xxPmD+04IQXWYBGYxDTPwg4QY21sbMTe3l68Sup0Og1bBB5CTuwHut1uqdS237F+WL5sa+0HmS9XPTq+i2hWr3FP/oxnZ9/C52AuPyPfG9GsmmWc9ME23rJq351tfuaL5buWMM1229cZc+Z40GNy37EByF/mXeaJsU6O8TNffY/7kfXXc/k0vGm8YBtGnJFxmJOi+Qc+k2Mxf3Jy0pg/j+FpmHIRsUjS6/VKVdbx8XH0+/2C5S9LHzsp5dVTg6WIudOMmB9oDbPW19fLq7stWACvLBQ2bgio37pEYIozQeFxdCQn+M6TZVBmB28DX8tSG9ByXS7PzwCQoJWEDrxZWloqySYCfCsYwSYVSCgrIJ2x0QYn4BOkUQGBYXUFFmMZDoeNsbC6ORgMShDIM0lYOcO9trZW5m86nZbqKL/anS1fBMfwx46Q71wKaKMIUOMMHO6DVw4msjFCEama2NjYiP39/bh9+3Z0u93Y3t4uiZi83eh1I17P2ev1Ym9vr2EAmTfvod/Y2Iherxe3bt2K8Xgc165di+n0/CwuZNbnmvBq6k5nvrUM+XRC0slG7wFHd2wDHJjYljgZ7SRRzSHw28Y6Ii7YEa/aWP78d8TcORt8efso1+akDrLuJDz9cIUCnzMeH2JOIOFEWk5IQXZs6BDz5ooQA2i/1QX9mkzmb3bieifbDMKp9jB481xiN7kHB5V9hoNRrtvf34/JZFKq8XLQwRZithWTRFtZWYn9/f3Y2tqKBw8elFc6e26fBhReJdUC5I/S1xpowTZmYDuZTBrnMXhBAX47uRIxD1gWAdEcSOVA1Uksfty33E4GTZY1+3TLLH3MVVWWYQNC65mrbg2G8WHuH7rsxAo+g6COv+mDdc5gPwNn+2MD1JpMOLjIINT3ZpDLj/njoMt20zbRgTH2z1Uonm/s4XQ6LWdfYj/yoomTl/DkeRJV5lcOeGp8uyq2AAzlSg0nUc/Ozgo2MS4kSIZHuUqhZmvtWzPZr/F/RNOn+m++41r/7R+3l+cl+wwHjYv6ZH3JyZgsz1km/LeDwdxOXoz0d1yPb/Q4vCAdEQ3s4Dmi705Sm2qBrXXTfTdfbONq8u2+ZNud5cYJzXwv3zGe2vYv95PneAfMqyTjQPu5TqfTKGqIOB8DurYIN+Zx1rAN3+WEaURzUSrbereT5zzrDX2iPzn+xl47RvLCQ57rnNTOyV36WEumZexlss7ltlk4dYLQW88gL5yAI/IxMB4v/QHD23+7zxHNaivsgWWkhiez/3UOw/mGbDs9FtqwL+Ra86gmK3zml4/x2dnZWePt18bo4D5f/yzyvMFzJ7tOTk7KwvFl2ot4QQedW7k6nflK/ng8boBKC723oRHI5fI8BuGqGQdPPrvIiR8YRQBtsJaBUl6N9P88z0FtTkgx0Vm4s8NDsB1wUlVEYgVDaLBsAY2IUp1AH7PgkazK5bYAHgsH+1Cp7PIbvAxIOcgvIhrBuKtiXLVC0OCAmwC90+mUV78D3h1kGoihMDkxRJt+O57lLKKZXGAsrvzgHJ61tbVYX18vvGWMvKHQh8G+bsRc5TN8kGlvvaL0Ej3r9/sRcW4EObvMCRG/rZE2kcGVlZUSeKCvyAP32dhnh0fQZwfvpDD9x6bwfcTFAMN6atBlMGGjn0Gpx1QL5mognesZu/vrUl94mVcSXPnHnOBEvbXYNgLdy8EoVX/sK0fvsLPwwwliA2jbNWQgB988m/HZzpIUo+/j8bhUVLKikxMJ3W63EawaEJAMx05gR9fW1hpB8XA4jLt378bBwUFsbm6WviIPBhpXiWqB2qLrDJ78ucFoBk/2XxCy5PMaDFBcyZoTuTlZlQG0gZ6vtU81wOU6j8uBkp/hMVo38/fWTxJwTooBGpEtV2xYHl3NaL7kt/9EzH2UK4T8rAwWM7j03DIeb/XLADevqrqvtbmnHXgH3zOfcyLaffM9OanmZzlYsp3GfntxjfadiHxeHa3JCf/X9OUq2IBut1vwsM9sdKBleWRRzTJkXXfSA/sccXFLZy0wqvm0RX/7GdxLeznBzN+Wd66tzUvtt/0iMpL1yGQZpZ+1pJeTKbnaOcuP8Saf2y7kBEXWF+uTP3P1ySI9zbzI/POY/KxsX7LcO7Cv8ZJA3227n+YXPhkfYpsL7vSRGc9TRfGiCbtvvUEG7Juyv1skZ1D2u8bAfI+sZPuPPGWb7oqdiMUYIF+zaK7ph2Uvx8ee29wG93vc7qP9PLEI/AbLuVq+pleWJyee+JzreF5ENDCq8bur/bwLgbFmHGEeep75nXXBem7K/OHajHP43gUsOW4xjywXnjP8ah7LbDY//gC8h34a8zwPWXY7nU4cHBzEYDC4EDPeunWrxNfPoo+VlMLZoTyrq6sxGAxKcuPJkycXwBMMo0KD6onJZFICWYCcHWl2gFmYXLHle1yO6axgxHwvrCsUMkCzgDvoysYrl+DaENNnv2oeoZlMzg/hpmSbLXkkizx2V4S5ygRld99Ho1G5xkCVQA6gw9tchsNhSc7wLJQZReA+Am6clN/+cnx8XBIPPIN7bOiogoKnXikimei3LFC9UUtecJ37jQx4pQYesGLLtkP+9yHqy8vzQ9GfV1GvCnW73bJVE31D1zD0fIaRWlo6r9i7detWCfQ7nU48fvw4ZrNZvPHGGyW5EXFxS95kMinnciEzEc3Kg4i5gbTO2SB7DLRdCx78OfpmZ74o2HIfbMCzU82JL8sR4+XaTmdeDeVA1Y6fZzvhik4Y+DkoRj8si7aBGSQxh7ZL8NHOCF7AKycJ+Y0DJKGETbBD98srDNzQNewkNo2/STx5jt1f+nh4eBinp6cxGAxKdR5jGY/HsbW1Va6nQnR/fz/u3r1bqq02NzfL/vYa8LgqlGWUz2o2aJE++FrLukGU/QaLBOg/84R94D745mC5BlbRKwPNrAM5UEXParqW5cHA0M/z5zk4ti9w0JgTzTwvt8F2dfctJ1Jq+kgli/nH5/TLK8N5rszHiGgkzdxvJ6AWJV3RNfcv8yUH8Zmfxk+eP8+Fx+r+LC0tRa/XK2MlIDGYt2wY7Ge59n2LdKWmS/5/0fcvmzqd84W6fr9fFiVXV1cbW5GZNy8Q5oSM59qLBFnfaM8+0vx2YsVz7L9ppzYn9C3LhwNxP9fzYB2utZv7Q7uuHvb1GU9YRvNWfMtwTqp6PNY/+J6Duow53GfarFVYZVmv2b08V3nuzZts//Pc+V5XPUO2CY6f6BPXmzcnJyflXOF8PIC3whF/vMqklN+c54QHu3i8GyTbJ2PfiLlNR/cyr51wcnLByQnuqVX+OvZ0P2rVqRHNM4wyFrUMOY70d7TtmMtFBD5rMSftav4NAssyrryzBTnh2ZZBJ5PyIqyxpOeK7zwOeJHxMb95jp9BH+2fPI/0ked77LmN3EfLgeeKe92ubVMuqiGmJSaGh4zTuIB4K6K54+2jkGVnNBpFv98vfWYu8f3PouffvJ8bUFaOg8sRSAL9LKjejgEIHo1GDYYgRBHN1dSczEAYSIbkwNFbAlZWVhpvpULRp9N5pZZBW3bc2aHl7YkYJCYH8O6EGAAXYcAokpAigcAbpgjEOIzPr5ClrwZ3zn7SNxtVyAK0t7dXxsfWGvo1nc7P+TJA6nQ6pQrDbxfo9XqN0kL3l+DVhpH5jIjyqnj+x+DZMTgQwGhAeXXQCQqveMBHthbSBxSJZCBJqRwEvE6EPGxsbDRW8fNrpG34vHLU7/cbZwPwRjQMp/XIzsay6vMx8upPDQA7cw9lgAc56WowFXFxlTWiWW6O7LgvtJcB8mw2a8gyvK05JDs9gxHLe67+MzgyCHT/kGfu8bwwz3a09NtzwmqRX4bgxJedtgEN9nE8HjdeRNHr9UpSw44tV0ISYHGA9tHRUezt7ZUFDMvQ0dFRGQP29PT0tFQuAnxZdSWx0uv1GmOnYnd/fz/u3LkT/X7/Am+vGtVAQQ7cnka287X7DKQimueq2ed6MYZ2ATgksbHn1mknJrmv9n9tq14GetYHJ04MdgneaTvzwH3KvsD9MFA00M3BG33MgYH5YD3qdDplC4IxiytfcoDgRJv7gc1Ad3lOTsx5RdjXWcdICuX5chBQC6pzAtz8yM/EtxofWA5yRbfHkRc7ngaYcx9zAJ6vyf+/at+OTBrvRszPa2Usfrs0/9t31nTdc+vnMR85wfi0v2s8yzKSg9qaD87BmK/L9+UkqvUUchvZdpgceBqD2DbkBYucyLWe+ccy6r7TvvtN39E188UVqp4fy7PneZE9si3M8+J7s3zkymU/N29l8/2eH7+5FZnN/fSi9auibrd7IUajght7HhHlKBW/IRxc4nszHzxmJ/TyS4R8PbJovYLX8NP22bFpxLzSJttpzyufG7fit9wXCGzJsS1gLMbgIzm8/XA2m12YX/rhZKtxsO2C+wof+I1/sa81DrBfo89OhIH1+T/7O3TcvtPHBfG5E4LMteXB7WabWdPxHPvkea7ZGT/fttAL7vYZftFaxhDZ/l2WjJOm02kMh8OS0+h2u41zXp9FHxuZG4SZKXkPtYOuwWDQ2DpEcoOJZHIdYACIOQjSZykxORm4ZuDMCj2TXAvSDZJcgeOqJSsQSQxnwTFgGDgHbfQXYSFgPzk5KUkQJpaqFldddDqdco6Kq62czCEhQMkixsEHfne73Xj//fdjb2+vnCVFQsorwvB1MpmUxBifcW1WIl7BHhEl8UMg49WIiCgBJc6Ktm1A2FqYwbYdCXNn55CdNME1z7937155dWa3242tra2GsQH8L0qIXHU6ODgoe7BZfchAiqSwncvS0lJ5lSdVE/1+vxyczrlRzAd65Uo+2sNBmofWGycJHDSSUPZ9tRUfgzjuhXJSGTmobRdxO8iUg0h0jP7bEeZKBz4z2MM5Gtz6M/qIzTCoMNDg/4im7SNotQ4wR04Yei85hP3yuUzw2wE884luGNBmkG7nzN+ulsTmYueZA/rGAgE6y/bD4XBYyoCxazVeYg9oH3nJY3+d6DI2KAOW2meeJ/73amktsLF++V4nV/3j5K5l18ArIhp6j25a/jx2A2uPw8kM95uxc20OoHLS2M/MAaCBYa1Syc/zoovBYbZxuZ8OGDPAdB9dlWjQbxxm/ngc/hwMkZMXBqgOSnMSAx3LAQ/2p7ZlzDjJz3BSnbnMgcqz6Gn6UbvfwdNVIHSQsRP8eiElolmdY7zmIIW54j7rRU7K1+yFn5PJelQLjLIOQjnZQV/8mduDFsmw281BeEQzuZcxCPcYb/tab1m2/TI2qPGnFmzmcXusfnYN5+Tra/zOSfVs3+l3bY7527bQ9tw8cYLF+p8TKB6TfQl6HRElnvioQfDHJeIYx4feibG8vFzehJl9Xs1Hem6ybUNWnCDJ/pX7wJqWI1MtwYyMWs/z3DsxFjFfnM02vTZ/jqOz73Jc5e+d+Mg+sJas9fmw1tUcv9mHWn7sN2jbxRqOL7GLtJXH74WbWqLKSaaan8afeYy2cxmLQXm87lOOYegPz+R+21AXfDjeyTLhfngMz0u5/+PxuLEL56VUSuXMN8EEAQeCAohaX18vCQgCoOPj47JtLwcPXuGHeQRXCLqdiCsgbDztzC1sBsbZodacAfe4UoBgzX9DWcEI6gBrk8n5ljSqt3IQi3E6Pj4ugZyBIEFXr9crFWk+4JgqBLa5eXvGw4cP48mTJ6XC7OjoqJHYyufamE8GQj7Xq9M5r55CWQ2mbJwi5oab5zjhxKvcawaSOfCKMwk42o2YH8ieA3KvNPOmQQyoq9x87+tKroKA13xumXCCM2K+5YSqFZKgOzs7sbx8/taUDEJsPL0fv9PplLn1G/vQBebTspJ1IeLiaibPd1LIYME6i2xaTt33DEjhEbakBpCdCOVegI2dPzYHAAwgySvjOQB08gi9o8IyO1g+Y45xyCRw2A4Mv6xHBs6Hh4fFHhgE2CZi/8y7nLwmkYktJylNVRWJIs66yi9dWF5eLg6MZ4xGozg+Po7xeFyqrabTadn6DB/oL1WXb775Zjk3LsvAZQLd15EsG5kWBX8Gxv7fYLq2VYMKutqzDN54JnrC/U9LNDkRlIMb/229zXYp9wXd9DhYLc/AjJcA1J7rcdh+IL9OqKM38NBtAJj57ZVM2jaIhhiz+5IDnQx+c1DrJJtXjQm88Qt+Zq1Nyx2AnL+xFfAlYn54LL4iA3rsgfX0Wfqa5flpsuDvr4Id8BxgEx3YTqfnlc68sAHKep75GHFx+6ef6TaeFhBF1BNZkPFzLTmU77HP9bPz31nvLH85gZvHExEN+XWcgL4YO3gM+R54bDk0zjc/a/OT//dv89yymJMF5k0euxNGtWfWEoh+fu6/dS/PrWOdnOSjLbBORDQwJ0UFbGcmZnkV1VLgE591C34Co9jn5Sp57HnWrYxTI6LBS2QuYxDLfc3nGReCId1vxzj+PienmTvsjf2IfYwLA/jbcRryzxiXl5dLAUJOXnrLJnGHeeWFilyQ4OotL2ra93c687eue+GVccITF7rY95mPtqHMf164cfXqIvvg3SaMhd/5p0a57Yxd4Kfjk2z/c/4i24b89utFfXke8rwcHR2Vt8YOBoNLnyn1QiqlEDYqTQgS6KDBSK/Xa4AOZ41xViiMk1EWWJJedkQOavkfJhkg+RwThBzFzArs15A7ucLBuhYqT7qD5Igo7TBmAjFen0hAxtjyloiI+fZAxrS8vByDwaCRjHNJbLfbjX6/X4I7BPjs7Kwko/r9fnQ65/unO51OOfybPd75PCWDpJwgIwEWEY0gE+OOUbDMRMz3oGMcmBsMpRUmJwUNULwt0DKFIc2r29zvYNhJ1KOjowsB2utGrv6j2gTdgO/wazabxdHRUYxGo7J9yknd2WwWm5ubZcWExIOTici39RYZcAIGvXESxz8R9a0cngc7pLxS4YQzcpET6N5SmEFFNub021uFGJvvM6BltdGJN66LiBLM5ySJqx0Yj7fkAoixZ9zD50tLS8WmsN3Otha+YgftvJzUJ7gkgbm+vt4oO88Bp+fMMkff+XECmXuwyz6vAP7gyDgrand3tzg75oO+RUTR9fX19fjd3/3d6PV6MR6PyxboRUHa60C1gGNRAFhLSuTvI6L4DcjgtcYjBx4G2fl7ZDiivnWAvw32aBeZt2+1fbDd51lOZjhwgAxe7QsAkDnAAGOsrKxcqKzMQbDlH9l3MjzbI651WwaY0+n82AM/wxjICTvbMK8A13hlHXXCAX55zrPdzP4zJ0M8npys5h4vSNA2ASLPzzJtoLuIclLAQNxyloO8pwUGL4vAXCcnJzEcDsvf2GB2EjiZZ37noCIHWDmpY19g2eBe8z8ne/Nc12yS//Y12c+7j/b9HpP7V7N1NRtnzEJ7XqjOQWZe1LKs1ZIH9Btcm4PkrENZd7I+5ooMYxB0yc/Of0fMq6nz4l5tXrg/41vzG1xfsweORTwe2yAf4YJNRH4J9n3Nyyb6lZOSjNk4LCeSSFrZR+WFCcbmGG4ymRRddrxifGSfVZOpRb+zztIu33s3j/XC+uDr+T4/w5Ve5ktEs3Lf+gWf8F1gTQhfN5vNGrEDvGae7PeIZcHX7p/xqfXWcSvX08e8ld3JVNoydvb88FPD2a46rPmhXK1keUAHs34xXuNv3+Pn0a53ZOU8iHUwLxI+D9lmw8v8ArvL0MdOUTMAwC2TEzE/BM9BFsw5PT2N0WhU3mozm80uCLUVz84DxsFMr5BnR2YB4R6Y5r+p3kIA/YawDMCcwIporgb4GhsfJgiBoerBwufsLGOvbach0OIZGPwa4CQJiDIeHx/HyspK9Pv9WFo636ble09OTmJ9fb1R6TAajWIwGJSDwOEZz3AlCPMVEeXVkzg5eMCcYwysWAaneW5tlOibExYEEM4mZ8DCHOEcyOS6dLfb7ZbkTAb9rxMx7xhRePY0Q7q2thaHh4extLQUm5ubZSUpYn4mkeV5aWmpusceR4KjMGCJmJfUunIw4iIIzHqXKa+0G1x4NYu+uCrAwNjBooGk551rDWJdUehDQi1/PtyXSsZOp1OcMfwjGYOdpKrRNo3DSEkOoidOYk8mkzg8PCxVgeYnzshv4qRf9B+9IynkQz83NjYu2BnrqhNLEfOXKwyHw8aZggRi2A7zjEQxSVXGgS3EhlEh6iTaYDCI4XAYnU4nHj58GG+//XZ5hgHo60SZ1897ryn7yQy4FwEoBz01kJz1wn7fwAv9ckWkQVlOyua+Q/ZBPNtyl4EvbaOnDoRZ3UWO4QPy7Coiry6iE/aV3Iu82hc7eM1+zPaCcWQ5ZWGLvlgectLbds1b4zJIz/KQ+2mQ6uflufFCkPUMXngs8NaHHLvtvMpsvuQAP/+9KJDLyQK3exVoMpm/rXpnZ6fYPHCKKxs8B8xVbR7hlYOgPH/+DD+S7TFk/uXgMz+3Rk6m5d/uJ9fWvs/41vc4EKtVo+Qx5LbcZg728j34O/Qr8zy37fazHBrb5D5kneD/nETI85ztTo0HmY9gB8uYg9aM4WwzHXTi2/1GZ5+/C/7Ixzu8bLJ8uwr49PS08UbunZ2dRqLCY64tmtqem7J854VaYh9fb7/l2DMn0ex/nSCjHT7PWAJ8ZSxL3Mo1jkfpB34Q3kVEieuQIeufYz1XBufESC7QmM1mBTPDc3TS25vzmBivddNyZt3CH8GLjDPwYZ5v427HFhFzX+yFb9vJvFV9kVzapxPjW4bMY66vxVku/LCdsK1/lu2+LGXbgt+i0vcy9LGTUh5gzog52FtbW4t+v1/2Fh4fH8f+/n5sbGwUI2Dw4KQQQZqz7hnQWiktbARJ2fnamXssFmwy2wielS0bgm63W87aAXQjxN7qRjIp4jwxsrm5WdqgtHE8HkfEfPUDgOKgb3l5OQ4PDxtCtr6+HoeHh6W6gX4S3PL2rPX19QKaAbqdTqccFGyDhOLNZrNy/pLL8xH0paXzN2G5Mgq+YNDJ0jrJ5oSTDd2ilRpnj8m8kxDLiUaP3xlkqjIizt/sxRsL7BRcvfW6EoYbw24nhgzyPRl9zjVbWlqKe/fuxTvvvBN7e3tx+/btiIhy8Pny8nIcHx8XvXQ5ew0I5yQFlUfMp1fXmD8HUdgF5sQOPetyDoDsbOABSZ18VpydqOXBzptnYo+QHYANyUwHxThUHDffUZHBlg2fl8dzSby4MsoVRdhYdAg7tbKyEltbWzEajUp/XBXDnHl1FX3PNsCBB89Hn31OXkS9MobvNzY2ylww5ycnJ6U6z9s6u91uedvecDgsthceHh8fF//B9fBqNBqVQ9kNgl71234+CuXgoQZkFpH9nhdTMoD2GxX5nN/wDb1EhpDHvCqfdReZcWIhB585YON+6yxtAV4j5sEa92TQbvn1wkMG58iQK4CNbewLDRAjmpXR6K71yaDRCwD2ofCPxOvR0VFZJQaHoK/un+ewJh8O9rIeOxDO/fA1jN2Bg5/tQMA209WiZ2dn5UxCdBI/zNEB+B2IayyHNcpAOicanqUzz6NPnySBZ/AH4GfOtsl4NSeo0I2IecWc5xmqJSjRHesm32VZyZg52wz3zbLhe7imlozwmGq/uddBln23Ezj5e8bvPrv9LNfMS01f4EWOO3KCexGfLNPuo/vkmCX7gfx/fk7WAwes/p/rGIvnxIt+7hexDuTkHXgSzNLtdoses6AGLzc2Nop9eJlEvENiEYy8vr7eeCs3R1cQw1gOfE6yz+uNaFYZZZ2y7Nbmb5Eeef7cRk5i5AWg2kIP15Dw5jqP0wu4HktOpnjRz9iZONC64pidBe58HzoMrkXOkE1XR2VfZ9vEW8CRPyftLOPoQ7ZT7vdgMGjgTDCA54b7fbSHcxo5uZifZ6rZG+aN+Xf1lrGPZQf9NV+NaVzg87T+PIuyfeD38fFxeXOxiw+eRh95yZgBwgyAnYO8fD1JF97CBIM5X8pO1SuJnU6nUZrf7/cv7OtksnlznZnkJBJ9AaBGNA885TkoI9s+IpoAPm9xsQD67Cgn1azgrq6IaDo+EgUknegziuXXmhvo872NoCtdAINra2sl+48Bpg8oMMFHp9Np8ICth/TVYJU+0H87ZwC9D0on8eOtZTnYd2DsebGiUsFB4pOD5W1YM0ihHZIwVK3UnvMqVnFeFMEj5DTvE+cHnpM0ReciosgVPOWcIm+7clWZnQvPqiUpclCXk8wOltxPxmXn5+os2jQ4ddsGInxP2xh8Vlloi/sBKDyXfkfMdcOrJxHz7azmPf9zgL/tJu07sW3dcHIBWaa/6BNl0CsrKwX4GUBzHX0lWejzxbAPHqf1yXYu98lnzfk+22LfR0IJQj4Yk1+FDo+oxLQDnE6nsb29XQL5TqcTW1tbDXnIoOp1oY/SZ/thB23Y1oiLB+mjE9YHg8ccqBrkWc/os5Mh7hP6mgPefD9yYwCWz3rg+5yEyW268slncHCfE0c1mUW2zaMcpFtH+cxtuT23b16xqsg1tWSag4E8Zvtdy00OSjzftaDXsuF7nwWofa1xlW2EA28H8Nx3GXnPgbX/rv1fC/w/Dhh/0YR+4ntdZZwTDARoEc2XsTiJkMnjh+dO4Po5bsv/57+5xgFXfuaiv2uBYG4v244cnNfkMs997Tn5eRC8rGGWzMPMk5qu5e+zvllWa3yNuLitJuu1g1bHQ1kXrVcZr+XFPXQ1J+Ucc5i3GWdTqOCtqPxYhp3celkE3iAeAgd1u93GUQWeY+Mix59gubw1LMuP41GwDd9bBnL1VZaBbNv8k88BNF5mTjzvjCtjTPyVbYP7D970eB13mU/GwrSHT2NcyAfE9cbFTkixmGu5dT9dbTubzRp4lGd6cddz5AWwTme+BS3LuefEvHbyy+3xmYtq+D4XYDjhzv/WG8fY9Ns6Cb53IsyL4WA7eJP970cl8xybsb+/XxbGL0MfuVJqkaF3KRvMIWEAwGElkEQAAkOAY4CZjZ+3o7CyZkNswNPtdsuquIXUQTCEQLnqwPtTYXheJTSwxChkQ4bC+WwAKiJY7Z9Op6XqxJUkJLZIXqGMtE1l09HRURwfH5fSbwtdp3OeLHL1FP2Dz1QeuHphMpmUSgWeAz/8KveIKI6HscJHOzAnfTAG+VBkzx9jZg5zZRRtwlMUz8aJZzh4yCs68Jqti4eHhyULncHA60rmLXNdAzUAYvh8eHhYzhxbX1+P4XBYymt5uyMr4A76mc9OZ/6WDK/EcK23jJG8MACC/zhO2nQptYPOPCYbR/5GbwzKbbsI9CwnfI7sco/bdR/QKZ6FfSLpC/l18dhLO3HLaMR89cNJMZJhVEqR2PY82GF6Jd46g974WWwXxC7QdqczT/ZY13IQz5jMoxw4OyECT23/8RFUU1Fxy7MJ3Kia2t7eLjYIW7a0tBTb29vVrUGfZrKfXhTEeN7hO4s2yJATU05M1trOQZEBcA7krGPYgCxTBl/uf5Ztt8/1uS+5DfcH/bZfZ7wOMpBHA0m3nX0ZehERDbtje0zf/Rk+iDPkaAO9IDCg33521jXGnxPZ9pXGUJkvTrKZx/aNObj2Z7PZrOAL+gsGcsKeZ3pO83NMWf5yoGwf4OtyH68K5cCJRQvmnwqVrD95Fd4VOPzOelJLYGTKegf5WvP3aXY164rtQ56nWns1X5512Dxx+/7fcu/vIQdoxiYeQ83+5Gfm/i3i4dOSXzWbbV7ktswjz6n7mJP+/m2faztoLIH+8n+OJ/j7+Pi4+HbwIjzZ39+PTqdTfPqrICeR8Hf4P+Mr4i9+Z56SHLEO2pc5YYW85AWVbL9oP+Mi+8TMe+Iy/uc+4+VOp9OoxjcWpq2zs7PGuVlZZ2r+xdXtOXkKT2gPrJHlh1gT4jn0lTEwF8gmMmY5Av8Sq2Q7QxuuEiM/wXM9v9mPev4zD/DJOQkM/41v7Vs9996pwPy5D56vjL2cN+E58Cr7DnzLZReBnkWLMObu7m70+/1LtfGRk1I2vhYkMuGelIhoVBsdHR1dCLpyuwgxzPTb/RAgV95kh2ugY4BKHy2kDgIjohHwOmjkHgTGCbPl5eWSePPZUfmQ7dlsnuUEbFKpgzB5u54NEMpDpRaH/QJUOPPJ4yFDmQEt/CWhMJvNSpk4b0PkbxtdryAAfFjpsLIxJq+Ec60dPYabcTsot6H3qnDeJwy/nPzwmC1j0+l81ZZrlpaWyhaiiIjhcFgSWZ+GhFTEHOB4Gxhz6JWBiHnFHAnTN954IyKaZ8TZwZBU9QoP2xCcDHFlZU4koRcRzTc/Igdch85jd5xoNFDzmUx51YF5R6fYQsvbGD3nXlmgP/AHnmWHZ3111SaJQJLKHi98dxKaeQEs0SbJrqOjo0ZFoBPRXvnje/fdlRjQ3t5ebG9vF/2D98wz/O73+9Hv9xvBdZYfzkfJAYR5wLX0eTQalb5gC6jegjcbGxsxGAxKv46OjhrnQaD/zD/3TafTxpt2akHY04LfTJ6TT5JqQRP0tGcvCu5qQSY2MWL+imyXWpu3tUAvJ5yQE/PZYMurqTnIy8FgDrwMyv2d73UbTmQbG0TMAXkG8Dk4dvLa7ecg2d9Zrm1T8LskY7PNgPDjOUD0+L1yj27nsRrU2h+an1lOPCb0MM93/t/b5w24vV2fRR/Lha/Nsp5lZ5HsGq/lQC7PVY0Wff4qaTY7P3eUc/MslwRV8CyPN+teTpJm35V1rEb5mhxwZfuQFya43xWauf2a3DKufE9NNmoBI9/ZJ+fqiHydbY+D8mxLnuYrshwbM/lZWZ98r/lXS2DweZ4fz4fHkvvrcWAj3I4D2lolEAvw5i9Y3Pwifjg7OyuV+A7gXwU56eQAHpvL0SY+a3MymZSYxjzgb//2uVRg21xB40pxf25biL9gDmiPfueFguw/+Yy5tMyZF5YrV2o67uIenxHovruQI+sc/xMTOOa1f8ROgXEdr9JeLUEE/vVROY5tuAYs6WRhXnTimA0/33yybtnveosg7fjZNV9kO2QbkH28iWc7jmGeXUSB7FgG/QwWsrNcfRSyreLZebx7e3uXautjnSllB5jL4fgekGvF4iBlDFVOLmUQZqF0ooPAIxtgK4MV0P3KZ0cY1KIUTKoDJKoSfD6FHYtLWL3KZcXmHhQGQ44xRFFIKKHITuY4a+/KCQtmfvsdDsGHtpE8oC0SUhHn+73Zjsf8jMfjRuUEz8qgkmtqh+RFzLc08XnEfAUaGfI4PDc2ngYs2bjxm3nyG8eQGQJukhfT6TTG43GjEuyTDjxfJuUETTbcdpbwbjAYNPg2HA5LxZ4TghmwYfRxJA5I6Mv6+nq88cYbce/evXImGdfniqHBYBBbW1uxv7/feL1oBsaDwSC+/OUvx8OHD+OLX/xiXL9+vRGQGTzv7OzEf/yP/zF2d3fjz/7ZP9vooxPb9McrMk5O0W/GbB4a3FmeDOxsOx2o2QkD5Khyms1mMRwOSz+y89rY2Ii33347fvM3fzN6vV45iwnCdkyn07h+/XrcvXs3/u///b8N3YmYgxTG/pWvfCXefffd+N7v/d7G2HPgh/0bj8elStQyQR8ZZ7/fj9FoVPahc6bDbDYrySd4u76+Hpubm8VO+nyH3d3d2NnZKf3GbtOeX+5geh49/6QSUjmQ+zjPyEFeTkYAwJ04duBTq2DLSQd/F9HUCb5zf7Juu48Gz1zvNnIAZz3mOj8PX4z/o10nWk0O/Pxcrncy3QFMnjN8lvnuJCbj8LZprskJMz5nvDzf2MW8ycDQ82Ab4QUfrs+A1n/nIAfb4fFY1hzg5+CXhTv33SB8kcznwPtplOfkee69CuRVe1cgGwf7BTYRF+2XE5/WlWfxIgette/8vfHbonv47cSYfYaDsyy3fJaDnHyf/U/ui/2pMXjmS636MWNJvuNFHdle+H/j1jymzB9/lxMT+ZperxcrKytxcHDQ4Jv76s/Ncyc/ciIxJ97MP9ttJ/Zs52iXyijeJMnftluHh4fl/NyXTXnRjviJ4wpyQYBlw2chzWbz7U/wnbYy1oZceWN/yXzga5zgqPlRruOziCYusd/w/8Tq3Ofnez7xnfTLCSo/C974iAX7Mf439vRLqTx27rF+0j8SxMSeyB/xB+2Q7Mpv37PPox2eZ34gu5bvnDD0OI0buL6WhHIeopYg9P2MxZjG/OP6vAUU/XRhDD/gYWQ4Y4uP4yNtqyD/z1nEl6GP/Rqi7CxglIWm1+uViRiNRmUinbjIbUEYDisfCRUUxquvLsnD8GSFdZLL19igo4R+204tUO50mm8UxEk562oQSX9JtLh9kiY2/CgY2VsbEFYgnEDhfC6qnkj6HR8fNz4jqM+ZfgfGCLIPcHYix1sda+X3yIATaX7VtY2QjWkO+LLTp30SZjYM8NdnhmEkasEGv+G3jYDPMPq0UE441L4zsMul1RjtnIh0gGqQwz05oPF9g8HgwgpLrW+rq6ulso/2akHsyspKvPfee/GVr3ylbBnJ+s31a2tr8dWvfjX+9//+3w0ZdgLZ8mhDm4FCdujun/XaSSoHZP7bTsKOG5tBmTaOPfMc28b5SozF+8oN3NfW1uLOnTsXxmsnTvuPHj2Kr3zlK9V2Mi98bg/zbWeLHSfR5EPT+c4Oz+DAh/e7fUAF8ov+Z19Tk7HL0vNc+6Loo4CGGiiGvwbaBlaeJwc2gLdcgexr8n215zMf/HBmg6t5DdiQF5+R4AAoB8i5HzlwdYDqNrKe22940YlglGTr6en5m4RZeeScQhKxfr59lfuC3EKAaiecn/WT+WDb7W1yBqOL/IH9pVfJPT85KUc/nJDnO4PiiCh2aHNzs+ixcYdttu3P0yj7mUW2mu+vKsE3zml0wGTM6aDZPq3bPa+EvXHjxgX5sM46weJgC9nJCRswteeqRvm+2k8Na9Rwdb6Pe/K5pcbsHo/bM28doGbcwbO8A8D4Hx5iB2jX4+l0mmfPGZ/SvpMOeXzMe63vERG/+qu/Gv/m3/ybC3bNc0s/HSvRh/xm0KwfbKtzvzJOiWieWWtZ4vqIeUDKMS7j8bjslvFRBi+T/OY/x3RgTB8Pko+a8D34JebGL5LJSQXrlvXQfPWZUI6n0V9jriz7xk3GrpCfwzU5nuW3fZSP+vBiTl54wGa48ijvfmHMmScZ4/JZjgfsd7KPj4jyEi901Tt7zB/8lOPZWiIbPcx2qjanmXc13JOxjXEAz/TzbQOMc42faQc5sc/NsbR9sV844PF+HMo2nrgtV2k9jT5WUsqMiWgeEmqD60wnW4JcRpeDNAyGnWF2ogSnVGrwBhcmiX4YXLk66OjoqAR3zthmB8L/vNWJ7SQWcK9oety0iXMlSKMqh+fg3EimUKmD0DjpQzIGIbRxo31nRCOiJKRIcA0Gg0bm2m8pIDikZNxGln54lYTEGUYnZ/hdXcJ8OzGV5cYGj981BbexBkDQN5ywg1YUzluRuNdJVB90byPw9UiLxr0I6Pu7y34ecXljmIHXonYxul65eVqbKysrRd79uft+2SAmX+d7LytHlwm+PP5F1zug4VqDpOzsc+XiomfD28uMJ89T5qufn/uTP6uNL7eVx1YDBq8jXVaGavLgwMsgzwsv2DmDSrYv0J7tpAHVojm1XudVaQcv7k9EfcU2B5kGkfbZecwAVtvyHLzmALsW0OJ3nWSNOPetVACAJfBHBssZY+B7fIZFBrr40pzQ5RonCMxvdNn/57nxGDKfPW7uI3HsYKyWTM+BbrbXYB3GCCYEP3kua7Z+kb2v6T39yXN51W2Ag1HjL+tEDqAimlhpeXk5er3eQhuaZcnPiGiefZZxeEQzwK3Z2Oyj+F3T7UW22jFAnjfrrwNzxkY//Fw/zzroMZmPtUoX98VJ5Kwvbieiaf8WjcnPcDBcm5/f+Z3fif/xP/7HBZsIOSllvaZf2cZl8iKBdRzKCWuPJSdLSfKweA8G9y6Kl03EPo4z/PIMV/JGNH0UnzvREjFfCHAMxHXYeWTKR8hwHe1nOc321PJbS0L4WtrOY7FfclKNz63/3j6cF+651n6VxQ/ziMUcj8W8c2Ux4zJ2pS+z2axR/IFsZ1klbsZW+riInDxz/iIvznkOHYvnuBsZYqz0xbpmWVukf7V5tXxxTeZ5LakH32pVf/jeHHd/XLLMogfWhcvQx0pK4fwMJvzwpaWlcngZikiHnVCYTCaNFWyDZCbA5WYASWcIefMSxsXVU1lwYFZtX35WSpJnPmAdIbUw0zbAlc+tABZErzhn8IoBR5hIhFESihL6LXg158yzADdsvZvN5lsC4RlGc21trRxyhxK6TNUJP1ZTSNaRSIM/0+m0vKVtNjtPFHie/MprA2/mwQYt/5ycnJSAgDYxeiQQmZMMgLx62Ov1ylgiomwf8rxcdRDbUp0uO2/WmU+SLmuUL0PZkX2UZ9bA9mWf/Ty8vWx/WnqxVAt8ciDn+TAIMyhyRazBeQ7q3E7WJ1ckG3RFzM/3MKDLANrgMYM9+uP2IqLhV/KqZw4oMg+eJ2jO17sd38dz/QwnHbxd0iDcZz5k8AmWqSXoHDDCj8zn3O9F/s5JNJ8J5jcuu+8OWumngw2vZPtMKK/wmwfPopwQ4O/X2Xc/K2mR6ZMea5bpRUmNmj3w39nu1K6r3Ve7vpYMqvWjFgwv8kWL9P1pfXmWX8vJhcvQ09rMSfinPS8/87L6lPn0UX23EyW5j68KDxBTkYjCt5CszwE1/YXs+xyER0QjyM/JIu6pVQBhU92meZ/tdK0dy0WuxKnpYb7OffSzSG44BiSOy4kUeGeeRETD52ae+JxgJ2NJzhMv8z/9JXdg3tO/Tme++4hxZn7x/Np8OJ9BQUWtQov7nBzyWPy/x57tacZN9CvjopxEck4CnrhQxsk9t5Hlhb69CII3q6urjWrzS907e529dksttdRSSy211FJLLbXUUksttdRSS68lfewzpVpqqaWWWmqppZZaaqmlllpqqaWWWmrpealNSrXUUksttdRSSy211FJLLbXUUksttfTSqU1KtdRSSy211FJLLbXUUksttdRSSy219NKpTUq11FJLLbXUUksttdRSSy211FJLLbX00qlNSrXUUksttdRSSy211FJLLbXUUksttfTSqU1KtdRSSy211FJLLbXUUksttdRSSy219NKpTUq11FJLLbXUUksttdRSSy211FJLLbX00qlNSrXUUksttdRSSy211FJLLbXUUksttfTSqU1KtdRSSy211FJLLbXUUksttdRSSy219NKpTUq11FJLLbXUUksttdRSSy211FJLLbX00qlNSrXUUksttdRSSy211FJLLbXUUksttfTSqU1KtdRSSy211FJLLbXUUksttdRSSy219NKpTUq11FJLLbXUUksttdRSSy211FJLLbX00qlNSrXUUksttdRSSy211FJLLbXUUksttfTSqU1KtdRSSy211FJLLbXUUksttdRSSy219NKpTUq9ZOp0Opf6+fKXv/yqu1qln//5n4/v+77vi2/6pm+KTqcTX/ziF191l1pq6ROn111vIyJ+8Rd/Mf7wH/7Dsb6+Hp/97GfjJ37iJ+Ls7OxVd6ullj4RanW2pZZeL2p1tqWWXi9qdbalF0nLr7oDX2/0cz/3c43///W//tfxn//zf77w+bd+67e+zG5dmr70pS/Fr//6r8cf+2N/LB49evSqu9NSSy+FXne9/U//6T/FX/gLfyG++MUvxj/9p/80fvM3fzN+6qd+Ku7fvx9f+tKXXnX3WmrphVOrsy219HpRq7MttfR6UauzLb1I6sxms9mr7sTXM/3oj/5o/PN//s/jWdMwGo2i1+u9pF4tpnfffTfeeuut6Ha78e3f/u1x8+bNK50Bb6mlT4JeN739tm/7tlhZWYlf+7Vfi+Xl87WIH//xH4+///f/fvz2b/92fMu3fMsr7mFLLX2y1OpsSy29XtTqbEstvV7U6mxLH4fa7XtXkL74xS/Gt3/7t8ev//qvxxe+8IXo9Xrxd/7O34mI81LJn/zJn7xwzzvvvBM/8AM/0Phsd3c3/vpf/+vx9ttvx9raWnzjN35j/IN/8A9iOp02rvvggw/i//yf/xOnp6fP7Nvbb78d3W4rNi21lOmq6u1v//Zvx2//9m/HD/3QDxWnGxHxIz/yIzGbzeIXfuEXPtqAW2rpNadWZ1tq6fWiVmdbaun1olZnW7ostdv3rig9evQo/syf+TPxvd/7vfF93/d98cYbbzzX/aPRKL7zO78z3nvvvfjhH/7h+OxnPxv/9b/+1/ixH/ux+OCDD+If/+N/XK79sR/7sfhX/+pfxVe/+tV45513XuxAWmrp64iuot7+xm/8RkRE/NE/+kcbn9+9ezc+85nPlO9baunrkVqdbaml14tanW2ppdeLWp1t6TLUJqWuKH344YfxL/7Fv4gf/uEf/kj3/6N/9I/id3/3d+M3fuM34pu+6ZsiIuKHf/iH4+7du/HTP/3T8Tf+xt+It99++0V2uaWWvu7pKurtBx98EBERb7755oXv3nzzzXj//fc/Ul9baunTQK3OttTS60WtzrbU0utFrc62dBlq92FdUVpbW4u/8lf+yke+/9//+38ff/JP/sm4du1aPHz4sPx813d9V0wmk/iVX/mVcu3P/uzPxmw2a6ukWmrpY9JV1NvxeFz6lml9fb1831JLX4/U6mxLLb1e1OpsSy29XtTqbEuXobZS6orSW2+9Faurqx/5/q985Svxv/7X/4pbt25Vv79///5Hbrulllqq01XU242NjYiIOD4+vvDd0dFR+b6llr4eqdXZllp6vajV2ZZaer2o1dmWLkNtUuqK0vMqw2Qyafw/nU7ju7/7u+Nv/a2/Vb3+m7/5mz9y31pqqaU6XUW9pTT5gw8+uFDe/MEHH8Qf/+N//LnbbKmlTwu1OttSS68XtTrbUkuvF7U629JlqE1KvWZ07dq12N3dbXx2cnJS9sZCn//852M4HMZ3fdd3vcTetdRSSzV6lXr7Hd/xHRER8Wu/9msNJ/v+++/H1772tfihH/qhF/asllr6tFCrsy219HpRq7MttfR6UauzLZnaM6VeM/r85z/f2DsbEfEv/+W/vJBV/st/+S/Hf/tv/y1++Zd/+UIbu7u7cXZ2Vv6/7OszW2qppY9Gr1Jvv+3bvi2+5Vu+5cLzvvSlL0Wn04m/9Jf+0kcZUkstfaqp1dmWWnq9qNXZllp6vajV2ZZMbaXUa0Y/+IM/GH/1r/7V+It/8S/Gd3/3d8f//J//M375l385bt682bjub/7Nvxm/+Iu/GH/uz/25+IEf+IH4I3/kj8Th4WH85m/+ZvzCL/xC/N7v/V6557Kvz4yI+JVf+ZViQB48eBCHh4fxUz/1UxER8YUvfCG+8IUvvPhBt9TSa06vWm9/+qd/Or7ne74n/tSf+lPxvd/7vfFbv/Vb8c/+2T+LH/zBH4xv/dZv/aSG3VJLry21OttSS68XtTrbUkuvF7U621KDZi29Uvprf+2vzfI0fOd3fufs277t26rXTyaT2d/+2397dvPmzVmv15v96T/9p2e/8zu/M/vc5z43+/7v//7GtQcHB7Mf+7Efm33jN37jbHV1dXbz5s3Zn/gTf2L2Mz/zM7OTk5Ny3fd///fPImL21a9+9Zn9/Ymf+IlZRFR/fuInfuJ5h99SS68lvW56O5vNZv/hP/yH2Xd8x3fM1tbWZp/5zGdmP/7jP95or6WWPs3U6mxLLb1e1OpsSy29XtTqbEsfhzqz2Wz28lNhLbXUUksttdRSSy211FJLLbXUUkstfT1Te6ZUSy211FJLLbXUUksttdRSSy211FJLL53apFRLLbXUUksttdRSSy211FJLLbXUUksvndqkVEsttdRSSy211FJLLbXUUksttdRSSy+d2qRUSy211FJLLbXUUksttdRSSy211FJLL53apFRLLbXUUksttdRSSy211FJLLbXUUksvndqkVEsttdRSSy211FJLLbXUUksttdRSSy+d2qTUa0DvvPNO/MAP/ED5/8tf/nJ0Op348pe//Mr6lCn3saWWvp6p1dmWWnq9qNXZllp6vajV2ZZaer2o1dmWnkZtUuoZ9LM/+7PR6XTKz/r6enzzN39z/OiP/mjcu3fvVXfvueiXfumX4id/8idfdTeq9Pf+3t+L7/me74k33ngjOp3Ole1nS1efWp19OTSdTuMf/sN/GN/wDd8Q6+vr8Yf+0B+Kf/tv/+2r7lZLryG1OvtyqNXZll4UtTr7cqjFxi29KGp19uVQ62c/Oi2/6g68LvR3/+7fjW/4hm+Io6Oj+P/+v/8vvvSlL8Uv/dIvxW/91m9Fr9d7qX35whe+EOPxOFZXV5/rvv+fvT8PtjVL6zrxZw9n3Huf8Q55M/PmzUwoaqDIogERKQRbpqZEacK2LCC0qBapUChA2gELpduggqEFGoJBA+zQEMu2ozoUlG6bsLREFKpEKWywtSqtnPNO555xn73PuPf+/XF+n7U/73P3zbyZ92ZR1bFXxIlzzh7ed61nPcP3+a5nrff/+r/+r/jpn/7pT0lD/it/5a/EAw88EP/Vf/VfxS//8i//bndn2v4/0KY2+9q27/3e740f+qEfij/9p/90/J7f83viF3/xF+Mbv/Ebo1arxTve8Y7f7e5N26dhm9rsa9umNjtt97tNbfa1bVNsPG33u01t9rVt0zj76tuUlLrL9jVf8zXxBV/wBRER8S3f8i2xvr4eP/ZjPxa/+Iu/GN/wDd8w8Tu9Xi9ardZ970u9Xo/5+fn7ft3fzfb000/Ho48+Grdu3Yrz58//bndn2v4/0KY2+9q1F198MX70R380vu3bvi1+6qd+KiLOZPxlX/Zl8Rf+wl+IP/bH/lg0Go3f5V5O26dbm9rsa9emNjttr0Wb2uxr26bYeNrud5va7GvXpnH23tp0+96rbH/wD/7BiDgLGBER3/zN3xztdjs+8YlPxNve9rbodDrxTd/0TRFxVsr34z/+4/HZn/3ZMT8/HxcvXox3v/vdsb29XbnmaDSK973vffHwww/H4uJi/Nf/9X8d//E//sfb7n2nPbgf+chH4m1ve1usrq5Gq9WKJ554In7iJ36i9O+nf/qnIyIq5Zu0+93HiIhPfOIT8YlPfOKu5Pnoo4/e1eembdpebZva7P2z2V/8xV+Mk5OT+LN/9s+W12q1WvyZP/Nn4oUXXohf//Vff9lrTNu0vVyb2uzUZqft06tNbXaKjaft06tNbXYaZz9V2rRS6lU2lHN9fb28dnp6Gl/91V8dX/IlXxI/8iM/Usog3/3ud8ff+Tt/J971rnfFd3zHd8TTTz8dP/VTPxUf/ehH49/8m38TMzMzERHxfd/3ffG+970v3va2t8Xb3va2+M3f/M34qq/6qjg+Pn7Z/vyzf/bP4mu/9mvj0qVL8Z3f+Z3xwAMPxH/6T/8pfumXfim+8zu/M9797nfH1atX45/9s38WP//zP3/b91+LPn75l395REQ888wzr0y40zZtr0Gb2uz9s9mPfvSj0Wq14o1vfGPl9S/8wi8s73/Jl3zJy8pg2qbtpdrUZqc2O22fXm1qs1NsPG2fXm1qs9M4+ynTRtP2ku1v/+2/PYqI0Qc/+MHRxsbG6Pnnnx/9g3/wD0br6+ujhYWF0QsvvDAajUajd77znaOIGH3P93xP5fu/+qu/OoqI0fvf//7K6//3//1/V16/efPmaHZ2dvSH/tAfGg2Hw/K59773vaOIGL3zne8sr33oQx8aRcToQx/60Gg0Go1OT09Hjz322OjKlSuj7e3tyn18rW/7tm8bTZry16KPo9FodOXKldGVK1duu99LtY2NjVFEjP7H//F/fEXfm7Zpo01t9rW32T/0h/7Q6PHHH7/t9V6vN1Gm0zZtL9WmNju12Wn79GpTm51i42n79GpTm53G2U/1Nt2+d5ftK77iK+L8+fNx+fLleMc73hHtdjv+0T/6R/HQQw9VPvdn/syfqfz/gQ98IJaXl+Mrv/Ir49atW+Xn8z//86PdbseHPvShiIj44Ac/GMfHx/Ge97ynUob4Xd/1XS/bt49+9KPx9NNPx3d913fFyspK5T1f607tterjM888M10JmrbftTa12dfOZg8ODmJubu621zkb4ODg4GWvMW3TltvUZqc2O22fXm1qs1NsPG2fXm1qs9M4+6naptv37rL99E//dHzWZ31WNJvNuHjxYrz+9a+Per3K6TWbzXj44Ycrrz355JOxu7sbFy5cmHjdmzdvRkTEs88+GxERr3vd6yrvnz9/PlZXV1+yb5RevvnNb777AX2S+zht0/bJblObfe1sdmFhIY6Ojm57/fDwsLw/bdP2StvUZqc2O22fXm1qs1NsPG2fXm1qs9M4+6napqTUXbYv/MIvLE8ruFObm5u7zbCHw2FcuHAh3v/+90/8zqfC0zQ+Hfo4bdP2StvUZl+7dunSpfjQhz4Uo9Gossp07dq1iIh48MEHX9P7T9v/N9vUZl+7NrXZaXst2tRmp23aPr3a1GZfuzaNs/fWpqTUa9w+4zM+Iz74wQ/GW9/61pdkSK9cuRIRZyzv448/Xl7f2Ni47YkBk+4REfE7v/M78RVf8RV3/NydSh8/GX2ctmn7dGlTm3359rmf+7nxt/7W34r/9J/+U7zpTW8qr3/kIx8p70/btH2y2tRmX75NbXbaPpXa1Ganbdo+vdrUZl++TePsvbXpmVKvcXv7298eg8Egvv/7v/+2905PT2NnZycizvb4zszMxE/+5E/GaDQqn/nxH//xl73H533e58Vjjz0WP/7jP16uR/O1Wq1WRMRtn3mt+vhKHns7bdP2qdKmNvvyNvt1X/d1MTMzEz/zMz9T6fff/Jt/Mx566KH44i/+4pe9xrRN2/1qU5ud2uy0fXq1qc1OsfG0fXq1qc1O4+xr3aaVUq9x+7Iv+7J497vfHT/4gz8Yv/VbvxVf9VVfFTMzM/Hkk0/GBz7wgfiJn/iJ+O/+u/8uzp8/H3/+z//5+MEf/MH42q/92njb294WH/3oR+Of/tN/GufOnXvJe9Tr9fgbf+NvxB/+w384PvdzPzfe9a53xaVLl+I//+f/HP/xP/7H+OVf/uWIiPj8z//8iIj4ju/4jvjqr/7qaDQa8Y53vOM16+Mreeztz//8z8ezzz4b/X4/IiL+1b/6V/G+970vIiL+xJ/4E4XVnrZpe63b1GZf3mYffvjh+K7v+q7463/9r8fJyUn8nt/ze+IXfuEX4ld/9Vfj/e9/fzQajVch+WmbtlfXpjY7tdlp+/RqU5udYuNp+/RqU5udxtnXvH1Sn/X3adh4hOZv/MZvvOTn3vnOd45ardYd3//Zn/3Z0ed//uePFhYWRp1OZ/Q5n/M5o7/4F//i6OrVq+Uzg8Fg9Nf+2l8bXbp0abSwsDD6A3/gD4x+53d+Z3TlypWXfIQm7V//6389+sqv/MpRp9MZtVqt0RNPPDH6yZ/8yfL+6enp6D3vec/o/Pnzo1qtdtvjNO9nH0ejV/bY2y/7si8bRcTEnzzOaZu2l2pTm/3k2OxgMBj9wA/8wOjKlSuj2dnZ0Wd/9meP/t7f+3t39d1pmza3qc1ObXbaPr3a1Gan2HjaPr3a1GancfZTvdVGI9WtTdu0Tdu0Tdu0Tdu0Tdu0Tdu0Tdu0Tdu0Tdu0fRLa9EypaZu2aZu2aZu2aZu2aZu2aZu2aZu2aZu2afuktykpNW3TNm3TNm3TNm3TNm3TNm3TNm3TNm3TNm2f9DYlpaZt2qZt2qZt2qZt2qZt2qZt2qZt2qZt2qbtk96mpNS0Tdu0Tdu0Tdu0Tdu0Tdu0Tdu0Tdu0Tdu0fdLblJSatmmbtmmbtmmbtmmbtmmbtmmbtmmbtmmbtk96m5JS0zZt0zZt0zZt0zZt0zZt0zZt0zZt0zZt0/ZJb1NSatqmbdqmbdqmbdqmbdqmbdqmbdqmbdqmbdo+6a15tx+s1Wp39bl6vR5f+7VfGz/0Qz8Ub3rTm6LVakWj0YharVZ+6vV6NBqNqNfr5bp/5I/8kXj7298eX/u1Xxtf+IVfGDMzM/GH//Afji/+4i+O7/u+74u1tbWo1+vRbDZjZmYm5ufno9FoxPz8fNTr9Zifn49msxm1Wi1mZmZicXGxfLbVapXPR0SMRqNyf/pMn+gjn+NvPjsajaJer8doNIqIiMFgUP4eDocxGo1iOByeCbfZjHq9Xj5Tq9ViMBjE6elpudbJyUmRyfHxcfnMzMxM1Ov16Pf7lX7Sv8FgUOlno9GIwWAQJycnMRwOY39/Pw4ODuLk5CQajUacnp7G6elpNBqNMobhcFh+IiIajUb5n3Hw92g0Kn2v1+txenpaXvdnjo6OYjgcVj7DvQeDQRwdHcXJyUlERJyensbJyUm5dq1Wi+FwWD57cnISg8EghsNhnJyclOsxZhrjX1xcjNnZ2cr76Mzs7GyR0eHhYezu7sbBwUG5l8fr+cpzXKvVylhz43W+62tkO+J+vp6/w/WPj49f3uju0L78y788Dg8P48knn4zr16/HF3/xF0e9Xo+5ubkik7e+9a3Rbrej2+1Gt9uN/f39aDabcXJyEgsLC0X+fH44HEar1YqDg4M4PDyMg4ODWFpaitnZ2bh582bs7e3FzMxMjEajWF1djWazWexydnY2Ll++HMvLy8U2G41G/Nt/+2/j137t1+Iv/sW/GIuLixU5oxfYDLYyOztbbIHP8XtxcTF+7Md+LB599NH4+q//+pidna3YKK3ZbEa73S5653lpNBrRbDaLLp+cnMQjjzwSN2/ejIODg2JH6M/s7Gyx08PDw2i323F8fFxe45rHx8dxdHQUc3NzMRgM4jM+4zNid3c3dnd34/T0tMyN+4EeY/f4DeSBreBrkD8/9LHX68VgMCjjyXIejUaxv78fvV6v6MnKykrMzc3FwcFBDAaD4iOxldPT09jf34+bN29Gv9+PjY2N+MhHPhKve93r4ou+6Ivi+Pi4zD8+F1nbrvldq9Vibm4uZmdno9VqRavVio9+9KPxq7/6q/HVX/3Vcfny5WLDh4eH5TvICN+MP9/Z2Yknn3wyZmZmYjgcxtzcXOzv78cb3/jG+GN/7I/Fd3/3d8fMzEwMBoNYWFiI4XAYx8fH0ev1ot/vx2AwKLI5PDwsskcex8fHMRwOo9vtxhd/8RfHBz7wgbh06dKrttnV1dWKH1xYWIilpaVoNBoxNzcXW1tbce7cuaKfzeY4hCNf5ghZ4LOtQ+j6wsJCvP71ry82TfxoNpsxGAyi2WzG0tJSRETs7e3Fiy++GIeHh9FsNuPw8LDiw/DPyHB2djauXLkSDzzwQCwvL0er1YqZmZniiz1vzWYz5ubmStzDTobDYczPz8f6+nrMz8+X+SDOY2OMd2FhochgNBpFv98v9+r1ehERxR6Gw2G5BnYzPz8fOzs7cXR0VPEXjndcHx+Cn8COuHe73Y7hcBjLy8sxOzsbc3NzcXp6GufOnYtbt27FzMxMuf/x8XHFZrFPfMDs7GyxEXzE0dFRbG1tlbhG7LA9bW9vV+YUOyDu7u/vx7//9/8+fuM3fiPm5uZiYWEhGo1G0S98Fn4VP4dfQ+/QPXRuOBzG5cuX4/HHH4+Tk5PodrvRbDZL3+bn52N1dTWGw2F87GMfi5s3b1awhH0TcpiZmYlarVb0LmMU7PH4+LgS1/f392Nra6voiHEU19/Z2XnVNtvpdIo9NZvNuHjxYhlnrVaLpaWlODw8jLm5uSJP5EQfwF6DwSAODg7i4OCg+AHm0pjoU7XVarUyt6urqwUbMN6ZmZlYX1+P9fX1Ehfm5uai1WoV2zLebTabRXcWFxcrn2m1WtHr9WI4HMbBwUH0+/3o9/uxs7MT7Xa7zMd/+S//JYbDYbTb7YIdwMSLi4sxPz9/G97G19tXeCzgTcZ8dHQUR0dHUavV4vj4ODY3N6Pf7xebcx4QcYYp+/1+iYt8/+joKA4PD6Pf78fJyUnxgxn7npycVHyEcc3dttnZ2VhZWYlmsxnHx8exv79f5NRut8sc4H/wc/wGxywvL8fi4uJt2MrxYG5urnx3cXGxyH9+fr7cx7kK8XV+fj5OTk5id3c32u128WWnp6fxoQ996FVqaZT4sbCwELOzszEzMxNzc3OxuLgY7XY75ubmYjQalfEtLCyUMRI3yT0Z99zcXNFdch70fm5uLh599NFYXl6Obrdb8ARjnpubK76AmIcv7na78fzzzxefgF8+PT0t+Q5xh9fBeczT8fFxHB4eVnLmTqcTe3t78cwzz8Tu7m5sbGzE/v5+PPTQQ/Hoo48W37W+vh5LS0uxuroaDzzwQCwtLRWbwG/RL+yTv3P+TL/wwY6p+APHMmIpOBJdd/xxLsznHTfBIxHjeM78I5/d3d0yDq5F3IuI4oNPT08rOSy6ubu7G4eHh3H16tXY2dmJ7e3teO6552Jvby8ODg7uqIONRiNmZmZieXk5lpaWol6vx8rKSly6dKn4Kec6+BMa+Nc4D106PT0tcmCeLl++HI899ljBRvgX8gf6xZxxv3a7HS+88EJ84hOfKHLDnzMfs7Oz0e12i93Th263W+T4C7/wCy9rm3dNSr1UQyif+7mfG9/yLd8SH/jAB2Jtbe3sBv9/54SAsxIDJOg0xoLSYrjz8/PF6aOcGCAJHsDFpIFBi4EIr+E0TCY5Ic0GQL8ApQQkkw8m3jJpxTVM4DC5Bks4XssXw4KA4770hSSCgGXDthPgsxERMzMzlc8yJ/SXz/MZjJxEDhkCmJxQ21EYENLHiDGRY0Mwecm46/V6zMzMFMeQyRucPEYZEUV+yNJOytdCxiYlsz5ksOz5zuPNhJV1bBJRNandLQn8Us3ju5NjvBOYyWPMzbLI38mv5e9lUg/HfKfrvtIGGMUu7ubzL9cmkZFZB17pNfncS3325fTlTuRofi9/zrbzSvv8Uve1v+E+r6Tx+bwwAGB5qc+9kv4SxF+uL69E9iRH99K84FGv1ws5Qn/PnTtX/Dx+B9k4zuCTuQ4AFVCIjUScJeSrq6sloXfSm0mgdrsdR0dHheBxbMzjGA6HsbOzU4D/3Nxc8c1OLgEvBsyOwYPBIPb392NhYaFCkFrPTeZ4YQL/RqLR6/XKvUg8jCeOj48LWc71jo6OKrGOmARYxNeQkB4dHZWEdXZ2No6Pj8v/zWYzNjc3y2eNEUyuRERJ+iLGRFrEGHiTONP3o6OjktydnJyUeGi5eFEsIuKZZ56Jf/fv/l0lWTfBVKvVYnFxsWJnzA96ZnxB3IbwHw6Hsbe3Vwga5gzATJKZYyjzTkNeAG/Pr/Gb4zSfIbknAX4pv/hqmhc0TDKBe1m04G8nbE5q0SHHLrAgNmjS71OxjUajODg4qOhhq9UqvmZubi52dnYKIQBRRx4A8ZhlMxgMyvf29/eL/rEwgU1yvaOjo5iZmYmDg4Not9uxs7MTOzs70Wg0yiI1fcUX4P9MzjOH7Xa7QjKcP38+5ufno9frVXwBc7+yslKxcfQwIsr8QjQfHR0VbB0RJYnmu+iNyUsWvOnjK9UH8LT1C7/HXLGA4GQYG2s0GrGwsFB8qfMw2yL9RZ/t/yH2Dg8Po1arRafTiX6/X/pIIQHXZq4gWe6lOW/C5/LjPAJ/SIyKiGi1WiWPcH5InsLCCPrJ+7du3YqVlZUSc5A/fu3k5KS8BjHHHKysrES32y33JP8inpv4dXy3v202m9Hr9aJWq8Xy8nIhnRYXF2N3dzcefPDBeO655+Lg4CCOjo7i3LlzRT9qtVpZiIaQQ+/AH3zOuNsLOPTVDT0A3zAO233E2Df6e8xjxoL+DIs9+Bj8CnGQOcIvu4CDWILdMp6IM9tgPoh3h4eHxX/0er04PDws+O1OOoi+dDqdsigG/4HtebFxdna2+AvsA9yCjLBldAmy/YEHHoiLFy8WQrzb7ZZrLS4uxunpaVk8AYfxXrfbjY2NjeIDwAoRZzjl4OAg9vf3S78PDw+LTTAvd7uYct9IqYiIBx98MN75znfGd37nd8aP/uiPxo/8yI/En//zfz4WFxdje3v7jt9dXV0tzB3O4Pr169HtduPSpUuxuLhYCA+zhl6ZQ+F4n8/DGkKqoEB5NdngxsYeMTYAOyccSCa/aCjzJBDFD0HMgQESj75FVA0bZ0xfUUpIOQC8QQ1BBiM14Wanyt8msOgb48eg6b8dEQEIo2YsNDt4B3yTYBHjCjMDdINyvm9m287XDK8rB7xCyXUgQzIwzCQT30fmJmAsp0mkhcko9zUn2Zlou9f2kY98JCLOnEa73Y63vvWtlYqiXq8X//bf/tv4oi/6ohJwqEZg5ZKAQ3UEq8KANpNJS0tLhTxmHhcXFwtAHA6H8eKLL5bXGesXfdEXxVvf+tYKUUCgymQQ+ukkEVILG9/b24tv/dZvLStNmSC1rR8eHpYx5Dn0PMzMzMSzzz5bruEKFfcR/aAvfJ5r275mZ2fj2WefjdHojJTmml61YAUs+6escwAQJzwO7CSwEePVFQcyEkRW7ph3bJRgyL25lita6vV6PPjgg/H1X//1Zf4Yk/tjf8fceAW61WoVOz08PIzP+ZzPiSeeeKKAUq88Iy8TAf1+v1yP4Nrr9UriMTs7G08//XR8//d/f4k9VP0BiACdJDzMpys28ecknb/5m78Zn/VZn/WyZNfLNfsH7kd/0A38K3/j7wGJvo5lZb/F57a3t+Phhx+uxEQSCcdS6z3v5USc3yw8UDnZarWi0+mUuIpOOw4iVy9A4Gcizoh1bJrVfccA9xV957MQPCTLJBzIAYxh+52bmysLP6urq8U+AHK1Wi1arVZEjBNNqqGw6eXl5aJ/VI3bt4E1sNP5+fmyEMcCkRMg8A0g0EAU28au0AfLCX0YDofR7/dja2urJIrGDDkJsN/Kv00k8ffKykqcO3eu+DETjJCPs7Ozce3atdje3r7tOu6nZeVYaiLLhFT+sb7mmGw88GqbkyL8JrqEPzaZ4MVE/2aePKfGVsjb/f9UacaQEVFW3yGaqCik8uTo6CjW1taK3THmdrsdvV6v2A9z1O12C1m0v78fp6ensbKyUuIBhDfETr1ej729vYiIkmz1+/3Y3d2NZrMZy8vL5dqrq6tx7ty5cl2+X6/XY21tLYbDYakSrNXOSNpz584VUg2CHuL39PQ0+v1+jEaj4nP4HnNG9VPE2O6wW+cpEWP8mfED93w18cYL29gV1cnWS5Jbk6kmRdF35y2OLSZGiF/4udXV1Tg4OChzDT7nvrbhev2sMm4wGMTW1lalWuTVNGK5/QKLSiT8fGZzczOWl5dLhb/zM+wSHx1xhj+IAegMY4eAIxcjHoLFIsYLU/ZrnU4nLl26FLu7u5U8DnIAvcAOT09PS65I/KXPm5ubcXh4GOfOnYt2u11ypZOTk0Km9nq9Qhy7Omdzc7PEOuIYfs25lMmpvNjihRiw3KTcJ2NGmsfpOGTbsa/39dFJy8g+FR9Mrpd9Lv4ZohSSb25uLg4PD2Nvb69g4n6/f0eyGIwDRl1cXIyZmZnodDqxtrZWiCkv1uHbjK2NmRwviDmQb+vr63H+/PkS61kkAmNQhbe0tFR0FTmxsLS9vV3JRVw5ia7Pz8/HwcFBZd7gde6WOL8vpBQ3+/CHPxxf//VfH8PhMH7rt34rfu7nfi7+0l/6S/GlX/ql8bf+1t8qCm0H++u//uvxl//yX46VlZVotVqxsLAQrVYr/tv/9r+Nxx9/PN71rnfFlStXisJjgF45ZyIMYpgMs5wGPTmI+loR4xViA0hXAPAZywCl4PpmW/mujc4Kb8Bl8BkxTi5w3HlyuQdAE4eF4qBIEANciwSHfpkUwOECigHzGACysuMw2ZVXF+ygMEjL3yx2JqToTzYkrxBZjpYPAS4iSlLhxJKkZTgclgBth0pDB0jAMzFloiTrktsk0slJqL9/r+3g4CC2t7djfX29lLqzeu3S1cPDw1hfX49Go1HKUJ3kdDqdqNXOyvL39/cLyETWzNn6+nrs7e1V5oJkEJkOBoPY3t4u5eAEApOa2Jxlg217vp04oQu8zkpFp9MpRJuTasuZe7gMOM8XQConRl7ZQhfcf+STE5dMSvNetkcnH+g/MnBS5coRj4WA45VAvuOVTdrc3Fy5j8Gyx2M/hY6NRuPtSiYKbPf4QHwCekRJNOOAALWtG2hg+4A5AD/3QbcPDw/LVkST94uLi+V7ECftdrts82T+WPFyYm2Cyr7Lc30vzQAjIl7SFrg3gNjxxYA7X5vPMpfYuwkA6yZkC5U41meayWL6xorZ9vZ2tNvtssIK4He/mHvHJHQxIgoRwxY/QCM2yL0dr+kXjc92Op3o9XoF5GHDkFkkoYzh9PS0kljX62erm8fHx4VANXmJ7ZkUZ2XZeoKeOj4zfsdWsAXjRGedmCJHk+HYO7aeX//Yxz4WH/7whysro8jCskMXsA2ukWVrX8SqL3jF90avSd6xZWzbOsE1+Qzfs0+yXCExnQjyvu3Hvv9e460TZJNnzG+2Qebb5B4xwb7XccBbJtCvjCMmjeOTRVwZC2EnxHjID4ghfne73XLMBnF6d3e3bCNz63a7cXx8HJ1Op2y5A99SXQDZNRqNotfrlcoC4sns7GxcunSpfI8FUSqEWHxBzmxnpS+NRiPa7XY8+uijJfkib4EkpuLL28ptP96uha8Eh7EwaBLZsRL/RRxzQv1KGtvV0D/+htxD3zyPJMeMIW+zxgbwCySr6OqkKjHs2guU29vbsbCwEMvLy5XjTI6Pj6Pf78fc3Fwhs+6l5dwFu8LmHD8ZG/dH5iac+v1+xQ8416JS1jqLzjPf+IFJfUSOi4uLMRgMYnNzs3wGH+mCAa7lBX8Wtpjbra2tYjv4U/Bxr9crVYULCwu34VL02zmc/ZLzbGNAY+mcf2diyTKYdO2Iak7J9xxLiKnIxTktlWh8x/d2TuPtcfhobIVYD35lLg8PD2NnZ6fCPbgZj87NzZXF/MXFxZKHsTsMsopYCQkJPnas4XPOnSGaLl26VBYFfayPsSWyAIPMzs6Wqj8q/kwYex7Jq8g15ubmynZqqsU/qaQUg9/a2op//s//edRqtbh582bpyMc//vF473vfWwADqwd0HIFFRGxsbMQv/dIvxbPPPhtf+IVfWBQHBXMy6mTewZ8Vi+FwWDFGfxbltSPgbwMVG42BO33yj43EYIwAZ+LC12Wvr50IE26gBxOJYbi81Uk7fffqGtdxQkwAot+s4CEfvmciKyLK6hcGEDFO2HBWAC/mw4mzHZW3z7kCAWeMzOiPSyu9bcXGdXx8XFa6nURhxJQtmtwkiYA9zwkcsnA/s65kffL7ObHMtjPps/fannjiiRiNRvHGN74xLl++XKpF7KRPTk7iwx/+cPyBP/AHYmlpKXZ3dyvbNZhvCCtkhC4Q2LDt+fn5mJubi+3t7Yl7nAGMu7u7FUBueWXSL68g2v6wG8sdvQMs5a18zGu2cSct2KFX/Pisk31sinnMvshyNFHjgG3d8tisDyYhsq9xUHZSzgqxSSITaC5X5hoEKyek7jv3ZNVve3u7AHts0ithvjdgz+QNMocgyGcfuOFjAAX8jU8A3FGt1+/3S/8402I0GsXW1lZJwokPEDOAGEB4q9Uqq5vIzluOnERbl15tM1DmeiwyoDdUSTHPJmoNqqxX9uuuRLUPdBWU452TDm9D8n2cuNmH4S82NjZieXm5nGEyifxgDn1uEQkRBCjnfnCviKj0hbi7sLBQAZyu2qU6AyAOeKQf1hXugUzQCZIidAI7WlhYKKuMJBM07m8y3npjcpM4TSw1EN7b26v4OPSCz7OliR/HHsZ18+bN+A//4T+UbZWuxgH05uoi45GMLfy3t6HYF+IrIaxYIDImwd9n35/1PPtVL1Bl+2F+/D3jwXuNt/YBtlMTvI4ZJpiQa0QUvadixAtk9JNEBtx4P8dxL42+QuQ47tdq4x0MJqtGo1E5dynijHjK89Nut6PRaFTOsen3+7G6uloS9eHw7LwntiKNRqN47rnnylYatkAxP/hv/D12wVYytuaw7ZBxra6uFvuu1+vl7C8IJXyKsakXt2nOFzjLyRjHpKurkL2dx4Qz9nc3sYd5wa+adFlcXCwV8tgliXGtVisL3ui7YzZJtH05dkv/qLwhxnLeH5iHhT8qovFf9IXtmJMWD++lcX9jeWMvsA7nSuGv8OHE04gouwS8kM/v/f396Pf7sb6+Hvv7+6Wa0JVp+GsILBMyOW/Oi1PYjIsHyIMXFxdjf38/arVa2YKPjq+srMTh4WFsbW0VmQyH462xzBf6gL5jx8az6FhemLWc8eX+XM7Hrcv5Ol5Qt280FufatkGugX2BM41PfUalq47tz5GxMeDx8XE5pmBra+uOO8Mixgs4EWf+jTNDIZbm5+cr55rhP7Ovx3bpAwswzNfR0VE5B6xWq1XOdzXGtf7DK/j629vbcePGjTI/VKmTQ4Jr2I5MpbdzHS+WvVy7r2dKveUtb4lf/MVfjKtXr8alS5diY2MjRqNRPP300/HUU09VPt/pdGJpaSlWVlZibW2tbA+Zn5+PN7/5zTEcDuOZZ56Jz/7sz66UqDEwQFRE9RBMXkfxmQhWPBwgsmOzs/N7OKbBYFAhMayseaUvYpz8EkRyImog5Uovl9Ob+UY5DTQdkPgeRpLHkYGilRyloVTX48ryd/JiR8N7OGGuZzBsuaD0jMFkgJMr/2/5+ZruHyQWxoHzPDk5KQdI5uBj3aH/lhN9xSBx2E76so7aidlOXqrl79xLM+hgv2+3262ANHT6qaeeis/7vM8re4vRTSeI6CoyODg4iOXl5bISGRGVJIozIJxM8B7bWVipyTpkWaBjEXHbnHgFHftGV1gNmZmZKYksMrHvcBKEvruik3tZDlkfabZXkggT0CaUDDhotnmPm9dceoysCOqAJcZFgED2HgefcUDCN3ol3zaWE0QOAse+DCrsaxhztnH3I5NfgA7mlu85Yfb2A5IdJ6HI0gk0sgcMsgVjODw7/8jnmphsJUEgKPN6Blz3mhjmxBlw4QPwmYtsl/Y//O145OTfenhyclISAcdG+zX0odlsFlBrW7I9+bvMc7/fj+effz5arVZlS4vnmzGYZCJxq9VqpfKNA4u93cl6RhwGZHIPkhuAue3ENuCKJd7nf8e7SYCOM3RWVlZKHDZwzwklCSwYA3/JtajO5DOQp05CLDPeY945f4XKDHz7iy++GNvb2xW7MCmFbdq35TiZ9d0E++LiYhwcHBQCzYsGAHCSXOOC7PfsM9Anjw9/7dji+M3ZPJlkv59x1jaGj6aP4A+wm2OBG/NvP05sg1D1wohjY8RZQvDYY4/F+fPnizx6vV489dRT0e1278s4X6oxPrCFt9KBD/hhKwp6BMaASMa/OsZik+jXwcFBWZQ9f/58wWatVqtU32DvvV6vyNQLo5DRbMHl+pw/RrLVbrfL9qeIs2qAjY2NiIhCArP4g12Qk2C7LCZbL8CnOXbkBTT0y687vjlGv1zjIG/0FPmbLMSXspXI2wMhTkyYghWxZVfI8R1IFXx9rkSC0GcbPvpkHG5iHt16tc0yd36Cj/QCF+NgQYBqPRa02MqFPqEDXljjIP7d3d24cOFC0cGsF/hY/Db2ZNIB+ULoci9XlRvHkQtBUpGX7e7uxrVr16LdbpeHPkGIDQaDWF1djc/4jM8oZ0vho5rNZnkgEtvTnVcaJzrfJkY69jsHpmWSCdkQf/M5m14AcoydFKMgB/E3fpAAco0Y4xi24xsnoJcm1Hh41s2bN+P69et31Dv8OYsLEFntdjtWV1djeXm5YD2wQc6HMudgnEZu1mg04vLly7G0tFTJ853bWVcoSPGCMGQ1C3Dgbcvfc39wcFDyQu6HnyMHvZt2X0gpBv3CCy/ED/zAD8Tu7m58wRd8QXzpl35pGYxBw8LCQpw/fz6Wl5crQCLizMm/7nWvK+W3VjoGD3AkSHhVj0k3+KBqxhUNvIdg8/84KIQeMQawHouBUnZwEdXqK9/bipSBPe9bbk4UDZa9CuggHzFOXF314QooJ7/cywczZjKBcXgMkEq+n5PdiKqTzquEdkgZ6OfkyYkD/c9EouXMfVhRvFOViZN9iBhkYYdmAJiDiD9nsJvBQgbduTkJvFfQ7JJvJ1wALuZjdnY2rl69Go8//ng89NBDcfXq1RKcT05OyrlQs7Oz5dwdttqSdLkMmER/eXm5nOuwsLBQ5IFz3N3dLatnEeMExGQfDR2zTWKrmZy0/vhcC5eYm3BzQut+uBGEaNZP24n76YBhMJxJYSf9fDbi9gN0JyVUXNf+EJ+H/k5KCrl+JhEsd/ygSUdADqtl+HPrK74Rm+d7AAXmweNyEjaJnGbeI6ICkpnDg4ODAtBMygMeT07GT5M0Sc1YABuzs7OVAywhWbmnt2aYWLhfLSeb2AoyAURkMOIY5dVD/3Y/TZ4DgCDhrP8mFJF3jjf2Z/hS+zD63uv1YmtrK5aXl8vBnvTDum59wVa59mAwKItXk4hG5MP/7o9JFY8fsgBZA/CRU7vdLv4NP4B+oOMkUdiEY5L7m1czPS76zPgd/5vNZtlOal9gn4McSWhINAxih8Ozc/1++7d/u/TZhIHxmPWMe3GfXMVjO19cXCxnYnj7JfLk84yHZqLNLb+ecRbzlrfVGi9mbOV2r3HWuu+YkO3UCx62WeKzt/9QMcj4beuOGxFnMj1//nx893d/d/zxP/7HY2dnJ5rNZuzs7MT3fM/3xD/5J/+kQl7fr2Y/RRW/FwxyNaTxv/XAn4Ncmp2dLdv4mDuqVpEdZ5fMzMwUovq5554rxDc+Bt9NApkXXIbDYXmyW8R4kRsMY2LAlf3D4dlTV0n4Xd1IfPGCGXbJIgALRtivSYOMTfEFtmfLlc/fCVeymMCT7xgDY4NQ5LXl5eW4fPly2ZYUEQXPceYXPpHdLOg4McFbnLh2xsn48+FwWLbKQ+ax1brf75enLEL622+82oYfc+VwxPgMTuJ+nhMfjA2BSdUOvtO2bH3zWUMzMzNlUY3+gDfw18aoPsS/Xj87LmN3d7fiH6lycZUs2Ib8zxVpN27cKHNvnfusz/qseMMb3hCrq6uxuLhYFoHwScwlBKFzRuPNXAjh2Ov8EFnzmheFjG0cW61Lk7ZA5jyKeOOF8JwPOiaj087t0Q/+9tbX3d3deO65525bDHRz/s52Pe7J0Qb0J/cLnxoRFRI8P5ym1WrF6upq8VkLCwuVohwW2cD2XihhMQFSutfrxXPPPVcwKARsxPjJw8bcFABRyRcR5SmPOae6U7tv2/cajbNHV/7sz/5s1Gq1uHbtWnzDN3xDRERFeTmQ0AEJJUV58jY/lN7JnJUfRTXQyxVNCJ9JzSuXmXwy6PTfrhoyADZwsAHmyqm8H5NVIxSV+7jSKZNRKFdElKSKoIxxo6woE0kfzsggB8XiHjg9AwrGl50Ocs+MONf304xyMsn7OBOz4vTN33FzBcQkIpBgzri5Ds7TZBafMePuxJW5MAj2nnhXx9DsDCcBhUnkFEYPKLhXEIljpyrAZZlHR0extLRUSoFbrVZ8+MMfjq/4iq+o2FKv1yt76dFXnFm73Y61tbUiJ+SGI5+bm4u1tbXY3NwsK2HYBQBjf3+/8ihddBF9RC5OoJEpIBEAyevWhVqtVrZfsT+b6/lsBBMyzJvPbPAZCuiR9YY5z/Pr1+y/7PPcF/skxnMnkhK7NiAYjUaFMDIplJMj7mlSwcQ/r5PYW95sOWA+8Df4aVe65PHSLJsMPlz6DyA2WDOIRE6sXmGXrPwY1FO+zsoYWwmoenRFArJcWFgo55l4VW/SCl+25/vRLHdvU2VurLsGWG4GnNYV9BbCCH8AwHAiaXKIJMZj5XMmaTPBcXx8HM8//3whrfEFjocmbQB/Jn8ODw9jY2MjLly4UCrc+I51wnpu4AQGMPj1Qo2JaV6D3GXVG91kPjKg5uyGiHF8BHfs7e1VMAXXs41nrDMzc3aGlhfqPE4SG2yTmJWreU9Ozh5f/eSTT8be3l6ZH0gE/japaXxlG87/m0ykAt7b0AzweY2tOMYa9nv8dpy2D+Pz9iPGW9ijk2FjE8Zxr802lxe+6DeywkaIldYB4xXrrEkV279jzIsvvhjf+q3fGt/6rd8aERFvfvOb49u//dsn+oP72ZgLtu1DOpG8ers2Z5S4Tz4LBfnhK5ALNkRlZLN5dvYbW6k4/JntVTs7O1Gr1SpP7FxbW4u9vb3iM2gcYD4/P1/8Hw+/oJqPMZCsYa+j0ahs3+Nx7pA4rsrkIRrEVleGM0ZXPkPmUE3h7Wts+eL+jtvebuQ2MzMTKysr5XD37O9MxNdqZ09me/DBB0vlgzEXZwviZ9A/8hh/1qQKZCV52MrKSvGt5IHYriuuWq1WqRglL/EC1b00+yx8KXjH/g+9ZJyOt17QiageGQFRXqvVysIr88Q9vECH/3a+CzkWMa44473BYFC2nKILfiqhcwxjTCoDd3d3Y25uLp555plKnvTGN74xPv/zPz9WVlYqOU9+Mi4LMPzv2IndoovGdpmcYg7so70QxjxwPeKJi0+4r8mkzCeQi+ZD5fGx/O2KdOYv5/d8nzMub926FS+88MJti3RutrFms1kKZViYZ95N5OWYDAGFDjoPJseBCyDGGJt5ax2yYpugdxBxv263W3I5OBnmp1arFR3A7rFvKvnI8fBNd9PuGynl5OD4+Dg+/vGPxzd90zfdBnxgZSOiQjx59YjVbZNPrgACKHFdJi9XUZgUQPEMaBqNRgH2NCenfBfDMpOcyRN+vDqWk2veY9IMLFEujI4+8Job5AXGZgMGcBNUaVZwk0x2DjD/mXiykZl4crKTHYdJK5x9Bqgu78UpGnjyGXTBBCLNAN8EVXaGyNgJk6/hBNcrFWbxmXPec+LkcWegzv2tk9avDJodZO6lGejzA4BkZdDgi9+rq6uxtbVVApH7CXgkCBEEAYoOfo1Go6yyQAhA+jEfgLqcUDKPGVR7Xh14GBtAx0DeKxsmdwnitncHFcAy+ulgx3xbb6x/+BF0yLqTt0pZB/g+/TdYtX4hPxrjo7w2J3eWle2D/7kmsvATxkzoEtgcDB30AKIGevTBK//0yQmlk2AafoO4YXvHnl2ZA4hjtSb7ZJIDCFKDbuKRK8yocul2uxXfYhB1P5vlZUAM4WZ5YUtOBLKeMi5a9jfcc29vLy5evFjs16vzVBB4jlwZmRMRxw37c2T74osvxtLSUpw7d+42cubk5KSyKJLjMK8xz9bbnPR7dc9xlDkHD7CiiC3bNh0v7PtNgDr+oGNs66Bf+Wk+3Acy1fZkspEEtdfrVXxClo39JeMBMCPT4+PjePrpp+N3fud3yvxme0MfTChmffR8YP/uCw/GAKB63IwJf4icXYHCZ70AOEm3sf9JcjAms3+3nvL3vTbHpLxQhu7Yjxv7OMG1X2T8TnwjxiRqBvjZzzcat5/Jd78bGMgH8jp5dUxg4YjKHNsscZZFU/sV5nl/f78kUBFRWQAhhnthMT+gotFolMPUqW6hPfzww+WQdJJEzh3FP4xGZ2dhsrUMgogzYdA1/GW/3y+Vit5mTB4EbkJPvBiHXVDZS0UYsrRteAHFOk5rNBqxvLwcq6urRQ+Rq+WPHV+8eDE+8zM/M65fvx5bW1uFTPH5Now/5xk8qc3xeDQ6ewAKyW3e7mZM78Vt4rvPetzd3S2Ewr1u36Nlf8FrxA8qoPDT+/v7hdQxgWSyAH0mRkBWYrO9Xi86nU4l/nhLIrbDQpR3l3B9/qcKBpvxdseIqPhQL+rNz8/HpUuXyqIb528+8cQT8cQTT5TFZvATc2w/7oIM5s2xLaKa56CP2V/bP3thxj7N98YX0jfeczM+HA6HZQtvxsxcG39MH5yjGj8Q15wDsHB68eLF2N/fL4tBGWM594MEZz6Xl5cL4ZmLHMC/2NpoNKr0CVyyvLwcrVarcg4dc4cP4lpgDHQVbsZYm4PKvWhQr9fLkSiuxKOCG7mTA3reTZi+VLtrUorJyQ2A+5a3vCW+5Vu+Jd7znvfE7Oxs3LhxI86dOxc/8RM/Ed/+7d9ensxE8BkOhwW8YQBZMRGCCad8fklOjHIyn5lGQHd25JNAXk6QMzhzImtCxWTFpEoFnApg3xPnyceBoGQmoJh4lA7Z1uv1sg/Wq9DIzHu5+cHYHay4J31hTDgMEzhZbgZ9NjIDMQzLhJ3nzIm++5oTMGRuBwYY8pz5dX/XoNcEhvUJmRrc23m6MsT3s3454Pl3tiMcE47vXhqHVrLCzjkfnDESEQUgnJ6eRq/Xiw9/+MPxpV/6pSVYUTnhFbpWqxXD4fgpZzzNbGlpqfTbCZZXESPGgBI5bG1txcrKSvkfB8w8ONjbCVuW1jfmMWIcbPzkMDtLAyi+T/O9ckLm+wEqsd8cILmX9YT/cxKDHTvwo/fYAj7Mje+58iOTTbRsB7Y1wJBtGcAFoCRBJoiRjPgQau/VN+mQAzW27n7adugregshEjHe6oGf5HG2BpKdTid2dnaKng+H47MvTDi7ghUfaVKDuSIJMjDKP/fSsv6ZBEEmzJv9N5/3ViB0wMkxMSSv/LElw/Eqk1nIIiLKlt5JhKbnzbrEdXu9XmxubpbDPH1WQ8TtdsJrTmCp9AHccx8/uRE5gQEYR8T4oR7oAPfCx9OXZrNZdNp+zffgc8QJ7nt4eFg5/NdJJH7JVV6Mk7ljnlxpgX5id37EOPeADGI+2PqysbERH//4x0tibFubtABgH+dY6JjmmMfqb6vVKpVakwhR+k2CaX/uBQT6kAkb64f1L+MBkzcZ6zjBvtdmDIlu1Ov1sjLPXDnxwdZMOPF6tm9kyz0cl2izs7Px8MMPx/r6evR6vTh37lysrq7e89jcTKjSl/yocqqV0CdIGmNc7MAV+sjJcdsPCzKeYCtKv9+P+fn5aLVa5al+kHEkT8TOhYWFODw8jP39/RKbeBoo5A+2dXx8HO12O65fv17iHpiJcUA4Ya/4RmIeW4XYHeLYzphJkq2/vAcGws/0er2yUMQ1WFhxPmM/u7CwEOvr67GyslJZWLWvgKCYnZ2N173udfH444/HU089Ffv7+4U8pBLeuuitk2BLdHY4HFdOeesd4wVXZuIduwazdjqd2xYqwJ73a/ue/Y7jF1iKM5+82IX9kRM5xnAuWsT4IR/oPTqCfdq/e675AW9FRDnnCQxMH/mbHQz4XfqbF4w4twsf2Gw2yxMkL126FJcvXy7Eq3NQ5tVECfbo+Eo8nVRZlBd2PFZ01gvAjjkR46d5O1f15/L1mCfIQ8efTGxlEsl2wmchKvHZkJQbGxvR6XTi9//+3x9vfetb47nnnovf/M3fjCeffPK2xVvmBOwDhnGfkAVzYHKfz7hCj91nJnjRXdswY3J1onMXqvyGw7PF2a2trbh69WrpkzkGZINPxC85/3DlZr/fv+tYe9eklAXmZoV6+9vfHu95z3tKIvvwww/H13zN1xTHh/ERFGDjjo+P44UXXojP/MzPLOWFh4eHJenJYBDgbRIlVx4wmZNIARu1gVgmLvixshvAZkInV3dlgzNQzEmwQRWBGcN2YK7VaqXs3cDSDDklelzLRm3CL487YkxAcR07CPfTjfdyRYUNy8mfmXUTnXnl2/NgmTq5IqD4s5a7ndak6zk593vuk42QOTDzDHHI+8jEssqEqd+jz+6vz1l4tQ09AhCjMyTsvV6vgA3A3WAwiK2trVhbWyuP6x4Oz0rXAXIQEgcHB2XfsUu/9/f3y8oKY+CpOCZarDtcA+eIDvjgaZM1WQexwUw20tfRaFyhxGedtFr+k3yJwTL3t91E3P5oXHSYe5JQu8LHNok+UxFjksA644pP66236/IZ9y1ibJvI3p/JK/AeN7ZKxRDbMLCPVqtVKjSs14zfNmi98DjwVaxm4ydMijH/yAmZ0Vf8HnKZmZmJc+fOlbMXiB3elkiSbB/HfKG79GOS75ikj6+2ZR/B74ODg3LYLjrhJAAdsz/L8cfEmuco35+4gi4xd+gC8d3xj89ZfuiQ7Z6E5plnnonZ2dm4ePFi+RxAHLtnbifp5Wg0ir29vUrlhRdNXF2L7zC5GDHezuwYwQHJEWOiyWQq1QCuOnDVAP4Em3DVl2Vpf4MvgnRFJ0mGqDLFHgCgjuW8hwwixocwkwh+4hOfiBdeeKGMgf74J+tCfs9x0I3kZ2VlpTwZjbhvfeB7u7u7xSaNn5CF8Yzl5blAFxi3bSZi/DRHj8u2ZV99L43t8ciBmGmflcdg8ndubq7Mk0nwvGDiBJiElTm6cOFCvPe9741v/MZvLPf89//+35ekItv5q21OVm07GYs5fmJzlj06RcwmRtvH8FlkyXu7u7vFl/A9fLQJXrYGgluwMQ76hkDjKALIrYiIra2taDab5RwlngLIar8XtpAD5DG5i/3jcDg+dB9b5jre6kX/+b4TQfsxywp/4Ti4tLQUDzzwQBl3rqBBp+bn5+PChQvx+OOPR6fTiWeeeSb29vYqVZnecQGZjOw5h3B2drYsCJicBIcgq6Ojo/JwnPX19RIPVlZWYmNjo/g1sBILUPhTchsWMe+15VyPnBD/btLl9PS0bBuFlDDJ4TkjbuBn0U0ITPAztu54AE7xXIN3IAj9EBh8LAvmjUajbI8nPjUajXKWUESU2Pvoo48WAoNzCE0sOHdwPHAOh33yN3adcxwTKsg+L/5nHXVOhzzRZ+cRxuS+JxjA1yAeIdu8kIzvzRjJcwQxyhZhFofm5+fjgQceiNe//vXRbDbj2WefjbW1tVhfX4+5ubmiz2tra3HhwoViJz7zGj+FHfiIG2IL3+PJfYyFPoAR+E0sdH6BT/GZquYh8Bk0ZAkRhb9DXo5H2I4xdl5Iv1O7Z1JqOBzG6173uvin//SflidSDAaD+IIv+IL4uZ/7uVhdXY33ve998Vf+yl+JlZWV0kmzcr/v9/2++J7v+Z5oNBrxV//qX42PfOQjBTihDGZ9rcyTklwzzgaLmaXl2pkBNoiwIjoJNRPsn8zyYgSTVlbzCoffc2JugAILThDKynpyclIhk7inExdkxLi4p402Iso+bj6TyTj3m8/4vjhgVsqcTHEtwJUdlgMuDUfkigGaCcgcrLP+2sGYCCD4exx5lRVCDQeSS7Nx5AbszJ/1yC3r10sljK+2OYnG8Xv1wATb4uJi/M7v/E58yZd8SQE0fAZygCczuLKBlc+Tk5MCRtApgvHy8nIFmDkpqNXO9i9DYJtEcXKciZR8jQwEDQ4ixk+ScGLm1Qxfk7FbD0girR+280nAj3kEmPC6nThzbwIAnTP4jaieq2XAC5C0/8m2a3/jMXIt+sdvkxwAIT/WeW5uroB8H2o8ycfYf/qevOfEGxvCHvBpgLksZ77H364iXVhYKI/vbjab5SBzqhJNXEZEOXePRROe9EQA9pjcj/thr+hYJkOYK4MJZGjg7BXf7Pcdz7xKRt95RDef80qtbY9rUkHk6+T5NlFM3xcWFmJ/fz8+9rGPRb1+dmCr9Rjwhk4BtL0YAZi6ceNGXLhwoSx6QR6ayHZ/uI7t2tUO+DIvdJycnFTOvfPWPf6nKorxUzVhf4VOIxP64b6Ac05PT0v5PP2yLjDnmYDOvnA4HMb+/n4899xz8dxzz5UVWsg//Bn99N+e80m+N8eu09PTQk5TkYLucB9k4DNp7O8ch63jxmPGdNmfed4Axl4gsQ3g0zymV9MeffTRMv7t7e0KDrCMkCE+w8kvr9uHMmYnVayKGyecnJzErVu34lu+5Vvir/7Vvxr/w//wP8Qf/IN/ML77u787fuM3fqP4hXttzgHq9XF1E+OgnxCBeQUf+8ZvkNwQa4wJfa4O+sPCH1Wv4AR0lkO8ITCGw/Hh0MQMdBPszXy56oVqp3PnzsXe3l4535MKH2SO3hEXfIC1Y3HOJbB9dN0kJoSFt906HnrHg8/rwm/MzMzE+vp6eYgUOuUciHt0Op144okn4tFHH43j4+N45plnYnNzs8gNXDgYDCpVPx5DrVYru2CYD+ZxZ2en6AqJNLKlb5BaPBCHfAPfif8zcVmr1QqOux8t5zHoVM5F0A/mGL88MzNTiKr9/f1yJhk+GrmQN3Bd2w2LYjMzMxWs0m63K/GIKl7mB/9AfDCRhN82sYl/9kIIFc/gd5/Byr3RQQhJxh8x3po1Go13eth/mfAx3stYEJmAA7k2Ns84nGNlbEkcIUYS57ABxoI/9rwyDhdLGIPx9/HxcWxvb5eni0MMHR8fx97eXmxubkaj0YiHH3443vCGN0Sn06nk7M1mMx588MESi0ejcWU78crEH2QjOZXjxsHBQcHj2A85GuSTz+LLRKJju8n8g4OD2N3dLXPgnN/+PZ89iv5g92DKvLD4Uu2+nCm1t7cXP/7jPx7/7//7/5YzZHq9XvyNv/E34tq1a8UhGZwxgFqtFjdu3Ij3v//9hXShNJOJstOlysNgyQ6flgE8r3EtBwgbkIGAAZAnxYCP8zcixqsK3DsibuujwahJKr5vksSgHuU0aCH4uawXR+XVWyefHr8TVpNC/Ka/TogsC5NNrJyYjHPfMsjMYNfJJEZqwiRv7eDvPLfZ6eUV9knkF7J2P3xdAjkrDpYjIAFdcuB2X/zbZEF+LTv0e2nef0wyzuGFJG8kiegZMnjmmWfisccei+vXrxe2nsoCb2+CnFpaWqqsLC0vLxeZeE4J4jhGAwG25dB3zyvzR3PgyHJizzT2wrW4H04TZ05CYdtAjyPufKg+MvMY6Cd6Y/LYVUr2D05McgWKk3mTDAbzyN3JP/3NiaSJMPfdssa2GTtzyHZrqlkBvqxeujqCa+UzUPw+r6GP7j/+B98DoQfR5FUY7HBubq6SfKCr6ADvsWUMMgo/S/+bzWbZcgCo9EG13sqA3O5n4z4GaxAV+UB/+s73MtAz2PBnsGHfI+LMrvyoaeuhdZh58L1sN3dKzGnM4cbGRtn2ifyJqX6YRCZrTXDs7u7G2tpazM/PV576EhGVR24jC+IKxBv272t7tZTf+GbAoheKBoNBITJto/QRgg9dxU+ig9wH30XFheMkOuEY6JX0jCeIp9evX49nn322VIRQ5cj8umrKWCrPqxMBx0xslGQF32v84qQD+Vmv6K9JUGMz98e+I8vDevZSIBjdNC59tW1paancd2ZmJra2tirngqFnp6enZftPtm90Hl101bnl4ipa5ozEZm9vL7a3t+NnfuZn4vDwMD7jMz4jfv3Xf/2+EFI05Iau0mfs0ckUc+KzpOzH0R+qE61PyIrvHx0dFZKLhwW46pVto8z9wcFBLC0tFTvFb/Z6vYJ9IMAhrcBBJOUkgiR6+ODBYLzliiSWOXXFBJ837mXbs6udqUJHvqPRqLIYDBmBPvhzttulpaVYWloq2xdNdqFfnU4nOp1OPPbYYyUx3t7ejhs3bpT4zlysrKxUkmVjOT/9FPzBPCAX7snTFnmoTcT4XDz7L+IJ+JA5bzQapYrNuOt+NMcUL5rnWOQKKmIH/o6YQJ7r7eij0ahs43TlCTpO3PMT7hgj13UehlysB8wvmA3fDsah+WEEkBVckzEuLy9Hv98vi+/4DjCGY6v/R99YnHFhiLErMmeMOUdzzHG84RrIG5/t/pikd1WabZnvWz7IwGQpss352NHRUeWJm9gEGLHX68XOzk4cHh7GuXPn4pFHHomZmfEZdRFROT+K+MuPcxd0jO3DrtRHTpCUyIcY61zChJzjp3NYz0er1YqNjY3Y2toq13UcpwrPVZ34SYoKWExEryDQ7qbdMylVr9fjxo0b8cM//MPRbDZjbW0tTk5O4rnnnosf+IEfKIJdW1srn89A+cknn4yPf/zjsbKyEu12O86fPx8R1S13BkcGaln5M0GVSQqD9vyeiQIcgfucQZ+BaiZaJr3Pfb3K4rJaO0X6wd9spSL4cz2DQs8J40OpzXbzeUgCy8AAmKDPSpDlDEPMHJCkYNSUghKwCMQ8EtUr2O6vHyvs8konnw4iGKnBrWWJzOmXP2MD9p52f8fO1Im1rwHoWlhYiL29vUrSan0wAZcT2QyM7xUoR0Rsbm5GRJSKp1arVXFABwcHJUE/PDyMubm56PV60Wq14vnnn49HHnkkHnjggdje3q4EJTti5hxA4/OicHIw6gQ65jQTPQS109PTUu5qAGKCBhmZNDWpZBBAkugEmcCOjbhCwHMeUS3Vz3Nvv2GHz31c/m6QMInMchDkM07Y6IvBJoDE16Av/EZmBsIGgx5HXrVmdfPg4KD4ZlakAeGuznHCRL/9PglBDvYks1wXUG6fZBCL7yL4AQLdSBDwR95q4Konth54kQEiloPjATaTSMVM7Nyvhq3xGwBlYOXE3dWJlq3nlz56ezCfh9AxWUU/kDd9ID6srq6Wp6ugb9g8zbGDawDWn3322ajVavHwww9XzutgLFnG+AyuG3FmV1tbW+Xx1cQdYojjA77JK7DERPqYv2ubYS6ciLp//ttnQ1kOyM5beVhs85k0jUajUvLO/W1nfB9b4DskL9euXYv/5//5f2Jzc7NcE79tn8lcm+zLPgQ7ReZe0Ik42+baarUqixMmmZvNs/O5ut1u7O/vV3QS/cmVdfaJJmuczLh//t9zmn0repWTrFfTwLbD4bAc+v3iiy9WfDvzxQ4B9Amf7Dm138PukT3+wAd92/aazWbs7e3FP/yH/zD+8l/+y3FwcBAf+MAHKljy1TZiJYktujMYnG0PJf6D4xzfkTMkg/uPf3GFnGXGggC6vba2VjnPjidAbW9vl2svLS2VuHh6ehpbW1tFTpAj+DKwCwkc/h895ilSPJTFTxC3nhrTZp/LHBKjiKHEF/wNySf9Nuaxn/KCAFVPnAXEArbx/9zcXFy+fDkee+yxyjlXGxsbsb29XVn8XVlZiaWlpXJW6MbGRuVIkIhx9QT3c7xxjuIFoIgoCS9+Aoy3u7tbiMj8lLTT09Ny0P3p6Wk5X+l+Nc+Zcwt0k3lpNBqlesR+krnwOVnIB/IC3JGrRqiIMnmQc0gwpHMTE2Pci21g3M8xHLIAncbGIBR4DRIRLE4sN3nBGPEJliGxJ2Kc7/Ad4gHfc77lvNNzkOOAP+t7YHvomjFTvT6uVjMZZ39IvPJY/DfYkArgRqNRtv56xwlnc66trcUjjzwSnU4nTk5OSoXacDgs54khGwhck0vGDRQPQGDi95xD4YucF+MjeY+54brgPeNJxyMvMlmPHK/Zuoiu4KuNq6y3d9PuiZTCUfLYVSaXgV+4cKF8jgFhgHyGpJaSQxw15fgmPcyum8HzRBpkZBCCYCYRADmxRJBeRfQ1AVomaiLGiSSgCWNnzDiXDKTMaBuUkRCimGZ+cZoGMgRbgG/EeEuAmXGXXjMu/qePrA45OXK1mks4uUZEVA6K9YoDP+xLB4AbzHsVlYTTT7ZwBRf3MwHlYGViLZOadnaMG8OlLyalnJChJ5Yf5xdEnFUO+kkD7mvWTfpl536/klsnhiR46BEAGpBF0PLhtC+++GI88MADUavVypkKOB6YfuSA7ABGOCRIxIgx+UhJJwSVK4r4Hk/PMfjNCUXWA1dOcW0cM4SY55a5BoxZT9Bb5o/r2t58rTzPXpXIumonbf/hQGU/5d8AQwKwAwbXNKnAvZ2g8X18MIHchIPlAGnAuWAAzf39/eJPaCaRPJ/2qfQvAxp01PvPTVL5nCkHWoMfAqZXw7m/D4A2EelV0JOTk/IkEW/T4rs58WDe7zchZf1gXPgU+guIQb9dlZfla1/n17zy6Dhi8EnM9TVY6DAocjxELnm+kRM22u/34+mnn465ubl4+OGHy/cAjyZlsrwd/09OTmJzczNWVlYK0HZf6KvPQ3GloUmnTGYhaxMxxH/iguOyF0/AAXwH/cR+PQdUdna73Yrd2JapsjKgtv3g0yAKXnzxxTg5OYlz586VMwT39vai1WoVv58TKWxhUoJE0m4Cn/fOnTtXWWlHdsifigDO4rAMkZPnzDZtPDYpUeEe+Bw/+to6je7R/PqrbVT3NptnD1tgPm/cuFFZ9KPvPmi2Xh8/AARfjn+iX9hajhdOXrxowfhv3LhRqmeo/LyX5nnAB2RyCkLBCQ5+lT5jM3wWW4MogazARiBRI6pPjs1YG4xLfOL/o6OjaLVasbW1FZubmxV5QpTi6+nT/Px8bG5uRq/Xi62trcpiY6fTiXq9XuYanFGr1SrnHA6H48oZ/I/jr3WD93gdksixBXtCZtggB7ZHjBfB2PZ18eLFeOSRR2J5eblyJtHm5mZsbm5Wngo3Pz8fFy9ejNXV1Tg+Po5r165Ft9utkPSMkTzg+Pi4PNiAsaOD+ABipzEX/h9CDnkix8FgUJJ4knGIbD9Z8X42y9qEFKQCjUV2clVsm23RYJZ6vR7tdrvy0Cj7dFfgoi/oPgtl+GKIFWOciHFs9edGo1FZIGDREN3gs7Va9YB5ZM410DEvVFE55fOOjNFp2DNxEPs0nkDOXhAhRkRUfb/9XUT1+ApkxncdczyffNb38jy75biLDmxvb8doNKrYPHgCW798+XJ0u91YX1+vEK+DwdmWutXV1bLAh7zxe14kR47OC6hwBiswt3zO+Si4yX4zE3nG6OZVBoOzrchUPrnqzN83viReUS3J9YxzXnNSysAexcqdjRgnj04CnGRmYQK8ETb7zxE0DhxhOLjwmpO8SaRURPVAao+Jz3iyfB2U26DM/XGfIqr78JkYB2fuC1gBYDsBy33h/nmiMwAjSUAxeA8HhkHaWRgskcjjkA0M6UcGfJ4PAxC/zn5XVjyQ6WAwKKyry8Jh+J3YZ5DGvXGuToIM2Dx/zFsmM3y+gAEQzsHJu3Wh0WhUDmm2PLLeZd2zA846+2obfad8mtW+9fX1AiLQAQgsAO38/HzcuHEjrly5Ev1+v+yVZz68whlxtvLHKpeTYle8jEajQmih2yZpncwBogFTzI9XA3KQo08AL+7Ld63vTiJNVHA9j8ErEplgNFlgH+Eki/dysm/CDn2YRCb4+w7CrnwyoLDPsWxyxZfJJ5NJvtfS0lI5M4LvHh4eVuYV/091i89wMRDGv9F/k0IR1YN80UeTLiRvw+GwlMQDrth24dXAiCivUyUCQEBW+/v7Zbt4rVYr4L7f71cOIDYot48xqLzfzb6AezKPzD8yRH5OfKxv6JoTf3w4usghtDnmAMRM4JioyFuK7KdNdLkahusCyK5fv15WE61bbL20j+caXId7DYfD2Nvbi5WVlVIV57FyLdtsRLX6kHm2nExuIBcImhyXaK7gi6iuGiNr9LxWq5UDZgG5bDHmfgBDk4PMEzJH5wHMN2/ejJs3b5bV7/n5+djb2yv264NSSSRzMuC+mwiyr4A44EmI3orEfCEb7sHcmmC0TjlRcyz35/msiXR+IOwzLqRftHslaiLGFZkHBwfR7/fj8uXLce7cuRgOz84ssq/I48fGTFBkGXsRw5+B6DFORHbXrl2Ln/3Zn413v/vdcevWrfjlX/7lyvbmV9uQN3J1Mmy8REJNMoZPQn/xyT6PyPIwUddut4u9EktI+LvdbvH/2OzOzk7UarVSyQDxsr+/H81mM3Z2dsp7Gxsb0ev1Yn5+vizKca1+v18wEnGAc3Z8Lg/2yjgcnyOiYufWY+RpkrdWqxVS17ENHXHFOjoNHl9cXIzV1dV49NFH47HHHquQZoeHh9HtduP69euxs7NTzr/iHhcuXChk28bGRuzs7BQSCF/FfZrNZnmgDfJgXCTojhedTqc8zS/i7GEI4AVibEQ196MggRjdaJxt4QPD3s9m7GhM77wEX24iHwI0Yrwd0X7dixd83uduMd/Mg/0rumT/aeya48skn1Gvj89Wg3wyxsLn+gE15ED4UCqmGo2zbf3kEui+fWomcnI+aWzC9/AH2Lbxuknn7O/5IZaCfewr83f4G7KE+TGp75w8IgoZxXy4wj5i7Oe8IHT58uUy/5CUVI7SL4hv4iJ2TB/wL5CyPrAc3EclI/ppn4wMXNBDjPAYXeRBTu5zsZgfE/x+aARYgwpg/Dh9ZOfLYDB4bbfvoWwYCn/nhNosX04GI8bkBg7bpJATGJQGRwe4dOKH4tmZOGmi2XByIuZElHHmn0mMH6/l3wZTvO4VI3+GsUSMt/k48ffnMDyvZpp4ySAmA2PLwUSfEweftWKwYfBukG7WnATAgHHSSgH3Zaxmd+00vRqQQaqJHRNt3NOfz8ljBql81uX1/k4mGCbdw44AR+l72qHlpNNkxP1oZtzpH2cacF4Dc4YTIShRBryxsVFWKrvdbjn0Myea2JADKeMx2cpcuzrANutk6/j4OPb398uBgJZPJiUdrBycKQN3guogxTyxmgWhwn2c8GeCCP3lmnyO79hGnOBlkoVr2q6sB04KTUY5aETcXplisGNg6/7kLQIed7vdrhAOgA0CjX12rTZ+chrXyNU11sUsGydWebsK88K5EvSHhAQ/FhFliwXggUCKXqLf6B4Vjk6kuSd9HY1GZcux/UH+fT9b9lXIzpUq3NvxyWDMOmoflQlW4jcycDzEvpmzvPIJsLIO2c4sH3TQpGStVisJ0+bmZnQ6nVLFhP06fpv05F722wAhAJcTXZM2XtnzFgiSBy9YGGvYx3AP67bBOiSSSXXu6VVRzrvwA0occ0yCWcZOLrBZEtCNjY14+umnY3t7u6y8R5wtUOD7jo+Py8MlDI6dgBrM03+T7RFn/ubcuXPRbrcr1ZvGG/hP3sfXMS+OwfTDumMixXZnf+9mG+B6OcZO+t6raSTVtKtXr8alS5fiypUrsbS0FPv7+6WiAn1zgmCSgvmmmSC2P8+LP+g+4202m/HCCy/Ee97znlhYWChPRGw0GoUAfaXNSabjFP7b+u1DqXOfnRNkMg7caVyAPeIzdnd3y/lRVIPMzMyUyksSwohqvIWQY+fBuXPnylOs0F1v26OqH3/SbrfLfdjGzlkxjUajkDrGB8jFOw5ILI0vvKADseX+UzkTEWV8yLLVasVnfdZnxeOPP162MDvv6fV6sbm5GTs7O6Xii2MyFhcXC6G8v78fN27cqFTLs0BgbMuDTYz1jHWpwGFrk5NXFoVY3GTLE0n34eFhOY+UrZPcu9/vl7OsvKhwP5pJCucQ9h/oIL7MZCiVtD7yhNzJupvzXMdOqq24l/XD8Zv+QXgYP2Xsy//EQpN9jJnY4NzR+kWMpp/MFXZNTPeiBrpnu8e+iZGTsKHjxSTMbPybY3HE7Yv+zkGROeP3rhH6j+4iK6rm+Tz3Zf79gJVGoxFLS0vR6XRiMBhUzv80d2G5G187hx+NRuV8OOeb2IL9GyQ545iZmSnVhdgRsuEzzufBfzTiAz4LvXVenrGpMQ4Ymyd1z8zMlIWbu2mvipRiEAQnr1iiHNk5mzzAYWLUlNtyXa/QYgwGzzZQmoObV7TNGrofntRMRNE/JsXBFcPjmhiGg2wmvjLwMHlDEmayws4IGcGEmtTzfLj/2fHZgQIaUEze97aDSeQQn+Nvr9IafJqRz47Sn3dQziu+1hWCGnKjnDCDzQx++e2tnhiS9QuHjbPBSD3n3Jf3JxFRdjY43wxWmY9JfzMW/77XRpLJVkv6xqPlAY8G5w4QvV4vGo1GPPHEE0WeXItHJ+MDIFvRIWTvZIPvOAF0gkX/TEbgzAyyHLSdoJhgNYi3448YbwHhvk4STEqaGOW+thnrdE7CnXRFVM+TAew44fD3+ax9nbfXGZgY2JuEzHrlcn/bMnOe/dPKykq0Wq0y78jX4IrP+pqMjfk3wMs2lcdqv2mgYF9JazQaBRzTJ4Cvt/0C9J2EOUEEBJ6cnMTy8nL0er3Kdi6TDIwj2z5zfr+ar+V7+H3rYC7zjqg+KAAdQ1fReV/fMnfiTuygogGdNcEECMFWDVZp9A/dxMZIKI+OjuLFF1+MiIgLFy7E6upqIaOpDHO/IuI2QonXICLZwmI7jaiez0aSxGfQOa7tRD+PAbkOBoOyAg4JRdWe5WB753qcpYJOElvtv7x9HVvl/hlbQMi++OKLcfPmzWi32xUfhUyoHIQAZO4y/so+2teiHwsLC9Fut6NeH58pYUzA57yl3+M00clnMxmTdd9yyPgp48OMjfJr99qYTxbWjo+P46mnnorZ2dl44IEH4g1veENsb2/Hiy++GBsbG7et8EPSubrcsjeG5Td+j0VdrukYMRqN4ty5c0WHZmdn48KFC3F6eho3b94s8fhum+MtSS9xwfPB+4PBoGw5cVUs89Dr9crCwGAwiFarVapiqILqdDolltrWwGGQSPjtVqsVi4uLsb29XbaIHh0dxebmZmxtbRWy6/T0NJ5//vkYjc7OZOIBAE4YScDBSLOzs3Hp0qV4+OGH49FHH42bN2/G7u5u0VF2FOAffY4NmMMHliMj5pL4ura2Vg50J57av4NHl5aW4rHHHouHHnqobAva29uL69evl3uApXq9XhweHhaCku1drVarVFVSIcWCDnKHYOTeS0tLZVy0TqcTvV6vsp2P7XngyFarFTMzM9Hr9Yrftu6wLW9vb6/E7OFwWBL/iHFF0v7+/iu207vRbeMYfAPb2IwVGTuYE5xh4gmbixhvO/WCAD7V+Qm6wN8mCyKqeMD+jPlGR3jPVesQuFTiOYZ7Lp3notuQh37CLAQT3+F140Hjf2RpPx8xxpD5b5NWEMXehWK8OjMzU2zWi7Ke14hxfmh8bdLbC5ls5XW+XK+fLcSxFR7fFnEWB1dWVirzkH02C/xeeGcMkFRsrc9YIG/vZnGfHMyEIXKe1A/uZ8LJxxp4y58r48E94INGo1HsnPzOxwoZ97Dl9W7aKyKlzOIykE6nUzEO3nPyYaeKkACbvAfry2AIBGZI/dQsg4pMUNlorZQoc04EmVAnonkCfc6Sk09/xvdkUgG67ofJHsbGNfLKUSZfCG71+vjphRgjiRcyRDksd1hgPsfrXr02+IyIStKe+0afIiK63W5ZFfF3s8Pjf3QJx22FRr6Tqo48R7kvvMc4nag5kOZ5y4SIE2XfZ5Ju8VkTfTnoevx3SmR97fvRmBd0AzDH1jicCYz+0dFRWe0bjcZba9BZdMorNMjXgDpiXGmHQ8cOsH3k6eTL5IXlwIpZflqeHS5zblIsJ5TI2ImVtw9iB4Bd65YTvxxoTbbwevYDvj824Nfp4yQCxEkX13aSyHUnEQH0Fzk7ifV38T2DwSDW19fLY43pE4kzcjcx5Xl3XzLAoC/oo/Un99HBkOtDfpAMOUagv+goMjSZOhgMyj555EyFXKPRKNVQNK7r8z183ftJRLlZd/wa/tv2w3vIPeJ2PWIs9lUmMgH6AE/OiLQ+UXpNooNc0QMDeINpEwf0zZ93vB8MBvH8889XHo9NrPR3rF+OLcgCm2arMOP2Fh/HEBZiiDFO/rJPiqg+AY0Y4acmoRvosEkYJwKcrUijT3nOuKZXmGmAdfwfh8c/++yzhUhEVsYdvMZYDc6Rj23TSal1azQ6q1YBqPNUMusD/R0Oh2U7lUkN+4asO8jAPjonHP6uKxly0nanuHqvdkx/vajHYtrm5ma0Wq04f/58ZQsEesoTGyHAnaR7DPajzMvBwUFFFug471n3XLkwOzsbFy9ejJs3b5aty3fb8M8kZ8g8y9EPh/B88jf9dIzyGSb29T78m6QJP3h8fFwqg+r1s2qmVqsVCwsL8bGPfawkzmtra7GxsVHky/3RQXCJSUL85NzcXKytrcUDDzwQy8vL5Yyu7e3t0v/19fUyfkgc9NDJGLmOz7pELsYNPPQBAgG/i9wvXrwYjz76aKyvr5enDHJgObrlqrNm8+zJe1yHsT7//PPxiU98Im7dulX6ji5bNmAiV6xzPlyjMX4i3OLiYvT7/XLsymg0Kg+fgLCiWjPb3fLycqnSYG7QDSfcm5ub9w0ju+ErmQtjATAJFVAQZYyZRqKOXSBDdJWqWGwoL7zZ//lv/id+mQwyaWuZ2m/TIDz6/X5ZQMDm6H/EuMrI28XQR/tix3/jYGM6Y1XnuibzjW8m5UGQRRm/813yDLBJRBWXT8r9LEvOIGR7qGOd/QSxPsehev3sieP02UUMxmkuLIEEQj9YMJqUQ3qXCfKD8GeevMDqeEDc4bvkgWwnzT6csZtcAh855vo+Jt7Jw7yQTg54N+2uSSmzonSY/aUI3EDOoNOJK9uCKMPkuzhQf54fbyHz9h++64Cdk6GcGGVAg6IaWMB42zkwcSgZ98og0qW5mcTKBuv7u18omp0NAcuK4kQwg7xJCTl9MYFDP3zejJl6A0z3B104ODgoT1/yYew2PICir0kzGYQcAAYVRRW5xTWcVNuBZ5nyeRuV++B+2Xmhi34tO1zed4Li/mRAwv18b657v5sPYcRJQLZYDlQq+nwAH85448aNePDBB8sqFwkSwdfbb01ympACtBBcYPdtK8jIRJgdHTI12WYnamLMCZwDYE6wHIztE9B1Egb7EZoDngO/78McO5kzsHfw43v4V5y+E/pMbmXfxzXcD2+jy/7P8p6dnY3l5eVot9sFgNVqZwepUv3B/UkuafTHT1Wyr7KPNOlrGfh7+bP2U5xdRjVLt9stfTBptbGxUe5j0gGZEPip1EGH0Evsgu9yD89v9iX32ib5SM8lZwFlApL3JzXHHQPDiOqBrsjCK+OukEJ22BdziyxN6EeM5w/98/exGceoWq0W3W63bOfgEdZsEfT4sG/HGtsD92HLB4kDSYA/6+qtvDLI+DJYNwhDB+gT/cNH8h1XixAv7Y+IIT6TAdk6hjlZwm6okNrc3KxUgHjM6Bd+3k8184qpSTjjCOs8spmfny9neJnwZf65PnZ569atyhluyHISuZhtwYnAJHLd5B+yzXaRMc1L2c3dNsvH10fG165di9FoFO12Oy5fvhw7Oztx8+bNMu/elumkg2savzGHYKZJ2JLXTAhabhFRqrg2NjbKNou7bV44cCyOGFf2OznDnvCzxrERUbZreRGsVquV6qXNzc1C3kKO1Gq1kjxis1RbPvDAA3HlypVYWVmJa9euxdHRUezu7sba2lrcvHmzQjrZNvALxAB+r66uxsMPP1y207AdhQW6er1eqoOI2wcHB4XIRxYmqiCFjB1JjpErlcA80QuZvP71ry8PoPHDOxqNRtk6tLCwULA3JE+9Pt721u/347nnnourV6+W/jBHVKMRR/FVvM6YvBWs0WiUJzgvLCxEp9OJbrdbfABndvFwlMXFxVheXo6bN28W7N/v9wsh5xwTnOEFj4y37rXlOI5vsv2ZAKAfVH/5kH6TecaIEWeEAGQC+AWdy3iS/xmvcyh+XCVtIsDkLu9FjLdZLi0tFXLKC+cQP/gJ42JXunoHE7gJuVl2lmvOwzOWot/4B8ve43b8No7L80ZzUQg+Ex6B3ALyGBKa110EYsy8sLBQ4pifemgdmZSDMybI9bm5uUIWj0ajCrnGwpW/b8yEfft1y867YMi30CHe43/0xb7CtlGvj5+iPhwOiw6DISAs8R/4MeT2mpBSTzzxRPypP/Wn4vj4OFZWVuLJJ5+MH/uxHyuPPLSyREw+2BJSKhuKAwATa2F6tZSJQwmd+OVkMqJaAujgboOxE6Zc12X/Xj3J4JAJ4f6uRLIzwqgywGPScAaZLUc2nmBknBPK4XBYAWMoCJ/D+ZkocrIyydG7P8iE1YCDg4NSgk3/IsZAxJUsnhuakzoMn9+uZmCuM7FlAINMcwKW58ggNyeVOCkTUe6L+28yDUfCfQ1uCCYGix6/AQtjuN+NZAO7oKwYcgigwsoPtoJNbGxsxIMPPhgRZw4Yh0vFou2NEvGIKPuyASzIAvlyn0lEKUBmdna2POKdpDxiDH6Zs0zQOul0UuPEISIqwYzPZN0iYGSS2tc2wePP2MYjonIN25wJAwAvQYP3+b51O6JKxtvWed+kvnUevWR8JAFO+rgmOg4ZxPkck1boLG//mGSi5fNJGBsJvc8BYiz4OCcktVqt9AvdMwmCrdrHIsOFhYWyQgsRyDwR2+wjs9+4n6TUna6HzIhDBkokVtYB659lCzni5DnbBHaK7LARYrQTXOKCE1X0zr6YcRk8oz9UiBADh8Nh3Lp1K9bW1mJ5ebnMSa6uwBYcIzwOZEZCBTHiz2GvyI9+As7wdY4ROflHnwBsfmgEP5w3wmp5xDg+R1QXfJjPrF8m4V19yt/9fr8QsZwpkTED3+dMGS8Q0OeI8Vla/o7xDPLqdDqVbb45icDPeNsU+mH5c237XXTK+oUO2f9Zz/ED2L/tx3K4k529msZWImIWT0BmOxmPWN/Y2Cg2hM8xXsjYbtL8oyMQIiYpfQ0WB+xDB4OzB4jgB2dmZuLcuXNRr9fL9reXkomTTy+aNBrjanfe88G+xql8lrlHPhHjLU2np6cFz5IAQXaMRqNyRtfs7GwsLS0V/MtZmcfHx7GzsxMPPfRQbG5uxunpafElVES44ndhYSGWlpbi3LlzBRfja1qtViwtLZVqLOyVxZHhcBgrKytRq9XihRdeKIepe2tiRPXIAMZWq9XKNZi/TOKbTF9dXY1HHnkkVlZWCrZizqneQbaNRqPy0A/809HRUVy/fj02NjZif3+/5FckwYPBoNg0/vXw8DD29/crZwnh46jWI9FGT/v9ftmazZYxDt0mD2QbH9sFkbnJORPr9mnI9X425ynOR5gTMCi+h3OkhsNh5awrcix8H0SIyVnnD8QU9BF5encOsRY9cc4DMZpzh4xRHd/YJjY7O1uKCpgD9M+5qq/lHIi5g3T0ffxd++hM8EzydVwnY5RJeYPxjokhxxN+4wuZT8aA78O3+t7G6Mw1ZAx+dHFxsRBNyJLrWK+RUbvdjqWlpajX62VBCR3g3F+f7ebYgE6ZSESHWEwFc3jsNPui0WhUbNkVfsiUXBFdNmFHwwb4jOf/+Pi48gTIu2l3TUqtra3Fu9/97tjb24uf+ZmfiTe84Q3x5/7cn4uf/MmfrGyr8SqJjRCDc9JnJg+F4Pt818DO1UsAQAAYZYmuuGICEYgVFyXhHhj2JALKDskOCmUhKESMq6X4n9eYKANKf5/XbVhOKAzA6RdOfnZ2tvLkswwuDGZzouvE2Amb78fnSBwixpUSDrTc14Qd12b8NhCM3E6A6xlg4ajdP+bWAZ3flp31zc4vg1/GbwIKQ+fzGXgzvrxi7gQasJTv4z7T7LjvB2DmGthmRFSeDkIfAaW5XJu5bLValTGT3DFPzDXB2LIjSaACI4MP7hFRPaiePhDkCTT03wfxm0DJ83AngspJqXXHlRLW+9w3ruvky82O3c0Om7E7eDAW9xG5mhTA/1jOOQBlMoqx5sQRH4oNE1QhGweDs8fL7+zs3FbBaN9isOIkFlkwVwZg/ryJjohqYpbJbFfnEDyJNQTmvb294jPwywZE1k8DDgKpzxQACBAbXqs2SU+ZW/p4cnISv/f3/t746Ec/GgcHB+UsE+bWn+dajrHZ73sxyERXTpSZ61xFbKKnXq8XkJtjmf01ugkxAjBEJ/b39+PFF1+MpaWlsiqHrViHrRMeq22eeGS78OGf9A+i2yDLdpNt3WdDoF/eisVYkQXjBFB6axPJADJCL5kTYwj8hW2/1+vF9evX48aNG0X//RnmxxjLlbTMuwkSn5cSUT0nNCLKKm+z2SxYgGs7sUGfnHRZv03M8Zr13fgtx07bR57r7HutJ77GvTb0h6QD8H56elrO1GH8u7u75fBsE4/YNTJzHEAf8oKYMTUyyiQ6Y0S3kDcHWM/NzcWDDz4YBwcH5YlrdyKn6CPzwhxjN/hHJ0fIPGOLWu2MwFteXq7YAovW2MPe3l6xgZOTk9jZ2Sl4HRuZn5+PCxcuRESU7WTGlOCO1dXViIhSwYNvmpmZiU6nEysrK5UnSc3MzJTqKJPhVPTMzc3F4uJi7O/vV84K43HqEDpeIDE2AhMZZ0DYQJzNzc0VIopkdXd3t8wxlTeQe/ycnp7G9vZ29Hq92NnZiW63W/q5t7cXEeMFOfzRzMxMrK2tVSrw6vV6XLx4sYKjsDH7B2JnRMTe3l7JRbAHthhCREKWkvc4zyPpt18Gm8zOzhbfdr+b/X0mNUxMoAP0hSoZ5wfogStSmGsevOInAXthN2LsF7G5TMhERGXByH4xL9C4X+ghukYV8XA4jP39/UImYt8R4+2oLjxwH/1EOh/tYXLPGMC+jL6TQ9r3ZXILH2B8yViIXd4t5AUz+IKIaqUnZ5ASt9xnk6z2segD+Z0/h5+l4UfYnueniefzshkPhJTzZGRnnOPFWcbp/CDnHxkTYZvOJxg/n8WH258SH7Bd5Odtwc7DXwkhFfEKSKnt7e2IiPjt3/7t+N7v/d5485vfHP/T//Q/xbd927fFD//wD8djjz1WWXmPGAM+l8MiOH+G9w0QUTADbYTP37Ozs6UMdGVlpZBjBLGI6pkLBj35ugjek24lM9lih83k5OTQ9zGA8sRmcM3nLB/6mB2Wv+uEj3v7rAqTLyRodsARY6CTmVknppzt49ViJ47ZsC1Ty5XfOYm2E8qVGtmpTgKt7jdJPAllJr9y8mcCw30xMeBA5e9nOZgQIUnB6P1+dhi+xv1oJnAXFxfLPXE0dsBUQDG3jBdwRALDeQAE2Ygq6WWGnnscHBxU9t1neyGQcF/rIfL3/FCxw+cN1i1j/53JJzt6QFkOdiYgbR9eFXbi7z643/lvE+W8TtDMOu7kAznjOyw7B5cMRpzUmaAC3GdC3glko3G2NchPkMoydX9N6tkPOnBmueFnLXcHfuTFPQj0rihDD+v1etk6ga7mJJWEgfnyggOVeAA7P5UPf2Iff78JquwTsh43Go3o9Xrx/ve/Pz7ncz4ntra24vf//t8fTz/99G0+jO+4atjX47MkB55D5j4/JIDE0aRDrVYr54YQHwyo0A/m0b59NBqVsnl0nGqhq1evxurqapw7d66yMugqHu7hxMF65BiMH8MeqBZCRtzX13SM5H3k6wQcXUVf2cqGfPG1AFlshOt5kYvrk0zbjr3ww+9utxvXrl2LjY2NyvYpy8QxmL7OzIwf4cx4JwF+fCTziK/odDqVkn5kgj/2PJvENGnAe8jG+mHfaXtwsujroJPZJo3BbGM5LrzaZtw3HA6j3W5XfClnTJ2enpYKGp8D6rmynXhxLeOeiCjVOzxxdDgcloQdzIG9Zj+CLuHnV1dXy5MCd3Z2CpFmW2asXnj2vdx/dMRVHBlTLi4uFgKEBMlx1rGLA9AZ+87OTiEENjc3YzQaxUMPPRT1+vislVu3bsXc3FypQpifn4+lpaWCUYj5jAs7hYianZ0tSTvntXH94+PjWFhYKFVEzCOEI+cTgoWybUVEqdLKuzqQ1dzcXFy+fLmMi6ovfDOHurN9aDg8O7Ntc3OzbMvc29srhJSJeOYB24s420bJWNFBHhhB1Tzzax3O+Q54uVarlUoq/Aw6y3EOtdqYjECPIDlMjnJoOraUF8fuV2OuPGfO+0ajM/KQXQLG0MQR7Bq7M95wAQQ/xlq52MGx04m+ZeyYYX9poiOTA/SdWDozM1MeLrK3t1cqx52vTspz0GdXI3Pcg/1qxp3G4MaQxmXcw9/lNeTheI39WLZ+KAL+7OTkpPjLOxE3xv85z6QfkLgeg30dfm5paSlWV1dvwwMet6/vBW37BPs/L7QOh9UnQdJvvmv/QhzO+Tf9cjziftY96zp2HRHFNqng5N62Y+fAL9Ve0ZlSEVGY+Oeffz5+7ud+Lv63/+1/i7/+1/96AXcWHpPCxDIpGCBJu/djwrShIP4sv9vtdnGUnDlhB2Hl4Pou18uG6onxdhJARUSURJjP2RlgpC7FzODfig0Ay+STATwrBqyEed8v90UpOcw1n7vjsdL4LkAyr6Q6gc4gnyDEPtfsuDOBlEEkn/OKuZsTUssXYieDM+SW5U3fIqp7oulf7rvvZdIpgyl/z4GYsfueJncsk0ktB8I8Z6+2IWM7K1YiSTQo9eRJPDxNARtEPw8PDwvY5jvYr4ENMvBKhKunTCp7brLeAYAMGiPGdkJljOVPsM+k66RAytjoV5Z9JoY8f1RvcQ3rlZ24ZWJdQzYOch5nDq7IiTHb1ly5Y4BoHc0BDL303DnwmGQ8PDwsIMWLBk4mkZmTT8aWdd5AySDEY3IyZCDjig6DUsAgPp5KJwMO+wi2DPB6vV4vT4/j++gq4GVxcbE8DYl73m9Cyi37tOwPjo6O4tKlS/GmN70p3vve98Zf+At/Ia5du1ZWKpEpcdmymiQTbM7kkBMM3nccJnGOiHKAK3Ph6+bkD/BtspFrU4U5HA5jd3c3rl27FpcuXapUFKCHjNUkh1dKuZ9t0nESwtHngWQ5WZYZcGagPhyOKzupGokYP+4dH8V3DR65JnqP7vE61/G9T05OYm9vL27evBlbW1sVX2S7ARBicz4SATBpTJJxHPKwHi4uLsb6+np5ohP4zvdaWFgo1dXWtewbmTvul4kJ7sn4MwbzfGeCOyccvuf58+fjLW95yz0/Xh5sjG5BzEFaoLtUSSGjHIuIa07Gnbyh27lyFl3jaXMZQxGrrHPWa5Oqa2trsbq6Wipydnd3K7gXe8v+ydUKtnknz06YWq1WOf8IWz4+Pi7nGXIWHKQG1RrclzFvbm7GwsJC3Lhxo+QP2N3i4mJ0u91YWVmJg4ODshi3uLhYMCyEWKvVikuXLsXCwkLs7u4W38CcQnRBmnAWz9HRUTmTy1vAvY3Lc4rfc/Uk5xLxebZCraysxMrKSvGH+Ck+wwMGarWzSslutxtbW1vxwgsvxIsvvliphDSpn0kAfDskIRjPxGKr1Yq9vb2KnaOv4MJOpxM3b94s+sLRLhCOzWYzFhcXY2Njo0LAnJyclCdC7+zsxHB4ti2YM6r29/eL7PCxk55sfT9axo7g5IgxNiaBN6kCWeb4BDFlf0wlHMSVz1bGNsAymYzg2o5Vk2I7tuf4YoxKM4lvv7K+vl7IhcPDw4L9XJBBc44OVqKizWdBOq7k/MlxlfhnjMKYG41G5Wl1jg3uP30knnohxyQOiwTGsZZrJqOwZeNkY/csZypB19fXYzAYVCqbTF6aLMSP2la5Ng1/jhzQLXak4Ff4LLGUuaBvueoVXIA8PA+ZE8E3gKWcr/He3ZJQud01KZXZ/KWlpWi329Hr9eLixYtFABiQHSFKZ3LJQRXFZlXCxj4ajQ+vxWnyOQJrxO3VJ0yUg4AV3olqRNW4DJp9HQfz7ABsZIAHvutEEeVAqQ2ic8mzDdZK5bG6XN5gAKPjWgR/7uODIw1kuR8ruozJwS1XYJix5xqWh8HhJGcyqVzcRmHygmswJv9vp2uSwPfORFletXUFj+cBZ5Yr0viM9cJ6gsxcjplbdtT++16amXe2IplIAig6GcDxUlnFezyZzzo7iVRCPqenp4Uxp5zcekF5fiaAIsbERU5o+R6kGWXyrupwpSTjZu5NUOHErR8eh9skciCTjFmfrCMmWCKqB9AyB1meJLH2n07SDNJygsY9mX8nccjJJdlZ7jSSA57W6HGbUHKyTMC0HZmIxj+ayDRhbZ/OfZw0M3cmDCLG+9q9msq1GSd+vVYbE+yNRqPo6czMTOUR5VwPIGxgZb9+v9tLXRd5NBqN6Ha78X/+n/9nfPmXf3ns7e1Fp9Op9M/JvwlOv2fQweGnlGObAHV1hHWAuQPEukrSzZ/Hdztpwm8Sd0jCTk9P4/r16zE7Oxvnzp0r1UMkniRl2PWkGORGkkWf0Q0nvciZn0lxFD324oOJX+u8EwrHznp9/JQ0yFjrKz4LH2Zb4kmJN2/ejO3t7WKDPoPGNsNc4j/BXyaBTUbz2zaHP5mZOTuLiGTUiYd1iznPmAy7m0Q62WfgB7Pf87XoK3OHfDIGsW01GmcHtL7pTW+KH/mRH4mdnZ072tvdNPsh7NP6zHzhT/F/JlTpG76J/52Iehv54uJi2Wo8Pz8fDz/8cGxubsaNGzfK55jfiPETIyclCyQbEePVdLaFXbhwoeggVV5eEMlxwDrmeELVCPbdbrdvS2jY8jQ/P1/e9xYTdAGZZqx969atuHDhQiFVsC3OXjJuhCw8OTmJTqdTFnZ7vV7x9SaCqABFdoPBoFST4feQH7YI5mPu7VPQVR7s0Ol0CiG4trZWKrrwB/V6vfhnZFCr1UoV1LVr1+KZZ54pi4uWOYQX/eOaLEryGz3wYsbi4mLUarVCEOZYzHXn5+djY2Mjtra2olarFUKJCkF89nB4VonG3PN+q9WK/f39ckbYrVu3iox2d3ej3+/H8vJy9Hq92Nrauid7fbnmOOJ8gbnmzGESefoZEYWoQ3cjxmSGm0kYF3Q4VhAHuAafR/6ZrLafx/cZOzsuYsOOJ1wX+52bm4ulpaU4Pj6OW7dulYpDroWvYNxe2IPMIl9nMSbL1LIwvnAu6r+pfs+V7LYJ/sdXWWbYNOQKrzlfc6GK+2X54cM57Nz21Ww2Y2lpKS5cuFAwDH12vzPut11ZPlkXXUhiQjMiSk7jcXghmHHyuskv4pb9sjEUpJd5BPrCWNwfKnj9ZMO7aXdNSgF2nCRxlgWHkjLJDko2IAdiSCiTWHwHY8ZhegXhTivxFronHnAXUVWcnGjSL4NbgyE7DidGfD/3iUlyqR73YWLZK40B+Rr00d/lXiZn+KwnnJUKryxGjLcyshXHhozccR4k+AaBzKEPVcdpZnY1O3b/mNjBKJ2ou8/MB3OX5e+5cnLtJMhyspM2ccX7JmiYR88xxpqv5XkykYbeGXzSMmi2rO5He/jhh2M4HMbGxkaZV5fyW99JyCOiPLnFSQV7onFEgDj0h5VLVrGsewborIgBnJGj58KrDfwejUaFTEHWnNcFiebtCNgzK1G1Wq2ABc8D98727L4zh563THZ6xSGiSkQhQ/SLHwcYCEOTcYyVcZlgMKFn0t2JsHUKH5qTPgcpxumKI4C3+2Xw5ErSLCs+Z/BhwtbXcr+dpEOQOHAjDyezyJzECZ0m0LLi2mg0CknKuVEQDyZgDaJdFZd9ze9Gc+LzL/7Fv4ilpaVSUWCAZ53JgMY+mDjEe5AWjNkrkJDayB6b8mGbGezkeTK5gx9xLPAWpFrtbAtIt9stCRKJmf2vx0hzLDcg5j3eN+EI6U4Cn0G/SRJjGuaFPhHPHIuIc+iuSVmTUH4PYO04d3BwEHt7e7Gzs1MWGwwOc+xz3AV30FjRBlxbTvZr+KjZ2dlYXV0tT9JCliYXIsZbuvH7tpsM9rOeeA6NP7LfzgkXeMsrxv6xTrRarfiX//Jfxute97qKbr6alhM17IU4wQHTbPmyfeRrgLsiomxjsg7b9xITiR2PPvpoqRqCnM8JKPqKfvt/Y85MNrLNjC3TTugcw+gf8RadY4wQasPhsOjT/Px8DIfDEs+pQML/NxqNCoEN2UFsIFmu1WqxsbERFy9evG3+FxcXi4+iogxboCqM/AIf1Gw2y2Hc9MHEHjgeX7qzs1MIGSfOriBFzozducfi4mI88MADpTqq2+1WKsXsF/EDOzs78fTTT8fGxkbU6/UiS/sY4yn7QusEZ0p6ITNiTIZAdiF39AR/iQzBhs3m+Nw+Fg6bzWZ5EA4yRp5LS0uxvLwc3W639Gd/f7/Mbb1+tn2Rw9Oz779X23XLJIDlORgMytEAxi4slDJu5OlKUeYA+flhACafnDvgC+zTTTI4d8lEhgkK+1v7EGNd77RxLOXsNIg1k8RcDxvHR1iWLPqYqMZHO78z6cM4M97KeQK5MxjOiyyeY/ru41z8UDDnkybDMkY2hgefZFkvLi7G+fPnS6Uw/TOZ6bjGtT2Xzodd4epxo2/Yg/P8RqNRHkDi2Mf/XgQD00HWcV3HNeeqvG4swZw3m83iX4l95CU5/71Te8WVUvy9sLAQTz31VPzAD/xASWBxPJMSLL+OQ6YUkgHm1RXYVxsuAreD94RwTwd3GysN4TpBd8UPSoShYkiAPhsPzc7MK6LulxlZV4rxPTuVRqNRSd4pB+VzeQwYJUkZikG/OOUfFptgB5CiugoQTrlqNhzACA0nZSftMXtMvG55GTgZFKEDfMdA1IkDffN9nDQwv7l/fNbgCofhZM4BwIk5JI776T4wHjsxXnOzw+Xe94OY+vmf//nY3d2Nr/u6rytzylz50HECoivjTFaxasJh6Dh55qBer5cyWJfKusSZw/gNjhzYLIes07yWiQtkzcpnJgs8HsAs8wZxZuLZduikPic2zKcBOPe2vhIA7GOYZwMeAp23UXj+sw05uXdwcz/oQ8T4SYUmkgxKrdfYAGdomNTPQNWy9bzbhzvA2r96nhn/cDgsW4FMqGCfHA5MHEEO9Xq9JOqcJzWpcsUEgf2ViXVW2O2HmKMM7nLLpMj9aNnHG1guLi7GN33TN8Xb3/722N/fL4sRNMcjJ+roGrKr1+vlCVcmTR1rsVVAN9dkjjKBl+Oi72uSk+uTwBC3GSek1+HhYXS73fI9iBgSxQz4nAjneGMyCH0g2cFncQ+2zJpw5rrcj+8YDNvP2UfwOy9WOZ5go05CIFuOjo6i3+/Hzs5O8eXMU8S4QtEYzH6W1+wPWH2mr14NxZ9QqY6O5ETLWIt7Zb9nWdmG7Mc8Z3nenCjYz3k+s0wz+eN7EpPutXm8jvcQGf1+vxwz0W63Sx9Z1HPsoKqiVquVLRY8AckJWcT4KUwnJyextbUV9Xo9rly5Eg8++GDcunUrrl+/Xh72YCzEd3kSJDqW52JSMphXyr3AwuchRrgPel2v10u1dUSU3Q4m4fb390ts5uEHvD8/P1+2k8/NzUWr1Sq+Gt/Cw3/wId5SxHYknuKHHdbr9Th//nzBQyS6LHT5nBXyAq5N5Rs2562OJONgLeTMQhl/Q8iwEyRifDabF+hZWNzb2ysVpE899VQ534k582Kj9cpjYz5YZGYO+Tk9PS1EJBiAvnjBhofgbG1tleQ7YxRyCJJU/Ca6cXx8HFevXo3hcFi2Vq6trcXKykr0+/04f/58vPjii7G5uRm1Wq2c7XWvLRP06Dq+EH02dvDcssjFQit9IqeKqC5IQySORqNYWVkpcnJOQZz0AhHxx/mOFx+Mi3OcQidcNemx5jzWeBLieTQ624p5fHxcxhxRPWqChi14odAY3oSoddUklPNt+zoTKpCkyAp/zoKic1DG52M3+C4y8OeRtX0m16DSk74wRzlOEleYJzA4ftcxnjny3PKZrJP2H66eQ48d+7Le5mb9oT/4Ogg14w/LCy4E+SMTqlMhpxj/KyGR75qUggkDwD3yyCPxe3/v740f/MEfjM/5nM8pHcPBGfChSEwo4N/ElUt4XaVh4EtApNnpRozLh09PTwtTzaq4E1Abno3AIMhK6u/yWv4+iuSVoYioGKEDO4HbgMSgw9sgzOLiKJyoGIh55Z9GIKIywKvaKDrGyPeRNaAA0sqOlHl0lYblyO/sDE2WTQJEOQkz0PdnDfzRo0xoGQBnx+N7OHHgNW+byIm2gwcytPF5jMjPNmQdvJNO3WuC++Vf/uURMS5JZasLv13ZYDY/JyrtdrvSRxwQYIhr5co8E9AGPfYLbsjRNomOkGRxXZqTKxJbgLLJDRM+HBB6fHwc/X5/IqHkBCOiurUkIkp1hasAcnMprseInnnFhIYumhTxeK27/q7BCLpqP+HvZCLcgR+gzjYGArYBhIMefTTQIIBZH0w600eubbCCHyEJN1Dm2iZfBoOz8n7mp9lsRqfTKTJh3ADFbrdbZMo185OSFhYWyvgJvPlcnEm2ea/2mvXAQDHby6/8yq/E4eFhdDqd+FN/6k/F93//98etW7cqn4cg8G+DSOwKooEHh7AY44oBYiBghDhgu6ff9fq4yg+b9Fw73tM34oiTH5I6g3qeDra4uBidTqfon1cN7ecN3j1H1mdeR+/ok7eeoEcmWB33+D7kFD4fPbbu5G1yXB/ATDKOXaDvh4eH5QlpfJ/5QN7I1ngixxNXrlvXHNeRGb6Uhg+1TJ3w5IVA5oLk0zbt/tmf+fek1dXsPyfhuUn/O6a81PVfacsPQ2BckCz8v7y8HBFnT+6iGhnfA/Fkfed6xFj0IMuY+WFb09raWly5ciXOnz9ftlQdHBxUSHoScnTcJEbGycYxEVG2pSFPiAuwOt9jpwP6VK/Xy/EcyAl963a7lUPD0Q/7p4goGBQCCLsfjUZlSyO2yZmzEB3Ea3wbSSyv47cYC+9FjEkiZMNnXFXQbDYrDwJC1oxhOByWLVH4h5WVlbItLyJKVR2HRSN7Y5/BYBBXr16NF154obLVmpaxqMnpiPGREp5bbBUdazQa5XBmdJuFDz/ttdVqxY0bN8pikqsvuCeL6bwODsa32PdzVtetW7eK7Pb29mJmZiYeeOCB6Ha78cILL9xTZSPt9a9/fTzxxBNlntH/Z599Nj760Y9WqoONGbA9yACwDrrlLeboq3ETeRa27sr+iOoDIk5PTwuxOhqNn7iGb/YiTvaf6IRjEPfy95gzfAzx2Hih2Tw7lB8/7jwuokpgo4/YCuMCT6JTYGjkCy7NxEomirwYxf3539VXjtPgCueTrrg2TqbPGdP7gHp/njms1WrRbrfjwoULt2EFMA1+2Dkpc8AYkKXzcs+nz7XKmJq5otLUOUjG/s4LTBwaZ/LZfC638yHiME8X5jUWPPy9u2l3TUpxQQ4MfOc73xlPPfVUPPjggxUQa1LGSmbDMxll8GQyCYXjx4pu0iUTETjP3d3dSkCzQ8hjcqkpgABgmJNTT6arCOhLNiwIASsyYwLQo2CZoPKYbJh+nT5xfxwar7N3FsAAiLYz4f44VD7r/fwO/gakzLmZXY/foDCi+oRDJ8S8l1/3tewoXMFgYIqR0/K1smE4KWC+0EUzyU6MDOyQoR0J389991hzH6xnJsDupRH8vTJHf518uCKCcRI0Yf29RaTf75d+miBgrk9Pz87OqNfrBVxl0od+MKcOyvQBnWMeXFmILnu+7CQNtHPQ534k3KPRqLI3nbmcZGuZ5EQ3ADC2SQPDTDJwXduHwQ//+zOZUGfcvO4Ve3TO9yNIeVUFveN9fF8+F8V25qTSK/i2I4Mw93mSjPgbsATx5Cony45KUPsarxaaHEe/8O8+c4cDPOfm5go5ORwOS3LHmJmrXIn0WjTLy8m0yZR6vR5vf/vbS4n4N37jN8bKykoBXb6Wv29ZYe/IiJV4x29szqCVOfCDMjizJa86830DE+slSSugyD/4J3ySAbnPAOOefvqPgZVtyXaUfRB/IyvGZdIVvcLurevcm366asoriRn8G3ASryPG1YEkFBAcrEQCzKlmNq7yarDlgFxts8gKYsSrt5PIcJJgN8ZCnLFvJkYwhybO89izbZlMs//JJDjNK+Qv1+5HfKVZHo6rJpaIY5Cqm5ub5VwutqbaLzvZcXLM/GRiiiSTakIeBPSmN70pTk9PY2trq1SSQi5ERLG3rLOee8vdCa7JD2N6+1zr0urqaiUJxF6ofup2uxERhfTAlpADZG/GwtgAlWUmjkx8oX/7+/sTt6p1Op3SLz8NkDnmulRpsE0vb2kkRs3Ozsby8nIhytCFlZWVStUhMmLBbDQalaM0WCyEADs+Po6tra3Y2NgoOZfzJGwp+1rujQ4xJ+gNuQ7EoXEX92UhHZ/dbrdLRQRzDv4iJ3D1GrYMYQiJF1HdFkgFGv4/IqLX65Xt20tLS/flXKlv/uZvjv/+v//v49d+7ddifn4+rly5Ep/5mZ8Zf+/v/b1417vedRsJZXtD95gXx7GI8XlebF3MW6zIt/C/JP2j0aiQ0M4znQ9nP4lsM47Ehzj+exGFe5mQo4/GCrTFxcXbKpHcvHuJnNo+HH2iX8Q740zrs3M55wPYg/Mr6z9yJPemmUiDFMaefSwI/cUuIs7sx/mwMRkyX1hYiPX19Qohx0IDW1ztY01cOS7S8viMRXNuYaKOsbHwYV/seTV28aKuuRaub71grhkL84GfhVhnTMjibtsrfvre4eFhXLlyJf6b/+a/iQsXLsSb3/zmIkyvfqA8rljwPu8sdP63sjhJNSmQCZV6fVxZZHKCyfP9HTwtZIIJ34mIyjUtZAyKxvVcmcC48oSgBKzcMB5fD+NhHFzz6OionMFhUG0ywEl1RPVQ5dPT08qhj1ZMmhM6kheMgaQMIzPBhEOy8ntMme3lf8+hE03LApnbadB/Aweuj67k+eR6DtYes+fWCZX75dUMO1v0wCSf9cDgYxL4dlLhSrr70ewEcezYl1eJjo+PY3V1tTht+k8Cypw3GmdnTDBWHn8McCSx4rsku/QhyzI7wCxXtg/aZieBr1ylaP31KgPy5t612vhw2Vz15/nCLk2ueyXHn7E+c2+vtPq61mv3i++apDCozwStfZhtiM/b/9k+nXD4nLjsy2xXngsnZb4mOm+fgp906bFLgfkOVUvoLmP1ajyyzqDPBAjyIg4wxxCxXMcJxeLiYmxvb1dIUz5vub8WzdfOc+TXl5aWyv/YbET1TDbGZtBHImO/ynxYviQDlhPzCAGEHIl73HeSLTtxRW984L79vnGAE13uz5zs7+/fdu/T0/HDQiYRwrYx+wHHa5r9VNZDyGPbnPURwM0WoFzVw3VYZWel1HowHJ5V8bGlyVU1JG7eEo3/M35ANtYl/JsrqozFvNhmUn44HG+B8CIL88ZKOPNFHEAmjsO87tiIb8HeeM+yQw+drDEu/Fcej1uOH/ej0T98oX0GyUpEFDII/T45OSkHirM1jRjq/tunm5SxbbFFsNlsRrfbjaOjo0pFzvnz5+Ohhx6Kfr8fe3t7FRlaxw4ODipb0ExMENvBeE5+HTMixgvSfNZPikNXh8NheWIciSFHg7DFDsyKbrRarZibm4vj4+PY3d2t2FG/34/RaFSeTIeeU51AohoRZRtVu90uuIYtvMi53++XOUXOXqDAFrz1eH5+vjwsY319PZaWlopPwt7wOVyD93m4DH6Jim4WYU5OTmJ7e7tUSpq8QPb4es6Hwd87D3Mcz4km9oWfcR4H9j88PCzz0Ov1in7bt3sR2+Qsc09FhWOq70NfWFidnZ0tWyUhUu61NZvN+Af/4B/Et3/7t0e9Xo+3vOUt8Q//4T+Mhx9+ON74xjfGxz72sXL+mXEMvplKe56WaELW+Rdjss9kYcD5iz+bSd2MpfIC5KR8NKK6AI0/8RZRiBPnV+h59mMQluTCjpmOq/7bD1iw33W/+N85v/NWYz0TXfhQVzHip8Ahjhu8Tywi58k5M/6AzzkPdH+QCT620+mUs8U8XnwEvhX/CvadFKu4Pq/R95zr5riJjPAfLPRa7rSM00zSWxeZDz5LX+iPMTT38EMRnH/elV3e1adinNjzCOC/83f+Tjz++ONlZdJGmX9sFHbMBg84PxTTijAcDiuHp2WQ6kmxIhiMGVx58ukPiY0TYc5gynvvCVwOMDip7GgNpCLGVSD8bUXJim+jRZZ8j8+7GXROKpez4XNf98+JnV8nmFMBw1kAOFUnOB6rlTCvCjsoGqwajNogM8g2+HclmB1fTspoNib3zY6L+9vB0XcbsR1yJhcsw5zUO1G0TlPZdj8a93V5LwSuddS65fNdZmbODi2k9BQnbFCKHCCfAHKQp36YgVcQcvLDXFs+yAY5G4hbxxzw8R0Z0JM852Bl8snEsnXLwM3AwvpAs87lPuT3GHcmLE14ZgLK18TOLT/PkwMbIMAES8Q42Hn1FBl49dSfR3452c1Bh/47MHJtGmcLOuFh7vFHNC8SuB8mshxPGDN+3YmsK3hY5fEqGOQk93Sgfi1JKfua/JrfI4FCLiSkzKd11D7TZCJyJvF3zPSqbV7xQie9NSeiek6ciQr8qfvkLS2eS5Itn2XCfZzssAq6t7cXw+GwUqUDMWG7on98JgN5CC3kRVVGXhk1ELdNMlZ/Dl9q4I68fc6K+2yZQTyBQer1ekkISOpMxDv+eZ4BytiT4xH+cmFhobKd2ePOsS/7Ffwz43XsI+ahk54T+zzfK8di45pJvsYJC3rs/vpe/k7WgVfbTMozHxBDbDHlXvgQz1mr1Sq4h/7kJ0R5scKJjMkWkiyu42qTRqNRqlMuXrxYCA3Lzf8bH0WMiXxswzra7XZjZ2enPKWV6+FvarVaqQBivBFnPmxzczN6vV7Mzs7G2tpa0VHGaxJqZmYmlpeXi0xWVlbi6tWrhXTiXhxaDrEBAcU9TZhSeVirjQk0tgb6TMqjo6NStcQWM4ibiDFpMDNz9mRKqq5Go/GTurgf8kWe6LXjLfdfXl4uiT33xXdmnSE5zpgY/cAv4Du9AGUdw/dxXWwSHzkajQppuLm5WSpW8UnIkWsPBoOyqMj1bNP85ruOf/YxbKkcDAZlbu61saW21WrFRz/60fje7/3e+Pmf//n4S3/pL8Wf/JN/ssyPcybyQWKuFzQjohx34S1y+ABkTYEB9uJcjxiHrJiX7P/sQ3jdeal9qXEcn4mobv92Vebp6WmF/GN+5ufnY3V1tXJ+k+OQq4u4JosFloPH4v4YlxubW08gdXjQRs4FI8Z8Bf/j7/G9jBF8ge3mQ9oZF9c3wU1s5SFQLBRiK64U5imZjMtza3wMz+BcAV+acxDjYOKB/Xi/3y9VofA0XjSyrwdbcO6e5yaiWgXHD9WNLKYxXh5WwTXNb9xNu2tSis7Nz8/H9evX4xd+4Rfi3e9+d/zdv/t3Y21trXTKk1Gv10vws0I4STWRhfMnkc3Jgkv9CCQGu5no4poI2O/xPqtUdtonJydl1ajX61USYJTZWxWcJKKoTnQNKAy8vKfXASCvaGD0lnEGDTTAn0Gpk0jADg3DdOI6qVzUZdEEZ1aZULYM2nltUn9xwHl+kLFJDObKeugECz0hkbRBeey85s9yX16z3vB9kpOc7BEYnKi6z9ZT9z2TUYyHuWDV716bk4+cQHiMOdFjTjkwend3t0JEorOuvPBZD6wYOqH1ylYmEO1wLSPblPvFj0kDyqRNrkFsAA4Ac/Sf73JfV/TwQ0LHPJnctv6iY3dKcjzOPEaTVJNet03wfQevHNBzwoac/Z4TQpOg9lcGrU4UHeCRiftnGWXSAjux7dE/iAUqQiBDvW0uX5PrARyyL0UXMhiwzvnzJFrYi8n915KMomUCxL7R9zdxyfcAz/TVPt16EFElUrjG8fHZ0xY5vNY2gZ+1f/WTv7i/x2Fw6pXMZrNZ2ToLaGKMXnwx2ZzjCLpAVR3JEvKJGBMGXNN2Sp+MV5xkGhR75RB5+H/rsUE1bTgcVrYWUs5/fHxczmNxZQcYxDGJOSBOUGlmH4SuIDMvsERUH1iDLNrtdnmcN8QGtuqtPR4LybErap1sYMsmqYgTmWS17/dPjgH2hdb7SeD8TvZkH5195attJiepuKnVauVMUyp4vHqet+rg89BZfLyTTGNB5Ml1nBjYXiF8SBZ8lil6Qowk3nkuiAHIiiTEc4798TCCGzduxM7OTvEXLFCdnp4WXev3+9Hv9+P4+DjW19fLAfA+mHw4PKukMsFEdSBY4JFHHonnn3++4u9OTk7ihRdeiHPnzlWSe/rN2WaNRqM8vMXYAVKFhWm2F/LUaleRGCM3m81YXV2N9fX10pdms1nxsXlHBrbJWVTMxd7eXplLHr7BGYLkKzdu3KjYpqv08A8Zk1vHrLfoiLe0Y7dHR0dlAYEtdPPz89HtdsuCSL1eL+/t7e1Vqu0gPSD7ONrBer64uFhIN3QmYry7w/oO9rkfDR+NrV2+fDm2trbin/yTf1LJCexfTOihDzyND3vg8HN8lTG4/ZX9EZga/cqLufY1XANfx7zbz3l+7WOdL9dqtWK76A/3xebsY/Ht6G8+181+HX/BvTLZ5rjpfMlzm7GmfTf66UUw/KPPTON+k2Tm/CjijJxkTiOqNhUR5Sxa+++VlZWy+MB4nffx9GBwJT/mSOzTwQI0ZJf7jBycP8ApTMK7loN3Jxizm6QCC0Giuire+SvbGj13xLusP3fTXjEphfK88Y1vjHe+853xv/6v/2uZeARsgGaAZOXzqqyNgeBlMOEVck+cJ9YTY0dsQOKxAKhYxbADNlto4ohrO9HOQJBm46Gv/iwKghxQiny4Ya4scEMJbOCeo+wEc7JqoiAnahHjsyIIYKzW8vfBwUG02+1itAAFAypfPyeBzD3Jiv+nv3w2s8Puuyvc8vXvBEj9fetV1h+Mi7myo4+okhTW7wyQWam0oeekl+vw+p3m/W6bCU47d+Q3HA4rJfVOMCFjcIgHBwcFVEAAMA4nWrZ1HHSuzDFwRl6Wo+Vp5x4xrmjAPryXP4N+ALTBT6fTKQDMJJfvbR0g6NH3nABmPfH7Jo18D89HrpzzeK0XJoBcJutEzX7Gvob75MTZ3zMAsz/32U7WaY/3pe7j1T+vNqKDfg8dmp+fL2Nk2wPgh9/0l3m3f8krUfTT/qnb7ZaxA3T5jkGiCTv6+lo2x4kMztzcD2+Lt+44xvKagRxxz4csj0Znq4ER4wNxTQD7fiQp3qZh0JOJDBNMTgIjxmd99Xq9is0RE/w9+uOxRERJ7LwyGDGOk/b9xhD2e/gS640TE8dzfIjBNv7RBCHgFUBvDIE8XXkCmWD51ev1Qm7U6/ViFyal7DP5jpNmLx4wxna7XQ6Cbjabsby8XLZFGcMwh/ww5knJFuPP2yjAWjk5YEzGeBkvZbIZXeNv657tyDYxyY747L00EiBkD07iCXGbm5uVc4WoHGGskB8G/Z43J7DIFuILm/DnGDO+8fj4uJzt2O12Y3t7u1LZgD1425zxF3PJD7iUyjiqNs+dOxerq6tx8eLFOD4+jlu3bsX29nbB2bXa+ElVXGd9fb1UWPO0V/wH+cTe3l4hOX3YMAnUgw8+GLu7u9Hr9SpP1+v1epWFM6oATYz6CYxUPRwcHBT/ur29XQ5hh0Tj3Ft0GXmdO3cuLl++XPQMcoa+2o/VarUylkajUbbuRYyrQbrdbkXWrVar+BHsf29vrxyf4AQTv2nf7zhLbAF7WfeQNTqEfoMXuffW1tZt/oCndjGP4LDd3d1YXV0t/cTfWf61Wq1sD4NEhCCjKoan696POEzsgEx75JFH4qu+6qvi//g//o/4wAc+UCoYvZPCmBRf3e12y3Xs931QfsQYB5sgpoEDmQNIfy/GGNth8/aVJh7ygjRznUkW/Anx3VvhiI3WS+aJ2OHqdi9g2u/muGRylH5xTduU/brHblyOLkEs54p8+o6vYD6QLQS0+2/bYQyQhfh1bHJxcbFsFXYxBw1CCrmid+6jsRD2i5xMajrfz+Sfr+u5Nb7hM3eqivWCo/0F38vYlM87Vpg7MYHsa79cu2tSyswj+8NZxaB80Q7XwC+zaygoxs7n7QgtaD7D9w0M828nd/6sCRsUij4yNj+hjknN1RRMpickM8+ZEOJ7Dggu+c59tRN0YMkVTDkB5h522FYkg3LAcyaUTF7kZJfSZxoJ/8LCQmWFZpK8LBODUSdTdvq8n2V6J9Bqx+zkKycTfN6OIztxZM1nrX+THIVlbQdMgPcWNq6dSbtMRPi6r7aZCMm2hm6zOkf1wmg0KuegOOE7PT0t2wE4JJTDWQFJs7OzlRWyiPEBlgBE7o2zJABHVM9GQp+sO+gE8uEzBmQG8/7OcDgswHV+fr7s/zYBnJ2mwdsk1j87f+saPoSkNQdpxuDrImd0DtBmm+N79BmZMc4cUAkOti10yytylhfv+VqMx812mBNLk9mWk2Vrf0QwBgR2Op3o9/vlsGnrCytZ1k8TrZafARhAj6oEfjvuAJbpI98HfJkoeq2bZZtfZ/yMNfsh2iRfyPf5DmegRETFH7ASDCmAPhpEun/uJ7pDPzMwNhbAl7Kth9iU/ewkcOv740u4J1U+BuaOrRFRec/xyEDNMcCxnvdMzmbwZWI2+yP6bIBIhbbl12w2CyEFgUVMcV/s47PuWN4REQsLC2UbhglarzL72IIsd8bu8eV7R4z9mauA7c/5vPXEumICygA4+1MTHdnPTvLv2d/dSwMjmmwdjc6IyIODg5ifn492u105Q4hEymQrvodkFTlC0DBm4i+2yni8FQu9sN/0ggG+Flur1Wpl4YmY4ESJ8RH/SYrz+XL0aXFxMR577LF4wxveEKenp7G9vV0qSrydJyLKFj5XUKP/jH80GpXtMZByjHN/fz/m5+dLZfdwOIyFhYVYXFyMg4OD4t84HJucxVV+EGszMzPlzKbt7e148cUXy5iIN8vLy7G6uhoR46dpLS4uxvr6ekUuNIgZxx9IRYiwjF9rtVqJf51OJ1ZWVopfpuKLg4wXFxfLmWDEPPsOz5Pxq20CIow5MYFNfgdxyW4L+kH+EDHOD6nyzPZh24B4Qh/BStnfYNe9Xq9sETWp/2rbwcFBvPGNb4w/+kf/aDSbzXjHO94Rly5dip/4iZ+IiCixz7iPZlnaRvw+2M2Y22R9v9+PpaWlClZxnL4TORFR9V+Og9lP46NzvLE/wW5NgkPgLi0tVWJkjp2Q3ZmAyHEUeyauO2Y6n3d+gd5kTMZ4yBt4jRzGBA6kE/bAeNErYrzzUo83YwNsywUt+OLMNeCn8rZ+59UQXNyD+zMf/J31z/F2Uq6Y55hxMwbnGeSCyJDtzvgln4OY82Lk5xhtbiQXbdxNe8VnSuFISbY5qNErKAYf/jERk4VpYsHkCxPhsvNJg8yMMtfy/fkcYMCGQpmoS9d9LTsnK+okwsdGQnCykfq3x8fY/bqva8Bl1velEmoHIJIBEw52mq4Gc/8yWPD1OXOIYOmx5/297pudged/0vyapKI5qFoWuZIkJ0o5oTE77vtnmWYn5b5Z75womKg5PT0tjzu+k4Fm3brX5jEC1Axkh8PxwbURUZIdCCe+RzKEgzk9PY319fViR4yJ+acagHsgFwNN9AddQf9wgJaDAbKDnJttPBOYTlpxspubmxViKh8a6vmYBD78moFglj/ELd/lugZVDmh8z34iYuwfvIo1KUmzTXAvVzsxB3w2+0HLKwc15jrLgf/zqhYgJccDPmtATL/RsZmZmWi32+U1xg1oIZnyuLgmYJkEBdCUk7HsQ/ke4zDp90pWeu6l3e09mKccEyKqB17T8tzzfWQAECEh9ryaBOXagCnbJZ+PqJaZO27aR2bfPzMzE6urq6XU3e/53o7pjvOeT1bTHe8zYQ0B7hXtbPOWrWOCQZdjXV54Yux8zmdrALq73W6MRqPKU7xMlGLjnONBNaEBK74DnbedEfeR/8zMTHQ6nWi1WrG3t1fx1Z5nyyOiuiUyV5p7G7/Bsh/dnbc2TALa6IdtLuux54C/XcXjNinWZpxxr82VUtaLRqNRnhrmBMO6ik7wt7ex0pCXY+n8/Hz0er3K4c+eYydp9M8YPfeVv09PTwtuQZf9lDjeI47a/0BsgCs4vHt5eTkeffTRWF5ejuFwGL/9279d4iJnUbHgxcHSJLwR4yMrvIgMKdLpdAqRxNZAiD3uv7m5WeR+7dq1ifqwuLgYDz30UMFItdrZU7xJzlgsWV5ejoceeijW19ej2+2W84M4GoB+sd2O7Wpck+26EP4+5gD/RJJ3enoaFy9eLBX2JgM5A+709Ozpdu12O/r9ftk2yRjxH/gArhtRXQzytjn0B520XfJ+r9crOsQ1kCWLSnt7e5X4sbW1VUgO/C3XR3/oK/1BVw8ODmJnZycibsdPr7bt7+/HW97ylvjRH/3RmJmZiZ2dnfjmb/7m+JVf+ZWKreZczPcnTpycnJRtpvaj2C94BFk3m82SOxEjjR95bdKCq0kdE1nGi851wI0ZGzquT1ooQJe9NTHnZFwLe4V8zViQ/hB/HMfJkX1NE3jYh0kP4xTvfMBX8J4XhY1rsTO/jq5N4hDok48Dca7pGGWSnwXUjFXspzN5aMxsMtIxwQvKngfPv9/PhT7cy8ScY7/jAbppzJhJxdPT07Kw4JzzTrnuS7W7JqVQgmazGe12O5566qn4jd/4jXjHO94R//Jf/ssyoQRABOKE1Irp99xyspWBpI3LhuL3fa1saBmkYtR+ogCTa+Xwa77fJIF7Mib9eJw0M48GdTZor1w5EOREEoWxM3T/UTDLIqKa+NqATB7ZUdOH4+PjaLVaBRBzfa9+mhjI84MjsKIT3Pwd5OPxWAboZ2a+J81Ndsbuhw3bcpw0L9Y5VgKpDiK4Hhwc3Oa8kLlJyKwT96sdHh6WsnnIAoCWSShki43h+Ayos33Y2VFablDqJMPEKvrh95lXzx86ajLG/iTLE13ItmmQwPcAVlQfOAGepK+Z7PFcYQv4E2SNvC1Tyw+gyvcsE8bNtXmP34Bfb73y9QeDQWV/PLbrs6L4LqXPnm++gy0il0xMWT4ZwNnne65MVPCabQT76XQ6Za4Yn/0012d1bTSqHiyLnC1bVzw58WUuIqIyX9b117pZd7MsaRno+bVM1OdYZaCPfAxerAOeGxNQTlggBk3u8xkn0+ggnzH4sa1CdHS73cqCjsGmq3kMzpgjrg+xlc/LsZ8D9PM76yjXIj4btKIfJrwg/SfFZx8APBgMSoVSr9crq5XMB4R2TlYhZXPssh7Tp0mVqWxfJpGPqCanyAvZexXYOkICxt+2K66FT3fMRW9MOoKz0MfsP/GHXpH1D/0kgUFnsu++Ew6512Y7Y8y2D29ZG41GZbuk+8K5PZAqzBe6ij+0rjMGEoHhsLqdlgoDrgs5hv8maaPlBSPkhs8wucWY2cpGEkJsIS5h+0tLS4VcuXz5cqyvr8fOzk7pY6vViuFwvHCG/WIryA07AreyvWw0Gm+RpYKo1WrFyspKNBpn585xfhQVMD4PqVarVQ4xRh/xhWyfu3z5cly4cKHML7Kyrp6cnES/3y/bdniNeOwKI86Po4qOqnISb+Ta7/djeXk5lpaWYmFhoVSN2dbA4Lu7u5XtQtk32t7QQW9/qtVqRT/BZDzlE1yyv79/G3lOog5Rx4Ma0GXG0Wicbf2C8IDw44w9roecOM+r0WhUqnHutbVarXj22Wfj7/7dvxsrKyuxtbVV5tUL9NgKf6P/xhWHh4dl3BCi9snoKn4ZX7a3txcrKyuVfJP72b8zlz7zxzpqvGOcauyF3PzdTH5wDb7j87/se7hexh9eqMZ2TTxyfZpJcutkzqE9PrA69g5msT3ir4g5Jm2M77mfsZ7zABduGBtZXo65jqWctZfzdu6XuY9JMvU1HfMcz5w7T8pdTHaZPPZ3IsZnivrsK2M+tqs6dhJ3+c3CmvUZm/W8v1R7RaSU933v7u7GL/3SL8X/8r/8L9Hv9+MjH/lIWVExs2sAzHtOKg0ELehsODQnOf4Of+fPGpyYhPHrlMybOc19M5j0xPrzEbcTV3zOMvHned8OL6J6vo+djwNgTooB7igDSgAwNDlAkGI+DKaRfa444nNuDricjcA9cO6MLzsC7u1ElzFnQ7acDML9OgbsebNzm8RW8xn3yf210SITmgEoToOkitJrJ0kOOOiEkx3L5n40X9eOfDgcPzaU1ToAGn0CJPOoZjt39CT3107ViRmAx7o8yYaRk1fyDKzshHNSkfUk21nWgYioVA1ad2yvOfhYV/zUFF6HHEH+JAxZT0wU2aZzQCTg5WAAEWCbR64Qxdw7+y5WwAi0JBYnJye3HV5teWXSzfNnGY9G1adM2YY8PyZ/+Y7/x4/wJCPO2GA8tlP0E5u7k5+LqB58jr/2IatUUnEdy/t3q1mP/ZoJY+tO/ryJlIjbSRcSV/tLyD1XYLDi7vmzv+ZemZTyb/vvjBXwn7Ozs7Gzs1M5O8dgLeOG7BOYexI/2x3zzzU4C8QycZzgerznFW2TZfxPX0hC8L18H6J4f3+/+GRvSTUAzYklciKxMZZhHo1BANKNRiNWV1djaWmprODSV/si27ITKMaCL0cnsDf8/SRbxgaJHYwJu81AO/8YaGccgb55wSTbyZ3sCf/8cp99uZaTi3r9bAsT/pdzcvb394uOM7fGmsjJWCHLhHgwMzNTCBwnf8aSPBmPOMVTohYXF8uDS7BBJ2DIh8SKRJAzmyCR1tbWotFolOvZH/H+yspKiWN7e3uxs7NT4tb6+nosLCzE0dFR7O3txTPPPBPdbje2trai1+uVCkKI2N3d3Wg2z46LuHjxYqngGAwG0el0ylycnJxUnvD3+te/Pl544YXodrtx7ty5ckA4emy/hD55cYZFm/X19bh48WKsrKyU7Yj2PYPBILa3t2NnZ6cQXJ4zqqR8lAG2SEWVcUSr1SoEEHO5tLQU586di3PnzpVqKZM5HLC9u7tb7BKsl20x61i73a747YioxATjKQgsE1dci2oZKss48wrZ8kRRZEa1ID4AHdvd3S14BZ9iIuheW7vdjg9+8IPx1/7aX4uIiO/4ju+IX/iFX4iv/MqvjI985CO3kQ+WFTEXnMDiM4SMt4baz+HLvLDOE81Nqts38HkTJnfKAbOvnIQXJi1KWbb2OfgAdNEt50m+p8kpkxGeO+ufyRdfx5gm4zkXwmSiLJNaJghN7oD/jY/RbfroRSD6RjMu4f/BYPywkoxz/TlXIzMXOQ+2nCbNc9YJxuUFbcZtEtlngdFHFgHcH+4/qWLKW/v5fI7j1tO7zWvvmpS6du1a/ON//I/jv/yX/xKnp2dP0djd3Y0PfvCDcenSpbOLKfG0Y+P/zIjyGQNMC92AzEkS7+VklgnOoNzfMbCNiLJy4QTO17EiO5nKhj8JIBug5XJ4fydPHs4JZcjGYIDrBAAlZEXYwcgympTE4GBZKTFhwvs5yXfCxrYCzyWO1OO0bAGyNhbPn38iqvu4uTf3czWXZe2EKSf+tDy36Cr3tB4xXvpNUsJW1uPj47Kqw/hIjJCPA4DnNjPg9wqWLfc3v/nNpdS90+lUCLLnnnuuEmgBvRFRnnRDEodMsGVAaQ68Xh0x0LY+GUQ7CBHQkQd6lAGCdSqiahcej18zkWi/Yudq+/TrXmHmdyZbqAAYjcYrtz7nxtV/BGzLgUQSGbCdGJlFRFk5jxg/qjUiim7h07gPsmNuAbK2VeYD38Nhp4yP+efcMeuXA6htzwmOr2UZG6jxPdsASSMr6d1ut9yTMU9KjgmiHj8yZD4ioiRd/mwmvFzi/Fo3+0i/ZsBomdp/5iSDvzM5lG0VORIT0BW+54oj5IsuO55yXS9uRFS3hdleIRLYCujrcXhoPtMo+4qImOhH8j1ZxWO7i0nLiDGZeycw6zjrfjB2kmGDXfsNA3FWkV29ap9ioGi/h6/IGMa6MBwOyyo9sllZWYmFhYVot9ulatfyMrZyzLQP5H3bN4QIgD6TWNZZg/5J/sN2mvFcrjjjNScaGZtk3ed1vzeJzHo1DTk1Go1SUYJ+81Q5CBVXwzWb4zNQckUQusB76J3jJtXOnKXUarXKFpparVa2v3FswPHxcWxtbcXu7m5ZjOh0OiUh5nvoL4u1BwcHsb+/Xx5pjlxnZ2ej0+nE+vp6rK6ulmstLi7G6upqwRDsruh2u8VXg4m63W4MBoNC5MzPzxdCCf+PbIhr6BxPuqOiqNFolMpvE3X1ej1WVlbK4+ybzWZsbGwUOzBhY93jWAqwHfgJPScWIruTk5NCIDFX+DDrbK02ftoeB5qTwDpP8SL6cDiM/f396Pf7sb29Hfv7+7G+vl4pBKCBM3d2doqe29+ZRIMkZV7RHw5VZxETco6dAIeHh+XMLifTJqe9mAnRxBiZL6rlvNhFhRTzn5Pa+7U4NBwOK0+S+1f/6l/F888/H9/3fd8XX/d1X1f6iS7yHftk+76jo6NSBQmRCZ51/MhzwQKB/atzYPutnH/a9/N+zk1zvpmxBTqZ8y9sN2K8qO3cLMvFPpXXcr/8Xf4GX2Uizn/TyDe4N7mB26RcymfQGce7kol+4kcc6xlL5h2cy3Jv/BRzZR/juOjcm/+ds1iunmvmwAvayMZ9Nf6q1apHp3CviLgNE/Mdx7GM2xyHnZ9wzYxX/ffLtbsmpa5fvx4//MM/HK1WK974xjdGxBmh8zf/5t+M2dnZWF5engiSMDT/byCcwXReXTd54etmxTZQy+8TQM1+RkSpJsiJqxOm/LqFbqDsxMcOiOuQ6DOBVlh/jmaQhRPLzsXAkQDmVQWXFBpAOmh4K6OTFhSMAOe9sWZ+SZw5BM2GzPxm8Ghny3xkIoPPIItsyL7PpCBl5285ug+T+mnZcy+v+vp7rmTZ398vpF6n06mcjcQ1ePQ31zJRYaKN9+9HGw6H0el04h//439c2Q7DOI+OjuJ973tfdDqd+NjHPjbRiaJr6AFz7nJvwLNXABgrQQcA4AQIW8kERnZw3C/bSUSUhNZznAMiv+lf1sGIMekJYLATNWinb7Z1/3Zil5+OlftiWzJxhE+Y5C9ygmjgxHaInFAa8GGnvOaDdLkeB8LzCHHbJX4sz1EO7vahGQjZn2If9qGM1yQp1Qe9Xq9c31uGCMrIxpVsJDcuf8f+qBxjLvAplp9XkF7LZpnRJt03g0LHPJOUTuptO7YzfjjfIi8mcB+uxXcNQPlMjsf0g345hiFXknnbCudLnZycPbkpJ1cZPGcw7soePoOu2Q+SkGWMYhl5nNZbE1rEWyoE/BQ+26SvjR4C+F2RhKy4B3HbVYwmpHnNmAmSYG1trZAAmeCgD1m/bU/4fmMz/CB9Iy544cXVDxAdVBbkRGuSHtEsD+sin3OyP8lG/L91xAnOvbS8+ER12tzcXKmAwbaYAxYd0S10tlarlSfIGQc7PpBgcLYY59mwjdJkQKvVitFoVLZRRZydt7a7uxu1Wq1UTrFQ43mChAIHgGl8hku/34/d3d1yJtOVK1didXW1bGHi89anvb292NzcjJs3b5atavV6vZAhy8vLZUsTxBikHm0wGJSz5x566KHSx/n5+Th//nyJa2zNh4RjfhYXF6Pb7VbsqtPplG2p6Abb+0yWMZbV1dUiPmu28QABAABJREFUJ2Tlre3kFxFRiCfmjTH1+/1CSti2ZmZmyoNksq/e39+PJ598MjY2NuKhhx4qZBl+BiK6Xq/H1tZW8Qv2nxHVI0qIjRBPVETOzMyUhB5dobITYo73GS++kG2drVarVIQxPuIz/oj479+ZcDCmuR8LRI4fs7Oz8Vu/9VvxQz/0Q/FDP/RD8TVf8zXxL/7Fv6j4SPpl7Ibd0k8OzM/5To6n+YFA4I/BYFCIbce4nG96UdV5oPGl4y0t47ZMsjhv5h7Mg3dToDsmRkxSGNt7kdmxP+fa9Mt6SZ+z/2Mucg45CQ94e7PJf+MDY3awiHNdY1jHbmNP8hQW8bKMc1yjT74+MnTuOilHcgP/Mx7uafwwGo3KLg5jDHIG/BPX9/xwDfLdnA+w9dhY0nHNfbibdtekFODGCSPlwDYgAocnxOSTSSUz/EyqVz0zCDKB5ddsSP6b3y43Z3IpozWh4SQ1A3orZU6g/Tdj5X5mJ/N3DIQtExt3BvRZKVzGT2CIqFazcF8nAb6vx47MuQcKxmeyvDMwNCFhEssGnpNsk0I4eII017WO3MnRuz+ZcECWNiZeo8+Hh4dF1u6by0O5Ln1CPqenp+UpMICXdrsd29vbERElMFNW7aSDBIb73K+VIBIFZFOv1+ODH/xgrKyslLmfn5+P7//+74/Dw8P403/6TxdAMilpsZ70+/1ot9tFFpC+AA/m0POOzNiik+efeXIzUGYcJoQ811kvHHzQJ5fimvihz5MSUMCmD6mHJANoAryz/tiRI0e+z7XyFhKPKYMS/kZOTvrop0FsRLVM2o8hztcyqPG1uR59IJHP33XSmOcGvzIJkGSf7vvS8hlTBwcHlW1RVHkhg0wWOvE24QRRz1wClAFhjOeTQUi5oZd3+t+vWYbWtZzIZ59PI+HMFU7YEHGNWIS87B/4nolj7pV9svtcq50lk2wTpnoIn/Lggw9Go9EoT11yTHKSagxiHUMOHpvBbK6wI6GPGFeP2X+5/1Qk+TBV9Af7dpwYjc4qEdiS6GvZJhzz6Cf+y9XIkK5ufgjN+vp6IUpothN03BVeyNI6cye9sU6hdybYOBIBOwMAe4sC79uPZbz0cvZnvzepn+4vLWODe2mMJ2IcH0nywcoQdFSxo7+j0XgVnxgHOQDh5BhA37FFH+zNI+yxSeRI1aG38YG1ut1uOWvJss84CxvKC8bYOfhna2urEErcixxhY2Mjrl69GteuXStJkONYvV4vVVQRZ1Xay8vLMTMzfugF8mIB9uTkJG7cuBFLS0vRarWi3+/H3NxcPPLII8WvNJvN2N7eLnYxGAxKPoOdttvtaLfb0Wg0SoxB94zTIaGI7f1+PzY3NysPswED5TPWqI6EqIQUM1bNMRTyx0QH/uH69evR7/fjscceKw+eMTFGRagxJxjI5A8EItsLqXJjIchnkW1vb0e73S5E6HA4LE//Q+fwL8ig0WjE0tJSkRHXsu+wP7JvBP8xZr9/rw2yOGLs2//Df/gP8fGPfzx+6qd+Kv7kn/yT8Su/8iu3YbiML9EzSAF0M+M7x3DikGUAWW3iIOek2D1+xRjK/iG/5rhof5p/cy/u7byZPhIfjP/8GeNOx2pjA/sXsIT7TjNZNAlvgkc9Bq7jvmGDxgKMgb/Je7AjX4vfxpOZCLPt8boxj8fsOZyEWUx8Zr3x+MBn1kfLGrlkXE98whexnZbKSPc3x2R0yv3B5n2mlHXklcTYuyalmEiAUcTtJA4TYdbfbC6DyACZz5mQYmLyvkuDWQvFzYrthIi+jUajsmUvB2IbHL/9wzVxPpncsLJGjA9/toK6UsO/bWgGhlZqAksG4Lnf3n5mUskBwI+WtrNhhYuxcl+TaVZWZJHn2Ay7r+e/Gb/HwbxnR5mNO7/HvbMjtkNyH5xImdn1dxmH37OcuYZBw8bGRnkkMTrNYfDLy8uVFWTGwz3ztsF7aQYxEWerdX/kj/yR+LzP+7xSJXf+/Pn43//3/70APMAffUB2rkTi2pTj+2loAF70ywlmruDxPKPXdsA5UbP9mrTNgcwEhIMrq3850TLoIfDaXgFTDqL0uVarFfLPeoxuWE/8N+Ozg7d/tZ9zVYb9UESUJNj98Qqj7T2DEpN1yIl7N5vNslLLdVzNRBLjCoWcpGbfm+fLc2C/6f46AWN8nJvh+2OD3rJs+8J38T7BGP0lSQJYILdJPue1bhmUvdRnst/PccENG6MBaLLMIyaf/5hjE0m0kzb65vk04Mr9t3/maU34zYizxPShhx6Kq1evljN5JiVxjqv8n/2KwXKOEZOAtHWIGIZ8vSJIYttoNCqkvFd9bZdO5D13yN0yJvHFd1lmGUvNz8/H4uJiOcdnMBiU/vC54+PjODg4iH6/X67jpADZ2d/SRxPN9A8f4oN9IcIc+waDccVYlu2keM7fWacYvz9rYtnymdTsm7Iu3o/GeFx5SVxg3Gztiag+OCYTWmCIiChEpg/b5dr8j6+GhLScvEXLeDwTvRmDohfM5aTV/8XFxUJe9Pv92NvbK9U7S0tLMRqNYnt7O5555pl4/vnny2IwBMnR0VHBEPYtvV6vnL9Ur9dL9RhEAgcJ7+3txeHhYZw/fz46nU5JstAJnxcIKXhwcFAORKcf9LlWO1t0ow0G422YEVGqmxYWFmJvb68sBPuexozgB+Zgb2+vVH8iY9sacwdx4x0OzgMGg7Ntj08//XTUarVYW1sr55eB2UajUWXLrnXH/pv+8uPK5FptvD2epBNCh4fFZOIaYp4+ZDIMHUK+/KAXjlPZ15EA32v74Ac/WLa4Ip/f/u3fjne9613xXd/1XXHu3LlKnHJ/mId8PufJyUk5zoLvuUDAPpf37QPxB9hsxquZXDIOyHiH17Ic7d/pA+85f/Qc8Lf10rHW+ZNzVsvAY6APzrccN1kQ8nvuo7GqP0t/+J4xDjmuH3gClqW5/3zXxRU0jyVirMPkQCbc+Tyfi7j9SAPLIZ/Zm/82qeeCDn7ct4x98SX4TopzuLbJamRL3xgTlZ7YOD4BTsW5dpbr3bRXREqZgXMik5OOrHwOZiQOBrEmsAygMhg2QPGPA6knz0kRE07A8RMwLKxcTRNRBWQ5kXOyh5FBUJAMO3HKFRCWIQ6Z79LscPzZbNAEEyewlgufYzUKBeK7BBCSUSf0dkB2EIzXTwDLc5DnzQ7NzghHzHW9kmAj5j1+I/dJOpENM2IcYEwsUuLNdTPxwHxguC6ZJPlgXprNs8e9kkQNh2fnAczNzcXy8nJ5KgnOgD4CzujT/Wp2EIPBoDw+GbDIZ7rdbqysrERElAPQTVDirACFOCfOE6vX62VVljkDjOHsDL4JzNmeCSDeWptJFduS597A2gEpB/hs25ARJn2YY1b36/V62QJMv3MAzv1xkLNPA8A42OeEwTaXk36+gz/gWvYfgDwHGXTAMvX8ErStg16xMUACcDN224qv6/47GSMJcUyxXeUkiXEAJKguwA+4mq9eHz9lyoki97ZsvDjhyg6uzfd/t9oknYp4+SdO+n8AEzbh92u1WtkWyQHItEw4OUk1UeN+ZF3JC1NOrKwjg8GgbNNptVrl+5BUV69ejYODg9tiOrjECwO87hiXZRYxTurRfWIOfURXTDzl7WfeaoIdokPYYibBrFP2D/TJsqUiwQSa4xFbfdg2BdjMAPvw8LAk6I6rNPtEv8e4s/+mz5As+Gx8vhMM+mCid1IzKTgJyFoXc0KXdT7jD8v6fraDg4Oic64AHg6HpYJ2YWEher1eWQg0CcN8sUoNRqvX64VgtI90zEFf2coXERVsGxElUfLDGzKGywS/Y1Ums7FnKjvW1tbKmDjfaW9vL1qtViGWNjc3S0XRpKSKRStkwFlF3APMitxqtVrZWjc3Nxd7e3tx8+bN0ve9vb3iE8Ba2Cb47vDwsCxQUJ3G+XZUYy0uLha5geWJF1SZodts3cEmZ2ZmCkFE3rO1tRV7e3sV/D5JH/EnriyiIT9+Njc3I+LMD12+fDnm5ubKOWLNZjPW1taiVquVp21iB2B7qsfBnox7NBqV7ZPMA1VBxFO279mv4R+ZC+TFofbug8kMjxu52K+C9R3rX22r1WrxoQ99qPztuPHMM8/En/tzf67879zI/ea7+Cv6BnbOOYLtifl0PEXerVarzM0kUioTQlwn+8o75cf+nMfBZ7P9+37Mr8lw46V8fcdbj5+5NZnkvJ//c78z6ZXzxEn+3vcDF/j6yN79d0zhfxO6k/rBtW2bOS45Z+EzvqZ9csQYe3vBwraGPHnNMcK4HP10DPBC0aS8GT/mymovpJmU5X2P2f9PiuV3andNSplswmDovMGmE05PhJWN6/F9s7IGtAjJ18pCi6gCFStlo9EoqyERYyCNkzUgy0maVy/47dXjSYGV+49GoxLkkBVgEgPPk2SlymO0Yg+H4y2OGegy5tFoVIAJhggQ5T0nlU5WIGjMUs/MzBQAYOCK04S8MAHEb8bhebUz4x6WC7/RCZcD5yCQ55Dr+X83+gWJRtChX4BrO2Su5Xng82amfU90pNfrxf7+fjHovb29WF5ejnPnzsXTTz9dVqz5rss/79aI79TstN1wbow34owF/9f/+l/HN33TN90WcJkrgrNJEP9fr9fLE9KonHMixTUiomz94z7ZpglYzAV6fKfk2+/Rf/rAPPte2DuAFP1EZn4AAiu0zClgFjmSDOfSZj5jW3CCx2fRbQcs5i+T3yR2vG/fAtDlPiZWLKtabbzijq7Zx9En5JJl7HsbZLhfDprcI6/q5KTcviLra05W7asz8cFcGPB6ay6+0X2OGJ/rQJ8zYP5kt3zfOyUx1mfrj5PJSXpkYiOiepgpZLQffuEVX3wy/sP2ZT1Gh/ntOI/suR5j6Pf70Wq1KiuarVYrHnzwwbhx48ZtZ5OwGMJ4HVe8Ou8ttnw3IsrrThZIug10+QwysQ5iz7ZzVwrwmz6ZlPPfOR6NRqPyFFTmDxlSzbiwsBCdTqf0jfnH75Mo+fyeST6Ucdse+RviKcdAPu97+P78zaPf/SAI2xb6NQnA2ue4r44rHk/u+0u1+2Hb6AmrxWAi5t1bqmu1WvlMq9WKo6OjstLshAL9OTw8jKWlpUoFqytFsg37ATk0cArXjxjvfIgY+0Sv4tunYtvG+hFRiFCqTSLO5nlpaSnq9Xpsbm7G888/HxFnZ9LeunWrUjXk6mOIjZmZmcqB6uAA7JoFPXQR3STWc74JY/XTcJELxLKvi+3nhyAwt2xvzjgUn8aDQ3z4OfgIn3JwcFC2IdPsWxivyTHHSK7J//QZn7m5uRlzc3Nx/vz5Ym+DwaDMz2AwKPaJj0df8LGrq6uxuroac3NzsbGxUc5KtZ75jB7+Rk9crZG3QXFwPPqfq52M/cFS5CPeXWCi49U2+wb7AMcO9MLf4Sf7KqpGwE34pVwh5SQ94z3nGywKZp+GbaIbtmH6/XL+z74yYzWTTTn28Xlit/PefH1/1gtbGUPSZy9Y5O/n73mRwzGX7/C/9Qvf5TMPsz5EVA/1zjmDP4d8TWTlXRxe7OQ7k3YxOKc1oWS/6z6ZhEI+yATdMamdCTb0xec78p5jhO/heXW8Y5HLC/pezJ0Uz1+u3TUp5eogHxjuoMXnPAEM1MrilU3+trBsJE46bGB5kJltRLj0m3uYcHGS43tbGfjx/Q0ibcSMz5OIIeBUHdSdsDthsBFi/Ab5lqllgkM04ZbJAPqZtw4Y2Pn6AK3BYFz6bJKP1TGv8mEQXiGi2ckhNwJUJiTtkDGOXEHjRNaGlx0L8+pybScwXIf+Wl42KhuujdQO03KPiLKatL29Hdvb27G4uBhXrlyJBx98MJ599tlS/k0j0L8coH65Zr12A1i4bLVer5cDQh1okZGDIPqFDZtg4W+TJ+6DHTlg2Y8tN3FBP7NdMIacGNknYLOAO9sy12WeDTDoa7fbrVQUdjqdShLuwGzdI8G1LWUfMUk/GVPEeEuHgYj1K68M5SoO+1rrAX3BBu6U/NEH2x/B3PNo0s1kkJP5DFzso+xjTCQyP8jHOsfYsq/IgMbyZ664jrcT28+iB5n4ux+J6ytpd5tMZ6DixrzYTjynBv6unuKpMXyfpM7bOCFAuS9Jpu0xA1rrpGNzBmMknr1er5xvB+F97ty5qNfPzqaB8MlbXW1jxGnsz+APf8bnwTSQWHl81jNwiwm2SYtVvJfxDmc5QErU6+NKCMvBK7ss+rgyZmZmJpaXlytbhbK82aZKjLEftN8EFzkx9HzjT21nJuZN9mbgbl3kGpPk5AqLjEPsd9wmJTO2Af+dv3u/7NpPOs1YttvtFt2AqKLfJDIsYCInJ8Q8Ln5+fr5soXICFDGOdVyn1WqVs438PpU7Xn13gkwiRTzBLozjken8/HwsLS3F0tJSRIwTFaps+/1+9Pv9OD0924b3zDPPxP7+ftFb5JMXAlkIst9CrpCaTrioYOIJgugW53u22+0KhrZfr9VqZasjfTIJf3h4WHk4CzpItRZy6Xa7pbILeXNAuHEM1UvWdcdz65DzCFcJeScBC2rNZjN6vV5l8Y2tjCzALywsxMrKSlmUr9Vq5Ql7zMHq6mo5+4mnIfpAbvyJCSh8IKRdxLjqBz1CJsPh2dPueNgKckB/bO/Hx8cxNzdX5GrfDQF0r+2l7D9X8OSknn4S+5gTcOXx8XGpOs66x3XQf95juy4yc95HPOB1rmF/n8mMXAGT809jS8+TY7gXlpkj55TeRePdJZ5L7mUi3XmsZe73uJcxC33imtbFjHcZDzLxIrx9LNfwtsRJGN25pGMY8Z3mSqyc+/oaWbdyDDPPYALSuQDveSHB2AT8gHzBv1TnmujO+Dli/OTgSQQt3zfBxefQAf99t/H2rkkpEhMrIA40AzwTVRYOguYwTieLGfiYEXYSlRMZA1AnQFZe+sF5CnmVPjsLxgpQnUTYeBsPn3H/AAnZyJAFYzWJkRlcAjTfoZ+TEk+cRE66rLCAW7ZqmMG2rDznTuKRM+NntY8x+jueCztm5OD5ygDESayv61LBbKCeH/fDzsWgyQmuQb3Lxw3urS9OdB2wrSvcj/GT3HU6nbh161Z0u9149NFHY3l5uQA465dlcK8trwoATGu1Wvy+3/f7yrg/7/M+rwKQCLiAWCqCKNt2sHSABChme8V/oMvYNUDVcuNz3vo3KWmyDvC3v+uDzQ28caie04goe6z9hDe2J/J9EyWMwfZm3+i95R4fPga/l/XW+p2DgStPPN5MpNIfAK0rU0wk8h3sAplBXPI682d/mPuMrds/81nmMK+QenzuI9fHr7lNSl6z/wRIcU/7RsvNyYNtOyfOn8yWQV1unoNJ82E/xWu+npPjiCoAJkaybQxZYIt8nusg37wiapvLoDkDNOvV6enZAcztdrv4BcbCYc6c+eIqavstQLBXAk2suwLDANmrnRHV7dl8B5sAm/BEL8cUFnGwh4WFhZifny9VB5YBJIP7Wa/Xi48lbhF3G41GkY3Hy/8+O8JHFRiXeTEwE0LWj2zjyM/zgqwtTydAlosTIe6Djln3rcfW+QziTZC65Ricx3M/26T4Y7I1E2r1er0QVOAMXjf453UeAsDT49hKVa/XK09BYwGRJ6hl34WPdMJgnTOudjz3rgjixPLycqUCp16vx/LycszNzcWNGzfKPEHY7O3txcbGRiHNIB2I0ZxfhN9x5QnYz1sjwdeDwdlxBMvLy3FwcFDRy4WFhej3+0W3TEbz3uzsbOzs7JQn7TmGmET2NZGFKy19uLUT9dPT03KGG+e4WDeYd5oXTPmfJDIiKltviWej0dnTFUlIwSTnz5+PRqNRDlRfXV2NwWAQN27cKLsf+A2BFxGxublZtlE7R3HlODnI7OxsubfPnIF8gzRznIIcBYshL8d5jw87x0/k83o+GS3HMF4zRjGRjDzzGZcRcZv/M9kEKYXeO2YRi+xDc5yf1DfHN+MA+xnikAs4rOMQoMYWjNuY3L4j4xI+7346pjA2k0y+n6/jeETBBxide+fPct9cAOE81VgkYyXHOrCl++0x+SgBy8h5gjGe5yfPmbkP66P1wbqUzw/MciCPJq+adB36yPe8YAtewBd5gd8V75mculubfUWkFALCyTAQO1B+O9Dym9d9aGcGsQZPFrwnwyt09M39pA/uX3biBsgInLF561FmYZksJixXJdFfPzrT22AMBLzSYKXMzpcJdT8yoM7KdycHiizs+FBMALcDEXIhWCJHKlBcOeJ+4FAjojzxJPeH79GsN5a1g7CTR+uXr2l9NYCAAEF+1j8b7NzcXMXoPOfZmXvlHQAzCYSahFxcXIydnZ14/vnn46GHHip9JtG6XwEXORlkfcM3fEOsrq4W0PLe9743hsNh/Nqv/VoZN+cdQUKQjJo8zsSMg4QJCdu3zyyIOAPCBs/WBWRum6rXq9sSGOMkmaGv9MEgMW9VcPAALJEQci3m2uW3tdp4e1MOytYr67UBADKg/8wX72MzfC7bjwnrTNThPzjzK5M9rn5hrljtwM4BmIBXEgj7f/cJWaEnTjQ9T5ZFBi00y8N64bm0zA08PHbGxHkfzAuyzYmuAW/2ob+bLffFhICJUD5rn54XdAyMTODYjiPGq2QGIq5OMkiDzASMRVSfssr/3DfHfZOkjIkzrgBZzB8JsQ+TdlKAr7JuWFaNRqNycLTJK+smffWPV0SzDWei14Q1RC1J2MLCQknMDw8Py2IR82VyB/9C/PKWEMce4iOkFImEMZVxjYGv580JAfLNC0g56XB//D3Haj8wgs96YYfPGucwX3kByDiI1d6ciPnekzDi/W4eV6/XK5Xl4AowET9UpBBHmHdjxNFoVBZIHnrooUJMmZDnnugaMcxVPsie/hhXM19O7DJmQrcXFxej0+nE4uJixScsLS1Fp9OJnZ2dote9Xq8cT8BWRM4163Q60el0Ynl5uTIfYC4TFKPR+MgJMLoTZJ8bl0ljxwSqzZwQEtO4LhjDcZp5Y1y0+fn56Ha7FazD97FDzrPq9XqVB6Igd8dlfCv+FcKR7WsmKCKisrBWq9XKAxFMHj7wwANRr59tpazX64WYOj0dP3UPAuX4+Dg2NzcrW3CRbT5gnwoqfLCPQfCCZC4MgFD3NmMSZD6D7zApeXh4WHnsfMaKr3Vz7J20WGU8GTFeGIDkxJ78HW9RzLET2yKPNC7M5IRjdiY5aP7MJDzzUpiVHMKxFH+MHdJ3E0wR1S1wjp285jzeeDLrGp+hQo7P8PlMnCNfy8Tydx+RNXab59VxE19JHENOXhzLuMD4O8+F5yPjO38my42+ITfsxMQeMQE50WfsCHkwNs+vOQ5jNPInFke86IsvcEx6Nfj5FZ8plZlFExg5CbVwEaRX3icBUydfFqongXtnptdEFoHf/c1Cd8Chb+6vlcUrSHZKDjA2VhTUK1AEFfphgiVXmzAOGgCOPk5yPv5tReDzlJUuLCzEyclJ7Ozs3LZyZmO3rFF2b2+Acc2kIfdEYQ22cmI9KVn3Zwwq7Jg93pdysgZYEdUqswxynZwhIwMezyV9RG99P+uL+zgzMxOdTqdU3lCeTJm4V8TuR3OAizh7RPDP/dzPVZL64XAY//yf//P4yZ/8yQJoOHQVncmBEcCVbc7JoOfIQBxdmJ+fnxhILVfuldl8l3rnJIT/7Yssg/wkPesWJeiunHN1ENe1LueEzAlSTjBzApfHTgD0diF8IH2yD7TsHeDzygX6a7/mIGT/SNBH1ugjYwMcG2B4pcg+1b48+00njZYfMvGc2heYIPCqmP0qsnRyTvLEIoHHZVDi/tlPfTJb1lk39ymTddgJn0Gf/D1/F13NMYD/mcMcC5FvxHgLNwsxBtYeh2Nc7ne2D1qv14v5+fm4cOFC1Ov1spWDQ505AJlk6OjoqLJNzf3NxIYTm7m5uRJfvbCQdYskEb+EvTUajcpWKdslciAh9bk0touc1HFvzj7kXDvHH99vMBiUxA3g6DhnsOr5RUfsV/1Zk7TEUfyQfQ4Hq5sMxBdFROUpPa56sN5ZT3PstL5kO8i+9U72ZP23H570nVfS3CcwGtuckA/VP9gH1brGuNgvBCPkCDKPOIvhji1sW3Os8vZo8A/2h30Yb9l30heTOsiHB7W0Wq1yjUajEZ1OJyLOKmzYQsgh4OiZffVgcPbUOOs7Y3TySV/q9Xrpt5M/b5XPT5lsNBqlqqfZbFYeVY78GBf957veIWH95Zr2idYlvmf/6GTOfgOfYzlCTNg/ZQKW64G9fX22CCKnW7duRavViqWlpTg9PS1bGi9fvlwOK4dkY8s05Bn3tj+H5EQ2jqX2DVQHOVHO8WV2djb29/eLTJAtOmId5h7GA8ZBr3UzFuB/y8ELOoxlMBhEt9uNTqdTsIdJBEg52ya5FfkxTzD11l4+i48w/st4xfgux0N/LudgOY+JGGNNYourcUejUSlSyL4Yu/CZxC/nd10cwXjRAT/EIZPnuZAk54rc03jDi9QmPb246twjIiqFB+D1iKjkNLyH/ZiEdY7CWCb5k0n65ubvIIe8KJhxdsR4sYxY7wWw/FkvZKAf+BrifiaKs7281BgmtVdESnkVxQGM9/3bxmbA7ATLwve1LHQrhB1SVnAGbuKMe+XVQe999PWtEE5Ynegx+XYS3NtkDsADAiInog6+lHLbOViGNJ9lQT8nJS5OmPnt4AXQ5ftUTJiUQ05mk11JEjF+9DCr2VkZSQg9J8jGc5zJBQcwHL3n3MYz6Xo0nAJ64lWpLO+8QkkwcJIDQHR1nPvO306AbPz1+lnZ/sHBQezs7JRDJJHlwsJCAWD3cyWIcZ+cnMTf//t/P5aXl2N2dja+7uu+Lvb29uJ//p//56IPyNfznh9JnufKBEGtVivjsLPkOzMzM9Fut8ujrgkA1lnPH0A6O27ed+BFZl71tE+5k1wdnJwcYMNsj+D+1nUTKryOngBkTQCY2JyUJNPf+fn5okfIyTaB3JkjEltAZZ4n/s/khYM09h0xrswyGYkOIVeANgSBK6+4r1dpJhG1tv88h9iZV8TcDBRMQuGr7CMYs+UGWHCy4n45Wb7XxPWVtOzPsl1kf2mQZjnlz036bXLASQ/yMLHi0mySN7YVATrtN/ifRNkxnB8DRI8TQMhWQifXNHQPH2VfnZMIEzmOnSaaPDY+bwLcZfEklTlxpOWVYq5t24SI8jkzXJfFO7bHGySDn+j3yclJdLvdytagDMqzTtnWsi/LccC2QTx18kX/M8HEtdnClEv776STk/62rzNGuFPsn/Q/18pY714bD1Go1+uxsLBQSWRIMvGtyNaVM+gP1Z0+V6bZbJZYcHBwUKqUut1uZUzEZ+ujMRwxyQlVlhMJJHHKrzebzUJIMS5IquFwGFevXo2IsweY9Pv92NvbK9h2MBgUwtr6zyG5w+EwHnrooZifny/9JsklQec9V0k1m83y5EM/2MeJPfbHtkbkneOvK6GoWqQqDfIHP8KCpQ+2tk8EJ1Fx7kon7jfpoQuLi4sVQhz8Yd+DvWdMRIN8ogLs2rVrMTc3FysrK+WzS0tLsby8XPDn/v5+OT7CSTnXByPQvBDtigvIl4iozJFtDn9PhSqkJOQf8kQO5F9cy2TiJ7NN8lUmOMGP6B9VcsTI7JORqbGPY6MXQny0hW3ZxJ8b15mUb/G+fb8JB+fCJqshI7AHL3ZaT2wnmfRGZ/Ax7qdjEP2i3yZNXJGFn2KRytyEfXuOxTTItbx7Ks9vRLViGtxr2eIjzJEg47zN3jg+y2ASZst4P88pOYIX1E2a8X0vXrgKMX8WW+Qe2CR+DP3mPrZXy8qyzDr4Uu0VkVL+zaTYsBCgHRDG54SWa6DELks1WHVlEYbpiaVhpP6fZkLEBBmONydS/z/qzqRHsjRLy8fMfLLZ3D0iMjKzqlrdpd7AriXUCxZsAIlFw4I/gMQOIX4D7JGQ2CAkJP4Aa1pi0xKbhiUtNdAjlV05RoQPNvpkAwvT893nnrieFZExVNaVXO5udodvOMN73nO+7+brmYxcXpqJE4wE7LEzLmb18+RwL/rq6gqfjzB57E3S2MFyfhZwxtsBsA1krurw82043ReME/PjNe/cx9kTy4FlCAPL/NI/hN5GNh9+lg2ZycgcDD0mJz7HSu7ANRtgO2i3KTPhOG2CeIy7M6s2GO/roL/L5TL+xb/4F/Gzn/2svCHn7//9vx9/9+/+3fgf/+N/1AgZACX7A2Ri1WPq+bccOWijb7wtKpc0Z5nLRj87Xxvb7JTRxYio2Q7aaV3j3q6c8RxnfUXms+NreiZjkAkTZMGBn/WT/hmgeNki/+fP+Sw7nTwX3BdbwD1wLrltZDYBLd5Q1YEpcp0DWdvg7Kwy6eMx9zx4KYfvkecsE5NU1wDmaY8roxzA0U6P68c+bM/cV8bCgCbLT/7JBCRjYzvHmKIPfjbVg54TbBjksl8mQDszQPTfEfWq5cf8FgB/Op3GYDB4TY45mGuIYwMwyyBts69nLKjwoF0G/K4Y4x4EibQFsobg1jbJlY/IMuPaalVLgBlX9gfq9Xo1DME8UbkaUeETKpXsO3NA6fY74PDnHivuhy1z1hcCkL5wH3wlbYZYdDLLgUommywbPtfkoHXEemH9afqMw4HY+ziMPW1zLYOukEIGeItrBv/gBOSKfYyoZBmNRqUqkGdR8YJvckU7PtpJOZMMxj5e/kW7IEcnk0l0Op1i609PT2O328XXX38dd3d30e12yxI97JIrXiAfXDFJhd9yuYzT09M4Pz8vpF2r1Sr7VjmWWCwWheRaLBZF9iGosu47+HKA7DFxcMtnLD10LOIx5VkeS2MfyBv6AAZmzG1X0HsqKhxjdDqdQrpn/Iptxd5y/9VqFdfX17Hb7aLX68VPfvKTmEwmNWyy2+33/PKb7SAQPXa2U8YLjCP+0rbbBD7jxDU5uDaxx99cw3251nbwYx5N/pLP+W184XZC3GXsh0ybJCXG4zlgMkhMx+FOmkfUfVu2gcZ9/p6/c5zk+IMYD1ufnwdOYN6NUawLPI/K0bwPY8b3xnj2sXzulVCOBzjffixXKuI3ebar9JxIdgyHXQPH5njHS12NRdBhj1dE/QUp6Ifjxia83ISTHGNFVFiBcWi1WsVf0BaTh00Y0TGviWLeFmuCypVTGZtbR970eGNSyptuRkRxsjmIsoA/xoaafXWjAbwZYDNZXOOJdRt8+P48D+fKZ7TRpJMDICbEZZKeLCsD57uvdoAR9T2knOW3QrjtOQDLY5GBG/e10hi8WXABwGSuaF82lh5P7uF2UvZoR2SDZtBlY+cgwYeNSybafL4F3eNlIJ3Z5ybD7PEEJNiwsZEjh6/3tZkA8Oc4XAcVo9Go7CfBfQGj5+fnZa+l932s1+uyZ8C//bf/Nv7gD/4g/uW//JfxR3/0R0U3CKwAn9Y5Gz6Pm50vOuPgt91ul7dpZX3KDqRJ7/N4c64dqb/H2NseQaY4ILOOYuPQU+7rSgqDTreD8wEVrVargMyIal812y3+ziAlB4kcBmfIEyRwdlgsWbC82tECJExwMa84X1dNAdr5zV4Ubg9go9/v18jGiHq2y/bhMVDiw44yOz2DcsCGARTX8L/H2Nkexs/zY7v1to71XY43eZZ9Ww4gOJBf20X7S3/G+cwlJAljS+a93+9Ht9uNbrdb23vMOmccwGF/ib1wu+wnLP+73S6m02ms1+tCTLmt+Ihut1tbhpf3BuO8TIJDKORnGxhGRKnyYCys6xGV3yXwtp3k/sY22D2+B8TilwGQ3juHdvC8m5ubAhZNkvG9EzxNgV3GaHk8XGXsTaDxVdvttrYMzQEKz6KNGccYH/G8rNd8brzB7wzO8+E5bjoew40/5Mh4CPlGVnKGHHvk/jGm3p+Q62nn/f19zOfzGI/H5c1ezKftOPpEW2iXkymWB+bdPtPBDm/Z414HBwel8uZv/uZvYjqdluoiV1DwTMskf5O8oHLaZByBD7LO/cAiBwcHhegZDodl6wMHgLyMBeIvIsoeg5YJ+ozetdvtGsHrKh7sF/eheov+cL+jo6NSCef+M55O5GJDwR74V+SJfT4PDw/Lc5kzZNirQRir9Xods9ksIiJevXoV4/E4Pv/88+j3++VN0K3WnryczWavkUVOcDmIzUSBsWImMrFf9D3bHtrrapWMRTKBhZw/ptcf6rAftX/L+CNjBmykXwxk352LHLg2or6Xqf/noPIvE4I55jHmzTjRsboxGm1A3kyguJLGcQ140QlsjwPt8VI2xgG9jajsqONWH45/M3mF/Psa20fG0m+VdEzv+WbMuc7VmLaTnhcvdzNZzb3Q/0xqOq5mXiwP9nnmFGgPJJ+JJO5rH81n3CsnKH2Ot65hHHlZHIkwxsbEf9aBt8XOb0VKeVJMKvA3k2MGMwsQk+x12Uwsg+Ggj4nkaBLErIjcx8EkjN7Bwf6NcR5ojswO2oDkiTN4j6gHB/TFpdMIYxNhB2j2tU3PM7DlHDt8G70moYZtZpzY58DZYld8eAkPTiZXfSGogFHm3Nk+G0P3iXHIymDly06M813JkEmLpsy25dTGxwabfnqsATbOMubn2egCiAwcfD735XXgjLcJgt///d+Pf/yP/3GtRP9djmwUMDjT6TT+63/9r/H3/t7fi3/4D/9h/PEf/3EJRmk/WXzaBoADcHq/KebUIJ0fKqQwXJYNzsnVYTlYzX3x2NouGKgZYFsP/LdBFzJnOcj2xoDKDsJ20MSXbQA6Q9/5zAG+bWsOZLMsRVSkFjIPaADgc3/0IqLaJJV+cm/aB4mT7RCfQbJxX+aJsaHKjrFnXJwlt97nebcfaJIn23mPCSXmZHWs+01LJyMqMt5gKMvir+PIQbw/83ceS8uc72NwkOXI5zipgb84OTmJk5OT6Ha7MRwOS6BimWbucxKCNtr2G2xnwsHncwB4j46OSuDoecxtyXPtsQF/0D9IH4L4rKeMJzpim2JAyTUs5bIPc9/9WcReRweDQQmybZuMQWg7QbJ9oMfdtqVJliwfyAiBk/tuu5xtJaQAy4WdNTWe8RjmH8ukMVYeuwzgPY78nWUnyxPnNsnV+whuXT2AfcUW8wOh2W63axs7Mz7OYmOXjdUA/9ht9JBNz708lv7b9noMbf8ckLq9u92elJxMJjGZTCJib1u73W6Mx+PYbDbxy1/+Mq6vr2sVXjlANBnc7/djs9mUqqB2u13I2Lu7u3j58mVERM33mpij7egApE673Y4nT56UyhGSQVQYQeBGVNVKzAX4nXuB98Cv6NlisSgETF4mc3d3V5IxtJOlccvlssynk3EOdKkIRZ7x38hAu90u5BsvaHBwbx/Id+jOarWqVUO5ssE+33pggm+3qzba91jtdtXSd++n52QA7UEf6FerVW1c7jbZ/9oHZ31v8l8f68g+OdseV8UaT7JPq3Evh/Ee88dvE3foDPYaW+w46TE/amzg84x/OUySu2gBPOpqPttgZMKEjP0I/aK9nJv9s+0WvspEvduY8bvnxuMfUdkj5pHnGIdnHsKkVBP25tzs329vbwvBnGMix/gZ29rH8bysT5lwI0kMXjY5TVvyPDoeyjES48R9kC2qYCGR3YeMlz3+Of78Vccbk1IuZaMDCJABcGZz3ekmIEsHPNhN92wiqnyPTB5ZIR1QIWCeWINknINJE2cjbfx93mOgB/CBozVR5fHxPe2YDNyy8JhQ4R4IECDbILXVqjIyjGEOXghcTcLYmHisDdhxdDyDZ7rCzk7QziULLX3zHPG/x8GKm9tnubDs5UouOwfLFtdBTJmozGPgebSR5V6WQwJ5mO2Tk5M4ODiIxWJRHMH19XW8ePGibFr5rkdTgII+/Pt//+/j4eEh/tW/+ldxfHwcf/RHf1RAF07GS1ytw8y/DW8Obtvt/Rt7er3eaw7MhtP7BWSQxeHAMRtsrsMR+PtsILOhbLWqqhkvS7GzhDQCBGRZtEFHNgxQIqpAxQDMMmI7aCIHeTVZmEktO3XmCPLAQU5EvGYPqAJhvrkvbfLfJu4ZF/bhYEzQFZYfeb5MnBnAul8GG3m+MjCwvWTsuZYlsm6/S9HdJ9tvy6hl7GMcbgP/Nz0/B/ecm++RSZFM4uX7EVRnYpS9MSLqPslErLOtEVEbV+TYREoGtc7acU6r1SrPRl7cRp7PZ5nsRP4diNpG0UfwhQEjz/DfJnM8DiY2s//MtsYJuUzwoeNOCDnTe3t7WwJEYyLLbBPYtY3y/DJmHsMs97nftNdLxaxbDoQMWjnHeutxzHPPdZlEzuC5CW9aHziMF/zZ+zwso8ZiVOgQ6DkQJOhk7rGrruyFALi5uYn5fB79fr8QKASoHnfmhQ2t3U/GzdVRDvbQg8FgEJPJpCy373a78fTp03h4eIgvv/yyLA8Dq1rXeE5ElI36qZS2XzLBiq3++c9/XluJ4Yop9pAzHmy324UQioiyJxfXWr5JGA4GgzLuyND9/X1JWEP6sGwOUmEymZS5gRBkHCHMZ7NZrFarMvadTqfsswmpZLxuPTERYXIJrHB8fFyWTEOIQepERM0Po8+MJUsdbaPYCzKTx8gIJLsrtmgf+kPbmMtWq1WuM4lue0l1FrLDWNgf5/gwY7mPfeTn+4f4EFnOuCwvW0XHjU2yf/ZYcC1j7xfOPBZvZzvs7yOqwo3sN1xljo03UWbbZlIH+5bjfMeY2R/Zf9kO2W8y11kmjFGM3/iMax1n2C/7YMm8fQRzaazstjue9jjxv2N/X2P/mcfFZJFteu6vP/P2JLSBH6qaLMMZO2a8aJtEX0g8UaCRdTKivmywyae/6fHGpJQDKAYGo9ckgP4+A2zOzwAwov6Wv4j6ru8R9ezW91WvMLgAN9j/rITZAGQyxm238jFpdsLcz4EzTKb7QNszELewGJjmPjqY8rLDTLDk9mel5xxvVEowRwaEPpmMyeDHy2IYI2eqncW1MHOPXH5K+3ieg+imH499Hi+TUnznMW9yBnwPk9/r9YqDzrLmw8YkG2sbGc8FbeQZ6/U6/uf//J/xx3/8xxER8W/+zb9pfNYPOeg/ywR2u128ePEi/tN/+k/xT/7JP4l/9s/+WfzhH/5hIXZ2u11tLyHLFLoOYEbfTWLRr8FgUAs2ckCKg30s45ENtvWWsUTmXCHFkY0l4wwoXq1WBdwTMLiqcbvdlmxEzjC3Wq2aU/CbRgyufL5lFRtpotQ6nR2H7YidkAMAk4gs0XUW220zkDT5nwMnnKgDJp9LH61LXu5hu2i7Bjll/2Jb6H57DjOgwRZnObOjtdzY1npc6W8Gvm/rWH/oke1Y/m171+Qb+B/bk9ufA9R8jm1WRJQKAG/g6+cg11Qa4DsMzHlOU6BheW6yrfgjJxXYY4Nn5yCG+5sg9tyb3HVVN+c4c2/9s43KPonnEOBxbbvdLlUXVJz6WR4f73eHbjFntMvVtbQJgoEDP5X9nUlH7su4e8mFMRbfW9bQX/bcoe15OazlznY32zN0kfMek/mssw6arBdu82O+OmOpdzkM+pEN5N/BPpv2o19Nb4JlfJEFfBGfr9frsjSMhBb9NlnLOHlLBZZYWXYt/9yn1WqVt7a1WvsEAntKrdfr+OUvfxkXFxdFHqlCMuZFJjqdTrEfVOxQiXRzc1PkOiJKtSCVYL6H+7DdbksyxcmzrEu8eQ/ChGoBv7FzsViU70kOrlar6Pf75c3IjCVVYoPBIG5ubsrc4N94i9dqtSrVutgSfCfEjpfDmpiG2KI9JnUiqqX0+GH0HxLBsgwZGBHx8uXL2Gw2cXZ2VmIAZKPX6xWyjvEj+MQ2RFQYhRczYafYz4y2mDQxwYV8PTw8lFUAOfnHObZBthm/DjKKo8lONJEcxivYaydNXI1jP5F9pP04emVb4fn6VUf2NdZ9xhsMxQbgxlh+DnJjUsWVmlSoe38sZMFtYeyMG5vi/HwdspAJvKa4kO8y/vNvxxBe1kcSiPgGXI9ddZxkUsr+vWkfpxwjWq6yP7KM0Bf/hiR2xTe4iNgsosIATYl/j48TS04ImZjK8VQuWHL7fsjxxqSUKxA86e4cE8L/zphY+VAIGm+lYsDIcPh+DnAMsnk290OZERScAs7YTjmvjUSBDIKZbIM8vqNdVhgCrIiqWsJ9MzC1IlpxnPV3v7gPnzUF6HlcraAeF9qaySKPuZfwYFC5r9e3Z4BAMAGAsFJkAOoAwv11v1E0K5bHnIPzTL5ZhgGKudTc5FEeSzboztUdGax7PrLhdPDSZEzJRCF7OTB4HweVDm7vdruN2WwW/+W//Jf4gz/4g/in//Sfxh/+4R8WY4YDynqYqyYgQNAJ5oDMY5NRdVCSxyWivseZQUvWJ5w+1/u3ZQyQ6KoovznCsu+lbXwPmWdSk7Fo2nOFNuQA1/21Y7JD5rucLeE31zhrhQ7yTMAFr+aGiKT9rDXPug2JRXtczUVwnYPqpuByu92WvUFM1DFn9NXBLPbdBCTzyLV5DD2uWe8cFDp7xRxnB2x58Zh/rONNntdkfzwG9D+iWkrqQDXfw+d7/gjsqG7IWVnm0UDIFT72Y05g8ZxsHzmMJ7zxcq7msB/wvi8eJ+tVDvwN8tGPPA6Z+PR9PW6AbnSfMYeohiwmUMFn4mecTaYKgnvZVhJkUrlBBYblwXvB2bfblhicMl9NWIrxyQS1dYXr8Pfek4f7cz/raJNcZ2xjeeB67uGKH/f/+2Qq++f3od/ecxKCA+LUNovvkbflclnkwzbPm9ubkMl94M2X3ugeXXHAx5sZGbOIqAVV9q9gkdPT0+LPu91unJ+fx8PDQ/zyl7+Mly9f1pZIU6llYtU24/7+vpwHngBXdrvdErjzZj98BnOLrW61WnFzc1PGC7+CT+90OjGdTqPVasVkMil7HrJtwMuXL8sSwtlsFu12tc/kZrMpb4i8v78v+zExpicnJ9Hr9WI8Hsfx8XEsl8tSiWsstV7vX8zwzTffxHw+L/fz2IMXut1uTeYhdrFP+Gn+dxVURIUTqJ7CvyE32Jzlclkql1gqTLLcFV62dSSlrYvoGTKJL0XmkWFsMe0xHmI8TEaZtMrxJLbUtuZDYONfdWRcYCzvZB44yolBMIgrduwvjH3BmOA4nmW8Yltsv55trscxx1EuIvAceNN7vss41f1vstOO8ZBdV4Ea40VUL2dgnH1/xjDbdsckOfZyuziHfhjDuD1OCtMH5JR20i9sj/d+8zO9pyTEEPaN+9CubHszfomIGu7hM/qDrwCvG0dAkntuHFdYdty3iKhhcmxBrgLm3n4G98r68jbHG5NSVkCDTg+iAU4mAxispuqo/Iwm4srP8mBmYGGBy4GVn4uhZPD5nD4aJHBfFNUOI4N7B08cGG87Jo+PFcqC4TY1ATSPSQ7GcRK+HkHG4TsINQsMqZSDCfeNsWM83Ac+j9g7QdrQxJZ7nEx6GFTmuXF23GOIkttxZ7mxo28yCpYVywlGiDe8mGzIDrIp8HNbuN6OizFEBtyGdz3W63X8u3/37wow4Zn83N3dxX/4D/8hVqtVTKfTWpBCRtNBG3PAPEPG3N7elhLXiKhl8ui35RID581cszPlb5MJOHdAV7ZDngd+AMYGRAAk9Jy2mYTi2QR7Jt+wUdyDzHUOxE3A0C5+52DD/c7yk8FgtpMOtgDwBD6ARVdNOKCMqF7vbFttm+J2WX+a7DZAnx/rLbpm8O0gHODQBDxpL+OcdcsgJBPIzLcBg8fBNuUxv/IxjvzsHEhHvE4cNAX6To7QR/s13zcToBFRXrPsMc4kET/eAwf7hm7wv4FfBo32n34WbchEML7dcuhlIj68bCL7RPQ+oso6uprIiQjGyH7cttKb+to+4AN3u115Kxp9pW/2V9geAl6qsQH36BTPyWOY93Kjvw5OHHB4LOxHkSHGxODdYN26DDGFrXRwY7vT5DM9P9bFJt3MQRJHxlWP6e371GfLNTbGBIP1hXZ7Y2/wLP87gMtVJiY4B4NBrTLPvhWiYT6fl8ogjxv3cMAJDhmPx4X46ff78eTJk9hs9ntIvXjxorwFkGvoc7fbLXvPITObzaZWpYSdYM8V9Haz2cRisSh6cXV1VfaHQrYgoNwfiCwnY/BjDoBPT09LZVar1SoknnWduXKb0IVer1eWMvKiGPtjb1nx8uXL+OUvf/maz7Q/vL29LcQY7Qc/tFp78plqKZNn2A7sCHLhZJFxPom3iD05t1gsSiDr7TZ6vV7Z/8pEUA6gqRY3pqOaLL8BFBmnjdhSPsu4O8dBnGMf5f8/9mEb+VhbmR/rE3psDGt7bxuQcb99g0n4HNPwTGNm+1TmzwUXxj0Q295zjcP+jHHg4L5e1ml9RK5NMNIu62v2N/ZLOcFFG3LM63jSOIDPnTzLhTJ5Xrz/mTFqRJWoxwdn+w6Otdzgy3u9Xplr5tc+z33J/aA9nncnwt3Xh4eHUljg2Iv7ZfzPc53EpY/Yb+aC53COZcJ+uQmLvcnxxqSUjZ0HxKSPAwJnTLPhdzCGsEVU5FXeh8SBCG1whw0wrJS02wERzyJI9WRa6TAwNppmTC3IuW92Rpxv42BlM8C1wc0GIAurjaHBPW9BMVuZs3WMJU6P6g/Gj/Mgp/jfxsElexZ0K7UDEl9vQ8O1TcDc4+FAOI+7v2s6Pwc7OH5XyOT1va4w4Bm5GsYyx31s/Fwtw5h7fnPQxrMhq97XcXNzE//6X//rkh3z0gIIyfl8Hv/xP/7HePXqVfz85z+vZTNyMBlRVcl5yQDtZz5NUDkYyc6Fw0SyHa/HmL9ZLpSDVd8LmaQiCIBmkIBTdOCJQ7LeEhh6aQ86ZpKUcbV+el8Lz7XHi799WHZNpHA4I+fDtrXdbhfAHFERZN7jxI4dkpBx8ZgbCDUFvg50PS+73a4EGAQq6Db3Yu5zwM//3DeT2jyTsWDumjZjRLeQXc+b5dSyav/wYz2aQAtHHrdMDGTw4+895xHVUrpMPprE8FJMzre/cJvtt3K7IaS89IHA1rKFT4fE4fAzWcZj+5IDirxMBjmhusn+xORU3jDYFdn4YyfXIuqEouXSpAJ2JhOFrrbwnGN3drtq/xH7Lwc2j/ls45EcOJFgwE+6MsdBEXYhJ/wYG+OpJvm1/DXJZp4Hj0HTkX2rj4y1fujhRKPHnmf774goyyBsJzebTalK5D6uUka/eAPmaDQq/pXqnevr60Jssdm3K1Bsy50pj6iw52QyidFoVJbQffbZZ/Hw8BB/9Vd/FV9//XVZ6k4W3ptxm1hxQOOEW6vVKll3kl0sR7QvME7IATJL59h8HGJquVy+VtlpInUymcTt7W1ZiuhKAvqw2+2i1+sVvMB4TyaTUsGIb2QJYUS1l9jV1VXZLwmMiY1jPLCXJsO5L+24ubmJ6XRaKpl6vV70+/2Ce4zpLb9NGBV5vLu7i8ViUduTimsHg0EhG8E3TjpAXIAZaYfftGl7wvX4EMdbrs62/vC38Sl6lQnoX4c/zuOZ/WVEPdlrDGbS2bJtn2C/wt/oVcbRjmcdM2Qf6zbYnxs7sYUF89tut0uVYURVIWRfgl5CQBtTcI05ASeWTHCZhHK85jivKQbOsZ0xiMfT7eUePppILccCEdWS2VyN7a0vPEfIe47VV6tVwSO5fcazTfFMxqHIhWWCfpr4dSI4k0g5QWwfTZvQW/rhuAdd5W/7bOvC2x5vVSlFZ1wBYOGxwnENAMXsqxXVgRyvb6VEvTRSpBZkQRberHT+LA+4g9THSA+DVhsZyAYLc0TUHDSAIqKqgnEAaIE3qGNs/DtnQj3JBrW0107dgo2w4KQza4/B9DIDzgcgu4IhL+uyUiGgLB0aDoc1mUGQc5DJePlvtzkbNK5D5nJg6TmycXZABElhecwAGJlzls/PzuOcDSx9xig3GaTcnx+izI8dm80m5vN5mV/mEsdxd3cX3W43ttttKdPPy3X4TTXUbrervRIZueOeLotvctBZLp35R08pL8/OFTBop25dzNWPXmbAvXHorqAy+MJWOcjKGRsDyRxEmTw3uOJ30/xbn5E7O2XrCrbNlVm+P8CC+Y2oKr4IEpbLZan8Y85cek//Lb+5vQYDtmvZQdmZWr8iKpuA/plUyyQU97INwX6577vdrpBrXt7htuWAzHP06wC9fv6vOhgnk5J5vHNfMtAzoZeBILrH/z4nkwVc5zm07UUXMng1ULR8oONkWF0FxeGKFD/bwRHzut1uCzFlopLDwSttZyNj5Ih7Qh7Zx3r5ks/ns81mv0EzGyMbSzUFCswtVQnMA7YwJ/m8p50DYObNWMbPaLJDOQgAy/iV9xDA/O378UPAmpf/Z3DNYV3kfJN92Q5kMvmxIwP7pvPfVdfBAxAI2CcnX3ISznODrHgPJL/V2curjo+PCyF1d3dXlrrh11erVW2ZaESUyl4qhZA5j0e7vX/D25MnTyJib/c/+eSTaLfb8dVXX8V3330Xi8WilpRh+RztxJd6DFzht1wuYzabxXw+j9lsVvCwZYR72Ybwd0R91cH9/X1cXFwU/3F/fx+9Xq/4t9PT07i7uytv4GNcTk5O4pNPPon7+/uYz+e14JpkGm0+ODgoL2pBHnlz8nA4jM1mExcXFyWWWC6XxR44ADSez/4a7EMbCQaxMcvlMhaLRSGnut1ueUkOftD22bbcxQG8MfPq6iqGw2HZwN5VY+iubbbxPHtBuSqWccUPuyIHIsoxhxMU6Anj5PEBXzXp8K/DP2f/aj+G7NhWO/703Bqj8L1tekR95YuvYXyMVUkOuYDAB+0ypuKHZA6Y2aRgJjToB/fyFh62z6xGIL6yLDrWdWzsONg4zRiC++ef3GePF8/NvsdtYhz4wR7h/zMhxZgj+1xvGTEBBzFNPNxq1fen4r5NNvkxeTPeQH8YP+Ic603mDYw7jAt8vsclIgqOdtvytdmP/5DjjUmpbOwykfJYUE4DvUQnC40HF4F2pUQ2Sn52NsgcFnImydUR2QFm0OR7ONjmmpwRyIbD/xM88p0PK7kJLvrGJGcGvSmQQnmbAluXlQJYzJJDNgDuc4loJvs8H/mZjCdLvw4PD2M4HNYcnYG8rzGYtux57B4DollG8+c+HjNkNgqulqI93W43VqtVjQTLzgryJ6ICUXY6HldXqPF3lpH3dTBXDiIxsmTCnYFB1tBL9Nhl9Xk5HnNnsoQjO8UMvrlHU5mv9SiTZTxzu91nA3GyyBZy7s0bIWT4jIwu59npWnctIwZr9/f3ry3rs9zudrsC9jy/1n3bDcYiZxEj6tWM2akZ0EVETKfT8tpuy2a73S5BDed6TjnXSwoYC7LRzId1Ky+jyLbjMTvHd5ZVAxLsg+0Nz/OyWjtcB0SWaQh1y9j3EVO/DgBMO/xs/5/bmG3fY4DE12SgxtgRXGfSCP9p+9w0n/xt2URfkP+MEfib+SQwtN8jEDWIJomSCRcHuQApB8t5fNBPB0MG1fQzVz1lXWfcaDs+jnFot/fVIFSGMA4OZL2Exs/CVhCEW3/Qa8aYsWjCG57vPO/8bbKKPpuQ8HiYDOSgUocNk3OyqcnfZkLbc+jPOdf3yTLP8RhWeJ+HN5M1mcicYl8yucgYInuQDYx/q1Vfitnp7Pdo3G63cXV1Fe12uyyDY68gbK8rsbCDkPSuWuCn1+vF06dPi2385JNP4vDwML744ov4+uuvyyblBNdexou/ALdHVOQLyWa2BmB/K+xDJsaNV73PpgkLiDD0ej6fFyIZmQKjDQaDcq/ZbFaSb6PRKB4eHkr1kasOwEInJye1LSroV0TE+fl5IQaJJ6j+ms1m5WUDXIMdYC6YU5MznveIup2G4J3NZoUkc9tzwsj7YUbscSvLPdmY3s9gfI3hkBEnBMBJEFl8Zt+UY61MSHG/TqdTPs/+mP85sGXZl33sI9slY1OPP/PLgd3OdjLj2yYiivM4kEeSNeDGpvgyYzTb3c1mU+TXB3JjwpP7QEAi06w+yHEzzzKpZdnmyOSdv89tpo/WDydx/B1tz5xAjpWRTd5SB470Ujf+tr2DILbNQdcsF24jtpk9CEka873tgufCh8fB8RxjTizSZEfQ6xxjY1d9fxL11nHa4vlu8s9NvvttjrdavmfBYRAstA5CmgTJE8vfDEREBbrIOmTyyffM5c0eFLN4VEXxHK+PNInjCgoLCNfZ6NN+2mBCIaJiX+kTgRABvQ1RRL2iy46h1WrV3qRjsi4DOhtz5iRnF1FeKxzj4CVL6/W6gP2Hh4fa2488Hw42c8BsUExZKG3CGeWAyPLhe/vIwZedA0c2cCbympxJbn8+TLywSSX7CmTDx5x7OYjBhcfOemAQwD3e92EyNX9m2XJFEZUIm82mgEPaCBjnMweR/tx958hzaD3GoDoj227Xl6Hledxu92X9GFM7Zzvg1WoV6/W6gOyIyu5YlwkUmG8CQfroTQ7JNqJb6CFA1jphUGDZy0GK206brKeZsOA5Jt7YuJUXOURE2asC+SKAouKLeXC2HfvJ51x/cnJSlgHQRuxczsw4AMnAwqAIW5uv92FHbwBNG7mPZcrPsz7nKpzve+7HOGh3fr7HjyMDE19jUoH7NvXHmS2ebeLCdsjBtP836HQfCICprDQg9EHVDXPpjV6RaRIn1m0niwys7B/pM8FxLnnnb4JRbIXfMmNfi/y6r36ex5LzwSGMJaQUY2yih2Da1QTIKM+APKa92CXGzPbZY2AC3KQX9t/n8LlJXbLqjLMrqIydaCf4yEvUs6+0H2hK9Lj91vtMqBoH+d7fd7wv/TZ2Wq1WcXx8XKpO3V5wgdtG/4+Pj8vSq4h6VWxE1HRit9uVwAZMy5xsNvvqyV6vV2SZzcQhuHxf5P7p06cxGo1iu91Gv9+P4XAYFxcXZcNu5NeBE/ePiJq+Qkbc3d3FcrmM6+vrsizM+NQ4FX+JLbD9stwgi341+eXlZams3GyqqsTpdBqj0aiQtYwN82J/xRJIklnoHufTPp7b7/dL3xjH+XweL1++LBucm8yFzLG/wjbYJnnLDGxbtmUE0Mgab0gdjUY1nOsYxFgTm7pcLmvV7ta9HCxnfO/qQJ6FTSEoZswYP+bfG0hb93kW45y/t736MRzZDhnb8ZlXSJi0wI8wJ2Br7mN8Y+yIXJhYsK7wvWPMTOIwV/i4THQwRxFRSHLHp1xvrIKMoVdgxRzH2O6YR/CcmsTN452xEX4r+5aM4xxvWaZIcMELwA2QeHfChXsw/n47vYs4eL4Jd+6BXycplV8yYE4i+zf7So+B/QJ6lZN5lit+mpJFnA+5ZZ+FnOaEGPduioV/yPHGpBQPzoRBHigTLQZsdMCNf4xIYHBxGhloeBAzIWVjvN1ua0wwBtOEiJXCk8Jkcc+cccqAF8GE0KFNPCNnWnwtAZ2JJQdZOaDgGges9IG2WCANlm2wHFwul8syvtl40i4HD9zLfff4W5FQel5P76VeGVDaeORAnvGgz87Gu70e96wkTc+zvGT55XMv8+ONNyYILJ8OFjMIy+2wPuX+v+8jg3rLR/4b2SAYgrRgrABvZAoYM87xczDEJmMMtmibjWy2Azb6dkSu5GJOchYWYmY+n5fNVCOiVGTwbFcbkDFZr9clIxIRBcThUBwo0hY7P/9kB9FEVmU5apIpPvO8+m+cxmazieFwGNfX10XvbNvQ6Yi9TUGusSde5smPr8eRE1C5EsXzzP0cDNje8b+dJM4/23TPq/vgvdJsG3w/3yvbYa7J4/mxD8tD/tyHfWf2fTmZ4/McOPKZAXJEBXIyIeXPnfn3fFqfTXRln5/nznghJ0Bubm5KoGebbT+JzD8GghkfyxTjYX+G/TAQt93KYNdBJUEYMo3fR64dbLbb7dr2AQcHB4VU9zPBLN67hbeVPTw8lMqPjKcgjExSe0w9X66AynPupYGMGXaB5UDII8k4xtNEme/BYbmz/c8APM+ZbcCbHu8LMDcdxgEQBVQ9ZV9InxnrVqsVJycnMRwOa8t7PP8eC+bKthZ8RaA5Ho9jOBzW3nxLEORKlYiIXq8XT548ifF4XPzYaDSK+/v7slwvokpe5DF0n3a7/ZIXfqgcgsgEc1rvvHTbdoTxJCimva5yNPHNAUblzXg83xiYOXOS2KTIdrtP6LgC0s8nIMUnMqZ5GwD64H0MvRTI1WrYCZYG0aasO7a9t7e3cXt7G8vlMubzeWy32xiPxzWMgJzh56mg87g75nB8Zl3G9rmC1BWqrLzYbrc1Ihq7yveWYRMbflbE68mV/Pmv67CdMm6OiNdkJZMAJPjxAdlm5zgnImrzYrsALnf8y3VNsQX3x0Y46ZLxp+fcRFVOOuCrXU3IXDI2toFUOLr6synWyXG3dc9yYOyWSSHPF/0w+WP75h8nsx3/eU7x95Zp9sBkrhgTk355vnimtymhP8a8TbJhX8gzHY873nDs0ORTmMccr/o59ll+UQIFJvQtxzE/5HhjUgpDiSHKgM2D5YCejlg4ODcrAodBp4mQiDoAtzHDeXKesw8AQLKQmXBgEuxI/DyE3mWyPJ9zfD8ELQednU6ngE4OZ/N3u13JojozauF0VtXKmwUvryttIt1wMGTWFotFbY8OCDiuY4lSr9eLiOrtGhATNgoEpm6fjSsAyeMMMOH/fDgoeIxdtozZmVrRuB45sSI3ZRksqwcH+z0GHh4eYjqdlrZlB+VgDQLHoN2y6iDGwfiHANEZ2Gewn+fCxMRqtSqvEz45OSkO1gCCfV4sd4wd8paJZBM5nhvkGzvgOWXMeCbZQ2TS7WETRztGB41e3mvHvdvtCrj1cjQqFez8cUhZjplPV6FZX+kH9zYxakeYnYOBQp5bjk5nv48EwSM654oXbDrtNCnFPQyurOe5aqvVahWSD0DitzzZITI2zry77NhkQSbibFf9uW1bRBQSDr3Lr/hlPqwHHscm3/Qxjvy8bAuzbbB8WIeaAIrvlftr8OZEh5M4HhPORbYdWHjOmkAyttQENPfi2ZBEAGj0CoBvn5bHzNjA44N8G7cQSNmHMIYmVeibxwb5ok/eg85BFge4hjaii9ic3F5XOdqfMS53d3dlM2bawNiShXXwnG2sgX8Orugn4+92O0ixLcl2LcuZ5fcxDJXtXpZ92z7L4/fp6YfUYfeL6qBWq/6mpayjtLfT6ZSld04QGMsYV0fs97ACczF+DqoWi0XsdvslecPhsMg9RIQrDg8PD6Pf75fnYDNvb2/j5uam3N8BC+MP2WGsx7J4NiN3hY3tM/6O771/HONDH9nfzUt2LbMkl+jD7e1t2W/Jexo54LJNtL/32+VoB3bKy5mY6xy8Rby+RyL9wrYZS6CXOR4yQWk9clxjfcR2QHxQrUefut1uzYaB0xhP5gVCiTkzGWW/aaxK2xgLV11iq5Bp712UCQbbffuPJiLi1300+V+wMnawqXqVsczkq3+a/A7nOTbmHpY3ZM3xFs83eWqfYgwZUb1Eg7E3AWbbhexSPeiKMNqF3FOFdHx8HP1+v/YSJONaDieGcoxsn09SK4+nY1H7E+YKwpp+2J/Y5maZ22w2pYIqImp+nu8sx4wLZI7bab01edc05zmONpY25mqKGxkL9482Wc88H3zvt+hyLz8vJwFzPPhDjjcmpTKLnsEmzgGj2XQ+A2MB83cWBib5sYHnyNm3iKiRKQQeONmsuA4S7UD5HiftybSA5A0/IcCWy2V0OvtXyVpIfTDBJkYMyu0A+R/HlIOAiHpwZmBjINEUzBFoz+fzmqNDWTiXwNbBhfdAYN4J/HHiOEEvm8GQ0l5kpYkdxjHb4Zk0yte5j57TJrll7hhrA3cHQR4r9n9YLBavkYd2sFTd5M3hPP45O/ihD4+LAQeZL4yjSzaRJ5jxo6Oj8lICDgdzBhEEgQaCHMyB5da66Hn393xOBs5BE+30kgo/hwwzckE/MnECkeFMkPWX/hrE0Q9XoeSgFfvgJTe+xuRlbntE9ZYwxgKdBiBkMuDg4CBOT0/j4eGhvK0I4GD7QyACoAfEIhsm8z2PdlIEZK6oyYGY5YN2Mp+uxGsiZu1rkEvOz0tHDw+rFxIwvg4+rM+Wh2xLfx2H7SmHQUpEfZmVSZRs4/L9muy/n2vyl3MycZTBtOfY/thg3H7D/tXPbvL/ruyw/zfR4uWf6L0xgW2ekzr5vCZs4HHG3qEvbjMAGQKaAA89Y1xN9DBmJmI9jsirgS+kL/3B3s3n8xgMBmWu7Fton8kDP49xsDzYpxrk2xd4+bMDT7fZc+UsetbrLLd5vrMNcXWN5/JN9dZ+5H0e2EHsGUGKsYqTLM48M97ulwMKxmOz2ZRKHj4jMIyI4hPv7u6i3++X5Xu24cgcyZRc1bbb7QmmLKeurrH+kkjNsmAsil91pZATX+129dYvfJQT2rvdrhBVkC95eRFHJgO9HAx8BrkXUVWRoKvGtVlnCNS9QXhE5cNtE319JnnAllxHAsfVo9g3J7n4DDlB1u7u7spSRvaQYv4dV4CdIO2o1GNLCvsNzqUflktjFNt5/3bMYayGrTV5l0lvbIKJrx/DkYkK99/9Na7kPH47seH7WP8i6vjKY875OT42GcX1YE6T0ZZP4yjabD9uAgx53e32S4j9mf2UCendblfDt1yH7tEvx7XgYA6TujwvV1mjY5yPLJtkjogyFpkstA8iPs1+KnMF9j2uGEOXvazZGNV6z31MYBoLZZkzB8J45wR1kx/kHGOcJixIcthj7/8h0zgv+2j//0OOt1q+B0FjwwQBBCiDdbfS2RnmAUBwmoBiZtT92/cykImor6HG2Xj/G2cqMlinHe5XNpJWGL53CTUOywLJubwlJQNdjsxKM8kmqjIB4zHJQB+htVHjXBMuXEsfPH60nb7yxpeIqG30lsfHwglQJfDM85aF2MpiB5e/dyCUgyRfb/nK5ImDZxszDgN85JEMo5nkpvHHADh4NnueHe6HdryWKcYgB0H02brBeRCTEVGWjmCIXSFlIgMQB9Djnhh3E9g2lLSDw9952ULWvWwnIBLpG/elatFZZ/pARRh2jbli4287QNs7/qdfEVED4pY9Ajg7wjzuHk8+M/nkv7OjZFxZwsf32Gj6Rjt2u13ZXJYlQsiL/97tdmVMrFtZl8miGXDY8WKTmCNsiufQgCvbB8aZOUTfCNxdeWISLYMB29ocrPOsj3U0BdbZpkXUl+9l25aBrm2//WT+n3nGZnF93gjYcko7HFy4fdkOM09+rs+HSOR5yAn2KR+AO5MxBucEvQbe9n3WY9rBWOKrI15Ppvl8jy3n0B4vK/DcZuCbfSiJLcCu22nyALvL8iwnwRwgAt7zsnO3x3pCf4y/uIbzHYQbGBu3MQ+2vVzvcXnM9+fD5xkX5aPp2qZ7vQt4bjqQRxImfkua5Y62mxTBH6Jv1jXkA5+FzLGhfE5IRERcXV3FfD6P8/Pzsl8UVVz458FgUN7oZoJjPp/HfD4v2zo4+LC/hvzibXp+qxQ40sRKU6DluWJckFV00DIcUSWb0KOmKoVsA5A53lDIWNEfxhBfx+fIMnPiwB/ikUDb42/ct91uS/WYdcV+Cb1kA3MH0/YBXmaUV29wDzCR98UBwxh7EatMJpMYDocFz0KuQzIyXxFRw4CQ7uAdyywVOcYHmUQxaZmxqePAj+mD3+Ro8sdgRye68zVeusZ1tsMZr/BZRHPca523vTeWAc9hJ+wPm3BPtovoQ0RFPhMHmkQypnOMnOXNOp5xc45Z6Y/HCt3MVWiMEeOR9cx8AL7Lyxkdq3FP9NDj6/Hx3EVUGHy7rd74m+fL9jyT/E5Ie0xtM3Nf+N/2me9zfxjHjN0zOWzcRb/8t2XIz/fPDz3eqlIKwGM2smmfCQcaOFIUMoNAT5CDlKZgAgfBpNtYZQG2Qc+khY2hmUu+h+XMgmhyB8F2ZY0nE3ACCGDpm4Hudltl1brdbrn2sQoDA/kMkLMx8TUIpteAM3/0GefqtwKhNAToNhrMg5dD4Pw514GMlTcbw+zM85y5f/loMqgYkSbFwNm6Ai7Lhc/1eGcDdHJyEicnJzGfz4sxyGXOBAy00c+zk0Z/GAfryPs8PI4ZEDLfvV6vLMu8ubmJwWBQ9Bdn5Oq+iOptLxhVAJHBUqvVKmW7eTysN8iZbYnnABLFRJQ3e+U6jCf99PJJ/gbIeizQSzY7jIiyjIa9kwxO6R/2EMBLP7Ls0vcczJpQ8Ph43qyz9DvLJQe25ujoKCaTScl8I6PdbjeOj4/L0kdnoiLqYAR5pATZnzH/JycntSwbgJSggmUFBiMGoJzD/9zHQQPn2TFbhlzlQlsymONcX+NxzHbmTQLc93Xktn3fke28r7GtzrJnP2FbzAE4Yv5NPlk3DVyyT/ezmX90KftS9Cf7BPxMtlNuB3aTYNp2PFd+WPcjqtL7Jv3kcHCBrBg38Dwn7LhH1qPHfjJRSqDoJYoR9ZdSQHb47ZjYZuwsc2mZok+umnqMKDDOoW+20R4n22sD4aalFd93NM2HZeZX6US+V9Pf/v9dwPNjB23EN2FXkT3knYw6hI1XGZi8sL6Ba5lzVy8vl8tCEvAMjk8++SQmk0l0Op2ywTXj0Ol0yss8qPy9vr4uLwWxr0P+mA+W5YBxWBZmXO4gxrEDsQH3PTk5KUQU5FFe9QCutA9wNZWTH71er4xXv98v+9JRDY6v4BrIQMYEfANmcSUIOs++bbQTO0Tix1UHvPXOS4UjKp/qijIn2ryHLfia9nOu7b+x+Ww2K/PsIB793m4rorLT6cTZ2VksFovykhTbMO8TZexq24HdRQ6Za9sw2u+KHV8fUSWPbCd/3UdTO0yYuFjD8VNTLIkcGoPkeMPne5wdIzbhZZ7N9egnspNXEeQ9kjORSBv85lu3mTe3Z0xHIpj28mxsk7F5k3+2nNEG42U/67HxcUGJ40JzE2B7SG/jxYjKVuGjHZ8zjvZxyDa2w5XmPpc2UXmUY0zbOJ7rcfGzjJsYD48r9zBWMOlkUsq4y99Zh31/xsZk+bvo61vvKdXpdGrrv60AOehGcP1qVT5HqBAwAyRnYuig/yfgtMHyRHI/wJfLzCwMgAEm20FlJt9wuN4kkmxYDgAcGNFWnK+BOffASHg/FguiQSNKS7stGBnA+zvaQTu9fAcl5ZWxrpbJ9+NwwEH72fjZQIoAnblBhmxomoBoEwh9DJhmItLjZkVyAGBw4+Cfc/0//TQh2ens94LwXmWuDOJZBA/IYFZwGyjLkAmfH3rksnIfHisbFkAzgaSDTYMxZNqbqVq/s+NAt9xXz4tlyRVGuSrDhpzzeV4TeeHPbcgBcyzp9Xyw1M2ZS/aaYH08pBtZTb81Z7lcFrDrpXYGhLSL/kXUgU1EVYXJ38xVHjfbUjt//u90OnF6ehrX19eFvAOMYAsMiF1V4WcDuNvtdrF77F3R6/VKlRXtsz4YzEMGZhKBOUa+PI+2f4yV/UyrVVWhurolk36eQ8bW40l7st35WIf9XP7M32WZdlszAHaf/H9EHfwyt86i2T82AR/Pb7ahj+ECz4kJZvs1gCskKvrE4UAUf+LnZ9DKZyyp3u2qrDXf+XqqmLLsud0eozw2PrKvM7HjeeWZ7Pvh75Fd99M+DvvrQBIsElHfv4o2uyKRMTCg5gd9cQLO96AtPNMBiwMA95lzPe62cfl824hsCzIGbJoDj7N9mq97nwftZD5ZNs7zIaTo/3q9f1V4zmDjI7gXxD4yD1FAkMf/zBWJpaurqxiNRuUNdZDE3M8+nD2hXIHIWCGbyI6rqx0I2p7Sb4grcHC2c5Z/J5WZd1fiUBlln2EyzzjB/h09i6hXQbiCn/vN5/MyRjzHcQTkE3vlMKf9fj82m2qJpXE51btuy25XVfVC/phoZ0ztr7jW/cwVlN1utyTG2+39Xlv9fj8Gg0HRV2zs9fV1TCaT6Pf7JWHnbRGYG/wOGMMvisF2OJnhABi5zLEbY+NnRbz+Uqgf22Hbhb452c05/LYvcREH32dSKqK+tDmivm+mfTy4LSdinLS1rbfce1VLUyLee1C5ssfPznbdMRN2JFc/m5SirzzH8u0+e/w47zGfm/2rsTO//QbCzWZT03VXObm9rvZqiokZO8ddJta51vfOHAHywDnWO8ft7q/xgHGKjzy32B7smolgE1fIr7cyyf45xyY/9HhjUiqDH7PCBpsmeGigA5mm4zGw0GS4uG9EfUNIhJisA2DWb9szmPGmkoBVA1mcH/+7TXnCOd+vaDaIoh0QXgBEv/nDG0RbwGgzDjiDK2clDXhp4/dlGxiD1WpVxoAxsUK5SgpZyAKIcvOmMo8TTt7XRUStcoU+5aw7444TxIDkYDcbJ4Msjw39ZJ5sWDIB4+xFXo7I991uN4bDYVxeXpbn2lnYSZsINRmAg/Kyihwk/ZCjyTDYcHq8DXxYdpCXsJJNyGCE+1nGHUjz3Py3x9NVTrTNAN2OOAcpDtoioqYTeU5vbm5itVrFbrcrG6A3EUE833Ow2ew3e0enZ7NZ2Tuu0+nEaDSqVRVAkvA/B3YlA36e7zHKgXATqem55TPAMGN6eLh/7TVVUcwfy+tsz3Kw58+QV+wsYDyi2reJZ2agEhEF4KzX61Lp0RTkAw5yJZhl2nPMPm9e/sUYILOWee4DSLaTz+TChwhaHzts3/1/Pse+Ko8NwSGHAS7XGNRyjquerb/87c+z3cykENceHByUCrp8vl/HzLnokmXJFQ1NwAwdQzYjqrdwmbTOJDuyxr1zYNcE/nwt/tXneYxzxtekNHPkqg/7AMgz+1wy1cYrrqRwppJnERDQdz7ndyaebXdNFHqcOR+9yplaQKsTeFyT5dty67+z/+Yz+w3f10f2e1mfs069C4BuOjJox68yT9vtNnq9XqnkcfKF+WbuTbS4vWBpsGWr1YrBYFDkxGQAFTObzX653snJSUmo9Pv9QlRFRHlpyMHBQfT7/ZK0gTiGlDKJQsUH8mCZyKQMf9/d3UW3242IassK+oa8Eiy52oKK3Ha7XXubszfydjKDe4Nf2u39Elc2cXcVI3rsTcDBxhFRC1LRX4LNTme/koTELBUYVFVzPWMLGQDhy3hut9uyLKrf7xcf6H0obaPtM42vIcyQkel0GovFolRg9nq9IguQkCSKDg4OYjgcFozjSr7sI2kTn2Fj2HMVG+VYyFgj677x9WO298dwZJuDzufvwV75fMebjCVz63vYb2Q/Yn9jrJjHNOM57oGvtA/jc/QFe+DYnmdbd5hby4UJRr5zYtP4zRjXOP+xfvi3+59xCIfJLg77e2JhF4ZwX/rot3hyL5OsxOjGJ8RT8AOZbEJGkCESD9YRJyUcy2Lf3R7HBjmGxGY1fc91+H/bS/sh9i60D27C+e/iU9+YlMJY2djloAVD6SyujTcDa0XiMGji3tyHCTBz6kF0YO/Nch2A8jwmnmcSfHsgEUQzqGYLLfwYHGd0ub+BI8v3Mni0Y+F/FMSsO22z4jYpMmOejaT77/mKqG9KTxDBJo4WdAeaKBdA2uC/1aq/hcvA2892SXAOPN3vDE6zEpjcoI+5jN1B/Hq9LktR6QvVLnbwTUEJ4BI5AhA5i+eAinkyM27gkAleBy3veuT557Dzt7NgPNbr/TI1xtDVSw5aCGq8RMNz0fQ89zOiKgvnb/TfFRoctg0eJ/fTGUDuyZs2AGUQqK6Yg0CCWDs5OSmAlOwh9/E8v3r1qgD43W5XKqcckNmWYVMsex435IV2We8j6q/E5vM81ialmKvb29sYjUaxWCzi+vq6AEwAMfuDuYw46yBjS0bHS/lyOTZtsBzSbsA+e9N1u93aBrf03cQn/UBWbO+8X5edKPOGHOeMtzOxtvlu66/jyPqTgXkTAM32z6RRBi7+3DbdQYVtQgaxBjYmt0x6+BrbFvyGXzyCPrFsnrnJ+CIiXgP+9ps839dm4te+JhM0kGd++1TWrYh4LQjzuNI+yxmVKJk0d4KCKkP7C57nBEi/349Op1OIBINVLxOARNhsNuXFBTkzzzw7ccXYOAnEuFtnTP4RSDMmtsnY2ezfM8ZqCqYs/77e8vDY/29ynnXhfR72gcgAyyrBixASkCbbbZW8tawb64HB8S35DVYmEy3j2HWe0+124+zsLM7Pz8s9qIy6vb0tRM9gMCiEBW+/82oBflxl1LRkx5VUfAYxwnX9fj9Go1HsdrsSX6Ab4GYSP+PxuMg7mMExCfrEdRFRfA0Y3ZsSc839/X0MBoMy9oeHh7Farcp+mN6CgLnJlcfggOPj4xgOh6X/m82mJDAZB9oM1gZL44+NrUme0Uee3ev1Xlt+xUbnmQyM2MdEt7e35Y28EIOtVqu8ffvk5KRs2cASSmQ5E0zou/G2bZF9ggN4+sGzwctZP3P882M6mId2u1qaHlHfZiJXhRl7NCVP7Zcei3c4TFp4jDIOR9bQXRPd+CDbmYhqM2uW/oFpXS3k/oOdjbOMX7mWlxPQfvtPY1/7ZsdgJnWMNfLY5fNznMXn6Anf0VcO7zVF3zx3rqZmDOELHFe7sAPfbpvNeVS2OWZGf60LXm7nOc/jkOWHZ3uesSsQ9cYIxsieL+LorJce+x96vDEplYEmA+nghSCFieIwgeIJsKFrt9uvgb0MYugsRtpKzvXcm30Z3AZPEobEFVN87oDNfQQ0GCzkqgpPNqQUE2VhhDn1+FqQHVz4OYyBgQBjZgdkIY6oZ7M9F54rV7hglHiehdoMbZMTWa1WERGvlYrn4MCGgyMHXE1ANAdRTcGkgxdIIsYURz8ajWpBkAF1E7DEgOOAaf/x8XFMJpOymbSvNdC3XOO4kR3PtWX+fR9uF88yUOAcDCOAzEQDJJ51lPMjKmfjqjb67CDUpKEddMTr5ctuG8+wQfZzcJCA3+VyWYIj5hA9h5Shj+ztBjhjfHgGmyIOh8PSb87hMFkeETVyxu32ciQ7ZMswf3Od+2nb5/GhrQaGBP04NMg2wDaZ4W63WwAxxJH7wv296aztGv/bjjBHzmAzFxBNu93+Feb0NaIi5QnoDGCwX7aB9JEEBjrL3x4n+yLbSMuY9eVjHX7WY/qf7XpuY/7bICPrjH8iqsCRZA0678SNn8nfnBtRySrEon3aer0uQBf5Pjg4KMFVU/bPumUfDuawbux21Us1TJyAWzxuOSjAPkHoIl8G7MiR9/Lx0l18eJNtiqhIZY+XwZ9tcavVKoEw1alcZ0xCQEe7c5UORHseA+aatxHRFuaQ9jW9Zcdgls8A1A4waJcrN33Yp/teTbLc9L8/z7L8fYfl5UMcyLbHwkmCbM9s5/Gl2K3NZlNbYkebTRzi36g+jKhXTIJz7u/vy3L0XKm6Wq3i5uamzOVqtSoVNn4LX8YP1iH7bc5hLKgaIrnd6/XKPlenp6e1pPPt7W3Z2/Lg4CAGg0G0WvuXdozH4yLXvHGy1+vFYrGovXbdSxhXq1Uhk9hwnLHxEnt8oW0Ae2tRuURs4GQI+IiKrcPDw7i8vIzlclnsGkGt/RCfk5zB37EcfrFY1OKLTAgxZiRIvRoDMhD7RyDL87E1VMxFRNkvlETbs2fPSj/oq5dYOh5C97w1iKvgbNttVyOiFoc4sWC89GM5mmwUiVnrR0R9FVDGacydcX/GJdmXOKbIvjCibrdzDIP/oHrH1UMQhvg2dML70tn2Y2+QJ8u249qICscx/5DfJKEcR3icclyYSRePh8fIvqcpBoOj4HwS0o7puRbfnrE7dszVYo4zm55Pv726gDbk+bQ+WYYiXt/jyf3PMYLb4rExhuNeTmo4ueY4zsUknqv3qadvVSll4cEgchgIYBgRRM7LSzFyQPCYcvlgADPZY1IKocOwZUKH9jmjx3UYUiYjLzuzwCCIXEs7MNoR9T2hmMBMvlmhbJQ83mY4MyjIbHIO7nN1heeu0+mU7LAdJ2V6dm42cFkOHHgQvLOfEsFwZo+bZKfJ0DQ5p+8L2CwnEXXCIGd8TQ56PjLpaLnKhs5LVLwEj/G1YcmG1cSBDZ8N64c4LEMmc9Et2oncOTPAmmfaDSDKQQrjDaDOTtnPYy4i6hUvfN80jg5eGSuWF8zn87LEAQcaESV7yjI2AoAmIpLlFZ5z6xOAF1ngXG/6aECW9TePlZ2bbVMmUxy42pYwVrapjCvOczKZxM3NTcxms2i1qk1cXfnU7/eLHWCccN4AMDKsllMHwvTVCQPmJyKKvcml7VQwMsaQDgRLDuA5CN4gQcj4GDRR+ebKKcscY5jBxK/zaArIMyi1zbXPsP9sspv2OxEVQZEr+iAzsN1NctnUJm9qz+eUsRts8wwTprb39MtAmLbkZXuWOfpqMEwQnt9I5UAPW0OAyfJc9y0TDtzPVb/GA9jX3J7s69xfV+66+sTL/Jk/CHX327LsIISqRNpAQJyX3jKe+MDs9whYTNhZp6zTrj7zYbvRJO/5PM9pk+x/n7/MmKPp3u/ryPJqssX+xjbVepmJKL5zFh0fFrEfN4h7V6vmNlH9NJlMCs4dDofFznPter3fh2i1WpUfdN6EDb9dSY3Nta622/vlXBBLEVGq/djuodfrFRnNFXo3Nzfx5MmTQniiD+gdVSom1J3sILHG0jUTTo5PqFbExnEvy/jd3V0hv+gnfW21WrWX38xms2I/vUTKdsvJOWNOlv5RbYU9pFqbscjEE+MLacZnJsNoD2T01dVVfPrpp/H5558X7M55k8mkjCHLl2xnmCMTc/TBlV4cOZC2/+WeXG889mM4ctxqm0cVkL83jo14/W23rNhgySc/2+22tvog4x0TFhGvrxbhx9fleNKkBOeC25qWXTsWAofRTmM4VwDudrvam5Dxhzw/9yFjmJwkdExr++aYz5/5fFdE2m/ncfLKp+wHvSQxx+yO47CBOUlvbNPtdmsb3TvOyW2n3dirzGfkeDGPV/bl2d/kRJZxmQk6YxfHiVnu3lVn32pPKQcXOej2IEJI8T2d8IRb+JrIJwuoAaS/8zPJ7FJyuFgsXps02uOsA88nWHHwg0Gw0JuAoS2eKJTUAsYEcz6v8XXwbyOUldVZ2Cx49MNBOs8za+vnmPl0CbTJPG9+bGWmzVQkcLjiyMGQlzDSFi/T8pED9KbP3O8MvrIhbWo/x3a7LW9d6Ha7teV8OfjNhhMQb/nrdrsxmUxKtpLn5YDGf/MMZDCz0x/qMCBAByxDWZ7s9Ggb4C4HjRjcHIzloMVzZ4NqWWTs7YQsu3wPkEb3AVvO/tNH5pbzeT7ZU+YWUEjQwNHr9cq9rVPZiWankbM+tBl7lEk2E3RZfvic+9rGcE5eQmhyaDQalax4Jgy3223J7gIwDLYg9PMm5RHV2/54pmXYy1m95MEyiR4A0rKjzgQYPsnz46ywK3D5nP+xRznh4PnxfH2I4PWxI9v4iNcD99zvfH6+NoNenmN54lzLmpMqHpf8N4RJRJTlYq72IEjinvb52R9nEHh7e1uqb6mSJsuaq0szEWffTD97vV4tkLafph34a8bFehoRhfCmzQ4Oqb72shf0Pet+Bof2W9zDy5kIfpB/k4fWe8Yqg0nGBkBsHMD3HjNXPhlU2y5bFrLvyPKZ9cry47n3Of78sftn/cw6a7Cd9ft9H9n+R0Qt0CfRt9ns3woHHs66iYxkP2vy0EvATPo6IMavscl1u11VQ+DL2+19JcV0Oo3pdBqz2azIkatyjFlpJ3KATzABiu0l4eEKVr9pjyQJyVDa6PPRK2MP7hGx980HB/t9myDPSDw5gFoul7HdbmMwGMR6vY7hcFj+Nomw21V7d+12u7KfZJYr9DEiSlvwrQTkyJvfvu0kuGXcc02SCNyFTYXMMDbjnpYR7LcDScYeosnP/OlPfxqDwaB2j8lkUpYxEod5LzrbCFeFUw1rksmBccaBHoN3DWw/1GE/mrGpddXJW1ckESNGRNE/b1MQUcd84GqebSxkH+rrHbM61jBORGa9zJsfL8V8DPuAjdGVTGx4jPCd3NfkVManObHt5/tv9yuTdE3XO/7P1Uy+N9c5Wctc2a+zYoK5wz55n0V+0OVMKrVa1Vty3R/+d8zAtcZc2Y81kZX+8X2yTD825nyPLPLbz2zS03fR3Tcmpfr9fsnGWCn4jYHFKHr9Np1ggikRZeANrqzoGSi7sxncYAwhlzCIBgg454ioBSw4QJYV4Gi8RM7MckSVHbCz47MsCAaGrVarvI3NGVeTQi4LpH+PEVMOFjJxx3jgCDyu2flyHXPhcmLvN8PzcpDEMiCMLksjPcePsaoYJAu8585HU5Ce58aHDU2WGztKytq9tw1jwtjR/lzSihyxlp/NaDNQtyGh3XYsADuAUxNx9z6OPB70D51F3mivs3y0G0dLcAf4gl3nGSZsHKDxbM9P1ns7NOSEtvqVy+guwNxZTmQWJ2MAjrPwHOFgLN84as6D7PKGjXl5KMScddj9bMpiWZ8z6WInb1nxnPoZ1q3syNnbYjqdlvsY5PsVuR7/3a4qOXfVK/bXc8+YeElJBt6ZtLy7u4vlclmIf+aDTDqvt2b8MulO/0hQIKPHx8elXJz9sHKAxPgwxwbL/u5DHwYaGVBlAG9A7GuzzfPh5RQmAmyHsP1e9siPZdDP52+qRn0+OpgDJsuoq5MhhZEby49t8Ha73zDa+tLUf9ud3W5X9Bf70W63i91HVgi8XO1BUIiM0V5/nn1yrjzLuClf8/DwUAJANkUm6GO5OXrv8USPua9BqGXcYBkbhb8Hf+C/aK99F7LoQNPLcmmTAxIO5s52LYPajGeaCKi31cWmoOpDHpZrZDtjFPY4ZN87fE6WjewDGF/Pswl/ZI6/vRRrOBwWfMG1kFPo0/39fSyXy3JP9h6i2iiiHoAyvpaNo6Oj8iZncCz9g8zgHiyN63Q6tcpKcDh4+ebmpuynxvWuUGdpoPd1Mt4Cm7CtR6/XK0T33d1dqQbzMsn1eh3z+Ty2222p/IRcQ/7RRcaJffKockJ3su9BJpsIBMYN+UE+IC/Zt5KxzfiEebCtYLyRJci/iL2NWK1W8dVXX8Vut4u//bf/dpyfn5e9xA4ODuLs7CyWy2WpePH+RLbLYC/skiulsKW0x0RZEy40zvwxHE1xXUS9gCLHaci242SuhZyhn9Z5jqxnjtmwpRwm/Lg32N2xFd+z1JS4N2ON/BIgdA58lSt0I6pqTsfakC/GE+Av+w37UPeLfltWchyVYytfa/4BjIKMGpcSAzGGxOi73a5UJ2KDsD2MayaEcvxkDID+4COwKdYBYyuPr32fCTnPW1PSgM+5L22GQPP32Arja7c9E2ce+8dw59scb0xKuWwcAwyBgsK5gUy+HaBBCYPtpTN2th7MiIpEyhPkSc33RmBoQ9793gaBDZANijCwdro4V4Crlzcg5PQDcsYG1sDNYJ/MgoWT51tgHGTmzKXBLcaPMW4KJnIGhfMhWQyuOp1OCQAAMDaSDpIBCRhjL5fJY5qND/OaA/isaE3CzxxlMtLnNgFUwMT9/X1MJpPCXjsYYdxsBBhHZII32lBibXYbOTdgz06GOd1ut2V51Ic8LC8R9TJTSMa7u7si4642sa5CYFjGbMwiopYNAigzxgbftMv3ysSKnS5Al9J85skBoNtq3aPN6OzJyUktI8LLCQCDAHrmnEAOgGjdtSNwPxkbDjvSDFQ9JgbjOWDJpH2+H2PCWB4dHcXZ2VkhjaiGsu3AtmSg5GCLc53xtXP053zm0maPB/dkzT2vrc7Lox3c0y/LMDaZv5FpKjtZ3sccZgKhafnLuzrZtz1ykB7x+rJmj51tcP7M51ourUMcBh1Zl5GPfL+ISjapwuMenU7ntddJZ/8EiOU7qhBNMmMzkHcDe3wqz3cbjTeMDchiI68HBwfF9/IZe6k5MDSw5zPuaf/u7DbPZkyckLMOgR0ImrEBEHKdzn6PN4LbdrtdNk12QGTMgM1Cz7Ndarf3Fb7eyysnhZA7B0Z8b7uDvHjuM2nR5O9sq2wjfV3WjfzTpD/20Y/d70PqtcF/RJUcJAhiidzd3V1Z7u2KE88rctLtdguWwsaBWyFX3SdfNxgMSjCJ74uIUhHFkncCT3Tt+Pi47NdkXeP+TlhF7JPX7AWFTnhpKMSWSZeIygcQ7DFGx8fHhaxCbxjDbrdbW+IIbqPtEGkEz1Qvsc+kA27vHXNwcBDL5bJG3OBXuJf3rPR4mOQdDAaF1MLv0Q9smH0UgSCf4Zv5n7YadyArJBEIoNFNYrSISqchvGkL8313dxdff/11HB4exu///u/HcDgslecnJyfx9OnTYpMiKvtLbOV45OjoKGazWS0OM/bNPg7blEnqH+Nhu2P7ZXvulTHgTFfVGPdFVLjORQUeFz8b+2qChue7OtYJEZ7lZCj+hljd59EHdLfX6xUCHZ9i2Xf/ud7xtgku2uCYwT/007Evz4iobxtg/5Sv9xjbRtFfCHL7R+NYx0HYv4iIxWJR+oIeeik7smG8YszMD3qIbOS42GPqg8+b4twmH2tMbzniMJbmN99jm9xHxo+/0e33pbdvTEp5bXVE1Ay6CRAzmgwWGQo6nIXKmXgreTZgBk3+3ACg1WoVo2khxbn54FwmlPu7bxYKnAbtdLBLfzA4XEv/ubcV/lcBKwscwk2fcnYMEiwbMQNyj1Fm7PnN/ObXUu521UbvVhorjOfR4BbD7CV+JgXIoOSg3vJEG5i3TFY1BedZ2WwYuA+fbTabspnj+fl5Y+AAaKF/njeA/Wg0Kkpq521A4mtNTNJeB+wf+rBB44cNQfP4GAh7Q9FsIL1GHrk02GX+s47ZiHOubQJ/G+hmstJkiqt/nJkDxOHAm6qcyIySefXyWsAFxKEdLlU9ls8MEnKlSA72LRdZx7AlTcEZ1zQBEjuQiGq5LTJP9ob2k0U2eGaMvDTAtjmDB77D4TpgdkWGlzFSKZKzvMxZzvgiew7kbGs42C+EShgTsG5zbnv++2McjBVHU2Bu/eEc2zbbcpMRnGt5w657aSagEr/vgDQ/n/9ZPkPwRuLKiRpncCGUsf9+2w++1sEVbcw2s2k5uMEqbaQfbn/eoJbEFDKWiRqDRvsVX8P4851lDHDH2EE45YrPiNc3emXfH+t2u90u+2Q5E+7+M+boDnOxXC5jt9uVpeuuLmTM6QuA1P7Cdp++Mt7GLNyDNmQ7lwM799nn5mCw6ch21P83gfsPdRgHYY+ZLxNT8/m8Fmgyb07WbDbVS3M4+N56QrDgCoV+vx9nZ2elepFxY04Xi0XMZrNCfuK/8YUQOycnJ7XlWGAicJyDKuTYJL8r/sAO+Fj017aZPaAODw+Lj+JN2uPxuPhe9qIi6cCYYIfo56effhq/+MUvythcXV3VdBzd5zPIvogo/ce++cUGjKdJ5NVqVYjC9Xodl5eXRXch3cAYEdXSIL/IgGeySoFKLmMn2zHGEixzcnIS6/U6ZrNZzOfzIhf39/elOs0Yy3bxm2++iT/5kz+Jv/N3/k70er24vr6O7XYbT58+jeVyGfP5vMQayCpYiYqu3W5XEvcR8ZrtMGaxb7D9+Nh+902PTI7wt3GW9ZnvbU+xbVTo2v5lux/xemLWttFxsskV+y/7A3QbwtHzkGO4VqsVg8GgFBnYx/gFGsac6B66Qr8d/3DYnjet0qDvtA8bYgLFsaPH+zFfYns0n8/Llj2ZNHW8ynfYiIgob6/kZQuMOWNKvMTcez6cEMxyYz/oefw+nWDebCM4uA5baHk1Rso8B3rpeIu9LbGzuU3vQ2ffmJTyHgw4Vgydg64c/FgJaLSBXQ7uPfgWEGcUfDiA5ciEjUsHDTIBwTYoOQDGsXItIJCMCUtd2u3q7QMEdwRadrwR1Zv5AOsGbCZlLDjujxXZY40z9t5dTcSN/zYz78A8V7/YYNJPlDYz5swtsrHdVq9EJuNug8E88gwb1Rxw2yhi5By8OPAy4HAQ4fvm8SRDNh6PYzweF2XHyAIoLbv0udPpFEDnyjsvxfTz+N8gymD/Qx52UHkMDNactTORiNxyDvs4UBIMOGWsnDXhO5MazBljaNDlNrOnjN8GhBxwDwwoz46oB452pGSeP/nkk+j1erU5QybJEB0fH8dyuYzJZFKCYZf0UilHkNdkm6xPDq49HxF18j4fmZi3fcjOnLHI+4Nw//v7+5jP5yUwvru7K8t22WeLpczYMeaJH9rgZ0M60U76ZPnJAJTfzLE31c36S3uzs8ZmU/XmMXK/HQByHb8zoeHvP8bxfU6+iaDykUFNDtIj4jXbi+1heYr3f8nkJue7LSwPIlsP+PHLBDif+TUuuL29Lec6m2dCFT1HppHDxzCBg0Xm23sx8TsTIQ7aGTvvqWbwi+3jPMh3zvW4MWYmvbgePWEejDXoN+0CCOeg7+7urtipPO/Wy2wPucdoNIrtttq03VjE98hANqLau4rDeMH+pcneOVDg2gySfc/84+95RvYdHovcTsbrfR8A+1ylbpldr9fl7WonJycl+RFRbV5vXGT7zhzhX4yX+Pzo6ChGo1F5cQX29/DwMG5ubuLFixfF9m82m1Id6DclI/8mM2kvLwLx2EK+mFSjvzzbhAlBNG+C5SUVh4eHMZ1OC76DdEXeF4tFLJfLsv8RuNd2wTqDjC8WixgOh9Fut4vN4VyINOaPcR2PxzEYDMr2DC9evCg2Dd2FkAETXV9fl4T3w8NDTKfTWlUZgay3vGDZIXiJN/BhU73XYg5ssz0k0UBygLlztYPJB8dYd3d38Ytf/CKeP38ev/3bv11WkqzX63j27Fm8evWqjJExhv36arWqzQOkg/eisk2xTmZM+mM9jNmyPWI8kF/0D8LVpGy2D5nIy3aO3032j3Y9lvgElzXFSX4u/szPMKFhmYE4JW7kQP7sM/k76xltNabgf/MKEfVVW8TvLmjIc2R/4N/z+by2N6z9DjbF5LTn3LHydDqNw8PD8gZhb/tj7Gn9arWqlyK4QCXPGTKEnmWs4vnJcQBjDiYhDvCKF88/5+x2uxK7m5DzC1cyaWbZfFe9fauNzumwl2nQSRwCjcqZxuxYDQptiMzqcaBMOGBPjAU64vUNwMiEMjEEUgibAaaZVpMDXJsBD4YFAUIxnKU5ODgoYJ3ftAPFtoCYJALwZqVxP2kX48ozI+pVFZ4/Z634zNVKBmqeR8Yzoh7cAqAMUHIVBU4fY2fwzzlW4izYuR8eBxst7uHAPzsOywpzbeMFMGq32wU8OANF1grC0oocEWU/AZaiIF8G3E3Bh/XgMULifR5N45X/J1NqQpp5BoT5czY1jahIUvqFM+EaEwU81zrOPDkYhbBwsGk7QkUkS+6QczYp5Yd2np6eRrvdjtFoFEdH+1dHQ0ySXSaQhJhheQIHWSS+J2vsIMzjHVER54ybs7wZcHicTPa7uiHrGuNPBtwkAOOGzSID5XJ7ZBsnBHFg+9XtdotNw+4RjHGOSQP3w6SQwR0VM/f396XiDN05OjqKbrcbt7e3NSIyk/sE18iKx5kABdvN2BAkAgIsk03g+WMd+bkOvB8DYbZvOdi3nuMLGAv6b/DItYwZ98zki5cGRVQbkjsZRcAC+QRBBZjFB0VUiQNnfjP4xwZ4OYRJIpNBrt7iWnSIe6PDBsDIcNO52EDGB9CIfaO6hOdxT5M+6FiTLaONBDLMpf2z/b6rHplTy4j9nHWSJQS8Ft5VFeiml2BlrJWxSH4en7vNxlq+ZxOBZL/pa/x/zg47ePO9/Z3l630flveMZXOQiAx4GTgH/cJ2ZbmIqDCZcUSr1YrhcFjentZuV5WM8/k8vvjii3jx4kXsdvvKo3a7XatQRCc9t0dHR2WfpN2u2uAbP0Oylc9brVZZjr3b7QnQ5XIZ0+m02Af6wcs1Dg4OSjKEJXa2NxBq6MqLFy8KoUVCMGNq3qJpfYTwmc1mRffYN9c4vtfrxXg8rlVNgCnQsfl8XhLVq9UqFotFiS82m00hkUn0QgoYm242m5jNZsWfbjabQvq12/s9nzqdTpyenpbPwR0O1tFR760Jxrm5uYmLi4siRw78s41brVbxf//v/41nz57FZ599Fn/zN39TkrXn5+fx4sWLWmxnopm/sZ2eZ+TJhQMZA/L5j5mUcnxBf+m/MXBEVZXuhJh13bFPxOvbahD75iSu25FtnOM53wdsBe5zBTJ2hXgee4rOcC9jsvV6XQhYDgpWnNQi7vNnVInRBsf/xsGOuV2EgJ6yvJfxyPGTcVxEtYcaVY2Q7saB2+22vN0041nzCYw5L09AJ+2jXYXIuBhjGTMhL1mXLF/2nTlu5nDBRFMs7YS5xzhjLNvRJkLMyUp//y7HG5NSOfvIxBBken+G9XpdHAQdM7jME8rnmSHmc5f82ZlnkMffPNfBtAc5M45MNP2woJg1NknkABLW1gQY94CMImBlgskW0TYLbBY698uACrBoo24H4zHyNRZw5pNr7EwcQJDJyQw27Xf1iQ0LyghwYZyRFa7JpYBZmXge/XvMYflzGybu73GlDQbpGOXtdhsvXryI29vbmEwmtfnkGcgKTrfdbpfPut1u9Pv9Up7vAB0yxw4tk1XvwyG7T4+NVRN4d6CT58Vvu0EukH/0gzHN1VE4ujzHjA9ZAztwA3IHrm4TTt7yz/+c4wod5tEg3ONt8OlxbLVapQrKesjfWZ7teLLRN9jODoYjk9Y8j/H19bQ5y40JjCaCCBmlRJ8gFT31m4WYP8hW3orGkoTRaFQLqLELHlf/7Xmz3d7tqqUULElAr/LadmfsMjAcDAYxnU5rbUEmbXNNMObjV9mb9304qDSocFCedcj2P9+L3/anPo/qKMbC2VH7X9tIt49xxrdhMyBDuQ9LBfBLOSniZ0VEoyzbVqBDgFz676yjfRVjCGHj8TJ57qW46B6Ekys1kBv7JMYQWQScI49UTljOPZa2JfhK7gvhRBupaGLPO8sEIB9bZ5nnMDHMfOAPmVeDb9ssYzDah700tuP+rkaxfeS+2S66KifLW5btfGS8kz/zeU0Y6X0eGXMwZq6QARdQWQi5wVgil1R9+jXmJjWQMeai1+vFkydPSrafqozZbBZ/9Vd/FV9//XUhOiCSkAOW0EJeHB8fx2QyKXPAnkwkB+jL0dFRqRRmGbbJZ9tik0z2MfgSv1Rpt9uVPkRUe8KBC3JwazzS7/fLuHp80S+WAZp4heQCuyPb3HswGJTnbbfbWCwWNSzIHDNH4Br6xHxSKeH9MG2fttttzOfzaLfbMZ/PSzvb7XZ5KQ/65Lfo2X9al03ckQzwPpn2p61WK66vr+Mv//Iv4/d+7/ei3+/HYrGIxWIRP/nJT+L6+jqWy2WxiVS+Y2cgDh23NAXZtgn2303B9K/7MFaOqMeNzFdEHXdjX5EHxsr7gvG5E0Fcz/1MOHNe9um2xU7urNfrkpSNiJqNZb6N0Z00xqfZv2Enbm5uSgUdyWuqprBdlkvahC4+PDyUqkXPt5Oc9MtJVDCCCRPa63YzNsgm5znu9hvYHQ+SvMIGMq7YBRK4jNVmsyn3gncgyUNbsDe208yz/R3XOEHPvDtOz/E29zKxlGPxXOVuPGmsgyx47pr0MseW70Nn35iUwuk4QDKIMANK5oYBM8gw2DYoNADLhsyT0AS8LdQGYgw4DgPnaOYyE1D0x9/71fAOihkDL9WLqBhpt4H7Ayi9sbABtQ2cQSrPxLgZ1HN/rsssM+3K2V2PIX3B0PF/RJWB6XQ6ZXmPK6a4L0CZJRkIeVYgg62sbG6PA2i3NbfPsmQltqPzefl+/gwZYZwgXmHAXW6NwWV+6BfLWQBis9mstiklAIj2ZgPxvo7HDITHxPrJnAJ86ZuXIdiR0Sc2a7VePzw8FELBwQzPtN5FVOAUY+0Ay2X2lhHLsQ29bYKXAmbD7WWndlydTqdWVu1lOZ1OfW8jy07OOln+0B23xUSygQIy6CwT1zmzlkELNsN9BVDgZAk67egGg0HMZrNaHzkX0EqWC50m42biaDabxW63K/bS9iwDq8f+d3CNbDbJM+Nnm+0ArQngOtCIiBpJkJ/pZ32ooLXpaLLLfG6f4LHLssH5TaRHllnrB/PtzYWznjrTRwBJ0MVce5Nygh/8blNC6jFyxuA4Il6rQnRGlzZ6n0LLHvbNgUREfSmsK1QYYwPPiDqgj6hwkav1Mm5xQoJrPAecb9237GYSEv9BsA5GIdihX4wBQLkp4cVYYCNYfu19hNA17ms5NOA1Ken5ZK4gYxxEWJ5pk+cg+3r+zjpJv7JN5Dl+pnXDpPmHOGyLjX+aAoXNZv82OpaDM++uKvGSMNtNro/Y68B4PC4VAN1uN8bjcczn8/iLv/iL+Prrr8sm3vf393F9fV1b4uetFvBnHBAxtB95M+GPLFE1xLx7yT1zkJcBgrPAzywph1Shn+iXtw0AhxMDRFR7o1I97eQUiVZkh0ogZHW325Wg1Pr49OnT2Gw28erVq7J0Z7erqrH9BmawOoEuNisHfk5k+yVBXjqMfC+Xy0IKYP8I1sFeyBW2kH2uOp1OSR7xXHDQwcFBrWLv9vY2vv3227i4uIinT5/GYrGIu7u7GI/H0e/3yyb5HIwTGDmT1rabxrzIBDbN/vtDHSaR3ubI/tk4KydBGFvPi5fCGsO46MH2mWcyjsiBY3CPJe3iN4lXKveQE8+F4xbfCx1zogfdvrq6Ki/twI5iKwaDQUlYo6OeZ9vow8PDGA6HtdgiImrku/0PY+35yPppueFZOSYES2B7wO4kZGlPq9Uq8a6Ja++bx5iBAUxesfVFrhwzwWc/v91uy156ToZxrWUnj0HmV4yjTHw1xUKWG8+/28fqg4j6nuLvW1ffipQymWDCwRVOdMgC6MqkDKrpoIWV52Co/Qxfw30siNyj1+sVpbSQ0R4Hm7QJIYRMiohahRCAAOMJMxxRlUvym+sNaCHLXGFkp2/l9murLYgGeh4PH4wXLLmFJgfovofn1Zk8HDlKR6YKhc4kpNuHUWOOGaumgJzvM8no9lpu3D8rtYMdB5yc48DIYIB5cxBxf38fL168iLOzs7LmnzZhNOgrRALByWg0itPT0wLGGA8HDg4I3ych5Xl97LAxp18eQ4wRh4km2krFn/XXgRmgmL47OAOsYcBzUAHQY913dkAc2agylswz+uVlxwTUBv3oJdkbAKJtA9dybwgq2sw5DvasEx5bCCPu735bph10NI0Bc+eKL+sQdiUHeFwPqcC9mV8y6K7+A7wzt4APgG/E/nXn7InhDV3t2GmbiTyej22mjfYXgCqDYfrEOdgFghyWHRJg2CZDxNiuGxharj7W8djz0A/k2e00WLXseHlZBpwQr8wzG2Zn0MdvVxBBPDIf1rHVahXT6bRsTJx/bNutVxH1RATEGOcBYt0X2mb98dIDj6cDNfyRddA/Tm54HCDom55tvcoAOdumu7u7UvWHj72/vy9VGmAV+0X+p0LC44eMO3hhPAhSqc5A3/DztCsHOH6G9YO2oI/cO19vW8RbL72c1uS6bZPv4/Hzz/cdvtZy4u8+1oH+GTO43yYzwRO3t7e1qvSDg4Pid90vxgJ7RsLzk08+iefPn5cq1mfPnsVqtYr/9//+X3z11Ve1tztuNptSzc2LgCBOIqpACxlCJqzz6FO73S5v8Lu5uYn5fB4PDw8xHo9rz+NvErPeAgPymkBwOp2W5AcrMO7v72OxWNT25iGpwtuTTbaQMGFj/4j6xtS73X5PK95mydvr3FfG5uLiouz9xdsTwQC8BZh9tRzQUWmEv0Rv8H/8MI4kxiCbB4NB7HbVFhEQfk4I84ZF4wV0PWOoTmf/Nm2CYL73OfT3L//yL+P8/DxGo1G8evUq1ut1fPbZZ3FxcfHaqhMw8263q1WZeA/RjE2yjfwYOvouPj1jBPQbH5r9CPFkxhS2Zcii/Z99eUTU7EZEtZ8ydsNYk3mFZETWeJs2OI82I+vII/OFL0L/IZtJNIEjuGa1WhXckTEctsx2brlcFluCDnBvJ9kcMzoZbTzXJDeeK8eK9M3L1DnwZRQjsI0EZJTnlm0mjGngFbAVw+Gw9nZMJ3/9Uin6GVFtNxFR4X4XvjCu2R8yho7nwAUcnmfaaj02sY/fYe65ziu03tQvv+nxVsv3IqI4EQTV2fwmcsnKQmd9MIgGJlxr5hlHZgLE16Oszv5ZuCLiNUdh443zdRDIvUyg4ACYbE8Wk2+A50wGk4kThg1mbG2EvEGhDTkCauBpIApwZYwMrA1i/RmGwBUxDvBMorgajLdk2aFCypm0dBsMbtzfDCTpa1NgQJ8doHOdCbScdbVhsqzxt42D54VNQZ88eVL2mIqI2hyhC/QPg3d+fl6ILS9Vwlmh5IxVU2bgQx3ZiDBXZNiZbxwaBswkx2KxKMEttoAMAP3iPpZNG3aMv41dq7UnP9gI1jbGZIyBZUTUAlnrg4PZHJgy94B1dIGqDD7nPovFIvr9fs2WZadhoGnAwfOaqk4NCjncRgMV9z3i9bff8b31l7FgDAiKWq16hpp+sIyDDV4j9rpwdXVVAtzNZlN7MxBjSSZptVoVYoolJIC0bIf5G8LQ5cv0n7ZbrqzTnj9sLG3k3hBRABLPo+erSUc+1tFknzjwJfn7HHA5iMyArdPpFAIEcMvcMEbYOXw3NpFXsRvgIUsATe9vYh1jzvKc2+9hv8EPyE62PSYzHdwhVw6CsEV5eSJkJbpBoNbkl9zfPO62f7bhBszIqjGJcQRzMRqNIiJKNaKXFFrPyVx6ftABZ1VNvGHH2eeG+7EMCp0BEzjpwGF8R/89ZvTX8+uqNftAY7aMAXNwl4O6Jp3JWM1tcRCcyf8PcRhnmMD3nkvojrExsuhAPttK67cDtdPT03j+/HmRqbOzs9hut2XJ3t3dXcHAzvpjcwlg7R94lvWZ87C1EDJe1kI/wAgkc5AFiG3miD5TPbFYLF5LMtrP5HEmKTIej4tvgpiCNKAtPIexWy6X0W63y5u4OKj+x77sdrtCrlL5TOUSY9GEWfGh7D2DvLP01r6RN9kRa0REzR5uNtVWGMwTe2GSDMqEH+e60o3AnIDZfpMxvrm5iW+//Tam02nBtLe3t/Hs2bM4Ojoqe3AZNyLX9lHWQc8ZsYK/8/kf6ngXv+65zb4Xe49ORVSEChgXnbUPdbKUeztu5v4eF/tOdI3fNzc3taQQFdAQqSZluB5yhvl0RRUyhj9yEgqczB6SbHFhf+94AH/GMlr2UgN7OHHJ2DIWxiRN/iDLF0dOfFBsMp1Oi89jPnibJ/bJ/YuobAn6b7KQpcRs9I++rlar8gxkwrEIn9kXUIWJjcxYPs+/sYHjfP/tcePe2DzHDznOICbIeuzz8lz80OOtNjrHuOUJptMmZvgfYfYEZMCSB8T3N+BzUIdBcFBGgIGioRCtVqsoUx54P8uglUnACfG5wRGZAITBTtvCbKFDKDnHbxuxcUI4na10/02ceaws1Jk5d6DOGHCdAY6BGu2npBvASll/q9UqSmwj4XECHDD3fvsA52AArTw58Abs098m0smKydg9Bj4ti5nYs2Nut9vlTQ3Pnz9/7W02yL0V2NUlp6encXNzE4vFosgmY4sTczszAP8Yh8fS8kIwmNe+84OjIIuJrOQKI4CumX6cA4QI9gLncHNzU6oTHbxlwsBzERE1YMizcQJsUn57e1v2RSCbZGBJdvL6+rr0H9lnPw2qJ9n81TLobBPjg13JMokuuxLFgRW2hWfkgDnbTp/H+ORsiYPn7Xa/5AC5BagyB+g2RDTkAbIMAeFsISQfn5MVAjDzY9KT34wZh6sLrCMRUZPN7baqQsTOdDqdUi3lzJ+XUtq+IlcZcL6ro33Tw8/Ovs561PQ5fcgklO0J92X+KC0nK899Td6gX7z+2L6B8b67uyvBGX7Cr5nPficHXF6iRHucmfNeFyYekA3OcWKIZ+X+k0gxYLctNsHjOeHHWULaTFuc6eT5JtIzSPQ59rFUGVqmd7v9G4NcgUCAbYxBssPzbeDMUirIRbfD9oNX0qM7EfXqXvqewT9kMhUnzlBnGbeNdnstuznQtwxlvfH/Pjx/7xtEP3Yw7vl5Ji2dtY6oL69GzuyL8R+5H8wjlUJ3d3fx7NmzGAwG8Rd/8RfxxRdfFJLH2BAZwvZbn+03+R586UoqsA2+1MQE8m4ZpSKQxAMJDAgU8ID3tWI8wRm0Yberlot1u91axS5JB2MO7z/lfY+ccOVa5o64wdXVLE9mvrKfo885eYB+Ggcw7xBXw+GwvAkY24vOErNwH9sx8NJisYh2ux2TyaRUcWBjsXO0wRjdldAsC95u928SnM/ncX5+XuYvIuL09LQk7Kyfxjb4GggPZN7EpWMdb0vyYzuMPfk/4vW3EEK+2FdwHgQk/jfHsU54OBY0mZV9ndtDJZNjPbAOfj2i0gO+d2KIucT/orvIBXKEvj48PBTfzwuDjC8tH/hb2mMbSB9oI4eJuYxtXTUeEa/ZeD/DPuDgYP8SA1ZU5dVRTsTc3NyUJa8R+4QyVYl5rsBnEHO2DxHVainm0jEVvo7D9s9V0I7vjYVNONuf+rAv9fYHxK38jU32fsmML77A8cNjvvWHHG9MShGkYeBMMGFMyIZ4kNig08YTAUMBTL54kDOQyQPt4Nlt4XvApl/ryLPM7j9WPWQwa+PCQTBjIsFLByjRd6WGHeDh4WHZbJLrmXDaipAyZs52dLvdIhxmTjP4y4SUlZP25yxvNohWWsYMRXEGpykAsTww9hhwNsXMxiMbGf5uOvy5SakMYpvAIfOMjDaBaAcN3333XTx9+rS2zML9h9ig7JF5evLkSfmfcctvQeH4WKSUQXMGZg4aTVZ4LvnO5AdZfuaN7BDn+RmQJZBQEVGyyPP5vGyUmnWs1aqWHNm421ADuvz2K4wtpBR7JFDxh/4CZAGdERVIZf6wg9gVrue1sLQXXebe6JrHLhtxB7G2Ta4KycQMz+BoAohc7/GEDD45OYnz8/PX9gBCl5fLZbknwTLtcIDDGBPsRERZp082fTAYFDAAGeaxcLm5CYlsl5qqCPgePwQYJqtLsEW1FAQcQI758bzlwPlDHtn25eCZeTOJ3nS9/S1/u38EIPghgkR8kf0ysjscDmvVy9tttUTAgRk6ztLP7BdNlBggcQ8CU+xk9tPZhkCAGzjxHNtX+uRAAFtisIt/cmUHIJ1nAgxt22z7XeViHXTWmOc6iULfkVOW5fF8Ktggf11NbcLBFSjIcp7XxWIR9/f3MRqNaktYPbaQlcYY/PA8Dn/vqh8OQLCrbzKmc5KyyWc32TXPdT4vf+dg2djiQx30BXnyAW7AR0F42BdjS233naRwYujp06fx7NmzYrdHo1F89dVX8ed//udxdXVVfDY6Suaee7OPE8QlCaOIyoZTOTebzUrASqYdffWcYl+89UXG9/mtryzpiagT3+galUT4BXBYRBR/YhzJ2KPP2+02er1eCdYhxoxp0RnHLPhL9umJaN73hyoKxgtcOBwOS5+YA/C8ZZ6+QRjgi5k/V20hBz54NnjaCSBsv/d/pX1UyjHmbGTOUswnT57UtjJ5/vx5LBaLWnWcEwnIt/1nE/nhcbb+/9gP+1PHARAWYBDvT4pcgz9yzIRMcD/OtX+D7DCmxv9QzRRR2R6IBV6+RCzjeSKB5+p0+3X0LiJKdVGu4uFcvyCB+A75wMbZJ2U7/xius6z4WmwH3+f4j99ZtigcMWGLDoPjqXLivowjy/ZcBMN9sD202+Q17YC4tu+3z/M+ziTAuQd6nGNlJ8lsY2mTMbrjXmQTv4wMek7YL8/4BbvOsx2HvAtWfqtdHik/pkMGzwA3Jo4BowOZMGHAYAcx+BbuiHgNzHIwkLnSJJMKKBftQfAckGchtxFAMBlws/u0l5JoNnTDsZC5QYhoj4M9A3SDVoM2HL4Nm0kzl9zaWWbQDNAxoDMhwzj6WgCmCRvAikGdyTGArF8TikDzN2DMsmEjwxx73m1cuE8Gt9mI2RB5rP03zzDzbGfhIIby66dPn5bXCu921SaXjBdAiPmaTCZxf38f33zzTU2GDXasKx/LKduQW69NUiK/6IJfYe5AzgSCQUhEFADI/3kDT+sXssT/u92uZOSQPaqW8hzlTBMO10vwmBNAoqsr7JB5Mw4EpDc0RudcnYVTAthj3B3IZXDGGJh8eoyYzcFUkw3znEZEoyzzOe0AoJJ9s13lwCY7k4PD5M1IzgCzXM7yRDun02ksl8uSGR6NRjEYDGoJA8YgonkvLTtz2gf4sa1DJr1EioCLahwv9WX8c7D8Lk72XY4cuNummUBizrLMOEj0degzem/CFN1jLo6OjuL09LSMIddBHLPsxKTw4eFhydT7LZfOkrt9TkxBGjvzhz4hh5DA2ATbfRNnBqYErFm3mmQG++Q325FBjXh9fzyDPpa8Mn7uQ7vdLlWItBGb50Ct3a6WEhnUEhASRJJxZ2y9FNH217pIXyG6WPZjv8N1x8fH0e/3a5iK++bgAWBr280zdrtdrXqOuXIbDcpNMNMe265MbDQRtPnaJl3+UKRUEylm7OcgijbSZ2NZ7DKybtlGh9GJJ0+eRK/Xi/l8HpPJJL799tv40z/90/jmm29KUoGx6PV65U1s6Cf39x4z6Lqx82KxiMvLy5q+m2Q1eeX+Q9Ygq8ZItIP2I4/IGEExgS52id8md25vb8v+UJbziKqywsR2RJRqMcsgCaocLPtwHyB3aCu6wEqL8XhciAO+M4nP/lFUKZE4Qw78DPqLruXqcHAJbzwcDocxHo+j1+sVnENCgblB/phLxu3+/j4uLy/jJz/5STk3ImIwGES/3y+JPWMD217myLbWcZ/tSsYyP7bDOuvKEu+LarkHW0JCEEthD63j3I9r8xhiI3wexCLL9XL8x3wy1sw7cuQEJ7rDnmj40rzBdbfbrcXKkJH4Ka8q2Gz2S+Lpk5NUjrP9GX2MqMga4wRfY5Lb/th4wPLlWLPdbpe4DJuLLbQf5p4knMAExo9UXVFhhJ8zngY/m/hh3Kkqtn2iDVR/2t7bX9DfrEOWIQhHjxcHzzF34jiUt4PiZ7BDEGe0oSkW+SHHG5NSBKQuGXRgZQEgG0bGwwwaHf2+wN+KaSBlJeW5Dl5wKrnM1cbBjtTPN3kBqWKHnIEPioiDwRDBaHIOY+PgPxsMxsbZGYMo7z9jkNs0pvlzA+08nrTRwg2oNpjFmCAHVlYMlt+OZkfLHACeMaI8w+tsDWAzKGXsOC/LHvPJ9w7Y82f+39d57B3sZcKGDSufPn1aMvqunrE+YIy2221ZkjGfz2slqu12vaLmYx4Gv4yFl79aPsmmmKAyeUVWMAeZOC+TK8gycx9RLeFEpziYHzIzkCHc11kj+kKlAU7DAAx7ACnirARLXyBNO53qrXlHR0dlnb5J2dvb2+h2uwVU5iUHEfWqqKYAyvahyYH6O19jXXCwYgfhYBNH6AymdQGb5aAHGaacF+DB9wQjEFOucIOct0zhrJkr5msymZRN5m137VgBDm5zDqRZLsC84pMgA5gzNl+mChA9tf/5dR+27+6z/46o+s41lhXLyGazKZVqkFGj0ahGCEZUVS2dTifG43Gx85TsX11dlSroiIq8cek7+smcG5Bm+xJRf4W2CUbfCxuadYQAlGAPWWm363s/RkTJ9vM8lu/av0ZECTRor3EEYwvBiR2MeJ0o6ff7Zdkr9tDVtCZmIqoNUD1mgEnAITqDD6Vt2GWeZYIRXcN+RkTZT4axIeDArmIn+/1+tFqtQm7Y1tBvJ/4coDJe2Oksu9Yz+2x/7s/s0/2ZbYW/95zYBn+MwzgOeTDW9Pz4HM5zos9LSrJvJnEC2T8YDOIv//Iv47vvvivBKlWhxnTIOvrCsk4SqlQ+3NzclL2UvO9grvSyDnGOKwxZDoq8cj6+JSJqCUv6yOvVqdIFN1LtgO+i6plYhb612+1yfxNjjCu6k/2SSRjsKvaElQr0lbHlvhBQyD067DehOdB0YpfA1vGK7WJE1LAm94qImk/mh03Rd7tdDIfDGimAjEIUYuMjqj21bm5u4vb2tpxHBR324+DgIBaLRURUOM5xC8QLcutEE2PLd5bzH9uR22WCAR/C/PA/djjvDWTiwyuMst9jbCKituzPpAEVUi5oINZCNm3/rZ8RURJBxO3YCGTO/cXG0ydWSHk5IsQmCQj2asxkMmSJX5zCGBm3Gj8zRratWZb4zqS27WVEFCx0cXFRxsQ6wdgR12RdoYqZcaFNLHsnFgCzmkgDf5IYwAbh4/N9mRfwMXYDP5vjjYwVmTPHmhm/M39OvHEe9ot5w7Y5gfK+jrd6+x5l4y5hxaBaUCGGMMg2QpkEsVKYqbfzZVAMKCxgCJ0nl4kHfEXUS0QJyjAONsgIpBlAjAf9nc/ntQwTbXGA6L4jkAip+2EDwJjyG+F00GrAh8PD4Jjs89xZaRlXxt8Kh+HIJJbnFdDsAJb+wure39+X4Mf7CTjbxHO8TMLrz92HbKT420y457ipv8yzP3dAayNH39wGxqrd3u8rtF6v45NPPolerxej0agsT3IQtt1ua68r/uSTT2pAzs9r6s+HPB5z/DawJlCQf8CXs6ERUYhcHCDOFNlwEGNgSZ/RS4gkQDFy4QoixshEAv+7Morv0SlXKV5fXxf5xjgbbOGc6Tcgg7nl3EzCeW8Oy6VtViaYIuqka/7Jc5V1wfrqezxGbrlEnDmybtiOQRKyBMPLVh2Qbzab8ppoAw1XoGWChPk6ODgoGWKyuR4/2oQv8P0YX+4dETW/BJGWQQy/CUwcAPwYQDHtaJp7z21ElSVssoUmJLyHAHJN4MXfzsr6DVD4mlevXhX7h94RyNgHOQMJUGPOaSe6SlvxFeiyg7aIqvIPMGuSw/7ZwT1tcjDHfUxogwcyKeIqhExuRNTJGMaY6008dDqdmi+kHbZ9BHjb7baQZyR8AMm2oTkpRxUZvhhSgP1zbHsgmyAjuA/BpBNnh4eH5Q1gGavlYAf/DWhGlhwEeXwsN8iysZfPt+w36WgG5dnX2/5kIP8+D9tiZB79st22f+Q6xpjP1uvqpRuZyOU5n332WSF8zs/PY7lcxosXL2KxWJTgsd/vF7LEVf3c07JHImE+n5c9FL1JN3YdjJMJYSetsAXYE56JvBjzujKZZXqbzaYQ4/gb2kr1I+2HfIEAY5zRoevr67InKP3Gp7daVfLMlQvIDXIJUcucstk3CTawB/c2Kc7YmajDduQtCfxM7CFVV4zpbrcr/jIn/uzbt9ttTKfTErecn5/XlotChKEnyCT27PDwMC4uLmI4HJZVEGBAnmdyO9trEknG//bBYCh8w/sMcj/EYftCDGlbGVGtLDLRbkLBOCb7cg5jHFfWoFeLxaK88dI205VOXjIH7vPSVTAY+5HRdmMtCCtiZCotwRN+KRDP5zNsvkkQ7LBJLZ5ln52xr22/EyJ8Z/9mXGBOIcd54/E4rq6uavrjOBs7QqUndtl7WDPv2FXuxX6mxATYgpubm1gul7UlldgI+o6tRh+owKbyjPEkAY98GQtmPoXDldcUEVlmwPvGYOAPZI3xf99+9K3evmcHFlFfWofgAZ7YbC8iXmu4hcZAgfsYfGSwbcU1EGXQeAYEAYpqoiYDBoCdMx0eeJSADIdZZgAn2Vbu7X7QXgQYxUboAac4Afaioq3cy8aN+7oyxaSGDwJA/kZpfJicMfhhHA4P928dszGmTzhIC3pElGoW9o/hnmataX9T4OVSRtrOb/522XYOeDmHdjrgNDi0QtMHt8sg0gYRA/75558X/cCQ2phAshwfH8dkMon5fB5ff/11beytE27bhzosm5alXAmJE6NNjA2g0oDC8+43+WAosRf5Ga4SYyxwCtwXuaGd3IexxoByP5wotoH+EiC02+2S/QUEA5oGg0HJdnrJHqQ8/1M1wD5aEF6Au7zE0LrM3w5KkRkO20X+tz1x5VomlfJh+8rfJvQAlwSxngcTbAbqzKdJBIgrV5hQXcbB3kXYCkA1Tn673cZwOCyZRYM2yMzdbhez2ayMIzYLx4neYUe22/2bUfIyW8hig6BMPtD3j01U5eDbbWkCY/laDttGNkJmzofDYdkDA9lArw8ODsrmtrvdfrnBxcVFWVJikJwJGPshVylHVOQJMgyoNgGMHDBP2IODg4Paq88dKNLvTEDSftqaqzAJCNEh/kaf8dP2X+t19Xp65CZ/73Y12QF/5vEg2cXSutlsVns1NWPUbreLrgJUbWu5LwG6x58KB8bMNoGEnqtQOIcNX2ezWURETWftWyHZTK6gg2Azk1CMr+etCUdZnh10+DvLv2UgByb5+/d92I4wFxnfMl/4qlar2i/Rsu+g07rBvVjSBvH013/91/Hdd9/F/f19IZbxMV4qmpPF2NHr6+vYbrdlWRCbaHtu8ya4xtyuyKXdyALJHH6zJP/+/j4Wi0V89tln0e/34/Bwv6/RaDSKfr9fMBT425U4x8fHRaYse65egUAieMTXOcgkOEZXwOL8YMvyPjTGQowJpNl0Oi3YGbvrpBmBJokZP8d7tniJtP2sx5bnQoDzYx8HdhkMBoWcYo4ODg7K8i37Q/CBiY2IejUpcw12xk7RNvrPtSZkjPl+Ewgpxw72TRH1BDo4kriCcWEMbS+tjzwH/NtqtWqVNe32PjEOCRJR3+MYbOW3WkP6ujpmvV6XlziRpPD2HFTIReyXa0KyoT/oLb4J/Wy1WqWAAXm3H+Be+Bja77jQ9hq5se/MZEvGao9xDr7n4eFhaTv2hUSNE+GQiMQg6L/bBX43jqA/VE55+w98/Ha7jclkUvQfffWWIcw5L4QAWyNX3JfnN8Wu3IO+QqQ7YY9cYjubKrG84uRD+M83JqUsUFYmCwAgzp02GeHzzXJi1H3YiBq85Pug5DYUdpic66DHguXJwegiXF4mwOceCxQU4XQJI8DUZI0BVH77E+PnN/ahdAiGQRr9MPFCX00qGSx6rLKiGsDyuZ0z2WzmAMfsgClnti3AHksHM4Am5g8g1lSplBUgK50VhzmnjWaOPSY56HfwRFsMuhlrZHaxWMSLFy/i/Py8yBFBOIQZssL8jcfjmM1mMZ/Pa+Sg2+l2ve8jA3zGnX75wFChAzgqiEZKWyPitTab3MN4I1PZsXjs/XZGtwO9MAFORab3Bso2gXvamC6Xy3Ieb5jrdDoxm81KgBdRXzpIZdeLFy9KX3MwmIlsE1HWPfpAv1yK77HznFmfOdekgAOBiKgBHw7m2vd0kIP9IQihyoL7+XkEuw7Ku91uLBaL8kYmPw9Sj8NEpgN2lnfwJiIH7ZzHG1ByCbH9kwlVdJHApN1ux2AwKG/EZL8bqoCz3flYhJTl5rHv8/zlQBs5McAzCRJRLTP3WzIhqwCXk8mk+Lijo6OYzWYl0ZADtKZ2+fn0iZJ29BUbgn+x/EfU36qL7cg+xERzJhK5xljDWIEDG+DqJSoRsjzRTuusMQ7PQa8J3CC2nTzzvUzicB73IyFmOY2ImEwmBUSDP8A+fEY/+v1+AeC2LbTXyTBIftpn2w5BQIDqcbCdAsOBC40bs0xnrJPnkftmArbps/ydbRDP+xiklG1+RJW4tB8zjqVdBD+eQ8s6us15z549Ky9yGAwGcXd3V6qk2H/RmI+2MUfO4PuNqZYj66aDqt2u2q4Du7nbVctVsMnI0OnpaYxGo5oetVqtGA6HZWUFCdu8LYYxyunpadErDpND/EAIECw7+QLGwHbwTG8h4eQwY4R9JPkVUb2gxbEMSyDBG1QRtVr7SqzpdFoS1LSTccTu2jaAb/JWGpZ/9A1dQl7QAe7H81erVXzyySelGgRCy/Pmpd6PVaxbn+y/wBKWdcaA85GpjFV+zIfbj11nj9mI+p6d9su2p54XKv64p6+JqN7OyHeMV0T9rWvMGXgWzOM9vcACq9WqlgQxIYr+E5Oy5yfP8hs7XfkUUb29DjnhXPQKGbPtiYjaVh5gPvTPGMYkXNN82O43+bqMYU9OTmI0GsVyuSx9IjZjzzQwI1tAzOfzeHh4iPPz8/KGS/ah9fzhIxkbVhJkvQULGQ9DpHt/OshDZCEn8Mxt2K96fpCfiKjhP+9pR7vMAeBzqBizPW7yvz/0eKtKKRtciAcAK4a90+nExcXFa0bKJIErF7i3iSeTLVzvMkgzqpxjMglw54ln4LgPoAsgh4FHcTDS3iAZJbHgm4jjMxTOII3PXJ5qooZzclCdDf5jisjzAPwGM7STABhj4SDXhBZGifHE6LL/jtfR42xMGLqyC+OGcUIBPJY45ojXX19vksjy4DHA0dqx0TfOoT0GZg4mcuBhVj4iatVYnqftdhtXV1fR6XRiMpmUZV0AEeaANbhkvJ8+fRoReyOFUTdQzIb0fR7Z8dvRMS78z9jYKUa8vvk0gM1G0hkyiAT+9xIT6wnPt/7bPiDzLjkmQwtB4YxVJr6cHeD+yC4OdzqdxtnZWXE+ZCywdewRQzt4lpdAuTybcXmMbOD5tpWWX4MV3wNbxrjQV5MxOSjPz+B7B4/WX2dkCVoiqr0NsBFs9MjYdzqdsuTDpcsGWd5rKgNniERAA+NE25k/2oMPcPCagwQcqrN5Bkg5+Pp1AeQcqHLYTnvMkDm+t33j+u22qj5Dl723FPbXNh1/jd0GiBBs8Xx+uIfbTbVPDnB5ln1HBtaZlDCRhEyZMOHemey0r7JNiKhvUG5sgl7TTtsPV1r5gBCC2OK5ZDfxmwSl3I+A06AUTMXyd0gzvmNuvNk8ABZwTB9ddYhOYc8g/CDCGD8Ci4uLi+h2uzEej4sMHB0dxWQyifV6XUhdjzXz6CQB8oRMgFE8t8YQj+Ec+3F/xjkZLzUdnE8bP+SBzCITHLZzBHAOVE3GcB/GxdjIeg3pQXWDCQbbDJ6JPTReIVPvZBxBrdsPrjORYP1F17x8u9vtxvn5eYxGo0Ky8hxs9+HhYfHjVBtsNvt9Jtlwn5cuGDNkwqfX65XPsPkEWyaMbTva7XZts2bbJPQXIovE42azKeNDAL/dVm8lhdQZj8cREaXqDDnnecwplTAQdF5O7EoL+/hM8HF+9ucRlc0lJuDNeeznaIxGlRYvhEH3h8Nh2djads421mRYxOuEtf0Asm+b+2M+7JutW+gxlbbgq4g6uRQRpQrfyVbPS8Tr/pExdLIdeWOfW8dwmaCxjIOfSTS3Wq2y3Y2JI/bNhVxkrhzTIq/4HHTFST58ivGYbTZyYfzGWHOO+5F9BniBfmefkePpHPN0u9344osvCknDSxfYioQ9IUnSzmaz6HQ6MZ1Oy95SLG/PCRbsV8Q+ib1arWK5XNbebEpV2enpaW1bFPMqtJsqZmOZrFfIGfaCa5kHfHNElMSFi0ywGSSkPS+O/azvtj3vcrwxKYXzZtI48kTf3d3FcrkswamDeJeHAZJMTkRUShtRr7Rw0IIRN9AhOEHpEDaDJIMA1sXm+3M/svXuu0tqUXyXQRIQwlLTb9plQ0b/3D6eZwPEuBos+76QJRYUz09EvAYcEDAbTu6bBZ3gEVLFbyOBASdYMettgw3oho2NqIA1xs/kHNcbcFoZ3A8bGGTNjropsMvt8zjk+dhuq9fmGnSZnb6/v48XL16UKigbZpd08uyjo6M4Pz8vb7rIwabH4WMclkeMPVl5ZIuNCtF/Zx8pA/cmqnnfITuTiKoCy8/HqVJCDGj02EREyVzwmzliXtBzDLD1jMyOiR6qCViSguyzXnuxWJT++x4RUUAqQWETqeqlGR7jDDq4n52l5Z22mSDFCfvItiY7Zh8mINBNAg6WYeKUsXGAD+wl53tcO51ODIfD6PV6Za6R91yybcBhgAFwogKLQILvGHuW0VJFkkkI7DPLNZFPnucAJxPWnoOPcVhG+D87+3yObYu/I4gy0cHYsBTAFT7+jW02OUS2Ez0wQI2o9JflX9h920GT/A5e0UF8s+WI+6Fj1mn7MgMk5B3fDYizXERUhDD+3OAuotp7gf7y2d3dXQnmAZSZCECWbA/Qbcs4bTc5hs6Q5OB7EhitVqtUtm2321p1J2PntybZR2PTOcdkh8El+kpwMRqNCll1cnISg8GgBNiZsKC99tnMC8SZ9csEQ5Z1/454ff/H7zusJ/meEVEItQ91IDu2M8YDyA8ERl6+ZPKKw8kO+ypXKyHP1lP6jW5zb4LU1WpV9g9EtziQa+TVy2cdDEPQ4O+chOr1egUXIEcQm5CbfMf+WJnoZqm9CUzjZfw59+fZ4Bkv8QOjmRxwwpVlfg6mDw4OyjjlPaCQdQJU+0fkfjqd1pbDQwqwnIlltvZzthfIhitHOND1JvvG58YWyBWB9XK5jLOzs7K0F8JzOByWVR+LxaK81IW+WUZcbcrzLHv2WcjP+w5qP+SR4wn7HvtcL7FCB02Y2A8Y+/k5/EbujBfzS1xGo1HxtzwnFx9wPsQT+Cqi/nKN9Xod/X4/BoNB8ee0D9tNFbxfOEBMQELW1X0R8ZpcZPtv2WC8jOVMyGe54jzbRPy4ST98IzwA7WBf4FevXtXsNPj36Oioxgn4pU7EDovFIgaDQYzH49didRLwPKvVahUyK1emnZ6elsQffaVvYCl8MPaO8bYcWTfRM3CJORknDHPM3m63C1mJHFt3XcCRdeOHHm9VKRURxYgiEPxNJ9nJ3vudZDbZBpb7MpgYW/5HCXNlBs93hc16vS5ldc5825jTFzPYfMfSDqqjIuqbbrIUDzLGDpoA3SxyDigNSOhXRLX0DeH1+GQSxmNlA+d7OZDwvd3nTIDZeVmwTAa6ZDOThLQlA3juh0KRmTPLDYDJJKb76j77bytKNuqZXHIbXeHmPhPAZSBrEgwjwmfIArLPcgqvs2fcMOKbzSaeP38e2+02Xr16VZ6DIf5YzjnLi7OjGHQCLhh8g7V+v1+cpJ0EBhIHY6IEQEgA5jlgzAj0IirbA3iHjGJ/IM630yELnAlero+IUmEQEcXpbDabsqdUu90uwdN8Po+IKHvwYLjtFEwAeL+lHEDbPtkZW+ay7kdUtqrJDvC5xxpA7vnANmUngl12EI9tt356qQeZNpOC2C8DTSpCkRfmxzpnP+CMqW0NQY2BOiDJy8Esc/QJuSPQBwAwZ5DvfguUdeRjH7bfTYfJmKZzDNq2230mHF9pH8bfkBPIB3sc2Od6mUBE/cUPBJfL5bIknQDpzGcOkPk+oqpmMJnE/KBjtNlAE8CYAS7y5KpJ+xRXITl4wrazkTFybIIn23X7AVeYgAlYtmVdRVe8f4flttvtluwzY43tZRy9553tLTYRgpaNVB2s5ESQSQYqs5wwuby8jHZ7v/wK+zkej+P+/j6urq5iu62qzxnf7AONNZoqfhzcm+i0jL8N+P0+Qqrpu/d92FbbLzIPnit8LsFLJl+NS4ydeZMawUquWkMe8afeRoDAjIpjlvM46OA+BLsOqE3eQMSYgOU15hDgXo0wHo8L+UIVBnPCW3Ej6kkLdAzdhMyBCOAaxo/EM/EIS77pDy/iABNwb+wZvs9bcNBOMJxtID6IAJ3+8CwSIwS0jP/BwUHZfB0b6DcZzmazslky9zS5nuMA+o4vYz49nhH1/VjZMHs2m8Wnn34aT548iW63G0+fPq1VUdzf35dK8l6vF9fX1zWbZmLAMu7Ks4j6G8kt0z/2w/bHvpHN7geDQZk3n8/4OxGP/DiGJpZxct8kq0lOx8YR1ZsQval8xH7O2U7BRRnclxf6kPxjKSlzut1ua+d0Op2yjG21WsXLly/j7u6uEFmj0Si2230ik+XEjAH9xWY4jnVchaxwjv2DiRPHtOAAV4k55mM8TcqCJbrdbjx58iRevXpV26qDJX3428wFOBEQUVVCDofDUtlJZRNt3+12ZV9M2wGTeJPJpGyR4upN42baFfH6y5HMB5jUQh6QJcdH/M08Oabi+oODg1LZFVGPTd6XT31jUopJxlh7o24GerPZ1N5+wcHf+beDGAdtfNcEIBBOB8wYVhhL7mlixPf0hoGca1AN+LTRyQCDwAYQ4An1BJsAY5zcx4ioOftMonAfG8Km8cmZFGdHDOCcmTUZgYC78oIxcgYcBxNREZS0gee67Rgb2kVm1a9nz0G2q89yXy2PDpAeI6T8mcEsY20nYGV2n3y92Wbfkwzay5cvi34QQFA1sNtVexkB2J88eVKqC5mfLLcf6rA8ObvtvgO4LB/r9boQNzD2LOfA4VnOXcGAMcxg3cbU+ot83d3dlQ04AZE8z/NqQMjzcJAmm+kj84CRhZzgjYmbzaaWTYIciajWwEOWoE9kEXOwiVPh2Sat3B4T2jk4gNyxg3OQS8BAP3NQmMle5A3nypgSlL58+bI44FZrv6EupBzPcJUo9oq/I6osFoGJkxrMs99GZD0zCXp3dxdPnjwpgBtb5NeMW26dKfR4e3wBR+xpwr195HH70IflwfNsn5Ztn+fX/oJ5AwgDCE2m8D3nex6RYZa+2Cbie6+urorsIEe3t7cxGo1qwbQTAxy5moM+5mCUNjJfkM7oFz6Jecr94rmWBYNJ66l9IKQXMoxuAKAt69gr+woDR+5P3/jNPNiHEiwzZyznQV8JuI1nAJEmvSP2S8QB2oxblg/bZn7oN/eczWYxHA6Lr4MotDx4uQB9dnDEGFnPsg30tdlne7ywL03Y4LHPcyD8oYkpB0XopqtCTPgavz1GpnFPrjXZc3Z2Fp3OfvsMbKN1nMCq3+8XrL5cLostxY7zLC/nQEe91QC+EkKZt7JBxEZUezGi+64uQsZZIpNxwmAwiIi9T6dSFnlCznhJAoElvhySajwex+Fh9ZIe9Bd5ps03NzdxenpaI/Hm83n53hX+kGImCzudToxGoxoWR1e9Lwwk4mazKVjQW4pEVBswkygYj8dxc3MT8/m87Ndo3GPdhVDgPswn5DXtyfYR27ZareLFixex3W7j5z//eW3vGnwndrPVahUSxntRMh7sJcW8cQ4ynHH0b8phWwUxwjhje/FrBwcHtWrxiKhV/9lnmyyxjTNuAZ/ZxuJnuM9gMCg4meQb+DciivyRzGC563A4LHPBM+yXId4Wi0WJt1nG6pgLLLZarWI2m5VlbRFR27cIbGefybg4HjaO9vgznp6LiCqWYfywaxEVPrDP5VlPnjyJTz75JL777rtiN25vbwtR5z61Wq2yfBV9Xa/XcXl5Wc6FyKfP9jftdrvsrccKJJOV8/m86Bfk13q9LrYHkt0YzdgfeTAeZDzs1/nMZLLtAjqcqyKxGdbn9+VL34qUMmhyJyMqosaO1hNgQMj5OAmXEpukMCjBaTF42YmhZBEVmWLSgYPJs9BH7B0wBt9gzf0xgHNwiMEwAKXfEfVqKweZAGBXTdBvGymeyf2yAvscHBTLjSKqvWcABZ4L/raS8rmrPziP6giTOMwDipOzIDawNh6ujvCznSWnHRlomkDxtR5rvvP1gAkOB3kmQbLsNfXFABMQeHNzE5eXl/H8+fOSuWROADuArIgoa/QXi0Ux+h8rALZeWPaawIJ1waSLM7MRUZyMiUiTVzzHIJhA2cDd+1dRLoxDZn02hpPsQq/XK44Eh0f2l3GH/PNSgePj4xgOhyWDCvBE5wDxLNPz/jsQ0+yn5IxCtlP020SvCTrOdxk+f2fHzJGDDsaDe/Ec5sE2FpuNDJtIo6rv8HD/WnCc0+3tbVxeXpZ2DgaDElTYieK07LTRRWd90B32MuF/KkjI0u12u5LZZk8RA2AHI84mMt4mKAH01l9kMZOyjPHHPCwPPrAxjK3tk20ufoVrRqNRmSOTGVRj2M4SwPAd7bBfwHfx1h5KuxnniCj7Hbm6yQkt/IX9ujEE/bBd4S1VtA9SCplh7JpIfWdUTZTZP9imA9zsTwDiBNkk5nh+9nsATebMbwN2m6gWdYaXrDW/qXjwfmgAZp5rm2VZIFMO6TqZTGpv0zJ5C44iqKeKZrfbV1NcX1/HZDIpegYR4GWzAHYHTowfY97r9QoZYNIq4zKuYx74PiedHjv8zKxTxj0f+sikqwMu+gM+YE4jqsAL22ZMjb+5v7+Pfr8f3W43lstlqQCmagK7DtlIUOg362H3aaNJbttkyMjz8/M4PT2tYUs28TWuI6lDO/12q+fPnxdSjOXhJLUPDw/j9PS0VBrg+zudTiFZ6Qd+Zz6fl8AavWYcnj59Gq9evSrYku0ovE8Tb9O9uroqcjIYDMo9CE69F8/Dw0Mh5miTYxTmFR9H1XC32y2VmswNY4Vta7Va5eUEm80mrq6uyvPZlwbfiO0gOGbscxyT/QaywXfEQUdHR2XbAmQL2wA5Tl/Pzs7ixYsX5Xn01wS8E/3Ib0S1F6R14jflsC9E1kiagF+Re8hIsBB+kHO4Hz4v+yb8J8/Dx1HZF1G9OTUiav4MQsq2Bv9+f39fqqKxx2CkiPrLvLAZkKLgYSdXSdhgw2azWc12Idt+sRjL3h1fIZvYOB/YJv9tG24MkOMY62P2IZ1OJ372s58V/USf2R4CHW6327Ul9VSHk8jp9/vRarXK54eHh2VblxyHMv+MP3NPBXJElIqpzBl4PCCdsz6Zb3BxDJh3t9vvB0v8yfe0BULNlVEQbdPptNwLuX0f+vtWpFRE5cgBLhHVW56m02kN/PnaTDJEVI4aMGPw60DOwNtl4QBqAlEYXFd3uAzSymPweHt7W94sxGeZYSSI8npwBzGcT7tcrkdfzHBnkiiX5lqIfBgsmAjzkhbayL2dgckADcOVxwmltZOjbcw/48O40I/MTDNWPBdwQ8UJgo4RoK02JBzMn4MQzne2m/GlrY8pTRP49W8+d6Dk7wwy6durV6+i09kvgWE8+U0lAf2gdJs14QbmH+PI/Y+oby6fqwbsPHC8Z2dnBbBA9kRECVwYJ55hHTRh4edwb0pFyfzgNLOdAeQBUgH4BJKAz1Zr/6YfAj0q9nCmu11VLksQzHc4UzJWLO3DtqFL9J3fGexbVhhTgw0+i6iqkEwkOfhjTLGxODvbVNqB3OZMqR0Y40/m+fz8vJCCXHN7e1sy1Ohxv98vjnW73e89BmghWEbe6ZfttEE9gWre74bnEhygV+gQ9+Qz2yNILj7n3rxJDCLUVascH0sXfTQ5d2xIRH15HmOEHDmwBxTRP1cpcL2XwHc6+81NvacB8wSRcHFxUauu8BIR2o2foHTdyaAcrFneyATSHgfno9GoEOImc+k3epj3vbT/MLYwJnCCi7FzgoLAg6U3tJs2OuhiHE0qR0RtqZ3topcfOiBgXkzcMLZeJoTMI9MRUcNbzngT+POWUfalyOQb4257t91u4/LyslSSrtf7vUfOzs5qugeWyc+1nUEWvVm2gya3hzFuIpb8fw54HyPE+P9jBcDGEciRcQqySBtd+WI8Ag5FviKqpc4QBWwSbqIVGWEeITPAhRAOPMsElUl8bK7JHgIoKiVpY8ReliGZWDbH3HM9byFm8+DLy8tCxLjag2D/6uqq+CWCMfSWOefziIrYJshENtgbCdtFlQMEEHLKUhz2UKPqyn6cAA/yjH2vPBcsSXQlA9VX+GT8JNVvyLzHljlgKTz2m5eKRFRkt0kpsAbyxb0h+pAxSPCjo6N48eJF3N7exmeffVZsBX4Ugnu1WsVwOIzlclnudXx8XN5eZhLL8hxRYXy+/00gpIzzkHWWWhLPEY8Sf2HfsXkkKRwLc++Mme3HXaHnODmivjei96fzCwCQz1arFaPRqJaMpRjDiSjsxWazKfu+2W84VvXKKcYIMph4jxjaiSTiM5KgTXjVPAHj4jFzLGhCyvbffp12e2z5vNfrxfPnz+Ply5c1v8jYY2N3u108efKkkPUQtOiisQt71Z6enha7hw93haNjVOwEK79Y/usY3zETz7MfoY/IDOOS42fbCy+1dBwMOWkS389/n/70jUmpk5OTso+MGTsGgCVIBrgGOQZxznhZ8XJQxKBl0BFRvZ7cSwZQCu5hcOK9VXAKGNfpdFrbKN3KgBBwGMxmg2SlMRB2dQ4EDsqJcNImOzqDKI8R44czdXaZtjoAI9C20JjEsZGzYWEMYfQZx/l8XiPUIqIEKWQLvBGf+81zIAS8lCCTV26vx5P2ea58mADJDpDrkAPOy9VbXGtG3aScsxN8B4m32WwKMcXeCTzTJCEBwcnJSZyfn5eAwbrwoQ/kw6CkiSjxvkAYX28CaALE409QjCwzfpBGvP0rZy5w5tPpNKbTaQl2uNayBTClagMngVPmXn67TLvdLoEVc8MbNui7iWUyBn4dO+voXb2AM2c8XVFmubPsutqCttrGZuCRyXVXlNhZUPHh/ZOaHD9/Y49s49hg2lVtAGfaDsk6m81it9vFcDisZfNwat5XIgdjfO43geQXAaCjvHqZrLHJLvsNMtrICzbee2R4nyAyRpkEz7bnYxwZcDjbZfKH722vPb9UlRl0mYzi3viL8Xhcs3GQzdgmNsU1EeUyeidpmFv2lsH+E5ganCMfyDL3QVewMZbRHHxZ7+z78av4WpNarq7GN9AOVzs6SLS95xmZkOZ7ZI/gwOSXbYLto7cXoOKTt4lZD1qtVm3jfpNljKmrAgnsac96vS5LmKjczePnsceuLhaLEpQ4iMqkkIMPJxlNfvB/9uf8z9/WC/usjJfoQyakfC/r1sc8msaWMUL+vLSWfmVSlbFBN8bjcW1pFASEs/m73a5UUbF8Dbm2D+Fz5pz2eok6NuHm5qb2Bmn7KfoH9kTGb29v4+rqKiaTSVkit1wu4/r6OqbTaTw8PMT19XUMBoOyNKzdbpdAe7PZ1Kqax+NxnJ+fx93dXVnuB3GDnvM3484bkOkvNsUYAN3lrZPoMedCMDvhzA8VRovFolSJsTm6bSYVq+hYRH1LDOaJOYqoNuZvt/cV3JPJJAaDQSyXy5jP56WKikSag38I4Far2n/LATlVX/YBBNRHR0fx/Pnz2h6O9JvvHh4eSpUZPta2gLl04tWkCXjhx37Y1kTs9Q0szJJrcCbjb6zJGJjEMgFsjOcYEUyHH8i+3ngBkhcMiMyBj7kXm54j78YK6CqViRBjEdW+zPTLxDH3YcNvEhnooGNFYmMXfCAHmUNowouP2fz8mfXA/tqxOf+DTSCXXCUEPu33+3F6ehq3t7cxnU6L/mAb4UvA+xcXF4XAJYGLnqD7o9GovKmYmAJiarfbFVKb9rtftB+ZcVxK25kzrzBB3pw8NnYzwefY0OQo3zPm73q8MSm1Wq1qhAENaLer193TEYIElM+g0cDDWXo6xudZ2Mz6Y8TtDCiNdQaXYMiOw8JN+TJl9Z5UDAyO2xMECPP/OSin/Q5ArUi0ifb6cIDJ/Wg3gNQC4YAYZaMNLnumUsRkjckWZ3VdKpoJRZY28L0Fkfl0lRYK4Tk1w8x9kRkb7gxwmsYpH55DznM7m66j3a4+83UOlBhzO3QTAQTrl5eXtaV6BwcHNWe+2WwKODo+Pi5G7n/9r/8Vv/zlL4uR/lBHBvZ85rFG/jG0DtDW63XZhwG9dNBnOXZmxXPqcm+ALfqLo+t2uzGdTossGxTQVshZ7k0bAH7MK/aA81nW1263Cwg2yXV8fFz2UMIO+a1X7KXhYDXLuYNLgwmDzYgqiLOdcD/tgLADAAzrF3oMcPccYwNyho72MnYAlk6nE71er7wunDfvREQJSvgfe0MVlZ0nc20bxee2b7QFB8leUszLZrOvyO33++W5EXuw7oytnSnj0+/3S/m5g6+bm5vaOJss+XUFr03Pz7ppu5RJEvxlRPVWJCppvOyKzykxZ9k3GTwAyNXVVVxeXhbQxf2xd9jMiCh6hfxScWjQCZHJNSZ7wQDGB86w5jHB91qXnASyz0PG7HNsV/xsrrceu0229x57Zz8Bksg+e0UA7vEjEBTYJu5FZRg+1a/edsUNoJh++l4eA4hZxny73ZZAdjAYlHOdlHAgTUUH53EumIA+URXBHEXUl8txL+u/8Y59PzLzff7b8/urdIuxzZ996IP+5eSC22BslgPPPAYQE2xkDwEE4cm2ANPpNK6vr+P6+rpWMeNnI0MkLfBtLMeOiFoW3W1mLqkSQq+pzGBbjHZ7nwiaTCaxXC4L8cTLL7gvmyrbX7LpMBsEz+fzEnzf3NzUKhmGw2EZXyo4vDeSExAkUKkCQicYw/F4XPo+Go1q5GpERSKBdwlkjZshfy8vL0uwy3VUajOe2GSWqs/n81JVlYmldrtdNih39Rk2nSWROZGBraB9BMBUg2X//PDwEF9++WXsdrv49NNPy/JLNjnHfk0mk7i8vKxtH2DbmfXMY5TjiB/zQTsZV1cd87kTIZBvJNN8H2w7mMUEf7YNkLsmsu17GUcv1+P/iL2enp6e1shqyFbkzclelp+zrxo6iR3HjyM/7Xa7LOvdbrelOgo8cHBwUJa0sSUGtmu73b729tomO5jJJft0v1zN+MnYyPFGTioxH51OJ54/f15bksqeduyXCbF0cHBQcKeXTnNf5GG325X9qlutVlkySczb6XTKffkb3XMSngo3E3RZTjKRFFHZVu7DdVRAMSZsecJ88Tn3chzh1XJ+/rseb0xKZZabztPw6+vrMviAMgQmZ30MWsygI9gIpTtsksv7kQD+drtdzdBiKJkMZ8R3u/3+CKwpz+QPk5UrEGirl+dlkEMgCAPKOX77iQNIrvEGkB4P7g8ziQIyzoyBQThz4vmJeD3YscIaDHFwb8bI2TSUhr10LA95SRD39rKMXMbKnPE9z/c4+F4oi8k3y1gO6viMcfQY5Pu5/34mbQPsm2RxoIIjJsv0+eefl1JxV2Jh1AGXED2/8zu/E8PhsEYcfOjDfXQf6LOr7px5YQlmDvbQbUgqxtVz6Q2ETWADrl1R9vDwENPptPYsAx/+brfbZcNRZ538RjXmGl3x8ibk2cu9+B0RNfIdJwppFVHfZwD54B62aTgD+m7ZMXHU5HQYA8urx8C2wiR4Hiv0DVDj4NXAA4ABAUZGzJWSlg3II7I+PIesKtdBPBroZ+dJUO62r9frUrWLfWKOHAgzdvQdH9Dv9wvA8z5U2GEnRT72YX9iUM9nHIBWAy8+Z+6o4jOhgO9iDujreDwuG296jheLRbx69arI+na7jcFgENfX1yV4dOYsIsqbbMjMevm0g0XjAZOtEFbIGBW1lhP7Gg6qebId8ngBOmmvX6tsMsY6afINgt3BveeGZwPijGnIgJo42mw2Zb8vE+TWfSe/BoNB2UMInJX3kPNc2KcCnG03wDMRUZYKjEaj4vesN9hWKinQRYJmyF2DceMiwDX3Ivh2lbIDDnyCiRDbAQeCTbpgXbF+fd/3H/IwtrAtzzYa+ccPWJY9nsPhsJAmYFZsG/I1n8/j1atXMZ1OyxtkTQbic/FFEFNUA0BkQ3ZF1IMeAk4TvHd3dwVfE8Td3d0V34o+dbvdGA6HsV6va/vakOTJ+/K42hZ/3WpVe7dANKHTp6entcpoEwIQOJA3xASu+uZ82sAPYxURxb5RRY0PZHwcT7Tb7dcquRlbdJg935BvSOAnT57U9nNk3ohhSA45SQSph69rqmiNqN5WeHZ2Vmw853Le3d1dfPnll9FqteK3f/u3o9PpxIsXL8qm2q3WfqP9+XweFxcXpU2OPawH2BRs4K/L377tYTtjrOX4AL/GuLhiFQKTv/O9MxFjPBYRNT3weVTEQwRz4MfBociO42F8TkSU6vTLy8viEyHTkF9kj/5GRCFp0E2IFb8kh7YSB2dewJg9E0rGwK7INN52rGYfwfm2cznW8zURUV7eRBwC5oS4QS89Z1RrkvDudrtxdnZWtp2AzN1s9ssh0XPeIm6eAAyNzYnYk/VgITCvV1XQD+Yl4988tow3nAQ2yOPLuNF/5MYxTY4d3/V4Y1Lq4OCgtreKJ9qZRphVBgZhtFO1gDmwjKi/5tCO0oONgWcQTS65HNBkiZUd5WXQreCPBYYoEk7VlVG0FQDL2vMMrDK5YVY8B9oYCoPsprFgHE3kmEAjsMOYMU6+H07IQaqJHQNXGG0U/ObmpgQODhi4B3NFFgogAHAAiOd9TRwU57lxu5yFQfH43rLG0URmILcmrMxSe3wdUPiVmTzb89FqtWKxWMSLFy/it37rt4ohxoggr4wRpdOffvppyVh96CMbEfphR4K8uPLIIM/BCbpH/1yZZGeQyUPPDxsuQiQB1kajUaluNPGMHPFKWtsk9l4hu0+gvN1uS9YWJ0NgSEaDLKqrjbjfer0uS6FMNlq+rIMm6g34kAVXJrhiJMuw7WlEvZIjO2RXi1ofTDZaf0xGkbkj4JxMJvHy5cviXNvtdsl+D4fDUtJPRpexb7fbZWk3WZ7NZl9S3iTjzJttOfbW+7Hx29cDiLjeJAggCTKMeWRjWdsdA0/G/GMdfi59cgBhH5DJSuYW8oDzvUG/l45D+B0dHRVZBrhEREyn07i8vCxBk5f8jEajUr1Ixh9f5zE2cEIX7HtMuERE0cUM2H2fVqtVW86H/PM5tgFwBzbBHtEGg1T3m3OxgbQVTGAd9Z4pXspvgIefRE9MlhOw0HbPKz6IwIaxJ6C1nABifU/LDRgJmwbZBFaDdCTIZrkWMgSOsC1nvKio8RYKtmfIKHMYUX/pgmWYv3PAYozIOfbf/vsxfXpMZ94HkP5Vh+cUmTMe5BzjaicV8BvYPJaPkLBhLtBn7rFer8sSeONZfvDTyE0OuKjUwe+7AtKBCnaH/Vlvbm7KvjrYF+zNzc1NISOpwOV19ZAjJAyQeXCiN1l2FYh1lDZi41lyyhgb13U6nRgOhxERZTm4MYt1E5tGdfjFxUWZV4JXVw26Ynm32xUbyfyz5xTJSRJhVCuw5Oezzz6L8XhcbMfBwX4vLrB4RL3aLSJKdRjE3ng8LpXCxq1UbEFEo2vEC/YV9/f38eWXX8bd3V38rb/1twpRYVv6/PnzUqDA55AhJsZsk3/TDsetHJ5XEkDIAXbOlfyOKSLqxIyJCK7necRNThQgPxBKxj7MJxiKOY+oChgi9oQHOov/RB69J+TBwUHB3yxt3W63MZ/P4/T0tBBYYPaHh4cYjUZlryV80GazqVX82A6j38SkjpPt7x2r5aQFh/0Pvph7PRbXOTEHOZevB/djlw8ODmIymRScic1dr9clrjPuYZkyMgK3QFvY69RLsb0HneMCJ7BcJAEGcKyAPIFZfDBfEVFW9xh/dLvd2rYXjs0tx+/qT9+YlEKY8v4wu92uvCIRYYuoZ4Ui4jUli6ivsUfgm0BDExHDd5vNpvY6cLctVxC129UeUhYyg1ra6IDZ9zSRQ9vznjoGvxw2LFaiiIoUo99MOoDDZFEGrZzr5+cxczbMSsz1XGsw6/aaNIuIUspIeTDPyorlexAgcB8cOMRNr9er7Wvh/rrkMIM4fuiP58YkkxUYpbVzMQFDnxycWxYsH2bnuT4D3ul0Gi9fvoyzs7PaWJnwoW0QAK4u+pBHJjIM4Hk+VQ025Gws7iDS45Udopl15jo7F4g+guBM4LVarZLhvLm5KUZ/MBjUXj/P2OMcAYrM0Xg8LgQHmzdSAbFcLssabvZscEDKvQgsLdfIq20gGWKTC9kGIgeWySYCyX0zMWEZtu5EVBWW2MMcCOZAzs7MJAJjBgm93W7L5rXsZcFzyU4DonHUELCUQwNMCHYZO+u7ZdEgBvnIQRyA3v0moMcRY8e9twL3RVY51/r8MQ7mw4CXw76M/03EOZOPPjEXrpKCcGDez8/P4/DwsJCMAMirq6vavgjcl3awBNCBrsEh19EWxth9yckBf4bMUg10dXVV5tPVcbQJ/befdrKMZwB2IXIiKlKMMaVfXvrgsTVwxd+TyYyob/bK/okeD86xf/T4AcabbAJkKkShx5V7MG4O0A1y7a/JnNN37OLJyUmcnZ2V5Z3YUwgCg3XGD/sIacwBocFYcS5+MAN+kyYmaxivJp3MhNT36ZfPf5Pr3vUwvnQAyv8Zg2KrXcFqXAvJYHm8ubkpwSX+kWVDvCnTSy9Zqtbr9WpVTNg+++DlclnsNvbm4uIiXr16VbOT4LWMl/F/3PvwsNowfLPZL8nG9tjXsYcReBpCluQ3ZE6n0ylv32Oj8Ol0GqenpwVTMM7IL+QL+u4lgK7oYx7Y3LvX68X19XWNpI6ognz6GxG1cel0OmV5Dm0gfsIHsYzq7u4urq+vYz6fx2AwiMvLy7i8vIyrq6uiexFREsTMFdXIEHnMMbrOGGDX2ey60+kU++/Y5fDwsLY8f7VaxVdffRWdTid+93d/txCFVKuZmMfWshrAftf6HvHxltByQD687eFYyHoJfmO+iZexcbbvxMrICPLF945jXACBrTbR4Io5krhOeux21b5F2BhXD26324J5eaEGbY+IYrOx68zx0dFRjMfjEgs6GTidTkvCGnvvZd/2Zcbm2Honh6xPtv326TlWz3F7RNTOYcw9l9ghZAOd8b3W6/02QYeHhzGZTMocUAnKvD887N9KzcsavM0F+JZ5Y48pYzuwK2OIP394eCgvEGCFCmMGT8C40GfG0f6FOc4yzPksMXbxCoQnsmd/3BQ/vsvxxqQUA4aTpDKBLIQznTQWoUfgzNRlAMtnHtgcMLmqhAFFST0g2RAwGQwsZJUHGAHkM4PHiIoAc1adzdvyMjrabePO5HMv39eA0gqAw/S5EfV9dCxYzv74b4Cp+0WZtUEA31lAHUxAfkVEyULx7N1uVxTWzs9VNxAKzLMDFJSV/rlyJR85qOZvK52NOLJmQM7h+cJYWIZMbGVAjMzjABxo2bg+PDzEN998E5vNJs7Pz2sEDAE4WS2AifXjQx8mN9xHdIF14xg/lgzQb8bJ681NfpjAcXk4z+H7zWb/mnnLno2tHcl6vS5jyWEir9PplPGEgGq326XSqtVqlTXgBKgRUZY4kDX45ptvypIVQPN2u186ARGGXthYm6ylv9ZvDjtS5NVVBPy2PDH2jI9tbR7XHNBmZ5JlzCQg/0N4jMfjIg8AWqqmDJSQhePj4+LcnD3mbX6Hh/u9PYbDYQl86RvPRZbQKQhB7CPz5nFBJgEIJgMA7tgzZIbKN+yV9fxjAubsD/J3yLOXiDibGBFlLyNnupFdbDFg0G+6gny8vLyM6XRaA8DIMc9iqR+VErbJtpkGPbYzVK15rrHPEXtfhD958uRJjTB08J7BKAdzbHvEeLnSG//N/TknIsoeTviGrIs5WcTeOPazroRy6bvHCPKIZzrYdfttS+ynHeC54hlZhphgz00ICvtY8BHPoB/b7TaePn1agmlXLTJmuVIe+2G8QDDjfSTR336/X/ah4zBh02QvOSfbVMuBr0MWsu9vIn8/xOFEoAMlkzYRVfLXAavJOogNNr535W7EPoi8vLyMr776Kr799ttie9lnicCKCiaWUJrojmiuuI+IsiQH0toJAOaCymRvJo5Ms5wHLM5YeBN/9ira7aqlcPxt/+TEJiQa84wPgijmBTKWD2SWKl/6AIEFjsZeIC+Xl5dxfX1dxgudZG8dgjfmC2KbuSTe4H8wM3tjOUBGT169ehXffvttTKfT2oqNiAqjM86MEQdzwrjhFzy2tismU8BM9vsnJyfxp3/6p9Fut+P3fu/3IiIKOdjv9+Pzzz8vlZm2dcYbxh8fm5CKiNr4vM3B3HjVA3YPeTeW8HUmpmy7/L3jKXQyotpj09gavMxecZ3O/i2XfvkA9zcOw0aTTJzNZgUz8VZOZIU2EaOcn58XLEtCZjabFRlstVol4WjcH1FfgQPZSYyc+4tNtI3MOMI+2JjAftrxRbb1xr5OinJtt9uNzz//PC4vL4uvIxlATIHtRe/RH9oG4cQSaq/Y2O32S4bv7+9jOBzWyCQ/D92jXbwJG+xNbO3kvWPfLPdN8kW7j4+PYzabFXwAIeXKP9vITCS+j+ONSSmTEigGzikiakAlZ2vt/Jl4Z2o9GblCJJMO7Xa1ltxElh0+bD0CTVuXy2URaLPagDsMhgG+SSUyzDjdiAr8ci7tRBG4pyuZmvpGGzLh5vMIDhlLDAzPIquUiRb+NgtL3/OeLhZqj4mBKv2BHSbbyf0wxpT8OSA3S8vnDmQcdNhANx0OHC0PGbSasMrZCeYFUIMyZyNo8MvYOYCiLb6fjSPk7Xg8LgGz7wFJ6DEzqP6QRw4YDZwj6htw73b7zXqfPHkS7Xa7VMgQfDl7HhFlKYj1wK9VNzHNnicmOZgXxujhYb9fmzcdxUHQVpPRlKvz6mnPMcaeIAkwBokBaJ3P58UomzxhzqlqpC0OcK3H2LY871nOTXI2BWAc2X5YpwD/3teOPjuotYzzPW3FUbkyxXIKECNgJUAhA0RlhYEnwSr3yeR7r9eL8XhcA0/b7bbsYUcARVuybDo4NQFBHx3E43Bpsyu7CKCbCI8PeTDPPgymMpDF9/EdpBEZc2fnCZxYOtNu7zenhXyZTCZxc3MTL1++LEGd7QK+z4SfEyJenoDvoT20GTJ+Pp/X9m2LqN4aZt/Nsh8AEn6SfiOXDiAZG2eZPTau6HZQaRBpvXW1A1gCGfT12ecjo7SL/xkfEzaW4ePj45I8MzFlfwXwJTDHHzMHbiP2HJvw5MmTMl+z2aw217QP20HJ/sXFRdkIHX9P28EejCt65jf+8vyIal9PE6b2rQ4uchWO58zXZR3NAUz+HnkG9H/Mw7JJW2mT5wKfZF9wcnISz549i263W1u6ho+7uLiI7777Li4vL4v/IoFAIMpyXoIQZMHBFLrJxuLL5bKQMQ6Q8O1srux2m0CiX9glKm7Bg4wLsousQlxCBLC8CBueYwlwAPLv5/MMzrPtcPC+Xq+LzXTMA3F0c3MTi8WipgvYJapv2RsL/cMvM3aOH7AN+LmIajnV/f19WQpENRJVLVSre7sQ7CRzSPLcnx8cHES/349ut1veWGzCn3YRZ3mJIMTF7e1t/Mmf/En8/Oc/j7Ozs7Lh+nq9LgkL+2Inik2cG8P/JhzGMRzGJpxDv5DN7Xb72nJHxwvGYLYHYBhwpnEOhBSyvdlsClnqSh3k1MtSZ7NZSfhSmbdYLIo+OLk6Go1iMpnUbDukMBWU2KaIqrqKdi6Xy0KCYrfB0o9hHcbCY2pSL8epTp45keOiBHws+urYOKL+ttiIassA5iyi2l5kvV6XPVPRO2y0f2Mnnjx5UvQZWwLuoppxu93WXkBjHMTYOa7CdpjUI0neFDvYbjp2g8R0YpA9kLFnJlId4+Oz36cev1WlVEQVWBGYUVKfhSXi9beFeFAjKnbTjCzX5uojZ5oYAJyAgy1nUwBLGN6cTchsKgqKAtOnk5OT6Pf7tT0WnA1EKQ3IaaMdc8SeSHIZu4UdpfE4mARysB8RJaviwMBLEnIbEG4AB+PLuYCbDOJw6igOQBIDxzUcCCwKAFmBYLsqzaw/CuAAm2fwGW31/W1EHBA8lg3xs5EngBFtAJDYcFm5rZS0BSNv+fQzFotFfP311/Hpp5+WjDhAi+UdzKs35fyhB3PrjE3TWPigvQaXDhIAa9yfv5EHZ99MzmD0CHQYJxwmJeA4X+YAp44MmbAzIeG9ZCA8ptNpXFxc1HQf4IfMrFarkvlAH8niYuQHg0Ep0R2NRrHdViXQeW8bDuTV1Somqw0usAX5ep+Tgypk23rnigvPEeMSUQF/7meCx3rUbrdrG5Wig91ut7zhDgeFPWPDZjJ2zJ+TBjyT+0VEAdkREWdnZyUY4vkmHHPb7YQ9bsiQfYHvAWAEcLs/tnFNJNGHPDIgdX8j6vrJ+egHOoj/Atz4Og5XM7DHS6vVilevXpXgB7ACOcRYMx/c26TxbldtTg8psdnsN/bM+59ga+mH+9jpdMrSEnTb8+I+mdiyrTKRiy1Cvw1OOZc202/IV/eLAC4TKDyTA/sPPgBjmFDhHNst2koSg4CBe7qaGHLe+3Ug92AD76vDeHj/Ht64yF4kjCPVNewRB3B2RUhEZWfG43FsNvt9dpyUwt7iRwisuH67rTaOdRaX7xys8Zn9On1ycGGS1uf5+8ViEf/gH/yD+M//+T/HcrlsUsX3etiOmCyz7XXlGstA2RwfvM0LCdbr/VvmkM/lchlff/11IY2oRGCJHP0ejUaF6I2IopMQKK6QYIk9e8tBdIELvBwHm4qusHm4/bOJDwhuJ1HRP+QdHeGtWt5IHBsDLrBd538qmf1SHvavQrZXq1XB816FYWzMuFAVivyu1+vioyCzGB9sJZgcn8kYGJ+CP3gG8QrxE7rnN4WDO/gMW26SwxVet7e30ev14vz8vLZMsd/vlyoNSOqIKkB3RSd6TXsgpv7RP/pH5Q29vAnw/Pw8vvvuu4LtsG0OtJkv29DfhAN9wmaDAyPqL+GB2DNpROFARH0v44jqjdG2eS6QwFbsdrsy1th8V5CDw/DL+APkFMITDO6qS/qz2WxiNBrFZ599VuYaItfbb6Dro9Eo5vN5wffG77TLFTeQLizNdYLJVXroiJNL4Gpslf2usZErJzOedvEAcmjdAXuRuIDss3+mT7QXX++944yLWUbJRvJgMPwi8QVEP/3EppGUx58TsyCP6DBzbltGWzwW7KHnJIPjgIhqnz3myW9g9di9z8TOW1VK4VBcKcArirODNXlikiYPUAYUALYMMmkDgBWDAMlhZp9KHAIiyhE9eAYuGF0bAK4nE4EgoGCASZfROsikHzgpDIwBl5ldfxZRKWFE9baUrDA8xwDP5/F8FIl+em4MlkwiWLkt3Cy/cTaMZ+OAPR739/clk5PJqszkZvKKecjgMsulx8BHE3vrQNWyyW9kx46YvzE0jDPPsMwQcENEePzX63W8evUqOp1O/NZv/VZEVBvlusIhB54/9DBx9ib3Qm4gj0xqAgpxOgAgxoDMpokHCCV+mHeTdzjp7777Lm5vb0sQZFlGz5Ej5s6EAzLn5Snr9bqWnTDIJbCynel2u4WAiqi/4c56grOigoN+owP035UK1l3O9dgQCNAW+ueg2hU/zgJ5bD12DuDslO3wsJk4PAIIE/QsfeQtQA8PD/Hq1auIiFJKzJJTbDPg1sQDgQFjTyCNfW632+U6HPRoNIrBYFBbitftdku76Qcygn9wBS/j5EqObPdpE8Ecrxz/mIQUc/7Y//7bwIr/GV/IY5bxRVSAl+wkoJN9R05PT6PVqpZwcbDB8W63K8ShSZxM7hBQrlarePnyZSlpJ4HDvXMG0zaCakj2q8g67uoHLz83UWTwyLX2g9wTu2vCAnm3PQfwIS98hr3m+fyGhKGNXoaMTXOW08QtPsfkW0TlK0xwPzw8xGq1ivl8XsaaSjjbBmSFZUwRUct48uYubP79/X3tbUMQI/f393F9fV2W3TKGBCtO5JhAJJDFjzhZw735nOutoxz2u5Ydj79trWWZe202mxgMBvHf/tt/i6dPn0ar1SpBx4c6sEu00Rlrt4/2u9/YYgKkTme//w8vjlgul/HFF1/EixcvXpMd7ntwcBCffPJJIeSoQrCPJ1hmzxj8su0zn2O7vXyaYDQTrK6wpG/MPXY6kx7IBHj7yZMnsVwua3tGtVrVvpGQMuzDCllrOUFGeQOlk2g8B6KNNvEd/cjJIAiXiCpJbN029nCiiOtpI8SAt9TwC4XQMYJDE04mPW5ubgrRRGIIW310tH+5CGOGXBDPUQHiZUkOch8eHkqbsMt//dd/HRcXF/HTn/40FotFLBaLOD09jd/93d+N7777rvTRFSXoA/c1tvpNOBi7HMvhl/Bt+M6I/ZyfnJzUXu7C9Z4//JGJXw78GFVzEVGSnmBS3vAGzkVH7u7u4vLy8rW4e7fbxTfffFO2VqFi+vT0tPgp+ghB7vjy5OQkhsNhITeoLHz69OlrMWRE1GwMvtQ4LsuB4+jHYrBMpLjYw7FxRNRiEHTS/cFG5/jZxQpeceGYxKsSOp19hTr6T/8YMycm2BMXWYFkhi/AV+J/0fnr6+totVplfylsFH8z15nf8LghU9jozWZT2ysau4hd4HpiXOOm93W8MSlFoO6qHLIMJmIYEA6TBmaRDUKbgjMrvIkbDjN0DCwAyYTAbDYryklW388yoLSysszBimLiDMGLqBhZA17a43sbXEXU31LHeQbTOF4DBwegfMc4G6SZTDAB5nZ7zgyWcvDDc1215n6b6KJNBlUAUpOGHp9s4FFGxu2x4NDn8H/Tj8GpiUEUF6DBs1C6iMrgGfCaEMhjhSxifOmvr6fs9unTp4WQur+/LwQvc/+ujvov/uIv4vLyMn7/93//e4mppsAXgwVAQqYBisynyUhAno07GQS+ByhZBgB2fnU0ctRut8trUJuIGOsyFTc47N1uV8pmyQzd3t4W24Au2OjSRjb9xGZQSblarcrbelwVt16va+uunWlEtgBkOVByxVm2SSah/L1lzZUDtjM4y0xWWZ+wNQBXDp5tnYVwJgimkgPHjK1BTiA4yMKRrWMjeb9mnHbd39/H1dVVLBaLuL6+jslkEs+ePSuvHHaww3h47JpIHT4jUMC5tlp7IpJlB5AGADzOedPj+3Tsbe7xWNYJmXLSh+fZJwB+CAYgHJC/iCh6FbEv9d/tdiVDzx4HLLO1PzFxZOLDweqrV69qy1iQOwIrB2gOjiC+eFsNSxDs65Bv5MYktfXLFRwRdb2hzYyDgyyq75pIEWwydoL5Yvz9DNpBn+2DmSvsPvPJmGUi3IGNn2lfe3x8XPanIOhGf+k/laieB88FsmNwCl4iCeEgZbvdxtnZWWy321Kd4+Wy/GCjANMOlAkQuMbkXZZ95spE1Pcd2fc3YSOCuI915CAc245cQRxzQJK3Wq0YDocxHA6LTEPc/vmf/3l8/fXXhdSAFKL6Bz3i6HQ6MZvNytKSPJY3NzdxfX1d27waWWN+8Hf0gXn2RujYWZZcYzNIRCHrrjLBFxLEe98x7Ax6ij1Hfr3Codvtxs3NTXkZB8sdW639XiyQathAKhh4tkli/BJvmET/2+12DQPTvoh4zYa7eoJlVSyvi4hCyFI1FRG16lTenuiKDFddYbOcVIBcmkwmpfKYwJklnMgdtsGyQtXW8fFxrRo2olpyvF6v48/+7M/id3/3d+P09LRsk8BWJ7wEC0xsv2o79pty2JZgS5n/HGexAsLyTDUgeu74jvMc95jI3e12ZVNy7CntMLno+1GpxQsJIqK2Uob95djP7Kc//WmpCHaxCTYJvUX/aR9VuxFRCA7kptPpxHK5LAlcZB6C1XFuPpri0hwH2N87AQNWcXI+J4aQQ2IA/DiYAju22+0KqUflKMQRiVP3mT1TDw4OyovY6OfBwUF8+umnZV9Nx1HrdfXmQSdLXX3sopiLi4uSYKT/Jtv4O/MoriIzbsQGQlDbBjIejtWtC+/reCtSiskjOMGp2bFmsoRr+d6ghs+s4C5Hy5kwlN2ZtN2uWreLoqKgbNgVUWUFDfQ4F0Fy4I0AARxpBwLCPXC0GBSDAQTbgSdOJLONNk4oPPeh7wbUrjxiXiz43MtZZtpmIctK6uoO2s74kx0myLRcoGyukvJ+CH6zE2PgShsTezb8Bk3ul+XBmTDO8/URdYa8CaQ6eKdE2p/TTsul2+Lx9JwwH7SV8fv6669rhhxywKWs73r883/+z2uZrscO9NaG37+9JAwjStas3a6qMzBmZExxYJZ5Z2fRL96iFxFl4/HBYPBakOQNSG1TWA9PUEZ7kVd0nBL23W5XAKqXTFxeXpY3CprkRgbY4B095m/m24RSDp4cSHP4fzsQf04/LUP5JzsFnks7TcI4k86Y+xo/i7+xQRBKZHY8D65ioc8EsqvVqoBr73vj/nkM0EG/HfGzzz6L58+fl30w8vh57OiLg5dc4Zp9Cs80ueOM9psc78Mx28bbTjkJkyvw6BNy63a7qoAxwrdkohOwDOl6c3NTs+G29cgEm9aT/DEJ1CTffoMi3ztRQHaYoMbyagxBv9FxA06PJW2nTVnGXZ3LnBvU2z9CvBAY0qYsTx5bLz3kuhzYcRBAPHYv/HP2Rxzj8bgEga1Wq+zz8vLly2i323F6elru4+B5OBxGq9Uq4BsZY6ky8+x9evw/NhNwTIIhorKVriDgWcwf+2dR4RURNRvBecY72bbaZj2mV1k2LIMf+rBtMPHog8Ckyb4zf5Doo9EoPvnkk/jqq6/i//yf/1NkGHIjYq9L/X6/EDP4a6p8yYiDeY+Pj0uFo/2jyRMwMsEfugvJYqLDONi6ShAMaYJO9Xq9Iu+8RINqPcgx5MhYGvky/l2v17UXD6zX61LJZ5KF77nWGJnPTAqA30getlrVXmmQvcgV9iRXHBmTIwcmwthnBnINEhHSy/bKMRJB8Xa7LedDumH7IaYcpEIMMraQJdgabHzTKon7+/v46quv4osvvognT57El19+WQiN58+fl2oOdA9bTb9NGvymHDnuQPbcp+12W8gFSET6CVYyFrH/QrdIFvHZfD6Py8vLYhftvyBF7B+J0V+8eBGr1aqQs3wPpt5sNvH06dMYDodFv5lnErkQKBFRrmOLDJKUyM75+XmcnJyUvag4wFeQXS5oAbc4WWxMSlvot8cTPJTnxBjTvzkcmyKT2Kwc+7FCZLVaxenpabG1rMz49ttvC+n6s5/9rLzJm6SCE1CQ9Ofn5/Hw8FAq0DmIm7ycmHGnH/TRfpUDmXOclfEusZ5lBZ/o5K1JKMtcrmZ+n8db7SllY7Zer8trg2msjbozjyieAwY7XANmd56D78wCWqFdbeLqCzZui4gamLPwRkTNobFnTUSd1CCwIxiPqLK0FoAMJrJAmBTCIOeAykGaFY/DhJ7BtAmdpkDCjCnn2OFyb7fX8+UKJsbH31MtgXFyFhoiYLerylodEHAOAIj+AoAwkrmixEGZnTSH/7Zhy8GHyxSdZc8MM3NiYspZbc5B1qy8btNqtYovv/wyfud3fqdUMrRarWL8c8XaDzn++3//7290nucUI+csNoEG32N8AXo4CJxwRNTKfAE21sFMBjPGZEfJTGYm37LC+vjValV7JS59MfgE+EbUX6O8WCzKNWQ+zs7OirPApgESyAZ7A2b6kx2A5cg6mKs2bC8i4jV5Yo7cdwdh1l/6yr0IMDnf5+YgPcsEAYxBda/Xi6dPn8Zms6ltsmmSDsKLgILrSR6QpY6oXtbAMyOqDe0B66vVKr755pt4eHiIn/zkJ4Ww9DIPnKr3n2IMXTmQN6n28gpABzLaVNn2oQ/LQUTUCGqXumPHvIci8kvQiQ1uCsi9+a2TDCxv4X827IWExEZSlcGeJswZtoOqAohi7Lhl0LYzIkpgCqbwXge0NdthE07oh/WE9mQSgs/5m/HxnlEmiGgzmUQIeC+HpG0A7N1uV5s7k3bWfQf2th0m4dBFxsr9MXD3/iBeEhcRJaChCpxlsBEVgW8SKZOzzBk+wssFWUZ2d3cX/X4/Tk9Py/5S3M8+1kEFdjqTbbad6IL77yNjysf06Vd9/qGOrNPYH5Oelm1ft17vl689ffq0zDmb1f/v//2/4/LyMiaTSS35gA6ShJnNZgXPbDZVZbP3MrIfNaaGtMiY3JUG6DK2gOBms9mUDdlbrVapqiU51W7v9w9kuTFvbYM8YXxoOxiBJYZeOsP34IlOp1P02djAyxHRvYwdsn3i/hEV0UpAy/h4PrEtxpOMNXuGDQaDoqsQEOzfgr1xpSnbiaBrxphZrpjfg4P9nloQjff39+VFItgV+kSCzsQAb2NlOS+yGlHp6dXVVcxms3j27Fmcnp7GarWKdrsdz58/jz/7sz+r2RXbOAfUH9PHvutBe5lPYhxI1dlsFqenp4VY5C1p+Geqh0wORFTJbhOW+If5fB7T6TTa7Xatch89b7VaZYUA97m4uCg2mPEHG1LN/PTp07IlA/tDR0R56Qnz1ev14u7urlRR8iY59iMbjUaFdMrE+na7LXIdETXyFLxifXMfbO9dcOGYl/9d5IDM4XdyTB4RNczimNYkC7p0f39fkjRUPuE/kQVihV/84hfR7/fLW2vBDCR/HLtCAFOtj96u1+u4urqqxUH2g06m8PIKlt+bQ+A5jtPpVyb4TRJ6DhlXJ5+5B997m453Pd6YlMIpINhkXTCCZtQQlkyMuJMO9i0ICJcBiIN8zuU7BpJBjtgzjbxVhutNAhmQ4KAA6bTTxEhEtV4bJwGbiuAT4ETU10u7L1YIxsxjQwDJPXIQCYGTz+XgPACeyzk5ciWGgze3zQrA32TNGE8DKsuHBdzjgHGyIeJAGQg0bLTNoDvQREkcnGSCyt9Zls26Z0bZc2IDYjLgMTLK/c5jST+5BlKFihJK1Jvm9occHv/vO5rAvMGm9cEZDfSLOfVb2ZxRM+EKycABKPPm45T/WkewNQA1NixlyRDZBgdqPJcyaoI0iBAy9NwbgoP23d3dlbJ3KkgA0P1+v8gWjoXDmUyTPnZ8Wd/yPNh2ZlnDvuT5ckWT5YjzXalgufeYZbKf53FvzuENO7a57PsE4QgJdXR0FC9fvizLHgDEzBEAiwB8vV6XahnGZ7VaxatXr2K73cann35aAjS+t3y5LxyAOf7O8wKwMEhvIus+9MH85AQK45yTEQBO7KWJEbJyEVFbVhURJailmpN9jxgjVz+ic8j7YrEoyz0gH20nIR0PDw9jOByWEnj7DgOnPF/4AoJj789ifc222PaOfhgwNj3Pds3l9Rlcu6qBYJ62sGeHK7YcGGdi2LYAH8XfTuJwL8aESirmA12CaOV5VCpZJ8ErJN4IQJF5JxI9l66KIaDn2ZvNJr788suIiPjZz35WKlGsOycnJ2VswA+2ZcYjTs7R5+xLDaaNs2w3fa2DmiYi6mORU8YTloncBst2ROVLzs/PYzKZxLfffhuDwSCGw2H86Z/+adlM+urqqjyHJdVUniGrjAl4lXl/+fJlXF5elv3GsPfoN3JiO4tfZVl9xsLIFxU2kCpUUvT7/YIVTKx62TfJXzDDwcFBeeHQ4eFhTCaTmg9rtVolLkFekd2jo6OCTxhzfBBtB4PgS902cAyyj+2lr17Z4Oe4WhxSwnGT8WFOQNs2gDkg8yD/qDxxVQc2E1tGH3nj6XK5LHtuWQZ7vV5JCux2+/3nILVJJNteoFu8jY+g2sQb1aGMQdPS59/Ug3mMiLISZDQalc3vwTDe/xIZcSzHvYz1vGz97u4urq+va/KCTLKtAc9D7y4uLmI2m5VkG/LOErSTk5P46U9/WnA2uoxekmDAt93c3MRsNqu9FMLxAC9nWq1WcX19He32vrIT+2N/Br7Hd1N5aFlsIoDxYybrPHaeF8YRm2J5xQ6bjOJ88wP22d7jiWT1aDQqS1yxt9vtNq6vrwtxvNls4uzsrGxd0W63C+dwf39fxoNl2fhXVzxGRIk3XNTi2NNcB7bU40Y/8a05RnYiG+IUX0WhhO/lMc8x7rseb0xKMVmTySRarVZh/7wkwsDOAYADbATEE873OOvMXNrp8L0DDu7nKguyALlsFvDmbI4dVSa82u19OS1vLMDQ81zakyslbLD8XAc+OQDJAsf4+l6MiYMTK5zBa3Z4rgDLgY/JGCt1RH1TUZcG2vFyngGlM8YORgguyOo2GQEDNRtuV2ZkQiiTXDwP+WLphINzFC2DXBywARP3YD6YfwNMB1a5PSYEcNbffPNNbcNSvqdK4F2OX0VG5XP5YUyc7cdJWuYxqgaxlmECIoMfxtMVOAa07fY+cxoRtbmOqN5wCDkMMcV1tM3ZKABWlsFWq1VIQL+ZZLValYqoVqsVo9GoZLr8hkTvjYP8uxqEseA+1kUHqf7M1QlN8miZbSItTUrYrnI0VW7xP+d6iQy20Nkm9IkN4V1pRlYJUhL9Pzk5iU8++SSWy2W8evWqvIWFewOKHPyjI8w9Gd/pdFqWIQGC7IQJHnCe6CMySiVUTqQgi3zmMfrYR/YLbgOEnYNY2oq/4g2ZkDMAzYhqE1D7FANEqv8ASxBKgFTbB0gsk4nYfQeL6AX227JFhp75c78iqqXdnMecGBAzZnlZCjbB9+Kwj3YG1gGTyUDslgMHfISTPw4CI6KWFbeP4xlOiBCwcC98f5bJiKoSFV9mIol227aih/SXeaXNZNuREY5sK5APgtCDg4P49ttv4+DgoLy8Y7PZlApa66fxg0Eyfc+Bmn+bhPT4NeFD4wbrTsaUlrMPfRhjWV/tCxgDbBh6AZFIZeLPfvazeHh4iC+++KKQj8vlslQ0Ur1gEtj7NuHTN5tNWeIJmRNR7aMJRtvtduX159ZjZNIVN2BDlvINBoMSBA8Gg3j69GmMx+NSCWXshawyLg6qTOxgz8DV3kgbefJ+VrQ3Y2jO2263xWbadyAnkC0R1Z4uEFidTqdWkRoRtSpA9ucxWWv8RFUqxD57YTmh1G63C8EIRsGOQkw48Uclq+0NcdByuYzlchlXV1dlLrCdrg6P2OsmlbC8gZznm1zebDbx3XffxW/91m8VcuT6+rok85AbMC1Lm3Jc85ty2N6gX2CKiGoJOueCNYfDYc0fgJdcmRZRt1P278ZLVLu6LSaZGV8IK2TQZBkJXbBtt9uN2WwW0+m0vGRmNpvFd999V+a806neynl2dhbPnj2LdrtdW+LZ6/XKtiRU/Tn2pB/omBNQ9rnYrWzf6VP2yf7bY4ItsA2OqJIc2AcXKtBWYgjI1N1uX/l2d3cXs9ms2B+36+zsLK6urmq6OB6PC7EEbjGOgMw1Uc65VKB5LCKi6CPkP7bOPsVjkbEV3/NDgoElzvh4DnM54L0cN76P441JKTNpm80m5vN5RNSrkBgQDgNEC0IG2S4J4xwmLJMT/i4PAgaQzBzf42Q9gAQ93gk/OxaCpdVqVd7iRVYEJ8W5Bo1kCDDuDn7on/82GZRJIYTLgoSxcbVKBvomq7gmkxQ4WJ6Lk4eNzkRLJmkMGh18m1jD0GCcGF9ntMgymDD0WJBhpU15Huk/5zsI8Dj4XOYrKyz99d5EOZtrmfL42Hj6GRkEm3idz+fx5Zdfxs9+9rMypwD3j3nYsNtQOftvw4NuLBaL6Pf7sd3u16vjKCOqJXM5wGeckFNvhO3qGHQQuXx4eChlzHlZnrMIZIyYMwAdemPy4ezsrCwBZAPu4XBYArDNZr9BcLfbjevr65KZMiEFQWv9ypU21mEDGBNJmQDJxFa2hR5Lz5vn0/NmkMr1Bkhui4NA/jcJAThBR3CQ3veLdpAF22w2cXp6WgAMWTeAFgEvy0PxOfib8XgcZ2dnERFxcXER4/G4yBsycnBwUIA97QAYQLS6jBxwTTsyAdhE/n3ow7qY/YbtHweySBALMOR/v4UJgGNShufwFh3vybZYLOLi4qL2BlsfDibd/oiqSsn7+AHImFeupxKLeyLH6DLAnn5SMQGQMlGFbSXYAsByb/fb48L9sy+lH/Zx1jMqFpgjYw2qh+yH+NtEvYG2v4PUZz5sI3JVEfJBFePDw0MBqlmWyPzyEoibm5vyJiWTUjm5ZZKP/kbslwY+efKk+DCWkzH/xhX4VoIOvncCMftuj7fl0D7Kspf/znrtI8v0hziQB8sf/TLWMUZhnEejUYzH41itVtHtdqPf78cvfvGLEvgYW7GEnkCPJbHIMP1frVZxdXUV0+k0IqK8KYpnOymGv4Qgov2QRyZQkKuDg/0y+PPz80J0uCKTCkrujw/tdDplCTH+2glu3o5HUE5VGKsjHPAhK+BWcADXdzqduLq6KlWh6/W6xAMQOX4jMBUF2BnOI3C0HSTAjIgaiWuSnb/Rf7ZwmE6ntXiEtmGPqYDx0lu/aZW5y6SudeDh4SEuLi7i4eEhzs7Oypixpyf6yHzQF1d8sIxpu93G1dVVqaqaz+clcUJsR5CLzjuZ1WSfPvTxLs8zmYJeQya7Us52iXMcV4EfaYvvw3OILcfjcdnUGuzlWIJ92i4vL2syRTvQp+fPn5f22Ket1+uYTqflraovX76sYUIq7x4e9pvu/+QnP4nxeFzkgETEeDwuWA6s7GdxcE4m9jwvJqsYD/QQGcxynmNrDuTfRIqvsW8weYPtbLWqQpxerxevXr2KzWZTljyCs2yL8d0PDw/x3XffxcHBQZyentYqipED4x3wCP2dz+cF/7J3mG0aPhbcx72pTMyxr2XYlWfYW2Mzjwu+O8cvGY++6/FWlVIwfZSYIXBMSm6kwb7BnTOeAA/AtMEy98sDaUEyEbJer2vVJgycq5g4F6eU28pnrKcl+MZpouRkVbjOTKqJC+5PW5qCVd/HytlEGEXUN8S00fN4ZsKF4MzLPRyk8iwTJs5873a72h49MNB+jsGAAygDSuRhu92WNz5wXSYv6JfJBDvYph8/JwcIjKXnxf0D4EdELYjNAbuNXX4ufWAss7Iif4xhxD7APj4+js8//7zcg2zExzo8XzljT3AH2GUPBAgBv2Uk678deHYurjRzlaVJMfTt4eGhvDEIMLTb7WrEhW2Al/9BIrFhI7IcESVbtN1uy745jMWzZ89q2SVkxfpLxZv1Bpl2cOx+M66WdTtny5RtKIA4ov7SBxPBDiIBktaB3Cb664rXiKhd62oM7H2n0ykkBnvFWe59nd9Aw2vk2XPo4eEhXr16VQJolnhYVsg8TiaT2hLi9Xq/1wrl8l56tl7vX5mb7SRtdzYaGen1egWUI085YfKhD9t/jmw/+TuTV35L1nK5jH6/H5PJpBZwQdLgA/G7AD1IjeVyGd98801ZNsB4cy0yx/0YY2wo5ISXthCEAWIJxKiA6Ha7MRwOS1UmQQ7nERAhW+v1uiy1JUgmWPUYZgxh20Q7eKb9AmPvqkbGEp/NnjXIr/WPoCMiit1Bnu2L8hupsAEQbpmo4lx0arvdFt9MgMxym4uLiwKksVXOCtOezWa/PQFvzjo9PS1zx9xHVH4xoqoCARNC7HFviOU8JvQXkpu++Se/mdPzZ79igizrRra9GR98TP/qwzbWAZBlDiL98PCwbHBOdcvx8XFcXl6WPaC2221MJpPodDolscJ8R0TRcxKr7Le6WCzK/OPbICpp33Q6LSQH8sCccZycnJQXE5BYYJ5doYTNQI4hKZARMIAJR+NKY0V0lsCXfc3QJ87HVrHPETEGOg2Wt99m3Kjyw59xfa6OwCcarzuAtr2JqBL8JnzQyYOD/dI72ks1MG8VBv+Ax9DN29vbGA6HZV8Zxo+4xBWl/5+6M9uNLEnSs0UE99hJ5laV3T096gEGA0GAoBtd6E5vopfUvSBAN4IAQZAwWrq7Kis3JpfYuQYjdEF8Ht+xPKzO6s7MqjoAQTLiLH7cbfntN3N3xxwkhljWgBiNSjc+Y/zv7u6KnWaRazCUk9YQbJ1Op/hUxgK7gL01nv5ax9+i+8Zn1oOIh2l8bBTC9/iQLHuOz2wHTFaYVGVNMWYeIC9U7ngNVMaba1mcm8ShcTFkWKvVipOTk5jNZnFzcxPdbrckauyjIJtduIEuovu2+eg/ONl9n2M996fjAN/LMVtdrGkMbT4hx8VOhNAWJ4gZC+IM+my1ethB8/T0NNbrh7WS+/1+zGazQtxAHGIbWYvr9PQ0vvnmm+j3+6U9+E5vMLCzsxOTyaRSGHF5eVmpkifGAE8wZdb8CX+73x2DQn7ZbhnngT3w/0yDtvx+bj/6kxA3xo15qTiQOqYykzCcw+BiyDOpYOIlEzYIrLMRfh5O1yQHRttTgWwUYapRShTWU4sYRK7l+R58O2AGivZl8ES2CcNhRTDhhuI5q2Yw5iwSAuJsWA7ELIw4D7cRQ2fF5vNy53YAAQAASURBVJ4oqCvQTM65zA9Dwfe+nwMYxsjZPSuH38uH+9ay5UDeZJwV0oCBsbdiocAm8ADNDvQ9Jh4bO1jGifdyMGd2fL1ex+npaWxtbcWTJ0/KeV/zoF0YeIwRY8RCf7QdctlrCJAZoP/tTFzFYR0hsOW5GHR0Y2vrYQrI+/fvy5oIGUxmQoT+BvzZeNphYcCZigYBhl3jHbrdbgGsDhw8JcJOwGPLuQYAznTw3pmER3azreE97Fhtd10VkmWatvAd42YygXe2TrnaxUGpp7w5UHBZOBUSEVEypGR0hsNhITgJsBaLRQmO7u7uClg+Pj6urEVF30FAElibmAIkmFilCovdp5z1pq+y3fyaB3aZvo+oBtHIsO0I8kNfe4cd2o99BYDmaS30U6vVinfv3sXZ2VlcXV1V/AltQWYiNgv5otv2MSaaaY/tABsOmDRGlmgfssaUfIhwkkbGEff3D7t2UnVp3cCPYTv4HfGxftAWA1reydMPM1nloAz7BIFKwEwyB1l2tUl+BucT4NEHjGn2i2RGW63NDkdMvURPPT3AMmWffnt7GycnJ7G3txeHh4eVLLkXYd7Z2YnhcFjG8fb2tizvYPIeufRYZ9zowN0kbPbd9rkZE2WAzD2zLv3YNV/ysP2mLdZh2mNsE7Eh9wh0RqNRnJ6eFlljrUOSLqz31m63Y71eF3L39va22Fj8N3pjLO4qutlsVsGFrgix3zYJRlBDNc56vS7VXq5ixsaT7IbsYI1I/EhElOl/VEZxD/Sk0XioEoNoQwbBGzyD/nGlEzLN8h+e3mp8yfOMHfGPmXTCbuIjwfIQC81msxBPth20IyJKsofKYtZ4QtdMvCHr2C4vKdBoNCpVSl5APiJiPp/HZDIp/ps1Tq0rrHFD4ub29racR5/zDGwvawUZi+W4J+vnr+GwvXIV63K5rJCM4IxOp1OmRzcajVLxCMHrJEVEdaMb5Id4u91uF6wE1mb8TPoQyzYajVI5i78kOcTC7Ds7OzEajWK5XMZ0Oi2E5PX1dYxGozg6OirY3wmiZrMZJycnZb2p3/zmN6XSljZsb28X+QD35tgbmccWOSFp2abv6SMTX46xkG30k3gFW8V4gYWY3sc9IzbEsWNS+oQq0Ldv35b3uri4iH6/H0dHR5VFv+kz+no8Hsd3330Xw+Ewvvnmm6KLJpCxZybL0ev5fB79fr+SnGbMTTaCtcAMxEW0y3yIp+lR1clhvsD3yAm8z6m/n0xKYdyYQkNn4ojcUAdOCFouE4v4OBDO5X0OFiI2pds4aJNUMMBkSSPqgyZIJZNNOH0cFvflN/ezMgLEDWgz2LAjj/jYCKMwdhCZiLNi+f44PQsZfc3zHdianHGf5mxkDoToX+7t4BUDxZxbB0zcO5MGjGFuPwa27plum/vJVUxc4zY6kHef+39noPmc9jnL54Aqk2C+t8ff8m5n4fbx/Jubmzg5OSlzjz/HmlI/9bDuOkizgwRU+j1wDAT5DoR5Rw73DQa72+0WIE2FBf3DnHZ2eqMaAuIwA0L02c+k6my1WhUwYNCAnE4mkzKlt9VqlXU6eHeCYt4tYuO8DAp5T1fh8Dl6SNWHs2c+LwdjfOZ3rQsu7cg9pg6eAdKZ+PRY+x2oKsN545xub29LgLRer+P4+Lisj8Y1nvK3Xq8ra4kwNeLw8LAsGjmbzWI0GpUpANZtQK93bqI0vd/vR0QUUpPFI5mewhiTUeQcdnva2dkpi0ry3p529rWOT9F53sXAhGo0yA762O3Hxg6Hw4rNoy+Xy2XMZrMYj8dlDalciWfbSADLeNzf35edKZ0Y8FRoKkBarVapqkEmIYU5zwEUga/lPyJKlZzfz2Q4NheMga/nXUzcca11mrbQ77wz5GwmcAH56LUXaUfe6S98nMl0/JptoH0r7QaDQBKYVCT4brVaZU0fZ9u3t7cL4ci7u8qLd7m6uopXr17Fzs5O9Hq9QkYwXgBpV8j4nsiC28a7IHtOMBq71JFWdfgl6w3XZ12yHeR///7SR7bHEZtNeiI+riKCgOp0OrFarcpUvVarFe/fv4/z8/PY3t4uOzpRtW6/jKyu1w9rQk2n05hOp2WMPI70PzbVusgaRuj9/v5+HB4eRrvdjoiokEjIvKvkGPNM2iI3yA76je4QFN/d3cXz58/LVJXb29tKFQf3BEN4ke37+/tCpIDxwQOWYZJfllFkm2QGRBw+nf7Z398vCRT6lVhhPp8XHAmBgY0Be3gXYt6H9aY8BRZiwRjXhDHBf8bpjsMc93Aea3RSjYE/zvEA7wHhRNUUla4XFxdlej3+odvtVuwv/gb7zOG44ddy5GIH7DnkKXIGUQc56RjVhBR64THDlhF3klTDr1xcXMRoNCrjyVhcXl5Gt9utTD1lLD1m19fXMR6P4+TkJObzeSGSPE33/Pw8Pnz4EJ1OJ7799ttC9p6cnFRmPp2dnRU9Jg6A2GVZAHy0eYOcqLB+mSy3XTduiNgUqoAjLK8mo+psn8l0VzE6Xs2x5OHhYfzhD3+I//N//k80m834zW9+E8vlsiQLiPuJVXq9XgyHw2g0GjEej2M8Hsfd3V08efKkVDfyrlSSU/nIzqnb29sxm82i2XzY+MI6xNRd+2+PJTLkyk1P82Sh+/39/fIM7o/9w/bkhPvn9qE/aU0pOuX6+jp6vV4ZVAeEGBwa6zK7iA0J4nULbPgMEg0iIqoLaXMdxtZleNyPrB+BVQZUfL5eP+xM4W1WabOBA0oC2MeAoPRmZ2mrFSmTciZ6rDwO2ukzDJadiwGg2VWclzPCDmhN/mC0TJrUAUL3ay7NXK/XxUhmks5ZYAfHJu4wXHb43NeVVb7OQUXdkQkjjwvtM1nk8/nMY5gJMY+ngSX94u9yEOl+pb8BYe/fv48//OEPZW2lr3nQDhNSmdxcLpeFrGH+soMdps/ifO0sfH/0mPMAX3ZINzc3cX5+XiqkXLkIECPzSNtYnwznhGNuNDYl5Th3MpDoNcQG44WRZrtqZITtV9nGFbDM+/AOWa8dhEZsCK08/c/EHX0XUQWGOYMUEbU2MJOMHA7Erc9+Dn/zXvQJAB7ggg6fnZ0VYENZ+WAwiG63WwIbnC5Am3GhugNHyjz6VqsVw+GwrFXgYBwZQZbYUSjv9uU+wUkDwgFmi8WigMytra2YTqcf2cqvcVj/+N92xLbQYIlxaTQ203EAwo1Go5CpLGjKDi9O0IxGoxiNRsV/RFQX4OdA5yOiTNk6Pj4uQRaVMyYOvVYVeuxS9Ezsoi/c0ySY9cjVySaTHHhxLXLNuDqwxJ/XkcwkCHieq8c4P2JDiDcajWK/vcYaC1Cz66crqXjnHDgah1jf8enIMHYkIgpxQGZ9d3e37JJHhRnv7H4hg8543N8/rKd3fn4eo9Eonj9/HoeHhx9lXJm66OlEkIpk9v2OgPA8dYL7+X/bMfof2TIWyPrCtcZfdbr1tQ76xevFcGQ5tFxRuUJQAqm7vb1dCEemhnKQ8MBGs9wG66NiB/MY2k+gU8gPOk1wDMFDUgkfABF0f39f1ihjt0De04mR+/uHxdYPDw+j2WyWStqrq6s4OzsrO64eHBzExcVFIbNubx+2Skf/8f3r9WbqIqR6p9MpgVa73S6bo1j+HBRmYpg+ZV0vps+xhgy65sQ648JY0M8QTyYjsAHb29tlijoJuOl0WvAVQX1EFN0moGT8GJPsM7wDHAfkNTESpBR90+/3ix2jfXznZF232y0+3lOOkSVjd/qaSmW+/1rk8Oc6jOfpB/p8sViUpBx6ClZ19QtjQxyQY1N8L7bezwDDuirONgZ9g8R1zAouOD8/L1NCIVJdFWn78PTp0zJFDRm+uLgo+Jiprt7p2jgfu8+sA5NRjofrYk/Lk3XU9t14wzjRJCjtyLGcsTC/HcPxHOJRrqHaibEdj8clIbW3t1fWNV2v1/H+/fuiJ15uguURwLfr9bqS5GOWAFP5tre3y5pyVCWv1+uC59yX9C39x/u6r/jfZBz+2T4ZQh5fQ1/wrM95fDIpZUCGwfbg5XIyv7hJJhg6wBuERkR1EXSDEgMKrjEwZoAJBK0IKEAdyYFjZtFdV+WYKeX9Od9Gg4P34b1tBExUoCg4rxx4GoTxPUJQNzXPfcy7GwDmDARjkTMVecxoO20yuHNQ7PZxrck/+jwTeciBATIZBcucx90Bmh1ZBpw2brm/uI535b25F+8DgHA/um/zYQdjHeBZrhry2GWi6+zsrGQkDIq+1pHJUBwQ70MGkOwmDtXBHIDKrLwXu47YELwmGchm7u/vlzVt2OoaY+++4lmQQ9vb26XSLAeeDjCp+HEGwRsXMF4sSmiQT1b48PCwBHO8I2OJrJt84r6cZ+cLIMxBEjLNffLfmaSPqBL19K0BhslnJwZcOYX9s96YvPBvdu958+ZNCSi49uzsLF69ehWDwSCePXtWEhk4UTs96zmO+8mTJxV7xRgCugHpBDbIJoEYu1Gdnp6Wd4REJIjCJnlaEnb659A/DvsDy4ttIYEqdhPQu729XTJ0jAVkG+umAZiY8nJ6eloCVogcZBt5oS1M72Gx8dVqVQl27buRKUhr5MNVPtkvR2z8EcCMrbbdBw6+AMHcm/ZCnBoI2xYbHNvucw/7TldM2X9j7yyji8WiADxkFTkn+QKGcmKKfkNGCSx4z5zN9btC4jmZQkD/9OnTuLy8LPbPa2CuVqtC7rPm0N3dXdlxjf45OzuLxWIRh4eHZTpCxIZUsc0j0OVv2k91onXMPpAqtJzwsS7YhtbpCOdwT+uTMcTPGQg7eDKmQq75nuk/yDc2i0TIeDwuSb3cV9PptFT/+N6soQiJCoFD/1PdhI7u7+9Hr9crsu7ZB+A4/DnYF51gbTLvCEg15t3dw6LJq9UqTk5OSra/0+nEer0uCXDWy/I0OKalYofAVeAHCBYSUp1Op5BRyCC2K6KKi12tif9nzRx8iauoILt2d3dLUgxbQR+aQEenI6KMK/pMG7HbkJJcDzGEb4fsN9bJGBSSC+KMd3OQTcIOezKdTssY83zrI89fr9eFmICU4PzVajNFK2Kzfh1+myobJ45+bUeOB9EpxpS/8S30sYmfvOQFPov7m4wnfkKPtre3o9frVdZ5a7fblcpXxibiYdxZlH4ymRR9oO20ERzFVPhGoxGnp6dlMwGqrSBGO51OWSQ9YrNEjavJGo2HxAmVZCZKXOBhQsZ2Otu4bO88BhzGvpzHtZlbsD3OvsFtQI+wla9evYrRaFTxeYyNCXqeYexCMhA7B4dhsgwcgZywqQO+AbxmbEibLZ+O0fnO009J+EF02r9iLxwj8DvHLX/r8cmkVKPRqDgCDyoNd4Nh9XBugE8TGvwAmHEImWxAaFzO56oCb/nIYQBtgsBAjtJF1o+AVHKm1kEcbQf0G1h4KpnBmUkIHJSBJO/mtjmgNnlikGyhMBmDQ/VUINqdqwYiqgsZA5BxnjiUzCBjxMxAu520gXdBuQArtNlMK9UNBrXeUtaMr+Uik070iQMMvy/feeqFjaYdg40IY21SK8uaP+O9AR+8b50SO1twd3cXb9++/Wgx0a9x5IA3oro7BcGLiQ+XwiMbJli4jsokr0MRsQlYKPul9Pj9+/dlJ5H1el0WQiZo8pjc39+XNSuo4FqvNzuNoWss4Mo1rHdApRWyRh9Q4cFcbuSCahMysh575DiT8uiuiUiD0YjNWgJ12Q7LOwdtRH7rKoMMjv0Mnm9iLAfqyAD2PmKzhhDBI9d6agfgA3Lo/Pw83r59G8+ePYuXL1+WbDBz1yH8GBNAD87f1SY5yKRSipLnb775pvifra2tsjW9/Q3vTfALgLI/ymT+1zx+DHgxRvYPLnVHHgk0DNIANsgqQRTTFvmf8c2JlWbzYeofi9RTeRGxkSt0iraZYEDPvWkA/plxJHAxAAWcci/aiA0AWOE/kL9ms1nWjbNvRDexZYw378G9dnZ2KksV2Od52oIzsgBRL0aLnnvKAuPImPn9reP4UwC+t30GNzlTTgUF06m4B21h2hDjwzhav9mNr45kpFLk+Pi4VLeYuEfu6OeIKNgPm2A/Sv8ZQzGuAHrLT7Z/1g3jAr7PxBZHs9ks08O+xmH7jT+y3+fd0Ankx8tN8D1TpPFnyDy6BZ6azWaFLMZnIZ8mtLFz+EVjcYJMAi5mTIBPsL2M+dXVVQwGg9jd3S1kGnKztbVVKi3Ql1arVSqWkSVkl0AIP8t0Rg62QTehhH6Y1AMH8t1sNis6RQUScoh/uLm5Kf1lwhYbArHnxdfRF8aYMfdaVa4mNCmPP+JdbMedTEBPWPic+3taLHpPP9iW1q03BVZCj9GN2WxWEnBUIdN+T1F++vRpwX70mbGZ46qIzfq7OZ75OUnin3qYpHAsw/+uZLX9itisR4r9z7jECQ9/h49nCR3uT4I4YrN2JNN9mSK7Wj0kjiaTScxms7JrdkTEdDqtxPURUda9Ahfjn54+fVpIyK2tzQYD6BokK1PE3V7WuDPpkuO4HPPynWXYfZrJEZ7PeRGbGCOfb9+cMbZl0b4IHQRfMCXv1atXH/UfJDNrdHn6HNWNEQ8xH9OqX7x4UXQWXEMVFMnXiAf7gO/q9/uFIK/jZereie/oA56DXaKPsXP0F/cFiyH7nxMr/6RKKQCsCSaAjkmpiE2gguF1QJFJqYj63aj8Y4NsI2eyAQeHACEIrF8CoYRjYr46z6dtmXDg/l6/xkbWzsyBZqOx2S4aoeE9HPRz+N0xBjggOyiuywJoB5eNvbMvtMlCWaeYJpn8vpnNp9+cPXEbeX+cnTOs7jNKTR3Q0C73k9vM/znQhgjI5f7IYyYyHbzRDowrAYDlMiu4p/7kPuFcv7eDIPqa/r+4uIg3b97UgumvcTj4Qs/py6urq5Il5zf6jXw7M2tZYDHxZrNZ2Q42Ikr2hDWKzs7OComFTCNDzWazBFNMD2BKHSSGwT1EK88CZEY8ZDQ+fPhQ5HQymUSv14ter1dIk9VqVZw2BJizjhHVtUywe+gga8mg94AJk1MR1eyjv6sLYJAbrxuEbtH/nGun5ADaFR/OfCOP6Lz7zlVfBEAOIpkCTTUdz768vIw3b97EbDaLbrcbT548iePj42g0GmUXTp7jzOHu7m4BM8gfWWrLKOuJvHjxoth8Z5AJsgHk+/v7BRgwPcXb5+ax+VpH7m8O93HWK2QdUObA1xnwwWAQEZspdxER79+/L+Qt5DH34bODg4MSdDYajULUML55fSWCLxOuDsAgqJE53sPA3JlWxsiLxUZUkx74Msbc44eMs00139uHUL1N36H7yB2y5iCWz9F1gi30nMCN9+CH4N5g0OOPDtonu/qNZ+YlELBz9AnTNMmm7+7ult3UGOtWq1WZBubA3mQFPh8f+vbt27i6uoqXL19WxtqBO4ezwrwj+IbP8f+LxaIChD22yB+/63Qk+8y6YISx393djRcvXtSp4Wc/7OONxfiOfiUggBTFr+agiOQM9hKysdlsxmQyKcFnXs+Je0ZUcZOfCzlBu7z7tHG4SWt0nuCJSi4W0N7d3Y1er1eeQ7UeeMABOrJG5r7T6cRwOCyJEHb5Qr/YAZZFllerVcEEEGuu+uD5kHq9Xq9MuUE3IN9IctHnvJdtnzG5kyjIKPfKpLin/KN36GWuIPR0vYjNbuidTie2trYK+bhareLi4qL0LbKB3QUjEFg74MdmOa6AMFyvHxIO7OLGuIP3IN0gLFhLC7Ixkze2tfTdr+3IsWdElAptZAUZAi8ZtzkO5j7YwYio6D3fMR4QThHVjZ1YJiEiKpW4FxcXcX5+Xsgl8Hyr1SpEExuYQYiAr1+8eFGpaAZbMYXTyUnaCLZkZoHlH/9n++Pfxp/mBUw+85l9PYenDnJfF0Zk3GBcTp8y9ZZlRUwIYsc5/+DgIJ4/f16mCGNnqE4cj8dxeXlZFpHnGdhSxoIqTHZYdUJ7vV4X/+9pr+zIhz65ms7kn5NBObYwR2Di3MQYMglO+JLHJ5NSzPPG2BFY2djY8dLRHnwHRxkoRGzInYgqWWBSyucAhlFulMHssdlmBBPGGDDugcJoopxkYlA22ul3o3+4BoNBewEcOYi1QqBgzmzQR4BPZzwBI7wb7x1RJf1wNvQpRoWg1QYvs8/OAHE4cPB9AT8okRUf8O6g2AaDe9AuAh7ehd+WFwN3y5Vl0ePq7HLOXHjcuZ57GOjTR3l83NduB211Vp+24CyyYWi1WvHv//2/j3/37/5dMT5f87D+1fUVn0dEWVvBJA3v6P6xg8nBiKfztFoPJffj8bj0k4GTDWu3242nT58WB0dAaPYeXQGIk10+OzuLk5OT4pQZY2dU0WWIFsAnGUxn9QFoGHCTqcifq0EJ9k3+GpzZyfJdtpn0T85I+t51pJdJDQedznZkciDbKgNLgjtIPMjKVuthihhgeb1el2wQWdiTk5N4/vx5WXjbJFK2GwAayBHAAsECaxpcXFyURSCbzWZZC8TVsJl0Jjihj2hvHeD50gdyhxw/dmDLACvo3XK5WSAc2Wb6ycHBQSFXO51OvH79umJjLGvb29txfHxc9D63iSlDkB/YTN6BLCqVFEwJ8pTI1Wqz40xEFNlYrx92DsOuIBNOhqELENHYB4gpgjEC/QzUnFBxIoRneTF9EnIOLhwk2E5hd/DNYBB0j4QEto8sNEG+CSxIWWMP+skA3/aHPjDphG6BJUzu8h6Q+jz76uqqJPIiNqQi4xjxQOhD4hPM0p8Zexkfesq+CTZPXeL87Fttf3xYT0325P+t17aBX+Ow7EZUp/xHbPTP1TSQKAQ4EZudKOlHziH4vLi4KItkc0+muUPU0O/NZrNU4eS+iXjQ+263W2SWa0ziovtUTnod10ajEf1+v1S9o5/IFj7YVTsE3tfX1/Hs2bMK2USCAX0ywRkR5Z0vLy/j22+/LfZnvX5IXLDwNnKBj2FKH33Q6XRKlaWnSE8mkwr5lPGp12yLiFgsFiXpAoHUbDZLUgx7bT2jj0iuYMfQafob/YUM4lr8NjJC8sVJM7CC+8IBN9+Dq1gvZ2vrYVkD2tfv9ytEGTILkWV81263S/KHNmA36kjmX/phf4htB4tiT51g4xqWKAGb5mQkfzNm9lXINz4Mf4b9xH96uQwqodA5E1/23c+ePStVkKzB3O/3o9lslml7rsriaLfb5TxmuICpHeNEREWWc/IFvXFCxD61Lt7DF/A/ttN42Oe5Csh/o9NOlHnd0Vy8wf0Zo4ODg0LEgiu9UyXLA+F3IcBJFjE2o9EoVqtV/O53vyu+DvIPm4sdoAr06uqqbPqATzP+p0/th3NiDpIQuZ1MJuVd7XPhHbCVEZ9/bcZPJqUA/gYfdq4mdRgYZ2RwqnUkhzNHCCuHBdCZBj5zVg0QiHEAALnCCQUFTNIewKgJExNWBKJ+PobIgNzZgxwEOuDgmSbZTLYYMNnZOGB0P2byy+vvoMxeI8RkH+PA8wG9JpV4bz+P9+OeOBvm3zsbaMV3ti+TmQZbtMvkHuOY59DaqZmIc/tNWNQZQxNTZBmQHRvFTDbYWDnQt/L7Omc/fE+unc1m8f3331cy6F/rsGxwONCjjYCgRqMR8/m87ALkMuKtrc10OIx/xCZLBiCln11FZ0NJH0GoEmCTOUWvGCcAMrJNtgaHgIOhfXaYgLDpdFqAo0l2Z5kAs5Zhgz0Tszk7wztis/jfv3mu5c1EHwfylafV8mNCIRPKPqxHJlVNyDpYcaBARpl1t9AfVznxLmS2Aexv376NJ0+exMuXLwsxmX2CM3j4BwIvxhXiazgcxs7OTvnMGUfbV4J1fJPH0L+/5pEDwhyAe6oZ9p4pNgQRyCcB0NbWVnzzzTelPw8PD2M+n8erV69ib2+vsp4aQRKAB90xyKGv0AeTP1Shnp2dlfFCb5fLZSEDAb34Dfs6SDSeZ0KKMcRPUNpuv4ZdwoYAVLFBED7olW00B21GBrxlNdjGCSQIGzZ/AETmytk6f+GEGzYF3xERlc9c5Wz/6+o6+jAiyrTWra3NOqDWC3wpxCxTpNrtdiwWi7L+CGQXz2EHtmbzYXcmwDv2yZUatisENTyPc1nHyn1qexXxMankMasDyCaAbFuz/f3coPqxw0GYxze/M+cgt+AAV9JwDtNn1ut1TCaTOD09LZUUjDn95CnWEBkHBwfFdhM8gYewtavVqlRFG/NiJ8bjcfGvrBXVaDTK2jadTif6/X4FKzYa1WmekLTNZrMsbo5tw0ahw0yn39nZiefPn5eqBrBBRFTWjiRGYPo/vpJ2QOYin44nIjZT1u/v70v1IT7DyRx0hL5GxmkL/b+9vV10jOon9zU2kqnvtMF+Dn0jAYGPXC6XpfKRNR6vr68rcZIT7cgftgECLmJTXYLOb21txfn5eTQajTIFn2pnfA3vORwO4+rqqowrGAi/bCLQbfk1HS4ywPcwfkwzZUwgaJ2Uj6hWq9gP2MehbxCexE60gf8bjUbxm9vb23F2dhY//PBDWXLCa4MtFotS1T4YDOLo6KgQh8bc9jeLxaLgfggSSChjYdarcrUfVcpc75gPXcNX5mrnTGI59s1xus/Lvpd+z/GND7fJcm/8bJ/De5CcOzs7K3KB/ne73YILIA+xsRcXFwV/4XuXy2X88Y9/jG+++Sb6/X5lNsR6vS6+3UktCgToS8dbllF0GztuewxubLVapSKUPrBvMt/xWCzxtxyfTEqtVqsyDcWkDcGDg3uXDWZB4jrumTNHBtwcVEPgfDHAnm7h6TQOGpvNZung6XRaBMKAzDsDuYzZGSvaQ1soc8YxWTEw7GZNbfgBze4zk14GKfSPgSxCmQPxHNDkIIv+yGDtMVLMn6PMBscmVADlZuN3dnbKFEru5dJk2oIi2GBn8EOwg4JmoGoAm3/8jq7mcdbcQNHGGHlmDFFKywTP4F7oB98xRp7r/5hRbDQa8d//+3+P//Jf/ks0Go34D//hP3yagn6mg3ZHVHe7dAYCY0XmDKNqOXPQgM7wkwkOiAh2PDs4OChACmeLc2DxcQgl2uyqR8adqqvFYlEpiSZYRu/v7u7KwuWUpuI8ccjsBEcFH2CBd/SYWzZMCDnwyBkw3oN7IWucY11zcJaJC+s23/OZSWlnprm/KxjcdsaWZ9HPzhKxCCyZOvQfMAKBsV6vyzTw+Xxeskbo/JMnTwrBiR6aqEYnDw4OYmdnJz58+FCmkwEIkSESErwvFSmsiUTgQBBv/ceHWB++xpGfR5uQGWfL+JxqIQMz9JJxAlRGPNi9V69eleDRPoStwAGGZAnpiwwWaa8rJJieR3vzu2EDqGCgv9keHr9BVQegkAWyHehEVMlbgoFsYwjyrLPY9Vz16MNJMvCPp/xDqnidJnwX7ST7jI9zm/x+9EOuarBf5Ddt572RfQLD1WpVAnfbGZMZtNcJo4iNj242m2XHMTK94BumUoILz8/Py/bWvKN31aUij3HAj3Bvb3zghE4d+W775CP7Y/9+7O+vfWRCClwBvq2rPoqIQl5AEqIjyMZ8Po/379+XqhzIV2SEe/K70+mUSkRsMnYSMtH9z3gxZsgOi6kTgEFmRzzILgukW875sb/hmaenp3FxcVGmVkJ2MLYsXs47XlxclEX8qfgCS4/H40LMmcgDu7tKiPeO2BBAkFARVdzfbDZL4uX+fjNlPW/24LV2eAZ6en5+HpeXlzGbzYo+go+wYWDQi4uLMvUH3XDiABtBQgK9Y6oyP1QLO/EHxkEGXTHKuSy7sFqtYjKZlB3b/umf/qmC6VxRgi+JiIK17G9c9Yo8/JoO6xOkBD7FGMSxLv1Of3EN/7tvuIYfSF/7XOJUprBGRCEcz87O4vT0tJATi8Uims1mmc5PFRTYHZ1i6qErbu2fIza7SJ6fn1diUSrtIGhoE+QyhQokshx7RWzsN+9vX+nz6uJVx8ecz/igd/S/MbS5DA5k0uQZ8S33itjM1DLxdnBwUHbag+hnGiBcxMHBQSGb+czv0Gg8TMt7+/ZtNBqNQhTCDzQajbK4/dXVVdndkYopy2QuHmEsjaHwyfStbYGnDGbc4xj7c+rvJ5NSlIAiTJkYwInmgCuTK3Q6hwNJA8CIKmFi4guhIYOL8NQF+2aZcZw4SNoOGIAwQkgzgOE9dnZ2ys5DDgw9WFYSE2c5GEUoHEzy7iiAhSk7aVefIRgOWDLYNnHjYJln5ntmIJiZYhtTFBtQDZAxecb7GqgAdhhb2oZzdCWMDxMftMGBiM/jWo9LHWNucovvCYozuM9tcsDA+3IfZ8jrwDAG0xnInwM0c5h8I8jhMw4TLATAJmH52yRLxOZdAXqXl5dxcXFRWS+o3+8XILlcPizw67ntfi4ZGhv16XQaJycnJYsaEcUhL5fLshMVoJGd/wi8IJwjohBevV6v9AcZ1YgopEomfVl7ws7MAQl9iB5xGCx7POrOtR22XbZD5ztXHllP+M73zqSMHbptA44LghJQhL12ObnBlte7IZClampnZyeGw2EcHR1VCCuAkt+50+kUcO8Aq9PplH7c29uLfr9fyVZzH6q7GKMv5Wg/9XhszHlf1lRBvprN5kfr+NgWR2ySRMgtOgUYIXDlvp6at1o9rM+Cblq+r6+vy/olEVECZWQcgG7ixDJJ9tiJLQIuZAICmimXXtC33W5Ht9stvgS/xcF7IT9uF++H7/G1BGEei9VqVaoxXGXhz+kvftfZ8Dp/Y3lz0GGsZP3jO8b4MZzS6XTi+vq6VL/wzlSMuE+QE94De8b7sSOqd7mk/QSdVEz1+/1SDYWNJvEAkCfpQHDPe/COlnuwgO1W7kfjlLrvjFUzfvlah9uOn2k0HiqGsI/oF+2CXGCNEvoA8nZvby8mk0m8evWqsgFARFSSReBcxoOp7JY5no3emEiFqJlOp5UEEDpJIE7iiHYeHR1VpgQ50UMQS4JrtXrYhY8FvJnyxrPYVCUH9Cx47moE49XZbFYCRPQfHVksFqXvndilj7zuU6PxQECTUDERAanGO2GPaTOkn4lq9IL7Es8QYH748CG+++67sug6VTC2UxyLxaJUn/J+xBzsSHx1dRXz+bwQbhDL+HD6pK5CAjsZEaWSfD6fx3w+j8FgUGwCU3fps263W5KNxFdOvoMBc3Ltl37YJ0dskgeQ/TnedRycSRgnOO13uHY8Hsf5+XkhNyEq0FGIEUiwi4uLmEwmEbEhdrDR8/m8rBF5f38f/X6/TOsHR+Hf8tR08Bf24Pb2NrrdboWYQu89XTxiU+yBzBMbcDger4t7jDvs63y9CRXrQMa6Hhfbf8YGHI9ugwfAC4wv/pdpfjs7O9HtdmM+n8doNIqjo6PKEhdswNTv9+P58+dlXKn2Rq8gzVerVZydnZWpgI7n+e2EpGdyuB8988hcA+NAv9EvJAdJYiJfluU8Vo/FtX/N8ZPWlMI4MSgYRwMhB4YZYLtkzMDAQKvu2ogNGMNxeLeaiM226hGbSgUGh908LPhmoZ09rWPuGWgMLwrme/iedSQK7wngc1BnBXAwgMM2WCDzwz0RRN43V0xlA+eyUT7PJFpWfvrXRsnnGPBlsoesK4rGGDjgxbjmfnNm2IGun8v/vja3wW302GZSiv7ECHMexKZl0SSXrycoof127Lkt+cjt/LkOGz7acXNzU8bPjhXHBNEA4LJMEzASDHkRv+VyWRYIZNwIqJrNZtmMgLUgkHPaAeGMrnz48KGMI9V1gFKAlXfli9gsEN5oPJSlE0xSWYHztIPzGG5vb7a6t3wjNyaZcJgRVXLOemodcybH8mqZsk3NJIb12M6Ec53J49mWW9rnczgPn7Ber6PT6ZQ1vvx+EIAOdkgouBQdeQGEjUajGI1G8c0335RpQuw4wzXoKkHwfD6P8Xhc5umbqDTpQPYnBzAGjQRcn8vRfuqRExQOTnPSA/v32LijU0dHR+WenU6ngEbOB/TgS6hCdPUfU37m83lZ5Nq6AIilKjFPBzGIc1B9f39f2W2MwIqxIBBH1rxTJmOHLiO7tk0EPZZb/kYeTJZaTzKmIbBH17m/ATb+mf5w5QH67gWeI6Li47EbvDf3hzR3lbXBOQEfpDkyQLXF3d3DDkjoDGPjtueKrOzfh8NhPHv2rEy14zvavFgsYjKZlF3X0FPIfOysKzHoH+5DuzMGeyyBhJ3ksO78paMOZ36Ng+fSR65qJGjnf6rETeC0Wq3odrtxdXUVp6en8fr165jNZgUH8U6ML4ku/DdBJ3aAoApZY6dZguzValXGzkleV6H6nbALkEr9fr8Q4Mh1rqiKiJhMJiUw/sMf/lDWKiPRgQ3ht8mpu7u7GI/H0e/3SwKJ96IKhApOV0+ZUPPGKASJEVEJzk2c89ysK8g3lU0Zl2Jbr6+vS9/RFgfq9/f3lcqxfr9fpmozVZEx29nZKSRRrizm3Y6OjmIwGMTFxUVZINk22kkHxpZKKO7J34PBIEajUdzf3xdCMCKKfWw0Hqo9nj17VnY7dOILu43u0q8/J+79aw/GdbFYlGIFiE7HVGBD+3hjZXTTOrxYLGI8HhecDBHBNdiH7e3tmM1mcXp6WmYQML4sk4AuMNXLWABSFoxM8oe1wdgsAF+MrkO0sCTO5eVlzOfzQiy3Wq0yu4FKQuQAAs8YB9kghs+xOd/bj2dfiD81eZMT476PcTj2yfqMDTU29vOJLfDf3W43rq+v4+TkJDqdTrx8+TLa7Xbpt+VyGZPJJHZ3d+Pw8DDOzs5Kf0OSO3H7+vXreP78eWXjoLu7u2i324ULoc2e6owNxl5nbgMbFbGZQs052Ef6w8VAngkS8eMx7V9z/KQ1pXKgmjOTlOUxOHZ0DgAAMbxQRP2UJhM5BrZkTc305dJTBAhj78NKQLtRAL638KJ8BMrZAdnxuO2AUyomAPsAwaxMHGZ5I6pBYc4U2sBxzWNAy59zjckVf08/cp3HgffLxE8dmHdQQXkhoCBnC11uno0OBo5gwOy3iRwH4e4PB+HOOFrufK4DcM7NmWnLLYbLJBR9aLBfNxbZKDsY/LkO3gNwgjGlnXZOOEscCOOYHYjnQuMwnbkj2AO87uzsxLffflsALfaF5wDsyDCMx+MCnlkclV39uD/XM2XE92J3IAwyJPRisSjt4v2bzWapDgKYeyqQK28iouIo64h5A2wTDZZBE9aWvxxE+m/ra0SV+Lc+W9ccQNN22zkHjWTqIqI4RJydHR5EGwEWpAYgl/UvqMrY2nrYPvy7776LyWQST58+rSzeDWlIX7Mjk4Nyv4dBMYE512KjnWkiWcGUh6+lj4/5BNsmZ8NoJ2NIwMQ1q9XDmhHOcI7H4zJd6vDwsFQKQhjzHMrGATXn5+elotEJGOQYe2DbiQx44XGDQWS92WxWtmC37kRUpzyge/bFTrZgp3PyzPbYoNeH9cBBM+AbUO1ECX2dfS8yx7noCeNEVSjP8rijJ+gdsmGZdmDnBZB5phfwh8hDd7CV7DRqUsj34bnodqPxQNybzEAmG40HQrnRaMTz589LFSxtxQa7UtFBBQFyTs4x7g4M6MesG/mw3Xvs+Jq+1m3Gn0G65LZmUtcyOxgM4vT0NN69e1cy2vhMYy/uSfabfuZ7zrGu0JdMy+S5PBvy2ERIxMeLGPf7/TLFED2jggN9xuYQ1K9Wq7LjF3jbFSEkqIxP0QGe1Ww+bEzQ7XZjuVyWShDwDPK2XC5L0IZuEGQ69uBcAvetra0yhZHKUBbppz+Ne8EZ6K43ZLJvtY5A7g8Gg4JHWA+XBbRdGWXsbHLR6/VFPNjhJ0+eFFvEWlNefD4n99E/2geG59p3796VnQBpOxUjVJwTFDM+9mOOm35tB+OGXjD+yIITvCxOjY1HHlarVdGniM3sGSrbnKBkmi/VZxBgi8Ui3r9/Xzmfn8FgEAcHB8XfkBw+OzurEFw5Jl0ul2UKZrvdLsTS+/fv4+bmpughNoY+2Nraina7XTiDiAc5c6UjPgtf53gvYoMXbJtdfIE9NP6N2NjOHNsZT4HxTdza/2HDGFvGw/exL51MJmWcd3d34/j4OFqtVumni4uLovOcs15v1sM+OjoqM7k8o4D2YDN/+9vflmt5B+QGGeK9+N74ynG3ORrG2u9MEgMMYd9gDP4ljk8mpdh9gcFxg3KQZKfKS2fSwwEY9/M9M6ngkk+CCr53BQ3XAgK97pTb6XtjyN1WgyRK77wlsx2PFQ0lqQOpBEIGyvwN0LKzxXEbVDswtKBk5TRxlsfI72+gnQ+ey3cob11fGswT5OGAccgAVEpP/b4EGjbAOQDD4DkD5edaUXAMeRxsrOk/yyB97HHy+iXISO5LjIEJBJ5nA5HHxcRNnq75cx2Z7COQAFz5x2PG1q8RUQEf3IODwJEqGrLolretra1CKHkaH86ANrFuFKQGYPvy8rKyKwi6SjYQYw65vbW1Fb1erwAuxpCFz739LWDL1TgRmx1syFwhu9gYZxkMqOmTLK+ZnDXRhMPKxEBuj/vcssfY+G/OY2wNZutIAhwWc9shICKi9N3d3V1ZCBPgzGKOVG549zjrQrP5MP2PTNLh4WHZEY7xbrVaJVgmY2Xy1GXdvV4vzs7OCmFmEIRMTKfTSt/ZtnyNA5uT7YNtlRM8JmncZmcgIWXpF34Dnqhkw24zDXO1eqjQ+fDhQ6lsQw/RGxMYjKGfjSyZRLNPc8UQU+O4bwaCHoc6Qgww5euQ5+wXvEDwarUqf3M/5IO+BURCrIMvGBP7MesY2UZXeBAYW8fcFgfP+DqmMFqneS7kkH0NY2zbA+HoPrm4uIg3b97Ezs5OHB0dlapD/LftzPX1dSEMu91u7O/vFzLTvvjs7CzW63U8f/682AB2wPSakiYTrf/8Td/QL/67LpDNtizbzXwYA36tw7KBbtBOJ+Sur6/L+npgWPu+9XodJycnMR6PK0ElOBa8QTAJfkbOGDd00li32Xyorjg9PY3pdFoqntA5V3yweDJrjmB7PIbo2/39fZlubbJ/uVzGYrEoMvPkyZOSIKL6mrXc9vf34+rqqhAzrnqmHyHT+Yzpb0yNwWZRrQWZtV6vS1U1FT/YASpMaBPT0re2tmI+n1fW0cM/U00GHiAJwxqLLCeA7WCqOr5xMpkUjIXOsog0utNqbWZwULG0Xq/jw4cPMZ/PKwQQGIypgMgia4IhC/hV433k6eDgIJ48eRKLxaIkEM7Pz+Pg4CAGg0GZEeEA/OXLl/Hf/tt/K7KeYxHbjl/LgezaHqHPYEjLpWNUx3gRUeTDRR5UtnJ/J1ixo/im2WwWFxcXRfaNrxkn1v1kmii7rN3e3pa11yCVI6IkByE9WfuP3eGePn0az58/j8FgEBFRCGV03TG08bbPMUaI2MwcyD7OfpXzTJT6Guu9x8kxm+NHcIaTMLTbY+XnOdZsNpvFJoGDIQ+fP39eCCuTwyQisBmMr+N67IBJR3arNtEP5jEOIk5zfzgRZ/I0IsrzOT9jmRxHuKL5SxBTn0xKLRaLilABjs0iGkxa6cw8YqQjPp46hpHPoALjxxxqwCAO0Pe3cLrEPCIqjhLAyW+TJLTDW3oyGFzjqWwYexMaPA9nz3UGISZRrDRWQA4bQAMt+pp3M2jnOgPyTCaaHXawXDcv1+/GO2RSDoGn7zlQBq/jg1JZWU0MEsT6GZ4C5MDe7XLwYmDKO9tIIcsm2gyATcxwP/9erzcLW/K/+9pkFONjssHZ4Ezy/ZwHjs0ksCsFmbZn42WyFaOH42TaK8AuIoqDBPiZoKE0nWDYO/Vw7Xg8jvF4HNPptIwlMoIs3t/fV3aF8u5fNsZ5ChFjf319XaYfAOaxgQSO1svsWDhwVAaTmXA2CMSuWNctE5YRZ20ionIPk6S2tZxXJ2d2SnWVILSJvuJvghqc7WQyKe3CdgPWkAn3n/XEvgXi8urqqszV965+3KPVahW5MdjA7roKiHHw5gPYaCcyvpTjfezIdhu9sKyxmDE2GzIJ+fUuLMg41/P+R0dH0Wg8LJCckwU7OzulMorv8PfoJlUwgHH6GPKWXZX29vaKLvBDsBIRFX3lPVwN6XOxwYwrf2e80Gq1SrDK1J2I6u6x2AJ8EFlszgFXYJsg4ZBL65iDEkhRpg6bPEQmAYnuVx8E/q6otCygUwaalhdPV3Slq4kHgvT5fF4WYn3//n1cXl5Gv9+P4XBYAnfrJ/aE6SDdbrcELGAagPnz58/LBggsdMs7O3DIuC2TS3zOuNt+Zl9ZB6Kzvcv99bX0O+M1462I6o6mto9+l93d3ZjP5/H69euy8D7TwDxOXG+SMSIqfoL+hJjBHmATTECRNCJB4wX/G41G2cUP+eh0OvHkyZOyAx/X2kfd3NyUBfSpTH7x4kUcHh6W6f7GtRBU3INA3gmf6+vrOD4+LsQs/p9kFIR6rpyNiJJkAqPmgBayZbValTV5su/f3t4u94fIiohC5tpOEE8RSNJW+tVrNJkoMgZiDSB8IqQEOxRbx/B5rqSA6MDWUWXsimMCY9b6xA9AmDWbzXj79m3s7+/H3/3d3xWcT5/v7+8XQgS7ZdyLPuD7fg1HJkNsZzIhgx5hc/IsoWyHGo1GWQeQMaOaEiwEQXh2dhYXFxdlQwGIDOzA0dFRRESZfYA/RVZ3dnbK5ib2j8jgdDqNVqtVEoir1SqGw2EcHh7Gzs5OIaNsi5mFELFJOnFvV+aAGZFt/BW2EdwPvgXLOIYzhub+xm1OhjneMyHlsaP/XUXlZ9C2HC/yt+NfZnGw9ub9/X2pSgabkPRmeZTt7e04Pz+vrBHHz3Q6jZ2dnfjtb39bqSKjL7DnFAdgzzw2xk72GchGxGZWh6s7jXUiqljqcx8/afc9Dz5GzQ6fhmbn72tRygwmcwBkkgpQ6cAepcolh2QXUWpATCanDLLcDmeeMQRZ6SyAAFqup30my1xaa1BikIKzcYCF4fcYuIqAd8mgjLZYaP0MPnss2M0KbxCXn+M+NWj19w7+AQHOuvJu7v+cEae/UZYcYGRwiiz4fbI8W67zu2W22OA/Z+b8TJMLHA5kcnv8WQ7Mf67DztXBgIFlq9WK+Xz+0Y4abrt1DLBE8Iqhdtkoz2VNkogHYNbtdivEhqcS3d8/lJMjT94dFOdC5tn6ZXu0t7cXw+GwOHqTqpDx7CKJLmN/CJ4hcR1M2565Lyzz1h8O26U64GK58//cu07GeFYGU/lvDle/OJgG2K/XVTKW70ajUQl0h8Nh7O/vl0XIvaYMGVXv9AVhCHAykYEMjkajOD8/j+fPn8fh4WGsVqsyfYU+dbUN9+Y9IA2cZQKA8U7O9H3tI4NTE2seU2SYYNJ2KU/DoCICn52nunMu519cXJSMaA7aaAP6zrNoEwEZpfrWOewgUz0ITAGi7KgYEZX13OzzqApyZpcEET6Xe1l+c/BQB15vb29LVRR9QUUg1Q0eA6+L4zHB74NbIjaLEiNznG+fycFnTG1iDLCBubw/vyMH7eA7APB6vS6kEvKDPrCQNVNwyIR7SmzEppp1vV6Xig8IDezv6elpqZ5wxUAO2rAp9rfZfvGTAxLbVs61HuU+eQzrfK0jk84RG9vJu7RarbI7HuNARRKVMicnJ6XawdM0XT3ja+h/bEGr9bDuEoTIzc1NjEajCtHsyinbIPwjNhs5w+73+/3o9XrR6XSi0+lUKurYih5yiDajK/avYH8IeOSfhZqRW08z8znEBxBsnk5FkpsEFH0TsUnQglNc/U1waNsF6UVii3H17pMRm2mwVIoxZY7q6/V6XSGkjLmur68LcQWeYUoQibLFYhGDwaBCTK1Wq0Io8l5c7ypipr9Dik0mkwpZTqUO55tYQSa+++672N3djcFgUPpsNBrF8fFxHB8fx9u3bwtucjUf+OlLBrqf+8g2yTFhxMc7qPMZ7+nYBH1Fjt3/xE6Qz1y7t7cX4/E4fvjhhyL71gMWIGesGXfHjuv1ulR1XV5exuXlZZyfn1fIFXCt1wVj0xPahJ1hMzRsKlMFwc0k00hU1cWtGQcj/xGb+Mr+EruG3csEk4sOuIfP4RmZG3C8ag6B9hlngztYn4573NzcFDIW3MOyCYyP2+PzwCNUO5Jcm06ncXZ2VqbH804mmCD5+Z72gpPqCCrLBOc6ZrDN55nWgc95fDIphYF00GUWmOCPBmegwAtizDIhZeXlGgNm7kdg4dJnnA1KyKJrEdWV592BBjcINvfD+QAWbGjyu5vwqgNRgLdMQiFAOLGc8eRcHwbWmRRBeR5TNt/XWQm/G/e0AlupTf5lgO62GLzQVwZjjKeJBAMRE2gGsAZxBFV1oNPEWA7uaQOfWUnpW8+75TOPDWNu0O8fnkFbbXg8ZcOfm8D4uY9MLCEHt7e3lYWGnfGzXjmTTV+5NBmDS6BGP1DaDlA0WWG5G41GMZlMCihDZnD6bGPOwos811leAiGeg+6zPhYVGwTQq9WqVP4AqMneci2gkbbidCKigBGIHAfr7mf6I6I6tdfOgL60Tnm8/Ld1hb7inGxvM4mGraLtXA/I5iC7fH19XdYIg+w7Pj4uwdV6XV0M24GtiUlPl8UZ4uzJrJOhOzw8jP39/bJ+hglDAxjbd+st7wuYcuDin699GHhlUNVsNkvwSBDh7KIrCCKqOyniI8fjcXl/ArzJZBKnp6cVIt5l4CYOTTIR9NBvVBhDUq/X67LTk8lckzMRm7J2LzLsBdGNExysmtTwmOYkl5Nh9s/WLc6LiBK4AcSNXXw9B5WNtMHTebL/ctVXxkLc15gIws/+Gttlsgfbir4ao3E+NnswGJTzPSUTYn88HsdkMol+vx+Hh4elLznwseBC1hyhH6bTabx48SKazYdqsF6vV2wzus/hakD8SrYL+fn0g7+zTctYpo7w+toH7XBii3dG55mGBYFxc3MTT548KT7o5OSk6B72jspAkgF5Cg366j4yATyfz2MymZQ2gc3QYcaKaml0FqKTygeu6/f7ERGFLIHAgdCxjfU29RApe3t70e12y/2pvnV1EXYETEaVSMSGqKW6BBviKlJkh+SofRzrHtIHfAcWsZ7iJ3kv7yxL241vHSDSR/gj+hG7hk1stR52VUOXTABw/fX1dfzxj3+M5XJZqqYgHRyg8pyI6uY8EE+s3RWxqZrodDqVOMZVfPTZ/f19/PM//3P0er34h3/4h9je3i6VXN988028f/++EkOYwHDl5C/9cByZSQu/h22TYxITUhzoEP6Z75AXqm7oe6ol0XkIEQhbyx5jS9UvibdGo1F21Pzw4UNl8Xs2HOBdwXTPnj0r8RqENoSa24wcg9mwG9ZbDvtTZMGxqAkjPstY2VgN7OIkuWPuzAN4fPBlxoERmymDjinNXbRarbIcCGtuEsc0m81CJhEvoHP0CfeADGKjhul0Wr4nbpnNZrG/vx9HR0flPRwjRESZqoz9pH3E1JZFr9FsmaTaE/LR13nsPvfxyaQUB0aMTCZAB6PKoNJgC0NEdZeFTJjgRCKqWUSAHs91IOppdMx9Jdi1omDgUX7akHdKMPNJ2zL5w7XcC2djY28AbZLOxgjlQTh4psm8HJC6HQi2FcvKnRXY5+S/DZ4zqVNHkkEa2Qjk8/N7uIQV0ACB2O/3K7sG0Ja6NltG8mFj8WPBJW01wPH4mnyyDJr0yvJg8oO2+Jy67ABtdPDhAObnPLJ+rlYP2VCcisETIMPZOL7n/QCNW1tbZWE/9JnscKvVqlRDOHhjdyeCRZwAgRaBUbPZLNO7IKU8p92yig2ZzWaVnVMcNFMtMZ1OiwzgOB2Yevop71knA+gWzsaOLQMZ9y+yYVnxWNmu8h0y7CDVRFldcO3MfV3FpsfG7WZHF+wvwAewC2lCddJy+bDmwWw2K1llgmTrEv1GgAYJ9d1330VElDVI7Dix7bnK1uQofezKRvyMr/m5wDI21vaS8ScLh5yjf67uRV4Ao+yQhI+zjr569Sr+7//9v7FarUoAwvn0iacQ2iYCWvBhThLt7e0VYhcf67Uec/YeO4HsOTHhrB5yC3GGXDtIrQsOIqqLptv2QuZ4amEONhgDnoPvoC9pvyuC0AGvuUeQ4PuhAxGbdR7wfWTTs3+1T43YVGfwHNa+yT7IMsWOXV44mXHC7iwWi0JOGVtAEvMe/X6/yNZ4PI7T09Oyrh9r/J2cnFSq94ybPE4GwdZLg28Oj2MdaDYuyp9/rYNneSqXq2to//b2dpnCBtkOVnrz5k1ZUygiot/vx2w2K2vHsNOVkzpOGtHnVGNMp9OYzWYREWVKmnWIvnKilvuv1+tSLXlxcVHegV2ldnd3y4Lg29vbcXZ2VogI7gWBfnd3V9ap6XQ6ZVoo/rvRqCZ5wB/WVwI63nMymcRgMCjrWHn81+t1Za09psph59B1nsXfrh7xNDgnf0xcMeXPMQefg1uc5OSdSNpZBzh/tVqV96FPvEg2JAL3ZroVWCdPT6f/sKs7Ozvx/PnzQiZSeQwRyg6A6Dzvj337z//5P8c//dM/xeHhYcxms5jNZoVUdTIZu8h4/BIw7085bN/xi9hpxoFEpGM1n0ufG18ZE5v0hXRaLpdxfn5eiYEhMpgibV9p8pWxvrm5KTvljcfjoqckcyFBIIPb7XZ0u92C0W1bWICf6aJ3d3el6tnxccQmAYiPMsYC/zn2Mjbjt2Mm23XLENyEfQL3t05xf653fMDn5iUsu/Q/vonv8KMkU4fDYdGd6XQaNzc3JTnDVHrGBo6DMQDfGbOwvAKbOdAnkO6MIdWbJtZMeDqGd6VVxlT0leOZHFt/zuMnTd9jBX0rUyYwDHgsXA6obBB5OVf+OPDnb6pX8lxPOgsG0WXyzrLxgxDiFL2FYkRU2sJ9XC3EuxsQopze8tbA2OSdlYz3MiGUgYD7lyMTG4yPwYT7OAfOBPqPKbUNJc7Q96L/HfjQljx2ORtk9tmBMw7NYJ7An3u4os3vzrs4g8f32aBlGaONOEcrpg0ZY0U/4wxy/1lW3AZ/b+BnOfgSCv63HOgQjgQi2kQwmZirq6vilLLMIQ+UrkZsACTZmKdPn1aMpA3/3d1djEajMqUIhz4cDmO5fNh5jzLh+/v7AtqbzWZZlDRioyMEd8ju7u5uIaR2dnbKQql2+BERg8Eg3r17V94l2wLWV7Bc8TntMRjxu+bDttS2i88iPl57Lsvcer0u1RrInStDGA8TZZnUsj7kgNykgTPRTB0hg4cjpfwcOVkuH9a0ODw8LJl6Ss0bjc10CohKxhF7ERHx7t27uL+/j2+//baAKAA0WWaCLt4FkGgChHHjO6YhMRY/x8HzabsJIYAhBPFyuaxdgwWZ4bxM5O/t7cUPP/wQ33//fZGFy8vLsoYbJKLtueUFcAloYgoIbbQNx39bDrkn02DspwCt9j8EuMgka1hFbPQBmYuoVqOi6wSVlnHa7wQTpCpT11z9YJnIlbX8ja/NwZ+JYhNPlkveO1cQONhgap91lnt53U3jJ2wORLEX/UeX3VdO7LEGFVvTW7/oo/39/RgMBjGbzUobqFg1+c5hm+b+s+3PxFS2mf6/7jv72TqZ+FrBsAMZB2HIOcTG0dFRDIfDQvRQPfjP//zPpXJtNBoVW8cGD5A5mVCimhL9Zw0wtpuPiDLdloNxoX34MMbABIeJ84ODgzI1hXHe3d0tz4mI6Ha7ZR0bdGO5XMaTJ0+i3+9XZAI9Rc8heegz/LttDUHV1tbDIuT4EuwW/R4RxWcwTcaYHpvJGmlcu7+/Hx8+fChYFdIG3w5RT4Du4Jz3cSUblS0QYvRpxCbYZC2ak5OTshswOo8NYRom7YiIsmshhD9jtF6vywLtroCCELu/v49+vx+DwaBUbLgq0zZ/vV6XCvPxeBzD4TBOT0/j+fPn8ec//zmurq6i1+tVFqZ3vEOSqo5s/jUcTqzwExGF7IzYYDL3tWMpfjs+8OYXrVYrDg8P4/r6upD62DFsOfbamJW28Symfc5ms5JkJbEQsVn+AMzEGDMtlMo7y8n5+XnxcyzKzRReV0yBZfkMnalLIGErsZMR1YID2srhZIs/q/vbPgK76xlV3MexJ/bOBJWrs+t8CMkqsM1gMCg78b1//z4ODw+j1WqVXXAZu0bjgYyyj3CSYb1el0QA8Qx9Yb+PLXDCzgk67hcRlSps+jaPkSuqkEsniz7X8cmkFHNCcQ4MjsEEHcqRs4yZWHHg5cDApIXBVMRmRyUz9DgOMgu+F4fJAYgosgsAL097wCHSVu5hUgWHb7IEQIow4uzNMvJu9Fsdm4vRph8zWcZ1HgMHk1aUrKgIn7Nd9I2BLYKZDYEzenbWBpeZ+DEQczUZzj8iytodmZzLwTtkIvKRZYpz/O6PBf8YHhyuFS0bP8YxGyoTDpzL+3oKo3XAgY3H/5d02AA58HKAygKlvKONP+cbQHEfz4+nioYye4JrZGU8Hn/kOL04IushbG1tlQyjS2idnQMs4hC2t7crhBaAnaA8YrPWEOWslLebuEV/sE8m4U3eGlDbdtbZmhxceVw4MrmF/Fp/rUuMnUlyyx99wnQLbDqZukyCLZebBVEJvFutVsmyAlTIIAKKOp1Ome5DP0IMWk/QRU93oJ13d3dxcXFRns+WuZA0EBgOGHDojAOy6PHIdv5rH7z/Y8E1h6eoU3ZPVpN3xu+wHgjrb1G59L/+1/+Kt2/fFpmlL9k1hmSL9Rr7OxwOS0k55AN6hy91BrjZbBawxQ/jgB+nWoC2QDxxOLEBAPM0PnCJwbizoSbycr/WBZeAf87lN/KFXuPDeL6D4+xTCUyxr26H/TA4y4GySbO8y459FvrBfdEJxpLz2QnTU6mtA9zPQe3bt2+j2+3Gs2fPSrtYX4t1epgGBC7CVmcA7+ytCTMqYO1PwVR1dvExP8/1dSSW8dLXOHiuCVlsFH7ReoPsttvtGI1G8fbt2wqJCNkwHA7j+fPnZZaASSLkhOlj8/k8ZrNZ2a4c+YRUcAU0FV3GNcvlsmw7z3iAZbrdbmUdMnQBmd/e3o5nz56VygBsL8QNlZmQkdvb2zGZTIpt83qRVHV5XCGhSFbZ12EbTf52Op2I2KwVhQ/mOeB3Kko4/+rqqmAT43JvmmEbiE4xzYnEC31Eu9DJ3d3dssGLYxySM7QLX2/5MiY3MeFqcmSKjWq80YzxBIQKeIz1tWwHwc7YLvrgj3/8Y/z+97+vyNNwOCw7vSG/9L9jjF/DYTtNsA4ONg7knTxVPeMKZA+/sbW1VVl0HzmCzJjP5wXrUoWM/UUeIPvQP+zdaDQq6wYyVsRc4CzWAKW9EFKu1G21WmWNI+QZ24NPwo6ZYMMuIQOOt+sKUzIhleOyHFsZ4/pvy3UmnCI+XtCcMY7YxGcuhnB8h645HrUNxsbv7+9Hp9OJH374Ia6vrwsZdXh4WJZF8dIF1p2IKJVXvMdyuYyLi4uybiN9QnzhJE6OWe0DHaNx8L35F2z9j8Umn+P4ZFLKgUkOot1IB18GZHSks5NcZxDGYRIDg0kHouA4GBTJ5AFttgEFVJmQol3MHbcT9SK8mWX0gKN4ZvttfCzcvAOGzCSHldBBFM7d72MFdLbfmU0LnsG/gy4rs8fQCmuW2z+ulEBg3X6cP7Jgoog+d1sx8GT4HYBwMMbOwGcg6jYY6LuP/T+Agv5hfDNJGlFdo8zBkYMKfqzo+T1NYhg48302Ej/XkfuPfqIaIfdZBo2cg47wfujK0dFRJYACGOF0z8/PCwgGrEVsFl12NQ0kydbWVvT7/bKwIrLEelWtVqssxkngzfQZ7An6sru7W8DY1tZWHB8fx83NTXS73QoIwBZFbNZ/4V55Go6vcTDGtSaNc5bJTtb9zvkORmxvbTdsM/geMIvMEVS7khNbRZtdveo1pbweBfpCeTJ2jHM4qDKDaEBGIDl8Du8N6dVoNOLk5CT29/fLlrmeXsF0P9qOHDqTZMKR/qL/vmbgmh0+/2fAsLW1Vcg0ZAwCkDYzNsgQGTsCoT/+8Y/x3XfflfNdpUYQgi43Gg9rv1AF4Qyh7T+y7mArBzGUrNMmxikiKvJG+9ED5Bt9ddbZ75mTPB4/fI7tevbBjDs+wIRQXsvG9pznYJu4Fl1i3ADj9DfXMsbWcd4D2wJRbCCZ+5h7NJubNceQD/6H3AAwR3ycsfc72VYtl8s4OzuL6XQaT58+rezIhU3e2dmJ4+Pj6Ha7Zfyy/7dPtHw42OeZjDGVe/bj9LN1xmOZ/Wgdbv0aB2NmOeY9wUcQrFS9kDz58OFDjEajIjuWcwJTSCrbdPqBpC27dRG0RmzGnQNMR58jw9gE9NxV96w9QoDstW3m83lZv4zzkePValXWnGF8Pb2n2+0WOXVlICTper2ObrdbSNV2u12utywiR8g9FQpUMSGnJmkiopB1BPj0D3rsJAp6k32U5dhTqJFr+gE7RPtZl+v29jbG43HM5/PSH7yXk0WNxmaKI4RGxCaJx5peJCZMaHgTBNYEW6/XZVoo57Pj2mKxqExHvru7i8ViUfqPhbqZanR5eRnD4TBev35dMIEXc+e8X8thnM//+FdvcGHbbVuFv3PyhHFD1qfTacGgd3d3cXJyUqn6ZyMB7IWJFRdcrFarMk0X22+/ATkMlqYykvZ6/dRMciM76P9yuYzDw8OC67Fp4AJ0LMdgJvAcGzmW4jzHp45F0amITTzA97Zp9m/2D9ZbY+0cn+e43n6t1dosP0Gbwb2Ma6PRiHfv3pVp19ghyCnjd/rQ8TYJeezxdDqNwWBQwarolpN62BfHH+4rr9EL9+F+92wH45XPHad+MimF0l1eXlYylwgNhjQTQQY3Bg8mDCKqQsR9IZAQYgYd5h/lx1EazPmZEVEyFV63iGdYCR0M8U4G4GbzDaJo12q1maZkoI2RcKCIA+MdMwjwcwgkeb4VxcSMQa5ZZoNCgzkHsla6bHR9Pu3OhCH35l0wRAg7jpSghiy819ZBFvJccxsMK0MdIDfIt+Fx1tWgjjH02CIHNogovbMP/J2PTJY5a8fY8J0DqjxuP/dhEhnQC6uPDmIonSEhC2vH57GALFiv15UMC+N2fX0d4/G4ABpK6B14AtBpI6DWQWKr9bDLECCZsUSGCOIx7rwr98I2oD8s5P3q1av45ptvyno6LGDo6XLYANuNbN8iPl4jxcSUnWrWTTt4yyBOIx++zmSv7XauXHAbCMZdJcb12K6rq6uYTCZFNkxC5d2NWq1WxSbf3NyUcUYe2u12XF1dlS3naRd2iEB7sVjE+/fv4/j4uLyvx9AyCWjDuZPFssPlsA5/jcPykQNqxtntoX0ENgAcZ8awpREPsrG3txcXFxfx+vXrij0zkLGeUAFhkhSwRYDkDKmJPYIt22ivueLAjvE0qONdGBf7BftE5AI9RTfsT23PubdJqOw7vbseJHUG8xGbyg76mjHDHpDl5joIYNrpRJHHyb6H98xkTk5gWEewDU7wYJcIJrgHQSR6iX54qj/2n+P09DTG43H83d/9XdmSnvUwvGYUY+osL/JgeaL/POUxB+51yblM5Fsv3Cc/9vfXOBh/bA6BnUkO+oupNZA1JycnhaxDpj1VDoxJEAk2Rh7n83mcnp6Wqe1U7VA550SeK9xopxOfEQ9TOR1sNhqNGA6HcXR0VKZ3Yv/X63Wcn5+XceZe6DSfr1arQkxQxbNer0syibWhWI8FXYJ0xbbhTw4ODorf4N0ioiSsSB4R1HstQ/w6Uyep5m61WmUdVCeMnLBxAp3PaRdtRf5IgPH83DfGD7SDKmRkCkKBqVWuMMROQUihR/f392WdTHbyoorcU868Jg3vsL+/H5PJpBBTjrciolLl9Zvf/Cb++Mc/FvmgT/DDEVH5+9d02HZYd01mmABhlpHttmMRdGF7e7tMn0O3z87OChHLOLAMgp/nJSTu7x/W9Ts7OytTPtFJnoNfdEIJeW61WmWH5H6/X/QJnHZ+fh6z2axMxyWxNxgMCmZ28YcrZo19zQvkmC7HwhHVqWUmnBgHJ5JycoV7OnbL2DzHdrZPvqfP55k83+cZDzSbzXj27FkcHBzEn/70p7JO6qtXr2K93ixjgl3D73vsWE8K0vLq6ioajUb0er2KLvq9jfXpA+TVSS4ISiffWFgfnfZ7fQn/+ZMqpTDiBswMQqvVKovi5cDdQpiDfBSXZ2RiJWdhDWCbzWZh7bk3htz3g+VzhVXEptqGgIosjYkDlIt7G7jhPExC2egYvNJek1s8n+sAbJzrMj6u9TQfGzbeywEL75mnUdB+kyEOdu1M/R4RUdplgJudIA7UYPnH1u3ymBPY4hBt5G286oJFzsll/1mGAdtWLpOaufTb19vZ5GDG7LYJgAyQ/R58FlG/cPXPfXhckQXkGSe7Xq8rxGqWJWQ3YuOEcHZM3/LC4BcXF/Hhw4ePxskODPAGwQnAQ5ecqfU6MoztcrmMg4ODAuIgTNFj7zLE9/P5vKxh1Gg04vj4uBAbEVGp+LCN4W+TwHXEgoknf861HPRBnWPITpT7eAxzu3wPftNXyDMOC6dk54t99XQiHJ3JYXYwQz5Yy4ugHQeIDcDms5YBO8QAbugTL1BN1t1g3LvFODinTyA+8pjQ19nOfOkj2waPTQ6kTRrX6R0LJlNBAcB4//59IXTpD3SN/vVW9GSB6Qv7BmfNbKsNJk0mIS+c7x3bIuIjGUL+sCMOZpAXzjPRaLIc+XXVBDJiIJz9FH2KrJmY5Z1MkuM/aCs7cXGep0P5ffy3kzL0Wc58GyQbE/Ac3gMiD3IJv0ybsLs7OzvR7Xaj0WiUhVIh1SAueDYBNOP3P/7H/4jf/va38fd///eVPoF0yfJlO8zn9i32kfajBv+PYR3buqw/Wb/r9OlrHDkJwfhSSfPkyZMYj8dlfcPT09N48+ZNRERlqhALyDsJAOHihNvV1VWptEEe6G/8dsSmSpHKDP7nXGNlZJRgtNvtxosXL6LdbpfxBU+znljWY8iqiA2B60Qu4727uxtPnz4tPh85ouKS98CHcz9mP0BMgSexEdg0Lywdsal0xh6QuIKEzwEpnxvXNhqNyo5p2AsqKajyZeHoi4uL4vOQ9UwY4i+Hw2Gcn58XvWeMIMAZq4ODg0JUnZ+flyDW8VFExMXFRdzd3cXvfve7gntc4WzCkhiPqdtMB1ssFgVnNZvNkrxjUxoSdxGbzRjc3xkT/xoO7FeOf/xe+JyITSLSxIGv4/0tK/f393FxcVFI6larVZZDcMWMsRhY6927d2Xts3a7XVl/EXKRe4LrTJjTftYIbTQahXQCDx8eHlbWiEPnIB6Nfc0FuECiLn71de4rcwMZsxr7gl1zTGJMnRMYmaDieTzDmNM4Psdt9KeJN4ovKOhZLpdxfHwcFxcXBasQW7C2nmMu7Mv29sP6Ya52urq6ig8fPkSz2SwVU+As41fjXtrAO7tvTZK6WIj4iOd+KWLqr9p9z50LkKATIj7eYc8VLmb/8/eZaLFjNbCg425vb8taMzl4ph2wtQhKxKYSh3NRSFcJeCANtDHYKDDvgbOyMpCJsjGg7Z5rbgE3OOV/zxelb13uyHdujwm5bABwiFYwBzf8zgGRDws77+22mFU1+UffI0comhUbEsBTQTgYR/rAhJ9BuYkQG0L6JWdoI6IovqttaHOds/F39IODQnTDSm4CkLbhfBgvG8Of++AdIjaGmKoX9JhqJF+DU80VYBA3rhwk2D04OIirq6v485//XLamZ5oPfZPHfrFYxGq1qqwrhRMFkDlYXK/XlcU2sUdM3QIwEiixmwUAC0C8v79fStYPDw+L/FomcCb0nQnzHLTTF5mQyORrJjnzeZZf29dsY7JeIbPO7PE8/kenab/tB4unUkkLUQQJTGDAAsnIBiA3Iirt3traKkAHHRoOh9Hr9UqmmoPvcdDz+Tx6vV6FRINoHI/H0Ww2S1WUExH0g9/5ax91YCjLBId9KFluj0tExOHhYQkiscGA3E6nE6enpyUApR97vV5Zs4TADZmhUmC5XFZ2GfKzTWQiNyYEXU3Ed+iVpxThF1yRYP/BkYnciCiAnWoPA2IO/I+TDNyPabsQUNyb98nTDa1L2HB0ye11Rbl9sKs0vEQCQZ6nKfMuJvqdBKRd9D/TBOlP2ybsLtMIHPgQ7HutIm84QPb24OCgAOrf/e53FXlBTvJW59hw+sm678CDc9mp1dVt9F8dAZUPB72ZkPpavtY2Gpvk6t1G42GKbLvdjvF4XNbde/XqVYxGo7Kg+fb2dhwdHUWz2SzLVoAfer1e9Pv9UvlLhYX9LPqAzGTMm6sLvVnF1tZW2VkL/QLPes0TiCj8KUHx1tZW8QXcGyKLezBdGNJ5tXqoAGWanolzKkBcgbe3txfT6TR6vV4cHR0Ved3e3i47XvF+yLATU7wvMc5qtSo7aDGVH2yADGIrLWsE5uAN42z0D5uJX6RyAozO7nW0n93XWNrA8QY64vVpmM6Y/T2y32q1ShLnz3/+c/zud78rlVOuWs5+ERk6PDyMs7OzQi43Go1SxUG/M73X2D1PD4rYJCN+DUeOEbGVvD/kvJN7ngJqDOdKSe5FEhVfjaxAGnqKKvE4a6Gdn5/HmzdvSgJvtVoVzGQ7ALbAnyGL3AucbrzODAam6jYajej3+4VcdUzreDvH+jmRU0cUIXPGEvk7238O+3hzCNaBjPcy+eRY2/YlYjMNDhuaK3sjNrEk8Tu2hI0H9vb24smTJ2WHUdbmPD09LXpBXAG5zXu7etWbP71//77485x09jt67DOxZLKTnWG9LMmPXfu5jp+0+15ElPIuwJIdGx3Abwfn+buI6hpKfJ6FNQsfQkqJo0Ge2xmxyRiaLUXAI6KAcXd2FnBANPeL2KxlYjIGh2TizRkH3heDQJ8ZMHMfgxQcG9f4GQa1uV/9TN7LhIzHKys7QYIrvzKb7ekE3BNwjiIaZNogwgwDtHO/YzwwqjZyBF+AZwx0fkfuk8fTwUk2aJCLlgmDSBtcBwQeEw7ahywYMOdnuu8zifdLONwfPtAvMokRUQGp9JtLg5ENnPV6vS7z8BeLRfzpT3+K8/PzMhasC8X0IeTLCydixCn5d3WUHb8NLmtK3dzcFKICvQDsARYZa5PxBocQz1SPuV30E7agzr7lADuiSjJHVMl4jjq94XNnkrKs8pntc84SMXZ2xibf85QsxoAFMQFBBlmNRqOsD0Z1VLP5sPg1pBb3cUaJYIEggGlVFxcXRb8ODg6i0+nEfD4vFQOcT7+zuDdBB1Ne2KEJe8vvTBp+jcM+8seCbY8100LQL+SU/mddkMFgUBbtZcqKyUrWcCH4RQapcMtTaKkQNoHvqZmr1apMGUHu0ds85d7AzZ8DkCGtAPwGzMiqiSCDf/t//BrtJhCyvYaIqfPfrqwymWr/zf2dWHF/oncOYjOGMb6wjptMyoEN70xwxLVOatmeOIjf3d0tO6FaBqk2HY/HMR6Po9F42FgCcI09bbVacX5+Hru7u/G73/2uBNnIAhUpgHzkKWJTtclz3U76wMQhY1qH2eyP+Z1t6NckonzwXOTA01lYf8eVLl537erqqvQzO9RNJpOCoQgivcbPxcVFsbdUYpmMwT5Ydt0/xi/ICLjOxJXJaXy9F+IeDocVIsIVPd1utyQbXFWIXF1dXVWq9iCKrYPgTsjQiM3i3vgagj5s/t3dXZlualzHPSEH6QeqjbBN9CH9gP+k+iT3jzE/dpL/vS4VftFEFraEvu31enF3dxfj8bjEJ+hiXmKFqmMIXQg5E42MzXq9jh9++CEmk0l8++23lencEVHWnVouN4u1393dlbWvnHyiUgrdXa/XZRMZ5Ivg3omNX9NhvE+sg61Dr/F1nU6nEJcm1ilcIFFnMsTJMjBRq9UqZB/yjqwtFot48+ZNTCaTaDYfZiPgZ6lSs35DgEZs1oRj3UjkAn8NuQpmOzg4KNPF0B/ILdugujgrExuOL41rbMPpm7qD84yd7R98nTGVsS8+2W0zd8G9s39pNBoVf+tiB2Sf6/iOheWbzWY8f/48Xr9+XdGD0WhUrj06OqrMWkLntre3y5Q6MPpisYj5fF6qR51syLGsSSjskElr+szJRFc0577/nMdPpqYBuc7w0DgPlAG9gRODboCBgGRiwwLE4ax4xGY9B76LiEqGlHN9P4xwHgRnMvyMbHysRCjAarWqLLxL1shKy/tFbLJRPCs/O5fY4mQNPnPQakXLCpbHAEfnYNzAD6dLBsTPM6HlMTJZk4khsgG0i3nrBkLuq0ZjsxCxy64JRLjWQVXdT+4HxoxrMEp2IC7RNjnldzM5x3nZ+OHMXd1jWaYdGH3uYfLg5z5MFDC+VDfQn5BHvV6vAk7pN5x1xEaGTB7t7u7G5eVl/PDDD2V7Wa/NRGA7HA4Lew+wYlFVABd97AVjkT1XZ7Eg9/X1dZkj/+zZs+h0OpUpyHd3d3FwcFAc7WQyiW63W4KqyWRSwEJEtdKQ9wTEG4xwDqDSDtgEfB1xikOz/ULvHVBEVKdZGQAgw7a1OWitCxbtrBy4EGwQyG5tbbbjxt5yX4h25Pzq6ir6/X4hG9Ad7muyzyD9m2++ieVyWcB5RJTtqyeTSRwdHZWgg+D7yZMn8ebNm4puRlQXBWZsv4TD/ZTDAdJjRyavIPkt4/v7+2WtLMj8s7OzSpaVSoStra1KybgTOs1ms0z9wRYzlR6bgE2ezWaFNKGNyDfBEdOOHBgx5tgNByoGrATpDgjxjXzv6kj6xnpHMoh7uQ+azWYlWLfvQFe9RqKzwyZsCSoNSm0PLMuuzM1V0hy2obTToNPkGosz+x6+lyvDuS/ZV94Xu0mbqHZiS2tXXWED7u7u4sOHD9Fut4svIOjytEgfDnqRE/qRvqOtOXDJf3/q8XMFvw5UvfZRxGbnR/tM+oPdZNvtdhwdHUWj0YjJZBIRm+QI40DSeDQalY0KGAN8NnbVcmesFbHxYyQZCDrx/RFRplaTjGBaCevauULCZDQyD2GGPcc+8KyDg4NYrVbFLkGwgKObzWZZW4qKH+MpfphiuFwuo9frxXg8LvK+t7cXl5eXJRhvtVpxeXlZ5Bsb4QWJvYMlpJOn8Dlh70ojgnk23YBgILGH3yH52ul0ot/vx3w+LxUVi8Wi2Kx2u13WCGPsTMBD+mKvnSjnmWAcY2J0/OXLl3FwcBAHBweVOMC7sV5cXJQk0Gq1KsR2RJSlGaj6w9bP5/OK/2Jsfo2HiUYwrW0rvtGVLhzZz1lWlstlTKfTYjshSpENEm3IMdVRy+WyVP5RiYkdyX4CH876VCRysRU8i0KKy8vLuL+/j263W6l+NdFhXIkNyzjXcSN6bNue/bXjrXw45vW9jbFz7OvzHUObGMrn5hgyolqBZKyC3YG8dfLLZHaz+bDR03q9jslkEo1GoxCO9/f3cXZ2VpIQkFDGv7u7u9Hr9eLi4qKMO2the5xNdObYAgxlfYyoVnu7T91XX+r4SQudR9TPwzQbmTvA1+bvuY9BnwMoB7AZoGFIMf7OYPCZn2eASnksz6eNdcSFwZ8BXq6CcACFAyD77/bQjlwJw/+ew+12ZbDOPQHo9FfuKz8DAaWtCHgm5xy0eow8NhEf70THfX2dA3GXiDabzcouThhBV7LxDPozy96PGSsO+gyAwP9cA4HpijvvbsLY+pmMex3xRZ97eqjlOxvI/D/v/CWV/qceEBAQg5TdI0PZMaGH9LWDVFdSOHv07t27ODs7q8gaIBrZn0wmpXwZ0opFmNEJ68/NzU1cXl4WgHx5eRnj8biAa5fP8wwcMw6bSi2CUKo1mL7HVDEcLoE2fZDH3w4O2anL5GRH64PPMuGaiYqIqGSVeW8/J9/TB/aEaw0gOR85JRC3/qNbAFMqdrxuxfX1dcme8rl1lgVneYZBAKCLLKCJxOl0GkdHR2Wtkevr67I2lYPpHHzjV3Ll79c+6uwvf+fDoMeJn2fPnkVEFPKEgJ+Kp52dnXj69GlZvwbgGLHJoDn5YvLC4I2MHfYfu4Ys8Hez2SyBI98BrH0O9o8M4dXVVakise5koOQspW12Jk/oR0/jzVPBwRQATBMk9uW02Ukdr4XlduBnkGNsAn1gspCDd814wQksYxPsLu2ybeAa2w9sKcE0Y8i6UfZbjUajVBteXl6WtYw4L2JDCjs7f3NzU9b/8bpklmdkyTaMd6JK7seSNVkvclDjvvy5DuMFkqYQGE4yOHijSgpsSN9zHgQhVRGz2Sw+fPhQNu3gemQZ0prxRV54JrZvZ+dhBzbst+08FV7gN/AScmRiCsyJTJEAev/+fXz//ffR6/XiyZMnMRwOyzNub29jMBgUMtQ+F71CPx3g3d5udm5muiDknas60Q/0GJtDhZerTHMyC3zv5DgYfLXarEXFOzOuJNPAxK5yYAqjSUr6CnsBBnFi++nTp9FoNIoPi6juuoWss/18xEavqKZguiR+EgzUarXi9PQ0Dg4OYjAYVKrhsFXY5YgHu8g6nldXVzEYDMqMGlfb5EDfGOjn1M2/9iDuQSbpH3Ta/gr9cpzDefaJYGYSeZ1OJyI2a5YaR7Pg+Pn5efETvj9rQmInvKbi7u5uqY5yYnG1WpWqOHwSiWFkBZsM7mApnV6vVzA4+M/+w3JZ558jqtVI9m+WE/tOPq/jGxgDPvd1lsGIKlY2rnbiNo9njvt8NBqN0l/ES7SJ57NxFJWgxD1g9fF4XKoRh8NhXF9fFyK+0WiUqivIfmZ+ULxhvXI/OyGVsYjtEJgvxxmPxSaf4/hJpJQH1YpI0Mphw+OBf+xlHIh5wEzgkLGIiBI8APT4zNkRnudMLMEJTtrOzUQPz/YUDmcyHXzbsFqpCKgymZEPkxqebmQAlfvDio0BoB/qwKeJj5y5IYjxfG4TDCZPshK7csjlwxGbhSY5l2DHATIO2kE559gguoLCxGMmJC1X7sc6GcuZcI8FoIoxcbt8Tp1c+5k8j3YDWvJ0Pu5n+fslHYyHAx5XCyA7rgaixDQiSjUSoJLx3t/fj/l8HqPRKEajUXG2GGhXr0AAYbjZDQSARNaTg7aSCWUnv/V6swsK98dhTKfTir1hjACdOzs7Jahut9vlvSiXh/gyiM36Zxmx083BmZ0pB3IU8fGab9ZLE7s8k/fN52f7nKs4XC1Zl8ECWDP26/W6ZEKRhfv7+1I5YcLX8+zH43HMZrOSEWTKCOcAyCEYAcV3d3dl6ofX9UOujo+PK+CfzPn5+Xl5d7KDlhtXiX1NsFxnQzJI8vcmoUyYLJfL6HQ6BdhCENAX2JqnT5/G3t5ejMfjCmnjpAznoofNZrOsMxSxIYMgGAlYkaP8fvgrB0YO8uyP8OEAOOQKGcceOyMbsakm4noTz/YZfE71CoEk4MwVAoBs8ML29nZla3rbPghPiCjjkVz542pGZ5wjqr6BNnBOxiY54efkgM+jv00Y0HYWnccWY+MhRFarVSGZnj59GvP5vKzrh11stR6m8h0cHJQql7wQNG00XrBsO2HmMWG8ObJu5sSV9YfvHby4LV/6QH+dnIvYbCkOLiCYR07v7+/j6dOnsbOzU6p8uBdT1lerVYzH43j79m2pUHHFmae9RWxmHIDTICXW63WpMs666TFAFqbTacHmW1tbZQ270WhU7vvmzZtSfYM+Ud2KvbcO2VcxPb7X68V0Oi2kB/YAH4Fv6fV6ZVrRs2fP4s2bN0UP6VtXQpuERz7AnqvVqhAt9/f30W63I6K6UDfjwDRDqq/p48Vi8dFu5U6A2nbznXEmJK6nCuMHIYzOz89jMpmUHQVvbm5KosdkOeMB3skbCYGn6Atw08XFRXzzzTcxGAxKNdzl5WWxRfTD3d1dWQft6dOnZUYNMm+7aTz5c0yV/1v1vi6Wxca4khT/wrtm+8W42BYhfxCkJhGwhWDk9+/fF9zKWC6Xy0JWzOfzgrVICkRs1iq0r1itVmUnN2TFthufbr+7Xq9LdTR2hoouE6joZ0SUNuTZFBnj5P7Kfo9zTbDwLpYn+wjbf/7PCUgnfNwO38vtysUZHousXxBO8BXocUQULBERpXL19vY2ptNpjMfjeP78edm0x5s3DIfDUkm5Wq3Kjogk68FcOUawr/QsHvozz/aif8BLX+r4ZFLKbCSVErDyERvwaDLDwmMDnAMukyj85nk4KhNHXuyQ56OoXtPFGd2bm5uYz+eVRcMMEN2uHBA4SHOVgAMYT79yYBARFQXBqAB4nVHlGXn9HQdHZtgNcOwYAHPu5zqGmcOBax2ZyLW0xcJs4WTMMvHieca0hWdxH1cl5copMtrOXrsapc5oGPTxucc3yyDf20m4QoNzHDzxDFed+R1c/eT+5L0MPrg+j80v4cgBjzOfLleO2Bh9kyKMPefSJ4CTt2/fxng8LusmoPPcz7LsKUVekDFiM4aALoLn0WhUKphYM4ddO5vNZtl2ezqdlmAZO9NsbjKwXmiVLPPW1lacnZ2VxQUJSJ1N5cfBBO9m24BNcRVKJq2yQ7WT5sh2zEQggNyyaxl0O/ibvvUz+ZughfY6yGQ8IQebzWap0uGZ9BvkHtfu7e3FcDgsNhQgw3NxyKyxwVhwH9ZVYkHgiCjl6Oxe5L5CZk3O+72/1pFtav7uMaLMPpP3cUaTChaXgPd6vVitVtHpdMq0C0+lILGCLWbHHfQTcojFr00Yua3oA3INiYsPhGj2WJDgYD0WZIt3J2lgfUVmHWxirwxosVecA5Zwm01wGWA6sI+o6pYDXf4Go9if2Yd6uupjxAljb92HvCfAzZVcTtIYiLqqKSIKQTCZTCr+FKAMIWX5Av/t7+/H8fFxbG1tlXX5PG16PB6XQIhgnWnWZHu5n/sQUMzzc9IzYzX/tt2r+8yB2Nc+sn1ZrTZTRpFfJ1GR862trWi32yUp0+v1KpU3zWYzzs/P4/Xr1xUilD5qtVqVNZ7oG/siy1NEdTkGdMwVOHwP5jRJTGBNVv/8/Lz4S+Pera2HXfvQDXS93W6XdewgpAiYsSPGrBBiJK5evHgROzs7cXZ2VtnBzokT23vuQ7WvsfVqtSqBNptssHMw44d8WrcYN2ZLjMfjWK/XhbRBT4ihSK6huyZrTDSAY0jCUc3daDwsOM3OmdggYzMqY4iHsPOsh5ntBrIZEfHDDz/E1dVVdDqdMiUJ2aNvGS92YnRsFBGlnXX282snYz8HGZ1tM2OID7QNzzpC3xp7Ya9cNOEY11j75uYmTk5OYjqdxnq9LjtugonwjxEbwgnZB6uBsZmein7hF129Ay5HflarVYxGo7i7uys7R65Wq+j1emXKIXGfd2W0jcn+CVnKftf4NhN6nGc5yvFTnQ+lDyDKaBP9RTu5n+Nn/C3ynTkC3sfEvskp/CEchdcxxt85fmq1WoV8/N3vfleJ+TmPqXu0az6fF2xNFajbRR+7mp3nWUYy78B7Og743Mcnk1J0Ig7F6+4AGiM+XtsEgIVg5wDIpILJDn7T+dw/4mMSi6DHQR8KsLW1FZeXlzGbzUqGpY5gyYFgxGaAPHXF2SKDYAJS+gphNotrZ0z7CZ4jNuWZmejhWfSHgTP3NylAn5gQyQANwcIx8T/9YhKnjiQxEOSg4oB+pGQ6Ij5aC8BTNnhPAmZYYmQH44GxpJ2uinNfZYLKh5XZjsDylIGz+59+9hgaGFtpnR2m3Tw/B582BGbefykHztbghaogpkYhT8gxY22SI5NbsPFZpwl2HbDs7+/H06dPo91uV85jXFgYf7lcxmQyKQE5gJhMH+NuRwCI81QyZ2t5L5yMt+jl2dgg2zM7DsuZK/98PkEF57miM2c3bP9yf9C36EQmSN0G2xXa48M2z/bb64o5aQEg8ToltIXthO3wsIm3t7clA8t1vBvVAxBWgCcW5US2KFenior1Qjx27ksIA9oNufK1ySiOTALmzx87fizYpg/ZGIRgyKQkRC3jjz9cLBZxdnZWbBnPYswInL3DlYPGiE1FrcmEiE01k7Nx2AJ+TGhxD54FGWUZ50DHM8GDzuZMqr8jCWZsYPnPABMsBAZwMGaCJet7RBUM21846WIM5USO+4pxqQPHjAG2tNHYVDZfXV2V3bJms1npg36/X6qe8LfofKOxqXC+vr6OXq9XFj93IM29WRuIgHU4HJYdxRyo+D0YQwgLbEFdMsl27TH/z7jZV3/NwzbeQSxyZ1KOvrAuUuGPf4PcWK1WcXJyEt99910J+h3kQPyjN8gMQSJT1tDDRqNRwdGQJ/b9ri7C3wwGg2g0GvHhw4dYLBYla++d+lgzChLGa9hFRKUqhKQXfXV3d1d26aM/sWGOEe7v78u0Um+Kg/xRhWtdIXAzDqAqBBuJDvR6vQqxjw7gVwj28S1Mt2QNGKZPu/IJHOtAkH7FtpLUyTa40WjE0dFRnJ6eFt/57NmzooNUc9MH9tGQlRm3gzlYr4wq4u+//z7W63UcHx/HkydPKrEWlbiMxd3dXankms1mpR0ZY/wlvP6lDmPvv/awf3Nc4Ngi4mPizX7J0+nAJ8QdyC/9SbXqbDaL0WhUMC56HLGJ09FZCGX7PIglsHdEdU1NyG7sMrafCq7ZbFZ24LVdhpjChjhJQV9wH77PmDSPi0khj53jT+7jeC5jY+tNTqA7JnE7PU72LW43nzFOdf7Xus4P48vz8MUkG2gnvmBraysuLi6i0WjEy5cvy7Wcs7e3F/1+v+zGCsnIBlE57jLutk21P8o8To4hvtTxyaTU1dVVZYtU7wbhwcMJ5IE0+WKniFGLqK4ZYuBLBzWbzY/WOuA8SlHzfF0CHQcbdlRm8ul4B20eHJwV9/bzMMZ8BvDjPIIjg2srIY7S1RWeVhBRXcvKRgah5XMrFEDC4+F7W5ENvPN7m5jKwSkGjHdx9RnO3YSfFdmKwDPoS19HUEoGl2dheM34ZtDpd/Q9eVYmszDoLCppZ2nFzs/j/g4c6gBwJqOcIaP/TOj9Eg5n4gGJDkLu7u4qO0uiF+i9CQbkhXnqLD4K4KUUHkO/s7MTvV6vZBjpF4wvzh3Ad3l5WRb5bDYfKqG8VgSLnC+XywL0PQ0WPWTBSBacJPNj+YEkwWAj+86GoJtknbEROUg3Mclh22fnYqdoJ2jHahDENVkveC7vgq3mOjux7LRtq3iGCTQ/x3YW2w9pSN8A6BuNRqls5fPValV2RGKefrvdLoSFAQGf39zcxNnZWfzmN78pssgYHh8fx4cPHwowh9DM6xb9HMdfIqQe+97nOVhDptg+GLJhvV6XYHMwGFTWibu9vS3byXNv9BE5J6NLObqnr/F8Ew3OYiJn6GFEVKZ/R1QBPj8GdfhT5CO/Lz4qopotzsQx3xtjODmTbbN9ANe6Ha52MFmYgTHX8Nt2jXPw7fYvriownsnvxPugt/b56DjBOlvPU7367t27EtBQZcOYRlR3SGw2m2U6tfEZyUB251osFsW+2jaapDb4rcMedfJf938mqvLxl77/UgfPspziP53wQR6ZUoWfskxHRJycnMT3339fruXe9sGWaaplWKeG8aLPuY7Akuohxgrf7UAQe/3mzZtCuDgBTcKKhZKxIcPh8KP+cCKQ54M1kE0SzYPBoMiIZZoxxbZg45hFAVnkADUiKueSoED+mJa8XC7j9PS0EEn4NYgb8A7vgD0j2ATXMd3GJBaEmO2ySXLaZ5uJfYIYojqCOIUp1RBnJhsiqhsNYccZt9lsFq1Wq7QVu/727dvY2tqKp0+flliCtkOSEUzjp8FlkHTGMXX+69d44M9y3ItfNCbiHDAmNnEymZQ1xlwlhZwtl8syZZr+g9iChARH9/v9Un2IHIF/8WOQxlRXgQHQx9XqYYqufSDTQ9EnV+743fAXyLirAfFN9nP4NjC2bV2208a8GZva50ZUq/zz/XxdRFT8Ufbd9hmekp/jvRxD22Zi89frzS60EZuKZa//SQXb/f3Dem3tdjsuLy/jzZs38Zvf/KZMpaXvSMgS3zhpbBzhhJvjeAgu+17H+jl++FL6+smklKdPOKjCkEVUV87nJVA2hC9nUD2gBG12Shi7iE0Wib8ZcDORKGCr9VCqDkufq1OyIDGIJimy8jjY8ntYeA3EnTGhvzLAs7Jldtb3RvBoM79NsiDgOArOo59pA4eByHq9WZybezB+Box1fefxyYGzgTnnAVpRTIwqCoEzw0DQbkobUS4HwA4+bKggGlDerGQcmZwywWoSlOxc3Vj48PduT5aXbMCazeZHWYSf+6Dv0XNki3ExwLbxBazkQB9gxW4yrD3CznsYUzIxx8fH0e/3K+tekOHDwOIo0TXbIgDkarUqgbTBON8RhEF8QYZeX1+XMvvd3d2yjoerx5bLZQGuXkzbYNIOgsNjTP84wOBzB4B8ZvCTZYx7c50BgzPhnGOddRWpZR1QDijmOsuwp04SPABEAPEQgEyV8EKo9Cs6QOUa/YbjfPHiRRkn+pk22ubgoLEBBCS9Xi/evn0bEdVMJwCKPvjaepjH8C8d6IkB03q9juFwWCFX0QUqAA1+uG44HMbFxUWcn59XCEHugW3CX7rfIAwJsAGYjAM6iDwhJxDw+HUWY0VvCAwhH7P8Wy/sD/jMiS5+R3xMDKELTCNCT7D/Wa6QJxM/9LH7NSc1wCW8g3FErt62XpkMtK9HJx3cc2/rNufYlrO+G+NKe6yb9/cPu5+22+3o9/sV4oP2odfYZSpCVqtV2TmMadbr9boses5SCsiQSSn+dvBtGc//c1g+uO6XcmTcRl9TvYMuk+iwzkREZW1Ngpjz8/OPEhWcayzFQuj7+/vR7/crpD96YV2hSgP9oz9ZAwo77ApUsAAEyN7eXpluhi+HvLFt9Xpm6BK6TuBrjEwF7tnZWQyHw8oaUTwrYqNrxnIkzyI2iXamCUOKMUboP+8OZnBCCR0xXnegyDl8D/aBeEIvSYCaeLJuoa/L5bLsEgiJbHKe6jn6Ynt7u0yDByN5Z1CquJgdQF/ZPvNsE5HfffddREQMBoOSKKbSo91ul+m6d3cPizRfX1/H999/X4mxsh11QuLXcjBGVJ45RjKWiKjGBMi37fnNzU2MRqMyLTP7NmR+Pp8X/Esf46+63W4pYsCfeaq8E94k71arVfFJyC1T8pEDpvbd3NzEZDKpFGLgR3kOMuvEl2Np7Ekd8UEb7F/y4URL7lfuadIp+/6IzbpJXGe9dRv9TA4TZrafOU7Gjvk68K5jS+RkMBjEaDQqUzKJdaggpR9vb2/j5OQkGo1GsVsRm3VEI6IsWeL3pj/87vhYfmPbeBdPwXTijff5EsTUJ5NSGA+CBoMtGscW7XYSAA5nJTxwEdXA3Qf/m5QwoGOgEDpnFMkQODtjYSZIysKNUDor5Hck4LLg4uwQAPrCwmrhy0GkARRgJVdUoaQ8n98WFDtH3odn4GBQDA4bCp+fM3Y4z0zC+J4OXv8SKHS/ESg6a2VjzOdUwpB14jzGKgfp+fkmf+gfnpEV0eQo7+93sj5kOfe45jb5c/eFjVyj0SiVf7+Ug3HKBttsPMCTvoRYYAFh+huABGEREWVdGwwyBODR0VFl+gFtgKCk7J6FzF0FQgCErYJkgtwG1AHe2P4WecZ+4Aio1OIHpwFYHY1G0ev1KgtDYisx7iZ03U+8Mw6dA/vj/zMZlMknE2IZEPFsj6HtXNZB7kmQY5LR2TR0BPBjEoEgAfDD9xEfZ9V4JhUa4/G4tIndpQ4PD8vf7NiHs+c5rsydzWbR6/Uqfsttz2SHbUlEFZB8rcO6n/1E/pv2EcASuED20C8AX5fP856dTicmk0mcnZ3F+fl5sXee6sd9XFHF5yYsLZMRmyla+D7ajT5DZiAzZFUz+cDfZHIJsG2r7dfxL3xPW2yL3dcmeyynBEy8B/emmoK+NaBFriKq67BhI7EdXtcqA12uyXgDXUOGeT/7a/u2uoSM/Z2DX4JPyEUIuevr65hOp9Hv90vlKbaYrD596sVYJ5NJjMfjUlVxe3sbh4eHMRqNYjwel3FlHGzTTG56vOr8+2MBTJ0+1+nWlz5sR21PkS0HrxBVTFPHltmmTiaT+P7772O5XEa/3y/nGedZ751IIZDlmbkqEBnDBjPV0oSvp1oz/Y8xAytg771mEbbENowkBG01YeGgeTAYlP4kIQHRhn2BgIfU6/V6EfFA0hHwe41Lqnewo7QXfTKZi+40GptEOGNru0IQnxNJxAQOLn2gt1RmQzL1+/2Co+hfiCmmVm9vb5edBnMCjDHd29uLg4ODuLq6isViUVnz0XgfrERbsDUk27APr1+/jul0Gv/iX/yL0vdsX0+iDsze7/fLtvcmUOkvMMKv8bD9wP56vdWIqp1B1jx9CztA/2JbjROZssdYMSas4cQY2G6jP1Q7Wg/RY/wkvpvZLVTugLM7nU5lsxgXMaxWm0qdxWJRWVMKPsDxZV0fIv/okX2qbUad785YwYl/rrMPzdxFHWbhqGuzz+UAe0H+5VgQ/Eyszn2w6d1uN549e1bkBx10IQ62Hb0lOcv9SeDhk5k27JlM9HPEhpzzd2AR9w/Pz0mkL+E7P5mUMuDhpXAmDq78gq6kAPBZQOhEByQGYs72uB0MOAES90PoWGDRCw+67c6qooxuBwplgMk1vJcBHkaArBDPxOEZFEdsstueKmQjzeA7oMyCRB9xTc6Q+nzalIkPEyuMk5+HY6lT3BwYeawNcurIGpNnKGDEhvi0IjvQgtyj8oI+Qw4ycWSyiMOgl+/pF4wD32UwC6kB4KNfAfK8D++SwS/fZ4KA8xkHG5+/5XDm4XMclpWIh7azoB6ODLBrcoTsHUEY8hIRJeBhF4m7u7vi+AaDQXS73fLsiE2/EhgvFouYTqexWCzKeWQWANxekJkMEM/udDrR6XQqRIh30nFmo9vtVuwdYDxiM/Xo7OyskpmiTTbkrohAxix7OUizTfHY5uCaa7IMc76fl51OdkT0T0Q1E8VvbDMgB2cIIbezsxPdbjd2d3dLEAS4QZ8hRxgXgBRBCO3ic/qVXeVms1khYAgkXEIP2AIg0geQC4eHh2Waku0iQVUmg77W8VOem+0KYNVT7fm+3W6Xsv+IzdqHBGwnJycFSBKwMP2CACOTloyzAQv96wQL+giha3IJObLsO8uM3OBLnZCo6zdsi20x/zs5Rj8460uf2U7ZZ+CXTXiZBHUQyGYwgEoHe7QVXcigmvPpN+MN3iX7bDBWzmRif+r+5z1yP9l/u71nZ2dly3fWnXL/cB429/z8PKbTaRwdHZXMfqfTKdUq2eY4eEGXs1187LBdyzLh3xmL/aX7fo4DncjBFWPhhBikwu7ubszn84jYEEJU0bx69SpOTk4K2Y4M01eeYpcrGL2uVMRmPRmmB0GcRESFjI3YYCB2+DPRvLW1VdZb4j1pr6tgVqtNpSSJIIJhVxZTadVsNksFgBcGJ1HEvbERYFSqQjgHmQSXmyBheYiIDVHiGMXVm+ig7Qc2JyffIj4O3kxYE8fQz65WwgeORqPSV/gxZKHT6ZTqrtXqoTKRDWA8tZ1EIP0KYdJsbqb7MI6QajnRnu1qq9WKxWIRr1+/jm+++aZMMfI1t7e3hTgZDAZxcXFR2uNY0om4X9sBZmFtVcsIJGeWAfyu7RsEQqvVKtN10Wl2QjQJdH//UME8GAxKogPd5j6Op/mJeNDj6XRa7gPmZayurq7i4uKijE+32y1+j0pX3g97M5/PYzKZxIsXL8p74hsjNvrpwgzjsRxTOsnDYQxqosm+5zESybGX+QzruslZx9R+tu/nz417nBxgvLO8oMe2Vbu7uyXRend3V5Yh2draKmsCGrO1Wq14+fJlZcYGFXMRD4lcyELH55nP8buAC3h3sJmLNz5XTFl3fDIplRvPAPs3nxv0WdjMDgJWcQR8jqDC5nIQnHCeOzdiU07O3GU/ix8608yrsxlbW1uVASTIZjBxVg42DapYyM8ZKAJuzkFBcbYmyvjO5Az9aMLFzzYQcWBlAOs28sysYHXA3dMCzO7a0DmAcd/43UwwMV45S+Q1ykx80B/0vclQk18Etznj4r7IfeWgnfey0cukFt/BYrt00lVE/M/9/LmBUL7Obf8cmaPPGUwjuyZlIGOvr69L5o5xgiQkQEZ+vAsb/UgfHBwcxOHhYUyn08pC4zgxG/rV6mEq3tnZWSXQZgHGwWBQHAHXeAH99XpdyDRkgfYAypBXMoz0AeQIGcr9/f3SD2SYWHiQxUKxay7Ht45GVB3XjwVhDtJsJxgnbJz13uuG5CwQ5+Tx/jHHY+dF/wGC0Z1+v1/AGlVn2EWyteiuZSwiKvaO+21vb8fh4WEJhJn+yQ5NfJ6JU9pE0EOb+/1+nJycVEj6OlDzNYLWTz3qdNrkhO0u78u7A2gg6QCuZ2dn8fbt2xLcdjqdytoCVDTxLIha+/cMPrH3W1tb0el0SmbVpAprg5l4wafwTgB3AjRXT/AsZMg23YmmiOr6fk5AmHyECDNZwDOzzzRJbrKea+gjj4+rErAd9hPYJdrsKacmvDwdNpNB6/W6+MIcCBkT5UTg5eVlSQbwne2nqzum02kBywcHB8VO0lcRUaloPjs7i+Pj4xKwZVxg7Mhv/IUTWD+mh3+JkMqH7d7n9JOPHbwPPoBxB8eYjEc3aSd9jzz9+c9/jvPz87JGIlNruD+ygT3GV0Le2DdwXU6cMHWHJBwkDnI6n88ryWZsAXjZwaoTPMbUkNQR1USrq6Pu7u7i8PAwer1e0R2ILu8IeH19HYeHh7Ferwspgy5tb2/HfD4vpJuJafx4r9cr9otp+E4mQ0ZjM5kChY1l9zP0wEsDOMmBTjUajWJneS44n+ozEw3395s1ASMi2u12dLvdGAwGlYrxk5OT0kfci/8Zd08f/Oabb+L6+jrOz89jNpuV9YqwBbQ521HIyd3d3Tg9PY3z8/P4l//yX8a/+lf/qowJ5CPE2sHBQfR6vUJMWQ8cQ/6aDseS9I3fLaK6iZT/529k5O7urlQj0X8Q+RCT6DP3giCy3jNetIlYG2ISX+pKNtpNEs9riBEXs74dyYKITZUjtobrHNdil4wP7a/oh0zYZ2yaiSBj1ExI+doc/zpJBHaw34YT4JxMjNk/cX/HkrZRjgcdZ9uWcg2zNdBZpsZ6hgib9iyXD7txMrvk6dOnFcKIQgDOpZ1gMhfsZPLSYweOMVmX/fXnPj6ZlPLgPMakGVxZ+DgvCx33zYJUx+BRiRSxcVzcF0OPQhF4OOuWDz/DVVEoottvBSAYd+YxB3kYY4TV2dY8N9tgFgWuG3R/ZsbbipQrIcyKc2SyxIqcSRRnfU1QcX42ML4PBo73dDbPBFce84goWX6AEu+FUXN5Kocdpw3XjwXgHj9kgHuhvHlNEK7xGiiePsG7AH6s8Bgbt8fv7779HAr/OY2G9Zwjk9IZeHEOcs+73d3dlTUUcJhkeI6Ojkq2hutZUL3ZbBbAhONkt4mDg4NKYIeeAr6oqqK9VPFAfvOc6XQaEQ+gb29vr1JVlStoIFxwyhyeeoQuMKbIvrMkmeChv/nbDpFz6ogT5L2OOAeAmJTNNsLt5BrG0zJpGch/Q8g3Gpt1SSAjIekB+I3GZu0KHB8Am+vpJ/TRAID+nM/nJYBii3rAvEubsZs5aeJsnR0372Yb8jWOOuD1WJ8biGR9R4co3+Y9AGZbW1txdnYWo9Hoo6weO7nc39/HfD4vQRQZUp5pHEBw1W63o91uFyDbaj3s5ASJz3Mc5Pl9kPfcZo+hAf6PYYlslwjWjUdse6lSgeRyRRHX58MZRI8PuoiMuSqMZ3uNFfyGgzUOj6/117KdiQl8OO9CQMr7olvGB9hpvifIMVlF+6fTaezt7cVwOCwy5iCIHXcByARArIGTSTXezYnMTB6ZUMlHDlp+7PhcPvanHgb/+KtM/kOMzufzQjhB8L99+zbOz88rScTz8/OIiFIxig124IQc4KdIwLgf8KkELFS4et2iiCi+OiJKNQUZfsaZtmETeCfsstduxEcbiyHH3W63JBu8dhGEtqsoCeyNuZiiF7EhedENflPN22w2yxIk6Kcr8JFp/gbnQz5hixyDQIY5EMSmeaYH+NaxhQkEAt31+oFAZvdf+pSg1vjaBFLEpkID+cP+3N3dFfxEnOVgnfaY5MJnkuBYr9dxenoab9++jRcvXlR0fHt7u0wB/fbbb2M0GlV09VN19nMeGT/9NYcDffdzRHXzKcuck+PWPfet++Xu7i5Go1HZAACSALzk+zFmEQ+zdEigostnZ2flM+w+FVLcE5zscbWOrdfrMjWV65A57A5YDMIZ31XnPzOWNQ42ps2Yx9/ZT/ylMfa12b9kvOekVh3WzvfNMTRj75jO8ZLjRZNiu7u7MZvNotFoRK/Xi93d3TI1l+d6RsD79+9je3u7QtznZGFun2XPnEC2FU7u+b2+pO/8ZFKKRhokA6D8onQaAuzGW9ByoOMAyIE6Tibi43J2X0+pG86G8/mdg0LaAZgiG8G7OcAFxBOgYqhN4FjQva6GCRYbIisHz7Kwcw7Gw/1SJ1j5fDskg24/y0rhPsvGFiDKGNmQZGWDDMhkG31go8v//M1vgFA2RDZqLnVFPqnaqgviHgtcsoGxfGZiNY8RMuMdGTkcRLi/3Od2Th6jr6H4f80BEedx5G8HqYyhyd6IqARNGDtkB0DV6XRiOBzGZDIpUwR4FqB5Pp9XFnikD00uWp+3trYqO3DifHn23t5eCaQB0ZmwiqgS2ThtyyrA21PS7u8366WZjHGwnQkFy+iPkepZpv3jYMTX+1241v/7sPxyTl17TB4bYPK+gHzsDGuR7OzslI0oADCAKQCvp1LT34vFosyZx96y/Xyr1YrhcFjxL2TvTB7zHdlz+s3VLciUwebXOLLeI991h+207bCrCl1RYLDx4cOHuLi4+CgTxjO73W6paHMSiGd5ncStrYd1azqdTvntdS48rYY2MlXJsm4A6iSBAWa2tblv6BPsEuPM822/8B8mSHwNfUIw4Ge68oKDAJU2mzyzziNTVLvYT3J/jwVtt28w/vJ19l3Zb/teGUMwNnmtELKtEVEIEgityWQSt7e3MRwOYzgcVqqaIcEhKP3uvV4vut1ume5lu2IsYUxZFwg89l3defmcus++5OGAxfLm5ESz2Sz2kLFifbg//vGP8e7duwr+Ni5l/aGtra1YLBYVe0zyzFPU2PSj2XyoXMav4lsjoqz1hr46qMw2gODVOgWx7VkPdWQ09yOJQFIIv2xsgf7ja02iUZkcsSHfba8gYXg+OkXlD4kUzqdNEFokKqn2pZ9MKEB0EfCDAfAnJt5on9exYi1A+hL8ANnHlM3JZBLz+byQUvv7+9HpdErCD/tku8H9IjY2abValQXQmU55f39fMBP3xx6Ceei/+/v7aLfb0el04n//7/8dnU4nXr58Waqk2A0Z/9/r9Qrxhf1x0vtrHAcHBwVf/q0H8khf0ydOCPhzKmJy4gGZ4brlchnj8TguLi7KumnL5bIyBd5VSdYNE9NXV1dxenoak8kkhsNhkSdmDng6PUQo+s6Oqsgv1elOToDNsA+uBsKHgQHpLw5snvUnx7l1+Mc23TGwsXXEx0uYoIPGu8YBvj7HyW4bPp7+N1fg+D4/G3tgfJljF+zebDaLy8vL2N3dLZWcLrhBL6fTabx9+7YspcA9WcvLyy4YY+X4HT104sp2Fxkzfv4Sx09aU4rBMngzkeMMRCZOADcGmAijDwIDOjQiSnnuarWqOA0TGi5xM1lEpZLLZBl8s/4WHAyvySf+Rgl5biYWMnliAXXAxnvhHBE2Z+gxFFY+jAHncp+IzY4rtBmBoh202SWYHksHN35PE5K52sfknImJHARyX09LQ2ZoH20BZNVN27KcAah4NzJenvtuI2MQyE4wmQxyQGTjZgOT3x/jYLDEuZlkskwzXtYRP/+XRkpZB6wzACnvJkPbIWU9DZZrODDEgOZmsxmHh4exs7NTtshFx+fzeYzH4zIO/FAl6SCXNpMp9Vz2m5ubMkWQaXwYXYy/pxZZ7q3vgEEAqgPA0WhU5uJnOTN5bCdpG5HJbp7n/z31g8/tOOrsjsGQ5SwHvjmTzjgaQALEGFOTfQbVAF1Pg8ARA3qYMkK7OZ8sLmQ3wRRTwnjm7e1tvH79OubzeRweHlayv3nBRwKabrdb7I0/xwbSjq91ZEDG3/l72xRsN3+bZKBSieQO8vynP/0p/vznP5f1KPz+VEMBbEy6kl3l3G63G0+ePCk7fPE8rvM0wUx62Z4j57brrphgHPAr+HDkjHvhc1y5iD/H7lvOTer4f9pJIEAQhv805jHJhc3IGcdWazNlLyKK/zF5lgG4p4DwfviabDd8DnKAvNiPuQqEPq2bwmUdQA+pwnHfRjxU2JycnMTt7W08f/68MpXeY7G3t1emVxNA53abKKoLUv2d/842ru4a61S+7mscxgU81/bT42Z/gQydn5/HZDIpdokAEv+5s7NTPovYEICMAT4UmUMGR6NRZZt45Iv15Eja4hchTcDI9vHolivNkdPlclkqsY6OjgrhQxCE7yfoBk95Q5FOp1OuW61WZUo4a0ZlghXyiHtjK/hNf9NX+Cvaa5zo4M3YGYya72m75iQRYw3u51xXsuEnITBWq1VZ+8kJf56HD1uv1zGbzQpZ1m63Y39/vzIF2qQd9s7kFf3LuOWkvQlG5IV1Oe/v7+P//b//FwcHB3F0dBQ7OztlKQTk5cmTJ/Hu3bvKmlX2DV/j+Df/5t/Ef/pP/+lvtgHoNISnCRLbtZyUwOY7fkSG0HemVbKDKeuferMAfoObbNtvb29Lcne1WkWv1ytyhG+x/DnORlecEFwsFpUdrnd3d4u/Z6op5Bmy1Wq1KmuYOc7Kfpc25L5zkYXHKmPTjJfsU/g8P9cyzzVO3ORnZB+FrTDuypyG41GuB5MwZmAsE8IkU1erVWXX1IuLi49IpsvLyxiNRpUdFUkGgNlt48A19ENdAsgYw3jhSx8/aU0pfntg6gbNvxFuZxAjPp5T6XuTQaXD7+4edsqyoeRwZoH78lwHmJ4SYibXjsIMqUkpGw8TKVZopkoAXnNfOUOC80QQ+Nylcm6LySeX5rmPI6rBJk7GJJPBUP4MxTQJh3HkGQR1HJmgsEFxhgrn7DnKBu4GEPwPyehKCRsMQJkz/66WcsDAGKGYOeAnmHGG2eSSDZYrvWw06S8TFw6ceK98HX2dDcDXBMufepgYMelzdXVV1lzKzD9Tdmz00C1knWwsz+BcdMkZQaZqoQfOfPrZjCOO1lMQMuAGzKPDjAk/dWBxtVrFbDYrATrP4d5kjgHnvV6vQijZORlIZHLcQLvOAecsTl3igP+5p0kk24h8nduEXBrccg8/g6DFNhlw7QCAe63X65KdpRR8MplUstA8y//f3d3FfD4v2SEy/be3Dzsxzufz+MMf/lD0mKAa8ITtYk2R3J+2AQ7qv/SRA2cfHif7QNt4kzzID/4Pku78/Dzev39fSB70N/tmAlgHuw52ut1u9Hq9QiDTn9g9ExjoM39nghW9z+Sqbcp6vSHJXAmHvNq/EQhBWvJu1knbXvtZV3WZgDWo9WLrlhn+d8ILGYLUs8474ULbnPjISQ78qQmkiGrVsQNlg0vOAwTzHPyOqx8YG4+Rk1m8v3X//Pw8Wq1WHB4eFiIE3xwRFf+e7aD9SSbiHATwrKwzjx2PXfeYjn3pw0SF7TryB3FzdHRUxopk23g8LnjaU9+pUlmtVjEajYpvY2ztX8CONzc3sVgsytQgJwsiHnSAKkdkeTablYoa7Il10btsWS6urq7K/9fX14W0hnzC/2NDwNXL5bJM1UNn2+12mc6PbyapQfKKRNFsNqskMI3fGAP6jSCbsSBApOIP3SPQ80YQ6Fqz2SwYwskxiEOegw1zVcpqtSrEDv1o3DGfzyvvgL+k7xaLRVxdXVXWHxoOh/Hs2bPSt9gN7nN3dxfT6TSazWZZSxefQLXekydP4ubmplTREVeYrPAOgRCF//N//s/4t//238ZgMIizs7PY2dkplUlU0ealTL7m0W63i3z+tVjbtinHx/xtP2wbHVG1nY6fiHnfv38fk8mkxFJUIxNLQ04ic/v7+6XydjqdxsXFxUd6ze7W4DgTE+AE2ks8ZT+FDXdlNDbo/v6+VOlxjn0pfWL/5pgr20P3p/29497c1/lvdJl+5nv+t320r/a4cE1dnOZCDuyeq2A5P+Nrvydjj857eiWfcZ9ut1vsIus6Mp7MOjg4OCh+FPxH2+nLjL3si93v2DbbqTwOn6orn3r+T1pTihcjc+hG+rwcfLs6IgtRnUHifxSKz+xcuY5yYwdSbo9Lz3Gwzhq7rZxvgOwgzoFKnQIRDKPEOBUTTBj+iKiw5RZWzsssroF6xGYb6jqW02DPwgdZ4Coj3sFTJWlzzvZkUO9+xuF5brwD70x8uX08h8OkwMHBwUc7Ljnw4kDW6j7nGZYJZy+y8TKxUWcI60CtgyhXLxjk8yzfz4bPffRLO2hXJkoyWZCzdK5QbLVaMR6PCzhtt9sFhNHPjA3gknUhWBOK5yNjOFRKiM385zJ4SGmXGWNDGCPekd/WhclkEuv1umyRTqbYz7y/vy9ZI+7b6/WKDjgzHLEBJ9kp2Nbwec7COovhe2Qn47Zx74iPSSX6xMQNNt9EfS5bxu5xDuvH5Ew6eoWdxYZh66ksu7u7K2X/BB0mD7h+tVqVXREJCsgodzqd+P3vf1/eEzBugMwYGFyYiHCC4Gscf8lxZ//mxI3bjM6hK+jNdDqN77//vhC7Xk/KSQCTryQjICP29/fLorVMswHEQhYjS04AmTR2MsPvkwNp2xnubxuMDLlaivcmcOV+XGsMwzUeY871upBO3tDvDka5zlggy7p9jDOX2JzValUCBXyH7Qq66oSPz+M9nG03Ae022Bc6CAGA2s+yVkUmDd0PtOPdu3exWCzi+Pi4jBf2gED68vKy0l7/bZvyYzqQ/W8mrnyuv/d9fyqw/luOx4Idt2kwGMSTJ08q47FareL9+/cxn89rg7J2ux2DwSAiNlXrBBAE/3zHOztgbTQ2S0ywMxvtu7+/LxvyIMPGRATZXuPGU39ILHpnu06nU+wK7TcRC/GEr2aNI6YS4WOur6+LHQJ7ttvtjyrgIzY7oIFx0du9vb2CeR3Aea0cqhstX4wlPr7RaBQSx9MJIba2trYqmy8xDiZ41+t1ZTqZ9Z3qMge5DpCdjGUH4clkErPZrNhGsBgEBZU1xESQaIvFomK/wftHR0dxc3NTAl/0/vb2tqzjiL3k95s3b+Lbb7+tVEjS5n6/H+PxuJLA/5rHf/yP/7Fir//aw3bS5ALf1cW8EdXdkBuNzVp/2Fl2suNgDTZknQQRNhlcO5/PYzqdxmQyifv7+6JvVKG7mhFsGrHZaAq/uLe3V2QJAhGdgSjv9XolacU7eG03KhSNsdwHJkfonxy30Y8Z0xoj+HxslAkm97/5g/y3SZjMNeRrsYcR1TWhHNs6hsT2YOeMBRw3ecr78fFxqThG500gUxHpIp75fF7wsDECMpcTyLn/bF/cZ2AJt/mnHD/l/E8mpSI2AARBRiH8PeeYDX0MZDB4dWSKyR0cBh1vRWadmAzuOCc7PCuFgSZK6kAMYeN6CziGm3YaMHMPKxzn+jNncLlnfg+Ey31Jn5uMsiLkgDQDGf8QvDympP7MbfCY03YvVsnvTCrmfufZvIsDAMYY4ADhkI2+20wfA7A5z/2QCafsmOjHOpD7lw4DubqsqEF9fq4NAHLwSztM5mUiyUEPOmlylb+vrq5iNpvF8fFxZb0hv3/EZn0xV9p4bAjeWMwRGcFpc+9+v18AugGxs7yNRqNCtHPfvb29OD8/L+tZLRaLePLkSSFMIN0oUc6EBn1DVY8zFv7JgS1tyrJgfTRhYhmzfHEPxi0Hqpzv4J1n0r/0EYQU9+AcyB3GdrFYlGACgjKXZaOj29vbZfHYiCiB63q9jsFgUKZvOrjNVU6j0ajYXe+weHZ2VgI95CmiWgljO+A+rSMEv8aRQZTHsy7wznY1YjOd/eXLl0UO0I03b96UhZG5P9ny/f39in0lWKENfEZlRp6eZz/Ed25znUxn+2hSy+fZjnBYj0xo5L6xvlg3/V5UExDE0WdUidE+dNxEIM9BLmmPKzgjogSuGeRlcg4fiLyamHIAk8kC7mtCkfMy6HbCCD0Ej1A9gh3kO2Mjk1rIHJnyyWQSrVYrvvnmm4jYkNHImLOu9t/ISCZfPN4+sk7wf9bZjGk8/l9bv43/MgmHvNE2xmg8Hsd4PC7EBdii1+uVReSRFxMfEBnY1NlsFqenp4W0Z9xZpBz/xJpSTAnzVB+3EX0xZkEGqMrEFlAZxTMIeK0/3HMwGBQMBbFNVZXJZ1dBE8zRbxGbKaj4CT7HR0A4mWDjvsi+MT7ne/oj52A70QfeyedBLDL+JtPX680up7Z7JHe4n6tPWJcIWSJeMBHJJhUQXkyxpnqbA3tF8Hxzc1N2ZUM2+/1+tNvtsvvmer0uVR0E1p4e/erVq/j222/jt7/9bWkDi2B/++238ec///mjyrWvddDGv/UexkbGsHd3d6X6mLFxEQTnOhmGrsxms/jw4UMZ2/39/aLrEVHxP8TGjUYjZrNZXFxcFLwEkWW9RE+QS+whOt9qPWxMMRwOK7uL5+pkKs1sT9frddFTbIRxFf1lfJHjOQ4/K9vMPAa5HTlplbGxr+U7rnE8nmPoujjbWN3ts47nin/jzBwLszQF4+7iDHAIstTtdmM8HhdbAJZm51t02dNt6VfakUlT/+0Kqew/v+Txk6fvGUihiAwQoAXyIGKzlgz3yJ1gogvBhcjgey+0FrHZCYu5snZsBmcIuxdhM1FBR2OEYfgjqrv9ZIHmOZyDoucpPq464gcFczaCe5v4Mmjn++zEEFSMh/uW66w0JuZsLGijDzLF3ItrDEK4t40xz3EZOAGlq7yy3OSqJL8H/UvZs4FJVhL3u41gDno4AFs2nA5KOXLwlA+fu16vK+sQ8MycFbIxhhioM9C/lANDxd+MJ+CQrCfjmYMt67uDRewG5wAAb25u4uzsLBaLRQHOlv/1el0JJrkXO+kAtF2xiG4CAAjiIqKAv729vfJO9/f3cXFxEY1GowAxStm9gHPEZgoNGVd0hQyIp5XyXcTGUVi+rBt5DEz4+3NfZ5LQ/ZXl3GPj98j2nHs7u0Kml3Yvlw87t3iBVO5tx8iY8D3lxhyMizehAJzTn2SPAcHO0uPsLy8v4927d3F4eFgh25gywg4n9BOyhKz8HDro8aj7rg4YZLIAQoK1RPB96/U6zs7OKvpJEAbpAIEAoAXwrlarsvYatjiiuhNuo9GoXXPIvtj+xvbedjfLKfd20Mhn2e5aZyzfJovy8xyAupICeTfJc39/X2TSpLD1h/7ifpxj0ts6Y13zvTKItS/nO2xKJuMAsParkFsOCsBqW1tbFRAMyOU8B9zGDWCx+/v7Mn0oIuLdu3exu7sbR0dHxQ4zRWpvb6+sl1En1/7ffZD18bFANvtt7ukj458vfWBj8jqeYCPax3QdpkAuFot4//59Id4johAK3W43IjY76VnH7A8hA8bjcVkTBtlj/Hk+/pNrso8jaGUKnfvXUwr39vbKjpuMOws5WxZzUmW9XpdKHNa+gVSC6EKuHaSRtOZdkF9iBOwgxBKYgkCOd89+kf5lah3YJJNY2TeC15Ez1tyz/oJBjAVsDzMWxN44YU2fumKCJB0YFKJ5uVyWdcnAOO43sDrtYYyJjfAFLJa9Wq3K5gbsrtdsNkv12s3NTfzX//pf4+joqFRAM6XS0wlddf21js8RXKPHtBuMX1elii7W2V8n5O/v72MymcR0Oo3VavXRLraMC2MDsXR+fh7T6bTIAecjq+i0fQwJF2QJzPzs2bNSWQjuQtbwDUwpth9zfJp9NO+PfNdVM9n/87/HKidXuL/9uv0S3zs5Y9+csa+xCfcyPsxJLb8Leul28Zt3NQmHvvneVLSR/L6/vy/2wRsF4N+ZWWCsDO6lutk8DTpLu7BZJlQZc4h58I9j5M+hOz92fDIpZefNy9zd3ZVFUf0dnYGCUoLGIPheGYyQCTBZRWfgUO7v70sZKWCHDs3VQxhYBMKEA0wgAZSfh4HBkADE+R7wbQWyUPs5Dj4x/D4/opoNd4CG4wIYwLzyTvRbxGYRc7PPtM9kVMSGEHGwxrO4V1ZG/9D27KC5P2vqAMJsrG2cfU/aZaNDH1Al4XHxdR4HZx8ysLWcQhI6c2xjkseP53isclDkz3DMGAv6BVmgf93WPKa/tCPLJocJAQx0ztxy/vb2wwLTlCAjS3biV1dXMZ1OK+tdHB4elqojyAVPj+XaRqNRKmQajUaFXI3YlA179yFAHLuRDQaDAoJZn6bdbpfF1pE1SCyALzqOE7cc8a6Mtcfe8p8dLn9nR2oZsU5CtEZsKtsM+jKYMhhElyOiort+D+Q6Z1HItDop0Wg0Suk4YwWYglTnfZEXHKCrZOk370riqSKQiJYnZOXDhw/R7/fL/ZgSiqzu7OyUHWU4TPLU2YEveeRx5bO656NzJo+wawQUnEe5t0Ek96Xqj6CTKUHNZnXhYftU+p5nuTIKueN8/IoBfER1XQ37RL+3ZdtAlvbx237MgR7nuwKLz7PPta+q8xsOKDLAxTebQKojbwyUW61WWbuL/yGp7H95Nu11+xjPnOUF33CNK40NRME3AOCIKOPF+CNTXseCsWbKrDHFZDKJ7777Lra2tuLly5cl+OJ7npv7if6uwwF1PjfLSh6vfGRc8LUDYZ5p+79cLqPT6cTh4WEsFouCk/b392M0GpWkCAvdbm1txWAwKJgV3TeGIXCcTCbx4cOHIgcQ7/wP/mF6FQFPHW5jSs96vS47choTgz93d3fj2bNnpSoK8p8gmCQwchgRpWoHP8mBP8CO7e3tRbfbLcF4q9WK+XxemSbU7/cr1yNb3j2bPqMPqS7I6/QQ7GHD2DTFhAq+NhN8DrIhElj3B/LAU+LpZ/vVZrNZzs9xif9Hf73UBfibZzh5MJlM4uTkJHq9Xjx58iT29/crMRf4LFd38czj4+M4PDws64wR9KLXEVEW0X/z5k384z/+Y3Q6nbJJje1/nkb0azmMiYwVTEI5NkOXOEwAgmFYsBrirtfrVRJv2CsSchERFxcXMRqNKssj8Hxib8YI7JXjbPDY4eFh7O3tlZh4d3e3JJvx766+wifwXPQBeQAb2+dle23c4Jib/zk3224TW4zHY3KU40nsXsbVvl8myDJWz3Gg41muzTwG+uEZFTkucVXr3t5e0U+wMrp6fX0dR0dHH2F5EsNU1uEzcjzhvnJc7tgc2UWOXNDxpY6ftKZUzqi4VJXfACsLUB7Q3Bmcz6B4sMw6AyxhElEqnoMRMPiqyzbAAFKimgMpgDnkGwNEIGOQR+bUwuhzOAhWvZVnzijnvs5kkBU/IirGLJdC0icZwNPenZ2dCtOdg12MC/1K+z1uvJfBnd+b5yHMJhB4Bv3KexukZgLRu6i5Db7WDi+3yffM989A1te4b+rG6sfGkPe0sSbAM0DNgPXHgPXPeWC8cKaQ0xGbKVrOvGRjjVNjsdDLy8vodrvlO2cKxuNxAW92RBhb+snjjk2AVIqoZi0ATIvForwTmSTKm+08uIZ3pD3X19dxdXVV1khx9m+9XheA64ViaScElhdJxf44OHPb89+PkVrWexOCDtidIfE11nfAPGQw9yCgQHeRcRMT/GYNMJ6RCf9er1fIa94P8O4pVLSPLDC2DtsNGCML72neZId///vfx97eXqnmgGQEdAPEIurXkfoxwPO5Dz+njoyqkwN/t16vYzgcRkSUgPfi4iLOz88rQAxbQ6Kk1WqVLdiRYVdHmYxi7D3dCBmKqFYT53fK4Mj+EpvB4e/qbHSWLz/LBJeJEN49VzhzQC470He/0xZ0wCXyYAL8eyaYclVkBrQOot0XtMP9bxIit9MZeMbEGxS4b/G3BCF149ZsNsv6JFSoYBu8AL6xEDulvnjxotLuuqkdER9XDvunTvZp34/5yvy9ZY9gLev6lzros1wZY5tIny6XD4vZTiaTStKMhadZ+4fxo9INP7Ner2M0GsW7d+8KzsPHGG+xzhAVVb4fySNX01Gpfnh4WNZ+wmcSvLAODZ9Zvwh4FotFWfPo+Pg4Op1OITy94DlVxtgn+md7e7ss5r5abTZ0uL9/mKbELn2u9huPxxGxmTZH5RQY1Dv6kXSm36giZJ1I40InHa2z6Dt/Yw9I0LNmFliEdnjapf0obYLM4n5UGlPRQpUrenp3d1cqw1zNA2k5nU4LOdXr9Sq2KiJKMOypy4PBoJCDJBGJ17CL0+k0Go1GvH79Ov7xH/+xsj4R5IhjkF/bgW2yjbftRkYci+biAL6jYo8d7oyTIWCRK8ZwuVzG27dv4+zsrJJEMPGBvnA/7sPSFBEPOunFsafTaSW5Cq5F5kgAuiAB2eK5JFuIe1wswjX4Csf9xrH0Mf/bP4Bnc4yRxyciKrggY48sd3WxnjGofSZtss57vI2xPO58b5xp8goy8vT0NE5OTmK5XMaLFy+i0+kUXIXdBp+x0QE2jSnUXnfX78Z9jO0dHxiDI0951tuXOj6ZlKojABaLRfT7/YpxjqiC01xBw/dZMDCeACJva8tvgg2CQdrltlnYm81mUQr+J4ghICPzY1CA485CzmcGiDgtH86um0zyZ3baAGoDQRMmFmb3SSaGuLcdoZWI96kLTPgOQ4Nx8fVmuq1EAI3MsjpQ8Lm8m/93n/od/WzujdM1AGNsfL2DEgN2GxV/5ixDHSh+TBEfA8b5/oA2gBZZMwN8EwW/xIO2MmYO7nBW6GDEx4FcRJTMI7oIYAIQssOPndB6vS5z5b04KouUHxwcFJCE0cYJY1zJQLJVLu8DmXF9fV0yzJb/iOq28gDgiGrVaKfTKaQJwBoSGn1HLum7xwJA5JrPLaO0J2dlMwEfsal4ABgYILg6JKK6GC5twxm5TWRvnRTAwdEfBDHYbe5l3eM72zv6eTqdFrDuQIm+HA6H0W63K+tXuZ8ZdycfCKogBPEh9Od6va6QXhnAfK3jx0B6tkUOsBl/pss44XJ6ehrT6TT29/fLAvTILVNtTCTv7+9Ht9stwDUnmvCXjJ2zmzmZkt/Hfetg0+9ov+PPeGcDwsf6x/e3fOYqI7fFvsa6GVHFN/zvdaUcdGDX0Qf6jH7CFmWdQ+8YQ+si7+S+st5EVNcs9GHcw72YBpt1hwCGQIR7EajQPnTUu4vZRmHbXSF6f39fFuU1lsAe5qSaxzWTS3UBTF1gYv+cgzsqAr/G8Rih9liFW6PRKDuK2u+dnJxUgiEHOOjS6elpnJ6eVhKYzWazEBUQ/Lm6EdvIeoxgbnwaNqLb7ZapI+yIC+GGLzemjoi4vLwsu8j5YNME1hvDNkF0LBaL0p5W62EqGueQYMCOIZvIXKfTKX6b6h/ku9vtFsLHyXDIIDZwwId6A4Bms1lJXLsK0wEoWAkc0Ghs1oTr9XoxGAxKdQz3JYkZEWWjDzAT4wwJnm0D5FEd0Y4Nor2sLXV7exsfPnyIy8vLePnyZdnhjb5wxY8TEezGd3h4WDaAQZfX63XMZrPY2dmJ9+/fR7PZLKQpmILFzh0H/doOx3jYL9sYF1TY19hvch/Wx4x4IGb7/X5lTULr+XK5jPF4HCcnJ3F1dVVZXw5ZNIZ2jOm14JAHbAxjGBFlzTdsktdvxqchi+CP+Xwes9msMlsAufX9kSHHlDm+zbGDbZ2/82+PC7/zeDju9fvST38pnqv7bTnwPXgXJ5Bc4OGqUH93cHAQnU4nRqNRnJ+fx3q9jm+++SYGg0Gxs/hiCHr0KOIh6U6y33JJHzjpljGuY3nbMb/nl9TVn1yLZfDkXS4cIJmZyy+EYmSAaUXFMLN4IMoDEZarJwxA7WQzwKGjIzbZGA6+czmshT8iKkLFkYmMiE0ZL9dYEXi2r0fR6jK9NlyPAVICO55t0I0A2VhyHyunf/OOOcjNQQKGyECbbKz71u+Vyc28uGzExwuhWwGomGo0GmXuuo0cxpKME/exkaLfbOxzpsvB1d9y5HFwVnG5XJY548iBgc/fevwlMu2vOZAdz4e2HDGl1mXp+X2ybDEVDDDp73DKrH+QszImoNh1AuDM1FxkhCAd8gvZIFtHIEYmL6K6hbrXT4J02d3dLSXPTG/AWdjGODjm3p4iYR1E7gze6wIubIYdrM8xkAUMAortUAkIfV8DbGwQ1U2ZnKZ9TDOABLI95ZzVarP1Me+PDbDN7PV6ZYqep3sgVw4Crq+vS9YdAMX3yBYVB0+fPq3YeioBIFMzEWGgZNv7pY6/5PAzgLBN9VQSAlAAI1Neh8Nh2ZmHd2WNLYDnYDCIwWBQISkctNlXQa6YuDAAZxzoO9tdv4evMWllIMT9GWOuyT7YYNP+L4NeE1H2ub63340DnXOmlsCXc0n88AwCS2wc/YE+cE+DaMaI8TV+ydMv3cfGaPxgA/mhsmKxWBQdIfB3f9qG+zNPFcHe5qkb9Ac2OK9pQTt+zNdmu2cMU3dO1o/8tz/LxNeXPCyP+QcCxNOs0N/pdBoREUdHR2WNOKZyQCCRaGg2H3ZkevPmTdkZ1niVxctvbm4q1cbYYidvjMvW63XxbcZPyBs+M9vdiOqupw7Oe71exVcjJ6w5xPR9xxI8m2n3rG2FD+90OmXKvROzYIV+v1+SE/f396XSqtlsljVcIGlY+wwsOR6Py/uCK/H1rjYAO1hvXD3oZJdJaMbDZBSksCvQ0RkqWCCN2N0Pko1+tb0wCU48xX3u7u7i4uIiJpNJPHnyJI6Ojspi1lx/f/8w7fHw8LASzK5WD2tLrVYPm4us1+synff+/mGNJBOLTJXs9/sVPPBrI6Xq4irG2ESAE/HGWowB78406LzQuH363d1dWWcVgrnReFhEnrGMiKLn9h9bW1sl6cq9GFP3P+MGTkRm+UE/wHmumr+6uqpUn1Pp5Q0Z0Ifcj+6niCo2oF2c7xg/Yw5jWd/fNt++lO8cq2RSyzGifQ1/Zwyd28szXUBC/JATMuiE/T96BQZnGSOwx/b2dvR6vTIT4PLyMiaTSfT7/SJ3lk+/l7EZY0y7eTfL4C+GlDKZkQOa7KhcAeMgztm0DDhNWHG4IqHRaJTpPpmo4RoTUpkIs1AQ3GKoTag5kIuorqVl4Op7Z6KNc/1udqruU4NRPjMg9lQ0PncmP5c+WgF98L52hl4o0ud7DGlT/p1JNBsuk2wZ7Jvt99hlR06bM6kD+QWYi4hiKAmiuF8ehyzPmZjKAU4mT3+MLMpgOTuqfA+MjskpnEMdkfPXHF/KcGRCM+tsrpg0KIv4uGru+vo6xuNx2UmHEnXrFVNO/UxIHzsoCKZGo1F2osDo4zCdeSRoh+j2fHuXwe7u7pbMP/0KeOj3+yVAdPu9uDfv7GynxzrLOX3lQIb/ba9sL2xHXb3Jd3n6rUm2OhvMNbYFtN/rXkRsbCJAKE/LwwljdyI2+p13b4NkZneh+/vNQp1k/3g+7YOAtDzSzlarVUA7Y0U/Ui3g9U4yUcE7/JKObI9ywBcRMZ/P4+XLl2W3LQgofABTPbrdbvGz7XY7hsNhqZBi3Bh3Ay9n/zKhhB3lGsuACRcDRSdEeJcMhnh3T0/LtgUZwoZk/5VBLoQJ9zZu8TMBwFknG41GhYC2ftrfGQSbJMZ2cI59hLEL753tOv7c05xoMwGf/ad/vLMg05KwHZ4S0mhs1ngDh61Wq8qUnByIMY4EL6w/ynUE/RnM5/HxkcfSx4/5er73mOYx/tKH7SXPJdlycHBQpg+DA1gnhsQVFdYQJl5nabV6qFL47rvvYjqdFv3I5Cq6hz0Ek0EcEdSyhgjPgsDBf7LekP09z2OHNTAfpKSnsmObp9NpHB4exsnJSWUHMIJq2yrGzLvUOeGBv1mtVqXKCZ+AfkRsiHTab7lxFb4rEmgLsUPEg/+CaGHs7Jd9eLo8uuy+QRcbjc16YOv1ukxpdAUZdgDbRqKO6jJITgJX2uMgFH+Qbfbd3V28e/cuLi8vYzAYRL/fr1TOkaxwfIfMNBqN6HQ6MZlMYj6fF39DUonEHtdBxBoz/xoPj7n1LKK64DbnOia2TUCeWccpV+MznXc8Hld2GcY348eQL/oWbEsiGdmjIg9C1EQmMo8MkizGb1BxzQYEnrLL2quNRqPsWm0Z8Xqe9Ekd3jIWqCP/fJ7Hwf1sfGL75+cZf7s9jpXzuPJZXYLN5JiTWjnWRj+4H+v6UflvPEpi6/T0tDKt2oSXY5nr6+u4uLgou5xm7iLHD8YE7jfahqzU+dfPffykSqk6ABFRnTqSDYtf0C9pwJUDS5MDdASBiQUI4fIaPW5rbi/g2oLpaXoOGt3xde9E+0wQcWQyx4DUCmLAnIkpOxCXJTebmzUBXFVjxjgDabfZgNgsus+nb+x4+Nv9DPDhYFwxglZMv6Ovp02AYfrKpJuDd36omGKOM6RUvt5jx/tiEPnJhs/yWCcHj4HefDwGeH2f5XKzCCFTaLKB/iUdDnRs6OlrQCpj6SyKHXS32y2lvqPRKEajUcxms2g2myUrDNj2mEH4kCWM2Iyrp9Ks1+tSZYNznE6nMZvNYrVaRbfbLe1Ftmg7eokceyox5/R6veh0OtHv94vdAKS6KsrBep3NAFxjD+gfnmfCyWSR5SrLLuCP8bIcmuRmzGyHaRtZchO0nJt1MgfxttsmB5w8APD7vX0f7kV7Aeqs38W0I8aGneIiojJNaWdnp+w8dH//sJPiixcvypoIZJ64L+3w768VtEZ8OjB3v6Ej9N2zZ8+Kv9zd3Y03b95UEg0HBwdlCjwl3kxnHA6HH9ley172z9nf1IFJkyQcmSyyHPlck5WWjYgNKZaJ1DpyIhOtBlg8GwI9+zhPX0W2sXHI32PA1r7WARltQxdsU38Me/j960C1s9t8h08naeSgBD1kbLkHgb1t0N3dXZlOlJNHLL7Kz3L5sJYZAcpisYjj4+OKbcgEvd8vByGPHZaLLIe5D+sA9dfS7ewvja+Y5gLxgEycnZ1FxMMi4JCB+MWIiNFoVPrww4cPhUwAA1kGM0ZFf9ldi0SAp4UcHByUZxlrglsgn6hOiohi06k8bTQa5b2MLUlCdbvdsoYNQTXrNYJ9PW6QSNhzqjzBBeBOpojSxp2dnWLzPJ2UBBV94gpEgm9IQ+sU51IVAunlZCNjBvFIv4BnkAMSYU7aYlsgKUw2eCoVssW7c/5gMIjJZPJR8oixRH89lZZ3W6/XZRmFyWQSz58/j7//+78v6w1613Nkgeq2wWBQiDEvFcDYI1+8p5/7az3sQ+yXcwEH5/p/DuwzJILjM2zv+fl5jMfjiuxynqtVI6JgY5KEJoAYe5JSkEokASGBWcTea5OZRHRhSNZv+zk2IjC+xu5n222ih+8zIeS/c3/mpG3ddY6Fc7WU72nfY0zuuNbx82O+zISQ78O1jJ+rn4ybIA6p6B+Px2U38hwb08/z+TwuLy/L2n+PYSj7JMdaTvy6L7It+RLHT66UsgDSyFzNg1B70AB6OFF3DJ1lA8YA4bgB2S4pzveI+DhojtgwwHkwHGB5qoKVinN5Bwu1MxVmRLkGg+33j9gYIIImB6UWYINVVyrksk8DHd7Jc1Wz4Dno4DkmFQ2YMhOcA4ks6FYqwC39gKzwWQ5saCPEjMeV8eP61ephCpZ3TOF7nB8AhHf2O7ntOahxgGQFrDNSGQDn8X/sc4+JM5fese1TA9SvfXg+eUTVYBH4kAUHCGaSZnd3txhOtsHm5/7+PgaDQVnYj75qNpslYxvxcfUEAJKsI9ncZvOhPJ+SchwswNokEvdjmjALjfI5u84QSHgNKS+OavIJB0O76TP/zvLHPbLTzrpie0E/cG22j3znqY/WTdu0HKyaWK+zj8is2wEQN9mGA84Eog87frebxR1ZIPb6+rpkZakA4trJZFICJi+oOhqNCpFI1cFgMIjXr19Xxgyi0WPycx05EWR/VJckYloL/sPk3Xq9jqOjo7i6uoput1tIk36/X+Q6YjNWGYTZ5/j5mcB6TNYtHxHx0f9Z5v199uEmUD1G+VzrA7JuG2tS1wQafcM59I2rB7lvBqjuG4Jt7uPkCL7X/iZnYA2msy9HT8FKvJsBN/1iIgpSNvePMReBD1iMAAX7b8I6IqLT6cT29nbFl9G3XEfwip9/TK/8nrZzlhWPd9257s98b46v6WeRE/6GgMJ3EGTSX+PxuCxhAdbhaLfbsVo9VP9++PAh3rx5U4j13F+Mp5M7fE4lA2QGv/f396PT6cRqtfpofUcwODJLuz1ND1KF75E13g+CrdVqxXQ6LYETZJT9k30f+ILKgXa7XUg1KroiohBSTtAgb2Bu/IKTq9YxYoJW62Edq/l8XuQ2IipTmuhLMAwJMe9KnP0Yz8OGcV/6zj7UMQ0xFFVW2BVXrPGezDbxUgAckFseH+TEi5U3m814+fJl9Pv94jNNwkVsSEsSrMfHx3FxcVGJ5dB97DXywlHny37JR7YdvIurbu3H+N+2n7GCgDw+Pi5yjk2+vLwsUyvX63UZM1dS4VO8DAY2mv5mitfBwUHBrFTUgcvcbvCtp/Y6xkNneP7e3l5JFqLbyB/Tw11pSJ/leIzP+dtkCUe22bbzjvHzOXm87DfyuHI/xuqxNuZCjohqvJ/b4vbxDKobeV8qJD1+2HHWXF2v12UsnYjgHnd3dzGbzWI4HBYdJF4xluPIvsl2GKK+Dq9/7uOTSSlnxnIWC0OWs6IoF9d60KyodIbJLhwFykA5sBXaZIKZZTtQ2FkGFZLCbK0D7Byc+X9X9hjYcvwYcKJN7jsHcgbStI/zzH5nIOtncH/u6yomB/Z+Nt9xnRlb7k3f5YCZawwebMRwbI8Rc81ms2Rj3Qa3DUXLABSlu7+/j263W3H6BACw8pYrG9c8Hg74TZjlPvb/dUF/naHLQNmf8wyy0AYtv8TDgJrxcOm8s4WXl5dlW1uuZawmk0mcn5/H5eVlGZeIzc54DgRbrVZlGoPlDuADqI6IkgFmuh1OnsoHjCy7/xEU8FyIqJ2dndjf349+vx97e3sxGAzKzmYEFGRzHeAxxtgxOzCPbQbejwXiHDnb4r+tA3XZT4M+y6htDX87k2S9t+64/2mD9aluIXGewTsCnrAxjJMzSQbjXoie9VXm83lMJpOyNTHBxNnZWQUAb21txWw2i/Pz83j58mUBcKwHxuL3AASCZ/rwax0GT7YpPgCcyICnzHrau8vA6UemnXIti6oixwaOtre2wxzZPtqn5fbWyXad7fczTPpEVKtr89oUvm8Gmbbrlv06e01fU1ERUZ2yzD2QVb7H14B76A8+d1KEd7KP5v3oc/sot9V9w3Osl7Sb93bFBn6TNca4D891pRXTqZgu5QDbMkj70CPGhkxuRJTKi8FgUBIBuSqN/qLNdZ/nvx87PgU41+nVlzyMfSI29pOkKUQPf4N5I6JijyOiVIC+evUq3r59WzCjEyAQFn5fkpoEt3d3d4WEImDhADfZttAO4yjewbv/sR4gPpZnU9HkqaZM73EixVPNsRsQIhmDRkQJ1JiSx7tBDvn+DvgbjUZpJ995GYiIKBugME2VqU0Qwq5ApN8so7SDNuCbIWUs13yGzQDjcg767pkInL+/vx+9Xq/sxEXCNqK6bqr9uBN4YDoT7sjGhw8f4uLiIo6OjuLv//7vyz2xGcQbo9EolsuH9b6ePHkSZ2dnxa/X4RXf59d2YD+yb7NegDt4fxP56BM4eWtrq1Qx+75XV1elahI9iNgkPiBbXbWLzHS73bKJANMCqSpHNrC1yJTXtmNGgfFGtis8c3t7u7K7MbLE8iSecsY19rl1MXVO9uTrOOcxf5DjM9/TeMY4ti6O5xyPb8Zmxkr4evcv41cnN64gA48NBoOyy6rjifV6Xfr5+Pi4MkbwG+Ck+XxeqlaRCeMG3ps2YhMcC9BffqcveXxy5IsgZsNGNiKiWpJvcIZCwgRGbAbHJImzBfmZLGRrEIkyOhPEfaxEBtW03aSDA646siqvTZEHxn1iAsSfRVRL+vOzDUQRKqrNrEzOstYBcoTKwu92ZhCeHYKDQZMCrs5yNQHvRkaP+eMw+Xamvi+VCg5e3a46w2QFMfFBNU4G+QbPvjYbRd/TfVsHfj8lOM3BTQ568n1ssJbLZUwmkwo4/KUdyBj6AejKmXkHOZmwWK/XZUodTtA6zJzog4ODaLfbBTRbPqw79Bc2iZ2h3E5A4d7eXtmVhzFHdjDYrdbD9IWjo6NCPEFQAb4bjUYpvUfGHAxyYODddmwARyZkHZRaZ/n/MXnCXkVs7Gd24Lkaxfey7SSAzUFxdqiWh4jqFro8k7bR1rrg2/7F7+Tnuj8B+GT12d6awOL4+LiUwiMXEQ9rLdEeKumePn0ao9GoyECuAvna+piTCj7oF5N3mSBYrValUtSVLezsMhwOi0/t9/sl4+ZK3NwXfrblrY4wyHJXd73vYXLC97Re88519vQxYsmBqL8zOcUzMnmF7cp2C9CdP3ff+13dPie0cn9Zr/iOvvE7gSkYL0CkE1ZZj7zuyGKxKNO8nHDDJ/MTEZVEnquUTTh7vFutVgyHw4ocNRqNkiTodDrl3iyejT76HeuCkSxTdeNed+TAIV/7tQPijG14PhV19h8EFCZi0WPsnXU/Yxfuw305kHF22CQ5CInT6XQiojpFh6odT2fn/lQVQw69e/cu7u7uyvRg43pICmQX4pPEFT4MTEBQ73fK64fimy8vL0tf3t097C4JUUp7kS0wnyuHjf/xo6z1ROKLtV98H+wMfpDnmXh1laZ12W0zXnH1Iz/WQdppkgLbfn9/H9PptBCCjBvXIHcZi7iPdnZ2SkKRKnMqnb799tvY2dkptoSqDAhnpqIdHR0Vu+ZKTdaayvb613Jk3+Y+ZdzAv8gR64QZf7VarTLFlMoi7kF/szFJxGaDC/qOajiq5sDKTMnr9/ulOoq22p/7XRgDE9/GbMYd/EaG8DE7OzvRbrdLwhacz3IbyLjlPqKKER+Lv7Ktzu2uw8n2uxw5Dsxxsc/N8XOWgfzsuhjcJJb11u+Q44Lt7e04PDwsa6n6/l4LmsQr6zXSnv39/VJRd3V1Vca/7h3s400M5nZz/x/ztZ/j+GRSCpAWUXXmZBnyWiVm4yI+nvpmAIaQu9rBzF1ekJf7GVhyromq3KE8y8a4DlAjNAiOs7Puj7qgykrmAaaNBpD+nTODOavK9V6jx+/ne+UxcntwbJxnY5ABta+1krmfeBZtoNKKKSTOqHEfrkc+/N4OjBzwQkLRNsZvuVwWUooKCis35+LMM8lYR1xZFuqCn8eObCQ9Bo8Z0Pw3gYMd9i/xsDMyqLZuefF5ZAAyAuLA+pDJXdYugBAyCHW2OaJKpjIt0GDRgSOVUc5EuuyfQJ0sE2AZ4+817Cynti91421bhxO37nB+DmotQ3VktGXYz2BMsmPPhFhuo0kC+tUOyjYvokqW8zefM/Z1NtZBdR5P+sd94owx7wFw2traKlnd0WgUNzc3ZcttdrAio3t5eRmXl5dlF6uDg4OSbeK97RuyL/saR50M5cN+z59FPPT97u5uqSDjcwDjcDiMy8vL8u70o7OurjKjT5C1OuBoubF/t41Apnx9vi6/dx0wNkiy330MkPq5thUmaQ2UDR4NYOt8vtvkKoPcFhNv1nMndyI2FSrGIsZVBvUEKPbjWV/dX3d3dzEajSrkUrPZLAs542+x0UxjhtCH3F2v1yXzzfkEpN1ut+zURpUV0wUZa4Byp9OJ9+/f17Y3A+hPPer0In/PM76mj83BTrZr9D9VLzs7O3FyclL6mfHa3t6ODx8+xOvXryu7p7lyOWMdEyMRm3XAhsNhhYyEGMMOc74raCCJkL/b29sYj8fFH7IQM8+l+oMkBzKTk9nEGCSxvbRFtvuci7zzPmAn1lxCZpGFdrsd6/VmAXHb+FxZHxGlX5gyvl6vYzabfeQL6XPrj2XQmwjktWAjNjoP1nASN2Kzsyr22QRXTiBBRsxms5IkpmrcyT/iN2MR7BBVyVtbW5Xpm+v1Or7//vtYrVbx4sWLsnHIhw8fKvYPv3p8fFyZOo68b21tlTGwnP0aD4+5MQp9bT/k6iHkk8ooZsKwvtx8Po/z8/NYLBalCimiuvEEWLnReKiKevLkSQwGg+j1esXXG/M5DkU2XRETsVl3lOssY8bStlf2Vd7QgOdk0pNrkWFjW2OGjN3tQ2ljjnnz53ms+N52oe5etDf7Ej+Hw/qR7+93Qf5zktNjhH26vb2Nfr9fCFxIJmSLZ8xms9IeioAgCK+vrys7SxtX4A9osxMQ+X0yF/Elj08mpQAi/BBIOGuS1xowGI3YdGQGZs4U4By8uwDBrYFiXZDjAPExUGLBdwYqE07cy0qSldffZcKF71FeznFAmJUpA1+cs9f5iNhsPYszsUEw+HXbsmJw5CCAv03E+V65usPg3gYG8GShZ30Tt8fOKAc5NkoOenHgGTxTVUO/OIiynNQRU5YplLOOAPix429VWN7X7f6lHg4i6FsDSstDLv8HJLVam11j7CDtyLArgFjrmglInk+VFAuYci4O37tE8d3d3V30+/1ot9tlu1XIJ7JOEVEpvzdhHbEx2rTXWddMptqu2Lln/f8xUoJ75c98PX0DqWuSKE99qnPonGfA7b/pP97f+ss7IQvOIHPQlxl8cD8+d1VKDrJtT1kMstFolLJntiJeLBbFLl5fX8f5+Xn89re/jYgHfWcxWyo8kTWDpa9x1PmC7EetKyYtlstlDAaD4p+3trbi9PQ0ZrNZRFSrm1qth/UrPCWS7/CzER+vH8c5TtpYrv2/5dDt5vmc6zE0qPeRQSr+wO+V39HtNcDOoJdzPM3C+pV9tgF3RDXTantoueXe3MMEbk7OZJuQMUd+Ps/x1vT0pYNPry1m+eF7fKmTQK5IxncjFxBL3MOLx5OcgvCgGsCBGhlhE3gGyBlPPSYTPtz/DhB8b8vF1wDZbpvtp20LeCQiStabyhOqcyEcptNp/PDDDzGfzwsRgx/jvbAH2Dbekf5utR7WSSIA9uYQLBxuPMSSApA+7MjVaj1UeoxGo1JRHBEFA2JzkUX7R/cBsubpqI1Go/J+BPAsts2BrHljEvqQag1vmHR3dxd7e3uVNWq9dhPt9M541uGMVYxHGVvuA3HgIM+EIbrnanGq2jwzxLbEz6Bfsv9mbJrNZsE1Nzc3JUnhxCe7c5mw5MeLomOrlstlvH37tixsTtBM240zVqtVHB0dxdbWViwWi2L7WXvrbyGff+4j4ycTv/YFjpHwt47fqIRB/1uth2r+s7OzsqSAp98jD+g7JCRLS6DX9lk5dqPNlhl8uIkf+zJkD5nMWC9iU5FpG8yU3RyLuf+MCeqew5F9vmNOf/5Y/G9sy3M5n9/+zPfwtcahj/ENfo9MVtYlrnIciq/udDqVxAVxznr9sI7u6elp3N/fF1+M/afa1btS25Y4Bqrru8f8KH/X9e/nOH7SQuc2uvyNMcWgOQPiQDULHAf3MlDxdxYWH2ZaMaBkijw3PwNGK14d+0u7/VPHalowM9AwcK4LtjKxgyDa2dG2rDQO2nObfE9/ZqPBvXKglQMI9wN/Z7LE2V2PKX3OdQTuXOPn895WEAIqKzX9hsF3dQWgg+1Jc9BEmwwKDe7rAi7+/msAazamOcD0Z/nz3O+/1CMbY5MOBEhZ7jDCkFIRUXbfidjIip0tRAMALyI+0hfsD+uWkCGMiEqZ83q9LguwY5cajUb0er345ptv4ujoqOgdlXfMw7d9MnjwVrfoe7PZrAByCLIc5GKf+MwALTvuOplw/zrAyud5CgB23LpOW9yn2Sllvff9bOMyqZD/dnCCg8Se2r9kQpzsugN4E5MGU8PhMNrtdrkfAQrrnNzd3ZU1wxqNh6qgdrtdpvDlbJL7/0s5Yh91trruHAMz+uPo6Kj0U7PZjOl0GuPxOIbDYamIwi8/e/asItMObC1XWaYek03Gse67DLjyPR2g1n1mf2s5ys+q85nGF5Z53h07YFIKAtsYgmdYPvmMccDWcD/kDf23/DuAs17i/0y6o28eL65xZput6ek3ql8Y88ViUVn3kT6zXtEGJ4z8GeM4n8+L7njHNqbyo9/YWypYPTa2p1mmPZZ18u8jByX573x8LV3Ohwn6jIMajUbxORB5YNr7+4dFam9ubuL169dl3cXlclkW7GaR8BwcI3vNZrWKgbVDmVrZaDRKYo8x47xGo1EWMV4sFjEej4tcEwPgM5F5xs66G7EJ4L3IthMo6A0L569WqzK9jz7zBjf8n3EjuAG95v70NVO7kXPIKXTba6gZ32AzCAwJFLOPXa1WJRYyyQwGMuG7Xm/W+KojwXk/J/YdaPIM1r1iLCGAdnZ24vDwMPb29mI0GpVKjKznPCtXu+Cnebe7u7t49epVvHv3Lp4/f14qzdlcxAtus2vfaDSKiCjj4Yox5OLXepikyLEhB7rOmmTIESSp7wHx4OmiEMDr9brsnHt8fBxPnz6NTqdTlriAGMq2xXJiPJttkW1znd+zT+d7Jyd5b8538qHu3o65rGN18ZN1KMemJkMfu959YT7ApLFxgjEoz/J4Zlzi9vl+EVHBVv4cm+2+wv9Tccgi9ZeXl6Uyiu/Btd9++21ZX9nTPFmrmL7N+CjHHXWf8051fMnnPn7Sasp54DCSBnV0iMHkcrksGZRMDJiUyEoSsSl7tTPxOQxiNvpWHD/Ln3uQDJzrMqr+beXy+1g4+S4iKs7QBoHrOHJbM4jnnB8bnyxUXOP+y8ST/87v5feuM3BuO+3NWV3uR/YnX2cgms/3eThJHJoBOSDC7aoDvBh2B7MGwDa6JqyyIbTh8ZE//ymKy3U/tivRL+Gw3FrWbLTcD9gJiCGCmeXyYUFMFhlvNBoFCDI3vdPpFHDJWlvO/GKcAddkNyOigGXWovLUV7L+e3t78eLFi3jx4kVZ7wSgz+5keYdMP99BI+/qDGd2AtnJO5Cus1mcixwbhGbiwEF8diBZzjM5lHXTDpvxpS1cnwFyPjJA4z3q2sr/JtyzTXdw7ucaLDE+nU4nrq6uCuFJJRAZ2svLy7i+vi5r2jSbzRgMBpXxcBXcY3bvcx/5GZnYzeeaYGw2m9HpdCq26/r6OmazWbx8+bJMqUIXIQ+8exYyzv0zmZSDS8tonc+0TJuAjNhM1bSfQmcyGLQcuDrB/syZ/pzNbDQ20xC9MzAy7IC0zt+5jZYL+3aeY5LI05VpJ33KdXnqjBNStrW2CxmgU2mDD3UfY+u8hbgDIoIgP5s+px1+bl01SafTiW63W4gD+1PsJ4F8xIYoZDztN/iec/6SvmRZzEfdd7YxX+vIOJHPbB9ZZ+vk5KTIB4tW7+7uxsnJScxms+IzkRv0HVyETWB6LjIPXqY60mtBItvr9aZ6hzG/v78vSZ/JZBKXl5elEt6yzbhDGqEXPJ/ntdvtQryxODmy4Cp9ExfILevo0H/Io5+JHYP8oo9pa8bfBHG8AxjE6zWBLxlDyBUqALkW+4tccz3v4SohsA3XMs3VSaysJ076O0HEPRgT3pGqNuyEN4xhWmWrtZm+yBIGxFW00/Z3tVqVBdVns1n8/ve/j+FwWEkc0ufMXuh0Oh/5Jd7D7/BrO7LPcswRUbUx+BN01tVxnH97exvT6TQajUYMBoMK6cl1g8EgXr58GYPBoJAW3v3ZY5pJEGSXthlDWX7dbvtEPuc9nTzxuDOeng5s3czYJmMfPzPHpbbpmQgz/uD77F/s290PPtfxoa/NcXqdPOT2GDfzuZNQrs7HB0Nae4kkr4+3WCxKcpVNILa3t6Pdbhf7CPGOHXX/uajEbbadNq7JvutLHZ9MStmIu9Q2zwWuA9U+MrMfsXE2ee2mnI2wgCLYlO7inBh8OxwLTw6MTEi5YoD25aDLQlYn6FZw9x0OwUbA/WoH5MDLRgBByu3j2W6DlSVPkYuoX08hA3L3YZYF3p3v7bR8f/oJ0Fu3Gwhj7yMToPyd38OOEoCMUrPODFnA3Ec2PO7jTGLmd//UI4+t+9b3wilFbNYO+KU7aFe0RVQJiPv7zXQ5j896vS7ZemzHzs5OPHnyJN69e1eAFAtXey0pMovMlfaaJTzXU0Qy0cjzDAL6/X4Mh8N48uRJAUzcG+Bu0skEOO/kxZANrsgUM/4Ew85aZ6cVERUwkIP67FDrdJjvc9CLbcT+RVSnhuEEM7GQ7QH/Y6cysHCgbsda10Y/30DFYN7nez0NO/fcLgAabYOAIpi5urqKy8vLOD8/j2fPnpXKOo9NLkOnDV/zqLN7PkxURlRJEJfvs4U3U1yazWbZJrjReKh4cR8i674vz7F/z3Lqc+1n7af4LstM7ltwhvXE7cs+Lj8nBzlZBrMOmQiybFrHLKcGefYXztii+4xDnsrO/fjbxJDJQLc9vz9BLW2EAHK/rlarsjMlC6B6KiFtJiGA7Oddnow9XKXM9JPBYFBZHJd3h+SnmpnPcvBiOaob4x876vx0Dmg4L+O1n+OwTEdsiMr9/f1SdWJdajabMR6P4/T0tMgehDIyaTmzf9vd3S3ZdGwgY0ylUtZr+zF+MzWeTL23F2fMITlMknqmBPKF3FINwjVUGLNOFsEYJAmJIt7RVYAQonWEJ9czZQ9MsLOzU+SX/nC/s3s3iWgvP0Ffe1orRI/tR0R19oOrDdEr67OrsqhOs0+jPayLSYVco/FQzQYJ5d0HIac5SNQRP0VEwU/IA32HP0c2aBuyc3p6Gnd3d/Gv//W/juFwGKPRqELMt9vtuLq6in6/X7G7k8mkNl75NR3WYWNg+79sZ+zLrBv8z+LWEQ/93el04vr6uvTrs2fP4ve//308efKk+ArkGznl+XXxpnExz812MPtkYyH7JPtddNc+Cv01kcN1j+EG+jLjL46MRXkvY2XOy8+0jauTuTpc8WN+BLzq681hGOMyFp6izHugr2B15CJv4NVqtUqinp31iHMvLi6i1+sV3W42HzaXcwLPfsf4KBN7GX/7sy+tqz+JlOK3g0+DG2c5fI1BbR5AC5KdCkbP3zvDiVLlrKTZPJ4LUI+orqOQwbyD6JzFtVDWDSzGIANSzs+AOX/ua/Lg576vU+w6o8ez/H9+p0zGmKRzJYTbjrJnRtn9nFl3FC/3eSYC/V4+DGJxpg6IIQiYKsU5BGd+Hxtk/+1A5jGwmsek7nsbIvdxHmsbHd6PhTV/6aSU9cyOmHF2ZR56SdbS6zpFPEzhm0wmpXKFKSA4aeSNzD4Zf8qgef58Pi/AC8Pu8n8qRPb29uLw8DD6/X4cHh6WKYJeeBVSzbYhYgMcskG3TXrM+TlAfUx+rH88L+tDduB1oM62lXN9b9uUOnttO5MJNAPo/Dn38VpE9Be23e2yDaFvkB9kBKBDltDA1ucD/J1R7/V6pf2eAnN/fx8fPnyIFy9eRESU7HS/3y9TS/N7fW3g/BhYtz2vs/1emPr+/j4ODw9LoNJsPlRPsGukp+BwP8uK25HlN/sv/3BuJrYyYHdAa6KxLuHDkQFu3Tn5WuQo96ttEzbZemI95ztkG5m0Thj4EQw/BgjtQwn8nT13lZUDF/eBdY42Mi6uJkEHDg4OYrFYlOqR9Xpd1piBMCJQJei1/2SsIIZtAyAwvLkF10J6XV9fl6kqHp86m5aPOhv4Y99nm5R1OeOir3VkDE0m2+vaGTttb2/HfD6PN2/exHw+Lz4oYjOVGvzMe3W73ej3+9FqtSoLYHv8vPi1idD1el0CYwhMpoDYTkCuoMteVwj/yzvgvyGe+Jx1sSBE0EFkDVKIZ4KXGo2HhNT29nZZiBwd4rleS6XZbBY/wfmQ8hBBJmHpV4g3EzT0p/EJ/cq59I2rRIw1bQPsK8Gr6KPtzPX1ddErb9SCjeL9/bNeb6bQUhEMJuJ57XY7dnd3YzweR6PxMK2RhZUbjYdpgH4WSyVgNyIixuNx/OlPf4p/+Id/KOMJBm+324UA7Pf7MZlMiuznmObXdNieR3zsvyLq46+ITSL+8vKy9AXE4Xg8LslWsDLVUf1+P7799ts4PDws+p5jGMe4eYZRTuZFVONAx8DZLvt/MDH6k3Fyjl8y7qz7ru5zDl+bsWm+Rx3GrYspObdOBrN/4H9jFce2nv7OwXNNKtm/oUuubASLQdZHRPH/tkPo1Xw+L3Z1tVrFfD6Pfr9fbGyj8UAKY1M8vu4j2sl3xFZ18vKlfeZfVSmVP89ZLQ+0HZMPKxLBorcZxjEi7BGbzDDCgcH/MQbWA1AHnp3N5chgxu+ZAzQ/08+rG3SDaQdVOVPo4LYOaPEcl9e5rQ70HgPjDg64V2anTcxxrwykHYRnw2jH677O4/TYmGVDYwKU5/r+EdXdB5CRra2tQlbUyYP7Lhuu/P9jgeJfOnLAAqkGKPUaN3X68ks7coDFu7mMfb1eFxDJ7kAmHLhPRJSFOFutVpkb7+kmlj3WwMg2hF1xILcjooDI+/uHhQCfP38e3f/f3r/0yLpkd/34yqyqXVWZdd+Xs0+32zYYLGwhkAVmYAnJAyaAkCdYYsAECfMGmCJgwDtAIDHiNfAT8pQBAoGwPAHExXYb3Ifu0332rS6Zdc/8D0qfyE9+d2SdfS57nz79r5BKWfnk88QTsWJdvmvFiojd3RqPx21zc4KXKOOq5c2RARAGahgQA4A07snXDqrYGae+HghYRfvkJxuKvL7Kufd7+N2OCSWDjgb6HkPrRTvjltOcmKA98APOeBpJ32MniLYAmplNpg0eI5aNPn78uN68eVOTyaQFDqqqZe2xdIY+9nTxhygel6p6a0yyXc4s8hIeNla9urqqw8PDtpm/neHkHwdBrC8z6Mq9SaucaOBeiu2o9Qdj7uBQzx5nfRl0qKq3sIFtlK9Zds3TtDFlNcfD+om2J07CFsGzPduFg49+zH2fejLntqVNR5bcn9vb29rb22sZHdTPUioybwjK0w8vi6Ivdtw5UIBlJ2yejM4jY4N221mHrp+np7J8nn5M8JzfE/d9iGLsRHvIjiAQ6EwbnIsf/vCH9X//7/9te8fYWai64x02tAZLm2/Y/Jh3k2HDxI0nBE5PT2symbT9SggweMkZ7bbc2NY4cMuefmtray0gyUTF2dlZHR4etn0m+Z1JBbLGwJQ4a15ST33IjgNPzhb2HnH4HDlJTkCNgCt4MvEK9VhmGa+tra06PT1tAUXbdC93BHNkRijXZrPFkkT0N9lPPOstK7x0znUgqxwug3yPRqO29AveM+9MJpOaTqcN/7Ckh73hLL/r6+v14x//uLa3t+sXfuEXWiBsb2+v6bKLi4t6/vx5vXnzpqbT6Vs+xbexmAdt88wfxjEOuFqmuHc6nbZ9LY2VNzc36+d//ufr2bNn7VRE217eb7sJ36b/5yCv25t2nZL9Sv/Jf7TFdgcbZ+xnW+Z6jQPT/0udnu1yH22rjCFdby/pIu2Q6+A7feJ+b9dRtZxFTOnh61xxxqTU+fl5O810d3e3Bdz5m06ntb6+Xo8fP66bm5t69epV81OOj4/r8PBwaUKKSXcCw4klzCu0y0sLsy/vu3zhoJQNqiOAVW87QwbGNpAIkxkF5yFTxO38VdVbANiOjxnLiqDn6CWYpo92gHoOUzqQCZjtlHqGv+ekui47bMkwVhD5Pis03m+AbLBPu3qOHrT1eLl9Fqqew5jjYUF2UKpHa/fNdbvwLKnFpq0zsDzjC7hBabgtHoP7AoAZHFoFhHv3WB6stOm/T7D5ou/4pouBJX1DmVEAoShcnhkOFyfqUdfe3l6tr9+d0OI0de/bwPtwoGazWVtqx+wCBceJMdjd3a3vfve79eTJk9re3q61tbXmnNNOB40o8KOdSHgdQ5Bja8eQ7+mIpNNuHrEMpZGkPvNRAgnrhR6/pyO+ShbSEHNP6jG33/TnOwEq7IVpCZ9ULetZwJTbi8PgvjLm1rduJ+1g43M2er66uqrT09MG8MkYYlaJZ5M+H6IkXXu60veav7xXxM3NTe3u7rYjgclEpP9sik1moJe3Wveje22He6CsxxPZn3Re3XbbEPOngX9m2hEAMq+m/TGd+N/BHPOZJ3Q8k082BX3ojYGXE3Hd9pTgDHow5c/4x46OA1W0K2fAq95eQp9to9+A3PX19ZZNwnJlghGTyWRJX1ctzw47+2NnZ6dGo1GzabSZZVleQmWZSp3iYlzyeXLX47lV/Jf6L595n8U4zLJFu7zkncDAxsZGTSaTevHiRQtWVS2w2mAwWFqSRyBkNpu1+6G5l4zyHO/DRjPGJycnNZ1Om90ms9I8aB2BfGSgIScS4I2qu9MF2dyboNLe3l5VVdtr0rrZWY0OKnl5XVW1696PCvxAYI57vR8VTlsus01fgD4iG5bhq6urtszQcsP9BBu2t7dbO42DBoPBW9uWeMzoF2Myny8ObfEpfrPZrGWZEVR0cJCJuPl8vrTtBRnqBASPj4+XnFUCUsiz5en29rZevHhRR0dH9fjx45rP53V0dFRnZ2d1fX3dDtvY2NhoE4hVC3v/bS2J8dN/Ma5hzK1/kJuLi4s2cUYAluDl48eP6+OPP26H7qDrbTMzQwr/B9lIP6RnJ+gP/xvrch/Ffovtre2v3+lEBQdn0jdOvz1pnZjYfMT39E+z3fdtO9TDXIyh9bffCw5BJrx9h7Okkrb+JGh+dnZWr1+/bnZgNBrVYDBYmiy4ubnbx/Hx48dL+42dn5/XixcvWtAZ3WwZcxyml8GV22PkxN59ePTrKF84KOUoWlU1ghhoOkgC07kOCy+CBIOT5UIqbdZXVc2Y0hZHPxOUpINlAekJQ/7dB7apzyUFIwG7QTb3WzgTLGX9CbQS3OdvBtu9djgQZhCc/bfAppIyWE+aWRAzau8Al52E3jg6VdzGMOs0CAb4A/B4Z856AWqswHhXL3jQK58HbD2zDJAgIGWh5/0fCih/lWI6GlR4BhFnxWnvzgQgeLS1tVXj8bg5zIDO2WzWQBN8wKk9NrrOvgJcoowPDg7q2bNn9fz586WlSyxngKcclIUnvf9DVS2BAQekeuvme7JQ1V+ikvclP9iQ9MYhdZiLwYvbYJ53EMhtTIB1n26yXs93uE821p54cPCv1w6epViWkXN0AwFGO2Db29vNUaZd0+m0tre3azqdvrVUxJMDq2j/dZee3K96rx0nZMUTRdfX17Wzs9Nm1jxjRjAiAwVV9VafzVvmNQdms622+1z3d/MpdTsAwnvyvhx/17Vq8ijtmHnauj+dw9RpTolPW+t+psPhDI6qRfZmYhLkwo685T4xQgadHUSzM0Jdbtvt7W1bmrSxsdH2MppOp20jawf/0cEsv6JNONe24QRDDg8Pmz4ni5F9+jxmq5aUvAvv3yeTq35bpSffd1kF6MEBLJOqutsHjr1kjo+P2/477P/m4I3tGOPC/kkEno3RmcipqpYNRRsIbvAey6EDTfCCM6wuLy/bGLOJ+mBwt1G7T2QkQMYJcMPhIstrd3d3Sb7gYXS07YH1PPvKEmCaze62/9je3m76jmfZ9Luq2hJF0959tA1zII6+eTkhmI7r1jvIhPdOvb29XdqPyhnC8AsHr1Csmz1BxyeZVGAoZ8N6WwvGGuzPO9AbZOMwrmActxceIChGAPXTTz9tY7mzs1Pz+bxNjMxms9rd3a0XL14sBWe+DXi3Vywb3ggf2pgvHZSynYTfTk5O6vXr1zUcLvbz297erqOjo3ayGvxIvYm5bN8So9JeYxv76IlLU6fkZEJv4sc0SbtM3fzZHqbv65LvWOUXuz/5bO8z7U/qFgdxepiaaznRYP+Y+lOH9mw+PtHZ2Vnbt4+DejjABh5jUnU8HteTJ0/aiZpVVa9evarDw8Pa2dlpkxUU9Ad8tIrGTtj50LbyCwWlcvYjCe0O0IlMTcUBzE4ClqkHowiBLDwOQuRnD+ilIDrDIcFzj6EdPOkxvYU+AwymkwG+HXfahmPmd7sPMEovcslznkG10bZyMlh1UKqqlu6zMqVvFCs+FG06EqnMMnsknRtfz5RpjKY39MOopnBxv9c6O1uKfpsf0mF3u3tj6e/mg7zHRtd7KSEL6ZilI/XTXDzedogYe3iD7z6e1MutchPP169f18nJScvumE6nbUbUvD6bLWaDqQ+a+fS+vb29Ojw8rPF43AA5s8sOxJinvI+LnV9fS+e36u1gDjRK/jMPWf/YCbUB45lVIK4HKFIXpYz5PdZh1lXp+PfG331yPe6f5d0OhwO1zqwFwOb7TGsCmv6e/baeZU+LV69e1c3N3cmPZ2dntbe3twSSzA/WEx9SHnsgyMXj5jGy/qS96+t3p7YcHBxUVS0ti7euNnih9CZ8bE8y2JGTDtZvXLcMJzj3e91XX897uce2PPUqxWOMvCcNq6oFjWxzKQTP0Ek4db7ffbIzjh40Hkpbz2QWzxBA8mSgZZf34YQajzlzxjaaNhFkur29bXvLMIFycXFR4/G43rx5U69fv27YAlvMSXHQkbYzk0s90Jhlfg7IWf57jkwPa93nvHi8e7qpB64/JOC2/We82KdpNBq1pelra3cb2r58+bI+++yzFliuqrbMjZO34AOyX7CLDpYQRHEdL1++bBsqIw8s4cT+MRYORlgGwV8ELxw0ob/cC0+DA7D9BHxdaKvxnYND1tdeKuisqqpq7zMGQ14JHri/FxcXbS8X5M973fL+29vbFhiA79P5Bs8gH7nHEwGn+XyRGeelg7TbesFyD+94WR+HyJDVdHZ2thR4Qj+ZdugAxovCc48ePWoHhcCjPtHQNp9sqY8++qiePXvW9pfynjt7e3s1mUza9w8pf++jwEe5UqBq2c/gj/0r4YGqO3n80Y9+VKenp+2k+p2dnXry5Ek9efKknQycfpP3UbZtcPCBks/2/NK8h/r83XjL705bzqf1HbrD+6i5/sRgqbPTr+rh7s/DpNYf1iMZB8jJEuMpt836J9ttHASPeNLJcQ9OhT45OWk2kwzZ6+vr5s8g596WhCwpdAy/c4BT+g5e6mtskONoue5NHr2P8qU3Ovd1M6YDHHbKVhWMIUfcYoC8TMup8zCEmReGTgfKzlDVcuSXkoyYitbXEuwYEGZ/+J139NoEuM9odPbNdWKsMxCYQUE7LRRol+3KiHcP9GU/fW9VLfXDitl9SppagLMNCWr4H5BiR7OnZNhTALr6eFvqz/cnoKJ/ScceuF0FgAEx3r+jB5Dvq++nsZjefLfStaKH5py8l4bMQUVm7b2nBbMDDmIwftAW/hkM7mbvDw4O6vDwsM0YcFw5+1F4v6F0Wmk/ey3Y+UtDnoZ5VbFR57tpmffaObOR+Ly6DDjS0e7JecpBry1ct9Hq3buKBm6T60Lm7Cybd9w36wgHPh3s8/jYyBLEfvToUe3s7LRjrNmXg2fpAyA9s7w+hEx6rD3eyWP003org/Msz9re3q7Dw8NaX1+v4+Pjto9I8lHareQ3PtmnL/mHYj6zTcr2Z3H7V9Ejs2KTP9zu/HQ9eQos/IJOyKAXznPa8AzymHY50wrGMb8709uBJHSdlxo4iGa+tS3lXQad/vQ7mTAgcISNHI/HbQnf5uZms6OvX79uQJjsVmTE9CTI8urVq5aBRcDES7AMznNSK4OW/r8niz3MYh7MexJ/fIhi/odOOCXQdDqdtkDB5uZmTSaTtk+Xsz8JVLLfEPc/evRoaWkukzDQe21trdV5dnZWVbW0vyWZUol7rZPdBxwynBYCU4yfl1ezJG8wGLTgDzqJDdxpK30xDmQTeJ8WSNuhC8EV62wHY2jbYLDYzwcMeXFxUcPhsAUFLB/wpHE3Y2KbhRwTyCIobFkmkG2fZnNzcyk4lpmV1kfYMi8ztOMLjcgoB0cNh3dLbdnugOw86GhfAJ5ib0+CeTs7O+2kQ061ZomlJ0I+/fTT+gt/4S+0vebIItvZ2VlaDfO+Hdz3WWzTcnyq+jgOXsm9/tjgnIy3p0+f1uPHj9sekMab9n/gjfSdrPd7fpXlI+/Pa1lPBl+MT6qW91SyDvFzbhN6xIdX9d5h7Ee/UxZdb0+v0yb2caK9Pbtguc9go/uV8ZAe7nBxANvvvbi4qNPT03baJvv70T5sM7aW9rGU7+rqql68eFHD4bAtv378+PES7/Hp4L33rbQd5h05afS+yzsHpRzxTCfOM2gIjCOAMIzTvKuWBYBZGiL6CCvMRzDGCtqOEe2yQXQhuJUgOsEcJcE/7fW77KAa0GdwyMoJ4+T03nR2M4jjKDt9cTswYglWqdNBLP9uRWOmcxuo1zRnfM3EmUFivknnx7T3+xm7BPrUm84sffQMmkGxZ9ngHWagPDbmbY+9eWGVI+5x873+PWnf46+s/0MB5a9S6Bd7NaDoB4NBmwk1TwL8ADwoWHgDxb+9vV27u7vtlCjqZGbQPGoAtba2Vru7u/Xxxx/X3t5eO8lvPB7XaDRqID5nOuA773PldmcwlGu9YIXHMmWbeg2OecbtSQOQDpR1XY/f/Fu2NY1OBqjd95SRLKt4NtuftEgj7vdD/zTa5iGfOOR6DbgYzx6YJyME58eTC2tray0oSrtSxt93SRvbs0Xo/arlMQSo4OARDGDjaYJU1GMA7SCh7QnZqT3g2uNLj40duZ7dpZiXXYcBp99h/k2g67o8/s5mStnnf/4ymzvfbX6lDr/X+xzSTgI6tHU4HLY9zdwGb1LqcU6aG0z7fttR+IEgJOOYdo9r8AZOatXCaR6Px/X69eu6vLxsy6DJkMLxxh6zcfXp6Wk9f/58aeKNiQfzqum8yk6u4h1f68lpD0h/U/bVs812erxEy5mIOBdgvKpqwcDT09OqqiUHl6AEuq6qWgDx5OSk7ePEUeJV9ZY+fhenik9jrKpa2gvJwf2Dg4N6/PhxC3ZCCz4nk0mNRqM6OTlpdhp5cjYzS8h4BxkEBI353/QZDBZbONB2ZxQ6K2wwGNTl5WXbuyezs+bz+dIJhNYZZEM5WOgxhd5gZ04+nM/nLXjHu3zynnWH+YYgorMYbJNZzkeAEjllSe36+npbFmSda4xAFhy0YDxZojmbzVr2Ozw6HA5bQIxnvL/X69evazweNz74NpeezTG/VL29vAveraq25PGzzz5rAUTs9dHRUe3u7i4tsffkf/pL9+nStFvYKy/B9T3o6sRfxpLYDuOS9KOzLebP9OMJlNznB7mf9+Hf7H8Pp2Tbbc+ramkMfb8znV1v+iZ+znThGe8vSRCZVSHoACbdbaPH43EdHBw0G8t7dnZ22v2np6dLexA6yA2tyYomW9b6PMfJPPu+yxcKSqWzUfX2yTMeDAegVoH6BF3M5LguZpF84oaZwcTrOYqOLieTZrGwufQcKg9Svnc+ny8JGIzq5SZ+zt+hazpz/O9NJX3NwTAHnaArQta7D2fENLTiSFq7neaLnlB6CUSOndvBc+kAp4CjPPyequUsuvl83tLgoTszOghfKnn3L/vzeeXz7ku6WRmuqu+nvTCeOP/+c2o6QUuAEkummAFyxN6BIWYWkX/PDKayHQ6Htb+/Xx999FE7VY90V/Y9YSNZ86L1k508B1f9zgTKyBb0SHmvWuZz+kVJneF6/GzvPtdBoW/WRw7e9kpPB/X05CpdxXt7ht4yxXfkzSDA9OEZj3f2ZVXGSNKYJbOAerKlDg4OajKZLO0pNJvN2ia1n376aavXffwQJWlI/xPU2Eaan2ezWQu6+dQtNpdl8sdgFPpx3XTEyYP+yb893U69dv5Sx5qePaDZs8Me4+x7BiUplmP+91Jg9938RB22cTznrIDe+FGHU/tzb5jUEalvcszTFrvdHjv3FVtnsG3d5foMwhlzsu0IMOzt7bWMGM8eO5sLR5UlZL0gw6NHjxoAT12TvJR2+D5b27vH+jNl6PPs8Nddehi66u3M7Y2NjTo/P69Xr14t8Sh7IYKTyf5l+Rb8tru725yd29u7pVxv3rxpTi/BfS8fQ17dVpee/bHTYhn2HkbokMFg0JaaYduHw2FbLkfmKjwEJoX32EScwBu4jvvpA4EqdD/LBL2vk2WU/30E+3x+N9nFBAb09b5Pg8FgKYCce52CbSybXrJYdbdsi7HgngwoUy84Cyzvw18I1CFnzpjyWIGJvWyRDdDpD0sxaVPuuUowzJvG+8TIqqqjo6PGb7SZwPXt7W0dHR3VT37yk+5k17eh0O+cPGMs4X/zmSfU8VWGw7sT9168eNHk6OjoqPb29tpEKr4d99vPcUZs+hd+JrGEbXniGuMz7k+bax22anKhp3fzt7wGrxjr8T7aljY6scUqnnJg33TJRBdsYdVyVhNtQKZ83RjIWMLBbn6HvkwOsBzv7OysLbtLvMGy3Kurq3r69OlbwUr0zdHRUb18+bK9j33+0qdnH1IfBmWaWNfQ1x4ufR/lS2dKeUCcEUVBiXNPDwQxQJzQxT1E9u1YpePnelxfXjMAyzbQNj+Tbb3PEXSgysIEU/aCVl4DzPOrDHsKpelgw519s0AZ6CXgt+FLB8AzQ67bBjed31WKx0Lfi7omGE+amU4AhnQ+bdBR1qzJR5Fvbm7WdDp9q0/egyBp6THItt1XPg9AW8A/D2D/tBbzvcfj+vp6aa+LqmWjAt/YIeP7+fl5TafTtiQPIFNVTYFTH7MJBKSeP39eR0dHLUNqf3+/nTYGsEu+JYBmXrAMWv/09Ao6jOsUO+7pfJsW6TyZ3yw3loHUDaaledt0df2MXcqh+5CAIu9LWc+SOiNlzqDH+id1vmeFGTc7IqYvYMI6nd8IRhOoYbabzLnBYDHTRfDSbU19/iHKfQDAIKhqednpYDCo0WjU0roHg8HSSZ/YWk9kmIZVCzkgS4rfzHOmL9cMCHNc/ZwB6Crwa8CZdOnxS48PekEf21ra6TbhDFsP5GQNzjJtSeDPmLiNDlpzf28fL/N1r17qMJZIPOJip8b2Gx6AFgQwqGtra6vJG3ywv79fg8GgLf9idt9jzOQCQYfj4+M6ODhY0nfGILTJtiLH9quUxHi+/r4Bdq8tzi5njNfW1lqQoarad/bu4X4yeMBB5+fnbXwJFG5tbTWneDab1fHxcX322WdtXyDuJ2jDuDiT74v2CR4hyES/wGLn5+e1s7PTMmxYvsVk0d7eXlta5n3OqIPMPZ71ZCO87RNEwSBnZ2dtCZmxYuJty4VtC7gQJ45MAt7ryTTbLHSCs4cYV/QT2eLwBGNAcZajZXh7e3spY8x9wckk6JTjySb2YGMO+vBy3Jubm+YkDwaDpVOKvUcW7dnf36/b29s6PT2t4XCxRPD09LROTk5a/evr6+0eNurvTTx8W4r1LI4+gYOdnZ2lIIT9KfiWsTs7O2v8eXR0VM+fP6+Dg4OW6dzDibyvajm4kvrfdteBjrT3PTzeszu8PwNRxkqU1K+py42v8/dVfle+I7EzvzvIZEzhSRDaZ1uD7Pey0G2fkhb2aaw7GJvZbHGCLzgNm0dWIRlO8/m8TSay7Qt65s2bNzWbzer09LTZYn4Hj4zH49rf36/hcFiTyaTpi6rlpZXJv7QfHJD3fSh7+YX2lLKCM/NjzAwwWRtLNN31+H9SuTmdAUfTYM17WDC4tMF1JbPC2D3mr3p7Rs7FzrN/79VRtQiKmMntyNDvdDJdr53cdAqt0FAsDuT1wGiPgdwv2pOOnJ9NpZY0hycyqOMx8JKojOivKqYh3x0VTuAOwEZZwytOqcRg24mxI2KnBQXl4EOPngY1q8rn/Z70fRf6/DSUnmNJkAdg4vGez+fthD3GBuOMIkRveMPPra2tpU3SifyT7vv48eN6/vx5y5Da3d1tWVJsaN5zhh0oNX9ULRvZnvOb/AOvVC1nVlCy3nR2eZfrXRW4dZDEBtcy2wMJPE9bU5/7Hv/f0w09By/BTE4gUIcdAAOh1J3Wd9SXfUQXGvh7U1X0D3qBE5y2trZqb2+vzXQzE099poGXILzvYllJp4nf3bYMnEIjHChkh6UsvZlROxoU6uvpa39PwJLt79kD98E8kmDPbXFdaZezruT9zGDIIJMLsutleT5AYTBYLCX0Bsr8njOx7l/WkYFl2+Gkh+uzDuCd1sEOTnGveSj1IPxuIMq7CTAwOTCfLxznra2tev36dZtErFqcZub3sjwAjMCzXDdvMI6pc3plFU+uuie/v4tdfh8F+oK9wI7YQJZFIb/Hx8dtSY8dr+Hwbrb9+vq69vf3W1ABfmfzZPYCIwBEJozp4Qzl+9qN7SCIMRzeZUXt7u42O7u1tVXz+bxl6qyt3e1dxOmf6Guy1y8vL2tnZ6cFrdCzYIH5fF4nJyftXQS2OAzFWScET29vb1uGdGbCDwaDdh2s4QDCbDZr/dve3q7j4+MlDEsbqhY87Uwl/CEfAkBxkN3yCyby/9gsgofmGwdBZrNZ26+Ov4uLi5XjCT4Ge8ELBMiY0IUn1tbWGv+x/YXt6sbGRsNar169qv39/aq6m0R8+fJlffe732288erVq4a1oME3IYNfl+zbloFfPeFv/8H+3XA4bHLIiXuPHj2q73znO0vL9hJzotc96bLKhrmvfCIv2BI/b8x5n89uXZu+Zy+zhmLM6v70gky2Wff5RMYh2S7fk31LvJL9d5uyT8ZQqyZB0UX4pWRxIltMEBJcnkwm9ebNm5aV7D22CDqRvIONrara29trenI0GtVwOKzDw8OWSXtyctJ0q+U5eSlp5f/ROWCEVb7w11W+dFCqatFob3boTfxubm6agaIO72lQtVgeQFCKDpNGmrPBCSQTiHENBsk1qn7OwuSARzpH/G5QS//9PcGiAXgCaO51gAWF34vUktrsdrsdFuJ8h+lmAM9nz+GxIGfkOWeA3U8bO/OJAXoqBf7SKc868s/P5R/GdjKZNB4EUOB8Jg+53h6vcU/y0fso34Sh/jLF40dAyjKVDuP29nZbPjCbzdr/ZHJ4KSBHVTNjTwHkbWxs1OHhYX3nO9+pw8PDpf2jOGkvjXbyfQauq952kPy7eQxZAKinA5+GEZokH/udNsKWsV5dVYsgs+/PwHPyUi8Q5O+993yek5jty2BbT//2ZJ7n0TG5VMwzTL3xyvYm+GJZwv7+fktfxuDj+AAEoKWD0x+qJCDzdffPenowGCxlP3g2GxvtAITpknznDATo4PdT0naYV63vKRmMNB9k/xKM92Qh9bgnKFLmeD/32s4yvtDNE0N8py5kvhdQM06iPvjYgSj4LJcqpd1Ne2/wb4B8n0317zlxZbvuDESDT5aIsfQGJ9b7SrJBKzo7AT7L+bjmg0dyr8/7QG+O/Sp+7Dklvfs/pExTDPCdzW2dd3FxUZPJpI0v2b5Vy8sv2YuEIDv3n5yctIAUYwgfkXHMu7zpdJa1tbWW6UKQh+tsRj4ej9u7Ccaga+kbASBnXpIJcnBw0NqCzLCJNhMCBEIIdO3u7r6le+ijT9glY8gn6nly0vtq4Xeg+wlcEYSdz+cNS3pJc1W1CbYMRLG3FHQA49hPwm+az+fNsURX44Sy7BGdRQCEdxjzIsu5NMc6MPUVdGJJ7Xg8br4ZPEOw1PxLO+AF7ClLRj/77LP6zne+0/aqm0wmLTiafsq3rVinmibI12g0qqo7v9X7KePncdra2dlZra2t1f7+fh0cHNTe3l6Td+pk038HEe7DZ3w6e8pYpmr1dgSr8Bqf+Y5V2NS/p4/F/4nDez6ebVTiDL6j93xvtsO+yCqsks+iu+wP8pd+qtvEc0zomEcYQ2zpcDiss7Oztt/nZDKp09PTNulOABpbgb6YTqctOI78MiGP3plMJjWdTmt3d7e1wbQ2XXxYgfnMPOR4xfsqX2j53mAwWEovtUBawTr9KwGRiWGhMDOZWdNgJ0NTDHIN+hiAdEaTKXtAKJmPa1Vvg8JsvxnUbfbg4hhQt4N7dhZonwFxgnHT2nTogbjep2nQA2r3KZ4ci8yQWJWVsQp4mp7ZLhSq22mlVlVLR1Hn3h/OgkjF5nrMJy6r2pyld1/yw33PfRNg+YsWZJqoPvQy4OI7jgpAjwAWYG42W2yYicEGKGNQd3d3azBYLO89PDxse0iNRqPa3d1dAqS5/4ODZc4y9BIWL6nIa+gC64WqWtpE1TKYMtwzuhgdyir57BluG5isw8HkVXWkkc73p3z02tjjCfruknLmd/f0SeouDKMD+TlLRV2ZBZefLGNh5pfJC2/66UAj/fqQwLmn3ykGPoAUyunpaX388cdLjtrx8XGjC58J7KxbPbHC+zJotGosrIfTLvZombxuvlwVaDQ2YIywi7wLubK9SJrxLuqwXbVjiUy7zc6ksgwajFof0N4MBhoneUmHbZvtZ8/+ZT2Wed7fwxQUBwodgKWd0JffkZXpdNqWaQ8Gg/r000+X9pDxJOPV1dVSxgd7/PROY70Pk5kH89rn2cweHsusyPddLCv0G3pjk+bzeb1+/bpOTk7atgNeVk42lZ0dB0mOj4/rhz/8YTtdr2qRweeNr20nLHNsYg9PPnnypI6OjtpJbGxgjeM8Go1qZ2dnKWPVgQr65JUTBDyqaml58Gw2q+3t7To9Pa3BYND2xiN4RBYe7SWw99FHH7XgzPb2du3t7dXx8fHSvkhgC/ZJGwwWgTPaCG4AO5IF5mzA+Xy+dJABY0SGFNjTAScHLewD8DztevToUWsbY1xVjd5cI6MKnoK+nMK1vr7ext8B7clk0vZ/4j47yWAu9uXa399vdZPdwVhZv8CfH330Ua2vrzeb8+LFixaofPnyZQtMc9DB52Xnva/ydci8sYzH8+bmpvFpVS3pUXiRMWOsd3Z26smTJ23ZlpfY8S7TyxMWPWxYtYylvLzrPv2Zv/X8SD9DAXNZ9/eCP/lpOvJ/TkQm1uz978+eD2Wfroej3Zash+/m/VV9cj/cH+TPPhIByvl8Xn/qT/2pevr0ab1+/bpevnxZr1+/rul0urREl3d4Vdmnn35a3/ve91rQnUmDqsXyQYJNngTq2T1sC9edLelsUvfvfZQvlCll4vg7wsJvOJTvwoCz2SI9nlkJDLQNdoLKqsWspYtn5/g/M3tWEXYV4/u+BNPc6+VrBvAGdBaM3nt9HUVmOvWAPEauF7XttdWlx1ip6PwOKwjTsEcf+pyg3MKFkOeyhQTl7lM6GW4zzyB08KZP4cr9NajX9PK1+xT1Khr2aNlTcvcB7g8JlL9K8ZIPF/icPZ+qFsug+P/29rZOTk7a/gLMMBHoAsBi7AAyVVWj0aiePXtW+/v7tbOzU+PxuM3UsaeGx858aH60EUzZMV85KEV9mUGTDlUaruQv6zfrVf+esoYMpNylbusFpRykT5lKx5CSerPHw34mA/h5PyV1W49W1GXn3gbeRtR072U15T2np6dtNhhDvbOz09Knt7e339q8NWn8PsoqOufsVPIHAZTT09OWqo0ssEkm/UU/ogttUx2gNc96DPzO+/gm5cltpk89YOjS0/MG9VWLJYk93dkbPweg7CSSHp8BuKRH2j4H53KPKNvR1D3ZXjv1Bvjmu15Q2e8wPVN+qvrZq9lWaOpgADrSy6rNG96ryHRxu3CId3Z2WuYMMpZj6DZ57FbxiHlr1e+96x/aKc6xI+OIyd7xeFyDwaDNnJNFhDPvpVNViyDSfD6vV69e1Xw+r88++6wtCXJA1QExB2AHg+V9g7wP48HBQdvjBnvtU9UIMLA0r6ra3lgsz6MueOf8/HxpeRlL/M7Pz2tvb2+JH2g72UXoavajGgzu9s/D9nNKJBkrBPDQj/A8y91MS9pIsAcZ2N/fb0FUL/GGlvg+V1dXbT9M9nNywNEYYj6ft+wj5HU2m7XlhL6XrN7hcNiWaLJkx9kLFxcX9fjx47ZvKpkU2Pjb29t68+ZNnZ6etiwKHyZStZA36p7P540eTCD2dODt7W3L9Flbu1s6REYlm7l7s2YOP+Ce95198T4LclW18D2MDaEPtCZoSxCQjP/d3d2lZXvJ/57MqFr2C329h6U8wZY21/dSb+96Xuth3cTc3JuTpvlc7zv2L3EzJW17tsvj4/s9+Wa8TEn77wlRYwjTr9cXy0i20dfW19drb2+vBYE/+uijmkwm9fr163r16lUdHx/XyclJyx4lK4r/X716Vc+ePWtZllWLJcHIG8v70F3oPXjV2XxeWsj3Xnbe+yrvHJSqWgxqLm1CKTulHcWDUAHWEtATiELA7CQaFMFMTss3GOMeK/Mc/B7DmBl9XwLAqrezuXjeTm0qjQSyZnTo6Hurqs2YpeO2Csz6HQayMF4qDJSc++oocoJY3pGMybPsV5DGzbPRPQeHQtvztK1VjoABsut1nRhj1m07s80AGHBF8CT5xXyUvPt1O6fJr9+GgkzAGxhozwQ5eg9wRNkxG0DavBUitPeGndvb23VwcNCM+Pb2dltWQFo4s6tun4MzzmjgGvyRMt/TGwAPl+TTDFJn4MWOlEs6WHZi7WhWvX2Kn59PAOz6ezqR/jvIkPXy/T5j6/dkn/xOO8ZZ7NRmOxPcuK7MPLUM2znGuZtMJnV0dNSck62trba5q09I8nN2Yt5HsV1J0OhiZ8DtYykPy16t1+fzxbK8pNNsNltaIm76ZRt4NrOg4LleAI02Zx99Peu0vXM2z6r3YPuqasm28hsy77qwAbyb9wHQaKfxgpcCJR5xv3ByLa/D4fKWApm5y33wIH2xTecen+yHfbYOs53NvphWljXf5xnv7K/ll4De7u5uWzLPO/jt0aNHDVhz0AC0MJDmL7EO7/88u5j3+Lt1km3BhyzWbQQx0TtbW1v19OnTFgCZzWZtE2mCUsPhsNnTy8vLFkg6Pj5umTGc4JTY2ZPHa2trtbe317JFCRIQJGM/JbKkOC2RySFsJpk7eQqgM1HBAfCFg1b0h+zoqqqdnZ3Gf940nX4krmVJIBiU06c8zjj1maHAslTwR/IeeMSnExI4pr0sofQfwSXk5erqammPK2dUVS329UMu2HqAd3uDd/Q4fYEe6Kytra12JDzHxIO3Njc3a39/v87Pz+vs7Kym02kLICUWub6+ruPj4zo/P284C7ozvgTA5vO7Ex/pw2g0asFHPqsWmcpMkFC+rUGptMMO9sI7iUWtr6uqLX/d29tr2NX2yYdGUFb5PX7O9rIX2OnZ9h4m7fmoxl5pt3o+zCr8Z9rYR+V9ucIm8aftUA8DG8+6XZ6UMj73uLo9SVPXsQrzuL3pA/MdHYIOwx6wN+6TJ0/q9PS0fvzjH9ePfvSjttE5OG99fb1evHhRo9GoDg4Oms73+968edP29PNkhk/8tF9ku8h3Jx983X5vlncOSlmI+G7gQXHkvqo/GwWjkPrr2UoKAm4nwErM7bDDYcbxPdSZ9/VATg/E9Jy0VcLeExqKBSABJM/QfmeLpQBSlwM6GOh04Pzd73U996X3mwYG926raevienlX0ov/Df4/L7JuJef6PbNMavft7eL0FsAWgSqPpfc7M+CxofH4Zh+SVp+n4PN5AN/GxkYdHx93+f2LFDto76uYx+Avrl1eXrbMDY/X4eFhnZycNCCKUgU4MaY+LQw+2t3drWfPnrVNqtl0FOAG/ax3MtAEbTwbW1VLgbDkMcsqPGHHPu/z2GUmVsqJjVcGYK3LLMdp3Hr6iXoziABNrCuyHZ+ZYMJcAACRD0lEQVSXxbLK8evdl/cmzRLcmJY9+uNUWTcC7pH7DCQwKwuYqFoc7Wsnh6UR0Mf962Vgfd0lwVPv/+QP2pbOGoEAnBVo4w00qdv7j2RAYJU9M6A08PPsrUHbqkBI1XImo+0Q9SRotM43jzoIR/GYm57uI7TzMmS330F35NubRRNgcN8cAPPmwgBRB8ITBDr4k/00X1uunUEG3Ry4SqyRuobPnBTwePCHw+3lQmyUXFVtbxpmbAmU0neWB+3u7rbTp1bhjuT9lEH/7nHnt97/31RJPEzghc1rf+mXfqkGg0HL2ByNRvXmzZu3MCu6aG1trU5OTuonP/nJUtaJ5dgyOJ/PW2CCDChP7j169Kgtx9vZ2Wn72+DAELiGJ5wt5exkZ0WxTJ+ToFgW4qVpxrgEjRhL729n+WB53Xx+l0l2cHCwFBAmkEfWNbKHA0gGEAEF636em06nLXBFIIi9l5ATJtPI+qNOJja8BIZ7qxb7xDoIQcYh1zJIUbXQZ2SS2yZSvzeipy8ExmjreDxu2YtXV1d1cnLSAo8UHGDosrOz0/iDsbm6uqonT57UcDhsNCf74+zsrM7OzpYOCSFA6ID3tzUoRZnP5y0gSxYdY479xcY6qMmebMibx97YKPW0bSz3JSbsZRn5Ht+bejPtZP7vcp9v5wkg12vbw/fEHD082As+WQbyfrfFxXEF0896MjFkYgb3uYfHs33G035P4hPjzo2NjRqPxzUej+vg4KB+8pOf1IsXL9pSWPbyY8Ln6OioLi4uGr/t7OzUyclJvXnzpg4ODlpCQNVi4s5tz+AT/GqbtcpOf13lnYNSKFKDW4jqNFYzjgNUvRlsZlgmk8nSfh4MjJdgGYQmUE9gnkGqdLJ41nWlQPWYPJ2rFPoE7b3nHXzKkv3KDRFzj5xVApkRed/rpXL+XKW83B6PAcWzqRianjIx0LdxNfN7vGlXKmW322mYvp81+mtri40a/R5m33g2FVjyBsbe9+W4eSxWKdRen/2u4XBY0+m0/ubf/Jv1d//u3/3KwaRV7f06Szo43hft+vq6Tk5O6smTJ0tH247H45aRwsz62dlZM8psLEwKOWO0tbVVjx8/XsqSQgaZZcglJOlsGRRYV9AH64f8rbfcyePcC1JxveccrXKSLI+pd3oOWU9f2dH1c26fg/02zD39sopvs2+f1y9+T3nu6fb8vkr38btP3ePT92Cz0BGDwaCBc5wM9k3BAfN4p75/n6UHRLNYl1YtB0zQf2QtZFDIWRTp9BnwUjJLNmXGAMu8kboQhzP1uoFYBmF5D9d69i3fb2eHMXcGkp3fquVMQZy4Hr7IoALtwAZy6qFBrW0nyzWSr8me8Gxz2mXu976e2W/LhnkksYv1n7deQE8z5rQtA5AGtgBndCyO+MXFRcN49IelPOY9bH6O5Sq+T55Kvvu8e75pB9jZYAQXGNPBYNACVFtbW3V7e7cfz3g8rjdv3jSHdzC429D7zZs39emnn7a9aUw38/BwOGwz8Pv7+3V4eFiPHz+u0WjUnJjRaFT7+/stw4mNu70Hk22u9Q0Bmq2trZYZk3vLssxvbW2tzs7Olk50JEBF1jr7K7G5N3KDTLGZOgGn2WzWdDl4rWrZFqB3nJ2A3ndQGvqiG8haQ5+ysoPiSSL27GKja4JEBCgGg7ulmbe3t225zebmZo1Go7c2s/e+VR5XsAiyCU8RIGKpDrxCltXNzc3SgQOMF22Ex1g66j46a+ro6Khl08EjT548aYEnxpx7Pvnkk/rzf/7PNz3jYKNt+re1MDZgXvqE/+Z74DN0IacWMl7GM9SZK2qsy3LCpudXvItN5p2U1CWfh19XYcAe1kt81gtI5TXX2eOXxN1pAx1HsN3zd/BA+sjGf/bxsr89jJy41fqQe3sTZl4NRGyEwywODw9rPB7Xp59+2rZlmM1m9aMf/agFtZEzeOjly5d1dHTUTsZ0soUnOOwPmw6070PI6RfaU8pMA9jju2enDTocoEpByIFFcVYtgh0o9J6QGWgabDqIZQbqCZ2v9wB3OgaePTQzZWZBOkwGctTjbItUAL3ZZqLuCa56znUu27PxQtCynQblPQZ0f3jWgtrL6IJWpiF1JN2T/lYAHstcsmIBBzzYyQBE2HEgcwLhXBXI4B09RfSuJWnsvvbu/YM/+IP63d/93bq9va3f/u3f/lLvrHq/GVJVtcTT6APv32XnDuNMmnlVtdlTQNzl5WU7zpRxBqCyh9Tjx4/b+mtmAuFn3uHN7T2m5tOqZR7MQFMaNW8kmzxpp4qSQXCKn+0FRHlfGu68P+WT3/O5rM/tzBn7bKfb2uP9lOeU01VBuvvqzHe67zl+7qN1j/Uxeg7A4VP1hsO7mV020KceOx1sPGuavM/SAztpg9yW7GvVspNkUGNdaYfSG6CusgW9/vt6b1LDoDPtp8fG9/UCUn5f8ga23gFHaJC6vGqxqbIDQA5Wu4++h7b7BJws/t06kPsdEORdaRuYUOE79MDRtuNivJU222DatOO5HB9KLkfsBQCty6sWS+W9PwWBia2treZ8z2azlhWFDQfP5BhnXz5Pl6YuTL7p4Y0va8+/aqH/XtoJFvEG2egesl6cRVxVbVmHTzVMJ6LqLjjAqV7sv8hejOiFR48e1dHRUQv2mPfRh14aj50fDAYt6HF9fd2WhKFzmSgaDAatj1XVgknIH4FLH07D9cziR28RiKm640Fn+TjriT5SH/RCZzhj0XKCjJJdxdiQ4UIfqYsMsKrl5bXQD2eRelhuSWCWwCH7Rln2bAfZUwuZGwwWGb7Qj2BU1cLXmM/vTvfb2NhoJzuyxPvy8rLJ6qtXr2o6ndbx8XHrD2PGuF9fX9f3vve92tnZqYODgzYJgk2tWmRuesN+22v6mM7vt6WgH+mDJ0DMJ2DYHjZjLzcv13QQxVmMxj6Wh8Sxtlmr9GavHzl5cV8dWR82L/FK1pc+oidN/FwvwaGnx1e9y/zUC7pVLa9gQJ/YXhsrJY2Nbf3uxLV+t59x8gW8kROuZKFaj7Fsdn9/v548eVJv3rxpem8ymdRnn31WT58+bXT2dg6sFKDv+MAej/RHrNfM3++zfKHT9yA+jfMfa15RwqTVokSJilNXgkdnSnF/1cIw5B4uFAPcquWTciy06bRwbwL6HJSqZWHImcsEbgkOVjkSKUQIhduQDoSFFdr5famkoC39TLrx6QCOBcT0dL3ZjwSSqcQsxBb8FMKkoduQfXTQzuOUWXX0Bx7zkpVUmNSHY8q7c8y/SEklteoaZW1trb7//e/X//k//6fm83n963/9r7/0uz+EobdBTjDnjR09Iw6wJa2b4BNg8ejoqOkNxmFvb6+Ojo5aKivAzWOWRslt8hj6maqFDCT4s8ynoecdqVO4Pw1WGvge/6VeSh1h2uY7ElC7H6bFKoOSOsx9Nx17ACfpkr8lH95Xl+mVDrPbR5tX0R59yu92QAiK0jdAIxvu4pglf3zIGd1VOsI60DaL/8fjcV1cXLTlFWTYVi3bVdPJ9IT2BE5M4wR32cZVOrU3rra7vedcX44zdKB9ecIqtDGgt4x4kgK6YDc8Uwq2cSYFWZw4XA7mUqc/7YxaFl3oH84e+iZtcOIPB7qglwOv5hePV9LbNEc3Jph21jY047ccE5xiQLAdKujAgRbQ1WPd46VsZ35fpVOS75IO30RJbFRVTQ95vx34lGUYe3t7jf+Oj4/bEo6qt7P0ec/m5mY9efKkPv7442Z3WTZJJuj+/n47wTZ5kMKkDLxv/Yrjwn5KODzcDz/xvvl8EeDa3Nxs/Sa4yYmODpA6+5MgUd6TS06x2Th30DkD0/P5vGVieaK4qtqSSGe28Z1iWXdAjWc9zozLYDBoATv0A5uoT6fTWl9fb1sROKDhILUzx5A5ZOj8/Hxp6RhL86gXG4feAZ/t7+/X7u5uTafTevHiRcucspyzJ9j6+nr9uT/359rewV4iz5i8fv26Hj9+XMPhXaYcew3npHvqxG9DyXY7IFi1vJVNzychKEhAypjL9sc8jR3LIC2/Z7YV/9u29/xB+pO6MnFx6tve532YjeedMWS97/tsk3r6zfX3fkusYdk2PWzj3Qdj7J7NuA8H9XQxBVnv9Tn9eNsBJg82NjZqNBrV3t5enZyc1A9/+MN69epVra+v18nJSe3s7NTTp08bP25ubtbl5WXb6w49zHuy7f6NU3Nz3N9n+cJBKYojw541ubq6emtGj+cZaAsVjquJw2aAzFBnBDcLzGUmdlllsO14cK0H2lLI/Zf3J7Pz/p4ysACnQrDguF4vbfMeFu6LDVgaS7ep53C6r1ZIFD/TUyCu2+Pg9yZdTfuqhSL0c66bdvQCETk7Rt3QjVnIHIcc31RcX1YY73tuFVBGrr4NBZpXLTby5ZoDyYyFU7vPz89bej50IKIPMB0Oh7W3t1fPnj1ryxh8uh7A19lV0BDe7+mGnlE372dGQs8A5TLT5MXUWan7qlbvpZA6jTp79/BbL/CUoMF81gu2Uo8DMqYV92R9q/rQa3vyfb6/19dV91ctZ4Z6LG2bMrhu4MdeG9PptDY2NtppkNAnM3DfZ3kXenqcCXpw/fHjx80RwMnJ8Xa/cgIA2qEnKT29bbDnkg6UwXvqWgdc0iYblNOebK/lHlnugU7e7ZR9F4PQnu2nDpxI62oDbG9u60meXgAp+cnjSvCBdvGd7BDu5Zptsm23Zzd7E1O+nvqQCRw7DoyfxwZdPJvNand3twU2Dg8Pl7JH3O7MdvaY3eeAZOnpWfdl1TM/DSWdn9nsbv+e/f39hqWRb2eaTKfTevnyZU2n06W6KIPBYiNqZtXH43FbSklAant7e2kvm5QLgg3wrjcCr1pkQrMkE14gGEPwyoeXWA/xnWwo23Lv/2ee82m+s9msTk9P6/DwsOHh2exuGeLJyUmbCGeSnPr8LupxMK6qlvYBYv9R5MzyT9/RAZ70pD7zIjRhciQzsKnDmZQEG6Ah7acd2DiCgrTF2x9cXl7WyclJ3dzc1P7+fpvUc6aUZZ99QLe2tur4+LhevXq1tGyYJX/T6bQeP37cJnVMH9rDfl5M+oD5jDG+LXg3S07aGFNYfzKGtsfIo/cxRN7ytD3rYGhnu5k+D+/t6UHbmcTFlMSf6adyj+/t4e3Ebembuv5sV76n52/aZmTgN+/luwPuxgy9d/d8A/el57e7n0lTPu03Jc0cPINv7LMit0wywkevXr2q8/PzOj09bdmr7vvp6WlNp9OGb93uVT4nNsj1vG9Z/cKn7xmYuDMIDEbDg5mAA4CV4KqqmvEFzDi9u+f8DQaDt4Td99qY5LM8vypQYiWdwmhF2ovk2mngmQwAudhxSrCefTf9M8pqkJx99B+GkLFKJz4dcdMgx8H3J49kG3LsfJ8Vn8HIfeAylQF0dqDCdfDJ3jEei17/HfzyO5IWq8oqfsvPd63vp7F4fMxHXuaBjNpwOC0UWR0Oh3V6etr4eG1trR4/flxHR0dtk03PiOZsbLbDfG4DblrzLGNt426noedk8x2+68mtQbRBuQ1PBk6ypIHO/1M/pZ62vuoZ+eRB2uN39WTA782ZH/PGfbzdqyvp7Fmr1Nd+h2WeoL3tE+9gvxL/zvMsK2FGqjer9T6Lx8rXEjD5Xto1Ho/bRrfs2wZ4gdeGw2E7rcqHO9g+ZF9tv3mv9SLy3rMPPVvoCYUEvAbn2S5nEzsgxfMU/kf3ZMA2+Rb+Ai+AUVKW0D1khDBOYBlnUlgHujiDNJfeoXvIPHcGx2AweCuY47E3z9jh4zP1i3GZf6O/3g+F+jMrzYE2xuHo6Kjdd3l52TItvJzJS9dcEj/cZwt7fEbpYaWftpL40jbMQQICDWSacLJar6DbWOI+Go3aqWgOhHhJXy+DwKfEoUOqFpPRtPvy8rLNpOMoEVSBb70EzMvNWH5Pu6k3Tz7Fr6CdzriyDPMcQbzRaNROKBwMlgO98Dqy5lPwcvJoe3u70ccZ9ARdHCijkOHUmySlfQ420Q/8ovl8cXjH7e1tjcfjFiiDJt6A/OLiok5OTurk5KRevHjRgkHO1oJOnNQ4HA6XApZ21qfTabuOD/bq1aulDcsHg0E7pObo6KjRBj0wmUyaTtze3q4XL14s8Rjt+WmUzXct9rlsv/L7bDZrm8yjx+EB+Ba6kHmYAamq5Qn7quVTUtN/tH1N3Z5Y4r7/e8X1+5meb2ha+f6kI9dXxQ647z4M6X71bAi/gQlyIiT1kfG660rbknUkbkk7le9L/F61nJ3JXn6Or6A/re9+8IMf1KtXr+rRo0e1u7vbft/f36/Ly8vGf4nXCL6zcoX25aTfquDV11m+0J5SNM7ZJvyhRG9ubloabo8h0zHFmLBUB4PFZrMQz04JxcDUg+t3WjlwzQxFPT1mTsH2vX42mT8FrOeYpRNmkGkhXNVvK78EpumAuX7T3UqvR5t8Hqb0u5Iubn/S3GnIdpDzPvedOnOGzm00fRyM8KyU2zscDpc2X+Wa6cvMT/LJKgO6Sln2xv3L3vPTWGzsDKKg9fX1dZ2entazZ8+WZhHn87sjhI+Pj9sYAIInk0ldXV21JXv7+/tLR0z3gh5VC54yDzL2TrF3oCnlA36BD+2YWq5WGd+UM9pjmc335m8py3lvBt+qlmeeLBNuO/RKPeVxTGNK6fWVcfc9vXFJ3nY/Um6y/X539is/PcbQ0ft4QAcDcJwSlpUCFn3iFI5Z9vF9lh6f9AA8Thq/s+kvmcYENRyEr6ol/cd7DDjgpwxaGPy6PasCHpQcp7Qhg8FgaT86t21Vv1287M72DBuZYL6q2qSXP+Ez6uAYdRxaB8vTvrsklnCQKPtmB9g06tnapLl1BPY1A2Guz/TN51MvwBe0DdzH794UfT5f3h9qb2+vHQs/HN4d4EE7vPcN/THoXWXbV5XUxdn33j3WEd+EvXU/yTohcwn64DTAl48fP67j4+NmM7Osra21PaN2d3drZ2enLZFjsndnZ6eOjo5atlQucSPI4fYRoKpa7NWEbhkMBk2X4ByxdQe8zt5GVYslcwSPHARD33ppGb+BKVjuZNlhufJkMmn08xIx2nh6erq0XI7sHcu7TwSkPVyjv+ZVNgq3P4RtcaaM9Sr7OlmPQDdjX8aEQwOMnefzxTJkn/I3nU7rzZs3bS+ynZ2dtnE+/ePdk8mkBYhHo1EbZ8aLwBc03NjYqNevX7cMLE7s+8EPflDj8bglEyDv3Ad2g3az2aydxGmd/20t8AL60TjX/ICNYruKDCRZN/K8fRNjVONT2zY+nTluOUh/J/GwiydrHCjye2znbMNW+UNpF22D3IbE0LYJWZ/lwlmM1JP4LX1PAujZLtuMVfbSuClxbK84XvJ5vj3toC70Nc8bx6JD1tbW6g//8A/rRz/6UV1dXdXR0VFVVW1vbzfeMq5Lu+4YxHy+iOl8UZv8VcoXzpSy4BlYVVVTNBiEqsWMAZ2HiHTs9vZ2aSPMquV9oQwqq5ZBBgNmYJTtdWDDjNJzbFc5PT2HJIEb7ewFr8xkZmZnNHGPmaUnUAkgzTyZst8L3pgWdiQ869tTONxDXXlvz6FlnFcJbK9t/ksFYWXtcfB98JHpQXAEcMdMYSpRAIhn9jwzlYqmxwP5e493VhUDsW9TccDGSh/wcXZ21mjoDUj39/frj//4j5tR8MZ75+fntbW11TKkvC8EMm+jULUMDijprFi2DIaYofTvyaerAqNpuDMIxJh6mRXPuV1p2J2FkM62+2u58/9Zt7+7bUmn7HcW0910sLznu3u8vUon9GTfY7yq/ZTUocwwI/vsKWUQyL5SHAE+Go3q9evXjeZ2Uj5U6fWJAk34wx47IEG2TdVyKj9HoRuMZnataWsHMNvSk5PkQfNVTgCkHCb/uB8GZOZ9rmdWBkHJVXKJw2d5hw5kfHj5kfmKduTeWw7+0Cb6bvDnPaksG9YDlj/sH5/mS+qh7fC7wWQuFbC+8Bh5zFLf2NlGz6Y84HyxpxnBAGg6n8/f2izeweRsQ5ZVusT6sert/Tb96Xq+iWJ7YD6sWugZxhj+3tjYqOvr63r9+vUSj1OGw2Ht7u62zcr39vba6WiMydOnT5cmeKwXqxZ21TLhwBhjxPhxD8GdwWBxsjHZpQTH2TuKtvAO2zoCcQ5Yuf1MGFCnM6qcWYgOX19fr/Pz86WNxB14Yyw2NjYa3oAn3Vb6TxBoMpm0zAX0KVlBBJawKbzPSxe3t7fbmNk/sq7IoBa+lQMb9JsJ/NlsVtPptE5OThqPXV1d1fHxcQtq2SHGyZ1Op+0a2VHIJ+NzdXVVBwcHzbmFDzm58Q/+4A/ql3/5l9vm/JPJZIkfmOhJZ9eB8m9jsd4C7zrwBB9Ay8FgOUs7T2KFz8wvPf1o+bNtN4ZMe1z1tr2+zz9ZhXVcFyV90mxTtsPfsx6/x/Y2sXzaSejE/cbGtq3ZfsczfN11J52MhXq22jTI/mTdxjOmhTOVwRaDweLgCfoDnp3NZnV4eFjf+c536vvf/37TYaaH9zwzX1nfo7OcqUX96Vu8j/KF9pTyDAHXfBpDpnYa2NjBqno788gDQhDKgDUFEqE1o+c9/j+FLoXXA5N9sOFMJeT68j0JeA1mzbBmcKdM0wZ/ul02MjyfApU0skPsGSuPbb4rlZMdgVQ4Hlf/zlims+lnaVuOrennwAAlxwwaGgy4L16Tn5tjmm88y5F0TP5Knst7cizzud5YfVuKlX/VMm85MAjN37x500DreDxuTp1T6Bn3tbW1pY3NnV6fsm/+TblLPrKsok/QUfmcdZB5PHnZMuYAro21lxon3Sir9Bh1mO75jEvKceqvpMF9utL19OiXOjSNdNbXAxhuc/Y7xznfmfXRX4qDoRlcmc1mbYPhk5OTGo1GdXBwUJ988slbS9Led8l3JWB3GzL7zw4WTqKBBLOqLOdL28Yz5m/bE9sUvz/bZ9mxrc6xcvDGOpxxQt97zGwzMwjaa5v5KHmM53AwsQu2hQ5GGZ9QTy5ns0yTYVFVbVygB45Hzx76/xz/+Xz+Fj6gPi+TxlkFk9nGZzao5ch9SFxmmnOv+2/+ICBSVbW/v9/sMRti++hq+mFe7emc+/gsadST1R6W6n3/ECWdJk+8vnnzZilrD5787LPP6uLiogV1KJubm7W3t9c2qWYvKbKj0GePHz9up4Hd3Ny0gAjBEYIfLAtxhg40gofIpoG/sM/G7Q7AElxCdnyKH1k6Jycnb02oQpvd3d26urpqQQ4msaATGMKfVYuJh+Fw2Jb+397etsAAmaXO8oZvB4NBWwJ3dXVVZ2dnLePIbcSZQw6rlg9T8TYlZETRd8sBMuBgm/dxok1kquF7Ecj8wQ9+UN///vfb0juWzr569aq9az6ftyAWEzZVi32syPwCJ2NHr6+v6/Hjx3V7e3ey5vr6er169aouLi7q7OysjcOv/MqvtOWB8C5LRsfjcQs8QuufhYKtQL+RfTebLZadUtDTyBnPJyaB7tatPT9sle5KTGrbYj2YOtS6tIexjY19n+2Un7N9oVjne8LIdcHX6SO674n/Uu9bPv1uaJub0rtet8e2eBXGzIBN4hXre++/VrXIlK1aLO3N9xjDzWZ3e8Ch14zlbm5u6vHjx21vqdPT0+ZjgXOQwfn87aX0YAc2Rafd0ORDBJDfOSjl2UAayP+kjZ6fn9doNKqq5WNvzWQpWCh0/sdAkDKf2TEWihSanmAb5KxyePxsOlu+N5VoCsqq9vQcKj+fzqwDPp7Z6TmL3i+DawYzqWxWOaK5XMBjniWDeaksesokI933KUzPKlihc81Htvu6+RL+84xDKqMMmFhJc6+BVk8puv2rSm/Mc6x8/ZsAyV+l9IyOAR9gt+qub9PptO0z8vTp0zo7O2tjyrhtb2/X48ePa3t7uzk4KGzeaX6CV80LPaPuZ3zNs5Q2Zj2nmfevokXqIeqzTPfo13OmLC8p51l6AMB12PHrld67e/Kdsm0Hlv7dx+M9PZxtXNUW970HdFyv6T4YDNqsJL9nAKKqWlYM6/Fdby8z5H2UVYbf7cz7GSeWobFsClnEzjowBf0cwMfOGSzy6dk/xqM3s2cA1gPAvscBpQz+Jg8kcPVzHlOu94Crg2u0FzDnE4INwB1kpx5vN4DN6tmqy8vLFkTAATw7O2s2Bf5DB3nShWvcR9/5xLnxWICXLi8v2x4SDuabVjnGtMMOjekFn0B7t4k/Au+0e2dnp2azu9PAJpNJ0/0HBwdL2I86DYLN98kP+b2H2fI3P9u7/qGL6bu2ttaWdeFwcM+jR4/q9evX9eLFi7ec00ePHtXjx4/bHlI7OzstYPPo0aM6ODiow8PDdsIXz8zn8xbk8d5OPjwnV0PYprodXmoIP9mGOjN1Pp83PQzvgg9YlkfQA/wwGo1qa2urnQ7orFVkjI25Pbl0enpau7u7VVV1fn7eAkymxWCwCDxZ19FegjD0H5pcXV3VeDxucjMajdp19rFiTE0j5HZzc3MpwyEnshJj+/1kP0GzjY2NevnyZf3xH/9x/fCHP2z0QtdnRhzvon6w2fn5eZ2dnbU2kG3mvpAZdXh4WJubm/XJJ5/UdDqttbW1+vTTT+v58+dLGW+MCbJu+5EBgW9r8RhhB+wvJu6yrfZYIAveZ4xn7IOYpqnvev4V1xOTJU7jPvS4/bKev+VyH6bt4TbbHPqW9rr3bvcx8affl7635Tp9RdvdxLw54WV76Xd4bDJAlbgHO0p8hKC338EG+NaztNm0oz7wLfr8yZMndXp62rI6B4NB06/eigAagxOdoUssxgfm/FQFpQwgs2E3NzdtU1UPqoXNQlS1DDRQxqQBI6iOqCcDVr29LIB7uWYQmYJr5k2HivYBVg3MLKjuB2UVgDLTwhhWFG6XvyMs1IHCyvc6oON+uN3QwenQjJMZ1UrLDon7QfvsyNh57ykjgIuFwTTK/uc9CE8GEHsKwEKU/Yb2W1tbbcNH0yuVpEtvjNzHLKuu+znzdtLsqxTk6n3OSKWims/vZuJIh4dfAcCz2fIpMSwlQO4Z3729vXry5EkDflVvZ+NBIztyDkT5/6p6S8HbmcpMTgNQ6y3LRY8PrGt6+sq6oecs9fiiZyyz3lWOWOom5CCNre9xW/2d35MmvTaYPvk/xSCh1/akTdXbICXbmP0x2AMEJkD0zJQDkdCKvabMe++z9HjAfXH/cXDoH+30kjf0mTdUtVPneu282A64XchoBi96ADf5sCdH7ncGszLFnPf7GdtWOwS2FQTdHJDCKaYe65Dsryc74BH0F/0Ht9gpx/FkXHyqmAM4zJbmGLNsKwNKHufEZfP5vC0Zsa4CP6SdWSWTBuYG0h4jfvNYerZ3MBjUaDSqnZ2dtg+R97XwfjzGOj190dNVPR2a7e/p3+l0Wr/5m79Z/+gf/aOlDP8PUczP8AlBy83NzZpOpy3gQLbLZ599VpPJpGXekIFBhhQ0Ho/Htb6+XuPxuAWqnPmDPmASyLR2hkzKtWXJS45Yrjafz5f2SSKQQaDU/Fe1HDyezWZNd3HSnTPZ2TMPrIquQ5ZoOw4UE9rGppPJpLWv6i7Dy0vavJG6MT8BMoK99H02m7VssePj40Zf6OGDELjX2bqJm7zcDz1iDFK1OBjm/Py8ZV0S7H3x4kW9fv26vc/ZstgHZ52631WLJYKvX79u/d3a2mr7kB0cHNSjR4/aITTb29s1Go1qOp3W6elpC+r91//6X+u73/3uUgYW9Vlez8/Pl7LOvq0lxxEd6SC9Hf2qxSQSe7FVVdPj2GzjUwo+mn2V1HMeW9v2HqZIfy3vcf/8LvfDmZH5Xp5f5dOYdvQ1g0P0K+uipI1ehc9sm7Mur2jqYby0hW5/D79kmynW9WkHuY6eubq6avYgs5q9zxP85gxmNjl/9uzZ0nvAsvTVNge9WXUXzOeETCcj5WTR+ypfeE+p/KtarGve2dlphCFSbiaoWt7okJk+M306BzCqmX3VEp6qt9PvaE+ClwTEvubr6ZDSbwJlBsRV/eU1GRgiMpuOn4EA3zOY5XeRBtijgWnqWVL3yQ4Yz/m9vNspiqapaeT3uc/ul/fC8Pu4p6ck00lyAMHAuKcgEEanNwJU7FzwnXdZGLOd+T1p/3mlB5pX3fNVy4dKj7aysvOCovPsqxUpY/P06dM2G4mjtr+/344uZjzue2/V2w6veTCDGQ5I+Zr/N5h2WeXw8FyOreW+x6tpyP1cvjMDsn6377Pe6xlLt+s+AJB60g53tjXr7o1N79p913vynXql9xv/oy9M/6urqyW74wD9fD5v+5rhXAG+vRzrfZUEPdkfF+stZAkda5rYXjgDYxWPVL29/DuDG8mrLhng5X0ZTDaIpViOoEeOsduUetqTUl4eBg/gJGF/AWKWA386o5bv0Ir60RUEL9NmefKH5UEGyQ68pUNt+4vjaBknmOWlUtCNLAk7Oqkbs73GT6lLjLnoG/f4VCCWKFH/1tZWPXv2rDm0jD8OfDoKyeer7G9+pg7zuEIT/t/c3Kz/+T//Z/2Tf/JPajab1e/+7u++xbPvq5jXcBqGw2HLnskVBi9fvqwf/OAHSwEf9oZiud7W1lZbFra/v18HBwetPt7HUjEHjczTdqIvLy+XMl7gCTJn/Az8D43Nb1WLLI/hcLiUEeDNzXnGm/ey7MQ8t7u7W9PpdCnQ4QkD8w6TEOzHNJ1O28bf6AICfBcXF60/xiu2C9ZNtJNgN4E1HDwCRIPBoO3fBS1SP3oJDZ/OXiNwS6AZ27W+vl6Xl5c1mUzq7OystaWqmjx6yQ66wLaddtuuc20ymbQ6yFTf3t5uAaubm5uWkfHy5cs22bi5uVnPnz+vqrvTYF3vwcFBnZycLAXTvy68+00V6yyWVl5eXjZ7gC4kexksQmGcLQvWWbY/jIPfXbWQMa7Zrvf81x5+TBtrn9G/p55dNZGR12zTE+/azid2N317tiADWz3f37jZNMjgne9D/2bWmvuQvNvzXz1pxPLO3NuJ/qM3PRFk+aEOT+gQpLe/v7GxUYeHh3V6etruAQ/Qf9ONuljxRjsJNvfs8vsqXyhTykrThgpg48bnPXSaZT0Mmjf8hOAQKQW4F6ypWmRSJbB06QmqmZ73OJrq+xJopmCvGrAe2MWgeF+AVaAYQUkHkmuAC+rNqKqF2anNFIAutIaWPIOxyhmWNGJVb++5ZfrYGTIdkpbpYKbySrCf4+FPeI5+8+nghw1ALudLJ6rHS3m9N/6fJ8zZ9q+r/MN/+A/r/Py8/uW//JdfW5294kBULoOrWjif5+fntb+/v7R8cja721fqyZMnbXkCe2Q4Dd31UD+faViypBPt7JE0vnY+KWmsP49nbYTz3asMb8/YWva5ZrDvNvn9vZlJzwLZaNsIcy3rdptWOYyr6ON3VC0vA089fJ+s9O5zm1M/Jz0Zcwy/M1WdMYOj4Y31q5aXpL3PkkCPa72gonmfYIfl4OzsrM2MQQPzU4KlnJRJmUqQmOPl+6w/Kehk6wYHKSwrtkOuO+U+J0Nok5daGuzZYXZ7csbX180jg8FgiXdyLBgPn67k35x1R9uw2+g5Ox38loEDf+cZ949MJYJuHhPrtrTZyWfmEU/keOmN9QlLftx+aEK7HMQwLXp0fNdyHw7r6djhcFjHx8f1+7//++/8jq+rJPZB33BC5s3NTZ2enrbgx/Hxccv0ubm5qd3d3fq5n/u5pufJhhqNRvXs2bMaj8dLAR42mfaG38aJxkks+YRfwOre79HL6JEDL9V3kMm4kn7aodvb26vd3d2lLMaqaqeGsvzU9sKbZ5+cnNTW1lbLymaJH211m09PT1t/ud8Z21ULefN+o9CCYBbvvry8bPslOagFbvEJ4sgmONzY2UEmgszY8cHgbrsKMqOMHVhKd319XZPJZMkGIFecnmdc5tMEvaR7bW1t6dTC58+ft6U/ZG7w/PX1dT169KhGo1F95zvfqYuLixZ0fvHiRT158qTxtMfr4OCg3rx5U1XVlqna1n0bi+XYfi7XWE6beAwe5xnbWgcq0j5aN8ObiaUo9rUSixqDGlNZl6YNp04X2wba3cOBPJvBY95lewYN3H+/2/bK9SdWx+5ULRJinCXq9yctsq/YTvfNeNx83MP2afdzvDMYeXt72zIefeCD6xoMBs1mOCOWCQu+oyvw9z2Bhg6Af902eMvxnfdd3jkolZFtiMYsnUsygzviGYNHjx612RKEFkVZtRA6mNVOnh3UquVlF7wzo5aUVIIW9kwf5Dpt8fsNEum3+98TTD4zC8P1Z3vNHF6SlM4tdHbacoL5dJgNJGBQR2d9D886cEdd7qff08soS6e0FzlPGtjx5K/3Xt5p54IUbNPI40P/Te/kNwPpHgDOtvrT99/nyPWMylcpv/qrv9pOPvq663Yh6JzHqvr3VHjwGbK/t7dXp6enNZ/P6+joqPb399sMMO03v1QtZ2QkX/i7MxKs/A0wrXRTbm0szCcZ0HF/DRS4pxeMQb4SVCSA8Hus/3q6gGtpYHzfKuObRphnkv49EJBtzfr97gQF2cfU/XbkUmb8f8qW74FeqTOdXcSnMwwYT+q0zn8fJWmW/cl7aRMOJLr/7Oys2VIcA0AJdeEQJX+s0ssZZKFdafMMUE33HsCxTat6+3TK5DfGyBkStsvmBeow6IeXHKQBYHI/uMTYBnzgoA86zGDXuIclF9Zb6D9nRNEnz5Z7ss/86iV41kHGFQ4aeOPk3himTLsk9kmZ9BiZjtnWra2ttt8R4wGNCFhY3rLP1msp9z09lZiLvli+vqlifUTbt7a2WhbK2trd6XKj0agmk0k7BXQ+v5uEfPLkSXP6yb4YjUb15MmT2t/fr6pqgSgCW9Cd63aE4TWWs9E2Z/bBw5xQxww6vOpJVuqsWvDa2tpaWypG4JJgEgE0soHYjL2qlvaRol0sIyOQxrLFzc3NhicIxoLbZrO7vakuLy9rf3+/1XN2dtay025vb1vb7AcgrzxvG0DfE1dW3W3wv7Oz07LDoCc6g8Lm/wQECYiRQUVmL3S0Dbu+vm5LPB0Ipy2crMfm9ywHYukc+8w4KH97e1t7e3t1dHTUaIpNRLbZR4qN9Le2thrWvLy8rJcvX9azZ89qY2OjBQDJJgVvWQd824v1H/oSPUYQtGp5A/yqxamTXppnmaFu7uW7/RLbPet0vrtO2yHjsB7uTPzqZ/I7JXHzffcnJrbdTx+K/3u63r/bbljPUm8v8zHtSs/OUPz+xO3+jWfBQF4lxPgbq3Ev/bUO4n6y5Hx6I/ri+vq6jo+PGx9h/7e2ttpSYieveAKCNrOFimngQOsqzP51ly+0fC8BDMSuept5AGwGpBDGwrG5ubm0tjuLnTY/z2+rALL/T3DidnJfz9nMKCzXeMZ9TQdsldOSQDzplw6T67ewea8eR9RT8NyeDDB53xE/6/5YOZgOpn0qPyuZVBDpcPXGxb+bxglSbQCSvv5O6jI8hEB6HKFDvpd7em3M8c/3Z78/r59uz9dR/vE//sddHn4fxXzg71a+KD72ieC0napqgan19fV6/vx5OyknjXjPYaEYrKXs5+wn96Onkp+dnZDFTpJ5hGID4HZ4aRH1uB2rSk8e87nkWzsK+bzB7SoZczvzWfRI6jj3wc9aRlPX9vgy6WY5T6CRutngwNkyfs73VC2C7DnDBw1XgaQPUeCbVbrDM3dVy6ec4tgBQnjGep6StHVWlZ1C6GI7kzrOy8U9BtDYNiYnWTz2Pf7K8UxblzJl3VNVS0vuAHTUYzva0w1Vy7Lt92CL077bzlM3fEYQ3xjATif10D7TEEfYY4hTnWCboFkGox0QYzx7mUvp+LiYvtTP0iECUgQGtra22uEVq3Qa9tqOV5YejrsP2/Wu9X7/0GU+X2RXgE9Y9sN+Ui9fvmwBoNvb2zo8PKyjo6M2E767u9smeHd2dpZWHuDIYPvm88WJkM7OYdwzaOq99szH1g8uOflju8vsPEvyyETFWTc/b2xsLG0F4r2gyEw6Ojqq09PTpSWJs9ldNvajR49qf3+/jo+PGz9ZLtFNBLDoG7IO3zEOLI1bW7tbUkjWAX2xXENfL7MiKGN9enFx0cZgb2+v1tbWWjYUfbETu7293e7nGnRxlrr3ckv9vbOzU2tray2wxiQGQWsmFglcEezkunUF/Dkajer169dtI/rJZFLz+bxevnxZR0dHtbGxUQcHBy2Q6T30jBe/zcX4xIGonBxnbKsWdsj2kbps0411E9tSbKvSHvfwsDGZdXcGfLKPrpP3+jc/m9g0cVva/FV0pa3Gnb13UWfaLo9Jr73ZFz+X9OCacbxxlCcu3S+3z35BD7OlXwvvkMUI33jSAZkm65OA+2g0aoc4HBwctOxNnoMv0KmmN0v34GPz8vsuXygoxSDAGI7w2mlAsEgf9mBULZ+KhMJEUVNcXxq/BBqrmMxM1XNiUxC5lo6U25sKP4WAtq8qzsows2LA0mE2zTNKy//QPB0vf2cMvFSFMew5AYyhDXXPGTVgTYVj4En9Nqoeg56Tmc5OXs/ItNtetUj/9DgzBj4qm/FMxy1npb5I+TLC+3UL/Keffvpe6u2VfIcBlGloB7dqwX+3t7c1Go3q8ePHbZ8M1j8DtpKv/X/PYKd8mY8zeJoZkslzaYx7htt9tfz6XX4/93sGI2m6yngnyPB91lMO5mUfLPMGKNnvnjHvgSLf05NXSurc7LOfsYH2dRvyfHfO9mS90GV9fb3tZeYxxa4xy+S9QjDi8PfXLVur2tx7j+/FuWVJiR0rp9TjbJ6fnzfnZDabtQwIBwLMs/CRbb7lOJ8ziMlAB/f4GduQHGPGC7CfE1MGzL7XsswfmUhV1VLY0Q2ATdtoCv870Ot67XQABBkX4wiPq/mUd3p5o8fX2U6JcXr6LnVj8hTj4klCB8B6mMB1Wf4Ya28lkP28vb2t8XhcT58+bTiEtvZ0RU8P5Xj37u9hA78nnYVVevx9lsRvVdUClA5Ynp2d1aefftpw2Pb2dh0eHrYlUY8ePWoBKrLBedb/Z5CI8eCToBhLiciqhL96E0zOOvY2FBmwJWDGvkTwsZeADQaDJTw2m91tpH1xcdFOESSwRZsODg5qNBrV1dXV0ulQ6KfE8VxzgMsTlMg/Dhn4w/qOd5PRBo0vLi6Wgnj0mYL9IAPMK0PYE8rLuFJf0NaqanplOBzWdDqt6XTabBXyzLtZusceM9DYmMNBOmetDQZ3ywZPT0/b8zs7O3V6etps4f7+fr18+bLm83lbPnp+fr6USUfwlO+8bzqdflBHN8sqm/pli+XZtgB6+kCA9M1sq7JO7GROpvrTGIl6er5tXreu9HOJKe0DerwSZxlf0ib0tetIHUzp4Vmu5zOetEia9nQ699IOxx3sL6YfYP3mMcpJk7R52R7LG7LuPlm3Uj/yTGYlNrM3huPxuC37ns1mbdXJ7u5ubWxstBNHHaAnMxX5rqq2lxTvtG5PH+R9lS98+p5nO91gC5cVODMSVbUEgBi06+vrNlsCoM4Zy6q+00mxIax6OzJrwOvfU7iraiWjrHJy+P/zHAeDvAwoQasMziTwtpPgo4O51mu72+qofNbHDJjBKvcZdK6imwGt2560t6H1b5/noK4aXxRHjm9VLfWHo4DNW1m36UHBIcuARI5vb8xXCXAPDL8PYJzLat9XMe2rFgoVAJbOLroDOnDvwcFBfe9732uOXRrGqrfTZz2by7vttPKMZb5nzNx+Owc2tD2ZSvleJR/Wjcl/yH0afb/HbevxrvUqz9mxcBt67Xf9ve/pDJpO/p5tstyvAiOpP02HVfRI+5D1eXYt3+Px9H4g5o21tbuTrdiXiQ12OeIbZ8jO3ddhsF1HL0jp4skh7AH6ynv3sNmtg5/IpYNN9+m4THuHXzPglcAFuuZymJRr078H8OADMjss+5ktBvhLEMm9Bp848LSdrKIeL3sWctVpXegy6G17z3vdNxyOzB7zd1/PwBXZXpbrBNX0v+cUcD3Hx2OSNjPHA9r4/x5WY/Y2ly/lxFYGoRNL9DCgabpKx6Uu+qZK0hJ7V1VL4396elqvXr2q9fW7gz+ePn3aglIEqJ4+fVqPHj1qy796MsR4+OQ4O2Tgm+FwuGR3ne1HXVXVHJgM1LpfBDEcvHImD7yzvb1d5+fnDZvBR17SUlXtAAAKy9rm8/lSwIflYcPhsHZ3d5f01uXlZTudcG1trU04sM8lvOXT6qoW2JbnwMEEn2kHk5rcyz1sU0LACLklEMSSHH4jmET/CeSZHuk4ksXljDvq3NraqsFg0LKYHLhiXJyFMZ/P2+l4h4eHbS+oR48e1ePHj2swuAsiHh8fN6cWp/j6+rrevHlTs9kiGwyfjuDdeDyuly9fLvHUz0KBz4xpNzc3azKZtIPAGMPz8/Mm87YXFAd0fC0TQHp6Nn0s2uYA0X24z8/7e2LlxF6rfOIeDvM9qdfTB/V7bEvd9rR39oNtw4w30pZA82xjD0OmTuT5pHViGdrkwJT7iG/kMc5ElsFgsQ8gtKVO7xXFKiqyTi339g2x2ehK60z//6Fk9Z2DUgkYHJRy4w3O03GoWgAClvCkA2MCmHHszPUYYRVISedzVUlQt+paAu7euxPU+zmYw6DUz/C7mdKOIYbPDD2fL1L8+M31Uh/fs30Ige/JyLsdr4ysVy2nLubMmscxC8Y/FWtPAPKalUPOXhvYYrzZUNN08DsR8N7se68t2d4Ez9nW5Kv76v+2FesFA19fQ5lyuhnOIMAGp2Vra2tJFszfPeOWQaf8c2Axg7/p+FetTuXlfTZeq2S5pxMcWLeBt/OXz7nuVTql137LgtvWMy49cJHP5f1ZT+rN1JP5PWXV8pqfKVc9OfKYGth4hq5qkQWSRpp9M6iL53CcmD337DqnlGSK81cpPTvT+z2LJxZoy2Bwd2T4kydP2n12sHwSVtXynlseM9PbwWXG0acS9gAq122/LVO2MTxnx9f2D14xKEzgnfbG/AbAoy84jw4skU2FbLPPDe3NwDiOPA419duRzEk88ysAkTZ4jzPT1DPw1E/dtmspl57Vtb7ht6wzMZiza7JueM2bpRL0hLZJjx4v9Camsh2+vkrHrvr9Pl35TRRoQcYN2Ys4EFdXV23fpLW1tRaUGo/Htb29XaPRqEajUdNHOzs7LYPTus32a21tre0jAv8T3PXEiHEVdEwHCH4mQFO1mDjmJMDxeNzagwPuCSNO5sWhurq6qul0WldXVy3jhv7v7Oy0DB3LOHqZie29vb0mk2Tkwbt7e3tVVS3rimUsBA9sv9kLCV5CJ3ijdk6bI6joLC/TAdo58OXgFqtKyAh78+bN0jimg042A4Ej9AL4ifvIgCCDAvtAuwkEbm5u1tbW1tJhDp7wQG/yGycYwlcEDOGJs7Ozur6+rtevX7egP/LHMj8CqKvk8n2Xr/u9xjHwW9XbyQiJJY1tqaNqecN9J3m4JN6xbu3pRt69CqcaB+Rz7qexlOtJ3smSPpDbxHutw9P2c29vctV2wvX5mR6+TlvUw/u9ANMqu2GaJj1pByvIrJsoGXxGJ4Mx0Lnod/TSfD5vS23Pz8+X9hFkD0ECVmR/eoIsxwUb7gDZh5LVLxSU8qywQXxVdQmbzMH9KGQGB8PsAIXTShlcz0T0iNgDcsnABtV2UHvOgBnVwuYZIj+foGjVQEKbnnPlCDC/eZbX4JLfHbDzzKvfZUWU45qCkbT1n/vHX09J9YJNCRJtoJNOvUh0jkWOfW4en46PHQ87nuYz6OHlXtn3pN9XLab3N2Wkv2pJpwIj4JR0z/KRmg8QIsrvtdKA0qrl2fSqtw1qbzYDMOUlmtZJ1JOB7Z5TY55z4NKGsGcozUOui997OqKnO3wt32MZsKz16s3v7lMaJ5f7dFmWVcY7r7utBg69+lfpgfwtgRq6DR7r1cVMtAOrZ2dnNZst1uZfXV3VkydP6tWrV7W1tVXHx8eNdjhUw+GwLa/4OsrnjSG87BlQf+Zykap6y3ab9jgQyefQ1TTv6cW8pwf4/Gz2K221bYsBu22D5d7XPSGS74Ae2Q4HomyTcAyhM7P/OR68384+mVdbW1ttzxjscwaSWK5M8IoxyaBtyiu2DUeG+7nHE1amJ4V7wVzwkNvmvnrZjQG3+XE2m7XJB9sBHNjJZLIEel1/j+d7+rNX3N5Vzlm+67763leBLvDJxsZGC8SA9c7Ozlpb2RScrE0m2NBvBEDImCLzwtiQIDr8Db+BweyQwWOZHezgCCfuVVXLAiHjZm1trQXx6aNxPP8zSTWZTFpGSVW1ZWLwzIsXL2owGDS5o92WYzYwv76+bnsdVVXLyiO7imDZ7e1tnZyctOAdyxgJMjmThYAU+6xwSh1jwPI56gQ/0j5oVlV1fn5eVbW0ZBrHcX19vfb29lr7yOJl6d18Pm9HykM7627GZ2dnp0ajUV1cXNTx8XELAhEgIiAIzmKZHs4veHoymSxlbrq9T548WaLHfH63Afre3l69fv26Tk9P275btotkJ9Ofn6WCrDE+ZEF5nz3kyEtH055WLWcP53XLZE4mcU+2C/uHnUDXe5J1VRsyKGMMtgp/Vb09GZr1+ze3M+/xEjoHm3gnz2GbEmPn/0mn9AMymyyxfvZz1b28z212jMO2PLOS0HeuG1uKnDOO0Gdra6vG4/HSxJ0nEpBv2tVbRg9G9BYsH7J8oeV7BhhOv0YIPfg8A0HsnFJHVbUNEKveXl7m2eqew2hAwbUUEgtbz+FKoqeS7IG4qmUAnBFYC2TW75kit4l7TUczW69vfpeVizNKDGazPQYg3Gvnvxdx9thSB0o2x8VjaDoikHYYVzk4ft8qh8fG1kDZSp0ZIs8e2Tmxo2D60VYHt0z3HMfe2NxXUpl9EwD5qxbG2bxF+rIDS3bCJpNJSyvn9JwnT560vSfI5PA66nR8e4EgX3cAkjp6QeRef1yvAUNP/lJmqD9louckZ718t1zkzJt5sefwMx6r3m19lfKUeva+Mffz9xWDAb+LYgc76ZvX/X8PSCUI4JoDGqYJAQO3rWqxOTQZfMxkEyTg1CJ0dAbmv2zp8TP98Wde4/3IGLpud3e32QT6zXPM4lct9iziu/V76mWDqZ7u9hgmyKINzhqgHjvQPVrk/5Y55MRZHMn/ttlkQmAX2GvGzjz2mOASGQ1V1WwFWRYJYlkqVbXgJZwT2xMcU/OqT9fhXYDZ5N+kM230RJb1iDFABvI9AbAqOwq601f64o1SbfvdJ2jmfQT5n/fm2Ll9PdubJfmD0pu0+KZt7Xy+2ONsY2OjZUYxWfPmzZuqqqZ7hsO7JWlPnz5tS/hy/yj0lTPHqxZ7wcFLtgHoSrCPHWfuJYA1n8+X9gtivyT2IyLoiDzN53cnv/k9yOHFxUWdn5/XdDpt39ENtncEZsgkciAOPoPPwXe0k/cTFKFvzjIgw4D2D4fDlr3N+8i8GgwWAWACcO6HT88bDAZLS7NoP+3z2ECfwWDQAmIEkra2tmo6nbYT9kwfgn+Mv5cjE1Rig3F0JWNWtbxcCDrTV2gLLXkn1/b392s4HNaf/MmftMyz3d3dlq1BkO/y8rIdT8/4n56e/lTI4NdVPJ7wJ4FEfA3uQc9dX1/XdDpt9sO4NevOSfrUbYmFE+P1JmC4N5/3hIZtRWI2bEpPT/Pp1TV5j0vvO/fZ/0z/tZetZXyY/VyFb5KuXl1knyNxdo5TfuZkkjEPxf6I6/dvtMk+hZfbVi1iKWtra22CYzQaLU02gmEJmlNn1WIVAXSGR7EdHypA9c5BKS8NcNo/BPESnaplJjHQ8EACTN3ZdFBSoDyw9zlGyQjZJt9n0JbMkU6G25PObTKcBTZnNg2moZ2XQ/KZWVDUidD4Xaah2wZQh9Y957xnIDLwZOe7B/R5xkEA6JTja2WXQDaVi2nrMaEfPGcllEEs082Ai0+/z8rO425+6ima/Oy13aUH9vPat6UwA4MiI8gHTxOYHgwGtbe3Vz/4wQ9qOp3W/v5+A92cHkR91MEG0xQb0ZzNtWx55tB80jO6PMun35fGwuDaxoX7qDt1WW9mKp9PHWIDm6XHKz2daJ6l7eZJ09KzbtSXfXGdfq/fs0pePq99llfTu6dDeM6/O0OG69yDvfGSE5yF4XDYPQUWZ5osBJwtgCTvWl9fb8dkf9WSdIVG8A0yZZ5GX11cXNR4PK75fN4cRfNcZqcw3jm7aB1s+2iHy2O6ahIkgWTP5tj2pW1NPu3Z8my/92BwdrUP+YCOmaXte7Ep3uOFLCnzmT8zQ4tAEu1Gp0EvHGeDTXQlz9NGxjrfQV95Hn1pfWQe4n8H0mzLXZ+d0wT8BJnMm+apm5ubtrzJe+eQNcBx9vP5vDnRaYsp1icef+sn827+3yvfpK21nai6m+F+8+ZNzefzFlSaz+dtsoYMYk4vhAedieMAIacxMTGELDLGDo5ULWTYp9EhR7SXQjAK+8qSMepBn8KH6E82ZD87O6vBYNCyZQhk2TkiaIL80iZOhWLzbIIylMPDw5a5Sv/8HZpAY+sy23Z4dTgc1ng8XpJN2sueSV7KbZtkm3t9fd0yJWezWQuKp821rDqTbT6ft2V7VYtAB20CP3kp8atXr1pmmJd2pq8GDdjY/Pr6uvGYM7Sm02njLfTF7u5uPX/+vJ1ISFbxzc1NvXz5ckmm0V3b29ttg/SflWK6Go94Qsj3pg10UKpqObHBtsF19ybqM/jktsErXEu/z/6eMXJVvdW+tM1cox2JHfKeVTRMrO1+p72ljfyfE0O027rW2UjQ1n1LnJl0znvs91pHGHc6ezmf89h6fOiPx8QBJE90mT4OKFn/MNFhOoADkVf0FFi551N/iPLOQam1teX12QnGqpaFgtkSMwLgz0qbeylE1yHwfY4SDJUMYCZMBymZthcksUHpgWIHRVL4DPYsnH6f28E7HMGFBl5KAXB3EMU06Tljbq9BMzSjbs+GZqCHOj0O7gvF70hgCO0pFuwElaZ/jmEKngGTjXpGz83DPn3FdDTfwKemmfkkxy9lIcfc1116xsPpmPcB6p+24qCUZalqEdBm7MbjcZsZZlkCEXz6bCPqZVf8buOaRr5HazuCVW8HQhI49IJNea95JHWBZTp15Ko2uj/cS1sygJ2G2kY6DUnv/vzfYNjyvopv870JptKZ8Tj1ZMr63sEB6/LepEFPp1v/uX8ETHFUqAsH7eTkZCmQhSNzfn5ew+GwZQScnZ219jPrPJlMWj1ftfT6w/UMHpnPzHs3NzfNGcGBHQ6HLcvBM+c9UGSepV7LZ+ps2wt+z+85bvTNNsbFMmX7VLW8VM78nDYq308wxHqcOnE2yW4go9aBb3Qz38mi8r4pzjJyQIpnrYts4zxxRxYWtPJeOgbf1odpsz0RYJzFkoAMZmWmlGXVtKXNBsW0xboeWhLYdabJ7e3dkqHJZPKWflylG3NMe/azpw+y7rz+TRU7EASdCM5Bq9FoVLu7u0u8al1IkMQnHyLf6DgHLxKXMta22aZhBvu8vAu9wvNkWdFG9AXBKwI3XsplWcFxYhk/S0iQQ97hgPJ8Pm+blY9Go8bHBOfPzs7afipkj+KEbW5utjZVVcu+8iqOjY2NFgCrqrZJNwEi7ISzrpAt742J3qH/4KDRaNTkA3lkLHE8q2ppMgCbg71kyQ57jJ2fn7fg0ng8bnWhV9gXC53FeLPHlE9CJIsY2wEdCFjNZrPa29urjz76qD777LN2X1XV8fFx2/MK/cUkYy9J4dte0vZ5LJEH24OqZazCc54wp77046qWsVpvMsz3IdcONKVv5zrRI74vfbve+xIb+jnbmt4kWAa9/H/2Z5WO5/f8n9/RJR4nY0T7naZdtoHfjFs9pm5nj9ezn7bH+W7X7eA9+o/va2t3ewZy2h76mPfg23pPQfS3t1I6Pz9vmY4OcH2o8s5BKfbSQGHdVwz0ALOsP08gWLWIMvp0kKrFaRfUk3VnfT2wks6LAWyCYINfvjtIk/UaNHPNRqjnODlwRgCqannD5DzlLumE4c0ob/YhmRvDORwOG/B2+1IoHRDz/wn6ewKbToSDSEkj6k9A5LalIs/+WYBTIRoYGdB5PwKMvgMOBllZ7jOoPYW66rlU9gAR1gV/W4r5IJ0cwAz0BHxXLVJOAVQJ1Ek1BVSlbskgYdVyEKcn5xl4XjUmfo8/e8Dd7bY8WHfYWQQUpoHuGSvamX25z9Cl7Pqe5Csb55785fimnu3pBTvb3L8quJLBpgQBSYvUSdDUtE+a8n8uh6atFPTQ9vZ2nZyctLrRD5mZAO2qqs2+f5XSG5+kNcW6DZrgiJi/6BfBGIPErNszi9DG7+D/lJ0Enm7fKj3Zk+WqWqo/+TtxgPUCbXNg3Mvh0Tk409hi6mO5DP3GXvI7jpidDkAdfzjL1oEOFoNzzMPcbz3i4Bj0MCDlfgNe61zG3FljjIkzRKoWs7U59hngQ77IzMtAiJ+9uLhobYEmZO5xcIDb5Em51HP3OUM9XbWKX3o2uVfnhyqWP2jg7Nu9vb168uRJbW5u1pMnT+ro6Kg5IdDSzijBHJaReSNvyxPPGaMxEZxjYl3szf7Ncw4O0S/zM7xMphCy54CXM6LgafMfy2Sn0+nSfm6cgjqbzdpeTdfX17W3t1fz+bz1nbrpK8GRra2tpe0c6P90Ol1y6Mj6IhiFDDlQ5MMZyN5ycJusQfTAzc1Nm5gjsMh4ID/00RMr5+fnNZvNajwet72jyNw9Pz9fynzo4VdPYjgQndk0YLOtra26uLhoS4NsG1hC+vHHH7c2o9/+9J/+03VyclLT6bQODg7aOKJXv6qt/GkrxkUsobZ9ysCHeRP9BM+vmvjsvS/trp9xHavwlbPZuHaf3fX7s/3ZNpdVfUncmHo6bakn1DI5IemT8QOPge8zTsl+9bCtn6Nk0K03PonHezRIbJZ2ubddBJ/2Y9HfthN+N0F5AtHoDCaNbKM/pC/6zkEpUmCZVUgHZZVDxv9W4KSuWoFVLaKAFq4MXuR7fV/V25FRG147MPyWAug6Elz7fXa63LbhcLhknAxqTSs7Yn6XhdWnYbgPntXqKZOMnOe7q5b3VrHCwrFJetgQOYCUAtRzmtMZt2I0DdyWLL17e2Pl95jODoJkplQqbdMFgc4AxX2lp8h6v6/6zXL2bSpOGXUKK33BSAOUCEaR3k8AinrMSxh5Sso1nx4v//V0RxoCj7HHKOXEssBzqxz9BPD8GfCuMng2cj1H6j45cXvSAOc7e/xqHWNa94z5fW3J+tMor3I+85nse7bTbbTB7gWnHETCobi8vGzgG8fZM8nmy8Fg0I4CN1DC0fi6iumXvG8aWe+bvsgWwRaWzeAQEPilTtPKv5n2OC/QmjFwMCSXhVnfe1KGZ2yr3JcEY9xrvoYOdiwdYOUZ2uj+57uhsx1vyw+ZFvQRZ5Xsix5WcWay+R+eATji2PM7kxNpw0wzLys0trEc8V7LM7S3zPDd7zS2SNpDL48vwar5fL4UnCNLwuPP/bbTjJEdjZSHnt1chdPMS73nCWT0Jp3ed0mnBb7ynkmHh4f1+PHjGo/HdXh4uGQ3CcwQ9CDQSn1V1Zbqki1DYMUz69aXvD/tQQbkM+Bt+1u1LP9kZnJKHqf/kcHp7H9m6QmQgQksG9a9e3t7NRgM2v4pZDrCP6nTLy8vW2DNAWnoxf5Qdno5lIWVH69evWq6g3YxWe/+W+ZZKmO6OBh9enraTrPjdwJslgsvvR4M7jKbyFzjN4Ig+AC2E55Moe1cQ395cufi4qJtXL6/v9/kF3sJfiNg9d3vfrfOzs5qZ2enbm9v69mzZ/XjH/+4Tk5O6jvf+U7jPSYo025824ttDmOBf+vgVNUdr00mk6qqt8bGgcvUV9yfvoqTE+7zlfwsvGWbwX2JmfP5VRgy/89ATVV/PybbxvRNE2f7mQySc533WCZN58Tpq/xxin/DNuakjcff+z1xL+2D3r2s6Zws84QW4+xxN960/UdvOsBfVUuHraDTvcya5dFeiuyJvQ9R3jkohWD1nA9KGvcclKpqabMoUwMgB3E8m+jPnmBmSaGl/RZS//G7P83AfOf3dBjz3fQ9jXbVsuPuZ3LZU7bX103fVDT8eV8JSoLspIHBfgaNYFoLhNvmfnCP60jlkYrTpadYTXd/dxsSIFsJwL8YVVKa/U6edcaU683/v0xJfusp50zx/TYU807yV9XySVKDwaCOjo6WZuwdyK2qJXkBjNux7Mk3Sj1PWUsHFkNgw5LOHSWvZRDYOsw872K5ow9peLmvx2s5s+WyavbM/aZu92fVtZ4zmve6bW63dd4qBzJLBs1ct+uHL3rLF3nGOtntSDrgJAwGg7bEYm3t7tjx4+Pjqlos3SCN2bOazM4fHx+3jAWe+aoOru2b7WavJE/MZrOaTCZ1dHTUZMoHBfheB2PspFo2zPN2XrOuLBl0MBD0WNqWGDSmDjBP+n8HyJwBhx5wO3Pmmskj/meCjM1+0cGJAc7Pz1twCFqx1wp8hNNumfeeLtCI9zBLiWNOP9GPBPKrqgUY7fTmWFlnJF4aDodtSY7rpy89+ee7M7p83WML3Ty2XoYEX5J1gZPt8e05PPn/59ngnk7Jdg2Hi+VW30Shr3YecFT29vYaDtnd3a2Dg4OWIQU/E5xio24CQHxC61xmZ2cmg1D8MV4EhuBtZ/GnnnDGBXYO2UYmWXbG9ao7PhqPx0uBHXiccRyNRm1DdIKc0+m0ne42m91t1E122NnZ2VLmEe26vLxsWdkZlHIGl/e25B5PmiPPLJexnHppHWPKNfZewgbRhul02pa9GXt6RQR6hEM2fDogzxFIoxgvub/gqpQLxo49sJDfg4ODVodPTF5fX2+Zj0+ePKnz8/P69NNPazgc1tOnT+v4+LjRhX6dnJy8FRz4WSn25Xy6oXVxLg+1LFQtVgh5vOBNJypY1yfm8Xj2/EYKPJO+IPX0/MBVujl93/RxEhvStvQ5oV1iOPQUJf1RPlf54O6jJ72yncYp1OssQrcj8cyq591n67V8nn7b1po/aLvbbexjvT8YLIKh6DUnBdAf/BeW7SUvfOjg8TsHpZxCmqBhlVNhwkMw72eA0JohKT0nqGo5NdjMv8ppS4crgxo9puzVkfX1+lj1duDIfafuZEgznxloVeDLSiXbD4P2otI9mvHujIb2nM4eXdPZ9/U0OEkLKyO/Mx3rDNLc52jb2EGLFPSeYgBEpNLp0firgNgez2WdnKLyLk79uxRAzfsujCezesibHQ945Pnz5y3TBHBVtQBYPYNrPjD9MKxe/mdwTOnJYeoZCvW6La7H99n5TEfNvM0YpHFPGc+2pV7KT/Or+5tG0SAl+dt14lRn/W63++02J4AxmPBvqR96ACafYywcwO/JYi/AQd30n8DSeDxujsVgsMiI8r4D6FX6SyABJ4i24uR/lWId3dPHSW+e4fvLly/r+fPn7V6W8ZyenrbMAWTSYwSvO3PKssU169ee3aek/VnFs840c6CMkjLCM9ApA1joCQesGC9+tw5w8Al8kvsuDAaDpWwJggjORHBfWR6EU+zsBtOINhLAShtMBhbBm6pF5gn1Ug82zbKduo2gRj5n+hKkWKU/5/N515k19vD+P9lv6mN/HWZsaQ8ZBj2baz3k6z2bmve6j6u+f+hi21i1vGT06dOndXJyshTkGI/Htb293ZZpsezLeAo+xR5WLYIcm5ubbdzQkV6OnFmABGd2dnbe0v/moVVB56oFzxhj4nDb8cJWejkd8gY/bG1ttSwbJrXZ8+nm5qZGo1HjO/iKJWjOpPL7nGngwKltF/1E5t0uT3D0AvnoIxw7gkxcc8bScDhsm6Y7i43N2Wn7zs5O2xv15maxBy+6guAF+pExIKhI8Iu2YPt4x3w+b3ttsTfNxcVFO+XPARP4Ch3+ne98p66urmp3d7dGo1H90i/9UqtjbW2tBd988uvPUoE+BIO9vGoymdT+/n7jUcup7RefPXsLn1F8LbETJfnSWJW2+XnrdsuA/3fd8JmDaMZuXLNv5d8cGPF3629nc6VfnBggA67QhP36jD17uDJxlX2P9Bk97kkv9BN6gz726OO20PZeICppM5/PlwLj9J2MRNNlNpstnSpKHV7ylwFK2vwhg8fvjKBxJhMcGwjY6bJRs1K0UeK53uzYKqfM4KRXeg6ff2NQXXrOXdbXY8h0fMykFtaMNq8CSxYaC0K2032BKS00KDo7b6a1jTLG1gKdiq6n+KrePp2vBxKzn9DG70na98Y96W7lYQXr9nC/lT58Z/ALHSyYDoYYeL1rSVnp8VaOLbJxfn7eUnu/jmLZfV8FB9gGDroRpGLPC/htPB63yLyzKgBwHgODKvOFedIzjA72cm9m8VF4j+uzLOXv9xkU03lVoN3PrAp4ud3+zPf1rvVkycY9jaPfbQczaWW6ZP9tlFP+fC11uet1XdnvBFOus0dLtyF1ku+7uLhY2gh8NBrV6elp2wMFJxFnfjKZNHrg5MFXOAdftfT6n85e1fIkC/qLYBuOAM7cj370oxoOF6drQS/PSFYtL9FiAsn0zQkF6OoJBmMBy7BBW8oJeroH2BIYm0ZpZweDQXP+uN9LtrmPZTzYWy8PZrkPcmDA7YAl3/n0cjzeCzAdDBbLjGh/1WLLAu/pAJ1wXNj3weNg2hg8Qk8+naXSCxDSjsxUTf1Au+5bRk17HcDNMQIA28a5/dPptAFn803qziyp77N95uPEmd9EsX6Ep0ajUdMzR0dHLbB5cHDQxsfZ3dfX120JJzrI42Qny9gbPiDAiR5gA2zkkYMSjCk93pTkaeqlzT0dQ1upZ2NjoyaTSc3ni8MDyOirqrZXkieykan9/f0lvW7+tn6vqpYlBdbjVFXq2tnZWcKW6+vrLesKuhA8gg4OQDsLARpAd+NQ5MT0c3vIJmIvpsvLyybL29vbtbOzUy9fvmwTI2TQsVwTXDUYLIJNnG5M/8lg9EQE+0J5nzA2en/9+nUdHh7W9vZ2szfGeui/p0+ftvYcHR21pYnX19dtCTl7bn1T8vc+Cn1ho2myDC1v4C/G2hPQxn7pOzjA2NNfPSy6KmDcwxZ89nxd/2YdU1VLexqC6xM799qdetrvxpa6/w7Q2Na7P4nhkx4OHvcSKHgOXZH+rSda6EPaHPfTOChL+r9+nzFDBgz53boTWSZAjZ5eX19vJ2USjAYb8l5PBt7e3rYlu2RY9bD++y7vHJTyoPVKOgFVy44QzzpllRkHhJLnHYVPJ5FiRykjuDb41GPmS8el16f7BNT/GwwZgBu4ebaiB/T438C+51zZKeCZjOB6rLL9CJwNN33xfQ5QebbTSsK0Ns17180XVrzOxvBY3KewevSzUnHALVNA4cNsh+mRQJg/Z+FkX3sljUqPPlnH9vZ2vX79un71V3+1/s7f+TttKdFXKdvb2/X3//7fr3/+z//5W+P+dRcDYjJIALvsOwA9Hz16VAcHBw3s4pjhCDuTzfxsfresOJhYtRzAtmGwTuoZ3jQ4/kud4zFMGaKYH3v9ob7kv6Sr3+H+8T/v6um03r0un6fT3+Wa256zUAka8nrKte9N2Ul55XnTiE+Pu4NagJOqaqCeZRXMgM9ms9rc3FzKsKla7K8yGo2WALn3TfsqxX3mvVmn9SP63NfICJjP5y1whsPppaOpk6tqKZjk4AZtM2Du8bxtRTrEBpbJ8743+ShlmjHkeQfS+J2+VlXLyORd7gOOlbOgvF8ODmsCa+usnNk04LTDSp/Rh/QPHsPx5F5+sw5h/xqCDrZ/AFEHqugbejbtr/WS5ahq4WDDM9zjk3qYcDDfuD3JF/AhTjmyljZ/VUmeNTbq4Ro+U0dmnR+62LHAYZjP5y0YvrW1VUdHRy27jSAzAVUHOuF3ZsupNzMXMqDjQBVjhlPjPUd8HysmwOxMMsHz8KufqXo7G4JrDog4WEXdk8mk8bqD186CN2bIbGkCKjxrHcIfAQKcNp/QhY5YW1ucYAfPsnSbvbEYG+sK5IwA0Pb29tL+QgSdoRP7iXr/qPF43PrEXocE2zz5QNnY2KjxeFwXFxdNz6QMeIkYskvA+ezsrNU9Ho9rPp+3ehgnlkkynixlvLi4qGfPntXl5WXLioJe4/G4/uiP/qjR6WetwIvee61qOehuPGPZskx6cqVqITuJ4WyD/XvyQ9UyZuCZtPW2c713fl7gn/+ND6i/5wOlT8c7kibUYczt+tLHc3scFHOAp2cr8n1V9daziQOMOzOIZTto3Mk16xiPvyd0XI9p6XHgfviJ+5hQYJIAHe09+tAzxGWYrLsv1vO+yxfaU4rP3qBSzOxVi2ALhoMBXgWObaior/f/KiDjdq4SnpzZMZBKJv88QTIgoq+99/ovGZ/raTR7bUPwDITdJoTamSc5bhhQfsPIu33pSCeQYQxodyqJpGfyigNfSRfXlTTqgU63h+veMNTtMHDBOHt8rfCtDGgL9M42vGsxLdxPFMiv/uqv1u/8zu/UX/2rf7XtofBVyvr6ev36r//6V67n88ra2lrbJ6Rq2dBBN8YD8Ly7u9tmenHs+d1Bmww2ZUamT5jJYI9l0vKV68ldXwakKBlcTj2QvJ6Ok9uXcp7y39M9vRknlwSfPb7O9vq3+4DDKlrZgFKss1xSP6ROyHbku3ptzPf0xsa6xnQCJEwmk7q5uWkzRO4H44XTSB3wHHrYe8J8lcKpf6ucbF+zPbP+vr29bbPZbHYO6LD+pq1ePst7sMHJJ73ACzTOQIQzoqnLwBZ7QlucSdB7t7FD1WKjegddaBd9Ru/YVtFWxpT+k9nkU7iqFqfY5RJAxr+XRY4eoz7um81mTa87y8UOAjSaTqc1my1S7gleEOSynVtbW2tZDtZf4IXhcHmDbJxS22nebzucgUSe8SbryDt7rxnA+znzLIGW4+Pjt7LP/L5e4XoC5x7eSB39eTr0QxbbGjKDWOYzGAzq8PBwSQextA2+995RyAHj6mCLx5fr8AQBGPbMGwwGTXcMBgvHGV7wPk0OMnk5H5/mRYIytrHeV5ZZ/sR83j8Jx+vk5KS2t7fr4uKiOVZMElxcXNRoNGoyyDPD4bAmk0kLLjlQRP+3t7drNps1G8C9ZGyhM2gbe2ARNLM8QQNvIO69wwhkXV5etgDgZDJpWZoEyV69elUXFxdtDzF0FcHynZ2dFsS6vb1t8re2drdH4mBwd8AFbb69vV3aUxXao2fgoY2NjZZZBX2qFjry2bNnS/qPZaZVd9lojA96kgMFWMJW9c0Eg993sdzAd14Bk1m8qa88yWQ7bR2assz/xsk8b7/Qei99udS38DC2zdjUJbFm+r62J66PQjtsx+0Pu6+JB/LdiZe4z3Vnu32PSy+QmzjTffBzvd8y4G7/3DxgHGe/KccO3euDXTLzcVVgiYkk+jGZTLoHftxng99n+cIbYPQGMEs6o/wB0hAmiAAg9YwiUXuE0cEB7mHg3LZkNp5Jx9BRVzOeGcRM6OBO1SKLyOvwq2ppVsX9NHNRMoDVc5StrEwHg3unUBqgoHSguetBMBwYNKhPxcF3ZxlkkKbnpCfo5T4vK/Cslfts53ZVsC9LKlYCIQboCCSZEO5/BiSgcS8Ayvu+rHFFCW1vb9fh4WF9//vfr1/+5V+u3/zN36z5fF47Oztfql6X09PT+p3f+Z2uwv66C+MJLQn44YhtbGzU3t5eA6Sz2awODg6arNjhZQPaVfLogIODjOmwV709U8613rhZd3nGyTzu78nX+XsaN/OL60y94DauMsD57p7hgp7uu9ua9bit2T63YxUg8PV0TO+jjZ/vtTdL1m1a9X7ze+1Ez2aLE7g4eQS6MWvuPTjMowYHDnx8lQKwQDcRrEXP41RkRgO6eHt7u+mUi4uLlinQA6jeT4Zr1Gfd52cTPCO36HMyKNJOQffkNexNBjJ5t2nM+53h48kTxo57HWDsBSMcDMER9Nh6eXfuoWN75JPyMsh2cXHR7uEPfYWjSl04t+Y1dCPOLan32DIH6Nn02ZOCXvLDZICd/qrlPSxwMHmW4jEaDBb7yORkljNneM5Od1UtbTCfet8ZcOkwJQYzb92nz3w/16wXvgngTTvAI9PptB4/flzz+bym02nLUkE3ZTDZG20PBoO2Fxq/0d+cXFxfX1/KUvGyD/48Pra9dqLsZPfszGAwaFk8yKODyHynP4wtQQ5nauA70Ne1tbWWIcaG3Jz4xp556M2bm5va2tpqp07hZ3jZ7qNHj+r09LRms7vJczZQh/YEVQj4wOfoDOOUqmoZVOmAJjbl/YPBoC27Rt6drYtcbm1ttc2xPdlg3mfsCCptb2/X9vZ2nZ6evjU5A60ZA8aByUXkE3kmED2dTmsymbS9xijQhkxd+gnGZvne3t5em4T9pmTvfRTrQNtC7LH3F0J/9/CWfVPrLPjackjJvSzTX0r7mvo0MZQnk5D9Xlv96cwuY7GkT+Jh43rsrb9zL23AbqIXbacT8/F/rz/QzRgjZcS0d6Efxjm2h6aN77evycl3GZzKeuxne3kw15Alxw/4c1B5OBwu2Qae5V7rZ9plWn2o8qV2ZV3liDvwUbUwut7IE6J44EwUmNSzLNzXixgmYKlaAEe/w8pvlcPq33vX/D6D8uyHNy/0sykofrcj3tSXdJrNFmnTLnYgzMBZl8clnRDPLCddkkaeCTKte9F0j1kab9PazlK+J8fU76eOnmA7mJTAIJW4hRqD4vZaAa5SfP5817KxsVFPnz6twWBQv/Zrv1Z/7+/9vZpMJi1t+usoOH7vs9B3Z74Bpvgfp2w8Htfm5mab1UQBehNC7ydB/byDcUW/+Dv3UpLfuNYr5h//n23IuvNallXGie89vjFAySyD3vvdbv+ejpvfex+v9p7J/vd+S2C1qk7XcZ896Y1BDzw5aJF6zyDPz3r/PgJPFDIMqhZOk2eX7GjbAWdi5csW61DsJjxAf5nJHo/HrZ+PHj2qra2t2tnZac7U6elp7ezs1HQ6bf0mqAUYc5DC/JJ09VhRrFcMlCjQeRWgM/DGtlEYH9sj9LIDGQ4g+l63zbaB/x18Qbf71CqcylV7W/Ae7xNFUIk/bzqN/cnMsdQHBBtub29bJokDYgT3zZ/QBr6knTi17DlDINPYwLbU9XBPD7u4/XZADGbBA4Bv9rfwnjaefe5hB95nO94D/EmDXrAq9Xl+fuhi3ENgBaeVfZLYwNtBGoISV1dXNZ1Ol/b+Y2mXg86MEddNd+Mmxinx+XA4bM9WLbKF+B2HkMwf6nRQw7yc+0uBDThwAt3mQJd5Gz1Bmz25PZ1OW+YQ+pK9jayfkBGyr6qqBWLsjPEMGYg+ydR0t6M/GCxOvsYJZNzoG7IJLWwDb29v39p7jmfIjvL+bwS16ZN9DzLJwFIEtk3TqrusKzIwoakntJFxgnzn5+f1+vXr2tzcrMePH7cN6GezWcvum8/vJlUnk0mb7MHOOoj/vnHphyzwtTPP4Btn0zmQyISAs4pX4UpkyZtaVy10p+tILGaZN0ZMO09dTljw5KwxWWb7wM/W1T2s5wBX6l/a4SB86mqeTx8PelYtn4ScdDBvW85cN/TtZXbxbtOOe3rZZ4l/Uj9fXFwsbf/gRBHrFmyscRrZkdzvLE/rdWwMeMT09/52PGt/4kPL6JcKSq0y5Nl4AwoGOrMPYPpkQgMIDDMM54HrOYkWOu6349dzlnoOZ/bFzGdh4P5epHRVlJF22plK4OV3J1B0VlnSM5f2QV8DeTOeGdTZWB5DR5bthKXQZZ97feKag5g9ReZ22cHJ3z1e2SfPSBskAXw8nr063OZsY894vGuBTqPRqA4ODuonP/lJ/fzP/3z9lb/yV2pra6tOTk6WHJivUr7qkqJ3LdAFEEegkxld0uodxMTRPDs7a04hgdeM/Js3CAp45mBVmyxnSdN8NnVHzyG3rCbv22CYB21EuZdPZwumXsl6rcd6xfLnvlnP9AI0KZ95H+3r6aakD8UG9V3uT4Du/00HP2sDm5kWlm3eCa2dPVlVS0DFs+Bs0uqx8bIH6k198WWLgaBlxdkrOKucyuXZdDu6tp9ct641rzJe3hSYa9gPn4xpwNYbR+jCJ+/xu9NOua7euBiUUScgzMEinyKFA4sj5oxt8wxtcOYAS/Zse6ExmRucUOWsJtsYbA8APrNDvPzPgQLbrATMjO36+nrLDDHdGSNm5T3O3nPI+tBONvRM8M/4mRb8Bq2cOeMT9tDBZIT8+Mc/ruHwLoPr7Oysbm5u2r6D+/v7balh8jG0cVYZtOGT8UnnG/6x8/dNOsXI9WBwt4TYtoTNktn0ejAYtCVdg8Gg7d9D5hz0Q3cQAKmqJT6yg+qx835TDu4hIw4Eew8S14XOZL8j6G396MxA+o9MoQ8Ipk6n01bPYDBYWhrKcjYCedBiOp02Hc2+R9fX17Wzs9OCaWQyXVxcLAVLnYGKXmBJ2u3tbQu8eN82nF/aznfGwPoDOtJ/grg4jF6GZ5oT0KuqpfewZ5P1CnaC/ekIZlUt7zXIWKIryc61PkD+vYIAu3dyctKCS0+ePKnh8O5UQG/JcHR01OQcuTcm+lkq6Ebohj24ublpmW3IbWJOxhg9aTxYtby03XaQ91YtB7kpxk7GL74vgy45ScA7HLSyvXHWst9t7Ge/y3iSOpwM4IA2/XVfjfsyyQKb6XuNp7k/N/G2TkxaerIEvWAcw6eTHhLD5lgYwxpb0MZ8p3WxJ1fsZ/D/dDqt0Wi0NKFJ29AdHiPr4oyrwGerYhjvq3yhPaVM6FUAHKAIWPKsAL8BptMBM2C1MFlw/O6e09ILelEP92YwKIMQqwSbe3ozdxZKv7Nq+YhLM1VmJVkh2FlwsVGFNsm4MJ83keTPM0WeyQEYeEbJ7+z1r6cI3Z9UKrQzBdXR/Yz+rypW0O67BTV5y+32jDZj5bE1uGNPDyuLXn/z874CfTgi9td+7dfqt3/7t+tP/uRP6k/+5E/qk08+qY2NjfrLf/kv31vPT0uZzWbtqF+M8fX1dZslu7i4qLOzsxqNRi1AVbW8nBQHhHG0o2G5ySB36g3LetXyunjLoscxdUDyauofj7GzOnv3+X7+7BjDc/QzAxLm66q396LLcUjnlPY7kJB99//WQxRkxvRwO1LPul6Pqeng9qaO6o1nGkzfw3OmWd5jkMcYu23cP51Om57EfpFJxYlX3mejF6z7KgWdDG3Y68QTC7x3d3e31tbWajweLy29My28/wq2N0GXA8QGr/TZmwV7fHB4055DU9qBTXLddkZtwyg9MOiMMe5Hb9BvgkMGnJ6EwFGAhqS2W1Z5p3kmA0hkfuNsIV/00ZlelksvbbEjiNOcWR5uo8fETqP1Dryyvb3d9spxmxIDUYe/W89Sr4Nh5jXa43fQzq2trSUnnv0rOGkNXch7Dw4OGr+DIbEjOPiMJ3vy2AbQL4JhBGx5j3mDPnydsvuuxePE+Dobx1k8VbUU3Li8vGz7I8EvOP6z2aztJURQB95kfybGFl72BrmZ/WCcRHscTHaGDgEclozQT+p69OhRC/ZcXV21sUnn8urqqu05Bu4igESAhbrQHc5WHAzusg/IJkNGrffR4bTPwUroTzvQxZubm0v8xjsJ6g0Gd5mTVXd8iM2gXuTD+zMx5mAmaEzwkf55WTS8wBh4gnltba0FpOCt3AgeWebd4NweDp/P50t0swM7nU7rxYsX9fz587q8vGzBZAeS7SzzTvPGz0qZz+dtkuLq6qoFksEXufohPxmLxLK+5nelT2obQDHOS1xn/Jo4LXFdz2dLDJn+pu9FzzjjMv275Bf7gf4922CblfVZxxkf5PJy/3lVl/EG7+rR2Fgtg22JjRMfE3RnIt8+gHGak3Z8Hdkm0Aa97Dtx3UsFqcu2w5OaPaz9ocrXmilFycip95JKBvIg9pY+mCAmuttiEJXBovva1RN2tyeFLwXEjlDvnVlHOkFVy85vChb9NVDPoAl1e6YT5nLddigcEfWYZN8ZnwTpSRMLbLbfdE/a+VnT14Kcyjuv5zt7v7kvvsdOi+nq95ge9xmHdy2m9Wg0qsPDw3rz5k1973vfq7/0l/5SDQaD+k//6T/Vv/pX/6oODw/rn/2zf/aF6v8ypUfDL1NwXtfX19smvSzPA4hOp9M6ODho4w+A3dzcbCftABJxPqre5omeE5z35YxOGmKPJwbV42kjZPnMenrZV6anQX6+w3XaqKdsrgICWZ9/s75ym3q0MoDye5CRDFJXvX1ghAOJLqZXttkBirxGnaaj9UYaTAAF/Jb30Zfsnx186gFccs/t7d2yClKtMfA4SQQ9vuqsEvTHSVpbu9s/hb2ifEKlbcn19fWS02fngTqt85E9j79lrdcm080OrcEWdqcnl1xzwCxBUvIhvEfbsG0J4BLw2V7xbuyhJ2A8MUO2gTcmpx0+mc/8Bu94f64E36enp0tAz/RhPGezWcvawHmEf8kK6ekfHDwvz7KTfnBwsJQ5Y4exJ3uJURJ75PhXLZYROXDBWPJcLxsOmhNsIFOFYNGzZ8/q5uamXrx40TJgCNwB4s1j7gP9IGAIH5mXaPc3VaAvOotgA3S4urqqnZ2dpQDHzc1N27gauho3c81yB/4msMwmzASjvK8K33N8rTcZNztCdoJsu7xczwFqeJqZeOvrvb29JlvGA+AEL89Lu0KmFoGnqmqn6vEMWwd4uS513N7e1v7+fpP11GmWCWQTx3I4HLbN6B3oYrklgWUH+Rh7n0LJdS/VgafJIDs/P2995pQ9+IPMq/l8XqPRqJ2oRXuQt1evXjUdSeA6nWrGDvrZYR4O75YTn56e1u7ubnuGjJXJZFKj0ajOzs7qBz/4QX388cct4wyd/bNUbm9v235wjDFL5+Fp68fEsJkx3/O34Ednn2awxLgnsbDxQNXbk4v+TL8A3uwFtXKyinfZL+z5buZ3eJM22ldN3zTxA+/L/oDNzWvwp/uAbjKGyMCY+9CbzHBQh2Lc6br8fiaQmAy1D5/+iH33+XyROe7DT5h0Q1Y5sbcXOAPPoqPSb8n+fIjypTY6f5d7AF+AJAATf4AEz1DCYGYujJXfTV3poPWCIunYQeT8nu1HWfScYQ+sN2R3G61I0rGyQjCwyiyG7I/B8WAwaLOsBlYG/hkNxvE1HRknK8QUetfL96RLKqn87DmEvoc6rBgyKODxcb/ye/ILffQyUCs/G4d05jx2vbZ/FYFlo8jvfOc79df/+l+v09PT+sM//MP6X//rfy0tk/y6S9L8i/SjNx4UnIXt7e0GitgDhRnzs7OzpUCgwRfGvGqh4L2nBuNmpyMDheax5D/zmGdne7NMqRey33xaf/VAhHUAbfV+ZWlQ8//eWNmouJ1uS/aDflYtHNkMAHCNcbGRtazQRmevZLuzpC7s6Tba63t6NO895/448yL1PUEIZ57why7f2Nio169fL2VmcFQ7bfOR3dYlDlp82XJzc9OyYXEk7RwZLEyn03YvbWLWFXnifgdPre8dsMVptS7FNhHw8r29wF9muiWo7o2/wSs2vzdx4llO+slz9NUZAbzXDhbtMI+kXDmjiOcMjo0F4CecNRxaMnl4P8Ad0Ige8xIgL1GDJt4nyMvv4Ev6jSPvfvIO7ydFW5wpAr1xalOvMUbIiXnJmChtqulLXfxxLw67cdT29nZ973vfq9ls1pYp0a8MblpnVS0mGNAltHc+ny9lvmBTPjTopm12fggssDk49pAgjAMtk8mk3edxMM4iWJVOK8uEcGp5Lj+dvQhv397etoD/9vZ2C0zNZrOWJdXD88aT2B7znOWIpSfwpseH921tbb21/JWxRH4IwK6vry+dLIX8sJyKYA704HALYwKCWIwVwWtnlTpAuL293eqsWiylw574wIOrq6s6Pj5umWNVi2DpcLg4oID60OnsDzObLTZFRxcQwHbAcW1trUajUdNJXhJ2eXnZbAhtsh5PvwA+o/+ffPJJ/cW/+BebvTJOOzg4qB/96Ef12Wef1UcffbSkv37WCrKHDs0AJuNRtThwwLT0716qWlVvffZ8xR5NbfdsFxL7+Dp94RnjgFU+lq/x3vTLXF/aD9ftrL2kr7Gp/db0A40vGBcHfY1rs9/WU7ZbtC1jAKY/+CT9SPeBkjY994m0f8798BdBffSzx9bbBXhM0OHGM1WLrMxVNv9DB4+/EIJe5XBU9WfduZ6DkgyLcTHQgzEMRFYB2l4AKdudYLmq70TZUUuB9qClM+R3p4DkfasUC/emQMCYGPEewE4HzLOlrrsXjPGY+J0OEmJETfOeg95zmGm3lYHH0GAq6ZTKpWqRXWanueecWpjSaWHmDSc2xwoaG7il45x8kE5Oflpxra+v19OnT+vk5KT29vbqN37jN2o8Hte/+Tf/pv7Lf/kv9Wf/7J9dOj3qy5TkK7fT5Ysonc8D8AbFNzc3zWnmWGN+q1rM6nqzVAARYOo+wJw05vecVbFiz7oMmNPpMY9Dp57h51n/Zjm/T8/05D/fy/02pOl0u45VfGk65eyPf7PTCD0TYPjdaXh7Ortq+cS27HPqdDs0afxpox3fnHyA13JNvEGAnVM7yTg1tIMsJGaYCbJOp9PmSAAUvqoBx+5Z71oforecMXRzc7f57O7ubluuNRgsNrC8vLys8Xi8tAyDfsO3zKgNh8MlvZP62s4J9DE/co9/tw71/2mjvHzFNgqQRGYG36G1J2B4nwM1lsvUK84YgfbOQKbu3DuDtjurZD6ftywAwCI0h6bwiINuXjIwny+WWHrsfTKOx82z8uY9Nr53302/quVtArDvmbaPXobOjIszs8EmXnLGvjJ2QtH9LHPBeWPc+B+6n5ycNJrihCPrBPvIDmRMTAP6BK14P7xCsO5Dg27KfXq6ajF5Qb8mk0lNp9M2Tg6IwJ/wqPdPqqolWeAajjOZNNyHLjMmc2ACfnAwxvrcQabElRTew7PoVP/GaZJVVYeHh3V2draUvcR9o9GoxuNxkxGW3iGHnsiFt6CvT8ckmIJc026CXWw5wNJTn2RFIAgZJRvMdmRzc7PtHWX9nLaO4NNgMKjRaLS01YEDXwQzed/W1lajN5lP1ttk2hIke/ToUT19+rSOj4/b0jpoQWFPRfgGXUmbodPp6Wk9e/as8YB1hzNAbbc+D0t+GwvyCl3Ye9BL/RlHtrtIPwysip61b1y1fAKssa19Esa8h02tJ419E2u5pAy77gwK9XzAxA9+f7aZ9nmZveujGCMkPs17/Md74UHTEno62SODTMawfj82yLT3O/0+08T+kCeKjI0dwDd2dwyB95sP3Tdk1u0dDAYtIA0OQHdST/ou77t8oT2lVpWeckmnKx297KwJxfM4A1au6UD5HcmM/N5zmHp1+fd8X/bXzlE6hyl03GcFknUbKGYwybNJ3kgx6e92WNgQFAeNzNg2iFZepq1Br6/Z0UwaZR2939xmt8vvTNryvGez7GRi9LNtVnwOhnhmzEYlxyeDDqt4aFWxUtvb26ubm5v6xV/8xfqt3/qt+uSTT+qTTz6pP/zDP6zt7e0lkPA+y9cNDKBLT3nyfTKZLI056esAU4M06J6KOemexijl3EY9lb1lgN9t8FxS9yT/2jBULWcjVi3zd89Y9t7jdtgBTh1iGc+g06qAWhaCHAkwqMsyaSDlcer1x/TOknTojZNlx/d4qZrf4/2g/DtyjkOB/A+Hw8aH0Cwzb3xSJJl/ONYs42BPkS9beLeXxnhZ2cXFRY3H46XMCsaLTW8JRLF/G4EzQDKz4QASB3LMX9DbwX8HU6ANPOLAVE8mbOMNag3onElRVUuODM/xR78zIOnvBCZ8qhjH3lvfDwaL/bEcbOIaWMTtYPy9j5czSiz7dho9uVS1vPTO+/FAD2MNZ2lQnCECra6vr5szm1jGwby0tw6+OQBFvz1DTbsc+OUexnN9fbEBOnIJCL66umonf0Ez7j05OWn7skwmk9YGb7Zvu2IbD+Zxxh80wNHh+1eV169SjF2MqZBpguPb29u1u7tbr1+/bsuDLGsEB6oWGRi2af7fQcec3PE9BEyqlu235aa3dCTbVbXYlBhZscNG4MpZOwR40G/UTZAVuSaQNBqNWuYWh1Nwmtzt7W0L5NEXaE5/zEMEKc3vyBHPERQdDAYteET91OW9WmkjtKi6wzxnZ2ft9FbkDb2fAVaCQkzcsYxvNlvOkLIO5XnGDLyLPaCsr6/X6elpO/UPWbPuZbwJHDIONzc3LWD49OnTpX3bsKnUSb+dpfWzVtIHdaasbZf3jbT8Vi0fEJEBJ+s5Po3H0ldOv7qqmg5NuU+8m7rT7+VeX3fSh+uxH5O4HV3t5/Bxve1P6kra4L6mz9nzScENyGa2K+ltLJMBncQvFOrw/oW5TDwxFNfW1ta6WwKAD9wXT/qk35UrzMAo2F73GV0/nU7r7OysBVHtK3/I8s5BqVXOq8FnXjcYNbBC4UFklHEGWtKZMjPyPTMg7NBmSWen1w8zm2c53Cc/D9hy0MnCkIJ/X10ZNEqHGeZiJiMjrj2HF/rQLisqmNKnnSUdecbA17NI3J/KIZ3qjNpzzcDFzlBvjHrObjo5XLOTDFBwWrgBVI5FRqWt8PP99zn7poH5ZXNzs54/f9720fj1X//1qqr6D//hP9R/+2//rfb39+uHP/xhd4+1L1KQr6+acfVFS2baQD8CgQCvzBAAzNkRsrwwBihvShoJGxGPbRpWB7dcevzneyxHWcxveU+CBjt5NrppXOmjr1n+zLd5n9tt+qReMWhy+/y/ZTjb5L74t2xzynDvu/Vnjr/BDH3IQHv23bLusUPfzeeL2XT0JKDAdqiq2hII05OJE2Y+v0oxXyMfBKLYGNhLWjLbCEB3c3N3QhJLXZA3ZKu3hMe2MwEq70Jezed2wCzvHjfLA/RNfkydYMCELUs77Xs9+QKP+LrH0xkKm5ubb+nxPPUN57nqDtSPRqOliQ0CRicnJ0u05UQ5B68o/G+w6Xea98jooB84FmQiVFWTP5zWxFkeD+gJeLUuzOwWj5v1gGXdmMkTZ4yHT98iWEWbPZnkE+SOj49b/63jBoPBEgbi+apFJhoZJS4+snw+n7dZ4VUY9n2WpK2DdwRiqqplML5+/bpOT0+XxshLMqsWS6otxyz7G41G7bQ64yTreDAPNtqBhwxgml+gcwYKeYflzVmaVbW02flgMGh990mDVdX6gWM1n99tFbC/v18HBwctawgdzB9BH9pGQPr09LS9G74gQ8DbY5DpQptGo9ESJkb3OaBnLO1JDhxtMrpYRseycGdm8D4HG42VoS3LeHge7IVeRtbATASx6CcTKjs7O1VVLWvKY05QKbO6GHsOJjg9Pa2nT5822+nAPf23H5KO+s9CceAJueKgH29wj6zaLuRf+mlVy5g2JwcYewdIq962Lx4/9DnjmkGtVX4lernnUxqb0WZo4zrsp6eeN1ZzHaYP9zgb17JHPdxnW+1goNuU9hC74kk1+pIJJi62fyk3xp3Gvqab+2Gc7Sw82+n0bQgq+fAJloJDM/jP74WnViWbfIjylZbv9ZynqkXED3BshnEUETCfGzXO5/Punh8OkhjkmrF7ziSETeen13bXkY5V/u82WfDMpE7NS6eo1xYHtWg7bczrdmyrqgFC3pdgjucBQAYdHpsUhqpFRgJ9zKixGTsdcdMtgwl2HHxfAuTeOKRz4zajDJzCaIc3A4dujxXvqgAn99/He6ms6BOzTRsbG/X8+fO6ubnbzPV//I//UYPBoE5OTurFixdf2cH9JhRK1bKj4fXNVoKWi2wv+15kQNrGNMek9x3623BTH9d6gepU8FkySGw+Tn1jOTGv8DvGzbKe7VnVvl7gwLRMOVzF0x6v5NmU7yzJ9wYEq4KC1iPuTzq/2W7X5fdkFo75hOsZXAYoe+YWO+RNJ10/joTHHv7a2tqq8/PzryVTyrbTThm6mswfOwrmLZZorK2tvdUegBg0AahZzhwwADSvGi+KZdLj40yMBKoGiDxH/2mjbVLOtlvH21bYhnKPl1MkwLf8pUPhZUzIKrOZ8ABtOD8/X1qytLm52TIbPv7445bZkHLA/+hFjz/ZRFxnzHFSAdvuA5k0LOHxbx5nBzgt+z0ZzHG2XbZsbmxstIkzB38uLi5aVh6yZYDswAD70lByNtg8SB20YzZbLEfzTHXOxlctLxf90MW0hQcJBuzv77dAwmw2awHGs7Oz2traavQmWAo/e2kVfF9VbQkYWT0EDJAj6OWMnV47jWEJehDYGAyWM3DQI162StANHqE9YCLrMeQOXsTBo26ygo6OjlrQs6qWsg3hq/39/cbr9JUlsnyfzWaNRpRHjx7V9vZ2/fCHP2xLCTmBj/a5Xw5W2UGHfvxPUI2xmc/n9ebNm7b0GloQwHI7kY/z8/OWGbuxsdFOtKTdzvjwhu6r9MDa2t0m6uPxeGnTffMhMkVwbjgc1s7OTrvn+Pi4Dg8PG60JVKEHrVvMWz9rJTEqKwCY+Mp7sZeMCTLDJE/eX9XXyRT7L7a9iYVdjNVcr33unl97n6+cOIFAyCq/yXi4apEdZJtsXU/xaZTwau6taBtuLJjxhIwz5HJh6kJueljW+tN4k35mEMi/OdPUtouxs93vZVunH+5xd6yF9tsfI3BlPf9NlC+0fI8OpuNS1T+ZLp0ZBywMUnNPH5gK42WmtcD7XT3nx+CpFwTiXeksrhLQnrBzf8/p6tEpo6tuq+/PIBfXnAafTgFCXLWYNfU4GLy77wne/DuKxM78Koc5+92LtiZNrXjtTJomNuw9nsp6+e4+r1KUVkoZ9EjnNyPZvfF13f5uMLezs1O3t7f1K7/yK/UP/sE/qMFgUL/7u79bv//7v19Pnz6tn/zkJ83p+SrlQ6ddVi0H9pDj09PT2t7ebvvaOGDFGEF/+AHA66Cv/3hXyiLXLfM9eU1+4D7rkORf86XrTJ3mtiWfwFPmSdMgDZedqbxmnvKzSQeXpIUdcesKtzeNU4/WBuFuYwa6UmeZVm6PnUf3P/udMupn3AbsiW2KnW90Khv026liyc/5+Xmtra3VZDKpR48e1Xg8XtpE17PqX7bgrDP7vbm52Rwh62f2DGLmv6rakjGWQqGDNzc3WyDcQSZ+J3V7Y2OjLTNx9mI6VvCQ6d+TKXg6xxg554+2GEwRkMmMS+73DB90o330kWChgTD3eCNk6mcjZWcspF10G4bD5VPRHPyivRcXFzUajRrPAKCd+QTQtMPopaHz+fIeTuiL2Wy2tC8JdIR3HaCxY+FTGjOgZ3m3TPo7fTVI5r1eyndzc1MXFxetnVwjGHF2dtZOtJzP53VyclI/+clPGiYEtHsCLfEB42D7zZjB59Zp/E4bfUz7N1FMbwJTHr/5fLFkkcweLyV1UJp+sCSN/zmBjYAq9yHrbBVgRwt+N92ceQ2vZLazecqBIgIr8N/W1lbrT9WyA2xbRMDVDpY3hfaeYPP5vMbjccvegY/Qj6bb3t5eC5BNJpMWqJrP5y3zB91hLIiO4tRMstAc/DGNbYPcVwK1yDr09tI361gOrhgOhy37Cz3PJ/rMGInfoSOBETuj7uPW1lYLTN3e3ja7w3gyIUKghb4zdv/v//2/Ojo6apvDHx8fV9XdnmDosPRnfpYKOgk7wvg64MtYY1csm9iH9FvMCy6JhVb5N1zju3Gl2+7vxqqJtXq+D/rDK4wS+7ouYwMH5+2DJo421kw8wLMO/CVeTtztOuxX573G9YkFctLH7XTWIjaXflOHxyJ1Lr9Rh/sBjqmqJTlFR/rET+judl5fX7el8/P5vO31SNt+6oNSZg6+Z0nmTeHyYNvB9/M869npVaDBzoudYb+rJ8T+P2dyLTirAh5Vy6cLWOhTeKxI6M99EXALcWYPUYf7msIN3ZxGTF/M7Nl26kFAeQ4BN9i2IaMYFKciMl1TmfWChVYa5rfeeCZ9rEgMZOxIkZ5OPxwVd5q068uAyKrSa5/7vrW1Vd/97nfr+Pi4ptNp7e/vV1XVs2fPmkKcTCZtX4VvY0kHwMYNRQswM10zoOrUeAcUqt4OABnI5XhDe7+rp3vsfPXq4rqDtHZ4XJcNbdVy4Drv7YGN1Ad+zsavpzfcj3yXabcq2HQf37nu+2TT31MO/W47I3k935VjaP6yLveMmJ0DADlOsvcQAtQAxAkMsZGsZ/y3t7ebEccRub292weHYMOXLc6OtEMC39vJGQzuMre2t7fr4uKineZJ1s5gMGjLx2g/fbSuhy44W7bR0N/gyIGadCJ5R0+HzueL7A07yGkfyOAwIMbe3t4uNvc1PeA52kibcQK8T0rKsG2ybbb77ev+fX19vY25sydyqaV/I4sN3jENDOh5t5fy2bGjP96riokk083jwDVPOGVQw/qG/mawOJc9eAyYfMDBJtNrMBi05aQ4xF6euUq39DCF+SrxpDESz9op8DLOb9Iptu4bDAZtI2wcCYJ10+m0BSWQ96pFsMf2x44Pexk5GOosHQcwHLg1dq9a8CL0Rf/RJu5z5htjQnCdU/OQOXRwjhUBLi978z5nBH4JuhPspI7r6+saj8f15MmTmkwmTY87YI9e2NraasEa2kq9zvra29tbOi6dNoJPeM4ZpQ7IOYA7mUzq7OysbSDu/a+cZQg+nc1m7RRGLzEnC6xqcdrWeDxutHCGLOMCvbCNBLNwbqHr+vp67e7u1ps3b5ZO5vLkA+PgAHlV1enpaT1//vyt4P6TJ0/eCrh9U07v+yzmdeh9dnbWAsDoXOjupeDIF7IGbY2ZjEMTU2Ux1va9lsGq/omZVcsrgbzHrd/f84e8FU/qbL+XNvEOrmE381n794kX19bWajqd1tXVVe3t7S3127rL9VGP8T6/95bU85szm5KuyKyDOokl0EHgEreL+zxJRDt5zn3wSjS+e4wZ36pq+opMZPtfjJtxUY7bhypfKlOK0nNC7PCYOAasFgAGzvdbGE1w6rHgprOSDqIZ3n1JZzGvu809Z5L7YUT3yc5htifrNrAys/boh8G3g5t9M8jMsTMtbZh8r2d0PI5JK95lUJO0SXpzbRUP9Uo60r1+9Zxit98Kx5ufegz4hP4OijIOqwJSVkyrChkLz549q+FwWIeHh/Vrv/Zr9YMf/KD+4A/+oP73//7fdXBwUCcnJ232+5tQCF+1QHMH/DA0zgAB1HrGl9kA6IgBHwwGS/uB8B7zsscpHSw+/Wee6BnOqrd1A/ckD2ZxOxMw9Hg/nbtVAV47DNzXM7QJCKyPeI8BoumzSi5WlR59/C7aaTr4N4+F9ajbYtBrO+IZWjvpqYvNc1WL000BW8wAk/WUs9sEGObz5eN3j4+PmwNA3V81KDUYLDakxaGnbziX3EPmFo4K/WKvOjIJrq6u6vDwsNVPW+1kkCVAnwnQQ1MHgdD3zqpwoMp2MYEr/fI+d6tkizGgrb6HCRMcKusCAoc49ynrONPGH/QTmaftNzc3bVPh3d3d5kwamJPhAtBz5if8MBgM2ibMo9GoLb+hPYwVNGYMquqt3ywr29vbLYPC+8DgVDNO4AY7tdxrMEr9zkQzWLbc5tLPtMFJJ3iIPVYyq8qBkft0TmIC9AD0Aphjx6sWe4Q9evSo7U90e3tbZ2dnK+36+y60zUHNqjvH/smTJ0uBto2Njdrd3a2q1SdRk3EDzTmVzjzuYKR51VgVGfX4GAMRjKAtXsqK45OY1hvsGt8ir+gpMANL0YbDYduo3Fl5Vcv2jEmslHWWKsJXDkyysTeZZcgGfYGWYI/z8/PWR3QmMkXGgfUagRtk1RuRX15etokRAkHUh97lNx+qAZ+k3iUI4kAvWYrpa8Dv6FTLDRMt8BzPE7SCtt5sn3HBVr1586btCcbYHxwc1NnZ2RKmuU/Gf1qLbVuvYD8Hg0HLXmOSazAYLOlh6nBA4aOPPqqf+7mfq//8n/9z4zPeabm1zUtMhM7o4U7/nn4ZdTkwkZmYPXvgABmTDsi978fe+aAJ63BjkKrlU++MTRIvzGaz2tnZqX/7b/9t/Yt/8S/q937v9+qTTz5ZOpgEm0hgOIM3yPp0Oq2dnZ23gqfYRNsK79/p32mrfXPrUfpGyUAQ90Gj29vl/QPT3oI/rOOMvQaDQbMxpqcnFa1PnJX9TfigXzpTatU9KQjunJ0T/zmyilGCoVcpLhvPfPe7Oo69dqdD1BscMxv9spF2X13S6LstDqihvGy4HURCOWVAyUJKG1yvGT8zqUz7HuhjTOijnckeEE3HOcc8x8fXUum5nx6bVJa98XM7SPu2k4uyTFBs5eh6s/j+Vb8D/EajUX300Uf16tWr+vmf//n6rd/6rTo6Oqp/9+/+Xf37f//v68/8mT9TP/jBD74xZfB1FQM/O6EEQqB31fJSO/M53xk3g8QcW4qvmzfNO+Yxrvm7Zd6pub53FS/0dE7yWu/9vbaYb90/tydlKXWv9QHj4oCh+5tGM+WL3xLs+Bk7CW5zBg1X9TtpCu0SvOV7APoYby99cRvME7QJJ8Kn5DhrJpcR4VDz6XFlBvSrFgJC1M0sIPIxm83a0eKbm5s1mUya03J6erp00hNgj/2O5vN5W6aGE0w/UsbIZLAzyX1bW1tNjm1rMrsCmqbs5FjzTu9nYrqmTZnPF5t44rDSV9tJ2zZOpEPnY198Gh+Bvtls1pxFwObZ2dmSfqFOUudZmuZ9msh4nc/vjk0fDodLjl/abArPM6tpGaDNfHI/14wb0Bm0z3oJPsBRQI7QzbbByGHPybXexZGCX29uburs7Kxlj62trbVNux34yz03rCdob+Is+m3+cRDEY8+EBwEp+Jhg7TdRTD8cC3gKHmP/LSa1ptNpO4HPyzZd2MOHQAVBH8tD4iv0Cu9CVzpICS71RBHBPWcyooer7mi8t7e3ZAOo17bk7Oxs6RQ55B49i1O5u7tbt7e3Sxuf02YyhWjv5eVly9rjPuvQi4uLJZqg76kXvXJ2draU8ZRyQKB6Pp+3DNXEMz6Nc5V/AD4iEDAej1tmmzNdGd+qxUlqyJZPulxbW1vSNQQ23DZ0H0EsAingt8PDw3rz5s1buBk9h65jsmQ+n9fLly/r53/+5+v09LTpGQL6jDs+X+6x9LNQVuHEd8X09ju+6LOfd/8qf/q+ez4P636R8mWfe5eyvr7elmP3MGXPZ/g627cK0+T/X7bcFw/5ou37afYx33mKCMBjw9ILiDiIYKfBTGJDalCFwvdMtoMkgEPPwlIcmcwgSBbuNYBDWbqN9BGwzm/+9CyMnaB0cpeIrtlbO0qZnYNxBZACUnNzTwNlDCm/2RnMsbNBMD0dnMpxNZDGwBmUeAwSOJp/MuqdhTYn6Eoe9LtMV9+foJqx5FnTiH5mOzzmq9prnjPfAZJJ83727Fn9yq/8Sv3kJz+p3/u936s/+qM/qo8//rhev35d5+fn7bSmVbMxP+3FNABkedkJY+H7PAuQwQjP0DP+8F3uLVNVXT4AVKX+gRdzBsnP0yY+LRd+V48f7bDbUUTneONBFzuD1iHJr+6H68gMEMYgHUB+97v8Zx7sOc353XrYTpfb0AsYuW+0385xT656wQ2/n/uhdVW1GWoH6Fyn94RiJsxBIM9y3tzcLC3HoN6vUuzYzefztsHu/v5+XVxc1F/7a3+tBXhPTk5qOp3W1tZWXVxc1OHhYV1dXdWnn366ZIPhQ9pnPTgcDttMPMuEkr+hJ898//vfr3/6T/9p/ff//t+XNrrNpc+WdZwy83PaTB/S4TrMWzinyA1OFcFr7zeDHTw7O6vDw8P6jd/4jbq5WT7WnP8Hg8VyYpxclrPwO894z57B4G52eHd3t2UPgGm8cTK28/T0tP723/7bzanf2tpayjqgTmj9N/7G33hL7qAHJ4zlPjKeUSeQ6om/qmp72sAn8HXilZubm3r69Gn94i/+YsvIcjCB98Aj1t3cw1InMi2m02mdnp42Z9bLNXv9pB5kF15MPoJmjDsBwP39/fr444/r5uamOS0ZoPgmCrw0GAwaH3sPoPn8bnPz169ftwAny/G8BJFxJcBH1k3VIvjExJCznxwoqnr71FwCID4cwDZ5MBi0YAQZUgTWGIfxePyWPPM89XFABIEQ2s397HNUVY2f4QPvncfSR+/hYwzvvfWurq6a3iCIhh50xgmYjSV29j2QITKaqhbZbugq69iqO100mUwaz3tPQvP8YDCo8Xhc+/v7S8vyCCw9evSo8cPe3l6TzcvLyxbIhB8IOBF0Qu7hQQexoS33j0ajevr0ae3u7jadtbu729rM8860nM1mdXJysiTPBEbTx/hZL1/G8X+fgZtvOhDxPt+fmDTL+6Trt6F802P/ruULZ0r1BtZgqveXs/B+DiBBHbnRWAaOHDxIYMs7esTP+3v3ZADHxjsdHfe7VxxMW0W7pBP38azT8RMoQo9evzwjksB/MFjMGGU/sj+9ewxkGDvPoFS97Vy6vzkmbrP5KJ3VdGLNBwnaXQfXPKbZDtbnO1Bifss29/jOYN68i6He2tqqZ8+e1eXlZf3SL/1S/a2/9bdqNpvV//f//X/1H//jf6xf+IVfqE8++aTNuufpEV+muD1fZ1klYy6AMnjIy8XM54BwBzcdAOYvs+ZSr2QgilRg6NgL4lQtB2C5lrxj3jcv8juF31wfv5sW5q9Ms83Ald/h+swbyW++14EAOx2ul355bHo6YZVe9R9OOY6V9VIGl1Lv9Rwf+sLyDrc9dYwzQnAu0nnGKSPIZLqxjM9OhwNQVdWCUJeXl+34bIIQ3nvqfZVewIb/ff19Fmj8Lk7F1wHKzTNfpp6ezKx6l79/Wf1733Pw1buWXibMu5bkjVW27PPKKt34Rdux6rd3wVPv8vt9xXrm66z3qxR0/uXlZe3s7LQlpRcXF3V6eloHBwdVtcBhjx49qr29vWZLHPSgbzs7Oy0AQmA2g/7WoZkhnpk0XGMfKePjtGUO8LJcj4AFS8GqFnr99PS04VECdNyPH0AfnLFFpiZBMZY1ErwnAMpJdqenpy2rjAxJMp8IRPE8NCG4dHt7W48fP146dZWAEkE4B/2hM+2F/lxjY/+XL1/W8fFxXV1d1Xg8bqfZeUmy+08G2Wg0apm6DmZfXl7WZDKpi4uLhrWwl4yTsyChoXlpd3e31tfXGwZlbFkCenp62pZGG+Ogo5wtx3YUm5ubdXZ21ibgehNjD+WhPJSHQhnMHzTDQ3koD+WhPJSH8lAeykN5KA/loTyUh/JQHspD+cDlm9nh8aE8lIfyUB7KQ3koD+WhPJSH8lAeykN5KA/lofz/dXkISj2Uh/JQHspDeSgP5aE8lIfyUB7KQ3koD+WhPJQPXh6CUg/loTyUh/JQHspDeSgP5aE8lIfyUB7KQ3koD+WDl4eg1EN5KA/loTyUh/JQHspDeSgP5aE8lIfyUB7KQ/ng5SEo9VAeykN5KA/loTyUh/JQHspDeSgP5aE8lIfyUD54eQhKPZSH8lAeykN5KA/loTyUh/JQHspDeSgP5aE8lA9eHoJSD+WhPJSH8lAeykN5KA/loTyUh/JQHspDeSgP5YOXh6DUQ3koD+WhPJSH8lAeykN5KA/loTyUh/JQHspD+eDlISj1UB7KQ3koD+WhPJSH8lAeykN5KA/loTyUh/JQPnj5/wHh5PRhN3stRwAAAABJRU5ErkJggg==",
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- "output_type": "display_data"
- },
- {
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- "output_type": "stream",
- "text": [
- "1/1 [==============================] - 0s 18ms/step\n",
- "1/1 [==============================] - 0s 16ms/step\n",
- "1/1 [==============================] - 0s 18ms/step\n",
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- "1/1 [==============================] - 0s 18ms/step\n",
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- "1/1 [==============================] - 0s 17ms/step\n"
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- {
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",
- "text/plain": [
- ""
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "data": {
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332WGEiVKKCEhQT/99JOz7dixYynuyD19+nSKY68v5vvv5VmuK1SokO69917NmDHDJYH8+eeftWLFCud1ukODBg00YsQIvfPOOypYsGCa+/n6+qaoCn7yyScpls64noSmlghn1IABA3T48GHNmDFDY8eOVUREhKKiotL8HK+rU6eOGjZs6HylJ7kLCAhQ69at9cUXX2jSpEnKnTu3WrZs6dx+vXp142dw5coVvffeexm+Ll9fXzVp0kSLFy/W4cOHne27d+/W8uXLU+z77/MmJCQoLi4uRb+5c+dO1+devXp1hYWFafLkyS6f5Zdffqndu3dn6s9NWpKSktSrVy/t3r1bvXr1ck7JyIzrTe93FfAWVO5guRIlSmju3Llq166dypUr5/KEis2bN+uTTz5xrkhfpUoVRUVF6f3333cOm3333XeaMWOGWrVqpQYNGmRaXO3bt9eAAQPUunVr9erVSxcvXtSkSZNUunRpl0naMTEx2rBhg5o3b66iRYvq+PHjeu+993TPPfc4qxOpefPNN9W0aVPVqlVLXbt2dS6FEhISkq5nkt4uHx+fmz5+67oWLVooJiZGnTt3Vu3atbVjxw7NmTMnReJUokQJhYaGavLkyQoKClLu3LlVs2ZNFStWLENxrVmzRu+9956GDh3qXJolLi5O9evX1+DBg/XGG29kqL/0iIyM1MyZM7V8+XJ17NjRpVpau3Zt5cmTR1FRUerVq5cMw9CsWbMyNAx+o+HDh+urr77Sgw8+qBdeeEHXrl3TxIkTVaFCBZc/IBo3biw/Pz89+uijevbZZ3X+/Hl98MEHCgsL07Fjx1z6rFatmiZNmqTXXntNJUuWVFhYWIrKnCTlzJlTo0ePVufOnVWvXj116NDBuRRKRESEcwg0syQkJGj27NmS/lm0+voTKg4cOKD27dtrxIgRmXq96f2uAl7Duht1AVd79+41n3nmGTMiIsL08/Mzg4KCzDp16pgTJ050WXbi6tWr5vDhw81ixYqZOXPmNAsXLmwOGjTIZR/T/GcplObNm6c4z7+X4EhrKRTTNM0VK1aYFStWNP38/MwyZcqYs2fPTrEUyurVq82WLVua4eHhpp+fnxkeHm526NDB3Lt3b4pz/Hu5kFWrVpl16tQxAwICzODgYPPRRx81d+3a5bLP9fP9e6mVuLg4U5J56NChND9T03RdCiUtaS2F0rdvX7NQoUJmQECAWadOHXPLli2pLmHy2WefmeXLlzdz5Mjhcp03Wxbjxn7Onj1rFi1a1Kxatap59epVl/1eeukl08fHx9yyZctNr+F2XLt2zSxUqJApyfziiy9SbN+0aZN5//33mwEBAWZ4eLj58ssvO5drub7MiGmmbykU0zTN9evXm9WqVTP9/PzM4sWLm5MnT07xfTJN01yyZIlZuXJl09/f34yIiDBHjx5tTps2LcV/7z///NNs3ry5GRQUZEpyfp7/Xgrluo8//ti87777TIfDYebNm9fs2LGjeeTIEZd90vq+pBZnaq4vf3P9FRgYaJYqVcqMjIw0V6xYkeoxd3q9GfmuAt7AMM3b/DMUAAAAHoc5dwAAADZCcgcAAGAjJHcAAAA2QnIHAADgIZKSkjR48GAVK1ZMAQEBKlGihEaMGJGhO/VZCgUAAMBDjB49WpMmTdKMGTNUoUIF/fDDD+rcubNCQkLUq1evdPXB3bIAAAAeokWLFrrrrrtcnrjy+OOPKyAgwLl+5K0wLAsAAOBGiYmJOnv2rMsrrSfv1K5dW6tXr9bevXslST/++KM2btyopk2bpvt8thyWDWj9odUhAHCTXR9EWh0CADcplt/fsnMH3NfDbX0PaJlfw4cPd2kbOnRoqk8jGjhwoM6ePauyZcvK19dXSUlJev31112efX0rtkzuAAAAPMWgQYPUp08flzaHw5HqvvPnz9ecOXM0d+5cVahQQfHx8erdu7fCw8MVFRWVrvOR3AEAABjum6nmcDjSTOb+rX///ho4cKDat28vSapUqZJ+++03xcbGktwBAACkm2FYHYEk6eLFi/LxcU00fX19lZycnO4+SO4AAAA8xKOPPqrXX39dRYoUUYUKFbR9+3aNHTtWXbp0SXcfJHcAAABuHJbNiIkTJ2rw4MF64YUXdPz4cYWHh+vZZ5/VkCFD0t0HyR0AAICHCAoK0vjx4zV+/Pjb7oPkDgAAwEPm3GUGz6hBAgAAIFNQuQMAAPCQOXeZwT5XAgAAACp3AAAAdppzR3IHAADAsCwAAAA8EZU7AAAAGw3LUrkDAACwESp3AAAAzLkDAACAJ6JyBwAAwJw7AAAAeCIqdwAAADaac0dyBwAAwLAsAAAAPBGVOwAAABsNy9rnSgAAAEDlDgAAgModAAAAPBKVOwAAAB/ulgUAAIAHonIHAABgozl3JHcAAAAsYgwAAABPROUOAADARsOy9rkSAAAAULkDAABgzh0AAAA8EpU7AAAA5twBAADAE1G5AwAAsNGcO5I7AAAAhmUBAADgiajcAQAA2GhYlsodAACAjVC5AwAAYM4dAAAAPBGVOwAAAObcAQAAwBNRuQMAALDRnDuSOwAAABsld/a5EgAAAFC5AwAA4IYKAAAAeCQqdwAAAMy5AwAAgCciuQMAADAM970yICIiQoZhpHh179493X0wLAsAAOAhvv/+eyUlJTnf//zzz2rUqJGefPLJdPdBcgcAAODGOXeJiYlKTEx0aXM4HHI4HCn2LVCggMv7UaNGqUSJEqpXr166z8ewLAAAgBuHZWNjYxUSEuLyio2NvWVIV65c0ezZs9WlSxcZGRjepXIHAADgRoMGDVKfPn1c2lKr2v3b4sWLdebMGUVHR2fofCR3AADA62WkMpZRaQ3B3srUqVPVtGlThYeHZ+g4kjsAAAAP89tvv2nVqlVauHBhho8luQMAAF7PnZW72xEXF6ewsDA1b948w8dyQwUAAIAHSU5OVlxcnKKiopQjR8brcFTuAAAAPKhwt2rVKh0+fFhdunS5reNJ7gAAADxI48aNZZrmbR9PcgcAALyep825uxMkdwAAwOvZKbnjhgoAAAAboXIHAAC8HpU7AAAAeCQqdwAAwOtRuQMAAIBHonIHAABgn8IdlTsAAAA7oXIHAAC8HnPuAAAA4JGo3AEAAK9np8odyR0AAPB6dkruGJYFAACwESp3AADA61G5AwAAgEeicgcAAGCfwh2VOwAAADuhcgcAALwec+4AAADgkajcAQAAr2enyh3JHQAA8Hp2Su4YlgUAALARKncAAAD2Kdx5duXup59+kp+fn9VhAAAAZBseXbkzTVNJSUlWhwEAAGyOOXcAAADwSB5duQMAAMgKdqrcWZrcnT179qbbz507l0WRAAAA2IOlyV1oaOhNM2XTNG2VSQMAAM9kp3zD0uRu7dq1Vp4eAABAEsldpqlXr94t9zl9+nQWRAIAAGAPHnu37IoVK9S2bVvdfffdVocCAADsznDjK4t5VHL322+/aejQoYqIiNCTTz4pHx8fzZw50+qwAAAAsg3Ll0K5cuWKFi5cqA8//FCbNm1Sw4YNdeTIEW3fvl2VKlWyOjwAAOAF7DTnztLKXc+ePRUeHq4JEyaodevWOnLkiJYuXSrDMOTr62tlaAAAANmSpZW7SZMmacCAARo4cKCCgoKsDAUAAHgxKneZZNasWfruu+9UqFAhtWvXTsuWLeNZsgAAAHfA0uSuQ4cOWrlypXbs2KGyZcuqe/fuKliwoJKTk7Vr1y4rQwMAAF7EMAy3vbKaR9wtW6xYMQ0fPly//vqrZs+erccff1yRkZG655571KtXL6vDAwAAdmejpVAsv1v2RoZhqEmTJmrSpIlOnz6tmTNnKi4uzuqwAAAAsg2PqNylJm/evOrdu7d+/PFHq0MBAAA2Z6dhWUsrdzExMbfcxzAMDR48OAuiAQAAyP4sTe6GDRum8PBwhYWFyTTNVPchuQMAAO5mp6VQLE3umjZtqjVr1qh69erq0qWLWrRoIR8fjx0pBgAA8HiWJneff/65jh49qhkzZqh///569tln1alTJ3Xp0kVlypSxMjR4uF+mtFPRsJQLX0/+cpdeen+zBREByCw74rfq07nTte+X3Tp96oSGxI5T7boPWR0WbM5OlTvLy2Th4eEaNGiQ9uzZo48//ljHjx9XjRo1VKdOHV26dMnq8OChHuj/mSI6z3G+mg39QpK0cNMhiyMDcKcuX7qkYiXLqHvfQVaHAljijz/+UGRkpPLly6eAgABVqlRJP/zwQ7qP96ilUGrUqKFff/1Vu3bt0vbt23X16lUFBARYHRY80Mmzl13e92tTRQeOJejrnccsighAZqlR6wHVqPWA1WHAy3hK5e7vv/9WnTp11KBBA3355ZcqUKCA9u3bpzx58qS7D49I7rZs2aJp06Zp/vz5Kl26tDp37qynnnpKwcHBVoeGbCBnDh+1r1dSby/ZYXUoAIDsyjNyO40ePVqFCxd2Wee3WLFiGerD0mHZN954Q+XLl1fLli0VGBior7/+Wt9//71eeOEFhYaGpquPxMREnT171uVlJl11b+DwKI/9p6hCc/tp9pp9VocCAEAKqeUqiYmJqe67ZMkSVa9eXU8++aTCwsJ033336YMPPsjQ+Syt3A0cOFBFihRR27ZtZRiGpk+fnup+Y8eOTbOP2NhYDR8+3KXNt8yjylnuscwMFR4sqmEZLd92RMf+vmh1KACAbMqdw7Kp5SpDhw7VsGHDUux78OBBTZo0SX369NErr7yi77//Xr169ZKfn5+ioqLSdT5Lk7u6devKMAzt3LkzzX1u9WEPGjRIffr0cWkLi5yTKfHB8xUpEKiHKoer/RurrA4FAIBUpZarOByOVPdNTk5W9erVNXLkSEnSfffdp59//lmTJ0/OHsndunXr7rgPh8OR4gMyfHPecb/IHp5+qLSOJ1zWlz/8bnUoAIBszJ2Vu9RylbQUKlRI5cuXd2krV66cFixYkO7zWb4Uyq1k5NZfeBfDkDo9VEpz1u1TUnLqTzgBkP1cunhRB/b+ogN7f5Ek/Xn0Dx3Y+4uO/8nd8LC/OnXqaM+ePS5te/fuVdGiRdPdh0fcLXv+/Hn5+vq6LHsSHx+vwYMH64svvlBSUpKF0cFTPVT5bhUJC9KM1XtuvTOAbGPvLzs1oGc35/v3J74lSWrY9DH1e3WEVWHB5jxkJRS99NJLql27tkaOHKm2bdvqu+++0/vvv6/3338/3X1Ymtz9/vvvzsB9fX3Vo0cPvfbaa3ruuef08ccfq3Xr1tq8macNIHWrf/xDAa0/tDoMAJmsStUa+mrTj1aHAViiRo0aWrRokQYNGqSYmBgVK1ZM48ePV8eOHdPdh6XJXf/+/XX58mVNmDBBCxcu1IQJE/T111+rZs2aOnDggO655x4rwwMAAF7CUxYxlqQWLVqoRYsWt328pcndhg0btHDhQt1///1q27atChYsqI4dO6p3795WhgUAALyMB+V2d8zSGyr++usv56rLYWFhypUrl5o2bWplSAAAANma5TdU+Pj4uPy7n5+fhdEAAABv5EnDsnfK0uTONE2VLl3a+YGeP39e9913n0vCJ0mnT5+2IjwAAIBsx9Lk7saH4gIAAFjFRoU7a5O79D5GAwAAAOlj+Zw7Sbp06ZJWrlypvXv3SpLKlCmjhg0buixqDAAA4C4+PvYp3Vme3C1ZskTdunXTyZMnXdrz58+vqVOn6tFHH7UoMgAAgOzH0qVQNm/erCeeeEJ169bVpk2bdPr0aZ0+fVobN27Ugw8+qCeeeELffPONlSECAAAvYBjue2X5tZimadkT15s1a6bChQtrypQpqW5/9tln9fvvv+uLL77IUL88kgqwr10fRFodAgA3KZbf37JzV3x1pdv6/vm1Rm7rOzWWVu6++eYb9ejRI83t3bt315YtW7IwIgAAgOzN0jl3ly5dUnBwcJrbQ0JCdPny5SyMCAAAeCM7LYViaeWuVKlSWrNmTZrbV69erVKlSmVhRAAAANmbpcld586d1a9fv1Tn1H3++ed6+eWXFR0dnfWBAQAAr2IYhtteWc3SYdkXX3xRmzdvVosWLVSmTBmVK1dOpmlq9+7d2rdvn1q1aqXevXtbGSIAAEC2YmnlzsfHR5988onmzZun0qVL65dfftGePXtUtmxZzZkzRwsWLEjxnFkAAIDMRuUuk7Vr107t2rWzOgwAAIBsz9LkzsfH55YZrWEYunbtWhZFBAAAvJGd7pa1NLlbtGhRmtu2bNmit99+W8nJyVkYEQAA8EZWDJ+6i6XJXcuWLVO07dmzRwMHDtTSpUvVsWNHxcTEWBAZAABA9uQxdyscPXpUzzzzjCpVqqRr164pPj5eM2bMUNGiRa0ODQAA2Jydni1reXKXkJCgAQMGqGTJktq5c6dWr16tpUuXqmLFilaHBgAAkO1YOiz7xhtvaPTo0SpYsKDmzZuX6jAtAACAuzHnLpMMHDhQAQEBKlmypGbMmKEZM2akut/ChQuzODIAAIDsydLkrlOnTrbKlAEAQPZkp3TE0uRu+vTpVp4eAADAdjziCRUAAABWstNIouV3ywIAACDzULkDAABez0aFO5I7AAAAhmUBAADgkajcAQAAr2ejwh2VOwAAADuhcgcAALwec+4AAADgkajcAQAAr2ejwh2VOwAAADuhcgcAALyenebckdwBAACvZ6PcjmFZAAAAO6FyBwAAvJ6dhmWp3AEAANgIlTsAAOD1qNwBAADAI1G5AwAAXs9GhTsqdwAAAHZC5Q4AAHg95twBAADYiGG475URw4YNk2EYLq+yZctmqA8qdwAAAB6kQoUKWrVqlfN9jhwZS9dI7gAAgNfzpGHZHDlyqGDBgrd9PMOyAAAAbpSYmKizZ8+6vBITE9Pcf9++fQoPD1fx4sXVsWNHHT58OEPnI7kDAABez51z7mJjYxUSEuLyio2NTTWOmjVravr06frqq680adIkHTp0SA8++KDOnTuX/msxTdPMrA/GUwS0/tDqEAC4ya4PIq0OAYCbFMvvb9m5H564xW19f/F/VVNU6hwOhxwOxy2PPXPmjIoWLaqxY8eqa9eu6Tofc+4AAIDX83HjnLv0JnKpCQ0NVenSpbV///50H8OwLAAAgIc6f/68Dhw4oEKFCqX7GJI7AADg9Txlnbt+/fpp/fr1+vXXX7V582a1bt1avr6+6tChQ7r7YFgWAAB4PU9ZCuXIkSPq0KGDTp06pQIFCuiBBx7QN998owIFCqS7D5I7AAAAD/HRRx/dcR8kdwAAwOv5eEbhLlMw5w4AAMBGqNwBAACv5ylz7jIDlTsAAAAboXIHAAC8no0Kd1TuAAAA7ITKHQAA8HqG7FO6I7kDAABej6VQAAAA4JGo3AEAAK/HUigAAADwSFTuAACA17NR4Y7KHQAAgJ1QuQMAAF7Px0alOyp3AAAANkLlDgAAeD0bFe5I7gAAAFgKBQAAAB6Jyh0AAPB6NircUbkDAACwEyp3AADA67EUCgAAADwSlTsAAOD17FO3o3IHAABgK1TuAACA17PTOnckdwAAwOv52Ce3Y1gWAADATqjcAQAAr2enYVkqdwAAADZC5Q4AAHg9GxXuqNwBAADYCZU7AADg9ZhzBwAAAI9E5Q4AAHg9O61zR3IHAAC8HsOyAAAA8EhU7gAAgNezT92Oyh0AAICt3FZy9/XXXysyMlK1atXSH3/8IUmaNWuWNm7cmKnBAQAAZAUfw3DbK8uvJaMHLFiwQE2aNFFAQIC2b9+uxMRESVJCQoJGjhyZ6QECAAAg/TKc3L322muaPHmyPvjgA+XMmdPZXqdOHW3bti1TgwMAAMgKhuG+V1bLcHK3Z88e1a1bN0V7SEiIzpw5kxkxAQAA4DZlOLkrWLCg9u/fn6J948aNKl68eKYEBQAAkJUMw3DbK6tlOLl75pln9OKLL+rbb7+VYRg6evSo5syZo379+un55593R4wAAABIpwyvczdw4EAlJyfr4Ycf1sWLF1W3bl05HA7169dPPXv2dEeMAAAAbmWjB1RkPLkzDEP//e9/1b9/f+3fv1/nz59X+fLlFRgY6I74AAAA3M6KJUvc5bafUOHn56fy5ctnZiwAAAC4QxlO7ho0aHDTyYFr1qy5o4AAAACymqcW7kaNGqVBgwbpxRdf1Pjx49N1TIaTu3vvvdfl/dWrVxUfH6+ff/5ZUVFRGe0OAAAAqfj+++81ZcoUVa5cOUPHZTi5GzduXKrtw4YN0/nz5zPaHQAAgOWsWLLkZs6fP6+OHTvqgw8+0GuvvZahY2/r2bKpiYyM1LRp0zKrOwAAAFtITEzU2bNnXV7XH9+alu7du6t58+Zq2LBhhs932zdU/NuWLVvk7++fWd3dkb8/6WZ1CADcJE+NHlaHAMBNLm1/x7JzZ1q1KxWxsbEaPny4S9vQoUM1bNiwVPf/6KOPtG3bNn3//fe3db4MJ3dt2rRxeW+apo4dO6YffvhBgwcPvq0gAAAA7GrQoEHq06ePS5vD4Uh1399//10vvviiVq5cedtFswwndyEhIS7vfXx8VKZMGcXExKhx48a3FQQAAICV3DnnzuFwpJnM/dvWrVt1/PhxVa1a1dmWlJSkDRs26J133lFiYqJ8fX1v2keGkrukpCR17txZlSpVUp48eTJyKAAAgMfy8ZD7KR5++GHt2LHDpa1z584qW7asBgwYcMvETspgcufr66vGjRtr9+7dJHcAAACZLCgoSBUrVnRpy507t/Lly5eiPS0Znj9YsWJFHTx4MKOHAQAAeCwfw32vrJbhOXevvfaa+vXrpxEjRqhatWrKnTu3y/bg4OBMCw4AAMDbrVu3LkP7pzu5i4mJUd++fdWsWTNJ0mOPPeYy+dA0TRmGoaSkpAwFAAAAYDVPW8T4TqQ7uRs+fLiee+45rV271p3xAAAA4A6kO7kzTVOSVK9ePbcFAwAAYAVPuVs2M2Tohgo7lSwBAADsKEM3VJQuXfqWCd7p06fvKCAAAICsZqf6VYaSu+HDh6d4QgUAAEB252Oj7C5DyV379u0VFhbmrlgAAABwh9Kd3DHfDgAA2FWGn+rgwdJ9LdfvlgUAAIDnSnflLjk52Z1xAAAAWMZOA5R2qkICAAB4vQw/WxYAAMBu7HS3LJU7AAAAG6FyBwAAvJ6NCnckdwAAAF77bFkAAAB4Nip3AADA63FDBQAAADwSlTsAAOD1bFS4o3IHAABgJ1TuAACA1+NuWQAAAHgkKncAAMDrGbJP6Y7kDgAAeD2GZQEAAOCRqNwBAACvR+UOAAAAHonKHQAA8HqGjVYxpnIHAABgI1TuAACA12POHQAAADwSlTsAAOD1bDTljuQOAADAx0bZHcOyAAAANkLlDgAAeD1uqAAAAIBHonIHAAC8no2m3FG5AwAAsBMqdwAAwOv5yD6lOyp3AAAANkLlDgAAeD07zbkjuQMAAF6PpVAAAADgkajcAQAAr8fjxwAAAOCRqNwBAACvZ6PCHZU7AAAAOyG5AwAAXs/HMNz2yohJkyapcuXKCg4OVnBwsGrVqqUvv/wyY9eSob0BAADgNvfcc49GjRqlrVu36ocfftBDDz2kli1baufOnenugzl3AADA67lzzl1iYqISExNd2hwOhxwOR4p9H330UZf3r7/+uiZNmqRvvvlGFSpUSNf5qNwBAACv5+PGV2xsrEJCQlxesbGxt4wpKSlJH330kS5cuKBatWql+1qo3AEAALjRoEGD1KdPH5e21Kp21+3YsUO1atXS5cuXFRgYqEWLFql8+fLpPh/JHQAA8HqGG8dl0xqCTUuZMmUUHx+vhIQEffrpp4qKitL69evTneCR3AEAAHgQPz8/lSxZUpJUrVo1ff/995owYYKmTJmSruNJ7gAAgNfz5DWMk5OTU9yQcTMkdwAAAB5i0KBBatq0qYoUKaJz585p7ty5WrdunZYvX57uPkjuAACA18voYsPucvz4cXXq1EnHjh1TSEiIKleurOXLl6tRo0bp7oPkDgAAwENMnTr1jvsguQMAAF7PM+p2mYPkDgAAeD0PGZXNFDyhAgAAwEao3AEAAK/nzkWMsxqVOwAAABuhcgcAALyenapddroWAAAAr0flDgAAeD3m3AEAAMAjUbkDAABezz51Oyp3AAAAtuLxlbvTp08rb968VocBAABsjDl3WWDFihVq27at7r77bqtDAQAANufjxldW86jk7rffftPQoUMVERGhJ598Uj4+Ppo5c6bVYQEAAGQblg/LXrlyRQsXLtSHH36oTZs2qWHDhjpy5Ii2b9+uSpUqWR0eAADwAgzLZpKePXsqPDxcEyZMUOvWrXXkyBEtXbpUhmHI19fXytAAAACyJUsrd5MmTdKAAQM0cOBABQUFWRkKAADwYvap21lcuZs1a5a+++47FSpUSO3atdOyZcuUlJRkZUgAAADZmqXJXYcOHbRy5Urt2LFDZcuWVffu3VWwYEElJydr165dVoYGAAC8iGG475XVPOJu2WLFimn48OH69ddfNXv2bD3++OOKjIzUPffco169elkdHgAAQLZh+d2yNzIMQ02aNFGTJk10+vRpzZw5U3FxcVaHBQAAbM7HRrPuPKJyl5q8efOqd+/e+vHHH60OBQAA2JydhmUtrdz16dPnlvsYhqExY8ZkQTQAAADZn6XJ3fbt22+5j50WFQQAAJ7JsNGwrKXJ3dq1a608PQAAgO141A0VAAAAVrDTQKGlyV1MTEy69hsyZIibIwEAALAHS5O7RYsWpbnNMAzt2bNHly9fJrkDAABuZaelUDzyhor4+HgNHDhQP//8s5555pksjgoAACD78qh17g4dOqTIyEjVqFFDISEh2rlzpyZPnmx1WAAAwObstM6dRyR3J0+eVM+ePVW2bFkdO3ZMmzdv1scff6xSpUpZHRoAAPACdkruLB2WvXDhgt566y2NHTtWJUuW1NKlS9W4cWMrQwIAAMjWLE3uSpQooXPnzqlnz57q0KGDDMPQTz/9lGK/ypUrWxAdAADwFnZaxNgwTdO06uQ+Pv8bFTYMQzeGcv29YRhKSkrKUL+Xr2VaiAA8TJ4aPawOAYCbXNr+jmXnXrn7pNv6blQuv9v6To2llbtDhw5ZeXoAAABJko99CnfWJndFixa18vQAAAC24xGPH/v+++81b9487d27V5JUunRpPfXUU6pevbrFkQEAAG9gpzl3li+F8vLLL6tmzZr68MMPdeTIER05ckQffPCBatasqQEDBlgdHgAAQLZiaXI3Y8YMTZw4UW+//bZOnTql+Ph4xcfH6/Tp0xo3bpzefvttzZw508oQAQCAF2Cdu0zy7rvvauTIkerRw/Xut5w5c6pXr166du2a3nnnHXXq1MmiCAEAgDdgWDaT7Ny5Uy1btkxze6tWrbRz584sjAgAACB7s7Ry5+vrqytXrqS5/erVq/L19c3CiAAAgDey01Iollbuqlatqjlz5qS5fdasWapatWoWRgQAAJC9WVq569evn1q1aqXExET17dtXd911lyTpzz//1JgxYzR+/HgtWrTIyhABAIAXsNOcO0uTuxYtWmjcuHHq16+fxowZo5CQEElSQkKCcuTIobfeekstWrSwMkQAAIBsxfJFjHv27KnWrVvrk08+0b59+yT9s4jx448/rsKFC1scHTzdR3PnaEbcVJ08eUKly5TVwFcGq1LlylaHBeAO+PgYevW5ZurQrIbuyhesYycSNGvptxr1wVdWhwYbs2LJEnexPLmTpHvuuUcvvfSS1WEgm/nqyy/01huxenXocFWqVEVzZs3Q88921WfLvlK+fPmsDg/Abeob3UjPPPGgnhkyS7sOHFO1CkU0ZVikzp6/pPfmrbc6PMCtYmNjtXDhQv3yyy8KCAhQ7dq1NXr0aJUpUybdfVia3G3YsCFd+9WtW9fNkSA7mjUjTm2eaKtWrR+XJL06dLg2bFinxQsXqOsz/2dxdABu1/1VimvZ+p/01cZ/lsI6fOy02j5SXdUr8DxyuI+nFO7Wr1+v7t27q0aNGrp27ZpeeeUVNW7cWLt27VLu3LnT1YelyV39+vVl/P86qGmaqe5jGIaSkpKyMixkA1evXNHuXTvV9ZlnnW0+Pj66//7a+unH7RZGBuBOffPjQXV9vI5KFgnT/sPHVan03ap1b3ENHLPQ6tBgYz4eMi771Veu0w+mT5+usLAwbd26Nd3FLkuTuzx58igoKEjR0dF6+umnlT9//gz3kZiYqMTERJc209chh8ORWWHCA/195m8lJSWlGH7Nly+fDh06aFFUADLDW3ErFRzorx8XvaqkJFO+voaGvrtMH335g9WhAbcltVzF4UhfrpKQkCBJyps3b7rPZ+k6d8eOHdPo0aO1ZcsWVapUSV27dtXmzZsVHByskJAQ5+tmYmNjXfYNCQnRm6Njs+gKAACZ7YnGVdW+aQ1FvzJDtZ4arW5DZqn30w+r46M1rQ4NNma48ZVarhIbe+tcJTk5Wb1791adOnVUsWLFdF+LpZU7Pz8/tWvXTu3atdPhw4c1ffp09ejRQ4mJiYqKitLw4cOVI8fNQxw0aJD69Onj0mb6UrWzuzyheeTr66tTp065tJ86deq2KsAAPMfI3q30VtxKfbJ8qyRp5/6jKlIor/p3bqQ5S7+1ODog41LLVdJTtevevbt+/vlnbdy4MUPns7Ryd6MiRYpoyJAhWrVqlUqXLq1Ro0bp7NmztzzO4XAoODjY5cWQrP3l9PNTufIV9O03W5xtycnJ+vbbLapc5T4LIwNwpwL8/ZRsJru0JSWb8vHxmP9lwY7cWLq7nVylR48eWrZsmdauXat77rknQ5fiET8piYmJmjt3rho2bKiKFSsqf/78+vzzzzM0vgzv83RUZy38dL6WLF6kgwcO6LWYYbp06ZJatW5jdWgA7sAXG3ZoQNcmeuSBCipSKK8ea1BZvSIbaMmaH60ODXA70zTVo0cPLVq0SGvWrFGxYsUy3Ielw7Lfffed4uLi9NFHHykiIkKdO3fW/PnzSeqQLo80baa/T5/We++8rZMnT6hM2XJ6b8qHysewLJCt9Rn9iYa+0EITXmmnAnkCdexEgqZ+ukkj3//S6tBgY57y+LHu3btr7ty5+uyzzxQUFKQ///xTkhQSEqKAgIB09WGYaa1BkgV8fHxUpEgRRUVFqVq1amnu99hjj2Wo38vX7jQyAJ4qT40eVocAwE0ubX/HsnN/eyDBbX3XLHHzm0NvZKSxJEtcXJyio6PT1YflT6g4fPiwRowYkeZ21rkDAADu5iHL3KW57m9GWJrcJScn33onAAAAN/OQ3C5TeMQNFQAAAMgcllbu3n777VTbQ0JCVLp0adWqVSuLIwIAAF7JRqU7S5O7cePGpdp+5swZJSQkqHbt2lqyZAl3zwIAAKSTpcndoUOH0tx28OBBRUZG6tVXX9V7772XhVEBAABv4ylLoWQGj51zV7x4cY0aNUorVqywOhQAAIBsw/KlUG6mSJEizsX7AAAA3MVTlkLJDB5buZOkHTt2qGjRolaHAQAAkG1YWrk7e/Zsqu0JCQnaunWr+vbtq6ioqCyOCgAAeBsbFe6sTe5CQ0PTfMyGYRjq1q2bBg4cmMVRAQAAr2Oj7M7S5G7t2rWptgcHB6tUqVIKDAzM4ogAAACyN0uTu3r16ll5egAAAEkshZJp3njjDV26dMn5ftOmTUpMTHS+P3funF544QUrQgMAAMiWLE3uBg0apHPnzjnfN23aVH/88Yfz/cWLFzVlyhQrQgMAAF7EMNz3ymqWJnemad70PQAAADLGoxcxBgAAyAr2mXHn4YsYAwAAIGMsr9x9+OGHziVPrl27punTpyt//vyS5DIfDwAAwG1sVLozTAsnukVERKS5iPGNDh06lKF+L1+73YgAeLo8NXpYHQIAN7m0/R3Lzv3T7+fd1nflwlm7bq+llbtff/3VytMDAADYjuXDsgAAAFazYskSd7E0uZs5c2a69uvUqZObIwEAALAHS+fc5cmTJ81thmHowoULunbtmpKSkjLUL3PuAPtizh1gX1bOufv5iPvm3FW8J2vn3Fm6FMrff/+d6mvXrl1q27atTNNUo0aNrAwRAAAgW/Gode7OnTunV199VaVLl1Z8fLyWL1+ur776yuqwAACA3RlufGUxj7ih4urVq5o4caJGjhypfPnyKS4uTk888YTVYQEAAGQ7liZ3pmlq5syZGjJkiK5du6aRI0eqa9eu8vX1tTIsAADgZQwbrWJsaXJXuXJlHTx4UD179lTv3r2VK1cuXbhwIcV+wcHBFkQHAACQ/Vh6t6yPz/+m/KX2pArTNGUYBnfLAnDiblnAvqy8W3bX0ZTFpcxSPjy32/pOjaWVu7Vr11p5egAAAEm2erSstcndAw88oLfeektLlizRlStX9PDDD2vo0KEKCAiwMiwAAIBsy9KlUEaOHKlXXnlFgYGBuvvuuzVhwgR1797dypAAAIA3stFSKJYmdzNnztR7772n5cuXa/HixVq6dKnmzJmj5ORkK8MCAADItixN7g4fPqxmzZo53zds2FCGYejo0aMWRgUAALyN4cZ/spqlyd21a9fk7+/v0pYzZ05dvXrVoogAAACyN8sXMY6OjpbD4XC2Xb58Wc8995xy5/7fbcMLFy60IjwAAOAlUlmRLduyNLmLiopK0RYZGWlBJAAAAPZgaXIXFxdn5ekBAAAksc4dAACAvdgou7P0hgoAAABkLip3AADA61mxZIm7ULkDAACwESp3AADA69lpKRQqdwAAADZC5Q4AAHg9GxXuqNwBAADYCZU7AAAAG5XuqNwBAACvZ7jxn4zasGGDHn30UYWHh8swDC1evDhDx5PcAQAAeJALFy6oSpUqevfdd2/reIZlAQCA1/OkpVCaNm2qpk2b3vbxJHcAAABulJiYqMTERJc2h8Mhh8PhlvMxLAsAALye4cZXbGysQkJCXF6xsbFuuxYqdwAAAG40aNAg9enTx6XNXVU7ieQOAADArUuhuHMINjUMywIAANgIlTsAAOD1bmc9Onc5f/689u/f73x/6NAhxcfHK2/evCpSpMgtjye5AwAAXs+TlkL54Ycf1KBBA+f76/P1oqKiNH369FseT3IHAADgQerXry/TNG/7eJI7AADg9TyocHfHuKECAADARqjcAQAAr+dJc+7uFJU7AAAAG6FyBwAAYKNZd1TuAAAAbITKHQAA8Hp2mnNHcgcAALyejXI7hmUBAADshModAADwenYalqVyBwAAYCNU7gAAgNczbDTrjsodAACAjVC5AwAAsE/hjsodAACAnVC5AwAAXs9GhTuSOwAAAJZCAQAAgEeicgcAALweS6EAAADAI1G5AwAAsE/hjsodAACAnVC5AwAAXs9GhTsqdwAAAHZC5Q4AAHg9O61zR3IHAAC8HkuhAAAAwCNRuQMAAF7PTsOyVO4AAABshOQOAADARkjuAAAAbIQ5dwAAwOsx5w4AAAAeicodAADwenZa547kDgAAeD2GZQEAAOCRqNwBAACvZ6PCHZU7AAAAO6FyBwAAYKPSHZU7AAAAG6FyBwAAvJ6dlkKhcgcAAGAjVO4AAIDXY507AAAAeCQqdwAAwOvZqHBHcgcAAGCn7I5hWQAAABshuQMAAF7PcOM/t+Pdd99VRESE/P39VbNmTX333XfpPpbkDgAAwIN8/PHH6tOnj4YOHapt27apSpUqatKkiY4fP56u40nuAACA1zMM970yauzYsXrmmWfUuXNnlS9fXpMnT1auXLk0bdq0dB1PcgcAAOBGiYmJOnv2rMsrMTEx1X2vXLmirVu3qmHDhs42Hx8fNWzYUFu2bEnX+Wx5t6y/La8KqUlMTFRsbKwGDRokh8NhdTjIApe2v2N1CMgi/HwjK7kzdxj2WqyGDx/u0jZ06FANGzYsxb4nT55UUlKS7rrrLpf2u+66S7/88ku6zmeYpmnedrSAxc6ePauQkBAlJCQoODjY6nAAZCJ+vmEXiYmJKSp1Docj1T9ajh49qrvvvlubN29WrVq1nO0vv/yy1q9fr2+//faW56PGBQAA4EZpJXKpyZ8/v3x9ffXXX3+5tP/1118qWLBguvpgzh0AAICH8PPzU7Vq1bR69WpnW3JyslavXu1SybsZKncAAAAepE+fPoqKilL16tX1n//8R+PHj9eFCxfUuXPndB1PcodszeFwaOjQoUy2BmyIn294q3bt2unEiRMaMmSI/vzzT91777366quvUtxkkRZuqAAAALAR5twBAADYCMkdAACAjZDcAQAA2AjJHQAAgI2Q3CHTRUdHyzAMjRo1yqV98eLFMm54gnJSUpLGjRunSpUqyd/fX3ny5FHTpk21adMml+OmT58uwzBkGIZ8fHxUqFAhtWvXTocPH3bZr379+qmeV5KaN28uwzBSfdTLvHnz5Ovrq+7du6fYtm7dOhmGoTNnzmTgEwCyh+s/q4ZhyM/PTyVLllRMTIyuXbvm/O5XqFBBSUlJLseFhoZq+vTpzvcRERHOfm58Xf9ZvNnPUUREhMaPH+98f/3Yb775xmW/xMRE5cuXT4ZhaN26dS7bli1bpnr16ikoKEi5cuVSjRo1XOKTpF9//VWGYSgsLEznzp1z2Xbvvfe6/G6oX7++evfunSLWm/2uADwJyR3cwt/fX6NHj9bff/+d6nbTNNW+fXvFxMToxRdf1O7du7Vu3ToVLlxY9evX1+LFi132Dw4O1rFjx/THH39owYIF2rNnj5588skU/RYuXDjFL/U//vhDq1evVqFChVKNZerUqXr55Zc1b948Xb58+bauF8iuHnnkER07dkz79u1T3759NWzYML355pvO7QcPHtTMmTNv2U9MTIyOHTvm8urZs+dtxVS4cGHFxcW5tC1atEiBgYEp9p04caJatmypOnXq6Ntvv9VPP/2k9u3b67nnnlO/fv1S7H/u3Dm99dZbtxUXvyuQXZDcwS0aNmyoggULKjY2NtXt8+fP16effqqZM2eqW7duKlasmKpUqaL3339fjz32mLp166YLFy449zcMQwULFlShQoVUu3Ztde3aVd99953Onj3r0m+LFi108uRJl+rfjBkz1LhxY4WFhaWI49ChQ9q8ebMGDhyo0qVLa+HChZn0CQDZg8PhUMGCBVW0aFE9//zzatiwoZYsWeLc3rNnTw0dOjTFczH/LSgoSAULFnR55c6d+7ZiioqK0kcffaRLly4526ZNm6aoqCiX/X7//Xf17dtXvXv31siRI1W+fHmVLFlSffv21ZtvvqkxY8akeA5nz549NXbsWB0/fjxDMfG7AtkJyR3cwtfXVyNHjtTEiRN15MiRFNvnzp2r0qVL69FHH02xrW/fvjp16pRWrlyZat/Hjx/XokWL5OvrK19fX5dtfn5+6tixo8tf/dOnT1eXLl1S7SsuLk7NmzdXSEiIIiMjNXXq1IxcJmA7AQEBunLlivN97969de3aNU2cODHLYqhWrZoiIiK0YMECSdLhw4e1YcMGPf300y77ffrpp7p69WqqFbpnn31WgYGBmjdvnkt7hw4dnMPPGcHvCmQnJHdwm9atW+vee+/V0KFDU2zbu3evypUrl+px19v37t3rbEtISFBgYKBy586tu+66S2vXrlX37t1TrQx06dJF8+fP14ULF7RhwwYlJCSoRYsWKfZLTk7W9OnTFRkZKUlq3769Nm7cqEOHDt3W9QLZmWmaWrVqlZYvX66HHnrI2Z4rVy4NHTpUsbGxSkhISPP4AQMGKDAw0OX19ddf33Y8Xbp00bRp0yT98wdas2bNVKBAAZd99u7dq5CQkFSnXPj5+al48eIuv0ckOecCvv/++zpw4EC6YuF3BbIbkju41ejRozVjxgzt3r07xbaMPBwlKChI8fHx+uGHHzRmzBhVrVpVr7/+eqr7VqlSRaVKldKnn36qadOm6emnn1aOHCmftLdy5UpduHBBzZo1kyTlz59fjRo1cv4PBfAGy5YtU2BgoPz9/dW0aVO1a9cuxY1HXbt2Vb58+TR69Og0++nfv7/i4+NdXtWrV7/tuCIjI7VlyxYdPHjwptX329GkSRM98MADGjx4cLr253cFshueLQu3qlu3rpo0aaJBgwYpOjra2V66dOlUEz5JzvbSpUs723x8fFSyZElJ/1T2Dhw4oOeff16zZs1KtY8uXbro3Xff1a5du/Tdd9+lus/UqVN1+vRpBQQEONuSk5P1008/afjw4fLx4W8f2F+DBg00adIk+fn5KTw8PNU/hHLkyKHXX39d0dHR6tGjR6r95M+f3/kz+m/BwcGS/qnAh4aGumw7c+aMQkJCUhyTL18+tWjRQl27dtXly5fVtGnTFHe5li5dWgkJCTp69KjCw8Ndtl25ckUHDhxQgwYNUo1p1KhRqlWrlvr375/q9hvxuwLZDd9IuN2oUaO0dOlSbdmyxdnWvn177du3T0uXLk2x/5gxY5QvXz41atQozT4HDhyojz/+WNu2bUt1+1NPPaUdO3aoYsWKKl++fIrtp06d0meffaaPPvrIpdKwfft2/f3331qxYsVtXCmQ/eTOnVslS5ZUkSJFUk3srnvyySdVoUIFDR8+PMPnKFWqlHx8fLR161aX9oMHDyohIcHlD7kbdenSRevWrVOnTp1SzK+VpMcff1w5c+bUmDFjUmybPHmyLly4oA4dOqTa93/+8x+1adNGAwcOvGns/K5AdkTlDm5XqVIldezYUW+//bazrX379vrkk08UFRWlN998Uw8//LDOnj2rd999V0uWLNEnn3xy0zvtChcurNatW2vIkCFatmxZiu158uTRsWPHlDNnzlSPnzVrlvLly6e2bdu6rL0nSc2aNdPUqVP1yCOPONt27NihoKAg53vDMFSlSpV0fwaAHYwaNUpNmjRJddu5c+f0559/urTlypVLwcHBCgoKUrdu3dS3b1/lyJFDlSpV0u+//64BAwbo/vvvV+3atVPt85FHHtGJEyeclb9/K1KkiN544w317dtX/v7+evrpp5UzZ0599tlneuWVV9S3b1/VrFkzzet5/fXXVaFChZsmtRn9XQF4Aip3yBIxMTFKTk52vjcMQ/Pnz9crr7yicePGqUyZMnrwwQf122+/ad26dWrVqtUt+3zppZf0+eefpznsGhoammaCOG3aNLVu3TrFL2vpn2rAkiVLdPLkSWdb3bp1dd999zlf1apVu2V8gN089NBDeuihh3Tt2rUU24YMGaJChQq5vF5++WXn9gkTJigqKkoDBgxQhQoVFB0drcqVK2vp0qWp/hxK//yeyJ8/v/z8/NKMqXfv3lq0aJG+/vprVa9eXRUrVtTcuXM1adKkW65nV7p0aXXp0uWma9Zl9HcF4AkMMyOz2gEAAODRqNwBAADYCMkdAACAjZDcAQAA2AjJHQAAgI2Q3AEAANgIyR0AAICNkNwBAADYCMkdAACAjZDcAfBY0dHRLk8rqV+/vnr37p3lcaxbt06GYejMmTNZfm4AyCiSOwAZFh0dLcMwZBiG/Pz8VLJkScXExKT6WKrMtHDhQo0YMSJd+5KQAfBWaT8tGQBu4pFHHlFcXJwSExP1xRdfqHv37sqZM6cGDRrkst+VK1du+mzQjMibN2+m9AMAdkblDsBtcTgcKliwoIoWLarnn39eDRs21JIlS5xDqa+//rrCw8NVpkwZSdLvv/+utm3bKjQ0VHnz5lXLli3166+/OvtLSkpSnz59FBoaqnz58unll1/Wvx99/e9h2cTERA0YMECFCxeWw+FQyZIlNXXqVP36669q0KCBJClPnjwyDEPR0dGSpOTkZMXGxqpYsWIKCAhQlSpV9Omnn7qc54svvlDp0qUVEBCgBg0auMQJAJ6O5A5ApggICNCVK1ckSatXr9aePXu0cuVKLVu2TFevXlWTJk0UFBSkr7/+Wps2bVJgYKAeeeQR5zFjxozR9OnTNW3aNG3cuFGnT5/WokWLbnrOTp06ad68eXr77be1e/duTZkyRYGBgSpcuLAWLFggSdqzZ4+OHTumCRMmSJJiY2M1c+ZMTZ48WTt37tRLL72kyMhIrV+/XtI/SWibNm306KOPKj4+Xt26ddPAgQPd9bEBQKZjWBbAHTFNU6tXr9by5cvVs2dPnThxQrlz59aHH37oHI6dPXu2kpOT9eGHH8owDElSXFycQkNDtW7dOjVu3Fjjx4/XoEGD1KZNG0nS5MmTtXz58jTPu3fvXs2fP18rV65Uw4YNJUnFixd3br8+hBsWFqbQ0FBJ/1T6Ro4cqVWrVqlWrVrOYzZu3KgpU6aoXr16mjRpkkqUKKExY8ZIksqUKaMdO3Zo9OjRmfipAYD7kNwBuC3Lli1TYGCgrl69quTkZD311FMaNmyYunfvrkqVKrnMs/vxxx+1f/9+BQUFufRx+fJlHThwQAkJCTp27Jhq1qzp3JYjRw5Vr149xdDsdfHx8fL19VW9evXSHfP+/ft18eJFNWrUyKX9ypUruu+++yRJu3fvdolDkjMRBIDsgOQOwG1p0KCBJk2aJD8/P4WHhytHjv/9OsmdO7fLvufPn1e1atU0Z86cFP0UKFDgts4fEBCQ4WPOnz8vSfr888919913u2xzOBy3FQcAeBqSOwC3JXfu3CpZsmS69q1atao+/vhjhYWFKTg4ONV9ChUqpG+//VZ169aVJF27dk1bt25V1apVU92/UqVKSk5O1vr1653Dsje6XjlMSkpytpUvX14Oh0OHDx9Os+JXrlw5LVmyxKXtm2++ufVFAoCH4IYKAG7XsWNH5c+fXy1bttTXX3+tQ4cOad26derVq5eOHDkiSXrxxRc1atQoLV68WL/88oteeOGFm65RFxERoaioKHXp0kWLFy929jl//nxJUtGiRWUYhpYtW6YTJ07o/PnzCgoKUr9+/fTSSy9pxowZOnDggLZt26aJEydqxowZkqTnnntO+/btU//+/bVnzx7NnTtX06dPd/dHBACZhuQOgNvlypVLGzZsUJEiRdSmTRuVK1dOXbt21eXLl52VvL59++rpp59WVFSUatWqpaCgILVu3fqm/U6aNElPPPGEXnjhBZUtW1bPPPOMLly4IEm6++67NXz4cA0cOFB33XWXevToIUkaMWKEBg8erNjYWJUrV06PPPKIPv/8cxUrVkySVKRIES1YsECLFy9WlSpVNHnyZI0cOdKNnw4AZC7DTGu2MgAAALIdKncAAAA2QnIHAABgIyR3AAAANkJyBwAAYCMkdwAAADZCcgcAAGAjJHcAAAA2QnIHAABgIyR3AAAANkJyBwAAYCMkdwAAADby/wCrwCP1ifK6hwAAAABJRU5ErkJggg==",
- "text/plain": [
- ""
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "data": {
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",
- "text/plain": [
- ""
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "Exception ignored in: .remove at 0x00000238A1965AB0>\n",
- "Traceback (most recent call last):\n",
- " File \"C:\\Users\\aydin\\AppData\\Local\\Programs\\Python\\Python310\\lib\\weakref.py\", line 371, in remove\n",
- " self = selfref()\n",
- "KeyboardInterrupt: \n"
- ]
- },
- {
- "ename": "KeyboardInterrupt",
- "evalue": "",
- "output_type": "error",
- "traceback": [
- "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
- "\u001b[1;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)",
- "Cell \u001b[1;32mIn[31], line 113\u001b[0m\n\u001b[0;32m 110\u001b[0m y_test_subset \u001b[38;5;241m=\u001b[39m y_test[indices]\n\u001b[0;32m 112\u001b[0m \u001b[38;5;66;03m# Make predictions on the subset of test data\u001b[39;00m\n\u001b[1;32m--> 113\u001b[0m test_predictions \u001b[38;5;241m=\u001b[39m \u001b[43mmodel\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mpredict\u001b[49m\u001b[43m(\u001b[49m\u001b[43mx_test_subset\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mbatch_size\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mverbose\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m0\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmax_queue_size\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m120\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mworkers\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43muse_multiprocessing\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\u001b[43m)\u001b[49m\n\u001b[0;32m 114\u001b[0m test_predictions \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39margmax(test_predictions, axis\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m1\u001b[39m)\n\u001b[0;32m 115\u001b[0m y_test_original_subset \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39margmax(y_test_subset, axis\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m1\u001b[39m)\n",
- "File \u001b[1;32mc:\\Users\\aydin\\Desktop\\Pneumonia AI Dev\\venv\\lib\\site-packages\\keras\\utils\\traceback_utils.py:65\u001b[0m, in \u001b[0;36mfilter_traceback..error_handler\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m 63\u001b[0m filtered_tb \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[0;32m 64\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m---> 65\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m fn(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[0;32m 66\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mException\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[0;32m 67\u001b[0m filtered_tb \u001b[38;5;241m=\u001b[39m _process_traceback_frames(e\u001b[38;5;241m.\u001b[39m__traceback__)\n",
- "File \u001b[1;32mc:\\Users\\aydin\\Desktop\\Pneumonia AI Dev\\venv\\lib\\site-packages\\keras\\engine\\training.py:2253\u001b[0m, in \u001b[0;36mModel.predict\u001b[1;34m(self, x, batch_size, verbose, steps, callbacks, max_queue_size, workers, use_multiprocessing)\u001b[0m\n\u001b[0;32m 2251\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m step \u001b[38;5;129;01min\u001b[39;00m data_handler\u001b[38;5;241m.\u001b[39msteps():\n\u001b[0;32m 2252\u001b[0m callbacks\u001b[38;5;241m.\u001b[39mon_predict_batch_begin(step)\n\u001b[1;32m-> 2253\u001b[0m tmp_batch_outputs \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mpredict_function\u001b[49m\u001b[43m(\u001b[49m\u001b[43miterator\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 2254\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m data_handler\u001b[38;5;241m.\u001b[39mshould_sync:\n\u001b[0;32m 2255\u001b[0m context\u001b[38;5;241m.\u001b[39masync_wait()\n",
- "File \u001b[1;32mc:\\Users\\aydin\\Desktop\\Pneumonia AI Dev\\venv\\lib\\site-packages\\tensorflow\\python\\util\\traceback_utils.py:150\u001b[0m, in \u001b[0;36mfilter_traceback..error_handler\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m 148\u001b[0m filtered_tb \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[0;32m 149\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m--> 150\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m fn(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[0;32m 151\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mException\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[0;32m 152\u001b[0m filtered_tb \u001b[38;5;241m=\u001b[39m _process_traceback_frames(e\u001b[38;5;241m.\u001b[39m__traceback__)\n",
- "File \u001b[1;32mc:\\Users\\aydin\\Desktop\\Pneumonia AI Dev\\venv\\lib\\site-packages\\tensorflow\\python\\eager\\def_function.py:915\u001b[0m, in \u001b[0;36mFunction.__call__\u001b[1;34m(self, *args, **kwds)\u001b[0m\n\u001b[0;32m 912\u001b[0m compiler \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mxla\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_jit_compile \u001b[38;5;28;01melse\u001b[39;00m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mnonXla\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m 914\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m OptionalXlaContext(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_jit_compile):\n\u001b[1;32m--> 915\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_call(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwds)\n\u001b[0;32m 917\u001b[0m new_tracing_count \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mexperimental_get_tracing_count()\n\u001b[0;32m 918\u001b[0m without_tracing \u001b[38;5;241m=\u001b[39m (tracing_count \u001b[38;5;241m==\u001b[39m new_tracing_count)\n",
- "File \u001b[1;32mc:\\Users\\aydin\\Desktop\\Pneumonia AI Dev\\venv\\lib\\site-packages\\tensorflow\\python\\eager\\def_function.py:954\u001b[0m, in \u001b[0;36mFunction._call\u001b[1;34m(self, *args, **kwds)\u001b[0m\n\u001b[0;32m 951\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_lock\u001b[38;5;241m.\u001b[39mrelease()\n\u001b[0;32m 952\u001b[0m \u001b[38;5;66;03m# In this case we have not created variables on the first call. So we can\u001b[39;00m\n\u001b[0;32m 953\u001b[0m \u001b[38;5;66;03m# run the first trace but we should fail if variables are created.\u001b[39;00m\n\u001b[1;32m--> 954\u001b[0m results \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_stateful_fn(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwds)\n\u001b[0;32m 955\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_created_variables \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m ALLOW_DYNAMIC_VARIABLE_CREATION:\n\u001b[0;32m 956\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mCreating variables on a non-first call to a function\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m 957\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m decorated with tf.function.\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n",
- "File \u001b[1;32mc:\\Users\\aydin\\Desktop\\Pneumonia AI Dev\\venv\\lib\\site-packages\\tensorflow\\python\\eager\\function.py:2496\u001b[0m, in \u001b[0;36mFunction.__call__\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m 2493\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_lock:\n\u001b[0;32m 2494\u001b[0m (graph_function,\n\u001b[0;32m 2495\u001b[0m filtered_flat_args) \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_maybe_define_function(args, kwargs)\n\u001b[1;32m-> 2496\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mgraph_function\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_call_flat\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m 2497\u001b[0m \u001b[43m \u001b[49m\u001b[43mfiltered_flat_args\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcaptured_inputs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mgraph_function\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcaptured_inputs\u001b[49m\u001b[43m)\u001b[49m\n",
- "File \u001b[1;32mc:\\Users\\aydin\\Desktop\\Pneumonia AI Dev\\venv\\lib\\site-packages\\tensorflow\\python\\eager\\function.py:1862\u001b[0m, in \u001b[0;36mConcreteFunction._call_flat\u001b[1;34m(self, args, captured_inputs, cancellation_manager)\u001b[0m\n\u001b[0;32m 1858\u001b[0m possible_gradient_type \u001b[38;5;241m=\u001b[39m gradients_util\u001b[38;5;241m.\u001b[39mPossibleTapeGradientTypes(args)\n\u001b[0;32m 1859\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m (possible_gradient_type \u001b[38;5;241m==\u001b[39m gradients_util\u001b[38;5;241m.\u001b[39mPOSSIBLE_GRADIENT_TYPES_NONE\n\u001b[0;32m 1860\u001b[0m \u001b[38;5;129;01mand\u001b[39;00m executing_eagerly):\n\u001b[0;32m 1861\u001b[0m \u001b[38;5;66;03m# No tape is watching; skip to running the function.\u001b[39;00m\n\u001b[1;32m-> 1862\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_build_call_outputs(\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_inference_function\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcall\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m 1863\u001b[0m \u001b[43m \u001b[49m\u001b[43mctx\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcancellation_manager\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcancellation_manager\u001b[49m\u001b[43m)\u001b[49m)\n\u001b[0;32m 1864\u001b[0m forward_backward \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_select_forward_and_backward_functions(\n\u001b[0;32m 1865\u001b[0m args,\n\u001b[0;32m 1866\u001b[0m possible_gradient_type,\n\u001b[0;32m 1867\u001b[0m executing_eagerly)\n\u001b[0;32m 1868\u001b[0m forward_function, args_with_tangents \u001b[38;5;241m=\u001b[39m forward_backward\u001b[38;5;241m.\u001b[39mforward()\n",
- "File \u001b[1;32mc:\\Users\\aydin\\Desktop\\Pneumonia AI Dev\\venv\\lib\\site-packages\\tensorflow\\python\\eager\\function.py:499\u001b[0m, in \u001b[0;36m_EagerDefinedFunction.call\u001b[1;34m(self, ctx, args, cancellation_manager)\u001b[0m\n\u001b[0;32m 497\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m _InterpolateFunctionError(\u001b[38;5;28mself\u001b[39m):\n\u001b[0;32m 498\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m cancellation_manager \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m--> 499\u001b[0m outputs \u001b[38;5;241m=\u001b[39m \u001b[43mexecute\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mexecute\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m 500\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;28;43mstr\u001b[39;49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msignature\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mname\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 501\u001b[0m \u001b[43m \u001b[49m\u001b[43mnum_outputs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_num_outputs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 502\u001b[0m \u001b[43m \u001b[49m\u001b[43minputs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 503\u001b[0m \u001b[43m \u001b[49m\u001b[43mattrs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mattrs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 504\u001b[0m \u001b[43m \u001b[49m\u001b[43mctx\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mctx\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 505\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m 506\u001b[0m outputs \u001b[38;5;241m=\u001b[39m execute\u001b[38;5;241m.\u001b[39mexecute_with_cancellation(\n\u001b[0;32m 507\u001b[0m \u001b[38;5;28mstr\u001b[39m(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39msignature\u001b[38;5;241m.\u001b[39mname),\n\u001b[0;32m 508\u001b[0m num_outputs\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_num_outputs,\n\u001b[1;32m (...)\u001b[0m\n\u001b[0;32m 511\u001b[0m ctx\u001b[38;5;241m=\u001b[39mctx,\n\u001b[0;32m 512\u001b[0m cancellation_manager\u001b[38;5;241m=\u001b[39mcancellation_manager)\n",
- "File \u001b[1;32mc:\\Users\\aydin\\Desktop\\Pneumonia AI Dev\\venv\\lib\\site-packages\\tensorflow\\python\\eager\\execute.py:54\u001b[0m, in \u001b[0;36mquick_execute\u001b[1;34m(op_name, num_outputs, inputs, attrs, ctx, name)\u001b[0m\n\u001b[0;32m 52\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m 53\u001b[0m ctx\u001b[38;5;241m.\u001b[39mensure_initialized()\n\u001b[1;32m---> 54\u001b[0m tensors \u001b[38;5;241m=\u001b[39m \u001b[43mpywrap_tfe\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mTFE_Py_Execute\u001b[49m\u001b[43m(\u001b[49m\u001b[43mctx\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_handle\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdevice_name\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mop_name\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 55\u001b[0m \u001b[43m \u001b[49m\u001b[43minputs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mattrs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mnum_outputs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 56\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m core\u001b[38;5;241m.\u001b[39m_NotOkStatusException \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[0;32m 57\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m name \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n",
- "\u001b[1;31mKeyboardInterrupt\u001b[0m: "
- ]
- }
- ],
+ "outputs": [],
"source": [
"import traceback\n",
"import gc\n",
diff --git a/Utils/Other.py b/Utils/Other.py
index 5775778..a29ba55 100644
--- a/Utils/Other.py
+++ b/Utils/Other.py
@@ -162,4 +162,4 @@ def print_optimizer_info(model):
for param, value in model.optimizer.get_config().items():
print_Color(f"~* -- ~*{param}: ~*{value}", ["cyan", "light_cyan", "green"], advanced_mode=True)
else:
- print_Color("No optimizer found in the model.", ["red"])
\ No newline at end of file
+ print_Color("No optimizer found in the model.", ["red"])
diff --git a/Utils/Timeout_input.py b/Utils/Timeout_input.py
index 508bb2e..0fc5133 100644
--- a/Utils/Timeout_input.py
+++ b/Utils/Timeout_input.py
@@ -2,6 +2,7 @@
import queue
import keyboard
+
class TimeoutInput:
"""
A class to get user input with a timeout.
@@ -24,9 +25,9 @@ def __init__(self, prompt, timeout, default_var, timeout_message="\nTimeout!"):
def get_input(self):
"""Get user input in a non-blocking manner."""
- print(self.prompt, end='', flush=True)
+ print(self.prompt, end="", flush=True)
while not self.stop_thread:
- if keyboard.is_pressed('\n'):
+ if keyboard.is_pressed("\n"):
line = input()
if line:
self.input_queue.put(line.strip())
@@ -50,6 +51,7 @@ def run(self):
self.user_input = self.input_queue.get()
return {"user_input": self.user_input, "input_time": self.timeout, "default_var_used": False}
+
# Example usage
if __name__ == "__main__":
timeout_input = TimeoutInput("Enter something: ", 5, "default", "\nTimeout from TimeoutInput!")
diff --git a/archive_git_branch.cmd b/archive_git_branch.cmd
new file mode 100644
index 0000000..e84e9ad
--- /dev/null
+++ b/archive_git_branch.cmd
@@ -0,0 +1,68 @@
+@echo off
+set /p branchName="Enter the branch name to archive: "
+
+:: Check if the branch exists locally
+git rev-parse --verify %branchName% >nul 2>&1
+if errorlevel 1 (
+ echo The branch '%branchName%' does not exist locally.
+ echo Fetching the branch from the remote repository...
+ git fetch origin %branchName%:%branchName%
+)
+
+:: Check if the fetch was successful
+git rev-parse --verify %branchName% >nul 2>&1
+if errorlevel 1 (
+ echo Failed to fetch the branch '%branchName%'. Please check the branch name and try again.
+ exit /b
+)
+
+:: Archive the branch
+set tagName=archive/%branchName%
+git tag %tagName% %branchName%
+git push origin %tagName%
+echo Branch %branchName% has been archived as %tagName%.
+
+:: Ask the user if they want to delete the branch
+set /p deleteBranch="Do you want to delete the branch locally and remotely? (yes/no): "
+if /i "%deleteBranch%"=="yes" (
+ git branch -d %branchName%
+ git push origin --delete %branchName%
+ echo Branch %branchName% has been deleted.
+)
+
+:: Suggest a filename for the batch file
+set filename=archive_git_branch_%branchName%_%date:/=-%.bat
+echo Suggested filename for this batch file is: %filename%
+:: Filename: archive_git_branch.bat
+
+@echo off
+set /p branchName="Enter the branch name to archive: "
+
+:: Check if the branch exists locally
+git rev-parse --verify %branchName% >nul 2>&1
+if errorlevel 1 (
+ echo The branch '%branchName%' does not exist locally.
+ echo Fetching the branch from the remote repository...
+ git fetch origin %branchName%:%branchName%
+)
+
+:: Check if the fetch was successful
+git rev-parse --verify %branchName% >nul 2>&1
+if errorlevel 1 (
+ echo Failed to fetch the branch '%branchName%'. Please check the branch name and try again.
+ exit /b
+)
+
+:: Archive the branch
+set tagName=archive/%branchName%
+git tag %tagName% %branchName%
+git push origin %tagName%
+echo Branch %branchName% has been archived as %tagName%.
+
+:: Ask the user if they want to delete the branch
+set /p deleteBranch="Do you want to delete the branch locally and remotely? (yes/no): "
+if /i "%deleteBranch%"=="yes" (
+ git branch -d %branchName%
+ git push origin --delete %branchName%
+ echo Branch %branchName% has been deleted.
+)
diff --git a/requirements.txt b/requirements.txt
index d42d617..096c290 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -5,6 +5,7 @@ hyperas==0.4.1
imbalanced-learn==0.11.0
keras==2.10.0
keras-efficientnet-v2==1.2.2
+keyboard==0.13.5
loguru==0.5.3
matplotlib==3.7.2
model-profiler==1.1.8