From f1687b84a2bc905b19b7439e72602289c9aab0d3 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Tom=C3=A1s=20Capretto?= Date: Sat, 11 Nov 2023 11:03:11 -0300 Subject: [PATCH 1/3] Add getting started to webpage and minor improvements (#754) --- docs/_quarto.yml | 4 +- docs/changelog.qmd | 3 + docs/notebooks/getting_started.ipynb | 523 ++++++++++++++++++--------- 3 files changed, 356 insertions(+), 174 deletions(-) diff --git a/docs/_quarto.yml b/docs/_quarto.yml index cf64b4e09..5328aaa6b 100644 --- a/docs/_quarto.yml +++ b/docs/_quarto.yml @@ -29,7 +29,9 @@ website: logo: "logos/RGB/Bambi_logo.png" logo-alt: "bambi-home" right: - - text: Reference + - text: Getting Started + href: notebooks/getting_started.ipynb + - text: API Reference href: api/index.html - text: Examples href: notebooks/index.qmd diff --git a/docs/changelog.qmd b/docs/changelog.qmd index acff05b8e..b5ec3eb5b 100644 --- a/docs/changelog.qmd +++ b/docs/changelog.qmd @@ -9,6 +9,9 @@ pagetitle: "Changelog" ### New features +* Add configuration facilities to Bambi (#745) +* Interpet submodule now outputs informative messages when computing default values (#745) + ### Maintenance and fixes ### Documentation diff --git a/docs/notebooks/getting_started.ipynb b/docs/notebooks/getting_started.ipynb index 2de9bc685..ce5954227 100644 --- a/docs/notebooks/getting_started.ipynb +++ b/docs/notebooks/getting_started.ipynb @@ -7,7 +7,7 @@ "source": [ "# Getting Started\n", "\n", - "Bambi requires a working Python interpreter (3.7+). We recommend installing Python and key numerical libraries using the [Anaconda Distribution](https://www.anaconda.com/products/individual), which has one-click installers available on all major platforms.\n", + "Bambi requires a working Python interpreter (3.8+). We recommend installing Python and key numerical libraries using the [Anaconda Distribution](https://www.anaconda.com/products/individual), which has one-click installers available on all major platforms.\n", "\n", "Assuming a standard Python environment is installed on your machine (including pip), Bambi itself can be installed in one line using pip:\n", "\n", @@ -53,6 +53,17 @@ "execution_count": 1, "metadata": {}, "outputs": [], + "source": [ + "import warnings\n", + "\n", + "warnings.filterwarnings(\"ignore\", category=FutureWarning)" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], "source": [ "import arviz as az\n", "import bambi as bmb\n", @@ -62,7 +73,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 3, "metadata": {}, "outputs": [], "source": [ @@ -81,7 +92,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 4, "metadata": {}, "outputs": [ { @@ -95,13 +106,14 @@ " target = mu\n", " Common-level effects\n", " Intercept ~ Normal(mu: 0.1852, sigma: 2.5649)\n", - " x ~ Normal(mu: 0, sigma: 2.231)\n", - " z ~ Normal(mu: 0, sigma: 2.4374)\n", + " x ~ Normal(mu: 0.0, sigma: 2.231)\n", + " z ~ Normal(mu: 0.0, sigma: 2.4374)\n", + " \n", " Auxiliary parameters\n", - " y_sigma ~ HalfStudentT(nu: 4, sigma: 1.013)" + " sigma ~ HalfStudentT(nu: 4.0, sigma: 1.013)" ] }, - "execution_count": 3, + "execution_count": 4, "metadata": {}, "output_type": "execute_result" } @@ -163,7 +175,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 5, "metadata": {}, "outputs": [ { @@ -326,7 +338,7 @@ "9 13.0 0.0 1.0 23.0 0.0 7.0 rating_c1 1.0" ] }, - "execution_count": 4, + "execution_count": 5, "metadata": {}, "output_type": "execute_result" } @@ -338,7 +350,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 6, "metadata": {}, "outputs": [ { @@ -347,7 +359,7 @@ "401" ] }, - "execution_count": 5, + "execution_count": 6, "metadata": {}, "output_type": "execute_result" } @@ -367,7 +379,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 7, "metadata": {}, "outputs": [ { @@ -388,14 +400,15 @@ " target = mu\n", " Common-level effects\n", " Intercept ~ Normal(mu: 4.5457, sigma: 28.4114)\n", - " condition ~ Normal(mu: 0, sigma: 12.0966)\n", - " age ~ Normal(mu: 0, sigma: 1.3011)\n", - " gender ~ Normal(mu: 0, sigma: 13.1286)\n", + " condition ~ Normal(mu: 0.0, sigma: 12.0966)\n", + " age ~ Normal(mu: 0.0, sigma: 1.3011)\n", + " gender ~ Normal(mu: 0.0, sigma: 13.1286)\n", + " \n", " Auxiliary parameters\n", - " value_sigma ~ HalfStudentT(nu: 4, sigma: 2.4186)" + " sigma ~ HalfStudentT(nu: 4.0, sigma: 2.4186)" ] }, - "execution_count": 6, + "execution_count": 7, "metadata": {}, "output_type": "execute_result" } @@ -407,7 +420,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 8, "metadata": {}, "outputs": [ { @@ -428,17 +441,18 @@ " target = mu\n", " Common-level effects\n", " Intercept ~ Normal(mu: 4.5457, sigma: 28.4114)\n", - " condition ~ Normal(mu: 0, sigma: 12.0966)\n", - " age ~ Normal(mu: 0, sigma: 1.3011)\n", - " gender ~ Normal(mu: 0, sigma: 13.1286)\n", + " condition ~ Normal(mu: 0.0, sigma: 12.0966)\n", + " age ~ Normal(mu: 0.0, sigma: 1.3011)\n", + " gender ~ Normal(mu: 0.0, sigma: 13.1286)\n", " \n", " Group-level effects\n", - " 1|uid ~ Normal(mu: 0, sigma: HalfNormal(sigma: 28.4114))\n", + " 1|uid ~ Normal(mu: 0.0, sigma: HalfNormal(sigma: 28.4114))\n", + " \n", " Auxiliary parameters\n", - " value_sigma ~ HalfStudentT(nu: 4, sigma: 2.4186)" + " sigma ~ HalfStudentT(nu: 4.0, sigma: 2.4186)" ] }, - "execution_count": 7, + "execution_count": 8, "metadata": {}, "output_type": "execute_result" } @@ -450,7 +464,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 9, "metadata": {}, "outputs": [ { @@ -471,21 +485,22 @@ " target = mu\n", " Common-level effects\n", " Intercept ~ Normal(mu: 4.5457, sigma: 28.4114)\n", - " condition ~ Normal(mu: 0, sigma: 12.0966)\n", - " age ~ Normal(mu: 0, sigma: 1.3011)\n", - " gender ~ Normal(mu: 0, sigma: 13.1286)\n", + " condition ~ Normal(mu: 0.0, sigma: 12.0966)\n", + " age ~ Normal(mu: 0.0, sigma: 1.3011)\n", + " gender ~ Normal(mu: 0.0, sigma: 13.1286)\n", " \n", " Group-level effects\n", - " 1|uid ~ Normal(mu: 0, sigma: HalfNormal(sigma: 28.4114))\n", - " 1|study ~ Normal(mu: 0, sigma: HalfNormal(sigma: 28.4114))\n", - " condition|study ~ Normal(mu: 0, sigma: HalfNormal(sigma: 12.0966))\n", - " 1|stimulus ~ Normal(mu: 0, sigma: HalfNormal(sigma: 28.4114))\n", - " condition|stimulus ~ Normal(mu: 0, sigma: HalfNormal(sigma: 12.0966))\n", + " 1|uid ~ Normal(mu: 0.0, sigma: HalfNormal(sigma: 28.4114))\n", + " 1|study ~ Normal(mu: 0.0, sigma: HalfNormal(sigma: 28.4114))\n", + " condition|study ~ Normal(mu: 0.0, sigma: HalfNormal(sigma: 12.0966))\n", + " 1|stimulus ~ Normal(mu: 0.0, sigma: HalfNormal(sigma: 28.4114))\n", + " condition|stimulus ~ Normal(mu: 0.0, sigma: HalfNormal(sigma: 12.0966))\n", + " \n", " Auxiliary parameters\n", - " value_sigma ~ HalfStudentT(nu: 4, sigma: 2.4186)" + " sigma ~ HalfStudentT(nu: 4.0, sigma: 2.4186)" ] }, - "execution_count": 8, + "execution_count": 9, "metadata": {}, "output_type": "execute_result" } @@ -493,7 +508,11 @@ "source": [ "# Multiple, complex group specific effects with both\n", "# group specific slopes and group specific intercepts\n", - "bmb.Model(\"value ~ condition + age + gender + (1|uid) + (condition|study) + (condition|stimulus)\", data, dropna=True)" + "bmb.Model(\n", + " \"value ~ condition + age + gender + (1|uid) + (condition|study) + (condition|stimulus)\", \n", + " data, \n", + " dropna=True\n", + ")" ] }, { @@ -532,7 +551,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 10, "metadata": {}, "outputs": [ { @@ -541,7 +560,13 @@ "text": [ "Automatically removing 33/6940 rows from the dataset.\n", "Auto-assigning NUTS sampler...\n", - "Initializing NUTS using jitter+adapt_diag...\n", + "Initializing NUTS using jitter+adapt_diag...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ "Multiprocess sampling (2 chains in 2 jobs)\n", "NUTS: [value_sigma, Intercept, condition, age, gender, 1|uid_sigma, 1|uid_offset]\n" ] @@ -579,7 +604,7 @@ "\n", "
\n", " \n", - " 100.00% [4000/4000 00:28<00:00 Sampling 2 chains, 0 divergences]\n", + " 100.00% [4000/4000 00:34<00:00 Sampling 2 chains, 0 divergences]\n", "
\n", " " ], @@ -594,7 +619,8 @@ "name": "stderr", "output_type": "stream", "text": [ - "Sampling 2 chains for 1_000 tune and 1_000 draw iterations (2_000 + 2_000 draws total) took 28 seconds.\n" + "Sampling 2 chains for 1_000 tune and 1_000 draw iterations (2_000 + 2_000 draws total) took 35 seconds.\n", + "We recommend running at least 4 chains for robust computation of convergence diagnostics\n" ] } ], @@ -610,9 +636,10 @@ "source": [ "The above code obtains 1,000 draws (the default value) and return them as an ``InferenceData`` instance. \n", "\n", - ".. tip::\n", - " InferenceData is a rich data structure to store and manipulate data such as posterior samples, prior/posterior predictive samples, observations, etc. It is based on [xarray](https://docs.xarray.dev/en/stable/), a library offering N-dimensional labeled arrays (you can think of it as a generalization of both Numpy arrays and Pandas dataframes).\n", - " To learn how to perform common operations with InferenceData, like indexing, selection etc please check [this](https://arviz-devs.github.io/arviz/getting_started/WorkingWithInferenceData.html) and for details of the InferenceData Schema see this [specification](https://arviz-devs.github.io/arviz/schema/schema.html).\n", + "::: {.callout-tip}\n", + "InferenceData is a rich data structure to store and manipulate data such as posterior samples, prior/posterior predictive samples, observations, etc. It is based on [xarray](https://docs.xarray.dev/en/stable/), a library offering N-dimensional labeled arrays (you can think of it as a generalization of both Numpy arrays and Pandas dataframes).\n", + "To learn how to perform common operations with InferenceData, like indexing, selection etc please check [this](https://python.arviz.org/en/latest/getting_started/WorkingWithInferenceData.html) and for details of the InferenceData Schema see this [specification](https://python.arviz.org/en/latest/schema/schema.html).\n", + ":::\n", "\n", "\n", "In this case, the `fit()` method accepts optional keyword arguments to pass onto PyMC's ``sample()`` method, so any methods accepted by ``sample()`` can be specified here. We can also explicitly set the number of draws via the ``draws`` argument. For example, if we call ``fit(draws=2000, chains=2)``, the PyMC sampler will sample two chains in parallel, drawing 2,000 draws for each one. We could also specify starting parameter values, the step function to use, and so on (for full details, see the [PyMC documentation](https://www.pymc.io/projects/docs/en/stable/api/generated/pymc.sample.html#pymc.sample)).\n", @@ -623,7 +650,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 11, "metadata": {}, "outputs": [ { @@ -649,7 +676,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 12, "metadata": {}, "outputs": [ { @@ -661,7 +688,7 @@ }, { "data": { - "image/png": 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", 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" ] @@ -697,7 +724,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 13, "metadata": {}, "outputs": [ { @@ -718,21 +745,22 @@ " target = mu\n", " Common-level effects\n", " Intercept ~ Normal(mu: 4.5516, sigma: 8.4548)\n", - " condition ~ Normal(mu: 0, sigma: 12.1019)\n", + " condition ~ Normal(mu: 0.0, sigma: 12.1019)\n", " \n", " Group-level effects\n", - " 1|uid ~ Laplace(mu: 0, b: HalfNormal(sigma: 1))\n", + " 1|uid ~ Laplace(mu: 0.0, b: HalfNormal(sigma: 1.0))\n", + " \n", " Auxiliary parameters\n", - " value_sigma ~ HalfStudentT(nu: 4, sigma: 2.4197)" + " sigma ~ HalfStudentT(nu: 4.0, sigma: 2.4197)" ] }, - "execution_count": 12, + "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "# A Laplace prior with mean of 0 and scale of 10\n", + "# A Laplace prior with mean of 0 and scale given by a HalfNormal with a scale of 1\n", "my_favorite_prior = bmb.Prior(\"Laplace\", mu=0, b=bmb.Prior(\"HalfNormal\", sigma=1))\n", "\n", "# Set the prior when adding a term to the model; more details on this below.\n", @@ -750,7 +778,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 14, "metadata": {}, "outputs": [ { @@ -771,15 +799,16 @@ " target = mu\n", " Common-level effects\n", " Intercept ~ Normal(mu: 4.5516, sigma: 8.4548)\n", - " condition ~ Normal(mu: 0, sigma: 12.1019)\n", + " condition ~ Normal(mu: 0.0, sigma: 12.1019)\n", " \n", " Group-level effects\n", - " 1|uid ~ Normal(mu: 0, sd: HalfCauchy(beta: 5))\n", + " 1|uid ~ Normal(mu: 0.0, sd: HalfCauchy(beta: 5.0))\n", + " \n", " Auxiliary parameters\n", - " value_sigma ~ HalfStudentT(nu: 4, sigma: 2.4197)" + " sigma ~ HalfStudentT(nu: 4.0, sigma: 2.4197)" ] }, - "execution_count": 13, + "execution_count": 14, "metadata": {}, "output_type": "execute_result" } @@ -847,7 +876,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 15, "metadata": {}, "outputs": [ { @@ -894,7 +923,7 @@ "\n", "
\n", " \n", - " 100.00% [4000/4000 00:15<00:00 Sampling 2 chains, 0 divergences]\n", + " 100.00% [4000/4000 00:07<00:00 Sampling 2 chains, 0 divergences]\n", "
\n", " " ], @@ -909,7 +938,8 @@ "name": "stderr", "output_type": "stream", "text": [ - "Sampling 2 chains for 1_000 tune and 1_000 draw iterations (2_000 + 2_000 draws total) took 16 seconds.\n" + "Sampling 2 chains for 1_000 tune and 1_000 draw iterations (2_000 + 2_000 draws total) took 7 seconds.\n", + "We recommend running at least 4 chains for robust computation of convergence diagnostics\n" ] } ], @@ -924,7 +954,19 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "If no ``link`` argument is explicitly set (see below), the canonical link function (or an otherwise sensible default) will be used. The following table summarizes the currently available families and their associated links:\n", + "If no ``link`` argument is explicitly set (see below), the canonical link function (or an otherwise sensible default) will be used. " + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Families\n", + "\n", + "Following the convention used in many R packages, the response distribution to use for a GLMM is specified in a ``Family`` class that indicates how the response variable is distributed, as well as the link function transforming the linear response to a non-linear one. \n", + "\n", + "The following table summarizes the currently available families and their associated links:\n", "\n", "
\n", "\n", @@ -937,8 +979,8 @@ "binomial | Binomial | logit | \n", "categorical | Categorical | softmax | \n", "cumulative | Cumulative | logit | \n", - "exponential | Exponential | log | \n", "dirichlet_multinomial | DirichletMultinomial | logit |\n", + "exponential | Exponential | log | \n", "gamma | Gamma | inverse |\n", "gaussian | Normal | identity |\n", "hurdle_gamma | HurdleGamma | log |\n", @@ -959,22 +1001,152 @@ "zero_inflated_poisson | ZeroInflatedPoisson | log |\n", "\n", "\n", - "
" + "\n", + "\n", + "\n", + "Although the easiest way to specify a family is by name, using one of the options listed in the table above, users can also create and use their own family, providing enormous flexibility. In the following example, we show how the built-in Bernoulli family could be constructed on-the-fly:" ] }, { - "attachments": {}, - "cell_type": "markdown", + "cell_type": "code", + "execution_count": 16, "metadata": {}, + "outputs": [ + { + "data": { + "image/svg+xml": [ + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "clusteradmit_obs (400)\n", + "\n", + "admit_obs (400)\n", + "\n", + "\n", + "\n", + "rank\n", + "\n", + "rank\n", + "~\n", + "Normal\n", + "\n", + "\n", + "\n", + "admit\n", + "\n", + "admit\n", + "~\n", + "Bernoulli\n", + "\n", + "\n", + "\n", + "rank->admit\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "Intercept\n", + "\n", + "Intercept\n", + "~\n", + "Normal\n", + "\n", + "\n", + "\n", + "Intercept->admit\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "scale(gre)\n", + "\n", + "scale(gre)\n", + "~\n", + "Normal\n", + "\n", + "\n", + "\n", + "scale(gre)->admit\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "gpa\n", + "\n", + "gpa\n", + "~\n", + "Normal\n", + "\n", + "\n", + "\n", + "gpa->admit\n", + "\n", + "\n", + "\n", + "\n", + "\n" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "## Families\n", + "from scipy import special\n", "\n", - "Following the convention used in many R packages, the response distribution to use for a GLMM is specified in a ``Family`` class that indicates how the response variable is distributed, as well as the link function transforming the linear response to a non-linear one. Although the easiest way to specify a family is by name, using one of the options listed in the table above, users can also create and use their own family, providing enormous flexibility. In the following example, we show how the built-in Bernoulli family could be constructed on-the-fly:" + "# Construct likelihood distribution ------------------------------\n", + "# This must use a valid PyMC distribution name.\n", + "# 'parent' is the name of the variable that represents the mean of the distribution. \n", + "# The mean of the Bernoulli family is given by 'p'.\n", + "likelihood = bmb.Likelihood(\"Bernoulli\", parent=\"p\")\n", + "\n", + "# Set link function ----------------------------------------------\n", + "# There are two alternative approaches.\n", + "# 1. Pass a name that is known by Bambi\n", + "link = bmb.Link(\"logit\")\n", + "\n", + "# 2. Build everything from scratch\n", + "# link: A function that maps the response to the linear predictor\n", + "# linkinv: A function that maps the linear predictor to the response\n", + "# linkinv_backend: A function that maps the linear predictor to the response\n", + "# that works with PyTensor tensors.\n", + "# bmb.math.sigmoid is a PyTensor tensor function wrapped by PyMC and Bambi \n", + "link = bmb.Link(\n", + " \"my_logit\", \n", + " link=special.logit,\n", + " linkinv=special.expit,\n", + " linkinv_backend=bmb.math.sigmoid\n", + ")\n", + "\n", + "# Construct the family -------------------------------------------\n", + "# Families are defined by a name, a Likelihood and a Link.\n", + "family = bmb.Family(\"bernoulli\", likelihood, link)\n", + "\n", + "# Now it's business as usual\n", + "# Note: 'gre' is standardized to mean 0 and variance 1\n", + "model = bmb.Model(\"admit ~ scale(gre) + gpa + rank\", data, family=family)\n", + "model.build()\n", + "model.graph()" ] }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 17, "metadata": {}, "outputs": [ { @@ -984,7 +1156,7 @@ "Auto-assigning NUTS sampler...\n", "Initializing NUTS using jitter+adapt_diag...\n", "Multiprocess sampling (2 chains in 2 jobs)\n", - "NUTS: [Intercept, gre, gpa, rank]\n" + "NUTS: [Intercept, scale(gre), gpa, rank]\n" ] }, { @@ -1020,7 +1192,7 @@ "\n", "
\n", " \n", - " 100.00% [4000/4000 00:11<00:00 Sampling 2 chains, 0 divergences]\n", + " 100.00% [4000/4000 00:03<00:00 Sampling 2 chains, 0 divergences]\n", "
\n", " " ], @@ -1035,43 +1207,12 @@ "name": "stderr", "output_type": "stream", "text": [ - "Sampling 2 chains for 1_000 tune and 1_000 draw iterations (2_000 + 2_000 draws total) took 12 seconds.\n" + "Sampling 2 chains for 1_000 tune and 1_000 draw iterations (2_000 + 2_000 draws total) took 4 seconds.\n", + "We recommend running at least 4 chains for robust computation of convergence diagnostics\n" ] } ], "source": [ - "from scipy import special\n", - "\n", - "# Construct likelihood distribution ------------------------------\n", - "# This must use a valid PyMC distribution name.\n", - "# 'parent' is the name of the variable that represents the mean of the distribution. \n", - "# The mean of the Bernoulli family is given by 'p'.\n", - "likelihood = bmb.Likelihood(\"Bernoulli\", parent=\"p\")\n", - "\n", - "# Set link function ----------------------------------------------\n", - "# There are two alternative approaches.\n", - "# 1. Pass a name that is known by Bambi\n", - "link = bmb.Link(\"logit\")\n", - "\n", - "# 2. Build everything from scratch\n", - "# link: A function that maps the response to the linear predictor\n", - "# linkinv: A function that maps the linear predictor to the response\n", - "# linkinv_backend: A function that maps the linear predictor to the response\n", - "# that works with PyTensor tensors.\n", - "# bmb.math.sigmoid is a PyTensor tensor function wrapped by PyMC and Bambi \n", - "link = bmb.Link(\n", - " \"my_logit\", \n", - " link=special.expit,\n", - " linkinv=special.logit,\n", - " linkinv_backend=bmb.math.sigmoid\n", - ")\n", - "\n", - "# Construct the family -------------------------------------------\n", - "# Families are defined by a name, a Likelihood and a Link.\n", - "family = bmb.Family(\"bernoulli\", likelihood, link)\n", - "\n", - "# Now it's business as usual\n", - "model = bmb.Model(\"admit ~ gre + gpa + rank\", data, family=family)\n", "results = model.fit()" ] }, @@ -1080,9 +1221,9 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "The above example produces results identical to simply setting ``family='bernoulli'``.\n", + "The above example produces results identical to simply setting `family='bernoulli'`.\n", "\n", - "One complication in specifying a custom ``Family`` is that one must pass both a link function and an inverse link function which must be able to operate over PyTensor tensors rather than numpy arrays, so you'll probably need to rely on tensor operations provided in ``aesara.tensor`` (many of which are also wrapped by PyMC) when defining a new link." + "One complication in specifying a custom `Family` is that one must pass both a link function and an inverse link function which must be able to operate over PyTensor tensors rather than numpy arrays, so you'll probably need to rely on tensor operations provided in `pytensor.tensor` (many of which are also wrapped by PyMC) when defining a new link." ] }, { @@ -1103,12 +1244,12 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 18, "metadata": {}, "outputs": [ { "data": { - "image/png": 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", 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Igtj5oOc34VOriX+5vX3rQRB1sU3xLze3bz12UUjsIggiGm4jdf83kHj5zygd+ylcteBK3PhLHSedBNz8W4YjDt9GcTESaVTf+FkU5/8d9rQDkb7va8j878fBhrompfgpUxiu+gbD168QVl7nXcjx3PP0wkwQxO5D12qOp56mcY/YySHLLkKCukNz3P8gx8KXqLG2G25HpQ67VSCxiyCIMLaF1H1fR+KVv2H06E/jU3/9DO6+h+HC8xmu/gZDLrftA8Dyqfui9KFbUH7bt6ANLEf2j+9D8skbAbOyxWUzxvCOtzHc/DuGmTOAL13KcdufODg9eAiC2A1Y1QXkC9u7FgSxpdAzm/CxHYsuRrPdhvQPbO8aECR2bR3o9icIQoVzpB68BolX78DQkZ/Fx2++CEuXAddexXDuxyY3NteEYQzmYWeicO7dMA86Hcmnf4bsH98Hfd0zk1L8nNkMv7iR4S0nAT//Jcc3v81Rq9HDhyAIgiB2eOhxTUi42gHl5yR2bFzLLvK33RqQ2EUQhELyqZ8hsegv6D/4Ipz16//GwADwo+8zvOXkHeh1ITsVlXd8B6UP3gRwjsxfz4X5f58FKw5ucdGZjHBr/O9PMPz7fuArX+UoFOgNmiAIgiB2bOhZTfh4YhfNdomdARK7tgp0+xME4WG89Cckn/4ZBvf6ED70q0tQqwI3/GQbxueaINaer0fxY/9A9fiLwF++A9nfvwOJF24GrNoWlcsYw/yzGf7fFQwvLgQ+/TmOwUF6iSYIgiCIHQ7X4pzcgAgJ0g4mBzayDsbSuwDb2t5V2UXhgX+JyYTELoIgAAD6ivuQevAajM05BR++9etIJhh+fgPDAfvvmEKXh5FC9Y2fhfGZR2DNPRapR74rXBvXPL7FRb/9bQzf+w7Dhm7gks9y9PfTg4ggCILYcejp5bj/QQ7L2p2fT+57yu7cBkQQ27Xs2sFfY3d09N5XxB82ZQvcqpBYv1UgsYsgCGjdLyB991dQnn40/uuO74HDwI9/yDB37s7zhsA690X5fb9A6f2/Fq6N//cJpP9+Mdjw2i0q97jXMfzkhwzDI8Bnv8gxQBZeBEEQxA5Cl5OYuLLluVoIYpeCYnZNEttThOEcqBW33/G3BW4/JVPErQKJXQSxm8NGu5G58zOotczFuffdiNFiGj/6AcOe83bO1wNrnzej+LG/o3LipdA3PIfsH96N5EPfBopDm13mIQcz/OB6hoEB4PNf5BgaIsGLIAiCmDy03leFq9AEIWMAgHtujDRZJHw4WXZNLtthsNEGVsBY9RBQ3R3SBdNgvjUgsYsgdmeqBaT/cQm4beFzz9yIdQNt+MH1DPvtu5O/GehJ1I49D8Xz7oF56JlILLwNud+dhsRTP9vsB+ZhhzJ8/7sMvb3A57/EMTxCDyWCIIgdHs7Bxnu2dy0aog2v2az93CfR7j2h361PnojBdrRPEoR3XljJSTxlVbdvRbYqbjbGiI5aHtu2VdkFIbGLIHZXuI30PZdBG1yJ7637EV5YvReuv47hkIN3nZdGnpuGyluvQvHcf8Hc9ySknrpRiF4L/gCYE39wHnkEw/XfYejeCHzpK5SlkSAIYkeHDa+G3v0C2NjG7V2V5tjMINC7t9hFEGEmReSqlaF1v0DxqgCQ5dFWwu2oQcvU0jCMNY9BX/0oUCtv+3rtIpDYRRC7Kcknb4Cx6kH8o/JV3P7M6/G1KxiOPmrXfFvmHXujcsYPUTz7r7BmHILUw9che/M7YSz++4QnFkcfxXDdNxlWdQFX/D+OapUe/gRBEDsqrFYSf1g7SVArPrFnkjs/2r3FLsrGSITxNIQtKEMbWA5tvGensA7dpdkt7m31HJnzUZ5VxqFvenF7VGiXgMQugtgNMZbdjeQzv8Si5Idx1b1n4b8/wXDqW3b9N2V75qEof+C3KH3wJvDsNKTvvRyZP74f+qqHJvQgPe51DF+7nOGFBcC1397ds2ARBEEQk8ZmWnYRAFme7BiwwsBm7WdZHJXK5F1D141xy7qF5yi8mbvzXcgqjO6vrUO0ZRfXE/4Pei5sNiR2EcRuhtb7KlL3XoG+7Otw3t+uwLvPYDjnv7Z3rbYt1p6vR+msP6H07p+A2SYy/7gYmT+fA617QdNlvO2tDJ+9hOGhh4Gf3sjBd4uvTgRBEDsZO5vJ0wSDrPM64V52G9xLvB0bgRUHgdLIdjv+jgIb74G+/pnNyoS9dh3w3POTV5dJ6Q5bWIjWswjG8n9PQkV2ALbL/bWTjd9bQrB5mSTTaMY2rcquBIldBLEbwfJ9SP/jElSS03HWP36Eo1+bwJe+wMB2tsnAZMAYrAPehuLH70T5tGvAxrqR/fPZSN/52aZf0j78IYb/Ogv42x3Arbdt5foSBEEQuz4T/IK/O2tcYbZfa+jrnoax9ontdvwoNmzgMM1t3Ca1IgCAmaWJ71oDqrXJq4oXoH6LSnFTOm7e3troetg2x8DAduqbVg1sqGvz99+tVfRtA4uL2SX1XMXKi5gQJHYRxO6CWUH6zs8A5XF89qkbkZnagWuuZDCM3VDoktEMmId/CMXz7kXlTV+Avu5JZG95N5IPX9fUV9qLPsnw9tOAX/2G4z8P00sBQRAEsQVMMGaX5wFTqwDmThKXbNJpLmaX3vXIlk38dyLGxjiWLAMWL9m2x2WeWDvxKSbnO7K2shnvypVxAEBPD/DiQhtj49v+5LS+xdD7loAV+rf5sbcGbKjLa9ddhzixS0IjsWtzIbGLIHYHOEfq/m9A61mEn/V+Fy/1HoBvXsPQ2rqbC10yiTRqx30SxfP/DfOwM5F48Vbkfv8OJBbcAljxnxoZY7jsKwxHHgF889scry7eYd/UCIIgdlj6+jjGxiZh/ORcWOcGJw7Boq3aDpnhik3Usss5r2TXAzBWPrAVarT1WPQKx/0PTuYzs35ZrJqH3reN1Z/thOV0/+rEE09vIa4l1Ga8X07y65Nn2bWdXsuM1Y8CAGomAHCUJ27stuVYTgfY7EaQ9tuebsJOPfS+JTDWPL7d6rF1CbSv3N46uTFuLiR2EcRuQOL53yGx5E48kfocfvfEKfjyFxj234+Erih4thOVt16F0vy/w5p9BFIPfwfZP74f+rqnY/dJJhm+dQ3D9GnA5V/j6OkhwYsgiJ2c0jC0nlcabrZmLcfQ0JaPeStWAuvW199mZIQjn28gaIyuh977CtjQ6rrb6V0Pw1j14ESrOWE4Fx9BisUm22gzxa6dkZ7eSSpodwzF0IDt1iJOh+RsMyy7vP9MDrZb1mSUuQV9TNcBBtsRvXZmdpDBpom4hlr3C9AGVmyDykwCXj8NZGOU25vp264+2xHOOfr7OQqFyetrJHYRxC6O3vUQko/9EJumnYFP/++FeM+7gXe+g14MG2FPOwDlM3+N0vt/Bdg1ZP56HlL/+kJs+un2dobvfYehWgMuvXxyB2qCIIhtgZxoQ9/wPLSRtYBZ3zRkxUrghUnIim7bUva0GJ57AXjqmUYFCbGIWa5LX/Tzjll1zovbYGMbGxyoOcbGgI2bgFcXN7nDRN0Yd0JsezOTulhV6KsfBSr56PU7s/IHQOte0FCknSjbvEnc/rs5Ytck13WCuR5iCtnyShm6iMtkbmE8soGBxmJ/EC8e1OaKdTvSPTWBumjjPdAGlgd251i9ZjvEsWuWuh12B63zJDM+Dix8GXi++XxhDSGxiyB2YbSBFUjf/RVUph6Gc/52DQ48kOFznyahayJY+5yI4sf+icobPw+j62Fkbz4DxsLbIh+6e+7J8O1rGdauA668Zgd+oBIEsUvxP//zPzjllFNw+OGH40Mf+hBefvnlLS80kRX/VmOEhUmmkdDVNLGZ+SYwURpcCX3ji2DjW256pDlv2vXOj3OOteuBUolPeIZuB0+rOARt4ySoj1uRBx8Cnnl24vuxQj9YZRza4MrgGue/O9kzN9BHtfFN0PuaVUXrs92M3bZAXNlaMbsmJUD9FtjKabooZ0stu158qQmxPwjf8vrLZbHh1XVDe0w+cr237Er29gIrVwFdk6snTwIxJojyzbAjiY5bEdO5RybT/ZrELoLYVSkNI/2PS8CNLD7/7E9QtdP45tUMqRSJXRPGSKJ2/H+jeO5dsOYei/R/rkX6/z4RORE65miGr3yR4elngBt/vns8nAiC2H7cfffduO6663DJJZfgjjvuwGte8xpccMEFGBwcnHBZyrt1UohdrFqYrKrWxbYn630+8Gq7ObN+N9B7PeuvZmvThNiVzwOjowwbugHYWzYj1jc8B21s44TdIbc145ujoXpCSvT0pVi0UansTM/dSaxrEwl1ZCoVvnXaqsE1ambXya7K5NDEOFIaARtZF1osxoAtt+zaIjZb/fQbkRUHoPcuhra94t41e0FjxlA3jp21owyN1QJQGvZ+sjrZGHcXy66toemR2EUQuyJWDel/fQEs34vfFH6KpxfPxP+7gmHObBK6tgQ+ZQ7K7/slym+9GvrGF5H9w3tgLPlXaHR+1xkM/3UW8Nf/A/52x+7xgCIIYvtw00034cMf/jA+8IEPYP/998fVV1+NdDqNv/3tb1tWsJEGALDathO7JgIbWh3tzsaay8w32cS55bnVsZo4P8Ywcd+rwGHDE6ZdCPfcgkKK08jPPc/x6M4Uu3qS+igrDsJY+4SwunGXNXjde/RxbJ22irtGzezKt86UfouaeQI7G2ufgN6zKKIMcV+ObYMkgmNjIumDHyNw8lrUS55h7eBZXxtkpd1RjKSMrodhrH0SfjbGXc+ya/0GjqXLmq/71jhLErsIYhck+fB1MNY/g2dmfRO/+OcR+Ph84A0nkNA1KTAG84gPozj/77CnHYD0PV9B6q4vAuVRZbNPfYLhxDcDP7mB4+lnds6HFEEQOzbVahWvvvoq3vCGN3jLNE3DG97wBrz44sRd2ZT3aU+lMYHSMFi+bwtrWx+7kQuTWVWEIL1vMfS1T0Zs6Ipd21b0eeA/wMKXwifgnpNtiTqxkZgo/E61Nzcbo992fh1sm+O55zlGR3eRZ5AnpATfZ3bW95tJui6OBSIr+lYi2D6arxSzy3EtLfQ3bXU26XX1bo5JLnei1YBwsR0dA8bHt25l3KQPA55hb4yQ0ixR+03mharkoa+4D6g1SFXJOZq+kNvUzXIy2UXGaYmly4D1G5rffms8timPJUHsYhgLb0PypdvRe8CF+OwNZ+DY1wLnn7uzvgjuuPD2eSh96BYkFtyC5BM/ht6zCOV3/Qj2rMMBAJrG8P+uAC75HMc3rub45Y3AvvvSdSAIYvIYHh6GZVno7OxUlnd2dqKrqyt2v46ODuV3Lie+hLe1JWEYAGMMvNwKXs4BrTlg4CUAgDbvoFBZLZk8MpX16Og4eLPPw7Y5crkqWlo0dHQkord5+Q7MtTow3HYC2tuT4LkcwBi0wLlwjIOP54DWVmgdHeDlKeDlHNiUKWDStnYuJ84psD8A2MUpQC0H1poDWjJgiXTDc8jlKiiWgI6OlLI8kbCRy9WQSADt1S7w8VVgUzvB2vbwttF1G/2pJNIpYEpLTqmncm5mFagI8xD3Gmada5fNZpHJaBAqBwdrb8dYQUPNrGFDN8PeeycbnsNEsW0hpHV0TOzbudvfXIJtFge3hsDzObApbV4bVWscC5elsPccjmw2AyOZiy2v3jXfUjan7Pb2dtGPnf02t37cqIGP5IBsyttX00W/y+UYOjrC1969Bs22fbPYYy2AlQNrawNr74C9/mFRnyPe33DflpYa8gV70uqUy5WQSqWQyWjKmGdvWAAMrW1Yp5ERGxs3pnHgrCy09jawKX4ZfGgteE8POmb5417w+rm/8wULWWRgGTlkcwnvfpnoNWhm+87URhjFx9FinIqOjqmwh1oAVhHXozW6X9Url1s1r4+ytjbwUg5obZm0e4hvXA+eToJpJbCOOaH19nALwEpg7e1ARr1fYsvUK2I7PaFsly9YyOVMtLb6z5ngs3Ci/OfhKmbO0HDoIROXVNz+wVrFs5a1tChjP9fL4MNim8ls821Joz771NNVMMbw+uPF9ahUxTWqt89EIbGLIHYh9K6HkHroWyjvdQou/NPn0N4GXPn/GHSdRJatgqajduz5sOYeh/S/Po/Mn85G9aTLUDvqvwDGkMkwXP9t4BOf4rj0co5f/wKYOpWuBUEQ25fh4WHlt5s99o5/FNA5VcQe1MZGoRUKsPURaAXhymgG9gMAY/BlpCrrMLJBB89N36z6mCZHoQCMpYDh4egx0igUgPE8CsYRGBnOi9+MherExsagO/W2h4ehjY9BKxRgjY6CG8NqeTHnpI2Ni3Ne8ZTY5jVnNDwHtw2Hh4vK8nxenJumAWNDfaIuQ4PgdtbbZnzMQqVchsaAsbFR2OlwnQBAX/M4WHkUrSec411D97iFVBG27bedOTyE4XEDhQKQTMS365awdCnH+m7gjScA2Wzz5QezFb/0cgGtLUBHR/0y2OiIuLbpPGzn/PN5jnKlgu7uIkp6AeVaIXQNXOpd8y1lomV3dHRgZHhI2W9z68fyw9ALBXBTh+XsOzYu+p2uRV/7uP66pbjjhjU6Cs5bJnRO406dh4YKYJMQYb+QryFVqaBUUsc8Y/3ipur0+JMc2f4iZmQK0EdGwC0heusr7gOzasjlchhJzfLLDZyr+7tY5CiV8qjpGkZHAF0T5zbRa9DM9tWBlahUKij0r8PwsAZ9fAysVIA1MgxuRn9IqFuuVfXOw3LGVs7HvH62pWhjzvg8Ngquh8vUx/NgxQKskRHwst1Uf2Kj/aKeiaxSz9FR0b/Gx8U90dHREXoWTpSBAY6BAWxWmBj3XOyU86xNjsLOSDG8xkehu9skxmBnJ3HcKg2DVfLg7fOa2rxa5UgmJ36OcX1raIiDc9/q66ADRdkjI+IaRe0TRTNiJbkxEsQugtb7KtL/+hLsGYfiihevx6ZeDddcxdDRTuLK1saedRiK5/wN1j4nIvXQN4VboxPUedo0hu9exzAyClz+9a0UEJYgiN2Sjo4O6LoeCkY/ODiIadOmbVaZg0OBBQ38CjTuBAPeAteRZuJ12XLawXpuNKFYQQ2egTwcGb9c5li+YnIy6npujPI5BifyvDnXRRZwl2+EG4hZ1ye0W9O4QeZrW+g1tGx5k6nmvb4Ynr5skWdVgxg/W43Jeh3wOlk4MPe2D/Wz+a6Dkx6g3hkzNqfY0VGOUknO8CkFap/oWMfhnZzIOLn1snXrXkIM14/VrcNmHM82Y8b1rVH3mHFaHiubPax7H2hbaeDbWtTLIDzJN4ex9knoPc1lbe7eyPHIY5Pngss5xwsvAgsWRq2blEMokNhFELsAbKwb6b9/CjzXidvwM/zn8Qw+fTHDYYeS0LXNSLeh/J4bUDnxUhgr7kfm9rO82CwHHsBw5dcZFi8Bvv1dHhvImCAIYiIkk0kceuiheOqpp7xltm3jqaeewtFHH71lhXvxbuorUdx9ldyCcS0cdyp+m8Z1mliwImPZPdA2LVSWbeplqFQwKQGleeSvoNg1saxbzT1D+FYXu7Y1LCZmF9+CAFVsrBvGygeUrGjbjsl6F3DTzEnZQ7fTawabaKYJCXfXyXpF4pLANFGefT5UWFP79Q+IAPGKOA9fNOMAVnUBDz0SrudkoGmiLD/03+YHqtf6lsDoetj7zSb7AjXJipUci5dMIGZXzHaTYCy4dYh91k7subC1GHI+gBUbhFVrlrrdh8QugiBClMeQvuNTYGYVC17zS/z4d5049RTgA43DIxCTDWOoHXseyh/4HbRCP7K3fQj6umcAAG9+E8NF/83w4H+A399MYhdBEJPDeeedh7/85S+44447sGrVKlx11VUolUo488wzJ6X8RpNXzhwlpUnrpChcUaZpsavuG3FcgPr4fbSxjXXK2/rwrRRMf1cTuzzrGGnWqhX6kLCEiRnbjJkSK4qZHKvEK5vDw0LAGBsLl1+pbMEHLPm6b4mA4JTDJMuu7Reb3T2nHeA9p0mdR1//LIyldynLkuYwWspdzRfiMDAg/jVDRnb+BentbaqozcK9MyweFIAb179aDQh0xcHAFtvn2pbLQP/ABHbwznlHVbfiCLSrko1x+2fZjRuiQpaK5TFo/cvqljNj7DFkqptC62wSuwiCUKiVkPnHxdCG16Ln5Btw6ff3wbx5wGVfZpMS74DYPKw9j0fxv/4COzcD6b9dAGPhbQCAsz4CvPsM4KZbgPvu3wFeBAmC2Ok5/fTTcdlll+GnP/0p3vve92LJkiX47W9/u9lujCEiRCzOOVZ1cdRqXLLs2nyxqxnLLuUluKnhcwcZY5V6Oz/qPZ6bET2a2mbHFruYXcXs0QeQNEea38mz7PKnL6me5+QNYFhh0YoV+sGGV9ctWutbLLLCOdRqftgBN7NdMLyPaXI8+jiwfEXzp6AySX00QpDe1gbkK1dxEZ+Hb771TzPjwMTKk1wPh9cIQUu2fnPXFfpDy2aMP4n20pLJqQfgtQvn4as+0fMNWo2pZTmWXV6XaL5RH3kssIAFBo4tuLZbDm/+uA2EoXR+NYxld295jSbf7xZ9fRyWtfnWeJOCVYW26aVIt+goulYLS8VaTdRXX/c0tMGVsnmhArdtJK0xTC0uDK+TTtm2eSi+4+ZAAeoJYmfFqiL9z89B2/giiu/8Ib7682NRKgE//TGbUKBYYuvA2+ehdNbtSN9zGdL/uRbV4TWonvxVfOkLDBs3cVx3PcesWcARh9O1IghiyzjnnHNwzjnnTG6hca4V1QIGhgx0rU6iXAY423I3xmY8n9RqRBzLtpSJLNuC+nij8iTMNZqxSHPrOtlPgzixa8MGjqlTJxZUfrJJWcPQ7Qpayysx2HJsk3uF209u32y1G5laH1j+OPCWGd5yff2zdYp0rMVME68uBlI6x777Mjz5NFCtAqedGr+ra73TPwBgajPV54EYRJPkphTj3tlMsZzzLf44Wq1yrF4D9PQCJ83ZcnfmycJzYwSgjawTC82w2FUPxm1RQIO6jY1xpKSJefAyK1aHdQx4msGyRMKLKNxh0ub1XKWbJaaiEyirVOLIZJrpX81s09xxvbE/pl9nxxYDrVs+9m2Bx64CG+0GABTzNl5aD8zdAzj4NfIGbJsKjNrgSmijG8DTU8A79lGqEYVrqVirAYkE4PdCH33NE7Db54G371nf7FRatnw5sL4bOPFNHKnU5l8vsuwiiJ0QbltI3XMZjDWPoXLatbjxodPw0svAVy9l2HsvEk92GJI5lN/zU1SPPR/JF/+I1F1fgoEqrr2aYfYs4Ir/x7Fx0w5ifUAQBBFFQOwyuh5Gdv2DAMSki0MoKSzmK25Th2g0h+I8oAuEN9Q2LYSx6j/wXrQDrix6/zKgVm6yRpP3HI0+J7V825roc6C+6ONuEyV2cc6xZBnw3AsTPGSoeBua3WRgd9sEanGZtSbQ1hECrOyllbScAP7VfPNlulV0ynGzg1UnpomEYIV+YUnkJBVgY93CmsRJXjO5uO3ht2WzPcra/NvWP7o8RPDNd3WbbMsutw7Nu0CHYW4Cjgb7PfOcsHCJr4Zfly09v3oiC3M6smVN3I0xRLCiE7y23Rs5Hn9SBPufHJq17Gpuuy21zJo0scsWSQBcV8CSGxvLvR+YhuC5b07d+/s5HnksHE8uxGa2y8uLxDmwiHGalUeg9ywCSsNS3es/x9xkOVs6RpHYRRA7G5zDuvMrSCy/F5WTL8eDQ+/H7X8GPvgB4NRTSOja4WAaqid+BZWTL4ex/N/I/N8nMCUxjuuvY7Bt4NLLOfJ5ErwIgtjRcF9YI940bd8lx/3cy7dA7LIaesfwhhNWLd8XKCRim95XYuug9b4KbYMTlXprP0pDAdYn+AyIaKiotnMnCcrhTCH4mVuYQVHrXoBpAw80ta2+7hkYqx7CeN8w1qzdAkXDjU3VYF9uTyTbXXMuoROFOf2ROYHv2XiP+FeODabE5JH+tqpAbQLRoL1YZhPPUlluVv+tgzvp1xgaupBtS/gkqBEaN0UbNu1CF72YOYJkpDHLZlh2NTp8XTdG2/RczOqLJjFiV5PXeGRE/FuI07knStMN1eQYszXFrsr4xJNeuBa+IdNiLVTXBx7kWPaKI+oHrJrjWL5CiPjKPd9MspcGuPUdz7tWXvHPYGPtk16W1CiUpMvOv3FWjM1CYhdB7ExwjuSj14O/cBsqJ3waXTPm49vf5TjsUOCST5HQtSNTO+ZjqJzxA2ibFiLzl/mY19aHb1/LsGED8I2rt14aaoIgCBl7w4LmJtLuy3XEG72aDV68Sm7caDkZsxzMim9JZVVj43cAaBwKhvNANSLEHrdSdSZkda3PqgWwgEXQZIzKqkYXUSLnMEZ8kxBWHHIsgsbiC+1dDHAONrgq3rqK83Dg/2oB+ooHncDbzvEGV0Fb/ywqFY5ly5sPtq7lm4+yzcojAIAXF9SwYmWgmhNxoYuwLInyiFm8RM1211zZankushXE5IdCjW5rffWjjpVic/j9Wrkxm+KpZ5o+TCzuvck0AJajom6GkDBZljJ+gc6/W3Aja01adgXJSwZ8wrBr8iy7gmLX8hUcL70sCrWlYVvrXgDmWRL6BzWW/xv6mkebOFLzll22zWMtuOreNo2E6wls62/X4MLz0B+bRb3qGKsfhbH2yQmW6Ij5boMp7pjqwdpLi2EvfRgwK9DXPw1jxf0NS48cv6LELue4eu+rYHLyFrsmnusQIml3d8TzgiF6UFaKd0S9qA1ksUuxGOUwlt0NNrohutA6kNhFEDsRiWd+geQLN0N7w39j/KiL8PVvcCQM4JorGRIJErt2dMyD3onymb+FNrYRmT+dhaPnduGyLzM8+xxw/fe3IKMTQRBEk7zy2CpYSyegBERYdjHJQ8Z9Yd2wnqNbei82Vj4AY5VwdzRW3A997ROxh/Ay2gNgw2uhbXwxWAnl10svRc2Kg1kYo16k48UuxnnTAXknROSw7j+v2eh6GKNr/d8VIXKxopR+jHOwIV+g4gOrwIbXQO9fiqnFl2IP7E2K3TmT40qYNv2y9f6l0Ar9WLwEWLceGBm2RTuY1eYmFhN4bsmTG//vzRC7YgyiXIuv0THAsArQBpZjdJSjVGpcx7jTCGfVm0RiYnYxs0n30Mhy1EWT+VqxdClH1+oIocNZlDKHwDwLk4kfeNID1E+CXO25MU6kUpzj+ReAYtEXuFhEHCNp8yaKjN9o7Tqgz42x76ldNWjjUra7gKjBqk2YW8W5MUZUZcVK4Nnn/XOeMIxhYJCHMkK6GHYJxur6z63ePo61a3ns15PQSNOo4a1a3cQWTYmzVrQJbbXKsWKlnwQjuj7uwB227MrUepxNbLDSSBMViRe7TJPj5UWBurj7jHV7f9vLHoPuiGrd3cDipb7bdySbYeXJOZCwxpA0h9TvbXYN4Bxa38STRpDYRRA7CYnnb0LqyRtQO/RMsLdfie//GFi9BrjqGwwzZpDQtbNgzTsOpY/8D2CbyP75HJxx1Kv4xAUMd98L/OJXJHYRBLF1qdU4ysVmZvDRM0/b5hgecf/2t2Oo78You29ZFkdfn1+uLelTeu8r0OSvyU4d5InF0JC/XDqCs0ya6FTGAS6da92Xb9u3Pot4pI6Nx2eGcieiCXM0ZJEV7X4pHcC2Gs65WL4HeuAlnznCHOPx1zJqMhZ3LHfb9PpHhOXHpoXQN73URIyp5p9bmjLh34znXcOYQf7yaflnoQ2swPPPVrByVb0y1bKCLjZbU+waHbWx6BUuBIKIC3P/gxyvvBrfTuPjrlX4JJgwNcH6bmBVV3i5e9skbF9AqZQn/gEvtHV5bLOVL3FsHl3uBGCb1bZi20pF/ulbu2xOfTgHNLuK9uIr4HXGMe5tHxzE4sauBgdVfsdbzRacYaJSR6cdGOTo3uiXuWkTR7nsWKTZHC8uBF5cCLCRdV7QdlehMewChoaixVaXlxcByxXL0fC2HcWX666X0foWQ+9dDFYYiFzfSOwqlTh4OZwdFgD6+oQ74eCgVBs7zrJLCz2S/HFfWlMcqhMb0d80GP9y4yagtw9Y431zCYyJzs+R/pKXmdb9kFKpqFVQBbWYPtfAjXHm2GOYMf6Ud9xXXgXMWp0kHA0gsYsgdgISz/4aqUevR+2g01E57Rr85a9V3Ptv4ILzGI59LQldOxv29ANR+uht4KlWZP734zjvpGfxwQ8At/0JuO1PJHgRBLHjwALWUAMDwOiovMQRu+ImYMUhFAoci17xvxwvWw68tEgISIBk2VVn+GsYZNqbIPiFGasfhTYkfZmv58bIbU9AiuKZZ4Enn65ft5njj8NY81idYzRStWKWR9W7oQjgxzmbyFNFM53JkhPbq/HX+QmIXVrUthN4h7EDpmqItwZqJL76BAUR9XxNa0slpPi9R4bFunw+frtNPdH72jbH08+KiaDbLvI9uDnXfnPxY3ZZyOc5xsc5FrwoJtATQr6WpREYax4DG6qnVNYpivvWg1tqKebuv2Ytx/0P+iJercYxPKIG/LYlYSvK6nDjpnC8vOYsu4C20hK0VNZCz0d3Cs45zJoozAplY2x8jIgSlV/MG6itkAjuxlWypNsneF4vLhQuxuPOuP/KYmDRq+qhCkVA71kEfdPCUG26N/qiWv1qRwuUHECusj6+gkE8l9zoMbCe2FWpcKxcBaxePoFkGXH1Yeo6zrnkXutjrHsKxqqHJnYYzr0EJr4lcHy7uPG+3PCA9QW/mA9mVh2xVtrULXt0DOjtkX2lJwaJXQSxI8M5Ek/9DKnHf4TaIe9F5Z3X45UlGq77bgFveiMw/+ztXUFic+Ftc1H6yK2wp+yBzB2fxBff+TBOeyvw819y3H0PCV4EQWw96o0wCWtMtagIvKhWpYmaZDyhimKSOGOsewpDw+Jv1yKr6Ogp7su1HX0opcbqx2D/h9a32JmMNI7ZVVe4cd6sn3nGxNBg/GaRtQsKLSPrYtcBQN+APcGMWJH+J86amHJiGtNbym3feiJ+84Yw2E3vq4pdEz8gi3BPihdBG4holbwaj8bbVRzDm7w3ZQQ5CXLYBItwJ4LDw/LxubBCqZXrFtfI4mrN2ngrxnp10ZiN1WuEhQgHUJtgEgRFHDJFXEEloH89zKrihizGponcYw1r52VbdHdbvsLC8y+o1n/y5D9qzBocCi5vXuxiMeKBy+o1QF+/I7SFtN7mLLvGxjgsK+44juWRVYPR9bAyzrv3S5QuHzTGkQPWB8fBOE++ibqRuvtyLgTKDV150UciC58g1QJQHosXeqyqt64wGh04PtqlkAfWufVTszE2/PATQ9BgTPywYBjiz2ayHirJKJzf8qmohl3+uKRS76tW+Fii3HDG2WYhsYsgdlQ4R/KJHyP11I2oHfZBVN7+bQyNaPj6lRxz5mj42lcZNI2sunZmeMsMlD78B9gzDkbmn5/FVe/9J44/Dvju9zgef4IEL4Igtg71NJ+ZY4851kli0jM8HJyZSX9KEzDGxczWtrlvFeQQcqjh6vLIUDDyGzlX6ywLPNrQarDxTd4MwbduiQhQXyvGn7wzUc6PWaiaLFSFegS303sWyVWXt8T4OMeypVxxB9s8rUSdkEaVEaVXmjWAg2FKeTlY94vY0B1OkOJNdgE0nFxMoPK6Ht6WT2jyEjd5cqsRJQBG189Y/Qh0KTac1yedPwzH2qGeG6N/6hHHaMLdxhX/uI34ftkMkgior39GDT4eUTV5EplOBddxrFgJPP9C84d3DTV0JrsMBzOoRsNGu2EsvQv6uqfROfSou6var2plaJterttGxsr7YSz/t2dxJPcHcEk04437RvA4sqgvF+HGlwr2kaBFGUf9mF2sOABtw3Ox60PVi6m2bEkX0tLrtB3jNbSWVwCc45nnHGtBsVPdMmpVC8WiiImnNWHp405ZCoWw4OqNA4FDNvomEIlk2eUeJv/iw8j2PBXYrlHhMWNH18Mw1jzmJ2YIbmBW/QyCdoziG3G6YfdUd1CaJLEryo0RUCy7tI0vguV7wDnHph4eEqyLBfF80Jx96lp2SWKXcr3rtLu8Rr723nOd3BgJYheBi6yLyWd/jdqRH0XltKthWgxXXsORzwM/+WErWltJ6NolyLSj9IHfwdrzeOTu/yq+/8Hb8JrXiAyNC18iwYsgiMmHc4hP8MWhutt1bwQ2dEMJ8K2+nPtLkpawBrMseBmbQscN/HbfWz2vBvllX03FFBDC1Dfsnh7upbn3XqRj3sK17gWRy2FVwTkH4+YEBZgJwLl3rnJsm+h3f/lNP1gMD7VBFEH30FKJe9kQdbuM0VFhGdTT629jmsLVqL/Pn6jUg0WFAa8WYCy9CyzvzL6dC60zLu23GUSod/KiWCu3CR1CNJruWDtszZhdmtMe8n+bRdVsAsHHrVpQC43dN7je6wfNeoHCt+bRgoJOE6ekjawBALDiIAwr2opL61sMbXS935/qYHQ97P3NGrVs4OS1viVghf6YRBX+qBAU5cN9RBIx0bhvJja+AC3fVz9jrar9R8Lgi7VBTzHmjYvhY7SVlqOttNwLej7mhh2M6xwOjzzG8cRTwONPRrsxuli2iD/niheFQvOZN6P6b0OrWKnzcw5odkVcg1rQpTC6nOERjg0bGndetxoh/cXyrblYTIB6rwbydQ3F7HILEQHqx8aFlZobMzNUQAOYZI0lHdQTIU0T0MY2glk15PMiZEEwAL1pAUuW+r+bcmNE8DzDW97/oGjz+NOJyDjbJCR2EcSOBreRfOhbSL5wM6rHfAyVU74BMA2/+o0I3njZVxgOPMDY3rUkJpNkDuX3/gLm/qeh9fFv4Rcf+RXmzOH46hUcS5eS4EUQxOTCAWi9r8BY91TdgLbuJE5+oQ1PlMUCjddg2AXh1hD0oQm8n3YOPoLW8ir/S7P70VYWyeQyAlYicnFubJQ16zR1v5jMi1q+N3I5s01nUtrcLL9Y5HjyKY5VXdEv6NWKhVVdkggHiMC8ssXJZvLSyyYWLVLFKFnQipsMlkvqb28uJTWo+yV/1JvwNhGzyxPKOAYGOVh5RJQruQnaNq+vsjSADXWBlYbDR48x1OFgyOd5fCy5QAG+taH4Q3cn7/XCvNWz7GoCxqRjT9CyS7Yaijp+veatJ5xE7VepcAwOxhfotpHGLEUSauoSS+KLZ4WTB5551vazw4Vm/41RLLskNMnFOLRuqAv6+mcbxshTLxVXXLvlbeXuodWpe/SnhKjtWN2t5Ps43J3cDqOeG+dysHOxkx3XrwPtIo/D9Sy7gveQaUb0M+k+7OvnGBxSBcOITWPhXiVEH0w4IirXAmaMMQVt3Ah0rRYfAha9wlGOCbrv5TMJqCnKmBNj2aVZ4ecuD3Z3r0AGgHthAAb6URc21h3ZcJGWXdx3Q5evU704mvm8X7xtq/VV6u66ZQbtm2Pafcmy4Dcu/wdZdhHErgK3kXrgaiQX/g+qr7sQ1ZO+CjCGhx7muP3PwAc/AJx2Kll07ZIYSZTf9UPUDn0/przwU9z80R+irY3j81/iWEKCF0EQkwi3JbeeuKBEMW5IQb1CfrE37KJ4YY55mXUXa2YebSX/87BrDVCtmF7w4gb+EQCE4LR8hbMo8CbP6llJ1AnEFBX4N4piUcSeWb06+nQH+mroWi3i6Ki1Vv8K/4ooa4BjdFT9Sl4shtvHtoXwt34D1OsXeQB/vfxW4U10nYX6xgV1XayYVM7Li0QQ6mpIpGBYsRIolSKuaYPJy8ZNws1SyUbJwy0ZFjeY0/bNWYF4bRHod8reMVWdNfjvusX39XG8/HJYhHQtu6Kq2CiuljxBXbM63GejXFiD61jMsZU6cY7ubo6FL0n17lkEY+ldAIRV5QYn9JvObHCmO2VHWPxFESF2bdwkRFeRqU5WcOJLNC2Onh6/jW3b317eTRtZ6/9wxq7+AY516+JNT+KEM2dtrPWf7J7NIpMzuARU/6iyYkRdGU2Z1Qf6G4dw8ayXpEPaVhtYETGGxrjZwReHm9FsOZf7oHuNfIGwt1cITvIRZMGkkdhlm75Swzlg2MKiy0pkY+uv7G+LNa64VIiJMW/L91GgXM4BDg3MCsfsYsNr0LLhoXAVQs889wCa+KbQ5FRA37gQ2mB8YoegZZdbrKV+XwIQ7FOC2YN3wXYKac6yS32XqDcyKCO49Cz23IAdZXHpMo5nn2uuQUjsIogdBauG1L2XI7HoL6i+/mJU3/RFgDGsWcvx7e9yHH4Y8OmLSOjapdEMVN72TVSPno/2xb/H7Wddg442C1/4EsfiJSR4EQQxOYiX9OatJbwX3oAvQvjrvB0pdrl6hlYZUesR+HrMuO2nP1csEILCm/gxOCQvaSJAPYDRUY4HHoJvNSKXytUX7HpNY0kfmiMFhVp4klMucdRM0fKbeoCRkXixg0mFLlnGsG59YL0bLDpiEuhmLOPxxSvlK9sGJjmsWhQuVnFI/aBYcssIH7FaDdakcb8bG+N4dTGwdFnooKjVOGzLT5UYNxFs6NoYmtBHt4uLvuI+JQFBM8dYuUqcv7CaC7eBzcMHqjextSz1fhgYmNj7gXe/aeHjBPuBvv5ZTFl/txI/R7aOWvQqMO6IARq3PLELgLBkbESE5Yub8a1QhDN4NB6r+nqB/gHJBQ/SBLlBNXp6JEvGCMS4wCMtYxh4yHLJra3SZnVidvmidB0RQFqV6lsIlEex6BUh8AW2jNxHG1wBo+thaL2vhMoO9t90uRvawPL6lYAah8yN4WRGCCZBbDsskvBgx3OX17HsqlYs4S4tJdoAVMs02xb9UvzSle3i+oUQSqPXya60bt1CgpAjstlaEiyif7PyqN+XlOOGGsXdA+FnYHCbAFEiW6Rll//DkmI3xrpoQpyvVhpy6lzne0VEQhGl8KhdlHvG70z+RzVxsPUb6t+zSn2b24wgiK1KrYT0nZ9BYsmdqLzpi6i+4TMAYygUOK74Okc2A1x7NYNhkNi1y8M0VE++HNXjP4UpK/6C2z94OTrba/jClzleXUyCF0EQkwBHE6EveMQLv2wNZDnF+BsxWM7EL3qsym16QpTiTqRHRJR2/x1fFtNUUSDqBT365b++acH4uKhzUfIiWb+Bo7dPfG8WLpihIFmxh4nLhM7N8CRnyVKOnh7/90svxxbfkCj3vMA0UREuKhWOl73A04EA/JJgF8y2FV8BJpUUu1HsLz+eUnwJUfHNxE42Hn4UWPCCpZyj3xcbiGpOEHMgLPpF9R9ZjGBWzUtAUP+6+eeVTkvnweX7RerHTfpr9fdz/OdhdaIXFfw8qiWCxeoRYpc839b6FoMVB5rRJUU9mAXO3DAbalBqbcNzSrt7+8hio7P5eF5Uslx2rE3cmyymHl2ruSS0qv/W2S20ZtErDZRTRK0Oi12Qr6vzL2P1ymxs2RU8vjayHj29Qmz0SlFT4UWeRl3h2qE9vzB6RVAYjqivbQsx1g3eH0X0uC3+kd3Ia7VoGyDbBoobN2DBA10YHubQ+peq682gyBnnkxddR8tyNw0/Z3TJytVW9RepWLHCYimRESSIFhOKxhGBgtkYuabeqMFxfqIoWQ657RVhSQHX3MMNDKiJDwBX7Br0ymLqwB6CBWpZz2o1VDf3mG6/IDdGgtgJKY8i87cLoK95DOXTrkXtuE8AEO4I37yOo3sjcM1VDNM6SejabWAM1Td+DpU3fwkta+7Cbe/9AmZMreALX+Z45VUSvAiC2HwYC3xYrfPiabGEukByjWPcdt7ppcm7Z9kVCJgt/T3atcqz3EgOCtc0P6uVHBws3kQgmO1MOUqdCaM9ZQ/nKzxX2mBkBOjrE6cif02uN9p6ll1xx4qa5HgFBybD9Y5jRa/146Jxv7w4wwCIL+GhLIURX+/diXuciIfSCFAelRbY4fpztWAelwZMbOT9tWQpj7S4C+GUMzrqT4ZDRXtiXLg8OYg5t01wzv1JVqgR5R8Ti6vlks2If8sBscs9VpSbXJzrXP+A+DcvuVaFYuQ1QLHsCqxjhT60FReDQ2Q6nRC2DduznhHup677rZbvgya7onqVkSbYbjEWkEyIX0XPuguR9zbnIrNpKRiPjkeLgOpG4UVx91vsXcrriF1+6Ki6Mbv83ZoXu6Im/RqzkDKHAYi7qhnDusgxtLmNla3dVaYpXJkfeUwa3wKnVc+yS+7LDz8qW++qx7bWLkRbaZmwYpUGq0x1o0gy4OwjhGS1CK+kmPb2xS4VNrwmcB5O3w41m1hua8nomF16IrKp7YDY5QvxjlwUUadajcOM7bOB+rsB6pXHgG+Za0vl1ItVyBi88Af1tpO+Eih1l8WuucN3QbP9rxlyeYqVtefGSGIXQexUsPFeZP48H1rvqyi/+ycwD/+gt+43v+d47HHgs59mOPIIErp2R2qvuxDlU69EdsPDuO2dF2OP6QV88SscC14kwYsgiM3D/0jceGJjsVRwofcX46Y3IXetcxhsZxITPUZxABsX+BNez43RmxSGhQBErIoqP+TGGIWRFGKXLG4EjsEiYnaF3EukumsR1jEAIrNwBS2P7MAkLFAbAMB/HvaXDA2pYpUrJMwduVttOg4wqxph5aLuL0+ltFoe0/LPSMHGo+oEGGufgLHmcfWcwmpX4LffP+K3EZk/V6+uu0lghV1nG3fT+hu8uJArcdXqFjgBV0MFpy1DboyS2im3qb9V+ABuAgHd05RsJKyxkLDB6/StepZdRvdzaK2sDluScC4E6Dr3F+Om4sa4dh2wbr3kMhxn0RKoF4dvDVc1GWo1ERuMx1ndxS5v2DlCS2ItU7kkFsjWJ4iZ8HNVUGesjhujU7DR9bCXyXbDBpF5r1YThbhZVD20gEsegHRpAzRe9Y7fjHaWWHEPslXVDTC+2YIFhi2BbBtqtkCERTfOAZTHAtctLHYBvlWnPHbYtuRmp0ERQDoLLyrjnW3LH0cCz5TlD0rCmFRfp3wp1QIAQO99VdnOjf0o6y89vRzFgiXKYEYoBiAA1dVXGZJ5YJmsSktWbtJOTzzJsSRCQ44iMs+D9AFLfs7VC+2mMYA7G+Tzros6kK2sR3J8jX88Hu4f4R9Ayhz0/lbELjlZjFfWxKUrErsIYjvBhtcg8+f/gja+EeUzfwNr/7d66/59H8cfbwXe/z7gA+8noWt3xjzyo6i883qke5/Draecj4PmDuJLl3I88igJXgRBTBzGgnPDeEsGznRw2cRHtuxyLHqYsx0gxa+ZwPDEhteipf8pr0xvuVxJHv1VW51HNCF2MR2a5lg9xGwWZSUjf/F2v75zG9CtIsCiy+JxUashTb6aCykE99y6N8pLrFAcL8XdZt3TMCr9MKy8P1GvM/nPFVciXRvwJxuBVw/L4igUHJHO5v4GPOwu5f8O+feEjpsyB9GZf97bSc5sZ0W06/g4R6HoT4wbtV2jeFqMW16MM1FF2dqqscjajOLlbjI0BCxaJFkOun0pah87UHdHBHG7lRsnKGkOQ+MWNhVmqO5BTWh2zL0RlCyo4prpvKzsw2Bh7si9mDX+aGy5zPYD1IP797N3K+iOpWiEO2MQzQ3ozhnWrWfoH5Bi3EnEieuyW+tE3ISjYg7GWdk5ayNidnFAciPkaODGKItijtWke6+Xy+J+29QT3Ck8hTfgx2liiIipGDquHRac6uwUdJ2W+6cbazFq2AsOyaxWRHLdY2gvLfGO51oxBbPheqJL8JrIhj5MU/q+ZykcVHwjzi0qNlkoZlfEM6Vc5l7Wx1IZePxJscOiV4AFL7pH1RFlDao8YzjAuRA1eWDsgW1KJymJz8EvGxNEHVd9y1wWu42K0Jf9sct1IZ5afBmpwVcDW8r/uj+DdfaPrFp2WeG/ybKLIHYOtN5XkfnT2YBZQenDf4A17zhv3SuvcnznexzHvhb43KdJ6CIA8+B3ofzenyE5ugq/Oe4cnHT4evy/qzj+8U8SvAiCmBiMOVOUJl8abZYEIEQGy/QnUIxbYg7AJbGLW5FujHFwDui9r0CvCF8VZXIf+LQsj3ZBASNbWY+kNQrOeTgGkwxjnkgQKVAhOhujXKS3X2kEs8ceQsLKQx9aAcNJb+9iRfoQqS//mzFPCbn7JKy8V1awvKkjz2LW2COoVEVzqvHVJNFDAyxdZCrzJhuBsl55FXjyaWDNWhE0vlRyJ6cRsYHiJjPq532v/plarzfJlSfLQRGhUuFYsxZYvFicbD0hy48F1px1jyzihtfG/KongkhmFHJfGxsLT1rd22VggPvZSOUVtTKMlQ9AG1jhtY+7yhWl1vS0eC6O4TpGL9N0oDP/PIyVD3jrbF1Yc+p2CYWCEDiFxaO4GIYVL1RxWAgFAZfRDLDhNcKCqTRSt66yUGXZ9dxgo90VxaYN1OSI8kT7xlznCMsujZtgxUEEdwiKTU25MQK+668kSEZew4hdNZjKFo3FLvUjAmu0X0jVjrd4VZYFdqtVaigUhVDrF62Oay7utU/Yvt+ubauB8ME0ZayQY0DK7qxRloEwq0BNFXYtW1guMa891ROoVDhWrARWd/kHLZWAp552RTUbxRIk4TdC7JEW9feLJBxuFlvOATa2MeBG3MT1bEC0ZVe0tW0990Tbhif0BVG/DUjPCOWQwVFVVGzu8F3IDb/kW4pz9cOAaXHfJX4CkNhFENsYfd0zyPzvx4FkFqWP/A/sGYd463p6OC7/OsesWSJOFwWkJ1ysfU9C6UM3Qa+O4foDzsaH37gY3/sBx81/UIPAEgRB1IMFLZHixg8u3BNtlkCpLL7cr1gpiSVcCg7uTXBtZ2Ljb1etciVrYuAQ4l/vS7ycCVF9CY+ysHGXpR03COG6FP+WzpnmhHeJdmMEj3NjjLAaMMUESedlGIPLMWP8abWo+jnZpbOIuQRcEkOkL9+u2BEVoD6u/L4+C2vWAQi6EXoCD2A78dlcjTE4QXWv4bij6dU8C6yIINKK6ZBfk/pWVmKdHHspaNXhNmm2uglzR+4WbnMNm8E/ZqnEsaqLK9fTnVDVuQ08kqVwcO8ooaVQEPGqAJHhTO0KzjUtjSIzsBCACMheqwnrHddChgPImP1CXLKE5RUb34SaKSqVGFoCza54/YBDV4TCeq8F7ulrDEjVVDcubogAY4ZVRNdqoGu1qEyUCBwuWFh2cTDHvVW9T7lmgJWEwMFqfoYIzjlGRtz4SuJ8dN3Zl/m9JqqtbRuRAjf3tSml17kibXiNIMqyy6WltAq6VUDXamBoWGzTXnoVLf1PB0RADnnM4hzQ6rkxBlI3yDVj1fFIrcSOmMIzWxW7Gg5Bofh0dsiyqh7BGcqs0YfQMvS8Uk8gLIBZNkO36jnpi12Ba+zumzRHlHJdMca2ATCmHMM36HLawC07okFYrQhj1YPqMU2OOSP3IVEd8guULoIvOKvl5Z0uwGCjpwfg7jUKtbP/7LRsPw5frSqJXYp7JVO6qvrO3+D9v1rwxDxvOK5I2S2UU2ssdnmW4baNGeNPYs7IfYFzk/+WboDY+ssVA5LFDZ41udwXV660HHdNErsIYodGX/kA0nd8AvaUuSh95H/AO/by1hWLHJd9jcM0geuvY5jSSkIXoWLPPhLFs24DEmlcNuPj+OzpT+K3v+e47rt+XAeCIIh6aJ6lgPv1NHpGJCx/GDj8r+alom+toMF03BhtgDExweW2ECi8Sa4aDyluAm5zIGGOorPwords3ToTA4PRL8vBSbSyuIEbo2ulERuzC74fpi/GhcUu25ttuS/kAYugujNNv7zBQe4JSEGiinDjo8RN/JWjOIdprazGnJF76woWrujjWkyEgkoHJj92HbfRCOkhtIYhYFzoVLYsedS5TRxpkQHAsEsxUz1ZzPL33dQjAp6XSkKQ6unloXaMC2qeNEeQGXrJ+60NLHcmbRzVqi+icM7x5NMixlKlwvHMc0BPr1S+23e7Fyp175W2AUSzTi0sxMyxR3wRg9uwTCBlDiA13oX20hJvQsiZocRZaySkAr4rpDz55K5lF1cjvjNeJ+GCt43pT/AhiU1u8XKAO+niDw+LBApiHzuwLwPnoswlSyyUywGrEA4osaMgsquWywj1zaEhjpWrhJWqbXPkx8PX2rJFXLogI4NlTCksxfSCyMbnijWGVcDoKKDZZT+wuFMx2YuOwQa4henjTyIhCTdK+wTaJVvtRnr9o6G4UmI4DFvQKZZdXIwH+Xw46UO1xrF8BUelop5/whpHuhZhHhiLur9hF5Es+x3ZGytjh0Kp37ndQnFdMxV3OjnPhSs42U62TsX6VhZWlKOJXyMjqPvOzK1aIFkKD5QUrmugBPFfNzNpcDupQfJ5OXalUJ64zVXXYsaEOOx8YJFPNm6mqA2vARvfBKPrYU/Mc9svt/ExqaqW/9FDul/ivPB1TbQvt22k7WFodccFUXDa7Iv8WOX/8scM0Raib2tKX5Cuh1UFs32X3UaQ2EUQ2whj8Z1I//PzsGcejtKH/wDeMsNbZ5ocV13DsWYN8M2rGebNJaGLiIZ37IPSWbfDbt8L5yUvwvfO/hfuvhf4wpd5ZDwLgiAIGebNNz0lJ35jDnB5IiFlbkqYY04RwgKMMw2MW4pl19q1vjgDf7H/U9KyZDcVAFi31saLC8XffX228vJdNxZPAzdGMfGMt3rQQgGSOUwLSFhjmDt8F3hxBIAkdklf6ZVqRJTv1lt+wi9brmbWk4n6ul5zLbvqZJoL1qettAwatxTrE8alLIqST6M3QQ1eq+AxbD/boWpoYEMb26hsG4qdE1FJt01sG55VlB97x1Z+uxh2EY2J7g9dq4X7kCc+RUz45OWarbo6aQMroOc3efuvXCWWu5ZpI6N+cG0ZBgvaxhcDGS0BPSZ2OwMHLHETFYs2qjXJhRi2b9nFdKVj1bXsck7RF7vgdVibh0VwDkDnEScTPIhlOhaCboyhqPvU/eEHD1Qm1s4yt27Fop8hkXEeGadOmaTXhEvYwpfDloRuFthqTQTOf/Z5Hsq+aEVM8vMFYPlyIQRrgex6rquaBtOrM9wWkLwoNWYjaY0jZQ6jvbRYPYAkcPmJNsQHAABAJe+U6ddV63s1ZJUnCw+uG+XqNcDyFerhhoaAcoVh0ybV1bK1shqdhQXhBogjyqIuwsJKcecMTG+8c4pwY9xj5N9IlkLBymDb/tgoxLA4N0b4QmNgRbAfqeWr/dK2AVT9scYXNWM+FLljSqwbY0QGW8B7dmbHlkBzBE7b5nj2eaB/gGNK7+POcVUJLwjnHBs3clQ3LPMXSnHy5D3yeQuLXg2vqcVoWO77A7dtpb/7x5Z/2NCtAjqKamD/eklSo6xg1Z04jBX3Y4/R++MLCUBiF0FsA4yX/4LUvV+FtefxKH3gN0B6ireOc44f/Eh8CfzKFxleewwJXUR9eG66iPU291icNn4Z/ueim7F4CfDJiznWrCXBiyCIeORJWF04d2NV+5YgjHt/t5WXI1ta47zYMwC6ED4kt5HxgIgTHJ0qVWDRKxz5PKAFvtS6E4lSiWPxkmCGLzcGS6jKXoDnwSGOlasCGzA/co77Uu0GXfc24aYipi1fASxaxJGuOhYL484E05ldsRixy7ZsP2OeU5g3GY1RIgpFJlkchANfA/4kJGhVwCOKjRM8MrVeGHZBXR803YoUEv1tFClEnjCXV3muaiE3RqnjhSeKYpqfNEdgbVwKQI7LE30iul1sNjycKCWyGHGQtevc30HzOEgWfwFsM1Q32Q2TMUmYddoiZQ4JMdC2lLrrAUMdpa5mFb19HCuWO33OE2uZVy+b6ZFh+CIvo1Q/F637BRGs26mUEi+HCys6mVqNY/0G/wQ0uwrYphf7LVLskjsp07w+J9fRrZLmBHTv6YUXx4zBCp0j5+rkXxVpo8cJwHfHDd5jlo2Qm5VlyscIuNk51juMm1LdOOTA3wCgwYYtxTZUzqGOaxbnkBQ4WUkCpuefRVvRF85UF2weEqy9unhGmRO4eSKItC6Vjmnb4iOBEuNMC56vKoQHrU+VzJe+Dqi4SPI4N0a4yUWkjR1Mbyjm6OkJWL85ncJNXMFqRRirHwmVH2XZZVgFLyOmd70LAzCW3gVUnE4nWVPJcEeYS5b8jwWmJdpraBBgdsWrcz1MU7idr1zFUChwlEpcxMlzjyO11bq1/t/+Bwf52aP2Ta2B2CWjb3wRM8eFQLd+g38fR1rqSh9b3LFSiTHpKccTn+OQ2EUQW5nECzcj/cCVsPZ7C8rv/TmQyCrrb7oF+OddwPnnMrzrDBK6iCZJtaD8/l+hdtAZOLT7e7jzoutRLdv41MUczz1PghdBENGIyYYEt5GrrItwU+LO/6TQyoEv0kZtGPAsuxiiYnYFDqXQJ4VAkrO/jY9z32IlojwGJzNbHXFn40ZVeBA7OlZq3Lfs6lqt7s+4qbx4A0C271lPqLI1EdvKSzsviUSWlgY30sKtk3NFwGgtr6wb3FsUxjwLIfn48kt/nNjl1r/eb1mw0ayS136VClAuBybxgX0T5ohq9SbVTT5OUxZAYmHod64FmDH+BHjfKsCsINvzJDS7EusHVb893QlTRJwe6VUrGO/J/a2ZkhWcVUVnYWGEgYYdKt/NTKYpwodPwor2WY1zQXWP39cni4VSBjVuO+KBpk5LI6xq2PgmcRO64rU0C9TyfdAGVnjr5POy7bBl2/oNwNJlfuGueFrTct782BP6lEpJFYp0R3Ysu9wYV4z5FpHcjhS7EKird76Ihkn/BsUuziO6K/OPEU5gIBpR4zXlmsv3hStiuEfWgsKpfI0CAiHnwmU2V1kjxWfz17dWpAHMqinlBPVrQIytwmKJhW/yzYTxGhKWiAMlt3+6uAYzxx6DXvYD+Gu6bOEq9Qf3vgs8hyLdzW1J7HLcGBWxyxYfSTjn6FrtJ7JQYi86fxaLImbW8y8IC0LL4iFhm1XHRXD0wHMhJIBzG7PGHsaU8krx03HHY+NCvPISGcRYdokPRWGxnYnUrDFtEn8NTZOhazWUZ0pwD67cAKLgOKsuAF7MLs55tGVX4LcrXpZKUjzCyH4XYb2ruGu6Qv/EIbGLILYWnCPx1M+QeuS7qB10Bsrv+jFgpJRN/vkvjt/fzPHuM4DzPr59qknsxOhJVE6/HtXXnotZq2/BHed8FXNnV/GlSzlu+xMFricIIgI3ZhcXsf7ssX50FBehrbRU2axU5qhUmRCIvJd71e2FK9NGoKWyDlqxP/bra70RSbbsWrNWnUiEXnA5x/oNjV3tQjA30G98zC55Iuqed9Ia9UQcCwaKRQ6z5rrGyJMFDt4yQwTpDlh2ZWq9ynZRcDDJpUv+uu5v7y4LT7Qii1QIumi6u4yMREziA+0zc/wJxXLEi9nlllIcQqraG451Y1Z8N7RAgHxFdAJHa4v4e3AI0IbXQisPI1dZB8uKc2MsNRGEO9ww6nHVAhhssEI/pg8/jEx1IziAVNGPGr9iJcfoqCjTGF0bEh1dVzmbM5TKzk7wRTRdEo3cmmkaIoUJD8d1zj2Wxmu+ZRe3vKDw8pmmBxep51Xoh969ANrACn+yHpHkkJVHQu1imr5ll3vPB939DKsAbgOmloUXoN7d3508cz9barViw7MUlI/vWpYxta84DRFygwtZdkmWpeBcyWYY7gkR1pMRYhdjsmVJYHPHskvjppP8QqqYXAZs+H0hcFC5QyrWj75YmK71KwePut2VAPWcR8ZdWrPWTzbR7Dti3HZuH5mWfw4zxx5zD+uhOYHQ5WQEuqZu5Ln8WSZ0q1hX7JL7q1uEZSMkdm3qEQLP8Iiw8HU3tkzVMk6mWAL+8zAwOqpaoAIAH+vDkiXC7Vk5/8B1TFpjym8vS7FTOVbJiyQTVRubNiGEzV3La8luljt9S+7HSlAy8bdp+u/83t3Co6Uh+fRsS37OumX56zVeRVp6dmlOzC7YFjSGsDVpgy5Vq4VjyImInxEfJaRlZNlFEDsanCP56PeReupG1A77ICrv/C6gJ5RNHn+S4/s/5HjD64EvfYGBbUY6VYIA01A96TJUTrwUrWvuwh9PuQDvPnkAP/8lx5cvzaNYJMGLIAgfN0C9aXIsXQasWi4m30H3kWXLHIstybKLBVzWhJ8jBxiD7rhYpMZWxAa9r/eeGrIIcmO4iGjPwZIiy6iUgbXr1Gx7URM1N0RVcB3XDEXQkde6IoVl63jiKWBk2M3iF+XGyGBz1TXNtQgTa5VGlP6WXss5h1WzAM4xpdVf7IldEW0cEn6Ck3bp3LysWn6NA7tGtZvq2ubVozIOY91TmDr2PHKV9X5MqOE1MFY+gMVLwhUKZdDjHC0tgGEAY2NArWZ7QoZtRV9vX/SJR27r5uZJNlhpBOBAwsqLfZxJq82FmLWhG+jp5ejvzntWHC6yeDI4COmUI6QWcetA06AE4g5hVp1zsZGtrIfGTdF/wZzrpiGYkc7Ir1MP65QBs+wtcvunUjMnMLY8kbcsQHfFLuYHF0uZQ97fOhfJAiwt45+c08E29YhA6a6SVCxyPPOsjd6esMLnXi9NWea6VtpYtRrYsCEobgUtu3wrLFckqFQ4xsaC5xsWuyJFJGmNfJ90TmV+5jhuSbGc3HP1m0JjtmShVuegTic1qkO+yMADgoDftLBZ0t81IFzUIsQu9bjNuTEuXwGsWRPRMk5dU+awt0jNCMq8zdK1PkzLP+NYA7llce9vY3QtZo89FJkN10WW1917uVYDBgaj723TdNtNrLSlm8yWro3MCy8CwYDyZlX8HnX6j3ufBS1Lk1I7uM9OAOCOCKmNrIXevQDr1sS4vrt9l6tiV/DDhjweuue2ZCnQ7Xo/xoxzUaIvl1V2VxSUFqXMIUzLP+99jPLcGAPWZs2ydBnQtTr8PFefB55DpbSEh5Y1C4ldBDHZcBupB69G8oXfo3r0fFROu0bY7UoseJHjG1dyHHgQcPWVDIZBQhexZdSOPQ+ld/0ExsASXDn7I/jGBUtw3wNVfOoSrrwYEgSxe6Np4qV6ZET8rhTFi7gXTNel5vsAesIG44F5mZjAyTFYxgsJLFuuCk7BciLrZatil5dljgPy63lH4SXkqusjy9u4UQglsvuiuo30ymxHuI5pCTFp9dxepPo5cVjY+gVImsO+W4VtYVVX4HgM4JYNQxa7mPzBKzxZcX/5m9hId92LKeVlaMn528SlhI+cPNURuzhX1wfFM9sChoZ9KyaxjX/w0VHXGonDKAdMHmKoH1iZI5kE9pwnfuULbvuL7GTj4zzklhrM1qaWpk6YmrViEdqqBQ4/HpMr8LgZExkTVh75PELuv5wDqSSQzXBPpIg+X3+RpoWvq+rO46+cWnwZSXNUWKDAciy7NACsvnbhrfS3C4RUU5Cvdbnsu5e54g6YiBnlujcyzoW44dQlSLHoCpzcs3gbG/Xd8qQzBwBommTJ4Qrfzj0nBxcXt5sqfsltzpgYD5av8IUYuSs048YojhFu3OnTNd/tlZu+1Rl33BiVQnzfu7iYXZbFsWKljVqNo3P0KZFt1KtP0LUbznGraC07fmqSZZemRVt2qVWKdqcDRCzFqiUGsGpVxF9c9Ipj7euQsoY990WvTKlAT1CyOTrzLyBdG1BEP/fse3p8l0+d18my5xx6fEzV6Vav4cL1Lvjxwnt2uGJXWPiOGhYaWbm6u6TN+AQBwhzQFbvUg1TK8WacLGBayDmcOJKqeBiF+0z3zk0yNeScw3ISfyjW2RGWXe76TCZ8bprmtIct7q2gnUY5wovdKz/GtVv06+jxkfEa2oqLkakJU7jNiTNHYhdBTCa2idS9lyPx8p9RPf5TqJ58eWgkWLyE47IrOObNA37wXYZMhoQuYnKwDnwbSmfdDjAd7x8+B3/9fw9haAi48L85nnyKBC+CIOCYNfmT6VTCEbsCqewNuwQwx3LBnZQH3BiFQKWOLbX8OEYGK+F4WYjOdOYiW3bZzFDFF+mgueoGz5Ig+NIf55rowdyJvnBjdFdV9Taxv5FzLNzCYpdr2VUqA+3FV32XQCU9uiP8MQ22rcbsihO7FDsT6X2BVfKwbSBb3QRDytRXz92t3gQOCMcKUtzXQtYmHN3dQlhw3U7kbYpF5kxsODYsH4apZ2FrSQQxZassHn3egOiWM6YD6bT4XchzT+yyTBtr1gK9fQjsIyb0mqbGn5KLlvSH5iy7uO11VM50sV9ACFZf69SplG2LujBwJX5dMDOgu8az7Ao0/+hooE7yEZ17JVvdhFx1g+fGWLf/cxucc6xeyzwLFV0LbCOfY8DCxp/Iq++srjjBYMH22oKFtnSvpXvvMXAYeli88dwYI6w6osQeUWTAsktyeYp6w/bE+wjLLkT0E25HW1LqujOYwmkfZTKvxrKTLYwYJBdO+N11cAjYtEm4aAfrqm6pLk+Zg2Dc9ATd/fcDDI17sZeq+pRQ3UUZ9W+IvuTRoW3Gi/690Fru8twXo7Bt2SZOIK65Kvb3D/iulXoTGVbH83LyCnFtKlWAQe2zlgWnobjzu7n34KCVc0iWt4WQlLRGA2skiyxonojJA4N2tVpHsJEDksERb7kFMHj3l/ohKbpPAOqY07UaGB4Ouw2rMbvU5968ufCePe6Yw5iwQi3khSVj0K14cFBkjozCHTfDcQuDboz+fZIyh9FaWe3f+8pmTV7PprYiCKIxtonUPZciseROVN78JVTf+LmQ0LWqi+PLl3F0TgV++D2GKVNI6CImF3v6QSie/b+wZx2OfV/8NO741Pew5x41XHYFx023RFtbEASx+8CcuMTei7/tTuzjXgk1yQKIKy/KOq/CM2VyMOwSWiprUY34QF+tE/hWjtnFmS5ckOwqUB6LFAqAsLjlToBk4UPes1rhGBkVSy3Lf3Huaz0BG9tOg5mdAY3XPJdM1YJCxE1xYzK5wk/ISoMDwnpGFbsUMZEDraUVjhuMosZJlR2HbQOmloEOv+HqubtFeAYqBAWMuIk0oE7Q3ElzKLA2gFy1G6lqL/oGk77bjlRUTeoHkdm1HA47hMPQOXSdwTAAY3gVLEtsZ5rxJ22aYesCpb9MJF0jhGjBxrodkctVGWNMOyKwbceog3FJ4FHr4T6HFTdGqWlrNe5N/oGwy2cwrpFod4aIUD7KVrYN9PUzrF7jlBtxy3suqNxULBOD/dy3CvNdDC0esA4NWFy5/meuIOVmXFTFSQ7GTWQKXd5vf/IrWXuWhkWGu+IwgsH05QDoZoQ47F0KzhWrTLcqIXEjWEkHIWq644DpjTuGXYSSuIG72/llTC2+HCrPrIk6Jw0eUQnJsgsICISWI3YBuRyQyTDoum/ZVUjNCx1L+eIRw3hRC48pkhtrI9ziVbc7G2G10hfxjSbELrlsl0rF76OmnkMhOVeMH+De8opkdRT6MMA5Zo8+gCml5SHLLvlY1aoQsU0tC8ZtJXGDOqbJbozBrzL1Lbt4QOzSnCyktvMMkddPzz8bW5bczsKy0sncKlfHjhC73I9bcgKLgBujJ+ZGTGMLeWBDN8fIqHrerjA2xbVElI8bOU6Hl8vPpaC4GQeJXQQxGdgWUvdegcSye1A5+XLUXndhaJMVKzg++3mOdBr48Q8YOjtJ6CK2EpkOlD7wO2gnfAIdS27GLW85Hx95Rw9+dxPHpZdzjI2R4EUQOzsbNmzAFVdcgVNOOQVHHHEE3vrWt+KnP/0pqlEqk4Qf5DcgdklijP8y7dgjuJYQThap0fSBqOktQhiA6sboEjXJdCcb7e0R9ZK/UEMD4xam559FYu1jSiwUmWY8GkZGgEJBlL3gJYZazT+WqecwmDsGYDpsLQlLFxHSDTsv6hEYKlMp9xz8jF1qPBXn7Z9p0M0xtI8+71lwyOfXVl6OtvJyTMs/o35ll9qRmRXYXMRMyY2+6i2Pc2N0j16PaGEOEecBpXH9OGFuX/Hrma71C1HBZt5EWC5X7Y7xNWSMexZVbgwrPxh//H5iQhsWvHzrIl9Ycoup12+mlFf6gb6dzIUssIPctxMB6w7Xssud2PmWSv45vLoYGBhwykZY7Ar2u2pFvTZ6wOV35ixduU9FIco/AOdenC+XqGxq3jqYSqtr0rVfvUZKTeH1KdtzswLcAPXSpJ27tXEtu2wYWlRnttFa7pJq6Ys83mTdBlhJjAlaoVfdWxYYG4rkHON5dZ0UasxfFmPZxdzgfxDuhG4f7Cy8iJQ5pMRIYoGJu+r+J3YUMbZsGIbqXijiNkUH8RZ7214cNxdd51JWPVeElkUnR1SpM2iUyyycBCTo7l4Ht53lek0dew7Z2kZ/AVcFlmDfBqTYcjF1ZbBRLvvXaCy1P6pGOywL6Cy8gEytL3pHqcy02QfdrmBKeUXsOFkuA8uWC3d5Nzadm7hB1EN6hjHNU5OXL7ewqYdHbhdRI/WZwOG7MTr9pNFHaz9bZNAi1FTOB4DiFhi0nJTFcNeCUx7XZs5Q7wEXywaGh4H166ESM+1lCH4I4d4xQm0li10RGYmjILGLILYUbiN1//9DYuk/UTnxK6gd87HQJkuXcXz2ixzZLHDjTxhmzSKhi9jK6Anop1+L0rt+DGNwGS6d8kH88KIn8cIC4IJPciVlOEEQOx9dXV3gnOOaa67BXXfdhcsvvxx/+tOf8KMf/ajufm7AZu+d0UmVJk+C/UmaCMrhZ28TboxVowMVY5ofayMiwUqUy6JraTB7dv1zcy273Amh+6KdTgHJsKdcCCUz1ybhwgEA1Zqr9IkNypm5KCX9ypi6iASfsMQMOB+YCOu6aBudVzzrGnUS7M4SAKM6inStF0lzxK1VqJ4ar6ov8wFTG9dKRV4cJ9RwIDxJDwzzoWyM0vahDGTSgfIFv74AwFlCcbkEAMvSQvUHVEs0+RjBSYwsBmhMnKdpAolE3Fd/gWlC6Itq7UN/T/iJx8VkUWg08ceXJ7uAaHNNc8S72DoBw8PejDQkdgWv8aJXgpNW1bJrSpuGtinRboyiTkJkEtZq/jVKJcM1KzqnwwLiiQ8T94UXjN237LIDrtDyNbac+G6uZRcDh675lkpSZQGlz/uimTtZr1SBZSsNMek3q2HLrghXSJm6InmEwROPKYsxf7nGa8owmKus9QVHpy8pGSq1nHrKcKwUOUe6f6Gyjjuit+f+Fagj4xY0XvMsBQHhxujXXwPnPHw97YhlEiIOlrpsQpZdPCzOGLURtFRE8gRZwFPiIybUQd4Vu2wbSKi5vpxK2ahWJWGbCfnEstTg+fVImkK0Liemh2N2RbSR6YhdqttlYD9XxKzaGBjwl9cTaRi3FYHR5k5/ZoDtWE6adSykVQJjbETwf26ZALfQVlwMnVcwMOhnKpazLbpujP59byGZdNcHxK6Y04v9UMPjsjGqgjkA2NKLRdDdNA4SuwhiS+A2Ug9chcSrd6Dyxs+jduz5oU0WL+H4/Bc5WluBG37MMGc2CV3EtsM68O0onv1X8JaZeEv3J3HnJT+EgSou/jTHP/9FghdB7KyceOKJuO666/CmN70J8+bNw6mnnorzzz8f9913X939GBMWILbzBu+KGtETQycbo6vhcNuzDhEv8ra3XZC4TGC6Dhi6FOQ4Jf41lDkUUwLFe1ZUGtA5te7pxSJiR6nWPqap1tvU0pFWanLdAWF94IpYIddAAIAG2xYxkWQxwJ0guQQD8waPLU86mkG1BgmP79F1ddZJ11/XAVtyHRzy4ukIscuOmPCatgY7wuoj3lUyQuxyW89JomBZQC4Tb9Hile9f2vjj1DdiURgedoOLc0+oaBbb4o5ll9t31X9ddMO3ZgkaR0w0BjPTdDBNFbvkownRwvZqAojg065w4Gp5pRJHn2ME44onDY/NfQtHmzsWRMyJZSUVYJrOgTw3xuiA6wy2I3a7v/1+IYsEfYO6sLK0qoqIKltugot37yCuAKvbZXQUXlLWcQT7rNs+EfcT84U1jddUty9eQ1XzDx6819X4dsyre8Iag17oiY2/x5kWqgmD7YmTbrtpstjFYoRQu75YYJphMczkjcWu1vJKzB59QKq0959YlBhkGR2HH8a8+H2GZNkVZY3IwB33clc8FZaOjQL0y8f1hHywBu7egmJyD1E3q4jp409i+viTSj/k8MV/d7llcVRrYQEnUCPpuBwDA74bqvt88GIoNnguhD8oyElfAJ7IQoOFlDmE1spqTC0sxKZNUptozO9PthOgXk7CwFTB1yXOuLyeZZ78QWGfvcWGSXMolLCAKZZdJHYRxNaFcyT/8y0kFv0vqq+/BLXj/zu0yUsvc3zhyxzt7cCNPyaLLmL7wDv2Rumjt8M88iOY3fU7/N/bP4J3v24pvvt9juuut70HJ0EQOzfj4+Noa2uru437IdZ9Z/SstrjlvY0qbg6S2NU6utCJ0yEC1zNuO66NGoazhynHiZtoGIG50owZwMGvUUUs4Srj18Wz1GhW9IkY0ixTFpOixS7RFnXEroi3ZjeOlcZNaNzEy4uYk21OTcTMwEOWT1FfrkU9uFdno1mvoUBR3RvDm9SLRSNPNoSoF2WJ5n7dD7fReIGhXHVjykjFxj5eAisY9+rANNF/OAcy6cbKT9BllknWhq5LjAi037AoAECh6FbJ7YMTELvsoLVDtGqhO12csXAGM8tW29fPuOhT0yUVR9Og6Sy+rXsWQ+9f5nhlinKmtIYvozxJdYXtRvgxq2zUrPhppew66Lox8kixiwvrIXlS7fRFwy5i+vhTaC2vQtIcEfuZVYT7KkfCHEVrZTVmzgDmzImuS0fpFeSqG9Q9JaspL8A4wsIBID4cKJZd8jpuwoLhFhSy9FOFEQbOfbfDerH3SomZyjJNE8fSeE20q3MJdD0gZdvhcjPjy0PnJGOaYeG1e5OOoaH6HaOjssxxR3Subx31NirzrZfF0D1HaQyUx2A5u1+5IrWpkxG0GbEr6D4p4p/Vt+wayRyCqjEVppZFws4jZQ4jZQ6HrHS5J7OI8rpWA8uWIVS+jBzrbXhYZBIdzRyMSnqOFPsr/JyOrK9zHPeDkisWcw7YnQegd6wVjJshQckfw+Ddhzovu6fl1dPLxtigvRojPp+1tAB77QkkDEfsssbQXnxV3VKy7GrWjbF5W0SCIHw4R/KR7yD50m2oHvdJVE+4JLTJk09xfP1KjjmzRYyuadNI6CK2I4k0KqdeCXO/U5H699fw9RkfwSkf+Qw++5fzsGKFjmuvBvaYQ32UIHZW1q5di1tvvRWXXXZZ3e0YA5KpFHJaFbVUCm4+MTOThpnLIlVKIZszkE6Z4KkMLD2LFEujlhJfz1MpIJvNIVVtQZonoBspWHoG5SmHIGWt8I5jGAypVPitN5thyOUMpFJiZtfaomPKFA2lkoVUyhE7ElmAMSS1FLJZHZl0EimWQibNkM1qSKXqv+RmMnpom0RCRzabQaqWwh7mq9BSKSRTLcjlfHeilhYDpXQm9otxrkVDseS/2GsaoGsJGHrKW1YzshgZzSGVqqK1RUfOTsNI5ZCxkjCsDGZMsZTJYjaTRoqL/TUji4RZA6Ajl9OQSJrI5oDp03X0D5jetQKAVMo/JgBksjrA4Z13sehPcqJgENnk3Guk6wkYVsppB4Z8PnzteEpDCilxvtyALlsSGVkYBkdKG0cmK+oPAIWC7V+LZBIdiSJMoxVZO4WUlvImuW1TWtHelgbP5ZDLmcjnOVIpYMb0HPrKaaRq6snYWsKzNgCAZII5VhOAmU4jYWaQrJWQyaSRslMwTb89ovqHSyLBkMqlkKqUkU2n0No6BUY1A55ON5zF5XIJ5HgOuqGjBRmUx1Kw0mnUcjlkqkkYpn8Otg2kM2mYFjB3roauLr8+6UzCuz8AQDdS0Gw3+6mglt0Hll1BtrwGU9raMIW3IDGSRi4npnb5goWcIfp2ttQLzjOwbSCTzSLRnsORRyZQLHIUU+IeS6c1VGt2w3vL0NPQLQuZDINlceQyaWjpHDJmEoxryOVySJXTQDINns4iVU1huO31aK0swJTWVnBWQjJZRcZIoyXbAnssA123vX6YzaShcQ25bBaplAU7nYFucyQ10XYpFDDFXi22zWaR0BLIZdJISW2by6ZxzJzlKGxKY2qnDgxx5bySSdHvOTMQ9DZNpxnSGTHGuIJ+KsWQtpPQI3yz0+kUDFOIBdmcOj5UkznkcjWkUibS6QRyht+PeSrpjT25bA75kSpSKRtmJoNMRpy7rov72tD9+9pOtyCbySKZFMsMA9DtBHKZJFIQY2QuZ2A6cugdFsfKZHPIZHIwDCCV8s8hZ5jieqXU8zrwAB3DI8CIPQdZ5zq42OkWVKujyhgeJJlJolqqAkmOFFLQ0mkk0imAc2TSDDXnPjX0FGyWQEJPIZVknjVqriWLXI4hmRSZcVta/H0yWQbLdscsYf2pJVJIp3NI6BWkqilksjlotoGEVWcAhLCcyqTF+bGEhqSWAkukYCSS3pgMiP4iW8pmsjnwTA66NQOtVh4G869pyvlb19PI5lqQqqTEQ5cbwh01BaffRQvDLWkNWjWFnl4GXQdSKY5cLocMa0WiUkQumYNhmqLcCMbzGlpyTPQNLQndttHRwTA8zJHLJGEks8jU0pjS1oZ1XUAmVQVPMeX+SSYZMlmOXC6BdNoEYxwsASRZCtmM5j2nczkD6bSFlmwGtpZGqqjWSdfqJ1RxyWYyMPU09puVRSajoVTRkEpFf5nIZg2kUgypVA0tmSZiGoDELoKYOJwj+dgPkFzwB1Rfex6qb/x86PPYvfdxXPcdjgMPAr7/HYa2NhIRiB0Da+83ofixfyD1n2/ijct+hAf/62Fc/MC3cMEn98KVXwdOeD31VYLYnnz/+9/Hb37zm7rb3H333dhvv/283729vbjwwgvxjne8Ax/+8IfrH4ABpVIFmqZaupRRQAF5VCoVFPIVlCtA2S6hphsosrKybaFYgmWWkahUUDOLMDUNBb2AirSRZUVbdxkGUCgwz6K0XBaiS7HEvWOU7aqwZjErKBSAcqkIVqvAMIBSubGFTqEQ3mZsDCgWS8hVKgDEypHRAgqFgrfNyDBQrlRiY4FUq2q5hgFU7AIsSYQosTLG7TKqlQoqFaBUzKNkFpAuFaHbVbA2tS1LKMJwFlTNCmyrglcXA3vvJc7DNIUF2oEHAKvXcOTzQuiqBE6wWBRaTLPWS4DIjumKTaame+eRTkWXU7VGwcwKTE0Hg6UEky7xKgAOVq2gWAAShniWFArydS0hN/4QTD2Hkt4BvVbx9KPxsVEYdh/0QgGVCkex6LZ5HqViFtlAhWp6AgnLX2ZzhlqVO21agmWVwc0KyloRqcC+Uf3DhXNgvJJBuVxBiRcwNjqGVH4cpUpNiXmWSEAKAO6WW0VxaCOm2BtRNUdQqVRQRhEFrYCWckmtr5VGpSL6gqEzxcJ6vGCjUvFj6ZlmCYzbMKT2LmgmNG5Br1SQLxZRLBWRLJVRKIh2LxY5ClXRt8f1Itasda5Tuoh9DyuA2wxjYxyVSgXFImBZDEXpWsVhaQy6Le7FSgUo6+MoWAVkSwWnDQqYUilDLy1DzcyI8aRYQqlWwdjYGFh+DMViGWW7gPExG+sXq9n3SsU8NF5DKV0U5fMidLsIboYrVigANbuMUnEm0lLFi4U8LLOIzs4KSkWgXFLPS9fd6x8us1QS2eQqFWEFx2CL7cs8Inh6AgfsW8LSRWJ5OTA2lewaCoUCSmUO6FUUqwVkKxVwaCjbRRR0p80y49i4vgTTgugvxSIqZWGxWMjDa2sAKKKKQrEI0xTLbBuomjWUMYZSpQJDF+Nra0seU/YX42d3fwmjowWk0mr9isXoe8G2gZFZpyG/qoqxsaK6D2oommVlDA/C0klUKhWY5hBgVVDO55EqV8DAUUr4xzM1HZzVYFsV2LZ/Pxmlkmi3ksgoWZPG3ZQ0NhmGGB+rZhGFRAHJWh4tlQoKxTJ0XkZGqngy4WcDHk/tg9bKanBuoFAQ51czx8CtCqpWEdVaAnqlqlidyfd6US+hYBegVw20lgdhOduVbfGcAoCankS+WMaUiBuKs1qsdRcrbEClUkF/v39+hUIBpUQFZrGIQq2AhFUMjf8ua9YA++4j2sjShMWaiCEIlAvjKNZaULTKGB3PA3oFlXIBFWsESam8fB6oVsR4Vi5zVKuAVRsCt8V7gdcXixWxHS/A1qxQndz6y2Sz8MZ2l5JeRE3TUCoVYdsM5SpDpRLdPoVCBaYp+l6pOB65TRASuwhigiSfuhHJ53+H6lHnoHriVxShi3OOW28DfvUbjtcdC3zrGoZslsQDYgcj047KGd+Htf8paHvwGtx6wvvwp56LccUV5+K885OYf7YwzycIYttz/vnn4/3vf3/dbebN81PJ9/b24mMf+xiOPvpoXHvttQ3L97Mxqss1bnnBtv1Ax06epNCchsHN3ihcrCKyMZrAhvZ3YM7oA4p4JMdc4WCeu43qOqn7MVS45Cq1BW6MIoZYIEZXwI1RfIWOd8UKxosxdDWGiFeOE5hZk2J2wXFjlI8Yij8jnWCtJgRDXY9cHclE3UeUINdSNsagqyngWLF5k33h2KUcW3KzU6+lVIbTDwyrgKrermxnlAehj63wjuUt121EuVRaLIW5M/MAF/Gv+gd8uzfV5WxiAbA0DbCQVLI3ih8aILWRrofFLgDI5ZchywbAGFDy9nXcr6SuYlrC+yfqmtq23DGYCFodiJNma0mYEDHgWHoKtPGIbIzcAqB5k3xRGg/1O3e/qCRvbkIL73dgkm5YecwdvguACO4N+P1kamGht53FnYK45fQ7NU6PXD/hhiqfSB1s27PEnDJFiNoiqQPzLn2wjd1+P3u2SGAhI7sxuqaUIuh9dD0MybwxeBzODM8lmXEuBVA3lPsNnMPmwNSpQL5Q332UQ/Pq5B6TcT8boxzYnzEmEjwwhmo1bOk5NhYt+jIGGAlxMkGxQpxTfP0AIJHLoDw6DsMqOKcXkVVPOiNA7Qqa5ga2F791Q14ngsNr3ISmOUlCwDGltAwJa1z0baYBXL0Ye+8NLF/hnoNoJJtHuzFqvAZbS3rLwve5Uz8tFTgvLv2lIThG+ivjL3DWHvBKkdueO+NAo/0N3V/txZNjwLROYLR3nXB/5hBtxETWY52rncCy/DHY7dNe+0jbOXkA0F5aiqHcUaG6RMVXi04woPrg13vM1RuL4iCxiyAmQOKFm5F8+ueoHf4hVN9yReDFlON7P+C4+17g7acBl32FIZkkwYDYcTEPOh3W3OOQfPjb+C/7xzj59Hvw5f+9BstXHIYrLgMJtQSxHZg6dSqmTm0uCrsrdB166KG47rrroEW9XQbwsjEG3hOz1W5kq93KMs4YKsZU2MGAs4x5ApewdlHHimTSif8TEbBcEbuYBk13Jn1cXe6KRJwD6ZqfyqqZUSlqMhaZHTKgU9m2OOe4eVmweXUDYNWg2MVgWZLY5UzEGbdEDCDpBDQGaPArJgeo13URV00WnuoFqo+KyQMAlpaKsEgJI08c3Bg5NjM8gUrXAc2xrtF5JRzXRprcKWKXPIlVOpK6P6v6qS8VsUvjkUIDZzpyWSCXE8fsH1RrE/VnEJslPVHVqwcDTJb0rHo4ALgxs6SyNA3onfJmzBx7TNk/URkAy4SFD0O3IXldepnJoi6pKcfscgJAB7Pg2SyBcmIGNiTPwL5TATaYB7dFcP2MkweBcQtgNkZGlD39jH3FfgD+9YrqP0Gxy71urkFkyvQbnscIxa2tDLxfbOEGC2TcBo8QikVgMVtqF153UsttEdsHkIVhrnywC4ldzmHj7idXAOFgXozDYH/d3zWsVTKMqlgsKa2TMg8yXYk31NsLL6GFxi0nCYhbh8D9JI2pHMwLRG/YBVhIgMHNEiuWJxLiutRq0dc3WuxiMAxxLUNjJOqLXXPmAJlZCYzLMQOlHdrbxLNBxIfzKyQLrWaqA0AeJWMGUtU+JU6XrgG9rW9CxuzHHHOhWGaXMKW8UtRdjxaaNEOHqza7H2rknAF+hlMO3S7D1LKx46bbz4PNqcREZAxxyU7ihT8gxfMoRoxLHBq4ZaIz/zwKqT1j99d0+TpztyqibHMYM8afAG8RC20YQuwKZJQtFIR1r6hroO5KzDQGxjiy1W6Mp/cN1yUqmYBU4GjmNWgrLQUQTEIS3z5yH55afAnAEbHbevVouAVBEAAAY9H/IvXId1E76HRUTr1SuWNHR0Ug+rvvBS48n+HrV5DQRewc8Nw0VM74IUrv/RlmTRnBrSedhdcOfA+f/0wB3RsnHGWSIIhtRG9vL+bPn4/Zs2fjsssuw9DQEPr7+9Hf3193P/fJVG/CIk+wKolpGMkeHljP4L5Cipdy9XnnZtGKQrZU4syIDPrOoXsTXFudPzSldkVZqNSUAPWCoGVX12rUPUBwcqzr4SC5HAyWY5kjrxfCiWrZBRbc31+rOfFOFMuumLf2UmIGenrEpDnIcPZwbGh/R+w5+fgN7QbF72t9oye2aRo8N74o8YFDzcY4OsoxPMxhOlYCe+whTygjLGWk4EnyJEnX1YnQWHo/9LW8HgBTJk6qNbJvRRM1sSw5c7tCaq6y3GY6GAMsJBzbNdtLkBAUcsz0NNT0KaGywUU/8eM7u/GJ1HpYlmt+ES7C5vJNIv4TtOxShBQGaDqDzYEN3cCKlW7RwuJHdhvSeE1kiiyPItP3rFr1iDEhOGENXnu5fYNB9AEh1nZ0MNiOZZdIhCEsCdPDS0LbM9ji/8w9nrD6SEeEKHLHBsPKK3Vl3BaWXW6ZMZZdUZNxzuV2cCx4uCpk7DkPyGR8E9m5eyawzz5a6P609Cwqe52MscxBAHxLN850pR0LRe7Vx20fv0JBsUtTxmf33DK1HpQT0/z+xN0yGZLJ+Oy4cSSS4mSCVk2NLLs6pzLomtrXs6XVfrkJ4KADGVpaHCtDZ0xQxvlsB8wD347U9FniHIJjoCMkudcv6HYu2ki96CwhxXdivigvPytE+9vQeQVWIHOuWlggerv7SxrLOZgiTAaJE8I0PTrbrc11EXOv1oukORS5b1ubag3mZQJmfpW9APhMg6nlwMCRModF35GoVL1CYGkpqUwVt9yg6O+cZAj5XhxP7ePVk3Hpng8G0ttCyLKLIJrAWHYPUvdfCXOfk1B5x3eUkXfdeo5LL+fo6wWu/gbDqaeQyEXsfFj7nYLi3OOQfPyHmI+bcWrpAXz/q9/Aez73Jhz7WurTBLGj8cQTT2Dt2rVYu3YtTjzxRGXdsmXL4nd0bmfLEqJG8Ms9EJ70mgj6Hqgv8sGv0Jm0cJGJQp5g2iwBTYuKaaJ5k6Ci9NG5WTdG1ZJF0Nd2EjCk1jNqAhhnnQKExSZdixZTTItBhzR5hTPJYFroHORg+PIEiPOwG2O84V70inJiOqp6G8B0DORei2mFF5T1wnLFdbMKZmMETL0Fpc6jkOhdGClKqifiu7ZWKkC/b4wn3D2hil3BmVCce6eucyHOOJhaDtVEJ1BZE2hLSdxQFILw9entE/8Wk3uglJiFGeNPii2ZAcYsWEgC8E0kuOofhp4pJ6K9owUIzDlXdXFUa0CL0lau2GXD7X02S3quQv4Ez6+ynI3RnQgGLbtqepu/DRNl1eSEARDi5B6zOXKOV+DoGNBSWYfksA6WnhUrApUSM5GpCeU0KPAGM3qq/Td6cs+YELtsy0KlaMPmgM6r3nXobn8bNG5i9uh/4Ko7jInxwbX6aGsHygExlzuX2XUJ9OvKwaROFDxPq47YBR4hlkvqUudUBOLw2mifmgASLSgWhgO7aeCJHGwYSgffa58E1q6W7gXOUdNbwbRxMAiXxjhrO7eNx8YBIfj692/FmArGNnlbuqRSDNXxukaOIdKOmFcNWPVypjd0Y2S2qpAprmdOO+y1J9A3DPT2qOMfA0cqrQGagYMOAnggxpMoS3z1iBL/HZva0EVnki+kZ9kVEBI15+MDsy1YRnxw+ziHO3V8c+oh10G2kgybTHqWelGiMVfCCUQ/CJMJcQ+7VqPuDtHPTYZqeg7yqRFovILx1L6KBbVcNdnKTdyXvuVZVNluYoeoj05a5JjNAdjez3qP+UZ9LwoSuwiiAfrqR5G65zLYc49F+V0/BnT/pf+ZZzmuupZD14Gf/pjhsENJFCB2YlItqJ76DZivOQOd93wDP8x8Enff9i7ct/4yvO190xrvTxDENuPMM8/EmWeeOeH9NObHQzEMKHGEXNyv+a05MZ8Pv2CqLhqGrUacrZcF0MrOBE+MAyjCZknPIocD6Gs9AeAc2VqPN4keGFD3rxvPw5ksBQPgAkDRagGgTkaLRQYE61pHUQtO/CMnywywnUxbTPNTtoPbYhLD/CC9jAWsEqRJztp14l9ZZIqrWZwFwUDLcd7f5eQsoKCuT6U1aLBQLquinew6mUj551IPDs2rR6i/sOC24TORjy+3K2OAxmxlSwDhOHHS71xlvSdaBsWZYD1qRofnrmmzBBirwGJJaGCeZRdsSxUioUHTw+fg9juuJSWXTXFeuhZw+3RciT0bEWfuy1jAsgtCzJGtPYZyR3nnm8uK6xUVZ5PBxoH7WzASDKOjHKOOAJ0obAT4TG8793p5/7KEN6FtdN1l19SoSToAp60YujeYGNvg1C0hBp6a3gLOEt5VErGtbMfyJINMrVfc15EWcK7VlW8ZJc6bKzdrKJaW67oWaVWKkKggHyOYXU4IkQxcT4X6uc10xyVRrX8moyHhxDrrnfJmMNioGJ1gLA+NBSy7ENBEmIZyGRgdFdULfjzw9T6/oi0tDKU+NK12mXu9ASnndIJil92E2KWX6lsXA8LiLGFwJekDd2JIpTOi/+u6Dj3FUCrJpm2u1ZQWOR4KO8CwCbDc/u7YwTlXPuyU03NgFIT/paXVMU92x6A6YpeIO6eu1zX/4xKHHhqbONOhaaYiGrtW0jbX/DhtPOKhDcA1XnM/4miMS/E3peM4ll02GEayh4oyQ7EKfEwth5Tz7BRil1Ff7HLOrWEMSS+AqHvfh5k2TX0H6FoNzJo1McsvcmMkiDpo3S8g/c/PwZ5+EErv/TmQEKOObXPcdAvHly/jmDkD+PUvSOgidh3sPV6L2rl3IH/sJThtzr9xytJ34dEb/wa7mRzCBEHs0Ljvl3bARU5mZFT8u+c87myrPt/EhMJ/hdStovKiGhXg3NuX6eAtYqJtall/OQeqxlRUE53ORCb8QssA1aclegtRp8BmIutdoC7S9lM7wstkhrOHh17soy2tpDpojiWNOSRe/p0J2j57A4cc7NgnxFh2eaVJx4iz7IoTGRrROkWPnKzIx0mnxQZxfcWrAzSvsrYNYVEmlacch7GwhVCMGyPjHAaiJmJaqEylPLf8ujMu1R2JQ7SHyRPOhNg3r1Es/phWx8oO4l3RKTpb3QTGa2DcQsUQsfiqhmgby/SrIAuprljqHY7bygS4mNzD+/sNJzAwxkLiG+eABgvMdpICyPckY4DkNuQdV7okrotnI2tK9T4NN4qwOhN+VLw4Kp2TqNdYen9RX0mcZLABze8jDBw8EXYrW79eBJj3Jv+eoYjtBTmvdw6RE3Uut4M/GffWB1+DhHIARFhtui5/dtB9WbIymjn2GHS77AhiDBosxSJGFd/EGJH3w9sF5RZpgb/TlCkMltVcptbRzGuATAcYE+6PJTWcEzgzFBfKRnGZ6uHGF5OXAEA6G/Sd9f+0Hcsujvh7UMSCDI4Hfh878EAn5lbAio9L1lzNiF1BJVizVWs9QHVJlDePtN5iOnRN/XgxexYwYzoAu+aLXTECftKxxXA/VrnXITT+OitV99ioyPFiP8Wl0ynHHd8jBUemYUP7O1FMzAmXF0yO4Xygks9JFYbD5ff1RVY1FhK7CCIGrX8pMn+/CHzKHJTO/DWQagEAjI0Jt8Xf3cTxzrcDv/o5w5zZJHQRuxhGEjjx0yjNvwOjyQNwevXr6Pn+eaj2rG68L0EQOyzyi2TcZGF4GMjlgJT7VTn4Ssu0kDUR04SLDxAURgKTcBuwZxyMvpbXo2p0RNYl1h1KA3gipwgpMor7W+DcaqafYSyqbn6do5/nhdSeofZKJiM39SYy7vat5S4krDy8OGcaA+YeJSwzEB2zK2pJ3PUy68WXqYfmT9BbW/zFsjCiO5NG99g2M7CHr7VIFfWt/WyuTuYYA8zsDO83B4tQDXxScruGxC7ulRHnxqgurfeRRt3HZgY0BphwAtRzxzqB215/F/kLoi2NvBoaGUW8estBy8C4jXJiGja0n46yIbIWmpZ/PxrSXNOSpmfuPRV0YwwSSk7BHRHIEbvsecdK2yJSBPQn0xxj6degkJxbNylCkKgJPIM06bbKfh0cscu/1919bceNURW8rXQnBlr8cwgcWRzLPQ6s2AD1cjPFuTEGu6YsiERaNTknGRK7oDvNHDiQFnUtGaAxYcVYxwuXQ/fiMvGAK5/iJi3tl2sRC8tNiF2y4F6J0JhtlkBf6xs8Ec4dK2QxWG6H4AcG5VpEjAHJJDzLrii3NpFcQB2TwmgR4jyHqYuPK0bCsUCV3UWhgTk7cTCUEjMx7Fg9hUtiyKTDhnJuXEbAF4jsQGw9eesgwq1fiF0V59korDYBZlZ8kTFmTHMzHZoBsYsncqplm2PZxblUi0Dnla2z5We9u5Ur3Ed9Z+HQAabB4tHjgcvxxwEHHuB+VJDEdxa9vctEXRlJ7CKICNjwWqT/9gnwZAtKH/gtkBGDzsuLOM7/BMcLC4BLv8xw+WUMqRQJXcSuizZjP3R+4RY80XktZvGlaLn1fbAf+QVgxZs8EwSxAyNPNiIeX8mkeMGeOQOxVjFBN5G+1jdCY8CcOQyHHVpvEgJYXFhBVBOdylfvGdNFrC8gwkVNPraeQN+UN0WmOk9KL+iark7AgIi059Jx3MmRvE9Nb1E2d89L14HDD2PePv0txznxhQBn5ot0Gt6E241/pBzSmfCyQHDlWbOCdfT/7OgQX/o7p6rtM57aH8Vk+Ct6I+Q4NmqWLWDqVOCYo+BlZXPFQ850TO1gaAvpjcxru1DseQaYrXOVbeuJUCnFqIIjaQQtQBCanM0OtptDS2Vt7HGCsXc4M8A0wORJYanjWXb5GUfFYVl9Szc94U/UGZDQXWFHzFzdviIHk95rL2dXHeDSJFHz2p0hn9oT/ZJrqowWcTMn7TEvDSnLSMH0GYMyuXSQjbc1g6FqdDR0Y5Th0MKWGEyUBc7V4Nmu2OVOpB2xVHZjlFUnjWmhhACuuObGm/MD1FtQrq50nklpDJDHqWxGCPwc/mTad3fzt4uKQyQsu8JWkm58q2DMLhbZeRg0TVh2yYfgypkEx0amiAFqAgx/LzdhVjDYvFpX17LOF9/32zdiO2ioGh0YHhMijieoyIIIY2ibAnS0qyKuvL1zsAAMe86DZL0bIQhJgmy8ZRdDwgjuy1FKCItizRKq3+Cg7WUVFS6EosBScg7AdBRSe4fKNnTg9cdr2Huv6PqVkrPQ13oCBp3nk635YldcVk2/hrrz3NLR3/oGAEJ00jSgxqUg8TEfCty2dt0YXWu26t4nqx+TZLErpg3da8+YqKscpB6MYSxzIHi2M9ayC/CznsrIx5vSypBIwLHmRKS4ORmQ2EUQAdh4LzJ/uwDgNkof/B1462yYJsdvfmfj058T8bl+cQPDe97FImMkEMQuB9Nw5Mc/iOeO/xce6nkrprzwUyRvPhNa94LtXTOCICaI/NiKetHdfz/gNYdlkMvJlgr+TnvOE79lK46a0ebNTxhjdcWurDQZtiXXCU1jzgTCEQUATA+ECmSANxEKuvzN3QPYZ++AeBV4RBuBCRCHL/h51kzOSVeNdoxkDlG297J/acqm4Ez3rKu4I/q4FjlK/eUAxpohvtgH4q/IIpLNDOV6JRIM06ZFZHtmbLPELk2y7FL6BQP2mMPQ2ck8IxTfsktcsyih1Jvk2IDc+Iyp03LG7dB5yxM4Q3ZB4hYO2DMchI1DzX7XOkUTAu1EcArwRA1mgMG17BKCEHdMWDwxwJmV1X39MxJqu3rHcWIRSYqLu10ywUSf4VAsIvx7ScNI9nBUEtOjjxmRQSBj9oJVx8XekomNpjFl5u3+6VtMcE/Mm8hrLmdaZL8Qy7gfOBuQYjUFOp7rxhhwdeWaBoulUZbOn3uhp22lJMYtJalBfYFFMHWqEMM5hxfbzDtOI8su140xsJQ7MbvsoMutM4apFqo2mKZBY3bIskz+HbR6VS27dP+3VGk3lpNZT+yCK777++27T7ix3D5cdoz0osQuANhzT4a5c1ldy8DYVQFXdbnN29vdg/LYvsmhQzfClo5j6QMwntoH1hRhmjo46PdJ0XYsdPx99wlXOplkjoVntCti1ZgKOO0hZ011xcapU/3nVy6n7utadnmHcywGh5MHeB94stXuyPM2dIahltf6oiYDbC0R/c2KMRHKIOZZrUtjMIfmhRzwxXcD1vSDIi+ilwAgMo5g4LeuwU2BGnU5J2OaTWIXQciURpD+vwvByqMon/lr8I59sG49x0Wf5rjlj8AZ7wRu+i3Da15DIhex+3H8W6YjN//7+OKLv8RQbxnZP5+N1ANXAeWxhvsSBLFjoMy54uKtJLPKMh4ULrgVetGXJzVxYtcB+wPTnbnqYYcAhx2ufvYPTpyCVgGmBV84CBy/o4MpboqaFmHZFRSJAMxwBBJXhHPPNS52l1u2Ul9oCL7Gp1IAT7cry3Re9ffRhLCiBSy75Ha0tHTDr9wb2t/h/MUwe7aw/oojn9pL+S1bmMRNKowYsSvaIsC3hAn1GY2ht/WNTtwyG0lrLLCnOsPfey8x0WSlEaTssZDwGbRqAdPqzJ6jkeuYywH7HTIFdrIFpp0Q5UsqkDuBYxCTuHqCrqYnAlURv+wIsStcJ9GfslkR1629w11ef8qmS76nYiIOpGqD0IZE6AE9EXCdixC7ZDFEl/p4vXsBEFaO2SxgsXRkv9A0EZenrmUX4LmOgotsirLwwjQdYAwDLcd51m2+ZZfqxsi4CWb6Pnty3y77npShujKIgOzBmF2yBWqkUQ1jiHJjtKGjWgVqtoFg8wMidttw9nAAQvyTLbs4BywtFRYqlEoH7gFokvWqLPZpMIzo7LNeXV033aiMJRKuO23fgHpk160tSCU9Mz5xQdzzh6kDrNsGU6cC7e0MRx8F7Lc39yyBAT/Glmu1pCeChXPMnKkju+8hYLovQKVnCJNQmxneeKgZvht6Kip0l1DvIwfN4H0quzECIgbVnNn+dlP3kXzC9YQTAF7UY78DEuCacG20bM1zf66H3TLLE7s0Jn2ckKvltBEQvgeiEJZd/lcqT8CLEbfjEqaIfQI/mQadV2DYJWlZ4zpNBBK7CMKlWkDmjv+GNrIepff9HNaMQ/CPfwq3xY0bgW9fy3DZVzRksyR0EbsvRx3JMP/KE3H+C3/Hn9afB2PRX5G95V3Ql/871uWJIIgdh6C7WjSuS0sYOzsVlp4NvSWrgdSjCxbBzsW62bMZpu7RAbtdEmCc3dzJZXAyZJoAc0yNol+o/QmnmLeo9QgHzmc4YH/gzW8Eci3+slCFHFyvv85OtRS1LsLqLZkEoOnexAUANFsKmiO5ugWq5CGyVdbfxhOYwNDeJrLzxTGSPUwtRrLsUoLCS3+7BkGusYM72Y2qlufGaANgTLEOYoyhZrSjkJwXWbeg2NXaypDLyVY/4h/f3dRVNjRlvYycACEaf6eWFqB9rzkYn3USbO66ZHLnsea7Mbr+NnUD1BuJwO3hxhlzxa6YOD6OwZXNRfBtXWdIZNLCpcjZ8OCDog+pacBA7rXhMh10Q7UWEUHc1W18Mcq37NIYQmKFPKHfc544YP+001CMiPHFGMB0YYnjlm8zw7veqjggBasWgcX8NZIw6wbTtr2+aCvn3VZeDlZonBFQafqAXr2HZCgp39/xboxRAep15PPiHOX4fm59S4nZSGZciypLBHxnfobA3tYTsWmTGmsrKKYEY3YlO50sm4oqx2AYqttsEM50zJ4FHHds/fc4GzpSSb8e7vUezhwaEtMFWkOhVq6nELtUN0au/AKmdTLssYfaz9173XaOFRrrOccRh4tkYppk4VttcxMk6J41oC6ZO4Uskdz7P8ayKziO2YHA77ou7j85BqC3r/N1R9QFSB5xGqwD3urVoZ57v4uR8JM+wBG7gq/mbsbKepZdXp2YatnFEzkMOuOM6Pfhfdxzk0VyNwYYY8Bo+kBsaH+n+G3XkK1uREtlTeS4RZZdBDFZmFWk7/wMtN5XUX73j7BeOxaf/xLH937AccThwC03MZz4ZhK5CAIADtif4Uc35PDH3i/j3Cf/jDybgcy/Po/0Pz8LVhzc3tUjCKJJfJcE2W2KeS/VcoY8F7PzYLGPK2w4L7QTCWQtY3eEJ0iRX6Mh3EB4nXguitilhbdJRASoNwwhwnkiSp1P3YbOcPhhLBQzS83Ux7xyRSB/f9alSZZdwSDVra3AsceqrlA2SzQxAQgLQt7xGlwTxbIrWKJzkLBlV7QQCXBvkmNz0SaKKxxzrWRiovpHBXgBwHPTlXPxjstEcG65P0SJDfURO7S1OS6EjqWCG2CfOW6Mml2DJcfege+elY+I64OgZZdVU+oT5cYo/21zx01KS4BnO0UcL4h4RnPnRl9UTQPKyVkoJWaKfblUdkiYFuKTjG1zYTkZqIvYVW1HLvWNtjZxMK4lgQiLNwZAD3RE+Z6Q7zfxNxduro5Lo3+CkuDk7B8SfmL6e9QtHYxHFTTUEZahbp8V1jXTp8FztQ4dOMKykDMdy1eKf+WA31bngRjMHY1KYhoOOMixlITlDFp+zC63nQcG5DLFybS0CGtW+ZAcGhL7vtb7JdfPMKJjdlWdOGgiqDig1U3oAD8bo9dB/OVVvT28Q0BFjOrvIQKdyLW0Uxer97svbLt9M2zZ5RUvHzchTLcKqXnQmLgB3HGRMYClW7xg8V6do6z4nHEt6J49Z14SU+RweRF/eTU00o7lqCvga0JEdZ/TdWQbd5zecy4wljkIZaMT8+YCndOTaGsLiKLO9eM82rouuMxmBsoJYQJtTjsYthe/izuWaIYSq8+vpzjHWbP8ZC4MwBFHaDjkkPhzIcsugphsbAvpey6Fse4plN72bdz2wsn42Hkcy5YBX72U4QfXM0zrJKGLIGT2mMPw8xsYim0H461/uR2vzPkK9NWPInPLe6CvuH97V48giBgUd0N3ohLMfugFK/YWhNYFX2ibDmQdepP1f3v1cUSBoFgjLLvU+EdxxWsRq3U9eGzbE2R8wxdPIajrvmXud4okFurKeWWymvP1nilf9keyh0tilx458ZOX8WZc85h6leRYoiefFMyMGdhVl6xRQnURC5JCw/AyJHqWXQ3cGCFZP2nSfHcsvV/I2kFsHT3JtuYdB2uPY1BLC9FLlyZ+DJBEw3BD2Q0yGLrT37331py+oWa3g+tSZ1X9yazTNu52I5lDsKH9nRjKHglACCLTZvgByTkHmO267Dn10RPe5FS2rGDwLbsYA5BpB5iGVIrhyCM1HLC/Wv999vb/VizzHC0rruuI9VI8LC5chEsJP+iZLFSGBeDwxXetR0KTZybENdklUY5jxKHh0EP8jUU/iHFjdPDErsAYEDVxt2YeGtkO2WxYsFAEX+ke4BCB5mfNiktKxYQAEljqxsHiTFeTY+hJEQQdgGvFxLgJjTHosKVsixHt7CxrmwLMmxdcx/wxTtEyGRKGanzPmBDM3HOsGm3iWmc6UBemwbJ9IVAWVKNcIJOB9ooa8ypGB2p6C/KpPZ0VAcsuNyi9LNi0zES1/QD0txyPcmI6isk5sJnhJXeQ3RgPP4wpJ69k5EymsKH9dBRSe0HXxDaa9Jyw9j3JCxbvHdsbl6XttGixa+5B09E2y4/NxuRnhloEmJEUXSngK+s/BxuLXZ4bO2NIpoC99kmIuF/BHeoEqN9/P/W3m5Sgu/1tsFpmSitsR5wzlKyb9T4yMA3IzpiOPeZE3EfZdqfq9R96ExXDSOwidm84R+qBq2Cs+De6D78CF9z4LtzwM47jXgfcegvDu06nIPQEEUdnJ8MNP2Y45FAd8391Lv4x62/gU+Yg88/PInXPZRTLiyB2QILWC87SwEZBtyVJUPHEJvdfsS7OiuiQgxs8Q2WxRrISUOsn6Jwa7SLhF+DPaXTdr9vcPYQIEYxnpfOa94x3JwvBCeZh0dnnAV2dsPt/A0cc7pvFuMJOOTENxaQUnyUie1vQWoFDj7Nfi17KoFwIxlDXu1zT/ToEr5/bdqkkcPBr4LkU2oiz7JL6hDNX06X5nFc80xVLCX/neIsS3jobSGT8wsRS1eUpKn6ONOlqaQmtRkjpY0wSgIVll0gqwCVrBrG9J1J5Vj1ix85O4aaoVCdg2QWm4cADDOy7DzB7tlIsANHGY52vhzXnaO9ebMmF3YP330++L+UTD7WEetoavI7R33I8ONOcpI2OgMG50i/Ck9coQcVZE9E1hU4liWuKZZffxzk0IS462RhlAVTOHCpnPq0nSHM9CRgZpW323UcSCYPCS4QQI45XXzSFIwgqGe/g9wlN15Vrp7SRlKhCuBVzKa5YxE3mnEyUm7ZyneT7iTHFjdJlyhS/MlW9A+NzTgFvmxveMLhfa/jYAKBF3MOZrBYSgJQ6Q0N/6xvQO+UkjGUOQmX/d0LKdgLAD+LeIgVzB2Mwpx6ISmIaBlqOQyUxHTZLwnbaJyobo7Srh0hk4j7DHMsuSVgNzgHljxJy33PF8HQyYD6XaUfLEW9Ga5vah2qaGJB40jf7YrroCxpXy2DSWBM5jsF3X/c+wLgfA/RwjEVh2aWH3Bj7W45DIgFF0JXHWM4CFqvJFjBNw2haDVQf+hAmFgIAirPeCKRDqXwFRjhAmtz85YQI3KhrQDkxXQkRUA8Su4jdmuRjP0Dilb/i6fQleO93z0ZPD3DtVQzfvlZkPCIIoj4tLcL68U1vBK76xb74eeVWVE74DIxldyP7h/dAX/PE9q4iQRAy8oTO+8ocFLsks5QA7qTNf6EVL9VxMYyCQeabqZvtWJrJZR58MDBnDgOTXTwiCoiyMEkkRLzBXIt6nnIMLd/ywj0/x1qDxUyoFfcrHUcdKQknUh2553IVtJ6LfseQJ1cM4bhKcYRiWMUcwtT9GSOTxRMGL9OdbNUCztXMXM75RMd6EQttx7JL97Ud9ViRsW7qB8YOdVF3Yi27MQb2cdt83jw1U6d8VPWnlOHRFRWqoyJYuGzZxVh093PX6777KWcaYIvJq9uvGQPSR52OXI4pbeuKkzZnsNKdTlw39yat3z7y9RCWN/FWgaKlhKBVMTrAmeFYE0myU1C4kfCyVwKw5r4O1tzXxVt2wR0zxAblxDQl2DWkrJqu6yi4Hb7giumaDg7NEVdZ5CaiQDero08ux9DSIk3C3SKZekjZ9ZY3Ersc8UBZJIl4rI55pSusuPc6Y1KAfKlCXgB2N1aW5lZaLk2T9lHHbtmN0js24I3xnDFwIxPeKILDD4uw7AJw1JHhPprNqaKlUlstQkiNuLFyOYZDD4Eaww/hLiJEULFQD7qsS82hWHZJf+tOPd0A9ZEjhjM2skAFPNf7mPvU0NWMoeXEdPS2vgl2u2+epzlfXOolLZk1U1mFXE641s6aFTwfV602QmVwDixfyTAyqrZhJTEdphEWotQwB9IKPYHRee9ULELF9oEPUkxq/npT6wixy2VDxxkoJcTJazowkDsWG73kLPUhsYvYbUk8+xskn/8d/jVwNj71l4tw2qnArTczvOVksuYiiImQSjFcexXDu88AfveHBK5/9lMofPRP4KlWZP7vQiQfvBaolRsXRBDEVkcRU5yv04rFERCauKmfwjVlma0lwTT1JdiaeQh6ppzo/W5t8V/GwxWKcNWJiNnl1UBzX6QjJpDMF7uiXQT9hR0dAFr9l3TDcCe2/qd7I+T2qB7LL04K9C4LRUyTXK60UL2iylR+cgtWNpyBi8fEnxETdFnsCh+st8V3yWGaLwS4woe/X3RFvRhtEc3vxTiyRTt6XSVwbpzpoQDSzKqgHv5pcZxwPDBvDy5EFNnUIji/DfTjYLwleTLmlqG4SXIbbcPPAgAspqoFIUs4vwhhrePXXLLskjImJtLg2Wng2anylgAA2/aFQj8zQP3A4YpFIAd4o37GbW/izh2ROGpSyxFv2cU5wFtmgLf491GUbi7cqET9x1P7huJ0+fswEa8LNrSgmhqIcceZAQ4Ne8xlXoZXxoQL1p6OfuBaFAXrxF1rF2m5FtAGFbEvTuyShSUWsMpkGo57nbNZ4GZRtnPWadwU7cTC2yh1R/w4wkPmO/4Boyy71Psl+oDhpB5iWTKpCkLZDJDJhgcF2Y2xv+U42K2zA1vUF7BcIpOeBG5fW0tKmXxDymfoGJaeVdy8Ky17wdL+P3vnHR5HdfXh9842SatqW+6994bBYIwxNfQaEggtlISEkE5oAUIJgRBSIPQaAoQWQvhCDT30alMMNgYX3Ius3rbM/f6Y7TvbpJVWks/7PPZqZ+/ce2bmTrm/OedcD4HiVDeraAVWHdYsl4MGxngjpxDtI4JfWATHwO+sIHb7tacMBficlXHrVsfcAhIFXZfLmswjvH+SwplT3HfWbQzlD0w4ZG1uu/tNTCGb81sneCmnyy2WavIaAJzJnVQl9FGlwtuY/Tg9g1QtCH0T3+t/p/TdP/GfdUdw08YL+OO1BvN3E4FLEDqK06k471woL9c88CA0NU3hwl/+k5J3bsD9wd041r9H+6HXYQ6YWGhTBWGnJlYAMQzYUHUIxb4NCYVsEu8krK+Vi7riqbS6BhKrOQDokgE4S0oJhPSL0TFeNWZF4mx88eIbRKdetxWswmGUGR52kx76EyocvN9h2A9prBWL3DB7D2BdcgmzanRSvUrB1CkQHKTQDusNtfK3YarikL3J29RaMgZaVlvLY+pvcw1A46C+eAq+QW7YniYPoiLkWaJCg+XUHg0A2nCzuXwRpe1fo1wxswImWhEelLkTZjQMLU/UIlpdQwiHqkVydsV6dsUO6rBPZJ6O2N9LSxXlQ4E6Yvpqcg2JIk1J0uSMKv5TGSgjOveb5WUEaGsgDdHjl+zBpCK/xw7+tDJicnbFrxQcOR/a6nGueT1uuRnn2BQarJrBxEMZR+Kg1dQOSJNsXMUkQtIYkWMW+T1sv44fvPbrB1vrss/ZpQCHM+raoTHiBs+xobo67HFGcjKhRO8oS0S2BvmxAlFxsYomyw4ZFetZBhAcszfOL19I2oZUnTBlGGPEJc2yN17scka8qRJDz+Kua04PzZ4RNLpGoRyfREzY1v8A7HSTeNE83uB4kSG2t6j4nGGRpam/hRk/DhqbYEPMLSLiVUb8YdJVo9Hbv0DFTDYROwto0ChJCBGExD4ad/myUb6Cw+YmWawMy4nP56iI9C1HQhijWR2dxtQwYEv5XnhK+zHGEfWiCjpL2VSxP0NcKZuPuQ9F93BpKQQaSvA7SmkpnQo2EwEk3YJsVM2qQaU0BRfT2FISdyTLyhSLF2m2bgNPQ3xfdhgQHLk7jq/fjmyb1Z4lZpshkdi6L0ROwkg/Sjxfm0omEhg3CudXL0Xsjg0XTNwlDgPsvBrjtt3mPm5Huslhwq1bs9RmqCgB8ewSdioCAc1H9zxMv3ev5vlN3+CLyVfy93scInQJQh5QSvHDswzO+p7iuefhkitdNO5+Lq3H3olqq6X4geNwLX0g4xtqQRC6jthBY/gla9IMeUmhMDECWYzK0VQ0hqDDy+jRMHJkXAXsuQD2XRxfbXDIbIjxZIlvK6aVcIJ6u7xQYc8u23wdMXbGDAZjPUcyEQnPcai40LvYAas5yC6RlxWS5nAaaK+VW0S17oiGMdo8yLdUTE1aZtngoKZ0HgGHNyHUMNl+I3HbspgWM+Aoo65kWsLAMnl7APCUERi1IGl5rPawtWyPUHhkSIgMCSRlpVBcbOXaSdyOdGOV4NA5ScuSumQkP1DUfSwprAknJSXhHEN2uy9B7CJ2kgSF0oGw1hWZ7S12gJ2S2MkHdFicJZKwPOW2hz1OdFQM1EaMoBJi8iSSktVHzxWrXCTJfcK6kYZiZ9QLiV3hbfQ5K6iqtH5xu+LPteKi6Hqx1Yb/tEtQby0Lu1wakXMsGiocqkM50oQxxp/vTZ5RtLiHWmJXqrYTSPImjGkiHEIY+z1MRrGLZLHLxIHHo5g5wwr7S4lS1Hln4ndWoByOSJVGUs6pyApx9sWfw3Gxp3HrpD7fwmGMhu3V0eVSlCWJdcr+UqoMzP4T4hfF3C8SZ5e1/k7IJxdXr42AHeMZFu9Na81COHCXXSgpJhKqChAYtw869IIivJ7fUY42PHGifWy+x1TEXzHiBe4t5Xvjs/GMguihibnShNaNLhk+XIHba3uBcLlUJKl7RczsjrGzhlrfwydTaEbTmD4xfZqVU9CaLTf55UtkgSsmnFXFi6jJ5ZO3NTqbZLr7VTz1RRPjbN1RMpPt3l3iygwZajBmdFa3uPg2cysuCL2XDz7U/O3cJ9hjx+V83L6Y4T++lrPOclNSIkKXIOSTk09U/PLnitffhPMu1DRWL6Dl5CcIjlqA56XfUvTE2dCyo9BmCsJOSWIYoyIaNhj9IWYQR9JwJKnOQQOJn7VYWR4XybMf2loUZ0+8rcl/q0gYY5rX7hDnyZDDS+DE0SMAkycpdt3Fvrgtbi/aVUxw4NSYgXKGR+4Ug7xYj6jA2H3SmwuojG/HY8om5Itqcw6wr9gml4/DETsIsve2Ky21kqhXVCiS/EjSveH3Jg8Yg6VWqK1Z3D9UKORFFlEl7CscPixN6IxNB4uG7zkwtA+Aeu+0JO+FVMJZOHQoKuCEhB1l2G907IyDYfHZjBGAKkagPWVxuX1GDFeMHpW+U5vp4hj9zRD0Rb00Q2GMfkcZA3ZfxIjZ4xk5UjFntiVWJg5203lV2ufsMiJhjFaurbBgHRrwx+ZQMttxBxtstK4EsatoLK3uocmejCkYOQLGjY1sRWhbovn4wtfClvAsiTHECeuxxsZ5diWGMVrrDBqocCXmj4ohzvzSAVHhzhm/I9ucA5g+LUE0V/H5yOJbiQ9jTHL41JmFpXSkDKdMuP7E3gNM5UpaQZHm5WeGY2vnZTl8uGLPBQqPJ9aO+Hri8nTFdqs0eefC+aIiTqAq/lgkhUQnYLpK436Phj0n7o/MjBgBAwbE2GqznyJhkwnJ9pUKXXKyvE+EhbwwifvGsNlsjZOB1fHXCbv0ArE0Fk+IeMCC1WZ8bj8YO8aguFjZhtCnQ8Quoc+zeYvm4t+YPPmHZ/nxiF+zo3IBY3/1F0aMtAtgFwQhHxx9pOKSixRLl8LPfqlpCFTRduTNtO97MY61b1Jy31E4vn6n0GYKwk6PUrGzm4UXpsm5YZ+ZPLnSrA2wX62+aCKB4buxtWyP0G9h0SC9Z1d1tTXzYnFxVGBJ9OxK9FSIJfqAHjXG7Y7m9Iol7sE/ISwlOG5fdNWoyL5NF5oZa7+1bTFv6uMEQ5tBTWJC4lzELqXo3x8qKy3vJ7+zki1lC5PbCtkzehTRRPzlgyLJ7iOeOjFtW14csXVE/9SopLfzKT1UwvUV92N91aG4I1Oy6fiyNn0u0ZsutUeVShJetHJGkk2HBcu45P4pq1Kgojm33EVGpD577Af7kdPM6SE4ZlG8t4UNbrfVr6tDWmAgmOypE+miZhCjbm2kA0dydqEory5j2DBreUmJShrsWuWjHnyJm2G3j2P7sDKMmOMSn/dJY+AONljLEme2SOFu43SruK6vHfZeqhUVCrfbTpCIv0bUF0+2LIvL2eWK+dtG7LIJu7S/PsWZFGln8sSQZ1npgEg1zoTrbG3JDILj9onLZ6hL47OVR+dqUNEw1dA22vZ9FRWbMoWFJ68aOucdHoKxM5Umijexxy3BIyx2VtAOkXypjqt88iSYNImkYxN7XYo1T9ssC1PjnUvAKE64zMcK5CqlKQCNgxZR450TE8YYvt5k/xIg1v6EapLLJE3gQcS+bHZ5YOzimJXSTbAQU3GIBQscIWE5eZ+kvT2ZsXG7KikXWLiidJ53dojYJfRZ2to0d/9Nc+IpGteql7hm1/Mwh+1C0Sl/BafNtCSCIOSVAw9QXPVbxZdfwo9/pqnZAf7ZJ9L6nUfBU07RP0/D/eZfwQxkrkwQhLwQO4gLlI8EW7Er/Po5OYxRZYoVSiif+V21vSDSWDwBSqvxORPDHuMHyokMHqQYPNiqKOmhWCmmT4NRo5LXixLz9K7SjaZSkeBfEdm3yZ4QxUXR6dQtj6Dk9jJ5rsS8O7e+Jwye0g1sDGXNcDZiuMJTFE6wFS/axf5dVqYoLrG2w/LsSiwbL3bFG5owqLMJd0tVFsAXyoMTmVVOJ4hdqKSjZOhglrqrImy7K6RJhcVJnw9M0/p7S+kCtlYttupOsD9RLCgpgTGjLW+EkDXR1lLYFKvJ5qBZhuxR7D5f0b+/tV/afUZSt500MT78MTwg3rjZsHpPogeQsygU8mWpwxqbkW2IxJxdsd1IOaJCX1lZfM6uuHUiMzZWx+VYAlCGg732hHlzo8v22RuKi+Mnh9BVaU9uzAET4u2PEcqUYS/6mCQINlGjQn9oMJwJM1hmNyJXCoYNU+y3jwKHO3LNSsw5hTKsELdYu6pGUzfkgMj3iD6mdUyYL6FLmY1QnuZboo013jlxk46E91Nz1Sw2Veyf9EIhduU2Z/+YrzHXNqLHPLatxDZS2pX2RwOXS+F2qbQl7YQTI6bvxtZnxol6of/iZqaIrjNkcKwnoXV+xs7QaTebZbTezJhpvNCsasI5+RInm0mox0zxc0Jfi/yesL6t06zhSG1Xuu2L9ezC8rq0ewmSzfu2OHNyKy4IPR+tNS++rDnxVEvs+uHeL3Dt3J/DkOm0H3NLxrdjgiDkj4ULFNddq9i4Cc7+sWbTJo1ZPZGWEx8hMP0Y3G/fTPGj30U1bi60qYKwU+FzlOMfOAND2eWkSR6whDFsQhMzhbKkJWWemRQPxuFZ2ex+THwQNxStrkGhAW54tdQzLk+amDgIST/y8HgUQwbDHvNT25A4G2MsEyaAHjk/aXm8uJgsOsUSGVTEJBzPBqcDXO5ofTpxcGP3Sj3mbys0MF4UjfVIMw2bjNgxdSSFX6b5BuAPiV2RJPORwXyagazZFq9rpFSZVKRcOPxVx4hdLe0hb0LDTTDs3ZXU5xM9NBSlpQp3KJxKGyk8u8L7rqQf7ZXhfprs+ZYtnpBjky8QE2IVOhYul6KoKFqx02Hidlllg4FQu7GnY+lA2ofsSqNnfHxIUqiPVVYmt2+bSypm3VGjFTNmJPTRsLgQ8qRrdQ3GcBfF1+twUFSkKCuLLnM6LUHaEYoQNQzQLi+BCQcQHDyTwOi9SDxQiZPkRM73BGPiPbtiBQOb81FbobeB/hNj1ok/3oFJhyStlvg3hpNwPvdM3itW+JoR58mWWkiwOZ8cpeBwQjaeXQranQMIOGJ2ftgzKals4vXHYHvpfNZXHhRvS0KOM/t20xdI+94lvQtthNj9HK4vbc6usPlEb1eJebMAhg6BsWNir+OJNdkbn61TdHxYoN1KiS8D7OvP5OWV9AIh8dRNIQzGzZhopw/bESN22U1SYXduZoOIXUKfYuWXmh//TPObyzUVFfDQr5/l1OJfYA6ZSesxd6RUqgVB6DrmzlHc8GdFY5MleK1Zq8FVQvuBv6XtkOswti23whpDs78IgtB1qND03j5nP+vNugKUwcaK/WPewOuEz5j17TSm5BFPDgYll62sgNkzo/lewmEyZvlQgp6KdJXFfTOciprSefirJtr+nsjIEYpBg7K3XbtKGFBthJIh268X9nCy8/RwOBSDB1vJeFu94yJ1xCccTm9DdPBlP7hJxT6Lown4teEiOHy3hLaTRyjaiE+E1n9AjGcL8bmdkiY90Kk9OJKw+XHkCOvfsFBKJbP/OLThQsdOeBBabXvpPAAM3Z59TwxtYziJeWzommlz7HKtd+iwTJ4+Ks7LJVfPrjDRXHVRC4OhCQa0I16AVEox2Yrao91HsicFoL0DscI8HaDC+0dRXm7lQ4uUC3uaqPjPeI9FcDgMysrjQ5GjPS4aNhorTG4u3zsSjpUsRCgqKy2PtUhuNocbXTkCisoTC8cYFTHOsjfiCBcjdkdEXGfyukCcZxdgVkVdecLJ/m3XiyHu/DYckaizjCKEjciQMpeRTdtbyhbROurAmDKpO5yt5ZHjnOgalKiGhJTIhDBu7XBneS9JQ7ZlbbZt5gyYPtUZCne3GDcWxo2B4cOhyBMKgYyh3VFl/eFwR8TGqM2xL0mSMVT89THBISznTQp7ZBkJ51eknkhOw+QwRrt67AiMWkBzhd1kLDH1JYTRt7kGoL3VVvizXRhjusrixC4zYXbR6NqeHIOzROwS+gR1dZo//NHkjO9r1qyF889V/O2nTzHp019hDptL6zG3gydNkg5BELqUKZMVN16vMDWc8xPN8hXWrT4w+VBaTvoXZsUIip/4Ee6XfwcBX4GtFYS+i/KUsLl8EXXFUwilFwLANDzRQV3CQCtWfMlqMJKTV0pyYbcbqqujy7eW7Ulw2C6YQ+fkpAKMGW3lmYpMRJOV7TGjyNDfOsVGB8cuJjDxoISl8WWnTHEyZHBMMm6bwU2LZwStFZOTbUg2LqW5KcMYQ58V5da+mL8bTJ0SX4euHAGusCeNjdhlODD7TyA4ao+4xaNGOaiutmZbDBWM/JYYxhgfrpR+NkY7XC7FpIkxkx4UVxGceCAk5mjCmmkNwp5d8Q3ZtxsVPcJiUazAZZd/KVmEjAqHAMFhu1izWIYa9BQ7mWYz+WZ0v8SGYXbcs0thCVI65Dmjiysjz7/BMYuTBK9wAvr2dvvtCp9urVXTqZ4wkrIyxdixiuHDsA+NM+I/kzCMiPAR9nqMeINFcqRZNtYXT6bdWUXAURrjMZZ8AhmGwuPJXSWxzsmoSGGFMRL5Hia+L9uIXSFlKjq5gUFDUfyshHGtK/u/MZyREK10IkR0vfhtzia8q801gB3e2VYFcZ0s9TlpCRcJ9bitWRGDrsSpGhMKpnIFCi1vLBpr+3M2ZC84J5ccNFAxalT8ee10Wn3b4VDstVAxOOHFR33xFOqq97YihGLFnIS/05kQ3o+zZxnMm2tzP8hyoyKOvKnayzKMMa2oWlxFm3d0WvsiMzqHrnuNnnHgcCWJ0qnaCYxdzKbyxVaZ2Jk2dTByzxw2zJqBNtx4RYViRnoNLt7G7IsKQs8jENA8+pjm+JM0Tz4Nx30THrxPcfTYJyj+7wUEh+9G69G3ikeXIPQAxo5R3PxXRXEJ/OTnmqUfhd6aVo6k9fgH8O3yXdxL7qP4oeNRtWsKa6wg9FEMQ1nhKMqIvHSPEv4SelDWicvtPY2yDmO0ezCPWTnVA3HQKEaXDbb/MQ0ej2LC+KzfK4cIi1JZlA2PkBOXxTBwaDEDqg2CRkhMSifWhcW1VPs7PGDSSatERZMUrmCzZsKE8YrysugU9mn3R8J2mNUTwVMWt05wyGwGTh3PkDEVkXXCg5yknF0xw2WdEC4X21xg5B6pbcqSoLL2dbNnRNJx3FK+V/IKCpI9u+zFLps0dtbyiKtLyGupbDAUV8WoPw7bva1dlgeQWTaY2GThuc44FosrbtfHtOp0gzM+PLCkGFCW2KVIDvENf/N7hzFwhPUsXVXlSJppNZLcO7S5qTwSDaeKSYwfSdZl/ZYgdjUWjWNbmeWVlng6lkQykmTp+mi3LKY/pxMd4rwU7Ty7QspDWGg2DXda1SJxRtzYL5H+p23aTrRPWQLV5vK92Vq2p22TwerJ0dBvYHvpfFrclktebPFcE9S3Fw9jfeVBaFdi+HMKtTTOcKv14JRDqS9OUN7jC8d9C58rSVVlpIPKcVI1KjKrYqjXoCN2pG8jcTf06++gqsomnDtLU+NzdtleWUIF0s8im60HYcrvkZSEoXD92PMpzakXwe2NhIbriuGYVaOtcjqIxhLs+1VZeSVjV66qSm93LCJ2Cb2W997XfPdMzfV/1UybCn+/W/Hjsw2q1vwTz7MXERy5B21H3QIJF0dBEArHsKGKW/6qGDgQfvErzVtvh+60Dje+vc+n9ahbMBo3U3L/sTg/+7/CGisIfZBE6Sf24TOcLD1xUBG3fidcu7SzF+TMVIl7KPYz7Yr2i51uAuP2oy00db1//AEEJn4j8rN9OEuKwXBac0OeXR2Kf7OZVi/jNitwFWFWT44M8g0VTcqs04hdsbUPHAjTpsaEoMWGJWZFjJ0xO3N91aE0Fk1Ae6vjizvscompSDXRWT+jAsHYcTmEMSbl5gqHgEbriDumrmICEw+yEqur6L7vqGcXhEP9Qp5iiR0obsYzy5vF4TAsTyKbRu36n62nY0IUbUqxy3BEvE2SwxgDoeXJxyi2yfm7wq7z0hiYgohXmzIIjphPcPiukW2JzHAXU19YoB433sngiNZuI3ZFvCoNakumsbV0d9v2x+27K2P3mhW3LHE/hb+HPbs2VuzLhspv4LadZNKyJeAoxeesjAhlcdvcf5ytLdE67L7YlLMVDR02XSZxQaqcUfaeZKlsMsuGEBy3j23ZjD0gVzfSLEjy5gq1MWG85elanhBBa4SOl7Z5gQREBMhsdblIGGOq90oh4TjdTIqx9UTWy/p+E/oe/iMc4h4qYInhyUKy3TPArrsQ9dQK1WOFNCfmdYt+cTiyz90lYpfQ69iwQXPhr01+fq4m4Iff/05x3e8Vo0aC693bKXr+UoJjFtF25E0xbvmCIPQUBgxQ3HS9YtxYuODXmhdfig6CgmMX03LS4wQHTafo2fPxPHsh+JoLaK0g9C1iE8wbCZ5dzZ5RBMbtiy4K58XKVvhIwEZwCQ6dk1LIMCtHERxpP0BMIkYz2VK2MJMhCV8zb0c0DFAnlTdLByaFgWVsE8DpiYZ1GM44QST+zXpIaEk5c19qMcJvWMmjg+XDksok15Pa1Gjur1T7ysaG0GdVFZFBedBISKwS0ycai8biLwoJq2aM0NUptE2GuVA44ZhFMUtSjbDj+2xsGKNhk1w+5UDLEV82IgylG3SGhDAjDzm7IGamTDuPE9OfVD48UYFhF5YYczrYeXqGScrZFR/hFyH2WIcFxcguCnlIJXsFxnfH8nKFOzK5Qvq+E+uxFBy1Z3S5d0BSCGysgKQU1Hh3YYd3NiPHFlE9wEZZSYhNUwqaPaOjkxgk4O43CM+gESm3C2z2m3Kw995O5u8aXy4sysauPmO6bbMpUSp6vqfz7Er3giM5h3h6T9eoY1fqiULsMIfNtbHL+szgnJRXIn01RuBSSkX2X3k57LG7siZPiFsxYR+raD9bX3kwO0pmhYtlRVyCervzFns1LLFkZz27IuJs6BqpYmcBTWhtfdWhkZyhsVRWRmdQ1l4rb6jPUZm2cadTsXBBetsjZbMrJgiFp6VFc+99mkf+ablon/0DxTePwbrhaRP3K9fgXnIf/qlH0n7AlSne3gmC0BOoqFBc/yc4/yLNZVdqmprhyMNDN7uyQbR9825c796G+62bcGxaStuhf8IcmM7dXRCEbDCqYsQQZfOc7CqGYGhAHE7QHPopOWFsqJrEOmwevnVxVUqbzMGhUVpzbsMWvzNdsno7shlKhGcZS7bFHL5r0rJs23A6IBi0/claK4Vpmd5eh38POkoITD4U3WTfSNpxZeyIJ1WcXpqK4iK7lAI0WjlD+cw0qnEznpLo8TeNIpoGzIfNT8X7lKWdwTEFNvaMGA6BAGzaHPo9RqxKreHFDKQcoAMxgqRt7G6qehKGVhFXJ2fmUXmMEFBVmaFsSrQ1o18KtUyXDkLVr49b5nIp/MQKvTEmxS3KPAmCM3QIU81o53Aq8IWSZ4cExcTdmzw7bHbHDUI5ymzQ7pKMKU1ivXXACkdsdSeKx7GdPd6zK9dZ4qx14u0Pe2cN6A/1DdbfLpdKCE1NNmXcGNLmLQuM359NX8cLnZmE9JSNxXzNqFcpw5rx04+Vo8of3kn2K+ZmU34I25cN8bONximfMcuTSRKlYnM4qlQvN1ITLpcyjDGihqXP2ZVdbrgsvoc9snQ0yXzkXLJuCVmhS/oTmPgN2jckX0ASBdls8/SJZ5fQ4zFNzTPPak44SfOPh+DAA6y8XN85PvRmJ+jD88z5uJfch2+X02n/xu9E6BKEXkBJieWVuWAP+MMfNTfebBIMRvMM+Hc/m9bj/gb+NoofPB7Xkgcyv4YSBCEtqt/o6N9k/3A9aCAYtiOuTA1m54EB2Z3eieE82bWd4rsdocFnJAzE+pZ7WwlMmmSJKEm7MC5ve/IIMlOoUeKMYam8JbLOBWNYOzj8hj0lcQO3FGUMBxhOdMVwXC7FAfspSkvjV4895sHx+2VnZApivR2mToE99yDkYZE8KI1HxQ04Z86A0aOdFIWc04yYEMRUUmDYkyJx9rOo2OVIOQNbomX9qojxXModw5E8MAxjDp5BYFz8fna57SdPSCLOncSeslJrYojwcU48p61VNZWVEDTiT2a/oyxUKHl4mkns0iX9CEw+NI2glbKChKqi3joZSRK7FPN2ybxaOlwuxeRJMGZMNqVz6CNOT5LHWVx4WQ4ecrmglcGCPaxzsaoq5u1KNtepDMcgIjx1yLIo83eDXebkvp7GiPRvnaHfqOhKoQWdu06PGA6DBkFRUfIKxUUQCB/rxOtR0uQzubWf0rPLWWpfAGvb3aFrqV2obRI2nrRZGZcC8ewSejTLPtPceHMDn3yqmTHdClmcPDmms/tbKPq/n+Jc+zrte52Lf9czCmesIAg54/EorroCbr5V89AjsGat5rJLoLTUOs/N4bvScvK/KPrvxXhe/i2Or9+i7cDfQoq3t4IgZE9iGGOEuNgly8tp4EBF9XA3dn5DEeGiuArVWtsVpkbwehW776b5cCn4fDBqZOwDdLauM6nxe6zwOsN2S7MgxQP5oIGKQQPTrBbzd8oE9Qn2DxwI5WXZvTa3Nyt5YdAoJjB2cZp8p5m8f7LD8qIh3vTOZGWPqUspS3goCW9CKvEwujTuh/79Ff3Lnaz5MvRrFrmsIh4NiQO1iAuGg0xdyh+wNsDjSV8uE85Qzi5b7yplJKX4CIcx2m1nGB13sJLLRcIYDev+7a/VccvDGIblXTZs1niWLbUUnfAu2lq6O07dllT3LnNISogfs0EpbYqrPAWx3qqJXjJDBsPQIYlr2HjnxGxkVWUObiwpcLkUAaUYPUpTnC4bi1IMGABfrYYBA3Jvx3LC7FioeuJeTydG2nqmdVC4SFWFy9nx86aoSFGUZdablB5VWW1PCs+uFEXsMMsGYzRuxuVSDAynI0yoZPf5YPp2J6ibktZP7JneEmhtTd1eslgd37/DTbeWTWBHSUkkN2XoVwD8ZcOpnjuTOQ0xMyNnST48/UTsEnok27drbrld89x/YdBAk0svVhywX7xirho3UfTEORjbVtD2jasJTDuqcAYLgtBhnE7FT85RjB2jue7PmrPO1lzzO6zZV8Ca/viIG3EtuR/3a3+g5P5jaDv491mGFAmCkAqlsntGDyeSjp0a3I7g8F0h6EvdWA52paOsTOEwrAfu4mJrcJgvtLuMbaXzKS8vzsuALGN7NuPisaMNlm6x/s4lrw2kFyyyJl24V4o392GCI3Zj89eZR52Gsub8DM8qpjs6a3aMPamdJtIPSs3SgaATlChXEQ0lU6hxDaLCJg4mKbF4CrFLtVsDTu0sQqc4NcL42q1Pj423Ri6EB+JZeVEALo+1McFg+nYjoo6N51Xivk/03KvxzqHIv80S4JSBWT0ZbcSH/2nDjZ9k181+/dLYlYPnaPr1SQo1mz4t+ndgzCKUvxVj2/KYdeM9u/JN/EyydijKy63xUYdI9KJK21yW+zdJBU5K6pW2vvjrXfbHdPHeXX+thoT+HXftSR8sF/YazORJlWkrzGG7YCx/Km0Zp1OBswhNsoLXXDGdrc1ljB0DVZVWjrGXX01dV9Y5vQyDFs8I2x8V4HQZDMjgLGzfgF1juSFhjEKPor1dc98DVsjiyy/DqSfDk09UcuD+8YkMjY1LKH7gWxj162g76hYRugShD3DYoYrr/6Sob4Dv/1Dz3vsxd1ml8M89mdbjHwSHm5JHTsH90lXgbymcwYLQQ/D5fBx55JFMmjSJzz//PKd1s/H4CRrFBMbtgzlgQvo6HK6MeXGysSVWSJgzG6ZMSi47ZzaMHAFqwiKCQ2enqjGFoeltaHcNwHR5k9dPQ6eHuzFNDRrcMS8VSDM7V5aeXZlJ7+WkvdUEwuFo6WqJScQdGLcfwdGZJhvID2FTB4a87AYPAt1vDLr/+KSyzSVjCTi8uc2MmJiAOZSwXpcNtikcz+DBmopy0noAZkYTNK3E+NmKXc6Q11Qgg9gVFiS1pzRjnQnOobS6h1LrnZWzeJuZBHUtR1JPBpGApwxdOhBsvHPymV4hMH5/y7MyGzq5Lzuy9p57JDSdUY9LIXZlFSaaQXztHn0rjs68S8iml3T1NrWXjMTn7IdSloiclEg/gUxdOzaHWfUAKwQ7kc5uUjSXo3h2Cb0YrTWvvQ433qLZuBEW7w0/+oFiyBBFSYmivT1a1vnpY3heuBxdMZyWI/+O7jemcIYLgpBXZs1U3HkrnP9rzS9+pfnuKZrvnqIiIQzmoGm0nPw47jeux/Xh33GufpW2A3+LOWK3AlsuCIXj2muvZeDAgSxfvjxz4RgMI4U4EuOWMXtmKOl0yrC2/DFggJXvZ9TImGX97R9wvV7FpIkA5dEZ7/KAsvuWVYRKRwfbma3IB2nNy2WwblNP4nh2771Srz5hPHz6KZSUQH246U7NnB3tqylTStk4iyhg8aIMOWTsIrxiwvViaXaPwDB9mP3GxS0PDp0LgbbQzH/a3r4Qbpdi5EhF0Gl0SjwtLwO1Q9kOPCN2DZ4R8UJzuKyNSdem1qDLhxJwFYOzGLYmiOoJ+z7irZWFvZ07fVXW7dihYxJ4Z5VgPi5XXRcoE04P0Mk41iwJ50+z/s/sRQa5h6GlnsygAEpVB5m/K3zwIQSCpNHqMmyPsi/TkbSSGStJQ/g4ZzuZQi4J7GfPircjHxKwf8y+GMXO5MZyQDy7hIKzcqXmp7/QXHSJprgYbviz4reXGwwZktCpAz7cL15J0X8vJjhyd1pOeEiELkHogwwerLj1RsWhB8M998JPf6HZti3mtukqxrf4Alq/fR8og5JHT8X90m/Fy0vYKXn11Vd54403OP/883Ne18ptlK6EprpahfLQdD1KKcaPUx0IS+y4J1ShsRNoUot32Xmm2S/vun2RWLPbrVImWB/QX7F4bxXKK0VeI8CaBuzGlrKFNmKXfSd3uVRW+8VQ1kxys2emKaQMGosnJOcdc3qgKDRraMZt7fzO0O5SPB7FvPke2+TVkXKVI9HlQy0TnWHBKLl8cbHlaTZjenhBVdrzKvF6ko2O2qmumSHheaawubBnV7B6Ev6h87JpMKbpQg+jO3dOG4ZdYF2OFnRI9MmiXA+ivFxRHcqPlaq7ZZXA3y4EPJ0wnzU5iF0h8Sqlh1qaqmLP7fBssfGzU8bjCnuNdcYdzlUcelGQwbg0iGeXUDC212juuEvz9DNQUQHn/kJx2CHYulSq2rUUPfULHFs/w7frmfj2/FnnE5kKgtBjKS5WXHCeYpddNNdep/nuGZoLL4CFC6LXB3PYLpaX15s34PrgXpyr/0f7AZcTHLlHAS0XhO5j+/btXHLJJdx0000UZZthNwaliEti3dFH0oJrSvk0IM4LqJs3LE1OJGu5ZU95uWJr2Z4Mn+BPVSQ7EgrvuQf2Sc1zqyYrvF6oAbyZI+KyJuCpxu+06ccqdqMUGyv2p3ioSb8M9cUKkVVVVq0rvujCGYE7mCw8FnPgVCtk0pf9yx+HM9zf7AbjipkzkpYmlbNLRG/9YH3svhs0JefLDrVhv3zPPYiL7LBDKxWeBy99wZTrh/qGsxiKB6UvnNhMocWuPFyfakpmU9G2AlNl8CZLaCv81RVSEqpSeRGmUHOy8sbNUCbcx8JCVHeQUlvNIuRSozInfO+8iWkxM9xiksqHxLFpU6Ph1XvvlXyfsLO7pARo6FyUr8rD/VjELqHbaW/XPPwo3He/JhCEE46HU05UkdnXEjE/eYKSf/8CDBetR91KcOze3WyxIAiF4oD9FFMmwaVXaC64SPONAzU/PcdKygpYXl57n09gwoEUPXcRxf88Hf/kw/DtfR7a241PQILQzWitueCCCzj++OOZMWMG69evz3pdr9fKvVPVz01lZYCWVuuJ1lBQVeVBt7vRXi8UlWPYjGK83vZIeVNDv35ujBRvb/WI6eity1H9q1Fd9JJKt7sse91eiPUKKylLst8MbbvddgFU7Ajg9QYpKzOorAxa9To9KctH6q0vB92MqqhAlacvG0t9fRCvN0BFuYOy8jLwe1HlFRz8jUoaGzVVVY44m8N/H3X82Egd4eNRVeXB79d4QtOSVVVVMXiQj8YmTVVV8mBW63p0oxfKrf2UYROtdVQTut4L5RWRfRI0TbxeP6WlBlVVidOu2VNVBdVzvbhcynbfZjpOEXtcAfQOL3jLKDPL8XqDVFQ4qKqKH+KE6ystLcVwOynvn1wmEa/XB0pTVeWiqsoaHXpLfRgOTVWV2/o9abtSiwaNTdaxLi93hMom9M3mMgh4UZUVqGwORkoGoHesQTd5oTT5HEikvakCj8dDidebZJMdOuCzzguix6fE204gYF0LVP8hQB0eTxCXS1GVom+F+22/fsn7sqrKk1V/DJ93lNtfq7QH9HYvFJXa/j5vXjlGczPeygo8niq8Xh/uVi9eb4p+uaMcjJDIrIzItTS2bOz5mIpsyqRaz9Piwet1pTyu4b6eyq4wVVVuXJVeWhiJF6iocFJVFX+NDtcVuWeE7C0t9REIagYOdDJ2rEFxsTUDqi5xW+djCFVZhXJHQ+C1rxzd6o0cr/33NXG74dXX/En7Q/s8Sf0skSMO03g89p6r2VxDsunvABXlfhoazci1JRjUeL0+PG3WsSgp8eJyeVMez7b2IN6SIMUU4fU6U/YXp9O6lobvxXboGd+Ahs3o7dZ0saqqKuN+ilz/HOFrZPRYx/aLslJFVVU072BpqZ/WNpP+/Z0MGJB8//b5rf1QbnPNHTSohoZaD05nadb7OUx0n7gjXsLa5864nXaI2CV0G6apeelluOV2zZYtVq6EH/5AMWxoCqXW14zn1d8T/ORRzKFzaTv0j1kl+BQEoW8xfLjitpvg/n/Avfdp3ntP87Ofwj57Rx9wzKFzaDn537jeuxP3u7fjXP0qvj1/in/m8eIFKvQqrrvuOu644460ZZ5++mneeOMNmpubOeuss3Juo7m5GYD6umZaWiD0leIiqK21PEKMoiGYFcOhttZmfR0p39oGtbXNKcUuPINh+ECob8jZzqzxNeNsbkb7NCoUzmxWDMcsG59kvzO0sQGb7QJoaNA0N0ORB+rq/Fa9Dj/BFOXDGI2NGM3NBOvq0MHsB7BFRRq/35oVq7GhwaqjsQHtrKe42DI/1mZnczNer5faGHvCx6O2toVAQNMecoepra1l0kRNS0v0uMai6utxNDdjuhowS9JvX9I6jgbMkA31ddF9Vlub/dt3p68Fn8/+WGQ6ThF7Wix7tOmmPtBAczM0NCbbEa6vqbmZVp+TxobMtjY2aVpboaEhGnXQ1Khpa4e6uubIeROL3X4OE95PjQ0A/eKOIQBGJc7mzwj4XbbnXS6o+gZrv+iijH2X1kba29tpbm6htjYLl4+gP+n4DB+mWbEC6uubUQNm0+Bsp/3TFwkGSd7OEOF+a7cv0+3HWIymJozmZkyjKdIf42irt85hv7LdDwataLOV+vp62ou8NDfD6qKFjBuqbY+Bo6kJ1WIZW1ptXUu1uySu7tjzMRXZlEm1nnWsfCnPDWfMzgzY2BWmvj5+vzfYnBPhusL3jLC9zc1WX66vt8KWfbFa5eDdQQcx6jdgNrdDc1RMUY2N1vVDWcdLKfD7U+wPf1tW14HWVvvlmdatqqpK2TcTqQ/dF6xrqcI0re9tbdaxaG5pxe9sTnk8G+o1zS2ttNJGc7OyPS61tS2R/WqodH3DCcXDcTZ/ZG1fXX3GbXUEXai2OowK6xppBqPHOrZfWO1G+0D4ftjYAB6b0PRAwPrdru9UVDTT4mynyNOU9X4OE3ttiKQ18LcmbWc2IpqIXUK38N77mltv16z4AiZOhEsuUkmJ7GJxfP0Wnv9ejGrcjLH3z2iac0bSdM6CIOw8uFyK006FRXvB1b/XXHqZZpe58LOfwJjRoWuJ04N/jx8RmHwYnpd+i+el3+L89DHaF1+EOTybXByCUHhOP/10jj766LRlRowYwdtvv83SpUuZMSM+xujYY4/l8MMP5/e//33GtmJzdpWXwayYnERmtc0UiAnsMhdqakgtdEUa6uJwH7u3+kNm5auqLsXjUdGE7vWdD2NLtN/Kn5WydIfb6eodFRw2D+3I7CUWmysnZYJ6m/LZmB/ObxNbduxY+OxzcCWYNnMGlHU2JLO4isDkQztZSYgczrmox2XHj+nQIYqhQyI1YoRnoUwTwhQO4MpHgvoOVxKznyJJ9ZUTHNklNQqO3B3tzmMsbgbm7wbFX+anrnycwrZ1hCacMKsn2q2Rv8a7kWDQ+nSlzJWeTcilynhadtVuCY6cD8EAQ1yKfv00Hk92DYXP31S5PdPl7HK7FWPHKEyXIkOe+5TEhzF27DlC1AOhS1m+XHPL7ZoPPoQhQ+DSXyv23y/Ng3FbPZ7X/oTrk0cw+42l9fgHqZi6d6ffcAmC0DcYN1Zx283wn6fg9jutXF7fPEZz2qnRUGhdNYq2Y27HsfK/eP53LSWPnIx/4kH4Fp2bJrGEIPQM+vXrR79+mbIJwcUXX8zPfvazyPetW7dyxhln8Oc//5lZs7ITepSKzkY3aBBpE1rbUVysGD48p1W6iPyNEGxrykN+mTxYkd2aHVm1M0lVOtpmBnRZFvmTEigvtz6zEp2yEbtsxLNhQxXDhkZKRJaXleYwU113jPNzaUOlGbHals9cznCEZnhM07VmzoCv1yXnLcqJjLZk+D0s9JlmljPUxdenS/pns1LeKC9TOIuzO07mADuxKc/k2pcjx6vrr6mBCQd0+toWqStgfTpDIrdSioHVmlGha04mW/v3h9GjYUhymsU8kIWAaDgjTiOJQteihZZ39wdLklfLMo1k+s3P2w1CcnYJPYh16zW336l5+RVrbPnznyiOOJzUMyxpjfOzJ3D/7w+otnp8887At+DHoSl4BUEQojgciqOOsMIY77hb8+hj8Mxzmu8cD8cebQ3AUYrgxG/QMmZvXB/cg/vdO3B+9TLBBd+HGSdGZ8cShF7K0KFD476XlFh5UUaOHMngwdmF/Md6doXfXO/sxD+Xd2Sg1JnBVY7Zg0MMHgRlZaFVu1xI6cIE7R0hZoMHDVQsXKCte0DqFRJXy0h2IkhPI4cNDO2MivJs18mP2DVwoGJgKOn1gt3B54f3P8jShFxtSjlNadidKxh6EZ+hf/cijySzYkTa3wu6KVk13kkDHSldWnMmfH90xigns2YqVMNcTF8T/vVladdXSjFmjIHzq+RtMgwYMypczvrMRqMLjFmUl6gnj0fh89k3aGaYvVEphcetU3gPd/5eEe/Z1bE6ROwS8sr2Gs3f7tX850lwe+CM0xTfPi792y5j+xd4XrwSx4b3CQ6dS9t+v0nh+ioIghClokJx7s8VRx1hzex66+2aRx6FU06GIw6zXKhxFeHf/YcEph2D+40/o16/Ee+7f8M373T8c062EloLwk6KUgqnw3og7RtiVx6EmBwGG3knmzg8G2ZMj5a3S9Sckk6Ndjs/UtYlA1At2ztdTyzpha4YT4Qs6op0gTyJAhWhdyzVA/JTX1pyObauEqbPdBEYl93kAtnUnWvX8noVJR056XL1Sksk7Nmlswu00nSPY15eyHI2w2zYdRfiBI1czqMEoxI+ewcRz66EFLC6fKh1nVDZ9F37bd5vn9jrdw5GeUICWzCcMC3/+zRTGCPAgg7O4ps74tklFJCtWzX/eEjzf09aKvAxR8MpJyuqKlN3TNW4CfebN+L87N/gKaftwKsITDuq63N7CILQpxg/TvH73yk+Xaa5827NX27Q3PcAfPs4OPJw6yFalw2i/aBrKN73Z7Q9exWeN67H9eF9+OefhX/mt8WLVOj1DB8+nBUrVuS8Xvgh1ezVYlf+HvLDOVkCASCUM8qsntxNNnQ+Z1dOrXmsGBzt7Q71JZngiF2zFhkyk51QEhYDsxpUhgd6eYq4KitTHLBfFu3mhez7kC4dSHDiN/La6xwOMJWbxqKxmQuH6Fg4YwbxJOOBscYcygxm1bI5cArG6m3WF29/6NHXzfhtHz8Ovvwq29LxVKYZz3XIpKxCw/PTZD6I5OzKUg+2J/8icbb1Zl1TQlXZiF3hyTvS1JofeyRnl1AINm/W3P8PzVPPWCfEwQfBKScqhgxJ07Hb6nG/eweuJfcDGv8up+Hb9UworuwuswVB6INMn6b4yx8VS5Za16Wbb9X8/T44+ijNsccoBvRXqEFTaDvirxibP8H9xvV4Xrka1/v34Jt/FoFpR4voJewUzJsL20IONeGH2ECPHrR1H2HvBZ8PUEYHEobnz7ssUqPh6pqBX1E5gYnfKNwEQMrIwwvO3HaMEU48nkPOrmw8YHpedFtXGpTdoH1j5QFdaENMQ7GfOaIjWemzPG89ZdY1IehDlQ/q2TmFE3bJmNGKMaPh+Retbe1Mn+3kbifROJcT/IH0ZQpJxLMrxaVy/q7WzMT5oiP7VXfigJaWWuHwY0Yn1NkxZ+O8ES92iWeX0I2sX6+57x+aZ58DhwGHHwrfOUExeFAaT66mrbg+uBfXxw9BoI3A1KPwLTgHXTYk5TqCIAi5Mme2Ys5sxRcrNQ88qHngQfjHQ5rFe2tOO9XPqJEac/AM2o69E2Pdu3jeuJ6iFy/HfPtm/PNOxz/zW+AqKfRmCEKXUVWlInM1hD27cgljdLujD/89ijzEHrrCYleXJBLOkgQBKDihC0WDXIWugsR35o90s4cl0ss3tevIJkF9twVpZBsWl8rzKxzGmOMFLY/5oLqMDEJyl83GmH4N2xX32B3afTbFewh2ObtiKS9XkQkyUtIF4b8dXymxCsWM6cnLzc6IXXm4gMaH5YvYJXQDa9dq/n6/5vkXLVfObx4DJ3xbMWBAGpGrdjXu9+7G+fkTYJoEJh+Kb7fvofuP70bLBUHY2Zg4QXH5pYqzvqd5/N+aJ5+GF19qYOJEK5H9/vuCZ8RutH77fhzr38P17m14Xv097nduwzf3FPyzvyOJ7IU+T79+1lvdsWOyX2evPbvOns6iXSXgKkr9u9OT9iWbJzSGNXONrsvHyFGnCGPseW5DvQ5tOFBmMCp2ZbNO6LNXenYV2KBOzbCYC7mol3Z4rKk7tTubKTx7G9nlr0uH2X8CRs3KDq2b3qb4CjwehSfRsb4HnVRVVbCj1pogqeNkL3blphN1XR60cJR5j5ikQzy7hK7kq1Wae++zZlcsKoITvg3Hf0tRVZWi42kTx9o3cX30EI6vXgKnB/+Mb+Of9110+bButV0QhJ2boUMUP/qh4ozTNG+8VcLf72/m6t9rbr4FDj1Uc/QRiiEjdiM4YjeMTR/jfvd2PG/egPvdOwhMOxLfnJPR/bLPOyIIvQmnU7HH/NzWMbJJYtSdON1oZxHmoGnosvQzUQbH75/29w7nZAl7eqhuyNRbMTRzma4mZuBRXg79+8HECQW0JwPBUQtRLdsxWqzvuXh29aAxd/Z0g9Fm1eiMZQZWd7ERRuiE7aAXifZWExi9FxRlcsvJDW9PcA5P0weKPNkJkmb1xLSThnXYeac7ZmPMI7NmhkLbO0Oecv91J3nxbi3wRonYJaRl+XLN3x/Q/O81KPXCqSfDcccqKirsO65qqcG57HFcHz+CUb8Os6Q//vk/wDfnJCjp183WC4IgRCkqUnzzmCL2XdzCx5/AP/+lefgRePAhzR67a445SrHbrjMwj7wRY/tKXEvuw/np47g+eojA6L3wzzmZ4Og9ZRINQehpKIPg+Pxk/raEvNyf8M3qyWi3F106sBOtZ05QH5h0MKqqH9TVpSzT7B5Ou7N/J+xIR/K+cTgUc+d0UXP5wlOK9pSi1oQVrCzW6c1iVxcLBdnksttn79xnaZsw3vI2zRYdmkQCM0XccTaj9Riha/fdOj+z3KKFmeuYNcNyHuha7PvAPnvb9+mS7hDockn21YNOPKdTpQxhzJ5OJzpLUa1Vn64Ynt96gZkzYO3X8TNx9jZE7BKSME3Nm2/DQw9rln5kvbE783TFsUdbM8kk4W/F+dVLOD//Pxxr3kDpIIER82nf6xcEx+3bO+LaBUHYaVBKMWsmzJqp2L5d88R/rJlkzz1fM3wYHHUkHHLweMoPuIL2hT/H9fEjuJb+g+LHv4/Zbyy+OScRmHqk5PUShD5KRTn0z1UrMhzoLDxd0pKNK5EyEvKYJFPrndU5O/owYREiF88uCWPsGJlnaUtm9Kgc1wmPMcz8JBG0HefkiMeTuY6BA7vh+KToA3bHZe5sUjoydA29y7Mrv6RJ/dOhnF2GNdFIF3gVV1YqKis7unbPSHooYpcQob1d88xz8Mijmq/XwZAh8NMfKw49GEpKEs6+oA/H1+/gXPEUzpXPo/wtmOVD8e96Jv6pR6L75ZD8QxAEoUAMGKA44zTFqSdrXn0NHv+35sabNXfcBQfspznm6Eomzj8L/7zTca78L64P/07Ri1egX/sjgSmH45/5bczqyYXeDEEQ8shuu/buQdac2VBS3NWt9M59lMtgMuJn17ucUHYuQmKXCmaYUUIOUFqKO3i9yD3MrYu8m3oDYQ+sNNEBHd4thZpRtxcge0agtlbzr39rHv831NXD1Clw5WWKRXslJOLzt+BY8zrOlS/gXP0Kqr0R7amwBnxTDsccOkfCewRB6JU4nYr99oH99lF8tcpKaP/cf+HJpzXTp2mOOdrJvosPITD5UIxNH+H6+GGcy/6N66OHCA6ZhX/GtwhMOhhcXT7CFAShz5KfN+ED+nfhQLKnTlGYpVlhLy0zh9lHUw1AHY7cZjHtTnQvFSNzJRrG2BOnh+095CqydFiUiay4EyrIoTGyLk+dc7GvbXJPQMSunZi1azUPPap57jnwB2CvhVbS+RnTY6b6bGvAueoVnF8+j2PN66hAG2ZxPwITDyIw/gCCI+dLmKIgCH2KcWMV5/5C8YPva579r+XtdcVvNbfeDt/6Jhxx2ExKvjGL9r3Px/X5f3B+/DBF//01+tVr8E85gsCM4zCrJxV6MwRB6G306ozoBSLHfRUOYzRz0OxSTciwcAG8+lqHzBDyRXgMksmzqw8RGLsYAu2FNgPoiDzfdTMH9niUQWD8AeBIPQtKeNKArs/n1rMpKYaW1vzUJWLXTobWmiVLrXxcb74NHg8ceih8+5uK4cOtC49q3o7jq5dwrnwex7q3UWYAs2wI/hnHEZhwAObQuWB0w2xDgiAIBaS0VPHNY+DYo+Hd9+AfD1khjn+7F448QnPcseUMmHMS/tknYmxcguuTR3B98k/cSx8gWD2ZwNQjCUw+FO3t6umoBEHoCyibv3oaumwwumZlVjPx9UTCopRpdr4ut1thKJ2TcCbkmbDYpVMd0D54cNxe618e6TbPrrxV0EtxZnYQmTEdKiu6wZYup+Pn3q7zoC2FnqvdpTnVJWLXTkJ7u+alV+DRxzRffAH9quB7ZyiOOsJKSKgaNuD88AWcK5/H2PAhCo1ZNRr/vNMJjD8Ac9C0nffCJAjCTo1Sivm7wfzdFCu+0Dz4sOahh+HhRzUHHqA54duKMaPn0j5sLu2LL8T5xbO4PnsCz6u/x/2/PxActaclfI3bV8IcBUFITW/w7HJ6CI7fv9BWdJhwGGNKbSSGqZPh63XZ1duTD1mfxnBg9p+AWTa40JYI2RA6T7ScMCkZPEj2jdutbGeADIzbDxy5yVcidvVxtmzV/PsJzX+etPJxjRkNF5ynOHB/8DStwbn8v1aI4pZlAAQHTsG34McEJhyA7jdO7t6CIAgxTJqouOwSxVlnah75p+bJp+DpZzQLdteccLxi9qxyAjO/TWDmt1G1a3B99n84P/8/ip4+F+0qITB2MYGJ3yA4ehG4dnI/dUEQhLyRnRdBWOwKZiF2DRumGDYsu9blcblwmNUTsyglBygdHfbs6miCejkeQkfowHOziF19kHCo4mOPa1573bqc7LUQjjnKZJdBH+Na9TLOf7yMseMrAIJD59C+6DwC4/dHV44oqO2CIAi9gSFDFD/9seK0UzWPPwGP/Uvz459ppkyGE46HRQvBWTUa354/wbfgHIwNH+Bc8Yw1o+OKp0PC1z4EJh1EcPRe4PQUepMEQSg4OvS/DAS7itJQ9FdHZ58TBCFKh+erEHV456HAx1rErj5EU5Pm+RctkWvNGqishO+e0MJxc99mwPaXcbz7KkZLDdpwERwxH9/s7xAcvz+6dGChTRcEQeiVlJcrTj0Zjv8WPPe8lQ/x0ss0Q4ZYyw49GIqKDMzhu+Ibviu+fX6NY/37OFc+i2Pl87hWPIV2ey3ha8KBBEfvCa6SQm+WIAgFoReEMfZyBg9WFBdrKirys497Q+TpTk1PnT10ZyWX2RiF3k0POfVE7OrlaK1Z+hE8+bTmlVehvR0WzdjIpT94g+nuV3Ctewv1ajvaU0Fg7CLax+1LcNRC8OSW3E0QBEFIjcejOOIwOOwQeONNK5n9n6/X3H0PHHWk5qgjFNXVCgwHwZHzrZlsw8LXFyHha/mTaIeb4Mg9CIzbl+C4fSS5vSAIQlpyHzTnS+iKs0LUrp5J+LiE41eFAhM6HnK+9H1COWq1K7+TKeSKiF29lO3bNc88B089rdm+uZWFw97n+m+8zmzvGxQ1r4aNYFaMwD/reILj9iU4dE7aqU4FQRCEzmMYir0Wwl4LFZ8u0/zjIc19D8D9/9AsXqQ59hjFjOmhgZHhJDhyd4Ijd4d9L8bYuATnVy/h/PIlilb/Bl74DcHBMwmM34/gmMWYAybIA6Ig9GXEC6XXMWQIbNxUaCuElBRVYPYfj1k5qtCW9CkG9Iet26Ckw47o2T3LmJWjJAKpl6LLBhMcuTu6pH9B7RCxqxdRW6d59X/w2iutmOs+Zk7V+1w77X0mzVuCQ/vQzmKCA3elfdQJBEYvRFeNloGRIAhCgZg+TfG7KxWbNmkef0Lzn6fgxZc1EyfAscfA/vtaHmEAGM5oqOOi81A7vgoJXy/ief3P8PqfMb0DCY7e05rdcdQCKK4q7AYKgpBnwmKXPLv1FqZOgUnZ5EcvFEo8mszqSYU2oceT63Bx2DDFwIEalyvXa1Vunl3m4Ok51i/0JAotdIGIXT2e7TWaJa9tY917yyjdsYQ5/d7n20M/wTU8gFYG5sApBIefhG/MQoJDdwGnzTydgiAIQsEYMkRx9g8Up39X898XrGT2V/9ec/MtcMjBmkMOVowZHfPgpxS6/3j8/cfj3+37qKatONa+iWPN6zhXvYJr2eNoFOagaQRH70lg1J6YQ2aL964g9BXkRWWvQSmFsyeOpoqrMPuPw6wcXWhLhD5K7kIXkrNL6HZ64uV55yXoJ7h1JZvfXMr2z5bj3LaMkY7POLp4K/SHQH8X7f1moMefTuuweVZoouTeEgRB6BUUFVl5vQ4/FJZ+ZE0m8uhj8ODDmimTLdFr38XJ+WR06UAC044iMO0o2rWJsfVzS/ha8zqu9+7C/c5taFcJwaGzCQ7fleDwXTEHzZCXH4LQy9CuElRrHRjyeC50EqUwqycX2gpBsEcEfaGbkLtpd9PeiNGwEdWwEaNhIzRsoG3DWti+mlL/OkwVYDwwRhtsdI+jtmIB/glTqZwyDT1wKriK8BV6GwRBEIQOo5RizmyYM1tRV6d54SV4+lnNH/+s+cv1MHu2ZtFeikULsZLax61sYA6ahjloGv75Z4GvGce6d0L/3sP9xg0oNNrhiYhf5tA5BAdNg6KKgmyvIAjZYQ6egS4fBu7CJvTtlUi+M0HIGYUVPN3t2pOIXUI3IWJXR9Ea/C0oXzP4m1G+FlRrHaqlBtW6A9Wyw/7T1xxXTVuwiC3NI1nbNIHtHIhn6EQqx49gyp5jqervJZyRRW7hgiAIfY/KSsU3j4FvHqNYtUrz8qua/70Gf75e8+frYfw4zbxdYJddFLNnQnFxwgOi22tNQjJuX+t7Wz2ODR/iWP8ejvXv4X77ZpQ2AWvSkuCgaZgDp2IOnk6werLk/RKEnoThlGTMuRIK39ZyLROEnFEGhB4RuqlBCWMUupfeKXa17MBo2gymCTponaVm0Hqg18GY5cG4Mkqb8esE/ahAOwTbIdCOCvog6LP+DrRD0Ifyt4CvGRX6h68Z5W8GXwsqjQRlGm58rn40637U+avY0jSKdTuqWFc/kE2tQ9nSPhT3gKEMm1jFnD0NZs+CPQYqqqqqqK2t7cadKQiCIPQExo5VjB2rOOM0+Hqd5rXX4b33Nf/6Nzz0iMbphHFjNZMnweRJismTYOTImCT3AEUVBMftQ3DcPtb39iYcWz7F2LIMY8unOLZ8huuLZyPFdXEVgYET8ZSPxOw31pr5qHwIZtkQ8JQX5u2rNiHQDoE2lL/V+gy0h+71AczKkSLSCYJg4SomMHovSeshCB3AYVhD4+4jtwT1gtBZeqXYVXLfURjN2/Jer3a4wemxPkP/tLsEXF50SX+CFSPxKS9tppeWoJdmv5eGdi81TSVsq/eyobaSNduq+GJjf+paSwif0CUlMGoUTJoAE/dTLB4PY8YkDFAEQRAEIcTIEYoTT4ATT1C0t2s+XQYffKhZvgJeegWe+I/1skUpGDhQM2I4DB8OA/or+vXD+lcFVZVeisvnUzxoPm63FUJJWz2OrZ9hbF+JsWMV7sZ1OFa9jOuTR+Ns0K4StLcaXVwV/ecpA1cx2lUMzmJ0XFL86AsgFQxERapAq/USyR/6DLRBoBXlj/2MFbba0u6b4OCZtH7n4XztakEQejtF5YW2QBB6JYXTnGQMLHQPvVLsajv2TlT9Bsv30nBYn8qBNqzPLdsN1q03CJoOTG0Q0A60VgS1g4BpYGoHQdPAp934tYdWn5vmNidt7QZtLdDWDm2t1mdrK9Q3QH09NDbapwRwGNB/AAwaCANHwqG7wYjhihEjYPgwa9ChRMEWBEEQOoDHo9hlLuwy17qPaK3ZuAlWfAHr1sG69Zr16+GVV6GuLrXHsWFAcbHG4y7DcMzHUPNRBjgdBhqTMmc9gz0bqHZtotq9kYHujVS6tlPurKPcuYVy5wq8jkY8RituI5CV7UFt0G568AWLaDOLaA8W0W56aAsWh/5V0mYWRb63m0W0BYtoN4ut8mYR7tJizj7bg+FwgOHA7D8+L/tVEARBEHZmwsPTbvPukjBGoZvplWKXOWAiDJiY8vdLfmfy6bLc6nS7wFMExUUxnx7LK2vwYKisgPJyRUUFVJRDRYW1rLLSErMcDjlpBUEQhK5HKcWwoTBsaGRJ5LdAQFNXBztqoWYH1NVZL21aW6GtTdPSCu3hiEBtfbpcLtra2jF1BdqsoFFNpVHBamXVHPtsqgBlgkP7catWnMqPRsWUCxXCIKCKCeJCuVR0XWVTX+i71jG+Ydr6roDKaoU5HrS8NBIEQRCEtGhvNZj+rMrOnQPrN3RftJGWMEahm+mVYlcm/nitYtt26y22w7A+VczfhiPmbwPcbnA65aQTBEEQejdOp2LAABgwwO5X+/tcVVUptbXZPRhH8YT+CYIgCILQUwiO2C3rsqWlVv7PbkfELqGb6JNil9er8MqszYIgCIIg9HFeeeUVbrrpJlasWIHH42HXXXfl5ptvLrRZgiAIghCP24t2l6LdMlAXuoc+KXYJgiAIgiD0dZ577jkuueQSfv7zn7P77rsTDAb54osvCm2WIAiCICTj9BAcu3ehrRB2IkTsEgRBEARB6GUEAgGuuuoqfvWrX3HcccdFlo8fLwn8BUEQBEEQjEIbIAiCIAiCIOTGZ599xpYtWzAMg6OOOoqFCxdy5plnimeXIAiCIAgC4tklCIIgCILQ61i3bh0AN954IxdccAHDhg3jnnvu4eSTT+a5556jsrIy5bpVVVXdZKXQVcgx7P3IMez9yDHs3cjx6/uI2CUIgiAIgtBDuO6667jjjjvSlnn66acxTROAH/zgB3zjG98A4Oqrr2bRokU8++yzHH/88SnXr62tzZ/BQrdTVVUlx7CXI8ew9yPHsHcjx6/3k41YKWKXIAiCIAhCD+H000/n6KOPTltmxIgRbNu2DYBx48ZFlrvdbkaMGMGmTZu61EZBEARBEISeTt7FrlzdAcV9MBnZJ8nIPklG9kkysk+SkX2SjOyTZGSf9Bz69etHv379MpabPn06breb1atXM2/ePAD8fj8bNmxg6NChadeV4937kWPY+5Fj2PuRY9i7kePX9xHPLkEQBEEQhF5GaWkpxx9/PH/9618ZMmQIQ4cO5a677gLgoIMOKrB1giAIgiAIhUXELkEQBEEQhF7Ieeedh9Pp5LzzzqOtrY1Zs2Zx7733UlFRUWjTBEEQBEEQCorSWutCGyEIgiAIgiAIgiAIgiAI+cAotAGCIAiCIAiCIAiCIAiCkC9E7BIEQRAEQRAEQRAEQRD6DCJ2CYIgCIIgCIIgCIIgCH2GHiN2vfLKKxx33HHMnDmTXXfdlbPPPrvQJhWUfffdl0mTJsX9u/322wttVo/A5/Nx5JFHMmnSJD7//PNCm1NQfvCDH7B48WJmzJjBwoUL+dWvfsWWLVsKbVbBWL9+PRdddBH77rsvM2fOZP/99+eGG27A5/MV2rSCcsstt3D88ccza9Ys5s2bV2hzCsYDDzzAvvvuy4wZMzjuuOP4+OOPC21SwXjvvff4wQ9+wMKFC5k0aRIvvPBCoU0qOLfddhvHHnssc+bMYY899uDss89m1apVhTYrb0j/77lk0/fa29u5/PLLmT9/PnPmzOHHP/4x27dvjyuzceNGvv/97zNr1iz22GMPfv/73xMIBLpzUwTg9ttvZ9KkSVx11VWRZXL8ej5btmzh3HPPZf78+cycOZPDDz+cTz75JPK71prrr7+ehQsXMnPmTL773e+yZs2auDrq6ur45S9/ydy5c5k3bx4XXXQRzc3N3bwlOyfBYJC//OUvcWOAm266idj05HIMdz56hNj13HPPcd5553HMMcfwxBNP8OCDD3LYYYcV2qyC85Of/ITXX3898u+kk04qtEk9gmuvvZaBAwcW2owewe67785f/vIXnn32WW644QbWrVvHT3/600KbVTBWrVqF1porrriCp556igsvvJCHHnqIP//5z4U2raD4/X4OOuggTjjhhEKbUjCefvpprr76an70ox/x+OOPM3nyZM444wxqamoKbVpBaGlpYdKkSfzmN78ptCk9hnfffZcTTzyRRx55hHvuuYdAIMAZZ5xBS0tLoU3rNNL/ezbZ9L3f/e53vPzyy/zlL3/hvvvuY+vWrZxzzjmR34PBIGeddRZ+v5+HHnqIa665hscff5wbbrihEJu00/Lxxx/z0EMPMWnSpLjlcvx6NvX19Zxwwgm4XC7uuOMOnnrqKc4///y4mW3vuOMO7rvvPi677DIeeeQRiouLOeOMM2hvb4+UOffcc/nyyy+55557uPXWW3n//fe59NJLC7FJOx133HEHDz74IJdeeilPP/005557LnfeeSf33XdfXBk5hjsZusD4/X6911576UceeaTQpvQo9tlnH33PPfcU2owexyuvvKIPOuggvXLlSj1x4kT92WefFdqkHsULL7ygJ02apH0+X6FN6THccccdet999y20GT2Cxx57TO+yyy6FNqMgfPOb39SXX3555HswGNQLFy7Ut912WwGt6hlMnDhRP//884U2o8dRU1OjJ06cqN99991Cm9JppP/3LhL7XkNDg542bZp+5plnImW+/PJLPXHiRL1kyRKttfV8NHnyZL1t27ZImX/84x967ty5ur29vVvt31lpamrSBx54oH7jjTf0SSedpH/7299qreX49Qb+8Ic/6BNOOCHl76Zp6j333FPfeeedkWUNDQ16+vTp+sknn9RaR4/pxx9/HCnz6quv6kmTJunNmzd3nfGC1lrr73//+/rCCy+MW3bOOefoX/7yl1prOYY7KwX37Prss8/YsmULhmFw1FFHsXDhQs4880y++OKLQptWcO644w7mz5/PUUcdxZ133rnTuzJv376dSy65hGuvvZaioqJCm9PjqKur4z//+Q9z5szB5XIV2pweQ2NjY9ybOWHnw+fzsWzZMhYsWBBZZhgGCxYsYMmSJQW0TOjJNDY2AvT664f0/95HYt/79NNP8fv9ccdw3LhxDB06lKVLlwKwdOlSJk6cyIABAyJlFi5cSFNTE19++WX3Gb8Tc8UVV7D33nvHHSeQ49cbeOmll5g+fTo/+clP2GOPPTjqqKN45JFHIr+vX7+ebdu2xR3DsrIyZs2aFbmOLlmyhPLycmbMmBEps2DBAgzDkLDxbmDOnDm8/fbbrF69GoDly5fzwQcfsGjRIkCO4c6Ks9AGrFu3DoAbb7yRCy64gGHDhnHPPfdw8skn89xzz1FZWVlYAwvEySefzNSpU6moqGDJkiX86U9/Ytu2bVx44YWFNq0gaK254IILOP7445kxYwbr168vtEk9hj/84Q888MADtLa2Mnv2bG699dZCm9RjWLt2Lffffz/nn39+oU0RCkhtbS3BYJD+/fvHLe/fv3+fyskk5A/TNPnd737H3LlzmThxYqHN6RTS/3sXdn1v+/btuFwuysvL48r279+fbdu2RcrECiVA5Hu4jNB1PPXUU3z22Wf885//TPpNjl/PZ926dTz44IOcdtpp/OAHP+CTTz7ht7/9LS6Xi6OPPjpyDOyuo+Hca9u3b6dfv35xvzudTioqKuQYdgPf//73aWpq4uCDD8bhcBAMBvn5z3/OEUccASDHcCely8Su6667jjvuuCNtmaeffhrTNAEr0fY3vvENAK6++moWLVrEs88+y/HHH99VJnY72e6TcePGcdppp0WWTZ48GZfLxW9+8xt++ctf4na7u9rUbiPbffLGG2/Q3NzMWWed1U2WFY5c+gnAGWecwTe/+U02btzIjTfeyPnnn89tt92GUqo7zO0Wct0nYCUaPfPMMznooIP41re+1dUmdjsd2SeCIGTH5ZdfzsqVK/nHP/5RaFOEnQzpe72PTZs2cdVVV3H33Xfj8XgKbY7QAbTWTJ8+nV/84hcATJ06lZUrV/LQQw9x9NFHF9g6IRueeeYZ/vOf//DHP/6R8ePH8/nnn3P11VczcOBAOYY7MV0mdp1++ukZO9aIESMiKmnsgMztdjNixAg2bdrUVeYVhGz3iR2zZs0iEAiwfv16xo4d2xXmFYRs98nbb7/N0qVL49xKAY499lgOP/xwfv/733elmd1Krv2kX79+9OvXjzFjxjBu3Dj23ntvli5dypw5c7ra1G4j132yZcsWTjnlFObMmcOVV17Z1eYVhM5cT3Y2qqqqcDgcScm4a2pqkt6kC8IVV1zBK6+8wv3338/gwYMLbU6nkf7fe0jV9wYMGIDf76ehoSHOO6impobq6upImcQwm7C3QriM0DUsW7aMmpoajjnmmMiyYDDIe++9xwMPPMBdd90lx6+HU11dnfRycOzYsTz33HOR38E6ZrGTZNXU1DB58mTAOoY7duyIqyMQCFBfXy/HsBu49tpr+f73v8+hhx4KwKRJk9i4cSO33XYbRx99tBzDnZQuE7vCA/BMTJ8+HbfbzerVq5k3bx5gzRy2YcMGhg4d2lXmFYRs94kdn3/+OYZhJLle9nay3ScXX3wxP/vZzyLft27dyhlnnMGf//xnZs2a1YUWdj+d6SdhT0mfz5dPkwpOLvskLHRNmzaNq6++GsMoeGrCLqEz/WRnw+12M23aNN566y32339/wDpX3nrrLZnlVoigtebKK6/k+eef57777uszYrH0/55Ppr43ffp0XC4Xb731ViQKYtWqVWzcuJHZs2cDRNIY1NTURJ4V33zzTUpLSxk/fny3bs/Oxu67785//vOfuGUXXnghY8eO5Xvf+x5DhgyR49fDmTt3biTXU5g1a9YwbNgwAIYPH051dTVvvfUWU6ZMAaCpqYmPPvooMtP1nDlzaGho4NNPP2X69OkAvP3225imycyZM7txa3ZO2trakqJaHA4HWmtAjuHOSsFzdpWWlnL88cfz17/+lSFDhjB06FDuuusuAA466KACW1cYlixZwkcffcTuu++O1+tlyZIlXH311RxxxBG9PlFuR0kUPktKSgAYOXJkn3jz3hE++ugjPvnkE3bZZRfKy8v5+uuvuf766xk5cmSf8urKhS1btnDyySczdOhQzj///Li3MzvzG5mNGzdSX1/Pxo0bCQaDfP7554B1/ni93gJb1z2cdtppnH/++UyfPp2ZM2dy77330traGvcmfmeiubmZr7/+OvJ9/fr1fP7551RUVPS5F03Zcvnll/Pkk09y88034/V6I57nZWVlvX5SFOn/PZtMfa+srIxjjz2Wa665hoqKCkpLS/ntb3/LnDlzImLJwoULGT9+POeddx6/+tWv2LZtG3/5y1848cQT+1T6i55IaWlpUm6/kpISKisrI8vl+PVsTj31VE444QRuvfVWDj74YD7++GMeeeQRrrjiCgCUUpxyyinccsstjBo1iuHDh3P99dczcODAyEuEcePGsddee3HJJZdw+eWX4/f7ufLKKzn00EMZNGhQITdvp2Cfffbh1ltvZejQoZEwxnvuuYdjjz0WkGO4s6J0WO4sIH6/nz/96U888cQTtLW1MWvWLC666CImTJhQaNMKwrJly7j88stZtWoVPp+P4cOHc+SRR3LaaafJDS/E+vXr2W+//fj3v/8dUed3NlasWMFVV13FihUraGlpobq6mr322ouzzz57p70g/+tf/0o5icOKFSu62ZqewwUXXMDjjz+etPzvf/878+fPL4BFheH+++/nrrvuYtu2bUyZMoWLL764z3mGZss777zDKaeckrT86KOP5pprrimARYVn0qRJtsuvvvrqPiEKSf/vuWTT99rb27nmmmt46qmn8Pl8LFy4kN/85jdxL3I2bNjAZZddxrvvvktxcTFHH300v/zlL3E6C/5ue6fj5JNPZvLkyfz6178G5Pj1Bl5++WX+9Kc/sWbNGoYPH85pp50Wl/NVa80NN9zAI488QkNDA7vssgu/+c1vGDNmTKRMXV0dV155JS+99BKGYXDggQdy8cUX7zQvFgtJU1MT119/PS+88EIkVPHQQw/lRz/6UWT8LMdw56NHiF2CIAiCIAiCIAiCIAiCkA/6ZjIbQRAEQRAEQRAEQRAEYadExC5BEARBEARBEARBEAShzyBilyAIgiAIgiAIgiAIgtBnELFLEARBEARBEARBEARB6DOI2CUIgiAIgiAIgiAIgiD0GUTsEgRBEARBEARBEARBEPoMInYJgiAIgiAIgiAIgiAIfQYRuwRBEARBEARBEARBEIQ+g4hdgiAIgiAIgiAIgiAIQp9BxC5BEARBEARBEARBEAShzyBilyAIBeeCCy7g5JNP7vD6d9xxB8ccc0weLcqeY445hjvvvLMgbQuCIAiCIHQ18pwmCEJvRMQuQRB6NXV1ddx+++386Ec/Kkj7Z599Nrfddhv19fUFaV8QBEEQBKGnIs9pgiAUChG7BEHo1TzyyCMUFxezzz775KW+YDBIIBDIuvy+++6Lx+Phn//8Z17aFwRBEARB6CvIc5ogCIVCxC5BEFKyevVqfvSjH7HHHnswY8YMFi9ezE9+8pPIQ8aOHTv4zW9+w6JFi5g+fTqLFy/mwgsvjKz/xRdf8Itf/IJ99tmHmTNnsv/++3PZZZdl9XZtx44dXHrppSxcuJDp06dz8MEH8+ijjyaVe+yxxzjkkEMwjPjL2bp16/je977HrFmz2GOPPbjmmmt4+OGHmTRpUly5SZMmccMNN3Drrbeyzz77MH36dNauXQvAf//7X771rW8xa9Ys5s2bx09/+lM2b94ct75hGBx00EE89thj2e1UQRAEQRCEPCDPafKcJghCapyFNkAQhJ7LWWedRXl5OZdddhlVVVVs2bKFV199FdM0qa+v5/jjj6ehoYEf/vCHTJw4kW3btvH8889H1t+0aROjRo3ikEMOoaKigrVr13Lbbbfx+eef8/DDD6dst6mpiRNOOAG/389Pf/pThg0bxquvvsoll1yC3+/nO9/5DmA9KK1Zs4Z58+bFre/z+TjttNPw+/1cdtll9OvXj0cffZTnnnvOtr1HH32UMWPGcNFFF+F2u+nfvz8PPvggl19+Occeeyw/+tGPaGpq4oYbbuDkk0/m3//+N16vN7L+vHnzuO+++9i0aRNDhgzpzC4XBEEQBEHICnlOk+c0QRBSI2KXIAi27Nixg7Vr13LzzTez3377RZYffvjhANxyyy2sX7+exx9/PO4t3BFHHBH5e++992bvvfeOfJ8zZw6jR4/mxBNP5LPPPmPq1Km2bd97771s2rSJJ598kpEjRwKwYMECGhsbufHGG/n2t7+Nw+Hg448/Bkh6C/j444+zbt06/vWvfzFt2jQAFi1axJFHHmnbnsPh4M4778TtdgPQ3NzMddddx3HHHceVV14ZKTdz5kwOPvhgHn/8cU466aTI8vB2LFmyRB6iBEEQBEHocuQ5gE9DfQABAABJREFUTZ7TBEFIj4QxCoJgS1VVFSNGjOCPf/wjjz76aMRlPMwbb7zBrFmzkh5gYvH5fNx6660cdNBBzJw5k2nTpnHiiScClut9Kl577TXmzJnD0KFDCQQCkX977bUXNTU1kXW3bdsGQL9+/eLWX7p0KSNGjIg8QAEopTjwwANt21u0aFHkASq8flNTE4cffnhc+0OGDGHMmDG89957ceuH29+6dWvKbRIEQRAEQcgX8pwmz2mCIKRHPLsEQbBFKcU999zDX//6V6677jrq6uoYMWIE3/ve9/j2t79NXV1d3EOKHX/60594+OGH+fGPf8zUqVPxer1s3ryZc845h/b29pTrhd9Wpqq/rq4OIFJH7AMQWA9X/fv3T1pvwIABtvUlLq+pqQFIOc124kObx+OJs0cQBEEQBKErkec0eU4TBCE9InYJgpCSESNGcO2116K1Zvny5dx///1ceumlDB8+PJIbIh1PPfUUZ555JqeffnpkWVNTU8Z2Kysrqa6u5oILLrD9fcyYMZFyAI2NjXEPNtXV1axZsyZpve3bt9vWp5RKah/g2muvZezYsUnlY/NAAJFErlVVVbb1C4IgCIIg5Bt5TpPnNEEQUiNilyAIGVFKMWXKFC688EL++c9/snLlSvbcc09uvfVWvvjiCyZOnGi7XltbGy6XK25ZNlM/77XXXjzwwAMMGzYs6e1cLOEHnPXr18eVmz17Nv/6179YtmxZ5K2j1pr//ve/GdsGmDt3Ll6vl3Xr1qXMHxHL+vXrgejDnSAIgiAIQnchz2npkec0Qdg5EbFLEARbli9fzlVXXcUhhxzCqFGjCAaDPP7447hcLubPn8+wYcP4z3/+w6mnnsrZZ5/NhAkTqKmp4bnnnuOGG24ArIehu+66i/79+zNw4ECeeeaZSLLSdHz3u9/l6aef5sQTT+S73/0uo0ePprm5mVWrVrFkyRJuuukmwEpE6na7+eSTT5g5c2Zk/aOPPpo77riDs88+m5///OdUVVXx6KOP0tDQkPR20I7S0lLOO+88rrzySrZt28aiRYsoLS1ly5YtvPPOO+y5554ccsghkfIfffQRbrebWbNm5bqbBUEQBEEQckae0+Q5TRCE9IjYJQiCLdXV1QwdOpS//e1vbN68GY/Hw8SJE7ntttuYMmUKAA8++CB/+ctfuPXWW6mvr6e6upoFCxZE6rj44ou58sorueaaawArwegf//hHjjvuuLRtl5WV8dBDD3HTTTdx2223sXXrVsrKyhg7diwHHXRQpJzH42G//fbj5ZdfjiRUBSs3xN13382VV17JpZdeSklJCYcddhgzZ87kjjvuyGr7jz/+eIYMGcKdd97Jf/7zH4LBIIMGDWLevHlJyV5feeUV9ttvv6ScFIIgCIIgCF2BPKfJc5ogCOlRWmtdaCMEQRA6yjvvvMNpp53Gq6++SnV1ddqyZ511Fm1tbdx77715a3/r1q0sXryYu+66iz322CNv9QqCIAiCIPR25DlNEIRCIZ5dgiD0aubPn8+uu+7K3Xffzfnnnx9Zftddd1FaWsqoUaNoamri2Wef5ZVXXuG2227La/t33303u+66qzxACYIgCIIgJCDPaYIgFAoRuwRB6PVceumlvPzyy3HLXC4X99xzD5s2bUJrzYQJE7jhhhtYvHhxXtseMGAAl156aV7rFARBEARB6CvIc5ogCIVAwhgFQRAEQRAEQRAEQRCEPoNRaAMEQRAEQRAEQRAEQRAEIV+I2CUIgiAIgiAIgiAIgiD0GUTsEgRBEARBEARBEARBEPoMInYJgiAIgiAIgiAIgiAIfYa8z8ZYW1ub8reKigrq6+vz3aRQAORY9i3kePYt5Hj2LeR4FoaqqqpCm9AlmKYp/amXI9eE3o8cw96PHMPejRy/3k82z2nd6tllGOJI1leQY9m3kOPZt5Dj2beQ4ynkE+lPvR85hr0fOYa9HzmGvRs5fjsHcpQFQRAEQRAEQRAEQRCEPoOIXYIgCIIgCIIgCIIgCEKfQcQuQRAEQRAEQRAEQRAEoc8gYpcgCH0DXzOqdi201hXaEkEQhJ0G09RorQtthiAIgiAIQhx5n41REAShu1A1X+L65J84v3wBo2FDZLnprSY4ei8Ckw8lOHIPUKqAVgqCIPRdXnwZ+veDuXMKbYkgCIIgCEIUEbsEQeh9tNbief0vOD95FBxOgmMW45/5bXTZYFRLDcbWz3F++SKuZf8iOGQWvj1/aolegiAIQt6p2VFoCwRBEARBEOIRsUsQhF6FY+0beJ69ANVaj3+X0/Dt9j0orkwq1x704fz8P7jfuonif55OYOw+tO93KbpscPcbLQiCIAiCIAiCIHQbkrNLEITegda43r+b4sfORBdV0XriP/Ht/StboQsAh5vA9GNpOe1Z2vc+H8e6dyi593Ccn/1ft5otCIIgCIIgCIIgdC8idgmC0PPRGver1+D53x/wTzqE1u88glk9Mbt1nW78u3yXllP+j+DAqRQ9ez6ep38FbQ1da7MgCIIgCIIgCIJQEETsEgShZ6NNPC9dgfvDv+Pb5TTaD/kDuIpyr6ZiGG3fvJv2hb/A+cWzlNx/NMb697vAYEEQBEEQBEEQBKGQiNglCELPRZt4XrwC10cP4Zt/Fr5FvwLVicuW4cC/2/doPeEhcLgpfvRUPM9fimrelj+bBUEQBEEQBEEQhIIiYpcgCD2TsND18cP45v8A34KfglJ5qdocNI2Wkx7DP/dUnMv+TcndB+F+4wZUS01e6hcEQRAEQRAEQRAKh8zGKAhCz0ObeF64HNcnj+Db/Wx8e5yTN6ErgqsE397n4Z91Au7X/4TrnVtxvX8XwbGLCYzbj+CQmejKkVFPMjMIvmZUWx2qrT7mXx3a7cWsHIU5aBo43Pm1UxAEQRAEQRAEQcgJEbsEQehZmEE8L/wG16eP4dv9R/gWnNOlzenKEbQf9md8tWtwLf0Hzi+eo2jlf63flAHuMjD9KH9L5rrcXgJj98E3/wfo/uO61G5BEARBEARBEIRCoxq3ABpdNrjQpsQhYpcgCD2HgI+iZ87DufI52vc4B/8eP+q2pnXVaHz7XIRv8QUY27/E2LoM1bAR1VYPDhfa7QV3Gbq4El1UEfdPtTdibF+Jc+3rOD9/EueKp/HPORnfXr8QTy9BEARBEARBEPosjg3WpF+ByYcW2JJ4ROwSBKFn0FpL8X9+imP9e7Tv82v8c04qjB3KwKyeiFk9MetVdHEVwcqRBMfvR/uCn+B+6ybcH96LY9NS2o64Ee0d0IUGC4IgCIIgCIIgCLFIgnpBEAqOql1DyYMnYGz6iLZD/1Q4oSsfFFfh2/diWg+/AWP7SoofOSXk2isIgiDsrNTXa5Ys1WitUxfSJqT7XRC6AZ9Ps2Wr9ENBEHo/InYJglA4tMb52ROU/OPb0N5I63H3Eph0cKGtygvBCQfQeuydqOZtFP/zu9BWX2iTBEEQhAJRXw/ba8DvT13GueIZjE0fdZ9RgmDD0o/g408gEBDBSxCE3o2EMQqCYI+vGcfXb+PY8inG1s9R/ha0clg5q8qGYg6eRnDQDHTF8A7NlKgaNuJ5+Xc4v3qR4LBdaDvo9+iKYV2wIYXDHDqH1mNup/jRUyl6+le0HXULGI5CmyUIgiAUiEyOW0bDBsyhszvdTiCgWb4CJk4AtzvPsxkLfZrWNutTnAwFIf+sWaspL4N+/eS63B2I2CUIQhyqbh3ud27BueJZVKAVbTgx+49HF1WiTD+qZiXGVy+jgu0A6KJKgoOmYw6eTnDgVMyBU9Dlw+wFMDOIY8MHOJc9jnP5k6ActC++0ApbVH3T0dQcOof2fS+l6PlLcL99S5fPLikIQu/mgQce4K677mLbtm1MnjyZSy65hJkzZ6Ys39DQwJ///Geef/556urqGDZsGBdddBF77713N1otZCIsHKQUEPKsLGzabP1zuWBS9ikoBaHg1NZq3v8QFuwOXq8IAp2lpcW6tpSUyL7sCaz80vo8YL/C2rGzIGKXIAgWZgD3Wzfjev9OMFz4px1FYPKhmIOmg9MTXzbox6j5EmPLpzg2f4qx5RNc792J2wwAoD1lmAMmYlYMB2cR+JoxGjdhbFuB8jWhXSX4Z52Af97pPW6K2q4gMOOb+Dd+iOudWwmM3xdz4NRCmyQIQg/k6aef5uqrr+byyy9n1qxZ3HvvvZxxxhk8++yz9O/fP6m8z+fjtNNOo3///lx//fUMGjSIjRs3Ul5e3qH2a2o0RUUywOwKwlJWarHL7C5TBKFHsyWU5rSuDrzegprSJ/jsc0v0npX6nYkg9FlE7BIEAd24leJHT8Ox4QP8U4/Ct9cv0N7q1Cs4XJgDp2AOnEJgxnHWskC7JYBt/Rxj23Ic25bj2PAhBFrBXYouGYB/6lEEh+9KcMwicBV1z8b1ENr3vgDHmjfwPPdrWr/zCDhchTZJEIQexj333MO3vvUtjj32WAAuv/xyXnnlFR577DG+//3vJ5V/7LHHqK+v56GHHsLlsq4pw4cP73D7Hy61PuWNc/7J7NklYpfQQyhw+GKkedHc80IwCEbfDJ4QhIyI2CUIOzmqcTOBf52B0bCZtkP/1PEE8U4P5qBpmIOm5dfAvkJROe0HXEbxv8/GtfQB/Lt8t9AWCYLQg/D5fCxbtoyzzjorsswwDBYsWMCSJUts13nppZeYPXs2V1xxBS+++CL9+vXjsMMO43vf+x4Oh+QH7ElkLXblOaRf8i4JvY1wn+1AOljBBrkGCDszInYJwk6Mat5O8SOnQHs9rd+8G3PIrILY0dam2boNtm2DrVth6zbYXqNpaIDGRmhqgsYm6++WFnA6we0CtweqKqFfP+jfD0aOVIweBWNGw+DBoHrYk1Jw7D4Exu2L+60bCUw+NL33nCAIOxW1tbUEg8GkcMX+/fuzatUq23XWrVvH22+/zeGHH87tt9/O119/zeWXX04gEOCcc+zzA5ofP07lhH1RxRVJv3m9Vi7GqipP0m+p6Mg6OyPl2wN4vUHKy12UlycLWtrXgvZ6wXBiVFVlrK8qQ5mGhiBeb4DycgdVVfK43xPJdAwLhdfbjssPlVVu3K7uf44qLfXj9ZpUVjipqup+0V7729CfP4MaOQ9VOSJt2Z56DGPxen243YqqKokoSKQQx6+v3jPNUMxxNvev7kTufoKwsxJop+j/foxq3o7jzMcxS0Z1aXOtrZqv18H6DbBhA6zfoEOfUFOTXL6yEsrLoKwMysth6FDr75JiyyXb74e2diunw44dVsLHJ5+Ovr4aOBB2maOZO1exyxwYOLBnCF/te19Ayb2H4X79L7R/46pCmyMIQi9Ga03//v258sorcTgcTJ8+nS1btnDXXXelFLsAGr5ehjlwStLy5mbrGlpb25K1DR1ZZ2ekvkHT3Ay1dRAM2tyPfM04m5vRDhfB2tq0dVVVVVGboUy4vYYGqK3tGfe/rkA1bAR/K7r/uEKbkpJt2zSrVsNuu0ZfwmVzDAtFU5PGH4C62mZcBRC7GmL6biGSqquWGhzNzei1nxLUpSnL9eRjGEtjo8bt7tvXgY5QqOPXV++ZzuZmAALduE+zEStF7BKEnRTPi1fg2LSU1iP+Svmw2ZCni1MwqFn7NaxcCavXaFavgdVrYNOmeFfqgdUwbJg1287QoYpBg6xlAwfCgP4dmyq9qSnU9pewZKnm7XfhmeesRqdP0xx4gGLfxVBZWbgbvq4cgX/uqbjeuxPfvNN79AO6IAjdR1VVFQ6Hg5oE9b+mpoYBAwbYrlNdXY3T6YwLWRw7dizbtm3D5/PhdrtTtCZxLRFaa3Fs+ojg6L3A6DovkkgYY6rUXF0UxtjXcWy0QnwDPfhe2tQMDY1gmlDQ6OKgP6d8oYUKfyt02J3Oc7Iw1bgJ7SkHd9dn29+6VePxQEVFdBu0Lvw+7ats3aopKobyMhEScyLgs65F3RCBI2KXIOyEOJc/hWvZv2jf4xyC4/fvcD1aa9atg0+XwYovNCu+gC+/grY263eXC0aOhKlT4NCDFaNHw4hhlsjl8eT/Aldaqpg2FaZNhaOOUGhtiW1vvwPPv6D501801/8VFu+tOfZoxYzphQl19M07HddH/8D99k20H/qnbm9fEISeh9vtZtq0abz11lvsv791XTZNk7feeouTTjrJdp25c+fy5JNPYpomRigD8Zo1a6iurk4jdHUtX63S9OsHVQV8qZALjq2foXzN0N4IxZVd1k54sGlmTFDfO/bbzkB7u+UR09nnhPCxb2wEl0sXZrbT9kacq/9HcMhsdMWw7m8/BzKeK11N5HjnxwDHhg8BCEw+tFP1rF+v8Xqhqip1//noE+szdpIREbq6Drv9DVhijg6Cq7jbberxaI3zy+cxK4Z3S/ocEbsEYSdD1W/A88JlBIftgn/+D3Ja1+/XLPsMPvkUPvlU8+kyy80crOmhJ06AI4+ASRMVEyfA8GHgdBbuwV0pxdgxMHYMfOd4xarVmqef0Tz1DLz4kmbiRDjpO7B4ERhGN9pZXGl5d719C/7dfoBZPbH72hYEocdy2mmncf755zN9+nRmzpzJvffeS2trK8cccwwA5513HoMGDeKXv/wlACeccAL3338/V111FSeddBJr167ltttu4+STT07fUJ5GP9qmnlWrrX+9ZkbHSDbsLvao0gmfMfj9mjdfNZldpuk3KD92iGTWOdrbNf97HUaPggnj81Pnex9Yn3Hnhr/NGhR30Ountk7jckJxMTgcqY+6arce1lTz1qzFru4SSRobNQ5HNGQxoxdkd9HDVKLPV1ifuV5be9hm7BQ4v3we6LzA2TexOqTRsFHELkEQ8ozWeF68HNC0HXxtViEbO3Zo3noH3nxL8+570NpqLR8zGvZeBDOnK6ZNs4StbhWMOsDYMYpzzlacebrm+Rfg4X9qLr1MM3o0fPdk2Hef7tsG39xTcS25D/fbN9F2+PXd0qYgCD2bQw45hB07dnDDDTewbds2pkyZwp133hkJY9y0aVPEgwtgyJAh3HXXXVx99dUcccQRDBo0iFNOOYXvfe97hdqEDtPQGB60534NrqvTtLbCkCEduH53U/hgROuyGXha3tAmO3ZAv8E9+z66s+DzWZ81NXkQu9KIDc6vXgQ6Pih+PySgGQbst082NvS8/vX2u9ZnooiTF8+u9ibwpM67tTMgYYxCjyLUGXU3RdaI2CUIOxGOlc/hXPMa7ftcjC4fmrJcS4vmlVfh2f9qliy1rkuDBsFB34Ddd1PMmNG749OLihSHHwaHHgKv/g/+9nfNZVdqHv4n/PwnMHVKN2xbUTm+uafieetGjG3LMasnd32bgiD0eE466aSUYYv33Xdf0rI5c+bwyCOPdLVZXc47KQa82RD2mBkypAMNR8Surr3uZwrNUjpomSA5u4QOYGbrBZVFPy+0MJIvzy7VtBXH+vcIDp2T9pnXZs2wJbz+pqZ/P5gyufc+83aL2BX0oRo2oau6drKrjuL3Wzl9x43teTO173x07wVGxC5B2Flob8Tz8u8IDpqOf9bxST9rrfnkU3ji/zSvvma9aR45As44TbFoIYwZ0/duEIah2Gex5aH2wktw862a7/9Qc+ghmrPOVPTr17Xb6597Cu4P/obr3TtpP/S6Lm1LEAQh33RkABUMatrbCzPLmi3dNLJPN4DXgKKbwil3Et57XzN6FFRXd66f5eOxp2c41WRnxafLNIFgF5uSgfC5EuxsGKOvCbBCODW5iF1RWlutWcOn9OL3kd1xiTM2fYzRtIVAcSUUVXR9gzny5VfWcSwrg0EDC23NTk43q+kidgnCToL7jetRLTW0HXVLXPhiIKB58ul27vmbZvkK60ZwyEFw0DcUUyb3PYHLDsNQHLg/LFwA9z2geegReOVVzQ+/D0cc3oWhjZ4y/DO/jeuDv+Hb6+fo8p6dNFYQBKGzfLoMtm7rQTm9dGhk382iV/IPpuVPshPcc7sa09TU1cPHn2YI7UtDPntDumPebUSMSN+/Nm3OX5N+v2bLFhg+PLc+nb+cXSq+wqxXC6/X2fY70HYX0B2eXSoYivsteKI1e8Kej2aBhdyuQmuN39+xmey7n+49J+T1kSDsBKjatbg+egj/rO9gDpoGWA+Dz7+oOelUzfkXNtHWBr/6peLf/1T84mcGU6eonULoiqWkRHHW9wzuu8ea1fG6P2vO+alm7dquuzD7554CysD1wb1d1oYgCEK+UY1bUFtXxC2zS1ifyPbt1qdpE8/nDtRBy458mJc9EZu7+AE8XRijBoXMxpgv8jmw79LHoEB73qrytq/B2L4yc8EcNqiz+3HZZ1ZS9aYmzSefWs+c2RAMCRL5m42xoxXlRe3KQx2dw9Q9wYqeQV/dDytWwKuvWZ7TPZ4YE01Ts+ILjd/fdXaL2CUIOwHut28Ghxv/7j9Aa83rb2hOO1Nz+ZWa4mK46YYy7vub4sjDFR6PPGgPH67447WKSy5SrF0L3z1T8+DD2nZw1ll06UACUw7D9elj0Faf9/oFQRC6AseG93Hs+DJuWVaD49Atxi7H0MDGN3B+/VbnjcuFsCdCF7s+6KQ/4lHatPaNhDF2mrwcyjx2h5T2BNryUr8z2ExVyzKM7V+ksyIvbeVCOMm/qWHzluzXC18bOu0k1NHH2XxeC3qAZxeabjj8PWA7U6B2rKb/pqdAB9MejkBAs2q1zuqlTU9j6zbrM9grPNei+3fLFvh6nRVmmo72dk1TU8eOi9xRBaGPo3aswrn8SfxzTmTV1n785OeaC36tCQTgissUd92uWLy3e6fz4sqEUopvHKi4/++KBXvATbdofnmeZntN/m+C/l1OQ/lbcH30UN7rFgRBSCKHh3ljwwcYWz/LW9MqjdhVCFRkRN3FYpeO/4y0X7cOR9PGqGdXvsWu3jdui8Pn0zQ05rYRvcWzS+Uppkppfy6l89JmVxIJOev0cexgGGOYnqaadpAueE+bhp7Xv4wdq1AKDO1PezhWr4avVsHmPIbyJtJVQprqZFfPN6p5G87lT9l7r8YYmW3ffOtteOudjtkiYpcg9HHcb92IdhZz67LT+O4ZmlWr4LxzFfferdh3seq6fFR9hKpKxW8vV1zwK8Unn8Kpp1mecfnEHDCBwJhFuJbcD+G8B4IgCIWkZQfGpo+hfhNsX2VbJPHBOpsH7cg8Zz3koby7csykmo3RsfljXJuXQEjsip2OXbXU4Pj6rR60s7oOre09Kt59LzpTZ/Z1hf/ovF15IaUd+TFQZVNPB/pQobpd3jy7Okzf8ewKn1NdZobWGOvfQ/XkyATDsJxmtZnesyscPtuF/a6rjkP4HUlPuVWo2jXWp22/iMkfmKW9/kDHbRGxSxD6MMa2L3CteIaH157M3Q9VcsjB8I/7FEccpnA6ReTKFqUUhx2quPsOxeDBcMGvNdf92aStLX93Ff+cUzBatuNc+Xze6hQEQbBHQ2stjjWvp8zY6/z6LYz6dSz7DFZ+aVskudZsxK4e5tkVoYOjhB07sgtxTzsbowYVLhDj2WVsXIJq2dHxlyDajCbg7+G8+j947fXk5a0diPTLx4Avr5FsXdSI7oiq1wu8+MNbkzfPrk5bUug6OtF6Co/SvOFrxmja2i0qi2rYyIf/+ZD163NtS4X+1112NGrrNBs25rl2bVovnLLI7Rfu6T3mvhq5n9mdg8n7qSuvSiJ2CUIfxe/XbHjwrzT4ynls86ncdIPi/HMNKip6/oNOT2XkCMWtNylOPAGe+D848yzNyi/zc3MLjtoDs2qM5d0lCILQxTi2LLPeuvqa7QvEPKT68uhwmu9wi0CgAxXZCny519PQoPlgCXyVId9INq0ogpG/7H7tCBWtyymrydEtqkD4A9BeCMdmM9jlLkRdpQPkVm/uRuTzvO8InR64d/Riky+FyN+GamvoXB2dpKd4+uQDve5Dits3Zv3yJYIyQFnX2FzySubC+x/AZ59nLpfL8VCNmzDq12FszVyx0V2eXW31GNvS5Qa0iLy8sduZMUZ2R/8UsUsQ+iAbNmp+/8tlTAi8wHuuU7n59gpmzRSRKx+4XIofnmVw/Z8Uzc3w/R9qHn40D8nrlYF/9ok4Ni3F2LIsP8YKgiCkJBJQmOH3DMQIBUkPrnZvpEPV5iuRbq71qIYNOL94NrXIlwNhMaC5JXPZdJ5d4QJKEf8mPMVIQNsJNP428LfGLXLodoxgfpKg9yZyGUA5v3gWx9o3O1VHoYg6dtkb29KiO+b9FeLd96Exx3xpeSEc8ps3DbIwB9P51Ys41nUw0VCeiFx3uq6FLqs5kWAolM3tznFFZaBUbH5Ge3LXRHMPH86tjUz36OSiXe3Z5VzzOkZNFrO+hmzW2eag7MIhqohdgtDHePkVzenf0xxReiM+ZyXzf3wqJSUidOWbuXMUf7vLSl7/15s0557f+eT1/mlHod1eXEsfyJOVgiAIKciQPEtnEe6kIZpYPbEqXzPOL19A7VidsdnOJO3NWexqtLIPGzVfxdvW1bMxZnAWid2PNmvHf/v0/3CseiVumfOrF3F+9VJCAzpp3d5Kpj5ibFwS+TvXAV9X5xtKaXon+pyx+ROcK54G7HN2tbRo3ngLVq1O+CHHMMZshNx8kw/HqtZWzRcrsV5E5lhRdH927bkTDGqCwe657nTHZaCpSdPY+XcIKQl0WOxS1n1HB7PTjbJ9z5PyvDbt/86VHDwTXcFGyluXozUYWz+PCPimqVm7tgtmk8+4XWnCGGM9u0Kf69bD2rVd00lF7BKEPkJ7u5VH6pLLNPtP+ogFA15F73EGuL2FNq3PUlFhJa8//1zFx5/Ad0/XvPVOJy7Wbi/+qUdZM5i07MifoYIgCLFoS6ZKT3ZP/CqFZ1fNJmvUYzRviy9v8wa6M4PajnqIGfXrcMTMMplVku9EctANUiWoj1aVZvBgs4NUghdXGOeKpzG2rYimAC5clu+8kqmPGA0bc14njGlqPl2m4/JwRvZfF70r1MEANG2mttYmMX9bPapxU9r1jbqv03pthUNC6+pSFumxRM6VTnTd5Stg6zbLA7/DBqQj4EP7OqgEBn2oxs387zV46ZWOVWGHnSDc5Tm7Yli9Bj74oOvqDycpd7ty3JiQd5EifYL6jka7JhEXJt+ZHZ+9Z1e/+ncob/uKoN9nzT7ZWgvAunXwxZewfn1uLZum5rPP46+Jny/XrPgiyw6V9nf739ZlaaPjyxdwrHo1u8KI2CUIfYLt2zXn/Ezz7yfgpO/AxXvciFnSH//s7xTatD6PUorDD1Pcfbuiuhp+db7m9jvNjuWRAfyzT0QFfbg+/WeeLRUEQYhB5SmMMYVIsyxFmpFwVEPsQLYzg9qcr7Tdknm8A82nGZWqxo0oGzEHYNMmTUtL/DrGjq/ik973AXLZlFw3u74eNm2GL2Nzr+Vz18XUZZhWWKnesJTaVetYvwFqa+OLO9e8jmPDh3z9taa2Lt3ovCPG5Kje5dshJIeD05nuaxgkXePa2zXvf6Dx+7OtOHU551cvoJc/l2bV1Bc1x8alODZ8gOnPb4ix3f7Kq9jlb0M1bUU1bEjfaBcR9ezK9YZh5ewydCCr7pztGdIRz64OhTFmsZJSobBBHW99+GVQIMfZDHfUwoaNlmgcZv0GaPeFbcrSs8v2J233Z9bPASrQjvI1ZVcYEbsEodez7DPNGWdp1q6Fa36nOPuQD3GtexP/bt8DV0mhzdtpGDnSSl5/5OHw9/vh5+d2LKxR9xtDYNSeuD56CMxOzLUrCIKQkuxzgGSqJlfPodLWr3AEW/Iw01rUhrys0IlBWy7eP+kGpDa/AODY+jmOmDC9WD79DN5736ZCHVp/J/Hs6mhZiHrbOZ0dryOtPaFPd2AHQ+tftIRLX3N0IJrCO3HFSivx9Qcfal56JbXXTnZeiWnCinoY4a3JR14/0ySyo9ashdo62LwlWwvSFUlfxvlFGiHMb3mEqTzPlJrbtSUzxrblGOujFxfnVy/iWP8ejo1LI5Ub9es63kCOBPzWp8PI7ZqmlbJydmF2z2yFcWJXJ+rJ+EIqpmioTFK4YidP92AQ6uwE92w9u2zL2a/bVbKpiF2C0It55lnNOT/VFHngtpsVC/cAz5s3YHoH4p95fKHN2+nweBS/+qXBpb9WLF8Op5+p+XBJ7pdv/5wTMRo34fjqpS6wUhAEAcJPwc6v34agz8bjIvVTciCg+fIrTXt79CE7GNSZB1btTZQ1L6eq5WOWLLU8LaCnTJfePblz0uXs6qgO4fOnbq9D4ZldyKv/06xaldqm2toUA6GYxWvXap5/0Sb8z6ZsNoRFFTuxK5/akDtQZ9WZY46wHbWZhJ/UG5wpiX1X4Pj6HZwrnklvTzry4I1kGLEZ6+IrylhvqIDujCKfZ5E5G484uyLha2tHtsSo+QqjKbUyqBo3YtSuSbQipvEgaseqvPW98OHI2WNVGSgscbGlJbtrTDZ0uWdX6OKTzfYaKRyuImm/cmgWADPIgKZ3adjezHsf2Mx6nKl/h2y2vf/EJezE/u9syHKmUxG7BKEXYpqaW24zueoazexZcMetijGjFY51b+NY/x6++WeB01NoM3daDjxAccdtivIK+NkvNX/7e27JIYOjF2FWDMe19MEutFIQhJ2b6NNxXFhK5OfUo/yaHdDaClu2QjiM8bXXMz/IK38LSoFWDgDaQlE8HRkLhQUP2/CMgC/1iqka62IxIDLotBkj6LAXFrGfZHz4TzcAjtbWI5REwLLX54evVqcu8/6H9stjBdEvV4WWBRO2LSxShMUSUnglJNYdEpJcsWJXzO//e02z9KOO94+o8BhqKNT/O8qbb1n5dDShsMjcRtCdajurFlq2pxwM52JqJhFcJeQDjPtNAajOadgFDAPevj0+X1Kn6YpNCSar7O5gXeRvY/sKHFs/z5h/LmsiImjuYhdYLxQ2bYYP3vdBKKeVbfHOJqhPk7Orq8IYSeXZ1UEcbTUU+bdR0fp5ggkq1EZGxTjn33I9rM41r2VVTsQuQehl+P2aq67WPPAgHHMU/OEaRXm5Aq1xv3EDZtkQAtO/WWgzd3pGj1LccYvigP3hzrut2RqzeegGwHDgn3UCznVvo2q+7FpDBUHY+Uh6qjRsHjTjn/iff1GzZauO/KIJv3W2RqT+QBYPq/5WFBA0iqxWDaiv13z8iX3xdes1GzbaV/p1KHrGnyB2qZYanF8+j2q090godBBXql3UEQ+sbJItqx7i2GWsfx/1RdRbuTODMh0RDhPriBe7AN5LlzBbx3sWOmI1qBjPrnYfbNveYXMjhM8VbSQMv3LcFc0tlqeXNmFo/YsYZBMO17H9nW+9J5v6sskzpeq+xrHu3aRcdnV1mk2bdC4T2XXMyBDNzbrDOVrTXYuWfATvvJubSVqDx78NV7Ahblm26+eDqpZlUXErLPqY+Q3XzNlrLuIhZdlR3fweztBshZ0yowOeXTm2kPCZmnB/L974lu3yfB5/KzQ4O8+u6AuIzAJXvieMDCNilyD0IpqbNb+6QPPc8/DDsxQ//6nC6bSuZI41r+HYtBTf7j8EZ67z8gpdQXGx4uILFRf8SrH0IzjtTM0nn2Z3NfdPOwbt8OBa+o8utlIQhJ2S2NfXdq+yVfIj4rp1scWVlQQ9B88hFWgDBUFVFKlny1aoSxHVtXwFfJYi0X1KQqENqrXG/vcsnvpraiyvsebm/D9923mraE2HRqV2RdvbLU/iqLdY13h2ba/JbZBvNG3B9EVnkPTbhF6Gqa3VvPq/+PrtIl90aGcGgyEvmATProyEBmxhB7HY0yCvObsiwmPnPLt8Ps3qNcmGGTrNzkyia+Ve1bCBYNB61qnPLsooJek8uyIJqoPtccvf+8DKYxfWE+POrRzJxoPozbfhgxTeiFm0kPZXu/DktLVpqG56l0ENr8Ut63Z8iVNg5seISC05i11G6A2NtV44nDhnD7FsiRO7OtFGNAY5Y9HItcsfv+8jWb9ydYZLs0IwGF+hz6fZvNn+xUP484WX4LPP09/juupwiNglCL2E7TWaH/1Es/QjuPTXihNPUKgYyd795l8xK0YQmHpUQe0U4lFKcdihittuVng8cM5PNQ8+nDrXSITiSgJTDsP12RPQ3tg9xgqCsNMQfwVSyQ+advqXivlUoUpiHuzj67CpINAW95PWHX/ATbmeMjIUyFzh1lB0VCoRriOkG7dYoqEm4M/N4ylxE01T88VKa9YsdDDqLZbnUUR7u2bJUlj2WW7rZevg8eVX1kC/OWbcZi8SWtu1ahWs/BLsPLvSEha7guH6YuvOso5smomEMYZVtXBYlQ1pdtLWrda+6d8U765ml+g8sW4VNSKe1tpOep/E49i4NK2QmYtnV9owxvCPykEgkDwhUGJq77o6HfEGzcKCrG0FaMj5ES37xONhctlvsZgpLgGffZ6cU3bdes2ny3Lr+OmvV/bCak2NZuOmTniy5uoCpM1QgvqE8yR1AsXc7ElcPe58yoPYlcXBj87G2PHmskIle3Z98il8sgx8cbOcJtu+YWO4Cp1Yyvo7i8tQRwRKEbsEoRewcZPm7B9rNm6ywhYPPCD+Suz46iUcWz7Ft8fZ4HAVyEohHRPGK+66XbFoL7jpFs1FF2saGtNftP2zv4Pyt1iClyAIQh5YstRveSzFeXbZPQ6meeJXoDHQxHt2ZU78HErCHn4OJvUDbibRJ+Wv4W1JKRqkjD1J215n0RocwWZ0ynximrZ2+OKL+GWmmX3Ox3Cx5iZQoe2PzQemar7E2L7Sdt26Os3/XsvOWyssDrW0ZGVW1D7T/m+/377NTLlzwmGMbWHnnpw9u3ScLTZDtTj8/gRvPzOIc/lTWacbiIpSaQwMps43Fz6+xf7N1vfwPsxKrLJWNravjOa68rfgXPsmxpZlWayfPUn7P4OI6PFvwx2oTSqettuH96Xh4ONPYMlSy8MkTLzmrfl8RXa2x1qQLrTYNDXBYOaOlm5wriKiWjb1ZCySYsW4jwgbNlq5F2NZvgI2bcbyzLLJx5VIU5Nm2WfQmPAsGxVW7WPoPlyau1AeT3x//3SZnWdRMkaSKBy/Tt6EonwlqM8hjDFV/TlM6JgClVSvdd2JLmgPXX9tNT67DY4si3/Jlv1lW7N9e/b3RRG7BKGHs2q15ofnaJqb4IY/K3adl/D0p03Lq6tqDIHJhxfGSCErvF7FFb9R/PwnirfegTO+r1m+PM3D1MCpBIfOxbX0gby+eRUEYedme2L+IWXj2ZXx9bYR8UjKBesNe2iQZ6Z+wPWlyTEfS3L6sfAoN9U1M7PYlW2gV6aBi9+v+fgTjd9vhRUOaXiFyq2pZtm1KmtKiABa9hmsXmPfaGz7gYCOboIKD6B1VFH0NePYtgJje5yaFmHVais3VUMnQ8/SETujYKztr/wvvlzMWCiCrWdXwsJgKB6xqzy73n3PCluLGmWtmDwjnT0RYTidgWYgVCS5TKoubWA3S0NqjI1LrT9CgoaKndVMa4bXPkVpmzULQDa7ct369MJPpv1a3fQuAxvfTCqT9rEnLGYrIyK6xtZtzU4X7v/2tm0PhSunSgSf7jCt/BI+/iT1fvf5rH3y6TLYtl3bK7cJ/a+z2Hp2ZehyiUIVgHPVKzjWvpG+YqAx5NHW2JTRMtulOec6iwgo8R1j+3aoq0u3Xli8zM6zK9sE9XbXpK+/1jQ0pA5j7Koccqk8u5xtW3EHavP6Kie7nF0RJd7uxw63rbV1rDdtjnpgZ0LELkHowXy+XHPOT61EmzfeoJg8KfkK7Fz+NI7tK/AtOAeMzs3wI3Q9SimOPUZx818V2oQf/ljzf0+mvvD7Z38Ho3YNjq/fSllGEAQhF6w3otH7ibHl02iIYZiEJ/4i/9b4nEaRt/Y5eHYlkC6Msb3dfnl05aTmLYFAZRK70hiTog2ApR/paM6REMUbX2dI/QuR75u3aLZujZb5ep2Vk2zd+piVbDzONOlzpMR7UMWLXe5ALYbpY0WChhUWgnSooPJn54aV7UDPKpxDWTLPrhfGLuLO9vCYZtyA2c5DK4NF8XbFrKj81vkQuz9aoinH4g3MsGFJObtswg6jhVMLdomTT0bMsKkvaXUd/+WzzzWrvgqHAsb+Zi0rb4vvUKp5G/gTd4AlGC1fEQ4jTWFfIHm9bIh13Fi1SrNqdaxqFg1jjCyK3cbw5SlN/RtDYVVJAm8WHoLpxPh16zUrvoCmkAhUW2tfLlX3yWcuqUw1vf2u/XIVk3cr4A+yeUtCTToqrhuqYxNONCem9spApIWE/WPG6Pz4mlH164lH2+du65TXlT0rVsJHH6UOY+yQZ5fW0LID5/KnoN1eWYw4cCX0pZJt78UJyTm3Hf4W8RBUIbEr04bE3qds+k7yn9lZFXOssxWJRewShB7Kh0s0P/m5pqwUbv6rYsxom6fKoA/3mzcQHDiVwMSDut9IocNMnaK4+w7FLnPh2us01/3JtA3lCEw4ANM7QBLVC8JOwgMPPMC+++7LjBkzOO644/j444+zWu+pp55i0qRJnH322RnLmiZxo3gV9OPY8mlCqfh7zoCm96w/tEa17Aj/GR/GGLdGuvAFHfk/lU6QtWdX0pJksSvOc6ODI5pt26M5R8I4fPU4zKgqt25dvLAVbsowYgYLKV90R/fNRx9r3ntfY78P48XFgY1vUt30FqaZULeOLjBqV6Na66K/BXzRGdMSbO1KEsMYAwFNe3tyw+Fxc8aE8TpeEHGsfQvMYPY6ZziMMdGzq7WWknUvUuzbaL9eTPsASmfnWRX2LFHpDExzIJJFkXD7NqO+dGqX1mzYGNtXY64FKeQRx7p3cax+NX6hrxnPutcwTJ91vtpsV5FvM541L+Hxb4uzOS0JQnZbm+ar1fDVqmgRlSEBnBGXkzxXz63OnQz1oVx/7XHXMDtlOEFsTWNXNvvN9loaf8ll/Xqd5M2VSVz75BOTTz5NDDfWceeebRXhWRAT8s4WF1ufuYZBR9pK2NDYbudc/T8cmz5KWCHs2ZVoZMeOs9qxiva1n6U5JiZNTaE2OxOVEXO/NFosd+xUnrmRVRLWz3Y2xlSzxWubtx+mGb9dti9IYmxPdY/XWrN1a/wyVbs2aYbVWBoaiWxkti9PROwShB7Im29pzj1PM2Qw3PRXxdAh9q9PXZ88ilG/Dt9ev0iRc0XoyZSXK37/O8XJJ8G//w9+8nNNTUKSVRxuAjO+jeOrl1H1GwpjqCAI3cLTTz/N1VdfzY9+9CMef/xxJk+ezBlnnEFNTYqZBUOsX7+e3//+98ybNy+rdqw3ogn3lWDCgD2Fi49q2EDRxrdwmO3JYYwZxw7h17IhsStRoEmyMbXzkAq2U9K+ni9jPEqSxB5g0ybNa2/EPsynMjL3gU+6sKEYE1j5ZdSLIdNAVqHZus1Kjp/k7aU1aJPaOtMasIZ+dwWb4rZAqaghWlt5mmIHSY6NH+DY8CGkzB8WT3u7ZtOm3GZftCNxcPz+B/C/15PL2Y0PLS9AzapVMV4Bphnv2dfWxOav63MOYwyExa7QYuVrQQPF/i0ZPN3SKZjxtkOsZ1c6WTi1V1GiJ0PUAy76QzgfV7K4Gq0wOlBNp44km5ooMBk7VuH0N1Ds32yVS7yGAO5gHQCuYGOkQtWcGEedYEF4MBv6fO0Nu1LJ8Xmx556K9ezKcHxSHuMOKMDt7drKexVpPE2DYbE1F7ErrVegjXCcsJs+X5HszWXnIbNxU9RLtakxKsqt/DI682lsuHG6Hm3UfY2qXR357gg54wVyCN9UjZtt+zKEPLsi+8c23jnhhUJ4ea4iqIX/689Y9d4qVq22/11pHQ0979RbhOhJrMOTWqTK6ZcgECd+SWfG9hrNex9YQiiAY/X/8GxbkqK0ss5LbdLamm7bYmxP0hitBZs3J4fAbvrgExwbU7Vt3TO2bYurJiMyOhaEHsbrb2ouukQzbhzceL1iQP8Ud2FfM663byEwcneCo/bsXiOFvOFwKM460+CKyxQrv4Qzz9J8npDHyz/zW2A4cH30YIGsFAShO7jnnnv41re+xbHHHsv48eO5/PLLKSoq4rHHHku5TjAY5Nxzz+XHP/4xI0aMyKod0ya/zpo1CYOEVCNAMxA3ZIj1Kskc2WB5A6iYAX3iOps3a15/M/qAHPnZ34axbQVaazZv0VTUf0C/lo/wtyXGO8a/UQ/n9cjoKRa3UVluTwKGvyku/CdW3Ek3sNPEe8ilLmipg2vWBFmzFnSC+JA40FEhcbG+XvPJp5pA6Liv+6rZSrRuNzC0OeyrV8Onn8GG0PuWjo7f4nJ2kTrXT7jc+pj3O6a2BMOvYgaYZoJKsHUbbPh4NXWxLkDp0GZc+JWrYbWVnNsMgAbDtI+lNVNNcZeBqDCcZr00daYK2zFiPMsc697F8cVz6MQE4zbZoyPnbkYXOoukyRLC4X7hRNNm6qTm4W1XNV/hWPdORsHLUNE2E22w/sgi2X9ooJ1jtG3MON2+bseXLyYtC+csW7I0RZU219PwOR93XLWJ4+u34hL2hynxbWB43bNWH7Vrw8bVxWjdhjPYbF1jWmoiYplh+iL92+dL3taaGisEGyDgt+ptaoa2tvDy6DXaUPaXkjiPwVjP0uimZo1jwwfRQx0ncEYFw/Xr7ZOWh61QceKPJqWwm0nsCnXzxDxhhtmOx78t4VreCbErRtxToVx+mRwb4jc/g8jbvA2CvkjKgPCsoqq9MUmBje2jpgk1NSavvwnbt6c4wRKFSa1xBq1+u6PWZNNmbevZV1uX1mQg5uVElrvWmV0xQRC6g9de11xymWbiBPjTHxSlpalv0a4P78VoqaFt4S3daKHQVey7WDFqJFzwa82Pfqw59xdwyMGhWVBKBxKYcACuT/+Jb49zwFVUYGsFQcg3Pp+PZcuWcdZZZ0WWGYbBggULWLIk9ZvOm266if79+3PcccfxwQcfZNWWu6iYsnI3mFaymmBQ42t3463yAlBV5cHcUQ6GH48nOoB1lpVRUV5OoKgEjyeI26Uo8RRBsbVeRYULr9cq7/E14fV6oawUo6oKALO+lKKiIoopIuD1UlHhor4hSFOz9WDtafHQ0AhGtZfycidebyBij65ZTcO2DbzzxWRMRzHVLhOHw4PXW4JpWNfEyko3hrMNXecFrxejqgqn04fXq+nXz0VVlYFZU4pWlvKlYkM5K8pQITvLywN4vUHKK5xUVVluCF5ve8SWMLrE2g+esjKqqlwMa34BDVRVHQdAWZlVTxhPiwePR1EVagfA9HppazcpKvLgMTzg9lj7DfB6vXg87aG/XaiK8shg1ePxUFFWhscTtaekxGEdF7fC5fXiLyqiqKiIxibweDROp4PiYkVzqwtfi2b23pUolxVTVFbSjKP+ayorJlFVFT+g8nr9eL0mXq+DqionLpeJ1+unrFRRVeVO2c9iMb1ePA1BvK7kvmLtzLCbksLjBocTdtRCaFdQUe7C5SJunfJyjXOHdQxCK1OldlDuq2W7d0akXOwxi0W3aHxFXjyeAKXFLvq1raaibgeqcji+4hK8HoWzvIyW1viBX3m5G6cT9PoPIwYaMcc0kdJSP03NJkU+Ny6Hh/JSL7rFR3FxMR6PSXGxEekT2hVAe70EgjrSD8KUlBh4vSaeFmt7iout461cLtyGB683Oht3e0kRXm8RZWXWMdKtZej2cH3K6lvtDVYbpeUR+0u99XjaPGjlxOv1UlHhpLLcRHu9LFnqp95Xyr77WMfcbC4DbwklzSV4vf/P3nvHS3KU5/7f6p48c/LmHLRZWUhCQgGJYAz4mgs2vsb4Z3yd4BJsMMHXNiYIRBQYbGOMwZjki7ExOBBMFDmjnLNWu9p44uSZ7vr9UV3d1WnObFbo56PVmemurtRVNV1PP+/7jjA+aiGrVcClWHSoVquUqVAuVyiXy7iVKqPlPCVZRVQK/nzTY1j3QaXaIZeDfh/GxgpUqwFTXS4XqFQE7sEy2H3E2CjVWg3LloyOBmNqbNRmrlKmVCpTG6kxUqv5hM7YqJrXIyM9Gk2X8fF8aMxL5pHzVXL9nH8PzDHkFm2/zvp8rVagVBIUCmq9KTaLlMsWxaJLqSioVYvBOniwBjmXSqVMrlBldCzP+JgqX7bn6YsOK927OFS9wi+305FMWddTKBYZrxYRtfh4y4l2rC+5+Rus76j5MXo4zyp3GXOjT2DFoa+DlOxb+stcfyNMTaqxpduk1/1qNU+lUsKxq5QKaq6VS4Lx8XFmajN0i6qd1apFsajW69HRUcTEBLI9Foy5kWCMjdS6SCmpjaj1ZBi41SqlUp92R1Kslv329fuSarXL/IIia6bsMitW2IjxcX99d2eq1MtlSu2SaluzSKWaAylDa7EeD2PGug8gnR7CiHQvqqofarUadi5gXJbM/Jhcf5752hkUHTUfxegoYjQoQ6+d6h4lr0t+uVKNQwCsrlpratXEtWamXKTYcimVLKpVVXcxPk7P/40aZWIiF5CaTg+5+1oYWUZnyROpVvuUShYTE3ncapW+o+awWyxTrVYZGVFzq9guUSw6tGSVarWKsGxGai5SSm+eW1gTE7jVCrh97p+usv/hGivEzVQ7dzNefTY/u6/MxEKRQqkQW+OKTdVv0Tbq31+AYruI60KxOByNnZFdGTI8QvCt70j+4o2SHdvhmncKqtUBk7g5TeGn/0B/yy/grjgjPV2GRxU2bxJ85O/gDW+WXP0OyZ13S172EkEuJ+id/Rvk7/gSuTu+SP/0557qqmbIkOE4Y2ZmBsdxmJqaCh2fmpri3nuTlSo//elP+dd//Vc+//nPH1FZjXqLhYUOlmdb1+1JOm2Lhvd9ZqaJXa8jmo2QT6XWwgLzM4dpNJt0Ouptb8tp0HC962YDcz2n16DRaCDlAo7nodman6fdaiNLLRqNBjMzyjm0vqbjvWJu1OvMzgn/+A03Njhw2zQbCk2a3QUc22W008Z2OzQaDVxLbebuuKPBqtosdqOBdHI4MzMcOizpdmF6GnI5gb2wwK0/b5DLwbatwe+sMzuHRNVzfl7SaMD8HMzMqDSNhvT7RqPZUP3QXVhgZkbQardxXXUvQSmqfCfMUtLpdGi1gvMAuUaDZkPSbpWh36HttmnkGl6ZDTreRnVhoYM7M+1H6+t0OswePuT3mUqvHPtLCYX7voKsS1qWUiJ0OtBqKgKh0y7h0mF2ZgbyXmCCvddRnN7D7N48N940yeZN+C/c5rz+mPX6o15X34UI+mcx5BoNWk1Jw3u7PzMddlC9avYrICz2jj2VTjuuhJuZgXwhfM3c9Awl7x6AGo/9PnQ70Og3jGtTnAM1Z3AWGnQ60Gws0Cq2qM+DdEs0W006bUFrYSHmSHt6ukGxvRd7723+sX6aF3JUxLtGA2rtFq7TYX5ujpE8tFotdV+MMSEaM9iNBv2+9OejxuysvsfePPHud7ffQDodGo2AFJq3p2k0xsjn1D2y5uf9+a6ubeB2F7w5WvbnaKPR8vLvUN7/DeZXnUF997WAIlwOHa7799yan6fVatJstZELC8xNT2M3GjSbkk5HlWG3WrRaTVqtJg3ZYKFex2k1cGankWLUy7fj3SddB0mhoBRHhw83Qv2/b3+DiXGBPT+P6DVx5uZo1Mu02mqM6LSzc9BsNmnKFvX5BRbqdf9cc+9u5hqHmZ87A3dmH3Nzq8nnDeJ7dg670aDTs0NrokauodbFYrFIo14HITh8uEGlouZGp6va1Gyp+yOAesPx+9iu1xGdBq1mg3avwfRhkK5XfrcBjQbttvDWyKbf973mLNLpMDtfh158C1+fbSf0ZTA/6vUOTgMadoNOu+2PAz2WzLGh1/1Go0uzUadvC5p9lVerDbMz09QX6mpNaytn/L5CaG4etzCDtRCMuXZ/Dqs6jWUF68fs7JDrh9tX60dbjavewoLfvl5Phuq+IFqMjAj604f9oF32wgLNVot2q6Xa3unQqHcYlTK0Fut5OnewQUV2kJUpaM2Qe+D7OKufgBxZjutKHnpA9cPCQj3kl22sOY0jHdrWPMWOmo/OzGGkE5Bauu0AMwcOg9Pj6z8YYeMG2LQx3BdiTo1DfYM6HQndIvZEfK1pNdt0Okot1WiofPoz07S83+rO/DwzM4Kvfl0yNQnnnt4m12ggu/up92dhejcz+TXMzNj+b1KnA23ZpmE3mPbW6067Q73eo1mcp9GoUV9QCt1GAxrFJq4r6M/MkKsvgHS59d55msUxli7spdPvcO3X99FqNql0OrT7Lg0rvMZ1vH6LrqeNhkTIHpIcrZYaaK0h415kZowZMjwC8K1vK6Jr544hiC6g+N33Qr9L55I/PEk1zHCyMDoqeNfbBb/+a/Cvn4VXvloyMytxV52Ls3Q7+es/eYw+ADJkyPBYQL1e57WvfS1XXXUVk5OTR3Rt4JBbIqXE6SeZ0aWZMfZ8Mw6VKsW2ysvbNdYrgTJqsKywGeOamS8w2rrdvDhkgXHHnSoimMpDm2CFzRVBmdr5pjze8Z63GfHNO6RymBs3azTqOUQkt1TErbw8pEfZSzdjNM11iNv9RByjh32WJddeua8RykxMutCeRyzs952sz870OXAQ7r7HqHm4SzmKwGvq+pTPAJbskaMTKi92beQi6fRCzezr7hjSbk1I179GSEdlLyxwlD+6gT59jup3WJv0JEVBNDMPZ59zGhT6s6kOmfUcPDwdXCTTHHyZ1/n1SHZQX+odojB3X/QyI09XzWHg8DQ8cH8vsSjzu1zERlgrT2xvhxpthu8WLMH/mYzMPeGvI+GyytM3Ys3vZXT2OqYa1yN6YQfqRwYZqucgH2/T05L9B9RsNyucdF+THJvbjt7dp/XdMThDDzIZqi7IkBgz5XaqdjqO5Iab8CPGJlgjDkTuzv8Op5fa/DPu/FwmfPLNds1gKkkLioeZH19L+zYVAV20Z9XflvKb+cCDhv/F2JVq0IbMigf4n8rd8w3s+76N64aDLyS1BuDOu+Cmm1I6LWb3D/Q7iWvM4WlCa5Bdf4iJ5k3KjDuSnUa7A0L2IBKNUVhpy22QQb4/568rc3OB+egRDVfpsnr2K4y1ghcMmYP6DBkeJfjmtYro2rVzOKLL2ns9+Zv/ld55v4Wc2HiSapnhZCKXE7z0JRZveL3g1tuUH6877oLe2S/APnAb1sPXn+oqZsiQ4ThjYmIC27ZjzugPHz7MkiVLYul3797Nnj17eMlLXsLOnTvZuXMnn//85/nGN77Bzp07efDBB1PL0hvKm29RD9lRIkWdT/AxI+DQgV4QQUlGIsEZm5HRzj08uBt+fr1QvkE6dbQPHcuIxig95mS0bbArxJ3aCul6/r7CzrWjEbYCf0puqEpJ/o7Ug7eXf68JnYRNr9MFL/okgHXgVhUCnuStkkkEhRxmL+KTy99QpmzeHQdmZx2uu87wkZbgEDwKGamPrlPfgVZTsnD/vTz445tptdWWoOOZ7OmIaWYeAWG4aLGRSiQQfQl5WFaknEgWsc2x21/U4f9i9XL9PZ8ibhAge21vvCRn1O3Cgw+6qT6dTHQ6koXosPLvdVDfXk+Rw0mVXzF/LcsWvpfqoF5vrvcaQczi0QoTSAyPJAudiexALSdZPrGwILn3Pl0HtVYceDgyHqXEdtuRWnjmYdJRRHuC/0AIxkI/kqU1txv7vm8bk0sm+tjziVkXHFfSCVcDgEJnP5BE5mjC0ZyLafc6THaFzoQuEfzsOrjxJvNIcjRG1aSIPz5z7TNfIMzch5jXzvSCjH72c0mjIcOUz1DzQsbaKnAp9g5FhkZ4jR5EdmliSftPTIum1+nEy06uoooo+Y1rA79i5e5eSr0D9LrE/RF6pGzoNy690kDg69EsEyL3ORoVUjuRHxCdNVrkwEAsifUb3D/m2dx9307MynK7iJ6e15bPIMteumPL266bY/XsV7Bk1/sdUW2/487A96I6bixqgO22Wb7wXQr9Wb+GJrWehF5P4jZmQvdLr3GVnrHIDbnOZ2RXhgynEF//puSNb5acfjq8+x2CSmWR15FOj+I33ow7spLuE19yciqZ4ZThaU8R/O1fqzHxkpdJvnbgWcjiKPnrPnWKa5YhQ4bjjUKhwK5du/jBD37gH3Ndlx/84Aecc845sfSbNm3iP//zP/n85z/v/7vyyiu58MIL+fznP8+KFStSyzKVKa2W2lDG3nqnSBSa84GsS5LsoH6kcw+F/izz8yqVvfvH5O77FqJ5CKSh7HKT1QgCmfAc60XU0m+UMTa7ocbpv5oMC30NpX9wt/rrOJJ9N92NuPtbsVLth35C7sEf+BlY08Hb71glI1yF+Vkr0dLImSgZVunuCTmcdl04fNBlbtZIF9lhhyJBJtQhmuaHP3Y5eFAyN+fSc9SWoOWRXSVtedNrI/pqY5TAx4F0EdP3Dn5NHyNeUtQsAx6BEnmgiLJrUN5pmfpkl3RoteDe+wQ3XtflwIE4kapx192we7dkbj44dv0Nkttvj6f/wQ+V2ZcqI3nMui5c+y3pqekCIjjauKhpp6+sSYjQJ11jw91rI9pzsTTJyq5Imn4CSwTcdDMcPizp9oKrbCtcj9H2HVS7D4VUNBJNCLjcdWuDH/33A4ltKjozQLzNhUM30qvPs2+f45GDwTk39lmtI3ffJen1AelS6TwEEu673yAhU6VoycSzCT1GEoMHLLoZ9wiB6bsUgS6lT8xEI7FOz8TLBLD334q993r1xeiA6RlYWIhwPkOTXdFDLkvrP4ocCxNTg/LW7TEJyOg1Ukq+/V245dbB+egMDnrxDbST+KnGdSyp/4SFOipKYpRYl+F+U7xOuNKhFwMpgmVzjYqTWp557wCyKwo9bnKJjqWGf6swTEp9v1bNfRV7t3c/heW/cDKJZYNOBSDvBtFEXJfENxL33ge33hY+FnvJk0Lom7j9DnjgG9/DvvtrRjAGnU9wA4ZVGGdkV4YMpwhf+7rkzVdJzjgD3vX2IYguoPDDv8E+cBudK/8c8pWTUMsMpxpbtwg+/HeCnTvgz68qcZ31q+Tu/HLwJi9DhgyPGfz2b/82n/nMZ/jc5z7HPffcwxvf+EZarRbPfa7y0/fa176Wa665BlBOnbdu3Rr6Nzo6SrVaZevWrRQK6Y7DlZoieFJ03LC6oV4nOXqYCD/gK5VW+DsMftiXgCVcck5dpU9kJuIbLsszM/M3LHqjH3mYjiq7dDMCIUj8CfnwtPfvMKFrpAyiiJlKi4UFydxMP9iISwn9jsnFhctU39KK9zZigVKt0J9msnF9LK/87N0sX/hucJmb3M9RsyJfmeUSUZtJFuZdZcInwsoufXnunq8zceBadX34pb3KY+Z+7AO3IWbCpEW4fZLD0xJTtJjUDbrfC/2ZWCREc8SuX+cdc5KVXUOTXbghsqteh9l5C8tXIyXvprQZb7+v/N0BHDwEuxN+lnuhWxQemzp3ZRYluf8BaDYcM0m4tlGZE0HdYzAysB/4bgrZlbSjH07ZpQksxTcJLLdNpfuQcQ5KPSWP6fag1nlA+dzR0eSkS/+OHzDRvDmWb7VzP0vmv0+xdzBGIi0swB13wMGDEuV2yhzo8ea7LtQ9f3sjnXuYbN6gjtXNtK7aVMdIL2VCumz+O8h+mupF+uUkn/GQ8IivD+W1qajbNxkdP53ryjAJlGYCGo3OmvIeIHwwfI2vcPTzkP7aFKGG/EbHx6q/OIfgrx/6u2cOJ+oHcLsdyt09tO+9UamAB8L1/TXp+BzRnyvHMSsVJy/vvidS8dYsozM/JeeosgdFzw2yjfQ3A5RdUmIdugvpdP3v4BFM0qHsJkUnjd+xNALeKCb1eCIhK4JXS1ZvXr24SCja1GOZyq5oweo3PRReNKH+Ayrpod0B2eti3/89dZ2/ZlpJyQciI7syZDgF+MrXJG9+q+TMM4cnuqy915H/8d/TO/1XcDZfeRJqmeGRgolxwXveJXjGL8DrPv9CHFdg//Tjp7paGTJkOM545jOfyete9zre//7388u//MvcdtttfPjDH/bNGB9++GEOHozaVxwdnL6xmXLA3Fz96Cdw6JBBHBmwpKHsksnKLhPRDbWUUGk/xIr5bymzwUR/MDLhedj1kkbUMcb1OaeB/fANXvJwvo6/+Y2qZYLv0eZahi8fkwy8/wH46U8dHn5Yfa/Wbyd399f8qidB94M+Pz0t6XlEiYy0w3K7Zgv96+y6YcIBvrN6M80guFFTQOni9CVCuv4mQivtXBfwiL7QMaNewu1heSSXcHt+JUQjMkalEzKxizXOg+U9Ci1b+H6I1POq6l/jq85SyJ9hNkFi7iHsPT/377mF9gnnYiWSQAG0aujhhxXxMixEZMzqerYN8dTProtKUoxvvTgBqOscr2TQN6LfiZ8nIHBNFVFsM+0kkzxSX+clX1L/CaX+jDoXyWLvXrDdDhPNW0BK7rtfcscdDhbJeeedBkJAzm2EbnGls9tX8vgqSqMwc8rrezRvqO+0SWVsWet3yd17LdZ+Tbx589J1GW3fScGZR9RT1t2IeZtJuqSOQ59ND2/grcN3gXS9vnUV4ev0EswcU9jcSMOi81268fsbezEh46aPwiDqdV1z93zDr4crk8m+2Vlot4MXF74y1FB2ieYhpZ69+2tMNa6n2tlN7r5vYR24VZmPJ0ishFSO2AHs9mFFRkb6ut+LS9piJu8GIWY/fB2Fzn71u4S5tEScxhufl81eS7n7cFCM0D67ergih1tb5pcvGgexDt1J/pCSPtlSjUXHgfHWrSyr/yhsRt9tYO9XDOf+xjjdnlSEUxrRGefzQt/TyC7RnqMwrfxllnqHsA/cFs7Pz8Yj8kRAUqbCWDOSTITTCbtw2k7H+13pNVk5/011tThysiuLxpghw0nGV74qecvbJOecDe+4WlAqDeFJtdug9KXXIUfX0Hnyn5zwOmZ45KFQEPzZn8DH1yznv372SzxD/gvzp7+Y0WXp4c4zZMjw6MMLX/hCXvjCFyae+8QnPjHw2re//e1Dl2NuTg4cjD+U3v8A7O8RgyC8Oap09zJf2kLfrvmEkvkUapJj6pxyagtgdeeRbty5viD+IGtJR/nski5C9vwH5rzboMc4AOPNm0zDo1BVdHuTHF5rkiX6DF57+Dsw5akbYgqI4Hu+e9gvS0pg5kEYWY7rFoz0Qeb9nsvPrhNMTcIFZX0+IPH8vI36uG7CZsWJE4kSS+VlbHLM81FllyovILt8tYYMSBIZzUtCrX0vy1u3ISbC3vzF9D3YB+/AWXM+0t/sxXfCrqv63dyfG/uYuK8nL12xd5B8q8vy+buR7lSyokZvxjxSwXUllhV+1tKkqK6aJrj0eHVFXpleJW2oFtlkHbr7ISYa12Of8QzQPqrMC6OsofEl5zTj6T2IfhdENTg7SMGWKOMIQ0TZBxh+B+nVT6e2ZSd0/yBOLgjZR0pJvQ71nsuYIBbeQivFhBfNwnRLN9m8MVy2R2hpPLwvODvI6XvsnKcitGYf9CKcS2ZnJfm8Wt8AXCxfIWIS5jroht/d5jBbpCt1/2jVjDV9H9IuoJWeq+a+hn1Pnu6GpydnECEioybhrhshrpLqIPtAESF7TDV+zlxpeyyhv/ZJQopft6/mysGDYcJW49Y7YKwFW7eoi3W3ueY06LV5eJ8MBT0B1RfW9H24I4E5frAOST/AiNU6TMGZJ4p+X3LLdSoy5iVLXe+FgjR8R+n7KJQqtd+LrbcemxpvmN8vLpPN69lTWOl1j+mg3kKWJ6B+gICZBeGR0Fq52nfUSxqroHxs9Tc9GQpVrEPKm3+vJ/n5zIWMjNgU8j/FdluI5mFkadyPNmli0JBLXRKkmUZi28E9zjtzTDRuoFVQ98G2PRI1xhibmRhkV2wNcFJrGf2N7XSU70jRjt9fANcdYv9MpuzKkOGk4stfUUTXueccAdElXUr//aeI+b20f/EdUKgufk2GxySEEPzWbwpGn/7blKwWX3vnp9n90JE8mGbIkCEDgMQ1FE2Os7h5hIZw42aLK+a/xUj7rsTNd5TskhjqB6eHdJJ27JL4YWVeY8kuq2e/4h+dbFxPwVOUCF/9pfKQhkpBCwRiyq5+QLLoU/4vs2GaEVXPmA/mjl2m1Qo2bNbDN2LN3Bc29zPUEY5nI6NNqaQMk41J/r3chDfzSQ7qpbC91sfhSjwn/6YMIPoK31QdBZ9tp0mh/oD/fSwUPTMo0Xd8bKqJEsgUCUFH642ggKhpkFl30Zpmaf3HVA9fT96pI5xOajsFDkL2qbXv5eGHJfPzRkpPsabTAuRsj+zyFGqOVSQgVGC0dTtLF76vrnGD9gbtCuq996a7ld+gXpgBCO5rklkYVDsPMta+00sTb9fk7PcTWhuGP77TnA6Z9fFJVWPsJfTogYNKjRWC93XfPjwfe1bI/DcNepMspBO3PTNgeVHeUsR7hkoumexSczacNtUhvBOsUaJxkEYddj8Ee0K+sIMtc9gvkco7NDe9OoXLSWpr3DQrSrCKpCAM+n450RcJbuRr8j0zoRWNeaehom86s6G2SGO9kN4V8/NqTJjlh4Mw6PmWcn/NW+d0OHQI5gwrW9eVNFo2e/dK+rNx8z4hnYAvThkg/Z7k8LReY1WBBWeOZQvfC6rhSui1sPffHChTQ/VMYkyN0zJ87zAc1CtSMD4h9Ec9Fh1Hrdk6IIPlKavCN11480CQcxvYD/7QUCHqjONVjR5Om0tmUTqNPpR36lS7D5H3zDsDsiudaQ8rSSO/mxgS3eh1MZcEwRkzlV/vQU4eDWRkV4YMJwlf+rLkrW+TnHfuERBdQP5HHyR311foXv463FVnn9hKZnhU4IJnbmF66RU8e8mnePnLWtxwY0Z4ZciQYXgIJE7/6NaNxDD0QLm7P4GgUuRB1JGxFtm0F7pEH4a9VLENnvAcN9tu3CQr5za9NE4ovdtpMtK+CwjMGKPt7vVjnIuPlqz5x3XeBw/pzXqwc3CtMnffE1ZASSuvrnO7rJn5AqXe/qCMiD8xZFgp5kfJM+oj3YTNSsSM0XVRZh4pplSBsku3wY2TeHoD7gb1lBKWNH5Cde5m3ym8FMo4ZHZOUq+bpFlcJiea8Q2rHgejrTtZM/tFvz9STQgl4PlN8vstosYw+0FIl9HWnYy3buPeG/dz3fXB+dwD3wulBbBFP9R+xyoRIrva91Dsz1DuPszowg0+Kaah6933xlfyxtIjDQz1nIby1TYTpDRIBlcECo6p+k8juUVK8MegYV5s5RMTVrQJVmhOh3NtNWH//rCPq6CcgDQ1N+ydyV0pNRR+WZbsJ1IhPhngnQz84g2SscUR2ovLYLxDnAcUBmls7fk5ePe2Yy41CYSF9w0gsvZ59y06JSIQ0mW8eQs4yUEANKIkd7fjqoiDEbIrSm65UsZ55sgip9ex4K/DnXcF5w8dDtY+Ne/VTT50CDX/UvCjnxj1cNVLikLvsP9d/xURdZorcvSqazh4yOXwNMzPhs3mAYqth3xS102RK/VD63yyQtN1pD8YFhakH1UwuMzxFWQacX7FIF8MB/USyye/7Ae+h1hQzGmnEx6LqvpWQMxq33q6z3UZQpE7ep0RkcjB/kxMI72MlyVWhP1JJMQi+eQ99ZxtpZkxGt9N8jgyb9VvTjKiaf0cRbyPFeLKtiRkZFeGDCcBX/iS5Op3SM5/Arz9rYJicTiiK3fzZyl+/6/o7XouvXOSzVoyPD5RuuJ/M1k4zHM3/gevfLXku9/PCK8MGTIMC5noj0uj0DtM2SNnTLhumJQJnRO5ZNMhXNptuO32wEeVVlId3NeiUU/wyYJUYdBlmABSBEC683thOBxHSnL3fJOx1p2smfkC03sXuPseGfLTAtBuEXWh4+9fHp6ucnhahpRd+zz1SLJzb79oQNLpQN5Rm5LR9t3eSXB6SQ7FDXIoYoIJycoukaTs8n1vJRThauJOnVxa/5FvNhdVyrjmxlgqhZ7eLEvU/QbYvRvuu99ImyDvsWZ3G7XwfBFJtemqdh8M2mMEQJARmzjX2Gj7p5xucjs98lCrCi3ZVwq+hYdV1LtIWstKUiDmQuOpbyl707HWbZRaD/kKLI1q9wFKvQOLWA+qytbnpfmV6JfZOclDD0G+P8ey+e+ENoGheZlC9KkPRkUSHjnzzlzQ5hAjEp4fM7PJLVHZGmSXsWFPW1qkCMguQT9m9mjC8myZ9eY75yY7yo92Qr4/S7m7J5HYSSam8Dfn7vh6hNvH8sinTi5wEyFdBzoLWIfuCl0ajcYojHLC4pz4TSg489Q694cqlKRWiSo4f/JTyfd/GCeKoiye64TNGPc+TAx6HfP9t0XW9wMHgvbExltUWabr6zoI2QtdUOzPsKz+Q7RPMvD6J+ZPTuBI21f8Js1vq99E9NS6JVM8yTs9k+VPqDsgpx8At8/BQyo4RBQPPeTyk58FBFUyTCLGcFBvvHQQnQXuu34Pd9wpleLTr5QmEG2D2LFAugjT+b8/JlL2jn7EwuR2aui5ZEWzMa7R5F40G012WbYa607fDZmEhuAE9zRmxoiTyshFX7xICTffoiK/mqn884MWEAMZ2ZUhwwnGf31B8vZ3Si68AN72liMgum77L4pfeT39jZfReeobBsq9Mzz+4K4+D2flWfz+ro+yZbPDn/255ItfygivDBkyDAGplF2uSI7YuKz+Q0Bt9uZKW0PXJTrDxiOoUsQX0zPqQXt2DtOVEpbsIpCM1CAXeUk7uveroXD3QjohhVUSos6WQ/6pWoe57/64sms6ENMk7BMEzSaJ5Zr9kPSQPjvj0miaJJJXJxlUzDQPMjcFuiyz/k7CHiEajVFKAibIS9tsBhuYwJF0kJFWxQUagriyKxyFUar/RNTtr6TfNx2CB2WYxOryhe+yau5r0O/GHmuU6ZpWNIQHhCpbEPxfbaoTFRuessuUztk2WDMPxtJK1yO7Ipt8KSwMVzv+XLE9oi6KsdadLKn/JOKoPLrRU/15z70JZoxGQ2ZmVDSyidYtyjk6MnFjl/SL73OO5tiIjhtjHksrHyK4zHEohUXfKjNSg3w+mkeQsUBt2IMqpmo3/Aqq+5NSfyE89UlAdllycDREjanm9Uw1rkd0F8JppAwc1KeZMebUPdbzyhVFP0mv49K//3rfl1KQtaHQ8aII+vM6VYzm04KxFoiENwZuN0z0+UpZT4HW6wuaTRnz2RWtgGnSp2HFlF3JStvgkzEvU8gucfi+kKl5aI66/bCD+oQol33DpXjoWqkVlyD63r10HHp2DYlFz64FeRgBWKank2+EnHkAa+Y+/wVGFNOH1XWmussy1npFVBmDWJPxeM7kjQFursNgqOVcqHjkrONI+t0euTu+5KthA7NHQvmZHZO771vmewkf/b4RHEDiO/WPLiWh25OyjdCKaq0Kcw89wLKF71HqHfDbHBRs3tOocjiFeST+O4oEVwqmZ5PrJFPXmTAysitDhhOI//gvydvfJbnoQrj6qiGJLinJ/+wfKX3pNTjrLqT97PeBnR5CPsPjFELQPf93yc0/yN+++Mucdx5c/Q7JP306I7wyZMiQjlxOPXC6rqRnD+ED0ny+TlASgXLmDYGfrejbXL1R7nYJmTEK6SCkg52DsTHjAumCVGoAvxpS++xKJ7uEdEMb2UQyIBql0QnUWvpUsH8xyajwdSaxJqOKChe6vhogXgvf9EbEFSACGZBMrnlNQmOcBLIroVTX2FzKJH9TGAoP03zG35UG1990k8N1NwTKLrPwG2+CW29XmzazDJNgLLiKEJC9ZsycxjRjjJI7WpUGhhmO4wwgfFzfKb3AxXFh/0FoNsNXaEf5UaJUqTRM86eADIxuCE0yT5MzIinKgr9xTVetmO0z1WZJxHSaqk1dEHIiFUvjm/SJnCKxZx9EaGfaHrr2GPvGrmRqikiUQU8x5O+kVYADXW8p08muQFkpYyZiQW2Fv+4E5qApnRU57HgEVb5ziOCeSUbad1NwlIlYGtkl7WKoUE2sANxwo8NNt8Xjuvl0aqdB7r5vUWvelUgSx81AJdEIrepLAoHbi3SUT3ap62+7A773g0CxCTDaugPZ68YIt7jSRpuVhud/KI2h9jTnZaKfKwO2jiobWoyC9rkuIRWQ1zil7EpYPiVKYSklWB5xKZ0+rsjTLKwKEXWmGePu3SlklwR3Zk96AxIjBUePxZVdwef0PV/Uf5yUcOddcPst0aADOr8ouSPjiQwcnpbcdrtnbqrzv1t9iJkxDs7Kg35Bo5CoCtUpDaVi3IzRSfQLmJSXThUakyGTxsyMMUOGU4p//0/JO98tedLF8JY3CwqFIYiuTp3il/+E4rfeQW/bM2k/5+8gX1r8ugyPSzibr8RZup3adR/kHW9xedpT4QMflLz7PY2BJkoZMmR4/CJnq/hhysRlmMdAc5erSKjoG1XHKiKky933pGThLUfaB47eL2lntTGFD06MUFDmD4ECIYqR1l0x9UfiXiXhoLaESSQPhIV01QP3aOsOoz5BPp1WfIOo1RdhZ/Dqj3S0yVD4lK5EEhmSGHUw6rMHdW/ShODS1aao8cyEv5mRQXmmSaOncjt00KtbTNkFh6cBITwTsuC4Y6gsfFVfrxmrZ8iMMbqRcbs+2RoItgKVSDQvG4etm1X6ydEe0oUHH7K4595Itq4aj1bEPFYKyyOFvfwjio5QWrOtprIrutGL9LHZR0m3TITIrrjfrTRVm/oQjMl2y+XmWyTdnkmuep+FDdLB3ncT9kM/CSu7tFlWpHKByZ4xxg0SRHvliW5qJYI774rOC7M9AbloCZUm4IXTZFLxMoCQ/yrbhkovsOGLziXhtDlwUPKdH3rKLscMUhCU73imrEnlS89/Vbm7N+YDsNMhMu6kR9xHvIGriofu696HpXLCHjJz1gyJJqY902Xjno+276Y8bUavTMb4qMvKFQGhUOvcH0vjm45Hb1kK2aWrmtPEtkmmOD0/G0V2xRlPR+YDQswnxtRcdEURxwHbU6RKx1FjWIjQHO11o+MiDlfG3heEr4n8CNn3fotS/b5IvsnO05UZY6JXOi+lubaqPtLErvn87pNhUdGTdH23AOHj6q+OjqmDpkgJE80bKfYPYUc5Ihn/HF1b9PyLCUz9A0adnYSx6ufvknw3oFp240lFZO01+thNJdXDyMiuDBlOAD7/75J3XSO59EnwljcNR3RZe6+j8snnkrv9C3QufgWdZ77Ll1RnyJAIYdF94kuwpu+lfN9/8/o/Ffzq8+CjH2tz9TtkxEFnhgwZMmCoL2T8yVU6VGObHeP3y+0rQiVCdGhn3mnQD+x6Y+F7IPGUXWbgKgDLjftiEtL1o+wlodZ9IFQWpJBX5oN45KdZkTRh80TpkQwCN/C7RZh067TDD+mhaFUyTHC4EgoPfZ9Kdw/lTrKqQG88TSfniWRXxEF02OQlDt+MMaFjor57Qn0RIji16ZUdqlO7pY+LEJkCYbLL9xfTb8XVcqYZY2Rs1nZ/leYdP1d19KsS+P+J3sux9m2M8zC7dsJorZ86Ol2pFWXRjXvY95mpLkgRbKlmmWM8IQqlmVdanXzCxyhzWLIrMFsKrp2eljTyqyIR87x8sf16yghLqed5qG81GRMqWxHngRpnwDNvSA0mEk+BNmN0DR9oaWqQZKWSMtVU5ywhw5vkKNnVrbN/f0BuCY/sahTWBGmko9aDFD9gfY94sJ2mfyysMo2boImIGWOnI/nxj/u0DKvFw4fBdfqR9ktG2vdg77/Vb3VSw+zuwoBV2aiZQTKbJOR86TR1zIgeGo4cmbwW6xx0BL9QlxlO7aQrI5H7FPpuXNl1512KNJTCokfRN0nFdZDYXv+qxLkctFrmfEvuBU3+pyJKVnfrFOcjZqzGOmWuWUqdGKdZfII4ouozq5ikeNTmvRr798G134Zu1ySwA/Qiy5nrOFQ7u1la/zEADz4ouelm71qznNiHSL1j/r4MaZqH++93DbIvaX5KajU4fVfoFDnLUxlGFb8pozgzY8yQ4RThs5+TvPu9kksvgTe/UZDPD56MYuYBiv/1SiqffgEgaf3aJ+k98SWJi2SGDFE4pz0VZ8lWCj/8WyxcXvEywR+9osKX/xte/wa5iGPNDBkyPN6wfIWnWElQdo23bmOieYtxRIQeKFsNh0OHPDUI0Mov52DtApQz78XJLiNbQCm4BPHoTJZU5jchkxkczxeTRwSZJpjSXdzMS6uUjMrk45Z4IF1Ev4Vwu4CKMue6MGIQXRDe6EUJuIWFYPMbU7d4hNNk43rGG0p5EdemqCO7HwqOJpNdSbIEwaHJJycSIXNzym9a0r3yHVQbJpT+Z1OB5G8AZahON9zk005BQ/2Kmhko4qY4czvLDv13pA6msis8Ng8cCEzBTMJWp4tukEr9ae+4wPYd1ceVKFKqa6MO0KMKoTRfdSpN8Nnfy4uoGsncfKsNn9ktKxeuDdUpikT/egPILmGofCwL+lYlZDLlEy3C9i+SEopev6lzSZt1NQ/DhKb0fZypbwljAELqm6Qx2GxIj+iRaq8sZUpUS4Wo/zkwiBlj0EYdcsfy9JI6oqjGlMfKu1aJh8Z/EeV830F4gQ5C5SHJ58Dpa7LYUK6kLoma8PDGundf5uZU/efmI6kdJ5SZJbuMtW6P5So6c+HrDP9YZtkmUW85HZbs+0Is4AIYZKfpQ9AkdBLal8sFVdWEVNeYdq5B0EeVqRqOa/v11rcxIG8kPbdErfMg1c79uI7j1dNCSMmO7VCtQLtlVi45GqPyk5VYBVW2Z443yGVyGuEisbhhoLBOj1P/AhbKyj9m2+D/QiboRlmz3q2evSv8uySlF1kyQmyLSF9Hx5h5vVedRETnUtI8nplxPV9mItGMEamiOloRhZklwmSXIldFOqmaKbsyZDj5+OznJO99n+Tyy+CqRYgu0ThE4etvpvKxZ5O779t0LnoZzd/8PO6qs09ehTM8+iEsuk98Kdb0PeTu+m+EEPze75R5zR8Lvvt9eN2fytAbrgwZMjy+MVJTTqLjCgV8Z7MaUSe7WqGj/TW5VoFOfqm3KU58Pwx45m0ED/F68+D77xDhDYUlu96DbsBGaX9c2vRnphy8FhY4vipDbcRV5MeYDy4ZJmhyEbKr0YA77nAY2/cNVix8W/WT11atUgjq4yR+Bth/APbujUgTDJhdf9/90verAipCW/Q+QEIEOYj5UFNWpgI3X6VTXBlL3++nb+5iPrtkUFGTHDHNMkMBAAKNFRKwGgcRs8ohvOsGDtYt4SlopLqn2vEx6KiIWtm1uD8WKUHKZFM787tNh1WzXwkRORquq8q1ZVhhost3tQon3dt4qB/CPrvUiUrnIVbPftm8wiskOJK3DBIgieyy4squJG8F/pg3HNRbQgKCVhPm573oor66LBcavyPtwN7ONCUN5qyMRwUFioUg8t+8P1XiNK5JlkY1iD/4keQnP1N9LYSa14FSLrn/o6odU9nlly/cWFnheim4VkH1vencX1jK+b50sWSfmZlo+Q75PDhdhwcfVBFYoyZqumZBgfoeRBSh3n2J+VRy+qwxxk88Cp6nKmsdDB9NefRbPRuQzLXmnelKUKEUU7pP5+eHU9NEy92718jTHDwJJowA9zyQDwifKF8qJR2pzEknmreA2/frCWrcFIrQaiYTzSaCgB3JmJj+HhPNWyLKxvT0oZPCMuZBPI3pB039tZD5CuC9LHHCii31N6iI9u14+LY7mJsLVyopsmSoryUcrp7t/2amcPKJiPPfLrXO/eRdo7FSApZHeMXnp9BkttNjzcwXPDUk2FZALgaBW9V9NXMIihmO7Iob3GfIkOGooImuJ18Gb/wLQS6XMgm7DQo//QfyP/tHcLr0zvw1ehe+GFldclLrm+GxA2fLU3GmtpD/4Qfob30GAL/8S4JyCd76NsmrXiN559tgZGS4H4YMGTI8diEEFJw5rC5AJXQuqm7xL9AfPWWMNqkKNj7C8EO0OLnumzNK1zdjNFcny+0xPw+uZZBdWtklNBliEmFBZEkJ7NmrItpNTarzgY8SN7RRtxOegh96SFLqqehTfaviK7s0OrkJ1X+Gj6dkp859yv2DicouKdVGUuDEHFeDGSExfF3sWJTsUiV7n5Up2EAznVB9dRs0WRD57N2gvDNHNzeOIBwMwCfGEMrHWfMwdvMwcvZBhNNFklebZqHqKEkga4RJwljknDp9I8Kahk8GSHBFCtllfM735kKqLseR2LZK4bpqfMT8vfl+kHSvpktAzHuj/b+ZDurHW7cGBIhHnHjVD7XJj+KXcK+dBGVXMz5MYj67lHJNmfHNzStFx/ZthlrHyodIzVC7PMLPJF8EDs2WJkCDOW956plDbGD/AcEUYdVHsQh1w9QsWQkanLM8ZVy/r5yRTzauj6Xe+zAszElaOOSdOr3cmJ+HuYHPuy3axMnCeOnKfE72+yFSR/lvU8qu/REeetnC96lPPYt+0/HVMnqshclgh5HWXSyUNgHSo/q8e6AJDzd5nMmIg/pihOyyZJ+lC99HVDueCi2Yt0O5b03z8YcNEWVNSHF5FO9Rna6hOIw5p1fouQHJql9ehPIwAgdYbk/VUwj/RUghD7IFOadBoT+tjqcouwatj3oMHTgIvbbL1qmkbMzfx+Cs6qd0TVHIXBw1IvR8m51VJpunbR5MdhW8AC6dDkHFpEcURSo6P9ujFCQhzbm7RJnTpkWojPnXlA7jzVtC48Jf52VclRVSPnooODOM17/PeL/DAgHZ5XpkV7hfzXmZKbsyZDhp+Ny/B4quVKLL6ZK/7pNUPvJ0Cj/8AP1NT6b5oi/QvfLPM6Irw7FBWHQvein24XvI3fEl//DTnya46k2C2++AP3yVZGY2U3hlyPC4h7YwSnnIX7fWUDyJ8JZU+/sIVDcBsYJ0GW/ezJrZL7LYLmhqSn9KNmO0PeJBK8gq3T1+NMbAp5OhOsEJ6uIGPk8aDS8/W6dzQ0HqconP+zJEIsQVRgJX5EPqCithg1rtPsTSxo/J9wPTIj9su6uuGtLliHFNpKaRYyHLQQSlEpS8Hc7atXFTPxO6DbbbxnaatObq7Lvpbubnw0qEiebN5Ptz6n4MsY0Q7Tm1YTWUXSH/TmYdDN9Bpf5hVsx/i3L3YU+ZFMfcPMzXrUQzI1OBYDnt0DnTFE3KuGmOykCPp7hPmijMe2OSqdb03bHrpMipDd8Av2mJG/OEgAB6fJt4cLe+IIi64PGLQV4yuN9JvsD8dKENrILldrjuerCdhmEOJ7GESy8/zn5rJ0kDW3j/D5yrJ9EGPlPndb8ipyeaN8eiZYKa5+22Or984btYbsdPZyq7hJCJBLLfTl2sEJ5KKNoPnrIrhfAsFMDtGSSOVitKaBZW07Nr2G6Hsfad1DoPBvWKOKh3HDUGYmaWEVJIE7OOI5mZURcX+zNetMRgvYr7VktG4vgHT9UmwgScCHzZxbhqj8sc9L5DmzFWOw9g9xOlTyEiZmYWHtoTPquhIgu7SJHDDKYgBFS7e1gxfy2TzcBUvFCA5cuNnGSyYtavq1dUvQ7N/fv8a8K1sbz2PEhUgZSogos46QpcO8rQvdN+2/R5V4aJHiGk/zvZd0JcF+WEOAq2QeZLY5z4kVUJ1FT33he/XiM6VrSpqqm8VP41PTPGiCJTm3CHldwOttsJ5S1E0P9pLxoyn10ZMpwkfP4/JNe8V3LZpfCmJKJLSnJ3fInKPz6b4jffirtsO83f+Fc6z7oGOb7u1FQ6w2MOzpan4SzdQeH77w/5QbjsUsE73yZ4cDe8/A8lBw9mhFeGDI9n6F8o9SCZvB4MIkXUVRECSChlV63zgFdG+jpTKuGb+FvSUU/XsbfFXsh6kcN2Gr6qQ8rAjNE166Bsj9At0oSDNoWxvfaMte4Ib24EjBjCoa1boCiCjaXaIFshZ79SWEiRC5nEpfkUESIc5c8nu2T6A3zay+oo2SURMdVGyImx94Z//XpFYI6PCcZGk/M221Bw5plqXkfl8HUcnoYHHvRr4Ke1ZRsITBPBuOdJ+zuMMSPw/TtFN47KjDHsW6bQn6FcCO/+dRPbbWi2bX+Da8I21UiR8azHh+NIul21CY4FK9BbJKnMhUSkvSaSyC7XBdGapdLdgznPdD9I10kgBeI+0vwzA0ipKKSUhime3swGDaw34ipNiK8GSaakObeF5bZZPnetb9orvDvcbgsWFlI2ob4psTaHTWJvNQmkzo10HyTXnx2oqhNI8o5yUGS77USfXZDsry0JUuS8jkhWdiUhn3NDAQE0AeBK1b/d3AS5nCaepc8URRV+ap2Jm4nSSzb3238gTATt2ZNAdkWQ5qw9CdqM3TS3NAnQBJdsqeVqOH2HvDPPRPNmRhZuTi43Ms9mZyN5WEUmJgzfTsIipHoSUHDCF+kmjI6Ejw1SdpnnbIPADEEIir2DTDRvomyYn8ukRcm8LGrKLIOXO6F6xz7o6wPfa64T5tBsS401rWyGZOWq8Mz6pSS0hg7ykxed2uq3IFp3ieuKRP+QQvZV3UNkl+pb87YLtDg1LFMb9GyRhozsypDhGPDv/yl593tU1MUkokvM7qb0b79H6QuvQhZrtJ73YdrP+wju8l0pOWbIcJQQFt1LXok1+yDuzz4VOnX+EwTXvFNw8BD8n1dI9j6cEV4ZMjxeIbRiJYVwCT+jJz+sBxtkrewK++xK2xRCnEizZF+90Q09/KqNqdpkJji4Jax0URtR11cMRR/WLVvVqdZ5IGxaJGDp0uB7sShYXv+u/912O8zXRchRvNqmhDclaf6cTFM2s+2m8/fEayKw7XiELinskK8vCCu9tF6ukBeMjXm6mkjfm0SfSdhZbjdGdIiIakFIN5JGBimjxJyh7BJGA2ObZSu+mbHoUypGMjRVSgilCAun8J0fy0I1pkbQZEKjoeowMqKtbuIMmeJiNSmR7P3FJCeiQrDoHPP7zHXjZF9E6WPiSMgu10WHBMV2Wp4vt6Bte/YY88j0BRYlT00TUa8PbbdNtbs7VPex1u3knQWabYuZWYxBHB37LqbPt+hdC4yipb8O1boPppLJOh/tz0xt6LV5WCA9GolbwkZygP2jl3ptzoXMdtV5m5zTCPmXM1HIO6H57BMAPp8hqFRgbFQTvVqlKr26qnTdrhoDMbIrKWKhVU4ZJ8EYTTJjFAxiMqIQ4DnnDx9LhlJt6nokm8m5Pcf3S6bMCJOeRwfRE5KF4iaaE2f781qZm3trnB6rUUWlrr1R/TSfXe38kvBFA2sjUhSuacf1WRXZM/DZBW5CpGBpnI86D9NKw6g6TeK9ODBuQYjslYCwARlS/5lqqjSkKbvM9Ung8sCDFg/sjttTCtnHV5v6eXT98v2/qcqujOzKkOGk4T/+S/KuayRPujgh6qKU5K//FJWP/w/sh2+g/ZQ30PqNf8VZ/6RTV+EMj3k4Gy6hv+YC3G++B3phyf5ZZwre9x5BswH/5+WS+x/ICK8MGR6XyJWYqZ6tNvEDFEmgHuSTtjZS2KxaZSo4BKM1Y2M7QEUR9/nRix3XZJmFZOtpaWSXoSqSLgIHKezECFu2FRAW0c1NlHyzI3u0Xk9wYORiOrkJVS5W7A18dANpmtSYfWFZcGDk4lAEriiSyK7xMXAiajrdVhOuG1bVDPJjBdE36eHoktGNmh0hmARuSOFX6e5lvHlLzFys2ZQ8tMcgW4TaQEvUPqhUNOog4uSQ5fYoFaJ+X8y62In7b8sCt7oUOboq1g96fGgCsVSC+sgOmoXVfvv8TbuhwElSXoBhOoixOU1RjPjBHRIiBfhKjeNBdgFj7TuYmr42MT8dCMAhTI6Y0OReyNm/2/I3pyZs2fETpm3ytQIM0hRGgRkjHtnliNLiZJfnz8yW3ZCyq1KGnTsUmQlQLwbWFBvWGzm40LOV7NHNj8Scx0thUXBSwtcBxZwbqmO4f1Q7bU98VOodjPvak+C4gl5PrVNRsl4mkF09eyRRgRtTXkXJrgSSPe1pUGozRtNn1wCyOkQQps2V+/uBE/4EdaeqzyBFlFQvd6ur/fK69hgQkOl6FIbydOPrYZqyS69riUFBEmqUpDYaPAfwA65o1bCU0KcYS2/ermi/aHVmrwfd/IRfQa3UMseHZRBK0s9LeuaReu1NbGAIjVWX0skFkjHb8/Op7/fy5arSCw2PfIyu5/r3MLSmKMJM17fbxffZpcZf2ETS6IHBldVlDpUqQ4YMIfzb5yXvfLciumJRF1szlP7j5RS/8RacdU+k+aIv0D/rf8VfqWbIcLwhBN1LXwX1A+R//vHY6e3bBH/1PoGU8LJXSO66KyO8MmR43EEI5XRdJm98hlV2TU0amx6EH0kJFEExoHg/D/A2+CkEmJQSO6ISC1Q2YYe4WmmkTTpM0kopuzwfRYP2zSRsiISgm5vAsUpUKqq02DVGP86VttK3q35epsrNsix6ds3fYLUThCJJZFelAjJCdiQ5GNakhnKCn1DPyGOImSK8oXBim1XtJDpwpB4366t17vfM9gIc8Cx7fEWTUJno4kZGA+WN0JUP1bFPMT/AObxI9tllW4CVQ9r50KZPCst3Iu/7w9lyJc3KJmNMGVImKal2d4fbMADKybg0VCvhygW+cgJl17q16q9PqCT8NI9NqPs/NrZoFTzSE/LOvB8QIaai0uNykM+uhOfW8UoLiKvS1CZ6gJJFQLWzm6LnVFsRW1FlV0AE6Gscq5Bs8mhco9cS220GeThtLHp+IALVnmBMm5FYDxoKyf7o+lj3JwVJMGHbjkcQq7IsGZ3YQq1BAmXq5vS5/Y7grJQw39TrYQIJ2o+Ti327lkzGRMwYh3nKSzM7VPcz6mQ83YxRq7VUAI7kcdVp9RCemtdNqV+aubAJIUDaihxy7GqICLKMlxsA7dwU7eIKdZ25FiS8GFHl2/752LkYIaxIo5Raxpzrg8RyuwjZ59bbgvz2jVxK366yb/Ry/8UKsZzDLzL0fWm1oNUKKD5NdtkJyi7tvF4aylWdHhEQfL66LYpiLXR/tNpR3+9CwVv/fBPYiFJX9lLNGLWJ6egIRjTGYF2I9kjmsytDhhOEf/ms5D1/qXx0veVNgkLBWGAP3knlk8/Dvv/bdK74M9q//AFkbdkprG2GxxvclWchdj6Lwk8+jGgcjJ3ftFHwgfcrx8WveJXkjjszwitDhscXBNLKpZoxQjLhYiK2GRECyzKVXck+ZlTpClrhYruteDRGw/E1qf56ImQXLq7I0fMuzRt7LdsONj/RDeJiyq6gZharVnpvmiMP69Hk+iFcKbsMsiunTFuiaoJWPnhOSHS2LuKqoiTixfTbkuw3aQDbZR6WTuwehzZt0iMXEwg3rViIlhEiQgwn13H1WVQJ0Cefi4+BWi3IN6nPLBuwcmAVwqZLoqAI0fK4F90TRKESJmsEbDlNUKuBKyVjrdu9axcPYm92U9SptKqvt5H2BmK1im9mal4XxarVOc44XSzqTw/w/fDo6KrSJdbRelx23EE+uwJllx6DOctJJMkVROivudGN36N4I3PC25B7Rrh6XUhyTh9kI/37lnMCVWG5s5dq675Q2SYBkxSJFZTSLcpP1AtKETZ15pn0EogvW2iy3euvWP+ETW0T+A8e8kylR2oJyqsE+ZEjSkxPxw6HCL2A6FwEKWlcq+irOIP8BxOaUsLuh4J6aFWdhiV7WLJHLq/Gad9Jyi84tn/0UuZLp8UqKwTMTF3qm58SIb1Nk9N6cQN9UTYpbJX3AYeFhXjpAwNvxNRs8YiD4KniiJt0A6ya+yrVbmAb71hFX1nYt2s+uTM9Izl8OFSYUYAMzQuToFO/7UQIJTW3gvVDSetc5S/eT69VhbPlXbTyK2J1t2LriPdiyqub8pWoSH51zI2k7xMly/W9sm2lxFyzRh3X66AlHByrRM+uhYnvLBpjhgzHH//vnyXv+yvJFU+GN78hrOiy7/sO5X9+AUhJ69c/Te+cFw49ETNkOJ6wf+H14HQpfO99iedXrxb89fvUg/wfvkpy2+0Z4ZUhw+MJ0sp5ZowDvPP6iZPWh+gG3gpFzBtoxqj9Vnkbz7xbj5vXmQRRQl5S2zj56ZUZkTScyeeNzaxtGcquRZocj0wm2LENli0XnoNptUnIDeI9DF9HpoN6YdmAiPtYMf2PpZjkRfu8NhqvgOkvSrpxIk9EDqRFYRMJ/pTC51UUzWEUGBpmBM9wpK2gKKVWiJqt9slZkWMCNm4QTE2qfNP8nGHZYOVC5x1RVBs6kcN1A0f2pnN/iYWwtBmOoSQQdkoETyN/Y987fViTrGYe3n3zHNQnPiYmmbfm8n7b0+CTOl6RttMCmayg0aRivWWQXTHfPwH5p+puk7OdVKWVrxYJaJ1Y3fzvCevKqsZ3QhVRPu/cRdcp3RbtKyt8UhhkVzBn0sZ+UqTQXm6cPeO/wNj6tYzU4hfaOFj0PYIj7mfQq6RB/EbLBMe1GB2FWiXi480z+Y3CtQop9T/yaIxJmK3sUgSMsMJmjCHCJXyNJrvabSOSbiQyoCW7WLJH0bPYazRt9o0+mQMjys2LgLCpJFZ4JdJBDASQC0giSXCfo+ueMvmW3ouIAP2+yifq0y3NBPOmm2VIBei1GlskjU8Bwhrgl2wAvPG+Zw/h4CheT9x0s6TRCJPAUfNVEQn2q19ACU+IJoGZylk4+RG08FOIYP2SiNB98FtlJ08cPeZtG+9FiKfsipFdPS8wTXDMnLe2LbAs4Zsxqv4NCO2wyisjuzJkOK74xKckf/O3kqdcCW/487Az+tz1/0Tp8y/GndhA6wX/jLts5ymsaYbHO8TkBnrn/ha5m/8Na/8tiWlWrBD81V+q6Fyv/GPJLbdmhFeGDI8LCIGw7IERAQME60J40xIlAsIP/IOUGP422Fd2dSAXNnnR1wvp+r6Fom0IQ+3upbD9N9N5Yy9o54K2Rs1WonxNbKOEoFyG9esshBCe0mGAsst/o63Jm7Cyi4S3/e4iZFfSsVw+/gg/b1jBJfpbsyJMzRG8jxsd9fKWUO4dTHBQrxQKUQR1N3zqCINQMOsgiG2O8k6dvAz7ASO0qRWhyIsa2owxSOfV3y7QbEG9qRR2PvkacuIuAsIyFInOjnVhFK5BMqqoh+H2aMfdbpINmk6T9HPsSZEG3bIQcYfqy3YHZmbiV+pxOV8fZMZo+2UKFEGdsweRTwZrqVoSnElUdoXzsX1LVxenNOmpoRxMn3hz5e3xUr36RM2no2WGnGindKRL3BceqPXKtmFyMn7OsgxllyCm9NFEjOm3v29VgjI9X3uVCpQKkfFi5WMkJIAjksmuqIP6o32y69gTft2tNDPGyDUmgaj9vcUDkihlV8G7Fbv3WvTtKl2tbCqMRZRVInSv5svb1FERvYfpM0NihZy/m1i+LBykBNKd66flbVvxG6RNQI8g+KUPkWAm7J3wYcluxGTcI+o9gjOq7NIqT+2zC2HRKqyktfqyEOnu/z5GIlyqNolY0BW/dD0HfRNSdYOS1gpLdsOUVYo7Bf07aQn9YiU5v8WQkV0ZMgyBf/y45O/+XvILT4PX/2mY6Mr/+EOUvnEVzqYraD3/45nZYoZHBLoXvhhZnaL4zatTdewrlivCa3wCXvlqyU03Z4RXhgyPeQjAc24upMvqVbBtazhJL0GYZZqjxEyzxHDmVUBM2QXg5mopexWZSMhFzUx8ZZfIJSq7rATFkH9t5Hs4nD0ghNqEG9+RcqAyxCcDRXgDblkWCBFzQO1YAZOYTnaF1+d8fsCGTCqH1zGrxchNqnj77Wo1JRMPO3cE/lSkhEp3j7dhiZJdqh2JTp+1mY23CfL9sRjtVc2MX1yZviHcDu+vZal8kwgoYSliS1ancEdX+cdzxQKtFtx6px0ipqJklzY7M5WFAjfBzDW8OXZdKBUlpZL23+WE1WER5U3S/U76yRbCQhYG+44KtyWWQzit7JPLQaOdLlF0vbpqfz4Sm7zlkEahhH2ehdVbcXNVGZuTAdkllQpSqDzMjXQnNxXPx6uPFQkUoSn5YE6Hya7TvcDoIR9JUqQ1zyO7rBg5YtNHyL6nwvI8r0dgNt91w8SwUhqqehby4WtdUUicT2k+sXx1nUcoD0O2JCbxb5gImaUPdB5vLC+adIvOTUv2sOkzNaX6058PwuLAyJOYHTmPMKEWlDdX3kbXu1dWhOwyzZljv0XC8juikBdMGUOoWktWgh2qnpdIrLoiz2x5R5A1ioxJgkTd6yMVdjki/tIgKC0Z0jAN910FmuSYZyro+1Xz+tgxzBgtEZgxmr9jQfHJAWtUzRydBPXiySLnNBLT2m7bH5cm6Rsjp12VNk/Tm1sRk9pM2ZUhw7FDSslHPury4X+QPPMZ8Kd/EiG6fvoPFL/7Xno7n0P7l94H+cqA3DJkOIkoVOk+6ZXYe39O7s4vpSZbtkzw138pWDIFr3qN5IYbM8IrQ4bHOnyfXdIhnyfkexICsks7Ih8fV2+/l3vvcqwY06NN/IaHuVlry0S2BYHrP6SHoR5fVRQulU5HB9QP66ZYTJl0JDh+Jv6AbUXIOIkmu7w351i080viRJKfj/A3HiKiVLJy6nijGb7Ysco8PPpkmoXVqWaMdqQf7ILaJEZ9CFUrBH5bonuVyM6zXFIb/vHxeJnRtiVGfosoIMx7GnXM7JOHEhBW2IzRLCvBBCyNSBWWscn1MtLmpb0eUKiClcNddY4/VixP8idRddB5O4a/dCmEL1MxnY1L7ESyq1UIyDTHAQsVbc5NMBXWisa+IxMjxEEyQVEsCZxNl9MtLU/uDIyxm0ByiEhBQjrkctDrB50bI1XMoAKo+20LN1kRapiBBjTTIGVXcF77BgqUT4qFFJZFqXfAT792DZx5ZnQwSEaqLuvXwYqpiBljTNllmDFaAiEEh6vncHDk4iANdio5ofswuvyV9/8Y2217+Sf7cEIE1XEccA1CQ6tpbNsK+RoERYotRnbVjCVAE3+OKNLtkuiTahjlUhqRYJouSxkp2ySfRI7RUSgWYLYcWLtYbo8Na3qeuVo4v25uHGnnIwx4ijoqsibJAUSMb8boYdVKwY7tsHNHjkI+6SpBu7CCZoLPqunqWdRLm/zvtuViJaxZUlhoM0aTTUx66VKOmHpOV8/lcPXseJ4DaZsQ80dz/PSIaNYw/zTSu9qi0FtDfVN4z+dYuPz0l1o+4ez/VqUTUZbs+n2ydVfyb79l1KXkzPpKMwFsOU0/i2RkV4YMxwQpJR/6iOSjH4Nfejb8yWtFKKpL/ucfo/jtd9Hb/mw6T39L/PVFhgynGP1dz8FZvovCt98NvVZquiVLBO//S8HyZfDq10quvyEjvDJkOFX41Kc+xZVXXskZZ5zBr/7qr3LjjTempv3MZz7DC17wAs4//3zOP/98XvSiFw1MryC8h3+PJIq/vPWh1VFjo2qjt2yZYOsWOPdcwx4H2LbVohR5YF8Mp5+ZZ+sWFap82epK4mY479QpzN7jfw9UCyrxgdFLaOWXKSf30vU3s0IQ860U8jtjbLDSyK5AgWOFyC4QzJe2KTWamb+Zj+FXxlRN+SRhdKNGEFEszRdY1HeZ7bGL2meNRqGg3+6L8KZcCGQurgYRQsT6yrLCmzLLSt7kRDdfuv/vf4BQtDkzrfTsj3xTuyhpmEAUxHyPGQoOKaxQO6cmVXCCiXGQRSVHs204VLuAA7Un+mSVFBFll6PuudBqIs87uiYZ68UNNAsrE00mF4qbaBZW44oCjguWkL7/mygxpPvozjsd2p1kEigJ/jgakF63ZffuONlVG7FYszr4LvDIrp7K8P4HJPv2h6/xSVu/7nnsFAf1ak9vsWNbslIoqirUmCtvpVFca2SkiGs8sqDgzEXyiXaAZPs2yehomqPwIH2yGiqcn0u6skslj98AISDvLCBFTkUHjZIf2m+Yd6nrQqMQ3AzHQalFcwnqFlFIJN+0z65CAVYY/Of4qCp7dLKYotiMrxnJ6q/wGh8gTHaVjZccIfWayLFkSs9RYaRxfbNky4qvIf4SqQnWFJ9lcTPGoALR48oPW9j0PJcTFIvCr0dyRkljNkIaizRll6c5jBDPUWVStQrLlkbGoFWgZYyPtLI1QvXXPvrsZHWY77PL6wwdzCLanxI7uYNTGcVAfWnJ3kDVle12/Htanggk46HiTL7Tgk5O2w+7lErC9/k2DDKyK0OGBEgp+du/k3zik/CcX4bXvEqE3mTnr/8UxWvfTm/rM+g8420Z0ZXhkQlh0bniz7AWHqbwww8MTLpkSvD+9wpWrIBXv07y8+sywitDhpONL37xi7ztbW/jpS99KZ/73OfYvn07v/M7v8PhcEgmHz/60Y941rOexcc//nE+/elPs3LlSv73//7f7N+/PzE9gO+zy1Wb2aTN9tIlUCxq/xvhTUKxKGJKsFwhTphAYCYH8SiHlVqeYlGwbKkgX05/cjXL7lnqwdg0BevklpB36uoB21Ms2N5zeuDUNkwWmWYi6cqunFdW2IxRm+E5ufBO0n9EEIGphRAqAq5G3vOzVShYFJJd7rB0iSIA0/pAwy5XvHqGN/BK7UTIHxWAs+Z82svOp5UPMtf5Ru+N3rBEj0VhKrtmKmeEHDtrhV2wZQ58dmFZvp8isx5RJVy0nknHJXaonbk8bN8mKJcF5BUDm8sJzjy7wOnnT/mPa1LYSkDk5e04gLBC0cSECBw7NwprlIosceckPB9KrkdwuQHZFVN2BX02MQ4r4uKRGHI5Q5k1gB0z69ZoRYgdQYhItWRPmZEJgeNIFhagXg9fEjVTlcL2NvZpzwfadDaBEArla36zfP9h2sedkC5Ytk+QaZM/RRiF896+JTBzDAiS8K7ZFxUmOB6PqVekfcR+rrSfIt1fiT6ICPqguewC2kYEVq1gsa34elwoJZsxap9YmzYGx8plqJQU4VeuFRkfS66vGzWTG0h2RZJGKtiubQwUtiGllaqfWufCkybfm1HpRVwdqrNfuTI5MIeGFXlhYA6NGNnlzfUheWV/TCQpqfS5w9VzVT0sSc5O8n2p7rgmk4KjZmRLRa7nCkPWLNIwxyqxUNwYe3Gi6iUSl4rgZY76cNMtMDcX7j9VVlzZRTRYgF9mQPDatmDNaigVB7epW1hKf/NTkCkWUWZdOtX1zJV3hIjkQWMjiozsypAhAteVvP+vJf/0afiV58Ef/1GY6Mrd8jmK33gL/dOeRucX3+k7QM2Q4ZEId9U59M54PvmffhTr4B0D005OKoXX6lXwmj+R/OSnGeGVIcPJxEc/+lGe//zn87znPY/TTjuNN73pTZRKJT772c8mpr/mmmv4jd/4DXbs2MHmzZt5y1veguu6/OAHPxhYjrBgtnI6+0cuiZ8TKoDF1i3eK+DkHCLfgt9J02TFfGBdESFwpGVsxnNDkl32SOxgqxCwBfoBPpcLVD9+PobqwzWcO6cqu4xNmCKDAl84ZrogI12H5LoDjIzZXHg+bN4cNc0JJzSdYE9Okqj2svKqz0x/OpWKR4poM0YjvbQLWLk8M5UzAUUgyBF1U6L5K2WX0V9Lti6q7GoU1+GS/jzk++ySarxI6W32IunSnBUvFINdfX/Tk9Vx72RUEOBOnUZ//cWYWLpUMDUlfNJIYiX47BIeGRbYgFqu6l9XKPOqaD9sOc2oJBK7M4st+tq1myJyjHttEi6jo4TMqJIc/G/fFvWpF66AOcbNut2ffxL7Ri8zzoXrLqTrWyuk+crX5m5a9eGKXLqyS3r9l0BIeQUadTb8lmH5xKoiF/vKp49d8PMpjVRZuQJGRoL7pzE2hq8q8YlVEY5cqY+7SVH2vERbTwvaPNgHdlzdostxRc7b+Mcd1JvXdEU1UTVk5SwsKwhwUSqBzBdSzPiUuXS+EFapOnml2nKKk6kmZ9FxVk7gG/z4EbHOCBoiJchc2ffVZ94aV6goqLYtAv9QXrlhcj3ameqP3nal/gJFlEhKERicC7fFUg7300jzJB9fhOdWtIKtwko6uUnyboMq07FU0pPyxZRdIYWxWv+K6TEiBqKdW4JjlcOBASS40kLmy8nTUOg+Te53nSZ2ENUmIdSLjUZhjX/ctfKhcTIxIcgXkwefP1ewIV8CO+Wtj4kQm2euIxnZlSHDEaPfl7z9XZJ/+Sz8+q/BH74s/MNq3/dtil95Pf31l9B+1rvBPsoVKkOGk4jOpa9ClicofvUvlK3GAEyMC973HsG6tfC6/yv5wQ8zwitDhpOBbrfLLbfcwsUXB5t0y7K4+OKLue6664bKo9Vq0e/3GRtLeaUP4Pm9qBfX07dHBppRaVogfiL+EKzhE1KRZLFyzN/PAb+lTiXwBt23q6xYAdVCQPA4VpnD1XMAyLtKmmLb4I6tYdrwe2JGFNObrn5+FHf1uf5xd3SVrybRpIQUcTPGpPaYGwS9oYkRabZgdFSQy4vIhjqQJCizmODM+Jg6rQk6f7NQUMoyR5R4aPyZrDzvdDasVzmpDVbUQb367loFGsW1HK6e5xOO0U2xbQfOvg/VnoC07KF8diUpZ3wS0KhMLi9oNAJlQzh5shlj3w6UdMIbL4pgsWObVZkrQnkiXhcCU1KJRd+J+LnC8r5rD89gez67lNlYXNlg2dqET0XprDTvDatMIko111BLmeo+xyry8NhTE9se8pE3YMKayRy7St8e8YmTpPsnPLLLdVJUPL4aT39Xyq60KK6W7HnKwnh+VhrZJURIaWm7LaWSsssIjxURpSpLluh5F2UyJOZkksKKkBTCqH8uoW7qu3Y6LxOd+4eTp819RYhaiQSRSc6YftJMaPJRGiSjS7KyC2DP+C/iGER0uQSdylr2jT4Zp7I08Z4frp4dUyxNTQomJ6FZWBluKPHxG1X7YVn++m+OP012qfZ4pKooUi+uj/jaCudnmjFKz79fEkslrKiyK2D348oui3phHYemrozlE7Q0WKP0/U9Udgm1RkxNqnGep0XFOZSYqwT27I2SXeHorsJS66FGkk9A7RctOi4dq6R+MyLV3DPxi7iFUdziKFGE+zV+DoyorgntF0KtLTPVs/xjSl3sRtIlrym+GbkeRym//ebllmE7bqqmh0UmScmQwUO3K3nzWyTXfht+73cE/98Lw5PV2nczpf96Je6y7bR/6S+HY6MzZHgkoDRG98l/QumLryZ34z/TP/sFA5OPjwve9x7449dK/u+fS656I1x6yRH8smTIkOGIMTMzg+M4TJmhooCpqSnuvffeofJ497vfzbJly0KEWRJGRmp0upJ8HirVCtWKRbGo1BWVik21qh4uy+USXbes0njHyFcQY2PIhSrlUoletcro6BhVUeWsM12+t38EaW2k1NlLtSLo9aSfb7HoUC4LqtUcYmIKOa8IDDExRbNUpliM7+pq40soFucByI2spFLeTb0/QrUUkB9WeS3F/q30KisoNucZGRGMbL+cwv5Zir0itZpNqSgRUm2q3dIYxfYc+anNjK/fRbF4CwCj63bROdygWGwhizWK1jzlcpnJyUlkfwzZqlIul3GqVSp2BRaK1CtbqTXvpFAQWJakXKlSFgVy/SLVimqr7tvRsTHsiQnc2giFYgNhqb4pVypYxSolr68rZUGxqMimas2mVCmysPJSit39TDSux3VhdOk67l52Jf38BFVhs3TpGJWZGpVOlQpV8p0KlXKZatXbOE9M0BZVqtU+3epF5IHR8QWK9VlcKf3y1L0S9MghGMUe38TY+B66s1VEbZRCL1AxlCo1SrKIBKrVKkUxQlEGxEW1mqdc7tNuS8rlCkW3SLlc4bRdNe76QZG+XaNYtCn3XDodSaViUyraWJFhUK3mKFeqWNYKkC4Tk5PIfVVaLZdSpUqlUsZ1JVJKqlWbsbExxEQy2VUbqUKxSLlSwW4UqVQtJiYmqFY7lOwaVbeEi8XI6BiyVKIiLQpWmUptjHK/SzVfpt4IKjhSy1Gt1ijbFUoyT1nOklu6jXzjATodiSwWKFpFf5Nbqo1T7Ks+mpio+kRWqZSjWq0yW/5lit2DjC/8TNW3llOqLK89M9UqXcNhjRQ53wywVBa0O9LrMzU/Sq0iSElhZISqXaFYDIiqarVAtV+lWKr6Y9S1Clhul25+ikptHISgVquxepXL7KERJscrlPflsZ2wOqhYFFRdm4mJMSoVl2InfL5Stvz57dhVtOVXuVKjVVpPt+hQzt/OaN+l2CsyMrWU3r699JsOpbGlVKvK9M2ZGOeQ0f6RkRHoAFaXctnxTaJtR1IuC2q1Mo6YolicplqrUWyXENKlWs175VewC1XGJwpUq11GR3P0iiWKRen34bq1NtKVTEzkcQ+P0Ci3/bYUi0VqtRzFYp9eZYRiZx7blQjpqZnKZVyRp1KpYNtQLDrs3jtCtVqg2AzakbNLjI5Z2G6bYsnCcjuUy4JOdYxCrxQL+FCtVhHA2NqdyOYeTt/lUKkK7rLHKY7ajI51qHXC9xvAmthKuXELRVTZ5ZJaoybGXR7Mn87a/CwHDrpUqjVcu0yxXcQ2nIaVK2oe25ZSBI6MjDJfalIURcoVi2bL9dKNemuZxcTkCMV+kXyhhMiNMDZWJZ8XlEp9im6VNatrzMyqMkZqAoSkUlV1r1RrlEWZoixSLpdxK+qejI3lyOckM7OqfRWrRKlXolrN0etF1rPqKLZtUR2rUVt/DnJhHxQq0O9QRQkdisW+P/bL5SqUqyAlxVZkHFeq5EdrLFlisXCgTCVfolYTfrs1SuUqFEcQnSJ2zo7cB2+M5qqUSy4jIyP+WLvi8gI//VmPhbqk2CyydKnF5MZR7rnXoWxVKRrzrlBVEuCyU6LlFimKIpYFlXKV8XGb4rqn0T9wG9WW8nspRY5KxaHbDdYHgGKzSLkksHOqLwoFQbVapSzL/jgBcOwy4+PjVKs9/zoAkR+h0OsBOX9eVSplGu4Z1Jp3AtAsradTWMbK7k/V70G1xsTEBLIkKHrzuXzGU6n0DyD330a53KfXUy4UqmMjVO0qpV6ZktOkWs3jui6lIR2FZmRXhgxAuy3509dLfvwT+KNXCH7lueGNvZh9kNLnX4ysTNJ+zgdVhJ8MGR5F6G97Jv1b/53id9+Ls/kpvvlKGkZHBe99tzJn/PM3SP7iz+EpV2SEV4YMj1R86EMf4otf/CIf//jH/YfHRAhBo1Gn2VKmW61iE6Sg422Sm83gRU+7XaTltGg1m9jepry/+ULE3EHsRoNWu0XDarBQr2M3VJjxXK7FHnsnazr30W5Dxwtm12yqz60WNBoCZ6HhX9OfX6DVavlpQSkDLNmn0WzQ8U7MdCscXH0ue9olGo1wWPNG6RJcWSDfuYl+H+bnZ2k0mox2OlgWTIxCqw3z87BgCaxOB6vRYGZmxs9/bm7eq0eHpuwhuh3aosnMzAxifkG1udWiQYN2oUmn06Fh9bG7EujQ6UDTbpHrtHCcDu2Oaqvu23qjgTszg91o0mq1/ShszWaLdr9Bvt2m1WziTm2l01GbhLnRc2hPLmHhTodO36bS6ZDPF5mbm2WmV4aeikC3sLCAbDRoNQvM9RtUW23axRaNhnffZudYmO9jdlu92aLvHdB1BOWvrdvuIUWORqPB3EID2WrQapeQ/eAmNe0e5X4Hx4FGo4HodCgaN7Fe7/hjoGW1KXQ6iJZAOnU2rO9w994iQjRpeWmaTZgct9n/cHiD3mp1aDbbHCqeB8C2uTlyjQauK2m2OuRGWn45zRbYc7NIayZx+Lda6r61rBaFToduRxHNjYbE7bbpiDadtsXCwjztdptecw5HujQaDaTbou2Gx2mz2aHRbEKnTd470ZMjOBOX0Tv4Axy3Ab2Or0hoNNtUug7lgk2r1fTzabeLNIS6F72eQ9nLq9HoYFmC/syMN4aa/ngFcEXfVy22W8rn2w2z59Ly7mu73UXg0m00aOebdDpKnZK3XSzLUffXbtLpqPrNljdRr2zSjfPq0KBYhLPO6dM90KTb6sZ8ugmg48yzMD+H47T8OvbsGodqFzDW+Ybfbz07T97peGOoTdNp0OtXWOa26bUO0ul0WGj26LQbdDpdnE7Pn+9z8/Oh9i8szCM6C4hOg1Zb0uzksWUX2+2Qz0O97tJZ/wQeLLToNdRaIqRLo9H1mtii02uwMN+g0YCZGbBabTod/DLXrFZzaGYG7HqdQkGNgWKxqNaKppr7DasH3S45p8VITVKvQ4sWrnBotZrYlhqj9WYbRI9WV/pmyFbJotW2EL0mrU6ZnNshl4OZfJ46m6h0bg31d6PRwBIw2xSw9nJyt3+BVhPmqNNo5JhbaFFsNUNjVV/ndgvkvBOtvFqjpks7mZ0p0OyrtjWaTVzLZbTTDkWCbdpqjmuF0EKzRbvdxup26Bjrfb3VpdloIl3B9q1NrEoHWWzRHcvRnWnSoUa73abtNBHtur8uCQGNBrStJu02NBot7HaLvDdnG1IlXJjHu0fedZ0mHadNoyHoOzLU7kazpfw2WjBbWgUlFT11XM5RP/gQjpe+b9nk3A4tu8XU6gYP7iY01nReebtOqQjtdoeO26ZUItbPLatN01Fz9cCB+HmArtOjnWtTb9hsPa1BPg+dTpNdOyU/+rEqu92Gen1etbPTomSur3lXEdO0affVPRACFuoNFhZUgJn9rGG1N3YcCzrtDq2OFfr97Hi/kXZf1VPdgwZ2u+OPE1D9Mzc36/e57pu220f29HqlxnOrVeGA3Eq+cxMA+8obkD2bpX01t2SrzczMDPSC9Wy22afXbWI3GrRb6p4sXQL1dotGo0G53QGnQ6PRpdWStNtJkZrjyMwYMzzuUa9LXvUayU9/Bn/2J3Gii+Y05X/7PYR0aT3375HVJaemohkyHAuEoPMUZcZY/PqbSHYCEUatJrjmnYIzToc3XSX5769kJo0ZMpwoTExMYNt2zBn94cOHWbJk8O/ORz7yET70oQ/xkY98hO3bty9SkvIfo01jFjcHMMyP8mXP6XeyKRCEzVKSXJ74JiNmYBdhxSP7ab8ywmJBh3oXAlkcox91sAy4VskvUPvs0o3TkSR15LBGYS2zlV20aypfbSIn7XzMmfXURPK650eFExZ7x59Gp6hfIIiYD6HAAtLyD4R8mMVugvG9UEXk0n2MxS4RwrNWjzgoTjB3MYPrmI6ubVubfQZttESCSZ7Ih8xuoiZiUefMEJg0FQqCHTts5UjewLIplzNOj7xwtKL+WdTnalVw4YUWIyMiciYdMZPNkEoqMGPUfpYs2fWDAIgEB+KBw2ePVLQqOJXlCDuPIwqeSWvIk1vIZ5xfdkrN40NjwNZNQLFWpGWYo42NKZOrVauC8WDbFju2Cwqev6eHH473bXL+2gF7P3R4xQrVQlt2EQIuOF85SwcV+c+xyhEH9WEzRgj8aWlTZJkrYunxZvjGFUntNxzU6yiFZnOsfIFebswre8BcA9/HUpq/K1B+1racBvXKdr9c1RYbiaCQl2xYb7ZXREpRme8dewoHRi4CYMvmoCph0z7BQnGj7z9tMfhO8i3biDxqsTzwh0+rsIp9o5eHrnPzhpmwwK+MViT2rYqXv8VDE89iunK2uq4w6qcNmycGZoy5vE0+L5Saa/kanLUX4C5RTtIs2QsHTtB94DsNS5kXVtjXYNcaCfq4OBJLL5Oy8n8fdJ09v4KI0P2LXKSIIdu7r0IFmihFff57PrsgmegCZdKsy16yRDA2pvtRUKnA/tFLqa+4lLExlb+5zoEyYccIMnOoeh77Ri4L2iTCc80V+VAEzJUrgnvm9A0zRv+SyCRI8Fmo841CSDf0my6xQFjUaoKlS2HDRu+c4btTGL9H9bKKcJvLE/xOGTdQ+R4b7gV8RnZleFxjZlbyildJbr0Nrnqj4BefEZk4vSblz78EUT9A6zl/i5zYcErqmSHD8YAcW0P30leRu/eb5G77j6GuqVQE736H4Lxz4S1vk/zXFzPCK0OGE4FCocCuXbtCzuW1s/lzzjkn9bq///u/5wMf+AAf/vCHOeOMM4YqK8KBDEzYyS1NdVjrHzWdtRiPloOJNNMpR475FU9mvrSFirdJdnwfVYK58g4emniWf9VgB9KQs3VY+3AFtGVbLmdRL27wN+8HahcpZ/2lscCBubep3rIl2gi1BkYdKAddZERj9Lpiy9Yc69YaBxBMTqqw8+EekV7Z5kO9Fep/beVaiTiV1v5+AgfE8f6PtcQgEarV4KzeRPr9J+yw3ygPrshFIoEFXw7WLlCEauwnw8hniA38ju26r00HPcFnOykM6ACYY/Vw9VzY/CT/u8RibFTdh3LF23i6XZ+csnPKAbtJPAgh/AidoMgWITRBZ4ccUnsXxAkZ73hQD8HSJYqkivu+id+HA7UnMlveoeobuU/r1wlWrRKMjQWRJDXRXPAipnV7wTXd3EQsf2eZF3TCu04gqRc3sGf86QAsXaIccdcLyh9TLm8ZG2bh/9m6xSs/dN8DH3lCQM5pIBHIXMmL/Ijvoy3STV5mgQdwgfZtF3bBHb5G+PWAYIz7m35HRadLJbtCBInkwMhFyDHlrFsRdsLz2ebT+n56Ea2QsHwyIp+XRl8YPruE5StA43WJH1q2TB0cGw/IzYdHr/SPa/TtWmK7dGyGYO1U9V+9xgqlaxZWq/tfqBnrRJBdKNKvP/C89a26FJlTC70tuyHSyrxXY3GXU0E6wv6tuvkpZsee4BV3ZMRx4HMt5x8vpkQTlMICAfmCroWac6tWxWuo+yXN51oQoCNelhCKKHYLoyyZElx6iWDZ8vCgdKwy+VwwVtuFFb7PTBUhNGijap9+maPKGx+Dc85S62C3Fye7YpFKvRcAsXYk0knBeMZoo5WzWbFckC94fW3ng6i8lkCPN9dVn22jvrpfzL/DICO7MjxucfCg5GWvkDzwALzzbYLLL4vMHKdH6b9eibX/ZtrPfg/uyrOSM8qQ4VGE3tkvwFlzPsVvXo2oHxjqmlJJ8Pa3Cp54Ibz9nZLP/XtGeGXIcCLw27/923zmM5/hc5/7HPfccw9vfOMbabVaPPe5zwXgta99Lddcc42f/kMf+hDve9/7uPrqq1m9ejUHDx7k4MGDMRO/EERySHLjdPBZSrq5cTrLz01Oq8mZkLIreLRM2XN4F4fJC2mXmS9v9SODuVq9ZYXJDCGUM/FBsHznxeEK5POCnTtg0+bwcdcq+soP30E9KoS6LzGIdFqwodIP6njXqX6DYNNQqJbVW/vUJ/Xow7zx3TIdbMPoCJxzdp5iUfhqggsvwFfp+OoQRLj/jTJrNdixDfKjcWIDTMWEd40mOSLEjVYKaGjCTV/rGs6+HasUa+tAlZJfFxGKUBdtSzQi9mKbIFNr0CqspDahNt1LptSmbXxccPouKJfVZl0gfeWCZYvQZtHPU4Ug9HIVxvhLjsyXpISINmJiQqmxEk7GjnTzU3Rzkyq4wQCSxhhV/jGl6FPfD9WeQDc3HrtUTm6kv/1Z/v2qlFU7pNGOh5c8i3ppY2z8+htZYWyiQw7qVZ5a5ZJzmx5hKMjbiiUwSdk46Rp2UB9SzXl1CROGIkRkLFkiWLkiGDfTnvXrsBxqNzcJFU81JvLKQb53rmfXWCid5tclaWwGcyZIILGYreyiNbIFh5K33qlz9eJ6Ziq7UuszOSl42lMEI6P5OOGYgIBQiqzbmtTziPslS/XN02u+am9oKkZIxeBjQvl5tR5YbjfsaN5IumaNUk0lQVhxZ+59LxKlqA1rgaPHpvpbq2lll8JTr0yquppHeW/op/w8ILFYu1ooBVjqy5kEta0+k/RTESljdKLAeU+wvH4P/yhaCeNNE8p6zglLvTRpFVbSLKzys/fvR8LvVOJI8tKtXxcqLbHyOuiEec61CgFh5nWo4+JFjMV4BoiUPiTjlfnsyvC4xH33S179OmVP/953C848I7oiSIpfeyO5+75N+2lX4Wy64tRUNEOG4w1h0X76W6l84jkUv/oXtJ/zt0P9YBSLgquvgje8WXLNeyW9Ljz/V4/g1UqGDBkWxTOf+Uymp6d5//vfz8GDB9mxYwcf/vCHfTPGhx9+GMvYGXz605+m1+vxile8IpTPy172Ml7+8penlCJSNxcJScOJ0tJaYeLK/PjQ+C8y1boJeChyjbFTCZkpqb86YqKI7N6Ftbiyq1DQG6F4hW1bIAYoinpLdtG/7xZcUWRiImq4Z1Q5Sn75XwU6MpXeMMt8GdGtEyiZEt6YQ/LOydKbdRUh0Dx14QXQbsPoiIC5SM6xzU7wpViANWsEME7/tKdiHbwday64Pz7ZpTffhqLH2Xgp9n3fUcdFLrThtHNhksN1larhUO0JOJZ2JmwSo+HNU8zkEQt/E5eyK5S5Uuj7oj9nkfN5LxLa2WcBWyzY7W1+I5sxUFHBkmpx8ROhuVew7wZVgCadlLIrGs3OSjRjNCs2NYFPyDirz8VUwCWqwkhX80khEDKolzoWjENNaQC4i20Lves2bIAVozbfvjOeRJPMMRVTUEzcvAmPOPGOOULdUz0Oe44p/YncASlBk7AiUIQOaETo265dFpQFrsegd7tQQhEtd08nXG6Qd+C111MpaWWXxsHaRYq4G2BwtW2bYFXDe22gowEKpTytjIATcT3njm+g0a2F6gDQX38xommYwBsmZ2nvBlavhskJvbaH123jZnnHUtZMkxizwieC8aZPGDXJlahWoNkRPnEUbZNlCYpFCDzbyVC6KNklc2X6Gy9XzudRQV1WroSH2vG8oxXeugWsiRy3/1g3Sq0DQkTXJXWskFef090BqLtuoYibJEhhheZf6GpzfPnHzLF1ITVbvQxIdBdgxevkipxHjKkTtqX8MwJMV86ktGQr1vy1Sk0lkxVbieurlIyOKl+/xsGEhEY0XHNNE3mEcHy1JICjlV02aNNlcxYdiRljRnZleNzhhhslr/tTSakEf/N+wWmb45Ol8L33kb/l3+hc/Ar6Z/zKKahlhgwnDnJ8Ld1LX0XxG28hd8vn6J/+3KGuy+cFb34DXPVWyfv/RtLtwQtfkBFeGTIcT7zwhS/khS98YeK5T3ziE6Hv3/jGN468gBSSRimmBEKYD6kxI73EPMynbfMBWQDLl1tsyoOM+gsPbYIFMmJ65D8MR5QcgnRll2XB5k1QLgv6QsQet93aMtw15zPZVGdWrIjnIWvL2De2jGrngeRC/MLMGpk9FJgx+psxKzCPUX/DG2YTUWWMFWEmzWsKBUFB7+0TyIU0siuEXDFGIPgkna8AUgfOPlPibh7jtvvUYRc7tBnL5cOkp/bZ1c2NxRya6zQDYVQ5dWNzJPYsGCaiMmEjmSaL8sybbFsTR9DJTVDsq0FdLFq0PYWOqexyhR1XdgkbRxRiG/XQCDLGvBxZGUrVHtlMO3eYUj/s208j3oRggutzK1fqcWgF/tASnRolZ27bgnI5XJA0N/2h9SCYH8HcNrefxgJkWYCLY5WUystL1nVMvz7ROkqE66i2CAfX8AFkkhQXPxHufwBWV5PbaHkmVD1vmNpe2WnKIo1CXpnlzZW20rXHleJN6nb6LU7tWlfG3zz4frcE9H33aOpcPi+gax7xUJ5AlicS25RWeOpwF+Z90zd2cambWYwhRkue58Ji2Vm7qDDFfDc4PDUFM7P461pavwniZJcQQDFsnjlSA9ppFQ7qVSwK3IDlT7kA30+XIujkABNF4TFOpJqh+v60cukBZczhbr4a6Nqjfr62IeoN/WLHiO8cmPfW0gSaVPfXVp2eywG9hAxI+c0i3kARfXPh/+bFWTxX5LH1oNbEuyP8ZDKQmg2sRxoyM8YMjytc+y3JK/9YsmQKPvg3yURX/rpPUfjx39E969fpXfjiU1DLDBlOPHpn/Tr9tRdSvPZqxOzuoa/L5QSv/zPBLzwdPvghyT/8o4yFxM6QIcMjGyGeacCTYMCfDP9kGfLHI+DMMwSjhu+VoLw0pY734O+rnCJmjAOUXUIoP4MamzZanLbZSFBQG6FKRZn6jI8P8TCfsr4FPrvCqgZJIAWIKqQMuUsorzNOj1bC/BwnDpIR1wiENrMDbVcjZFf0Vbih7DIhI2aMWw3/ZhLBfffraGmWXzMpjLosQnaFzGNTtywJ7RryJ2nL5siBkHol2Frmi7Z3WvgmQwdHLg5VQRhtEkKND4lSdpn9Nj5u4VoFP7ppUpUHOUfHU/34aYUybdX9kHatIuEEZ5wumFqiTYmiXPIi98MOVFMy5d5ZFmDnjTVD+H/iSp/ImPZIYU12acVP1zGVn+F+s+oHQLq++tAVhYBUk/hzsVoV7NopmFoSHS8i9KmryS4LLrrQU/wlpNfVmJwELJuF8haPPYgT/wI31cQ0NNc8ElD3j/I7GIaVGzCPU5A2d8LLjGHGmGCqGwQUCdcoRIxF1q1wz8avZWojtanRUElrVsNllxALXBGFZQWE5FEjOoa98Rdad2IBNtT81mSc64IsjeFUoxHOhf9yIo0Q69tVGuOn46w6Z7gqhpgvQd8LRJLgv13zbCG45EJktM7uSRfBpU+Cbt8js7XPxoTfDPPQgdoTOVw9N6Ze9a5ObI+lzXZDyq6CX5acWI87vo7ZwkYEbkjZFcdwcyEjuzI8bvCv/yZ5/RslO3bAB/5KsGJ5fJLk7vgihW++lf5pT6N7xZ8d8RvDDBkeNRAWnWe8DUSO0hdfDU7CW/cU5HKCP32d4JeeBf/wj5IPfTgjvDJkePQgUB6sXAE5e/Hfuah/Kh++Y+g4KXFg5GIaSy/wj+uocMv1niCy89N1aqy8lM1PvyxU31BdRLqyK9qSTZus0KZJFiIe4T088QL1wB+u1uB+ifnsCphBWgUlGYv5vkoy5yGJTAy+WxE/WItCxD743/PeBi3q3D7qFy34GtmYeJua1avVV1cEyq7NG6FSDe7p2KihcEmuoN8f6a0a1t52+GRuTjV+5boK69ZFEovk8vQ9sHPhaIymMlBElV22tzGO+DnbuCmHI0pUI/7Bw20Y/tnTsvCCH3iz0GhDzjTlTWS4tVJQ130R5I2BEyGhQxa4wnTCr/J2SlMB2YVJiMdNoLUJs1buhMiuNOfjTk+pPkUhKEcCMUPk6D03KBkrUOHYtqBWE+RSyKV8XnD2WTl2RILfhgnpsB+iKM443fCFhfQJJVPZFa23NruNn4/DHVVe02vVIbb7hqmYOSaqVdi+jdgaYdbq9J1uKNrjQ+O/aOQDi5HaEZFvyKdaug+6uF+1pP7obnxy+vnoAc3yDOxYfR9AK7vcsdU4I2vi6SJKXBPLl6n73KmuH1rZFWET/bGq+8H8XUzz2aVqrU7kPDK5UhGUSoKeF6giZ6vfwx3bBg+wbn6KVmGlInMj55IJMAyfXcHNc6xSMG+tHO6KM3BkHm3GSFTZJZJ9J6YhI7syPObhupIPfNDlL98vufwyeM+7RMSuWMG+++sUv/hanDXn037mu1IX9gwZHiuQIytpP/3N2PtupPDDvzmia21b8Jo/Fjz3OfCJT8Ff/21GeGXI8GiBnqqm4krDLQS78FTeJLZhjD9OdnMTOKWl/ncVsUoE5FraBihXpDA6GqhhokUlX5WM6NN+xL+TxsiI8BVh8c3JYGVX4K9G/d22BaYrZ7N37Km4W66gf9pTg0x9+VfEj5P08kgoa2jiQ9fDrGNI2aV8e51zNmw5Ldaa8DdfpRaYu5n1Dvz8hM0YTSJi7ToriOoobBL7cVEH9UF+W7fG08pcKXTDmoXVixIAvcpqDoxcTL+2OqG48GbS9ck6ddyyA1PAyQkVhVCnLZWCz5YwlF0G2SIEVGsWp59dZN3a6DOmSPw4DNJ+ea94ckpGpupKhO+zZUG5nHwZdopzJaMOun9cu+QdV+layy6AnCLAzGiWYbNnLxKbKCoywxs/q2OR7hRGRw1TqKktSu1ilf256booE0cTsTEXtCPvlTdX3o47uTG5UKPdoyNWgsN8YdiSBfNaCHBLE8yVA3ZMvXQ3rtdkVyRKoAn7CJRd7sqz2PiMp3D++Ytf49+n2iafsARFOObzInWuCgHVkhuO9ig8JWdkjUwdqAOqlzafLREnwqLf+1YZqxi84IjlVRwJm34KTTYOlFZ6qkOBhfRe3lgxEtaMRpkYkEIo5dRi61WYfw/uiTTILtsmZjYY9dk1MkLMR2KULNRkVz6vCLDayLA+u9yEe5h8s8XKHbgTG5DVIIjAXHkbh2rnJ+Qr1T31fdl5PtTyFQTQs5NfXkWRkV0ZHtPo9SRXXS35p0/DrzwP3vQXIjGcrH3fdyh94ZW4K86g/ZwPDGTZM2R4LMHZ8nR6Zzyf/I8+hLX7x0d0rWUJXvmHgl97PvzzZ+A975O+k9cMGTI8QiECp7p2wlNgd93lOGsviJ9QFyceTTJ3CCVP9Nlih5RWca482V/YkQmuIxsQe5EoeJhET0gekpB1ckUsG9ats3CtIvlKVT1P9DsqK022DfCwb6oqgIiD/sU3RyHOJCHtkikRiZpo+kTx2hDdpOpNeGXST6MJkbBljWEKZQdk19Ilgp3bjPvpy4AWMZszGuD7mfLQ3/RknI2XBt9zI0M4J1fo5iZSzoSVXcGt15tMK1lpIgSFguXfO2EFDuo18nnYsV2pVsamygkkiVGLIdSWUUiTXEhEiAb1DlmhyHsSgW3B+ecNKCdf9q+NVEAd1tPGU2cFZLAFnj+tsLIrqFetqnbvjmeKKFfs5PQLpti4QSQlZ/169cUdWYm7dCuNtU/FsasBNzvM44iR4dat6u9CaTPusp1DXJxUQKA+CaDMGJ3RNSyUlO2s38chNsN38qf+H+KP1BdTabb4WmBRqpXJ5QTO8l3Ui+vT03r5t0a309r8LP+w/xuRGjYQcPv+R/OE//0IlV3R7DWp0bfCxFXM3NrAnrGnsW/08sHl5ks46wNzZGnFMzxUu5ADIxcFaQyC6rxzYGrSIwIjbdSE2fzo2ewfeVK8bAFgLTpGk3x2KVJW+P7ckvpBiMhcWQcXPCG8RkSv6/VF6Hj0d2LracljTpvpDoJtqzFfWrIMd/mu0D5bijyOVQ7lsXa1zlcQpavkyErc9U+kVdowuFAPmYP6DI9ZzM4qs8Xrrof/82LBr/9a8htS+8EfUvqPl+Mu2UrruR+CFDOHDBkeq+g8+XXYe35K6Uuvo/nCz4KxoVkMQghe9hIo5CWf+BT0e5JXvyr+I5khQ4ZHDkwVhqwsQTQPeUdE+G+wWxmcYT5QTG3ZAj/9mXeVfrO9bDuivg9hmksLgbPxstiONCgpOab7omKgUOJIva3FyS6xSJvTFGfmVdu2CracZkQO63nxxLTzZE9torMIkYUG+QCEFAMhp8+JJYefc5LMoJIvjZgxeo6tfcLPslWUs3wg+dmwASbXQv/64DoZIrsCMzQ7Z8VNI+MVHIzojdfPaj3D+/Sxup6IEIuaFHa9zVYuD7QDpY6ZFiGoVqHRFWozKiK+qaT6XewD2AkvVM37dkSV1qRcZO5GMzPy9xUeWinhJ7IWnV/u5Eas2d3I0njied2FttsAoGuPxaolE5RdTzgPRvapeeF6RJmc3IQzuSncnEg+Qrr+xjlXVGRnPg+dbgqnHOvc4IDyfTYYMikL87ywkITvvV4z9NwsFkj0FxhTdnlZFPLeWu1AsXB0Y1xObGC2EvG5F/pq+ce2bYVOBw4chH5lOXAg1hb/KkHMVBcGmC4nwDwbI7ssaBVWs9+q0suNhy7K5QSXXyrZ/RDce19kbHhmtIOItHhFIuZyQCe/JGQeKLHi+YiEY155nfJqHMcLWOGq60tF15t3R6Y50tWzPbJLr09JZFeUSxdCYOft0MuUONkV5OcCdt5i6RI46D0eVMeKNE2yzIa+Q6LJooiovWw7ZcynYPt2gV0GWmZjAiLZHl3ChRcM93I9U3ZleEzi3vskv/cSyS23KjXXC/6XSCa67vsOpc+9GHdiA63n/j0Uh/ily5DhsYZ8hfazrkG0Zyl94VX+W7phIYTg939X8Du/LfjPL8DV75D0+5nCK0OGRyr8yGkWOOsupL8hUMiEeZe0aIyRDHMBCTIxLti0UX32VQF2AXfpDgDcsbX0tz8rqIBmQSLclr9BTIjGeNQ4AmVX0BFhFdKO7Rbr1g6oh7eDNJU7oqs2/lKbiMo0wsyMXaeLjSi7UmuuztjJopnBLGHCuU0bYesW5SwaUESdd6+cFWdgLdkYdwlhFGjnLN9MxnR4bUbL0yRbxeOtCjFhVkjakl5/D45V9hykpyu8lnrWM0uXJpyM+OzSt0lbBJTK6couEKxdC9u3CpYvV2M/pGAykUuqXzLBefyQ1Jci1q1Jak8TcmKjIqlLo6xZDas8E0MZmb+tka10c+O08itDxyFKdoXHrRBhv1saztoLcNY9MZmw8O730qWCnTtg1QrPHDLxMSR9zJaSrZxDkFPRqAZp+RsdKT1y1Bs8qX6oUnx2FYtBtstXKF+LoaKOEibZZfarZQnWei6oCpvPpb/laal5CIFP3hNZb4Y1CU8ipJxV5+Au2RJ8L4yHrtFTpFAI1sSkfh0Y7CFWkcBv2YBExmdj0McKEv4psx5SCE7brI6nKqJTYM6VZcsEZ58ZztvE6GhSlfSBSAAVDzu2qTVYjGgHbCK0TspcMXSvLr1EjUUR84sH4ajOcfItCbHuiC4qXr01GVyrDdd/mbIrw2MO3/2+5E1XSapV+MD7Bdu3J08G+66vUvrCH+Mu3aqIrkjI3gwZHk9wl26n87SrKH3pNRS+/W66T/6TI7peCMFv/5Z6o/rBD0n6Pcnr/4xU564ZMmQ4RTA28akmKvFX1ynHkw+vXwf9fuDIPFRGSh5p9Hi5bLFtC9xxl/o+aPOy6N5hCLIr7YFbjq/D7bcZmTqNbZbg8I0idD66uTHhjq7Cmt/rK1CEVC8UCl51YkECQuqsI1NCmU7xw8EFB1yb4KO0WhXIioWT4PpBjq9L27b6n/L5wIzRsoDiCLPlneRHVyMLD6t8PIfnS6YEIzUZczMxeNOpiwzS1Avr6awokh9LcfKE8s/2tKek5RUxl/H2cCtXCqZqsGyJhTtttCl0rbqPI6MCVwjKZYmwLGo1qNcTitp0Cdz034u2aXgE5lXnnJUUHCFhHPk+fILNZNT8aRB2bBeIhfNwpCR3WClD9LXd8ioOjAT3wcwy5MMoIUiBY8XJLln1dt3GizThKqWoNEyiVq8S9DwlSrKJWHrjtKJxEGRlCmfpduyDtyefRygT35BSzyO7RMIakSvgTm7CHVuDNe/Ni0g621aR+2w6CKGI6If3DazmUDD7R5slj3jv/CcnBZddIikWbcAmtd8EiWaMobnrrbtyLOrE3csiNDTVFzm6SqnoZlUlK5XwPAoT+eqPOSefdFGY+Ad8s78oZKGG6NaDDCKKzEhtzSuNysT7x+A3/b8LpdMQ4o7kiiTVzSjflcG4OOtMr59aRXK5sP+qpUsUQd/pRCpvK/NgS/a8fMJ1XrNGwIqn4WhzzuhzQYTsyuUEhYJkLjeFYCFSbxnqkiMiHYNMvA9eu6U3qo5I3p2RXRkeQ5BS+eb64Ick27fD264SLImFGFbI3fgZil9/M+7Ks2j9zw9miq4MGYD+jmfT3X8zhZ9/DHf5Lvo7fumI83jhCwSFPLz/byStluTNb8Rw3JshQ4ZHAvQzZFzFRMisSaurUt1WGRtDd3ITeOZ6uZxg29ZImaMrcVszuEsjJ+KZAlAqSJaOqgMrVgRk1zGpGYZ4SNZ90heezEObywkLd6kRes2vx+IqVnfl2bjLTw8OeB26fFWOSrVPbSz85jqtkaEIZ1F4J0ynw1EHxKnw+kU5ow6dGDKDcD6gHDjr6I85W1WvXtrIuAVyfD1OoapM4fbdCJDoTzUt7/Q0AllbeWR1TitDBA7qbVsoB9xS+biK+pcBYdy6QHXwpCflkA/AbQm8iKiFpWUmOTAM2WTbQeTA6BBMfvZNUHYJpeySBtl1pJtSOaL6+9xzJIcOp7swMNtkkl2mKZe0CwjRQSY59E7Ix0fE/YjvJD/RjDGdWAY49+zoHDgy7NplUWkTGa+uV5QI1c8/u2yHVxdFdsVUrgJmaueBPcNmq+AHmDzWJytz2IxP5Lj4iYrk1lh0Tuo6RoMAeLXzu9ay6W/7xfQ5PKAYncfYKOzaAbfc5pFeCdyt2a864IjpRzaN7HLWXwT9LqKrCZvBFfJ/D/0PcZ9d0Wy0smmhdBpyrA4zewaXkwDXFaG8ACXUWH8Bm5dPUW/AXXfHFbJ7x57Kjs0uoqt+n/XLlkSEfFZHXj7Yxdj0sW2YLe9gdW4/EJiUd4oroRukOyqyy1d3B4q0tWvJyK4Mj090OpJ3XiP576/A058Kr3tNsiN6XIfCd66h8LOP0t/0ZNrPuiYcSjlDhsc5upe9GuvgHRS/8nr1tnH5riPO4/m/KqhU4J3XSP7wVZJ3vg3Gxo71sSxDhgzHA2J8rb8JtKImb5EoYhrBZjj6N4C/YUst2MJdcXrq6SihduYZEmte4ESUJgI48wzYvx/2H+C4Qz9Xd/NTOGsvRFamEtPpjetIDc44Gw5dPyBTIRJVZVYuz/iYQ99ofIpIQJ9ddH9kmqaEnaAP2sCpgbBzRzTNkZJdQXo7J5iahGLZprARZufC6WR1aYpTpbS8T7bnlWBT6zuMFwJn9TkceHiSJaGkwmhLhFQa0IXu+HpwulgLD4eusyyxKIc6Pg6HDwfffV9SsVuYoOwy5rEQMFM5k9H2XfSt6lG7PatUBOuMx+lkcaiOtmiSXUFCZ8OTOHx/Pfl6I1+JCBHtshB+Ye2bjA2l7Ap/n5o6tmeVatXC6omwzy5txuiTjGlXh18ymCTO0uUFdu9ZjmWl3NKjgB6y/XUXgZ2nehQuiwUMILsSCNa0PNLOeSdtG0ZHg/suEtIkESpmFdLILuyC+tdXZI0r4vSIFJbvmyoYVwErmWZ6nCAWGwpJ99Zxk81gZW0ZS2pQb6j6aLJW5+FaRcgLpNMDAZZ0Yqq3tEqE6pGPh2nV0RL7dg1NdslcieboDjgUpBsUTMAoLtyu4giiswCe0mz5MkmtJnCOsDMzn10ZHvU4dFjy8j+SfOWr8Ae/J3j9n6UQXe05Sv/xMgo/+yjdc36T9v/464zoypAhCitH+1nXIGtLKX3uDxAzDxxVNs9+luDqtwjuuhte8jLJvn2ZD68MGR4JEBOBCVpgxhhVlejdlH90cJ4nlMsOP3BbFixfpnzznAiYGwlZXZLaOH24VJIh31VyqBBwurDwDkD4ph8iOVz9ELDTTMcHmjF6vmrsPO7kRtyxtYtfk1iG0XlWHlEaobr1nHRzdmHRP+1piVHQNm2E7aYI8MQOsgQEDqBN0tAaX4VrldL9EYXUYepzOzeVSF65K07HndyoizNKHlJR42HJEmVCOTlsbBnDZ5cloJcb43DtCWg/S8ejqwcJqKRHJmxYD2Nj2k8SkK/QLSQ5Uwvn41oR6UrEB5plDZiDaSbaR4OkuZ7IRCmyS5uhpfkv0uaL42MR9+4Ctm1T/vNyuSEisg6JohbxHNFeKF43rRQK86nDV3JQUn1KkyVJ72NEwrEgb8ETzlWfnSROzoCsTHFg5CKPuFHQfbR/5FKmK2eGjoXVnMKvQ9nghAYRcUes7NK+3FLYm56npCpEyC4fnhkjxE08E+EV5FhFZss7kCMrE5VdAK40KmUXYs8UZ54xRHkRuCvOVESsH8V4uCi+UWRkV4ZHNW6/Q/J7fyC5735421sEv/kbyY7orf23UPnk87Dv/x7tp7yB7hV/muijIkOGDEBlUvmxk5Lyv/0uon508olLLha87z2CmVn4g5dK7r4nI7wyZHgkwHdQHyWzIrsG34wx9Zn8+M9pv04x57Q6QfJhfcwdWYk7sSF2zhlSpZr0DJEEqTeux/Ik7ZE80QhnCMG+0cs4EAtZvzgNMswb9CgCh+ECd9lOnzw4IgfKgsgOVOBsuhw5ssI/nYhcIfFmVquCUjnsnP+kQliGsstQ6Xh+rmJVThyvSrckxRDPm6az8GEsNo3PxaLgssvEYJcBaT67ImUd03hOKQ60sEuZSS5fLjjjdMHIiAgamzLd4/kGRLA7vi5xrg/MYxEzxqEwkJ3RfWt2ZNg8Ob2P1XkdGEKXotRcwYv846Hs2rZV+a871oyEAFn21K9Hmc2g4rUVYrTPkq5J61e9JvYXIbsQgm4uzBifeQasWwt9u4aYWMsTL8B40RI4TNemdrYFY2N+dinc59H9bk4tVdEUJ8aTz2syT5sxJpFdGvlhfie8DKTIUS9tiiu9MMmuhPXFw5IlR2ASa8KyVXR4//Ek9uAyFDKyK8OjFt+4VvLSV0hyOfjg3wgueVLC4JeS3A2fpvzpXwfp0vq1T9I/63+d/MpmyPAog5zYQPu5H0I0pyl99ncRjUOLX5SAM04X/O1fC2wbXvYKyXXXZ4RXhgynGr6Den8Pnqx20OYQphnXia6TD+07xMoN/RYfwF19bqL5tUzYFB8L/E2YCG9kjwixl26Bt7Szzy2xZst45OwwWR7N5j1i92Uof44wo/QzAwmIYdidxdIcn7HpTm3GWf0EEKayK5zGspPak2ySKYSKLlheRDxjmvMl+dKLZzw4v3hCcxJ5aiphxbKxE9t27NB57top2LnTtJ/26hKZRoPqMFveRd8q4y7bmTjXBxPWx1HZlZh9YLd24QW6BOkdDq+r8WvVedv2yIE0BdgxLDkatZDJ4hAZpdqVgrPmPPqbnnz0lRlQvCZw/N+qIxHtedBk12LKriSMjanoifkcbDlNEYS+XzqT4PZZrfD1ei7PLH8qe8eeGjo3KADHiuW6/OBYuZLn9F3ppPbGjbB2DSxfnpKpZfvVjEe+jUPqiJpRM3sDicquaLuO8bFf+N2dKbsyPE7gupKPfNTlL94o2b4NPvRBweZN8Ykv6gcoff7FlL7+Jpx1F9F84WdxV555CmqcIcOjE+7yXbSf87dY83sof+Y3EfN7jyqfDesFH/xrFcL4Va+RfP2bGeGVIcOpROxteYraYe0ayRm7YCQa4vsE7IbHx9XfEc+CxF26HWfFGcjq0kRRyqmGezyUXUQ2TcbxqamEZ5uEN+tJmJqEDRtUQnd0Nc6a8wdf4JFuQqrdoG9WOERhzfFdHKqe56W3WLNamSBGccLJruM0Ltyl25Ejy8PKrly4bDupKgn2VbKyBLliJ9su38m6tSkFJjEXvp3bUZKHiQkTJpGx+dUYyrxpmGLTqhE5KyJTYDEyG6CTX8K+sSuPzkIjRSl6RCh5DEQ1yW40YOtGR4RaH6SLAEZGVWTZVBPsyL06HgquNIRNDo/8+tER41IrB4XqUU/BQddpwjk658z6p6m/NI5G7WrCtgVPvlywdGm0poPJl5D5faGofGeBr+gdpPicmhI87SnCd7Svrhs83otFwfZtwje7Ths3EjEU2RXcGZPsCmeqg6Bof2Khy44Qi49zIyDAESAjuzI8qtBqSf7ijZKPfgx+6Vnwl9cIJsYjs8N1yN30L1Q+9kvYu39C+8rX037O36qIFRkyZDgiOGsvoPW8jyCa05T/+YWIw/ccVT7Llgn+5q8Eu3bCG94k+cePyyPzbZMhQ4bjBhlTrMQ3n6Ae8lesiO+KpOdDo2f4NjlWrFguuOwSGNe/6ZaNHF8XS+c7/D3FpJev+hFhZ9JH9BpbJG0mBl4wVLbnniNYudJLmy8ha8sWqYdWdrnh70OU161toF1Y4SUXTEyIUES3oTDMzTzZN9wgu6IRBu1cEimU5LNLICc3MTJWwLIEshCfL74Ja4iLWqStIsGcdVG5j8RZeyHOsp1BHa38omaMyxcZOul1HPBdxNeblNgYRwnJmtVw2uYjrdiQuVem6J/2NMRoEPlzk6eqSTJjnK2cgTuyElmeYN06QaGQXKYcU2aZ7uQmlUUK2VUswrKlcMaRxw8KkEI+pkF6SlttQrrYOnwkvTronkeVXUmPje4i1m3HSnYtisgkmpyAZWtHWb8u6CdzvXCX7aA9vp1OLhTm4ojLWTR5Qn/MLr2EfWNXDEd2CeGZbaf/piWaMSLYvk192jd6GQtLnjh0nROrof/GwkgPh4zsyvCowZ69khe/TPLt78Ifvlzw2lcL8vnwTLb2/JzyPz2f0lf/Amfpdpr/3+fpn/2CI14gMmTIEMBddTat538c3B6Vf3o+9t1fO6p8RkcE73mX4JnPgA//g+QtV0u63YzwypDhZMN/P3q01mrlCfrrL2aheJq6/DjxEGl+PU41sZUEqcPAe33Yz42q43Y8YlUU/fVPwll5dvoWIqXBkuPnnDpcnp38fYjCIu6qB5QxsAKRr0f+zJbm9PuoIYTheydcn9N3wvooD1tUUhdZGk/Mrr/5KTgboj7YDBibRd2WgT7Thm6voTSrLkFObgyOWXYsm+g+8swzjq5jEwVUCYEIRjyFUNSH0KCmb94E5583uPyJCUG5fAIXjohT/M2blKrG2JqrPxL6dhV39bmLj2vLVmaZXuTWNJWbEIKzzhRHHOXaVJSFua4h5vnkZg5Xz6ZVWMWTLhp86bKl8NSnDF+3gT67Ii9mBpFdiYpLhiCPjxYJBLNjV5G7ns32XcVQMIHQvLLz2Ms3gxBHFgHzCNuRlLzNGI5VDpzsD8xg8XVYk13zpa1IO3AWViwKtm2Bvj1Cv5gc0The38Xad3RmjCea68yQ4bjgRz+WvPEqFc3kmncKzn9CeEKIhf0UvvNu8rf/F+7ISlrPfi/Oll94ZD4hZ8jwKIS7dBut3/gspf98BeX/eDnd83+P7kUviz3wLYZCQfB/Xwfr1sEHPyTZ+7Dk6qvUg2mGDBlOLkyfXco3h/7ZjMzHqI0RKLW0ODlktdCVw1QUBMeChCelOkDcdKZV3cT0yBImtYnTIJTHkeVxxPwe9X1olWtyEJ5jRpJTqqPBgE3IYK7LkIlIibRshOMGSrMhsH49uBbUjp/YkDWrYdUqYs+SvvrQgKwupb/5SsinkJ35UuJhWRpHVpYwX90Bfe9gkulhBEc+DMwxFpBd0VufRhgcK4QAZ+35iIX9gT8+lPpq+TKoeabSUfdxSdi08VjmwIldJOQQJm3D4nhP9dWrBHNzkj17oz6bhlNWtgqrAahUBEIk/CYESY8MQyi79JK0dCk88GDY79mwrpxKQxA8Z50RrO2Lw/TZlZxC94VtK4K2Xlffl0wJzj9PJq4lxwtJv5GaXE5zch/Jwfub3iF6/eiJEu7qc7Ef/KF/3apVsFCHDRuGr/NAeKxm2hxLQ0Z2ZXhEQ0rJJ/8JPvRhyWmb4a1XCVatNBaGfof8zz5K4UcfAunSfeJL6Z7/O+kPGxkyZDhqyNoyWr/6cYrXXk3hJ3+Pfe836F7xepx1Fx5RPkIIXvgCJfm/6mrJ7/yB5K1vhh3bM8IrQ4aTCZ84SXt4HJKEOZnvlU5GWdu2QnURh+JTUzB/n/KPBYAQ9HJDEF0mbB0jXve/eqmXrugZpvEpROXAS9KUXUfIfAwiuwZV3XeYbiNkH/IVcOYQ/Tbu5Eas6fvSr/XaVywIthwTCRKHZQkswBl20B3Ns6dl46y7kP5uCX3lOHzZMuB+GEh2eX9LMQ4tfI274nSs/bdCzkyoN412rIjj5aA+0Q1gvuIpywJYlgg54fa31yeIR5eeCagsjoLTTVSbHRP0nHZ6qrwho0wOwvFc87ZthZUrCPuCOooCki45SiuzgauaJrUK3t8tpylVpWnZE5iUp+fzpIsGBAYwsGzZkfSFcXO9cdQthu1+dV9YliLJtW9KSCbNTzSWLxNcdokcKjriMOPCN2MMOf9X1+VyynXJ8UJgTnlk/ZaRXRkesWg2JW99u+Rb34ZfeBq85o+NCBRSYt/zdYrfegfW3EP0tj6D7mWvRo6uPrWVzpDhsY5cgc5T30j/tKdS/NobKP/ri+ivu4juxS/HXXn2ET00XX6ZYNUq+NPXS176csmrXgnPfmZGeGXIcKKxeRPcc69xwJu3vplcbB4fh/BfxwlDPaQbkJUp5DBqKwPr1i5eRqVW4IzTBU4ld9TBptzlu6BQQ1YCM4/jp9w6gnyiSi7/+xGaIg2o+2CyK0y6ynwF0Z4DUBH3lh3HHdPR4CS4wtCbxnPPgZyWV6X0mcRGWMpPVKnizc58GVmo4C4P95WsLsXZdHkkg8A3W5QgOFpRXxSxqkc52FwR0e+kXn9sZNegkH2qb93Rlcip046lkGTkPZa83w4XezTT+gQst7YtmJg49oIS3A3iSujaYxSOcAzpvMoJ4sft29QLhdFRT/knRMwEbxhlV4jcO17w2y4QuTx7x57K1NKwtYMuNXec5tXxwNC/oQMG7do16q9PdrkcH2Z3mGoc4SKVkV0ZHpF48EHJn75esnu38s/1K88NHgDF4bspfvNqcg/+AGfJVlq/+jGctRec4hpnyPD4grPhEpov+iL5Gz9D/sd/R+XTL8Cd2Ehvx7NxNl6Ou3TbUG9Mt5wm+MjfwRuvkrz9nZLbb5f84cvj/vgyZMhw/LBpo4hEzFNqh3Y+GrP80eFT74InwI9/mnzOWXdsznHT4E5tRlo55Jh66j+q53u7gLtki/dFLv7melAhx1EKI63hfXaNezzi2OixlKjJLu9vislf8qXaA/QQso2jhDwJJO/ZZ8G+/d5GtD+4vE5+Ce3x7UxZdyAtgQMgLJxNVwxXmBuQXdFbbFnHaa8azTfy3dl4eVQOAih/T/c/eAKdih9B8IWjgfTUfcLpqu+6tGMwYzzhatZjKMBcdubXPosDTdDvCtzacuQQwcH6nvluJUFNm8sJVq6MHzcxjLLrxCBYrwsFwblPKPpmghrCUHY9+qAqLSK/LU8z/LFpskuZm6p0J+qpQcYCqAyHjOzK8IjDd74recvbJIUC/OV7BOec7U2q1gyFH3yA/A3/D4o12le+nv6Zzz/+EuQMGTIMh1yR3rm/Se/055K788vkbvsvCt//a8T3/wpZqOIsPx05tgZ3bA1yZCWyNKqiURVqyEJVfS7WGB0t8K63w4c/KvnEJ+HueyRXvZGEMM8ZMmQ4IRCCfeNX0nYWcWqSsik61e4x46ZcJwHCiplkPdohPSfrgVnj4jd2akrw5Mvk0C8oEjm5KAGRspmp1RLceBVrOMt2IkdXDVX+UeEkDPByWbBxQ6zg1PTd6lpo3XF0hcl0smvJcL6kF0V0cx8zIbPziQTlaacpn55pEQuPGSeaQcon2z8fjWJTX3Gq11eNyy8NPvtRNE1lV8SZvLvmCUPlOzamTBNjQR+GxLA+u443ZG05YuZ+FZ6VZN+zps+uRx2GMmNUfsFGRzlhyi6/Ohxd/hlLkOERg35f8sEPST79GdixA976JqFsp50e+Rv+H4UffAC6dXpn/hrdi18O5fFTXeUMGTIAFKr0T38e/dOfh2gcwn7oJ1h7foZ94Fas+79Lrn5gYOhimSsjiyP8UW0Zv/5bq/nvn63n7197Blf8xplc9NSjjXueIcOjE5/61Kf4yEc+wsGDB9m+fTuvf/3rOfPMM1PTf+lLX+J973sfe/bsYcOGDbz61a/m8ssvT02fBtcua1c+cXj+ftxaVPl18lCteM7CB+CRsCc8LuZXp8DBT3/Tk0FH0zoCZRdw7Epc30F9QHo5y3dG/EzBRRcml3PiScdTNLJOWBQ57fXbCpFSWrHhDu+hOxVRcqswZCybJDO1I0VUiRI+eYJZByFAWLjjirm54Amw/8AxZnkcqnU8YBKQPtllnHciZNewEEKwdcvi6dJwqpRd7rKduFNbBoou/GiMj2qya/B6cOH5UC4D7RPk7sDPzo8jfUSXZ2RXhkcE9h+QvOFNkptvgV95Hrz0xYJ8Dux7r6X4rXdizdxHf8OldC5/7Ymxsc+QIcNxgawuob/tF2HbLwYH+11EfT+iuwCdOqLbQHTr0FlAdOuI9jyiPYeo72dF7y5edNrXEW4fboSZG1ZR2vkk2HqFMkfKgk9keAzji1/8Im9729t405vexFlnncXHPvYxfud3focvf/nLTE3FJRc///nP+eM//mNe9apXccUVV/Cf//mfvPSlL+Xf/u3f2Lp161HVIXF/nS/T3/K0gAw5Bbj4okfKli8Z2qH90W7Uly0Ft34sNThGgqJQDT6fQEIg0bG15zQ82DQK5MQjSDV30qU1wwQVOIY6+QoMOzGb49HcKLmVyz1C5u8QkS6PFX3j+WdsLOyA/6jwCOk6Ez7ZZbwgkUdJdh0rtK+vYyVJjxhCLBqRXPfF8VJ2yUJNPT+fFAw38LQ/NbSbuhNG0h+dhC8juzKccvzgR5K3vFXSd+CqNwqueLLAOnQnhW+9g9wD38ed3Ezrf/4dzsbLTnVVM2TIcDTIFZDja4ffivW7yH238rP/vJHuPT/nYufL1G77F2S+Qn/rM+jt+p+4q8975Gj7M2Q4TvjoRz/K85//fJ73vOcB8KY3vYlrr72Wz372s/z+7/9+LP3HP/5xLr30Un73d38XgD/6oz/i+9//Pp/85Cd585vffGSFm9OpUMWd3OSrE4BTSnQ9GrBxI0xMwMRRRtg643QQI0DnePuIOgoSTO/QTsAamyy6icofHmG+4k62fZTuj4Evd45F/acdCZ04UrP4SF0uxGDn/48kuCfWKuyYoP1rmQq+1athegZWn0CL4iRs3gwTk4pYfKRB18g+TkuIs/HSxRMdLxhRgofDCQ5k45NdR3ZZRnZlOGXo9yUf+ajkE59SoWSveqNg7cRhCl/7a3I3/QsUR+hc8ef0znz+CXU8miFDhkcYcgXEmrN5wkvO5uZbfpMXXN1lWfvnvPjiL3LunV8if8u/4SzbSe/836W/5emPUn14hgxhdLtdbrnlFv7gD/7AP2ZZFhdffDHXXXdd4jXXX389L3rRi0LHLrnkEr72ta8ddT30xspdtuOo83g8QoikKGfDw7LEI8evyxH47Bo+zwHnPFJH5ooqmdM7fuUeF5zkTXSugLPqbGRl6QnJ3l2+E1kcQVaWYPVPSBFDmy2edCTZ3z3C8Ugku07bDJMTMG6Q+8Wi4Annnfy6WJY4br7mjjfEcVZ2nVTi3SsrHrhmuOuON/qFCeDAESuPM7IrwynBww9LrrpacuNN8Jz/AS//vSYjN/0j+c99FJwevbN/g+5FL4UjDBeeIUOGxxZO3yX4h48U+IePXcgf/POFLJ/8E67+X1/ijPpHKX3hVbhj6+he+Pv0d/5yFqwiw6MaMzMzOI4TM1ecmpri3nvvTbzm0KFDLFmyJJb+0KFDA8uaSGBlatUOnTyMjxeGdg5drXYAmJwc/prjCV3+xESBarVLuSSYmHik7rIXhzszAqKNGB9DVMP3SLe1WlXmhtF7KK0WcrYKI6NYExPI/mFko4oYGUUcBQvnVquI0bEjvtatVsGysSLXWbZLtdqjVovfIzl2EcyuBctCPrgA1XLs+lMB1+trMTGBKNZSzx9tXZPmoXEy8fDoaJ9q1WFsFKqiClbu6MpfojawvZ6kWu16RQZ2YMHcOjrbsGo1yPdY8jkauNVqyL7O7B/ZmUB2DiBGakc1L6IYeA+PEbNzDtVqn9FRm4mJ4/984y5bD41DRz1+JyePc4VOAU7k/QOYmVX3cHIyz8jIyQ/JeKzzWD7heXRmoCqsRfOQ4+OQ68OyrYjc8OUtVsdGQ/Uhqy6ltrWLKB8ZN5DtDDKcdHzlq5Jr/lKJ9N/0531+Ycm/UvjUB7Cah+lteybdJ/0hcvwoQ3JkyJDhMYdSSfB//kDwtCsl73h3mRf99XO58Pzn8H+fcy1rH/h7Sl/5c5yffZTuk16Js/nKR+Zr0AwZHkGYmZmJHas3JN0uzMw0hiauGg3p5Tf8NccTZvmNBjh9mJl59M5/e2Ee0WzQn5uHbvjttW5rvdVhZP2ZsXsoFuawGw1cu447M4OYn1ffi/O4Cfd7MYjKWiRVONJrl5+vXjxErptfkDQakLNT7pEYRSwcUHUW00dV5+ONXKMBQH9uDvJxtZlVWoVoHsI5irpOTEwkzsPFMD+v+nFuvs9ks4G08kdVvobjqPwAZmaa/vFgbjWTLlsUUgb51qpHn8/RINeoh+xl+0b/WAsLWI0G7twsbv7YxtjR3sNhMTer+nBh4QStaxM7Yax/5HP8MYITff8AFuaDe9jvn8rfyKOff43mEeRRXgMLTWD48har45zXh/N1mG0LaAf3bBiyMiO7Mpw0LCwokutrX4ezz5K87Te+xvKb34t14wP0115I+zkfxF1x+qmuZoYMGR6h2LJF8HcfgM/9O3zkoxbPff2V/NKzruClV36TJTf8JeX/eBnOqnPoXPYa3FXnnOrqZshwRJiYmMC2bQ4fPhw6fvjw4Zh6S2PJkiUxFdeg9McbKuh4hpMJZ+svKEVKZJMma8twJzbg6iA+x7ivklObj+5C09H9kZbpqXOFe4Js644ayZ3pLt0KHF0giP+fvfMOk5u6/v73SlN3tnrXvQA22IBtsB0IMTEhgdAhoYSQhEAooYSW0GsIGAjN8BIIvYUQyo8klNB7MbhgG7d197qut/fpM5Lu+4fKSDOatm121+fzPPbuSldXR/eq3a/OObe77LkHEIkAo0eL4LWlUKp6MI0d+u7bEGMM06dxlBRrM7UNELgeYtWzaVP7BSMDUl9pJEygXIx9TG8nqN8d6enp3//+dMRuyXfLOc75PceXX8i476wP8OxBv8Dor/8E7nAjfMqTiPzieRK6CILIiigy/OJUhv97ieH004B332c48fojcK//dbQcegdYVz2KXv0N3O9fDxbo4XzfBNGPuFwuTJ06FQsXLjSWKYqChQsXYuZMe/F2xowZWLRokWXZggULMGPGjLz335MXykI7U+oDiX7S+PqOngzAmQBl5FQgOXxkEAzqDfRQdHmAiV39nqA+PW43w4EHMDicAuS9DgMvGdWj+gSh7y7eUSMZfD7Wp/uwQynOkGPI6EslfZkBgpGPe/A6q+72jBoFHDgdBfF8JlQGzt2bGJIEgxzzHlRw/TUB/Gz0a5j/y5NwdOfVYHIckWPvQfi3r6uzLNKdnCCIPCgtZbj8UgEv/ZPh6J8C/3nTgeNvPRX3R95B+/Q/wLHxAxT943g4lz4HyLHsFRLEAODcc8/Fa6+9hjfeeAM1NTW47bbbEA6HceqppwIArrvuOjzwwANG+bPPPhvz58/Hc889h5qaGjzyyCOorq7Gb3/727z3rQtF/T1tfG/gcDD8aA6w75RCW0L0CHcxuLsUysj9C21JEvSOOphQRs+ANOkn9iuNBPWDSOwqrBlED3A4GEaMoB4sJBTGSPQZCxdz/Ofx9Tiy9DXceMLbcCMIuXg6wkc+ouXVGYRv1ARBDCjGjmG44TqGs8/iePFfHK+95cVrb16Gn//o57hkr3tR9dX9cFb/F9Gf3AR5jx8W2lyCyMjxxx+PtrY2PPzww2hubsZ+++2HZ555xghLrK+vh2BSo2bNmoV58+bhoYcewoMPPog999wTjz76KCZPzj+0at8pwMS91JfzXGFMHZANhO9VbvcAMKLHpJ+6/eDvAeFw/1rTm7i1aKmRIzIUYqq30oBjKJxaWfD0X/74vkcQAaEo3Ur1x2DweEx/OyCI3QZDn+7mJUtiF9HrtNSHsfAfH2K/4P/hyQNWQBHckPc7AaEDzoAyavrAeCsmCGJIMWY0w/XXMvzubI63/sfx9rvj8foXf8ep0+bjCvFulP/395D2PgrRw68HLxtbaHMJIi2//e1v03pmvfjiiynLjjvuOBx33HE93q8gMHg8Pa6G6CPKyxnKywttRfdxuxmO+DGHKA7Cd8Ah/nH2ezMBX/dTrQ0ujJxdg8Czq9AGEIOe/aYArh6mZRteBQwr/OS43YbELqLXkBq3Ytsb/4e9Ot7Er92daCuaiNAPb4Qy7eeAJ79pQgmCILrDqJEMF13AcO7vOL74EnjjrcNw1FuH4MyJ/8TFyhNw13yFjv3Og/uI88F6kEiZIAiiz+iVj4IDT1QalEIXgIHYlr3JsGFD+/gsDCaxi3J2ET1k3LienzwzDhzcJyCJXUTPkGMQN3+K4Ff/hyr/YkxVHFiFozHiyF+h4oCDoNAdmiCIAuByMRx9FHD0UQwNDW589sUFuHL+STjB9f9wIn8czcvfwKfi1XDNOB7f+x5DZSXdqwgib2g6xt6lF9uSF6mhr0rpmN6rdHdliHt27VYMwpxd/ZzfnyCGFCR2Ed2Cd9bB9c3T4Mv/DXesBS3B8Xih6yrsfeopOHD2YJ8OiSCIocSoUQy/+RXwm1+NRl39vfjgq19j/x134Vfitfjuy1dw3d9vQqB4P0yZAuw7hWHKZGDyPkBJCb1hEgTRfyhV+0DctRToDa9TdzGkfU/oeT0EudYMJUSn+lMY+ENgrxZW7qbwcmJ3poe334F/pRMDB84h7lgIx4pXEK/5DA4OfNnwY3zSeQYOPPmH+PnRQl6JbQmCIPqbMaMZxpwxE+CvIVz9BqZ/9f/wauXpWBw7FY+tuwSPfZ6Yxn3sGI599gH2nsSw9yRg0kR1GmlGAx+CsFCoS2LMaKCstDD77gt48QhIU44vtBkEMSSQR+wHCE7LMu4bDnnk/uCl4wpkVe6MHQt4vSDPc4LoASR2EdmRY3Csfw+OJc/C0bYZHfFK/Hvr7/Fl8HQcdcpYXH2iGjJEEAQxaGAC5OmnQZ58DPiix3HIihdxyKy3ETzzDKwsuQDV26qwYSPHps3Al19xI5zA5wMm7sUxaRKw90SGSZoIVlRE90CC6G+m7k/XHdF3KGXjIXTuLLQZRDfhwybaL6/Yq58t6R6MMVRWFtoKguhbPG4gEs1ejmZjJHqfWBDO1f8G+/YFuMIN2OTfF89vvBdbfUfjvPOG4fSDguTJRRDE4MZdjNjh1yI+67dwLXoCvjWv4FDhPzh45pmIn3QWePEIRCIcW7cBNTXA5i0cNTXAp58Bb76VePKOGcMxaSI0DzBVBBszejAnZCaI7OgeXeTsSAxFlFHToYyaVmgzCIIghiyzfwDIct/VT2IXkQILtUJe8E94ql+BS/FjUfNsvLj1Dnj2m42fXyNg5gxg2DA32ttDhTaVIAiiV+AloxE96nbEDj4froWPwrn0OTiX/QPSPsdAmHUW9ptyAPbbl0FPHsA5R3MzULMF2FwD1Ggi2IIFgKyoIpjHA+y1F8feE4FJkxgmTVRzgfl8pAwQQ4vufnEliAENS9zzCYIgiN7H4WBwZFCkevoxjcQuwqBr5y50ffQ89mr/L7wsio/rjsFHofMw9YhpuPF6oLycHvgEQQxtePkERI+7F7FDr4BzxctwVv8Hzg3vQq7cG9K+J0Kachx4+QQwxjBiBDBihPpVSh8QxWIc27erApjuBTb/G+Dtd1U1gDFgzz04pk1VQ7CmTgX2mAAINN0SMQgZNRLYVUeeXQRBEARBDDxI7NqNkSSO1dXA5kU12LPuWcwpfQfDAXze/nPU7Xk+Zp6yJ27bi5IxEwSx+8HLxiJ2+LWIzb4EjvXvwrn+Hbi/eQjubx6CUjYB0p4/hLzHoVBGTgcvHmGM9l0uhn32AfbZBzB7BLS1cWyuAdatB9as5fhqfkIAK/YB++/PMXV/YNpUhv33o5kgicHBfvuqobsk1hIEQRAE0VdQzi4iK6EQx5q1wKrVHKtWyShrno9Tx76K3434GvFSD9YWnwnXj36HQ/cfXWhTCYIgBgYuH6QDfgnpgF+C+eshbvkCjm3fwLnuf3CtfAUAoHiHQRmxH5RhE8HLJ0ApGwdeOgZK6VjA5QMADBvG8P1hwPcPBgAGzjlqdwFr1gLVazjWrgX++aIaAml4f00Dpk9lOPRQGWWlnD48EAMOxhhcrkJbQRAEQRDEUMQhqj9dzszl0m7fe6YQAwlJ4tiyVfUiWL+BY/16YMsWjonFG3H0mI9wz17/w/DxdQi7RiF04GXAQb/GJG9Foc0mCIIYsPCS0ZAO/DWkA38NyHEIjWsgNK+D2LgWQvN6ONe8ARYLWLdxl0IpGa2KXyWjwUsTv48ftgfGHz0Mxx6tiljhMMe69UD1GlUA+/Ir4O13OIAOlJUCU6dyTJvKMHV/YMpkoLiYxC+CIAiCIAhiaFJZybDfFI5Ro7q3PYldAwk5Dkf1f8GifsDhBne41Z9OH+AuBneVgLt9gKsE3FUMONwAYwgEVGGrZouaJHnDRqBmMwA5igm+7Zg1ah3+uOcyTJv2LcqVneBMhDxhNsIH3gh54o8BgU4DgiCIvBCdUMbMgDJmBiR9GedApANCZy1YVz0Efx1YVx2Erjowfz2cdcvBIh2WahTfCCgj94cyYn+IY2dh1tQZmDXTB937a+dOYMvWIixeEkR1NbBgYcKPe/x4jn2nAPtOYdh3ipr83uslAYwgCIIgCIIYGowb1/13W1I5BhKxINwLHgYLt+dUXOIOBKVixGMlGCb54IoXY1/BiXP26ETF5HaU8kYwKAAA7imHPPZ7iEy8ENLeRwLkxUUQBNG7MAZ4K6B4K4BR02E7k3I8pAlh9RDatkBoWgehaS2cW+fDxWVwJkIZORXyuIMgj/s+Jow7CAceOAw/PjwMAPD7OdZvANZvADZs4Fi9Gvj4E1UAEwRgjwkckycDkyaqsz9OmghUVlLuRYIgCIIgCGL3gsSugYS3HIGLvkZbSxy7dkRRtyOKproIWuuD6Gz2I9oVRJEQQLEzgFJXAGMr/RhVEcTwygCGFQWwhysApxAFvKPAPVMQKxsHZdhEKJX7gA/bC2BCoY+QIAhi98ZZBF45CXLlJMh7zkksjwUh1q+EWLsUwq5lcC5/Ca6lz4EzEdK4GXCNPgjy+O+jZMxMHHyQFwcfBOgJ8NvbOdZvBDZsANat51ixEvjwo4QHWGkpMHEvjr0nARMnMkzcC5i4F1BURAIYQRAEQRAEMTQhsasARCIc9Q1AQwNQ3wDU13PtJ1C7CwgGnQCcAIrh9QDjJwATxgOTJzBMGA/suaf6t9NpHajI2j+CIAhikOHyQd7jUMh7HKr+LcUg1K+AY+diuOu/g3Ppc3B9+yS46IQy6kBIE34Aefz3oYw6EBUVLsw+BJh9CKALYH6/Gt6+ZStQU8NRswV4/0MgGEyIYKNGckyYAIwfB4wfz7SfwMgRgCiSEEYQBEEQBEEMXkjs6kU45/AHgNZWoKUFaG1Tf7a0crS2Ag2NqsDVnhSl6HYDo0cDo0cB06cDE8Yz7KEJXFVVFH5CEASx2+FwQRn/fcTGfx++igq0N+2CuGs5xJ2LIe5cDNeix8AW/h1cdEOpnASlcm8oVftAqdwbvHQMSnwjcOD0Mhx4AIMugHHO0dgE1NSoIti27WpOsA8/BgKBhAjmdAJjx3KMHweMHQOMGM4xciTDyBEMI0YA5eWAINBziSAIgiAIghi47PZiF+cciqLmFY5GgUhE+xcFwmF1WTgCRCOAPwD4/UBnJ0eXH/B3AV1+oKtL++cH4vHUfRQXA1WVwIgRwOQ5wOjRDKNHJQSu8nIStAiCIIgMOIsg7/lDyHv+UP076oe4a6ka9tiyCWLtEjjX/c+yCRdd4J4ywOEFd3oAhwd7CQ7sxWUACjBBBsZzQJGgxOOQY3EokgTIcTAlDoHHIYYkOHZK+Pzbn+D3S/6umuJUBbARI9QPMuVlQFkZQ3mZ+jwrKwNKSoCiIsDrBYq8gMtFzzmCIAiCIAii/xiUYtdf5iqoqQEUBVC4+pPrv8vpl3EFkDVhy1jGs+8vGa9XzYFSWqL+3HPPxO8VFQxVVUDlMHUQUFUJuN30gk8QBEH0Iu4SyBN/AnniTxLLogEIbTVg/kYIwSawQKM6u68UAYtHACkMKBLARDWHoyACYOqMvKILouiAKDjBRae2zAlZcCIkOTDePQP3nsLQ1Aw0NnI0NQGNTcC6dUBHp9UzzA5BALxeDq8XcLsAUUz953AkfheSUkyadbJZMxnO/DU9VwmCIAiCIIj0DEqxa8J4AFx9TxeY9s4uJH4XBdNPpq3TlgmmvwWBqcu1ZW4X4PYAXo/60+MGPB7tn1v10CotTc2VRRAEQRAFx10MZfSBwOjezd8oABil/VNJfQZKEkdnJ9DZqYpffr/qFR0O6/+48Xskqn6QkmX1nyQlfpdl1UNaURJ1J3+UUvOO0XOYIAiCIAiCSM+gFLvOP5dmFSQIgiCIgYLDwVBZCVRWpitB4hRBEMRgobgYGF5VaCsIgiB6xqAUuwiCIAiCIIYSHR0duOOOO/D5559DEAQcffTRuPnmm+Hz+dKWf+SRR/D111+jvr4ew4YNw09/+lP88Y9/RElJST9bTxDEUGL2IfSBgiCIwQ+5SBEEQRAEQRSYa665Bps3b8bzzz+PJ554AkuXLsWtt96atnxTUxOamppw/fXX45133sHdd9+N+fPn4+abb+5HqwmCIAiCIAYm5NlFEARBEARRQGpqajB//nz85z//wfTp0wEAt9xyCy688EJcd911GDlyZMo2kydPxiOPPGL8PWHCBPzpT3/CtddeC0mS4HDQKx5BEARBELsv5NlFEARBEARRQJYvX47S0lJD6AKAQw89FIIgYNWqVTnXEwgEUFxcTEIXQRAEQRC7PfQ2RBAEQRAEUUBaWlowbNgwyzKHw4GysjI0NzfnVEdbWxsee+wxnHHGGVnLVlRUdMtOYuBAfTj4oT4c/FAfDm6o/4Y+JHYRBEEQBEH0AfPmzcPTTz+dscx7773X4/0EAgFcdNFFmDRpEi677LKs5dvb23u8T6JwVFRUUB8OcqgPBz/Uh4Mb6r/BTy5iZa+LXdl2Sgrq0IH6cmhB/Tm0oP4cWlB/Dk7OO+88nHLKKRnLjB8/HlVVVWhra7MslyQJnZ2dGD58eMbtA4EAfv/738Pn8+HRRx+F0+nMahedT4Mf6sPBD/Xh4If6cHBD/Tf0Ic8ugiAIgiCIPmDYsGEp4Yl2zJw5E11dXaiursa0adMAAIsWLYKiKDjggAPSbhcIBHD++efD5XLh8ccfh9vt7jXbCYIgCIIgBjOUoJ4gCIIgCKKATJo0CYcddhj+/Oc/Y9WqVVi2bBnuuOMOnHDCCcZMjI2NjTj22GONhPWBQADnnXceQqEQ7rrrLgQCATQ3N6O5uRmyLBfycAiCIAiCIAoOeXYRBEEQBEEUmHnz5uGOO+7A7373OwiCgKOPPhq33HKLsT4ej2Pr1q0Ih8MAgDVr1mDlypUAgKOOOspS16effopx48b1n/EEQRAEQRADDMY554U2giAIgiAIgiAIgiAIgiB6AwpjJAiCIAiCIAiCIAiCIIYMJHYRBEEQBEEQBEEQBEEQQ4Y+E7s6Ojpw9dVXY9asWTjooINw0003IRgMZix/xx134JhjjsEBBxyAH//4x7jzzjvh9/v7ykQiAy+99BKOOOIITJ8+HaeffrqREDcd77//Po499lhMnz4dJ510Er788st+spTIhXz687XXXsNvfvMbHHzwwTj44INxzjnnZO1/on/J9/rUeffddzFlyhRccsklfWwhkSv59mVXVxduv/12zJkzB9OmTcMxxxxD91siJ7p73yD6nieffBKnnXYaZs6cidmzZ+OSSy7Bli1bLGWi0Shuv/12HHLIIZg5cyYuv/xytLS0WMrU1dXhwgsvxIEHHojZs2fj3nvvhSRJ/XkoBICnnnoKU6ZMwV133WUso/4b+DQ2NuKaa67BIYccggMOOAAnnXQSVq9ebaznnONvf/sb5syZgwMOOADnnHMOtm3bZqkj3/Ev0XvIsoyHHnoIRxxxBA444AD89Kc/xaOPPgpzxibqw92PPhO7rrnmGmzevBnPP/88nnjiCSxduhS33npr2vJNTU1oamrC9ddfj3feeQd333035s+fj5tvvrmvTCTS8N577+Huu+/GpZdeijfeeAP77rsvzj//fLS2ttqW/+6773D11VfjF7/4Bd58800ceeSRuPTSS7Fx48Z+tpywI9/+XLx4MU444QT885//xKuvvorRo0fjvPPOQ2NjYz9bTtiRb3/q1NbW4t5778VBBx3UT5YS2ci3L2OxGM4991zs2rULf/vb3/DBBx/gjjvuMGbrI4h0dPe+QfQP3377Lc4880y89tpreP755yFJEs4//3yEQiGjzF//+ld8/vnneOihh/Diiy+iqakJl112mbFelmVcdNFFiMfjePXVV3HPPffgjTfewMMPP1yIQ9ptWbVqFV599VVMmTLFspz6b2DT2dmJX//613A6nXj66afx7rvv4vrrr0dZWZlR5umnn8aLL76I2267Da+99hq8Xi/OP/98RKNRo0y+41+i93j66afxyiuv4NZbb8V7772Ha665Bs888wxefPFFSxnqw90M3gds3ryZT548ma9atcpY9uWXX/IpU6bwhoaGnOt57733+NSpU3k8Hu8LM4k0/OIXv+C333678bcsy3zOnDn8ySeftC3/xz/+kV944YWWZaeffjr/85//3Kd2ErmRb38mI0kSnzlzJn/jjTf6yEIiH7rTn5Ik8TPOOIO/9tpr/Prrr+d/+MMf+sNUIgv59uXLL7/MjzzySB6LxfrLRGKI0NPnANG/tLa28smTJ/Nvv/2Wc855V1cXnzp1Kn///feNMvq79vLlyznnnH/xxRd833335c3NzUaZl19+mc+aNYtHo9F+tX93JRAI8KOPPpp/8803/Le//S2/8847OefUf4OB+++/n//6179Ou15RFP7DH/6QP/PMM8ayrq4uPm3aNP7OO+9wzntv/Et0jwsvvJDfeOONlmWXXXYZv/rqqznn1Ie7K33i2bV8+XKUlpZi+vTpxrJDDz0UgiDk5TYfCARQXFwMh8PRF2YSNsRiMaxZswaHHnqosUwQBBx66KFYvny57TYrVqzA7NmzLcvmzJmDFStW9KWpRA50pz+TCYfDkCTJ8nWLKAzd7c9HH30UlZWVOP300/vDTCIHutOXn332GWbMmIG5c+fi0EMPxYknnognnngCsiz3l9nEIKQ3ngNE/6Kn8NCfu9XV1YjH45Y+nDRpEsaMGWO8a61YsQKTJ09GVVWVUWbOnDkIBALYvHlz/xm/GzN37lwcfvjhln4CqP8GA5999hmmTZuGK664ArNnz8bJJ5+M1157zVhfW1uL5uZmSx+WlJTgwAMPNO6jvTX+JbrHzJkzsWjRImzduhUAsH79eixbtgw/+tGPAFAf7q70iYrU0tKCYcOGWXfkcKCsrAzNzc051dHW1obHHnsMZ5xxRl+YSKShvb0dsiyjsrLSsryysjIlf4ROS0uL5eGsl0/ORUD0P93pz2TmzZuHESNGpLy8Ef1Pd/pz6dKl+M9//oM333yzHywkcqU7fblz504sWrQIJ510Ep566ins2LEDt99+OyRJsoTDEISZ3ngOEP2Hoij461//ilmzZmHy5MkA1Pcsp9OJ0tJSS9nKykrjvdruXUz/O9d3b6L7vPvuu1i7di3+85//pKyj/hv47Ny5E6+88grOPfdcXHzxxVi9ejXuvPNOOJ1OnHLKKUYf2N1H9fFOb4x/ie5z4YUXIhAI4LjjjoMoipBlGVdeeSV+9rOfAQD14W5KXmLXvHnz8PTTT2cs89577/XIIED16LroooswadIkeoEniALy1FNP4b333sM///lPuN3uQptD5EkgEMB1112HO+64I+XhTQw+OOeorKzEHXfcAVEUMW3aNDQ2NuLZZ5+lZyVBDBFuv/12bNq0CS+//HKhTSFypL6+HnfddReee+45elcapHDOMW3aNFx11VUAgP333x+bNm3Cq6++ilNOOaXA1hG58P777+Ptt9/GAw88gL333hvr1q3D3XffjREjRlAf7sbkJXadd955WU+W8ePHo6qqCm1tbZblkiShs7MTw4cPz7h9IBDA73//e/h8Pjz66KNwOp35mEj0kIqKCoiimJK0trW1NeWLk05VVVWKF1em8kT/0Z3+1Hn22Wfx1FNP4fnnn8e+++7bl2YSOZJvf+7cuRO7du3CH/7wB2OZoigA1Be5Dz74ABMmTOhbowlbunNtDh8+HA6HA6IoGssmTpyI5uZmxGIxuFyuPrWZGJz05DlA9C9z587FF198gX/9618YNWqUsbyqqgrxeBxdXV0W76DW1lbjvbqqqiolzEZ/N8v27k30jDVr1qC1tRWnnnqqsUyWZSxZsgQvvfQSnn32Weq/Ac7w4cMxadIky7KJEyfiww8/NNYDap+NGDHCKNPa2mq8I/dk/Ev0nPvuuw8XXnghTjjhBADAlClTUFdXhyeffBKnnHIK9eFuSl45u4YNG4ZJkyZl/OdyuTBz5kx0dXWhurra2HbRokVQFAUHHHBA2voDgQDOP/98OJ1OPP744/R1pAC4XC5MnToVCxcuNJYpioKFCxdi5syZttvMmDEDixYtsixbsGABZsyY0ZemEjnQnf4E1NlKHnvsMTzzzDOWuHWisOTbnxMnTsTbb7+NN9980/h3xBFH4JBDDsGbb75pGUwR/Ut3rs1Zs2Zhx44dhmAJANu2bcPw4cNJ6CLS0t3nANF/cM4xd+5cfPzxx3jhhRcwfvx4y/pp06bB6XRa+nDLli2oq6sz3rVmzJiBjRs3WkTNBQsWoLi4GHvvvXe/HMfuyg9+8IOUZ+20adNw0kknGb9T/w1sZs2aZeR60tm2bRvGjh0LABg3bhyGDx9u6cNAIICVK1ca99Hujn+J3iESiYAxZlkmiiI45wCoD3db+irz/fnnn89PPvlkvnLlSr506VJ+9NFH86uuuspY39DQwI855hi+cuVKzjnnfr+fn3766fzEE0/k27dv501NTcY/SZL6ykzChnfffZdPmzaNv/7663zz5s38z3/+Mz/ooIOMGWKuvfZaPm/ePKP8smXL+P7778+fffZZvnnzZv7www/zqVOn8g0bNhTqEAgT+fbnk08+yadOnco/+OADy3UYCAQKdQiEiXz7MxmajXHgkG9f1tXV8ZkzZ/K5c+fyLVu28M8//5zPnj2bP/bYY4U6BGKQkO1cIwrLX/7yF/69732PL1682PLcDYfDRplbb72V//jHP+YLFy7kq1ev5meccQY/44wzjPWSJPETTzyRn3feeXzdunX8q6++4j/4wQ/4Aw88UIhD2u0xz8bIOfXfQGflypV8//33548//jjftm0b/9///scPPPBA/tZbbxllnnzySX7QQQfxTz75hK9fv57/4Q9/4EcccQSPRCJGmWzjX6LvuP766/lhhx3GP//8c75z507+0Ucf8UMOOYTfd999Rhnqw92PPpvmcN68ebjjjjvwu9/9DoIg4Oijj8Ytt9xirI/H49i6dSvC4TAA1QV45cqVAICjjjrKUtenn36KcePG9ZWpRBLHH3882tra8PDDD6O5uRn77bcfnnnmGSPcob6+HoKQcAqcNWsW5s2bh4ceeggPPvgg9txzTzz66KNGYlWisOTbn6+++iri8TiuuOIKSz2XXXYZLr/88n61nUgl3/4kBi759uXo0aPx7LPP4u6778bPfvYzjBw5EmeffTYuuOCCQh0CMUjIdq4RheWVV14BAJx11lmW5XfffbcRGnfTTTdBEARcccUViMVimDNnDv7yl78YZUVRxBNPPIHbbrsNZ5xxBrxeL0455ZSUZzlRGKj/BjYHHHAA/v73v+PBBx/Eo48+inHjxuGmm24ykpsDwAUXXIBwOIxbb70VXV1d+N73vodnnnnGEomUbfxL9B233HIL/va3v+H22283QhXPOOMMXHrppUYZ6sPdD8a55ttHEARBEARBEARBEARBEIMc+vxPEARBEARBEARBEARBDBlI7CIIgiAIgiAIgiAIgiCGDCR2EQRBEARBEARBEARBEEMGErsIgiAIgiAIgiAIgiCIIQOJXQRBEARBEARBEARBEMSQgcQugiAIgiAIgiAIgiAIYshAYhdBEARBEARBEARBEAQxZCCxiyAIgiAIgiAIgiAIghgykNhFEARBEARBEARBEARBDBlI7CIIgiAIgiAIgiAIgiCGDCR2EQRBEARBEARBEARBEEMGErsIgiAIgiAIgiAIgiCIIQOJXQRBEARBEARBEARBEMSQgcQugiAKzjvvvINjjz0W06dPx0knnYRPP/0UZ511Fm644QYAwOuvv44pU6Zg6dKluPDCCzFjxgzMnj0bDzzwAGRZNuoJBAKYO3cujjvuOMyYMQNz5szBxRdfjJqamkIdGkEQBEEQxKCG3tMIghiMOAptAEEQuzfffPMNrrnmGvz0pz/FjTfeiLa2Nvz1r39FNBrF2LFjLWWvvfZanHTSSTj77LOxePFiPP300xBFEX/6058AAMFgEJIk4fLLL0dVVRU6Ojrw8ssv41e/+hXee+89DB8+vABHSBAEQRAEMTih9zSCIAYrJHYRBFFQHnnkEUyePBmPPPIIGGMAgH322QennXZaStkjjzwSV111FQBgzpw5CAaDeOGFF3DeeeehtLQUI0eOxNy5c43ysizjsMMOw6GHHop3330X55xzTr8cE0EQBEEQxFCA3tMIghisUBgjQRAFQ5ZlVFdX45hjjjFeoABg2rRpGDduXEr5Y4891vL3cccdh1AohI0bNxrL3nvvPZx++uk46KCDsP/++2PGjBkIhULYsmVL3x0IQRAEQRDEEIPe0wiCGMyQZxdBEAWjvb0d8XgclZWVKeuqqqpSlg0bNszyt75dU1MTAOCzzz7DlVdeibPOOguXX345ysvLwRjDhRdeiFgs1gdHQBAEQRAEMTSh9zSCIAYzJHYRBFEwKioq4HQ60dramrKupaUFe+21l2VZW1sbJk6caPytbzdixAgAwLvvvosf/OAHuOWWW4wysVgMnZ2dfWE+QRAEQRDEkIXe0wiCGMxQGCNBEAVDFEVMmzYNH374ITjnxvLq6mrU1tamlP/ggw8sf7///vsoKirC5MmTAQCRSAQOh1XDf/PNNy0zAREEQRAEQRDZofc0giAGM+TZRRBEQbn88stx3nnn4fLLL8fpp5+O9vZ2PPLIIxg+fLglPwQAfPrpp/D5fDj44IPx7bff4uWXX8bFF1+M0tJSAMBhhx2G2267DY8++ihmzZqFFStW4KWXXjLWEwRBEARBELlD72kEQQxWyLOLIIiC8sMf/hDz5s3Dpk2bcOmll+Lpp5/GDTfcgKqqKpSUlFjK3n///Vi3bh0uu+wyvPbaa/j973+Pyy+/3Fj/y1/+EhdddBFeeeUVXHzxxfjmm2/w1FNPpdRDEARBEARBZIfe0wiCGKwwbvZJJQiCGAA0NDTgqKOOwh133IGTTz4Zr7/+Om688UZ8+umntrP/EARBEARBEP0DvacRBDEYoDBGgiAKSjAYxH333YdDDz0UFRUV2LFjB5599lkMHz4cRx99dKHNIwiCIAiC2G2h9zSCIAYrJHYRBFFQHA4HmpubMXfuXHR0dKC4uBg/+MEPcO2116KoqKjQ5hEEQRAEQey20HsaQRCDFQpjJAiCIAiCIAiCIAiCIIYMlKCeIAiCIAiiwDz55JM47bTTMHPmTMyePRuXXHIJtmzZknW7999/H8ceeyymT5+Ok046CV9++WU/WEsQBEEQBDGwIbGLIAiCIAiiwHz77bc488wz8dprr+H555+HJEk4//zzEQqF0m7z3Xff4eqrr8YvfvELvPnmmzjyyCNx6aWXYuPGjf1oOUEQBEEQxMCDwhgJgiAIgiAGGG1tbZg9ezb+9a9/4eCDD7Yt86c//QnhcBhPPvmkseyXv/wl9t13X8ydO7e/TCUIgiAIghhwkGcXQRAEQRDEAMPv9wMAysrK0pZZsWIFZs+ebVk2Z84crFixoi9NIyuyzykAAQAASURBVAiCIAiCGPD0+myM7e3tvV0lkYaysjJ0dnYW2gyiD6C+HZpQvw5NqF+HJhUVFQXbt6Io+Otf/4pZs2Zh8uTJacu1tLSgqqrKsqyyshItLS1pt+GcgzHWa7YSBEEQBEEMRHpd7CL6D0Egx7yhCvXt0IT6dWhC/Ur0Nrfffjs2bdqEl19+udfrZozRh8lBTkVFBfXhIIf6cPBDfTi4of4b/OTyUZLELoIgCIIgiAHC3Llz8cUXX+Bf//oXRo0albFsVVVVihdXa2trircXQRAEQRDE7gZ9jiYIgiAIgigwnHPMnTsXH3/8MV544QWMHz8+6zYzZszAokWLLMsWLFiAGTNm9JGVBEEQBEEQgwMSuwiCIAiCIArM7bffjv/973944IEH4PP50NzcjObmZkQiEaPMddddhwceeMD4++yzz8b8+fPx3HPPoaamBo888giqq6vx29/+thCHQBAEQRAEMWCgMEaCIAYtksThDwChIOBwAl4vUFIMSr5MEMSg45VXXgEAnHXWWZbld999N0499VQAQH19vSVH3KxZszBv3jw89NBDePDBB7Hnnnvi0UcfzZjUniAIgiAKhiIDglhoK4jdBBK7CIIY8HR0cFSvAarXcOysBerqgPoGIBBILetyAaNHcUyeDEzdn+HQ2cCY0SR+EQQxsNmwYUPWMi+++GLKsuOOOw7HHXdcX5hEEARBEL1HuB2O7QsgjzsYvHhEoa0hdgNI7CIIYsARjXIs+w5YsJBjxUpg23Z1ucsJjBsHjB4NHHgAUF7OUFoK+IoASQaCQaCllaNuF1BdDXz8CcdDDwMT9+L4yY8ZTjoRqKok4YsgCIIgCIIg+hMWVmc/ZKFWEruIfoHELoIgBgTxOMfCRcBHH3MsXAxEo0BRETBzBnD8cQwHTAcm7wO4XNnEqsT6+nqObxYC87/meO4fHC+8CBz9U47zzmEYNYpEL4IgCIIgCGL3RJY5wmGguLi/3onp3ZvoX0jsIgii74kGINYugdhYDaFlI1igCSzcBkhRyHEZwZgHHUEvxseKcBaKcc6xo1E6YSxGTh4LVjEWvGwsuG9E3jH+o0cz/OJU4BenMuyq43j9DY43/wd8/CnHyT/j+N1ZDOXl9OAlCIIgCIIgdi/WrgMaGoGfHM7hcPTn+zDvx30RuzMkdhEE0TfEw3Bs+giOde9A3LkYTImDMxHKsD0hFY1GbWBPbNrhQUeXiCJnFHuMCmHsuBDKPX4I/gVg2xvBticehlxwgpeNgzJsLygVe2r/9gKv2BO8qBLIkpR+7BiGyy9l+PUZHM+/wPH6G2qY49VXAj/5MQleBEEQRIGJBSG0b4MyYv+szzSCIIie0qZGFUJRCmsHQfQVJHYRBNG7RDrhWvo8nCteAosFoJRPQPx7v4M08cfYHtsf/33bjff+DwiFgP32A075GcMPDgeKitQX+4hejxwD8zdA6NoF1rkLQmcthPZtYO3b4Nz2DZgcNXbJ3SVQyveEMiwhgCnD9oJSvgfg9FrMq6piuPZqhtN/wXHXPRx/vo3jp0dyXPVHhtJSGlwQBEEQhUGsXwEW7oBSNg7wlBXaHIIghjhcE7lMk/z2LfSaTfQzJHYRBNE7cAXOFa/AteBvQDQAacrxiM/4DZQxM7GqGvjX3zkWLFSTzB95BHDqKQz77ZvhqSe6wMsnQC6fYLsv5m+A0L4VQpsqgAntWyHu+g6Ode+AmdyjlZLRUCr2gDJsEpQxMyGPPQi8ZCT23IPh8UeAl18FnvsHR3U1x51zgX2n0JOYIAiCKAAU2UMQRD+iaPcc3t/3nn7fIbG7QmIXQRA9hrVvh+fDmyDWfQdpz8MQ+9E1kCv3wdJlwPP3cKxaDZSXA78/j+HnPwMqeponiwngpWMgl46BvMcPreviEQidO8DatkHo2AahbSuE9q1wrn0LbMVLAAClbDzkcQfBMe5gnH3q4fj+wRW45VaOSy7juOpPwIknkOBFEARB9DP06CEIoh/h/R6+SDc5on8hsYsgiB7hWP8u3J/8BRAciBx3P6R9T8DylcCzd3CsWAmMGglc9SeGE44D3O5+eMg5PVCqJgNVkyGblysyhNZNEGuXqsnyt3wJ55o3wJmAGeO+j1euPBq3vX4c7rm/FJtrOC6/lEEU6aFMEARB9DPk9ZAXrLMWvHgEILoKbQpBDCoK5tlFEP0EiV0EQXQPRYbrq/vg+u6fkMd+D5Hj52FT00j8/RqOpcuA4VXANVcynHA84HQOANFIEKEM3xfK8H0Rn/lbgHMILRvh2PQxxE0fomLBXDw07j6sHns87vn0DNzaMg233txPAh1BEARBEPkTC0KsXwnFNxzK+O8X2poBhdCyUc1d6nAX2hSCIIiCQGIXQRD5Ew/B8951cNR8itj3zkHzAVfh6WdE/O9tjtIS4I+XM/zsxAEuFDEGZfgUxIZPAQ69DELzejhX/R+mr/0fXj78dXzTNAf/78Y/4pLbp6K0ZAAfB0EQBDFEoGdN3iiqDzeTolkK7maE2yG0bAIinVDGHVxoawiie8QjgOgEBLHQlhCDFBK7CILIj2gA3jcvhlC3HOGf3Ip/b/8Vnj6bIxQCTj8NOOdshpJBKA4pw/dF9Mi/IHrYNXCu/jcO+uYp/FA6HQvmHYOJ516O4j0nFdpEgiAIYreAYoqInsG0ZExMkbOUJIj+DGPMb3zgqPkU3Fuemp+XIHKExC6CIHIn0gXv6xdAaFqHXbP/H25+/qdYuYrj+wcDV1zGsOceg0/kSsHlQ/x75wDTT0fd2//AgTXPw/PfjxGcdjaEI64AnN5CW0gQBEEMRdgQeIYWCmo7ggAAsGALwBU1j91AJQ91jYU7+s4OYsgjFNoAgiAGCfEwvG/+AULzOnxS+TBO+8tPsWUrcOvNDA/cN0SELjMuH4afdilW/eRjvFl7OkrX/APu538OccfCQltGEARBEARBECmIOxdDrF1SaDPsIVGa6GdI7CIIIjtyHJ53/gShfgUeb34A1z77Ixx8EPDiPxiOPoqBDeGH1/SDKzDm/L/g4iX/RGOLA97/nAfXF3cDcqzQphEEQRAEQRDEIINCtYc8nIO1bwW0kOpCQWIXQRCZ4QrcH90Mx9avcP+G2/DCkiNx7dUMd9/JUFU5dEUuM9OnMZx140E48+v/4u2Ws+D67p/wvvpbsM7aQptGEARBDBm0Z2r/JdAhCIIg+osCCz/9Ceushdi4FkJrTUHtILGLIIiMOL/+G5zr3sbf1l6JJdJpeO4php+fNLS9uew48ACGO+7y4M5lN+C+nY+AtW9H0b9Ohbjl80KbRhAEQRC7KSQM7vaE28nbvocM1AT1QwrOIW7+FKxzV6Et6R+4pP5U4gU1g8QugiDSIq96D+4lT+G1rWegYdL5eOJRhj2GWm6uPJg1k+Geuxj+U30E/rjhv5BK94TnzUvhXPT4bvW1hiAIgiCIwcAQf2fjHI7tCyDuWlZoS3YrJIlj6zYO3l2VbHf0XuUKmBwDi4cKbcluBYldBEHY0rx6DTwf3YylrQcj/OObcNMNAtzuIf7SlAMHH8Rw51yGBWvH4PIVLyC278/hXvAwPG//CaAHGEEQBNFtmPb/bjgQHKRwziFJA7m/BrJtvYCseY1E/YW1Yzdjw0Zgcw3Q2lZoSwYT2rU4wIS+HTs56ur70KZuHG88zhEM9o5NJHYRBJHChu+aUfT2ZWiPDYP08/+H005z7XZhi5k4dDbDzTcwLFrmxk1L7kT48Jsg1nwG77/PVd3pCYIgCIIY8qxbD3z+ZaGt2I3RQ6QEZ2HtGOTkq0dIWoRanwY1DDBRqMcYxzOwIkE2bATWrC20FVaWLgMWLOqdukjsIgjCwldfRiG+8UeUOTsR+fnfMe37lYU2aUBy9FEMV1zK8OnnDPfPPxPhn/0dQstGFL36m90nHp8gCIIghihdXRxbtmYecNfVqT+7Hc7VVww0e/oKRVNdBLGwduxm6KdX97+DD67zMxLhaG7uqc26Z9fAErv6ju47SQSC9ssdG96D0LA6r7pI7CIIwuDN/3F0/ec+zKhYjuBRd6Nq6n6FNmlA88vTGX77G+D1N4Hnvjkc4V88BxZuh/fVX0FoXl9o8wiCIIjBBKPZGLtPDgOrPNt18RKgZkuWvQ5Yp/eBfw4FAhzbt/fMTqaHMQqOXrBo96Xbt5y8z/98djRwzuFvlwArVvWwEv1w+uD+zjp2AvFwr9c74OAcQseOvDYhsYsgCADAq69xrHzlA/xqr5cRnnke3DOOKbRJg4KLLmA44Xjgmec4Xl8yA6EzXgIEJ7z/dxYESphKEEQeLFmyBBdffDHmzJmDKVOm4JNPPslYfvHixZgyZUrKv+bm5n6ymCAGB0LjGjg2vNetbXPx2hpw+qRh0IBV47BkKbBxcw8rMTy7SOzqTwzPru5umMsFM4Auqqg22WeveHD2tmcXVyA2rALroqgSO+jOQBC7OZxz/OOfwEevbcO/j/gzpNEzIf/oT4U2a9DAGMO1VwGdnRwPPMRRXj4RP/nVK/D+93x4/3sBIic/BnnCDwptJkEQg4BQKIQpU6bgtNNOw2WXXZbzdh988AGKi4uNvysr+zb8vLaWQ+HAhPEDdyA9uMkwoIr6wSVfxq1XV3MMqwDGjqX+0RHat3V7W86ze3ANoHH5oEGS1Z+c8+7nhdU8uziJXVlpaeVoaACmTe35faF/zveBd1EpCiB2O2K2j8IYtc5gnA/AFis85NlFELsxnHM8/hTHiy9E8OQRV8PldSJ64oOASIk+88HhYLj9VoYDpgO338mxbPMIhH/5ApTyCfC8cTHEbV8X2kSCIAYBhx9+OK688kocddRReW1XWVmJ4cOHG/8EoW9f79ZtUJPaDkZYqBWIpUkIUmCMgUqGkaRj61fgNV9krKehEVhLkfRpae/IbUioSwJKDmPTASd2DTiD0tMjU3uYs2v9Bo5Nm/q/rUIhjlisf/e7fAVQ35D4W1F6vv/8NUqe9DNT0YFzDuuH2SOTeB+JXcn1DxgGxscWErsIYjdFUTj+3984Xn4FeOykezGGrUP0uHvBS0YV2rRBidvNcM9dDBPGAzfczLFh1zCET38eStXe8Lx1CcQtNF0TQRB9w8knn4w5c+bg3HPPxbJlFD6dCXHHIji2fNH7FUf9ELd8Acix3q87ZV8DU6wbDNTXKVi6DGhozD4wZNooKRexa+Ax0Aa+6enJGD2XnF3M32B4gCWzsxbYll8KoKysX89RX5/5oL5ZCHy9oJs7CLf3Sshab2gj3Re7Bhf6vUCWe1JLHiGc3al3oLZtgUU48vkkiN0QWea4936O9z4A7vzl+zgo8n+IHXwB5L1+VGjTBjUlJQwP3Af84TKOq6/jePyRcow77Tl4//t7eN6+AkpZBVB5QKHNJAhiiDB8+HDcfvvtmDZtGmKxGP7973/j7LPPxmuvvYapU6em3a6ioqJH+/X5olo97h7VUwgUnxoCKPSwDVLq3bEZcALMEQcrH9m9OjpLAYTAykrBSu3t0+3P1Ifm/uGcQ5IAp3NgfGXvbXiYgft8QFFxxj7V2w2sFD6fAq/HgYqKzN5AJcVRSDJQVuaCx2Pffj5fFAoHystdebdxT6/DTHAhBN7hA4ozt4tOJMIhiICrH88T/TwtL3fB4ejefnnYCx7xASX2x8kjfvCd6wE+FsIe309rQ3fvZXZ92NYRRVsHsP/+6evsyX6VnV8AAIQ9ptmvT3OPS96nJHH4fKo4X1bmRHFx7j4wPl8ckaiCsjInKipy347LbeABH1BSmvW85FJMvbZtjqW3yPUaLCmJQpLUe4HX281zNeZSj6fYl/F4AgEFHg/L+Zrgchzc5wMrKQHrRjtlOhe375BRvUbCcce4IAj5HTdXOtS+Li3Ju//S2dSd5zeJXQSxmyFJHHPv4vjsc+Dac7bjhMBfII/9HmI/vKLQpg0JqqoYHpynCl5XXsvxxN9LUHna0/D+51zIL58D4eQnoIxPfeEiCILIl4kTJ2LixInG37NmzcLOnTvxj3/8A/fff3/a7drb23u032CQa/WEelRPX+DY8D64swjyxMPt1wdVryiph22QjOD3QwgGIXd1gfPu1W3U0dEJLntsyziCQfh8vox9aO6fVas5GpuAo44cmmIXwh1wBIPgshNyhjbR+71V6kQwKCAUAtrbM7dJKMQRl4C2tmDaAW4wqOava28P5iV2VVRU9Pg6zATr6oAYDIJzb8Z20fn4Uw6nA/jx4b13nnzyKcfEvYCJE9O3HaC2XXfFLqGzE0IwCMXZBaXI5jgjndr50QS5NHV9KBBDZXA52psOBJz211w60vVhLvfHntxDs93D0q1P3qckcWhFsfhbYMaByFnQ8PvVbTu7ct8GAFhnJ8RgEIrYBSXbeSnF+ux+DeR3DYaCiXtBUVE3r5FYUD0X0Znxmvz4U47KYcCsmTnuR47DEQxCcXdC8eTfTpnOxRUrOKIxoKkpCLc7v+M2+trpt782c7CprS2YyOfHecr5kItYSWGMBLEbYRa6rvxDFL8SrwJEFyLHP0Az2fQi48cxzLuHoaMduPYGjqBcivBpzwLD9oT3zT9A2PVdoU0kCGKIMn36dOzYkXtcjqJwrFnLEYkM0BCIHFFDlWIAV8Bigf43oFdDNXqvrsamXqvKgAVbUo43GOS9M1NZd8lxHCZpKZ4cObzymMZYWRlw6XK6cQ7Fpd63oGZrDuV61Ha5hobZr/fFdsETb4bQVtMTIwYVsRiHJHFLeG5rG9DUB/eKVPLp7IFzUfVqSHOGnF36PTQ/ba/vwhi7O29EzzdWsV7W3Ts+ErsIYjdBljnuvk8Vuq64lOE3lfdCaN6AyHH3gZd0L+SCSM+++zLcdQdDzRbgpj9zxMRyOM55DbxkFLxvXAihfmWhTSQIYgiyfv16DB8+POfyLS1AXT2wcVMfGtXXSDGIu5ZB3DUQ8pX1aHSg/eSQ5f5PYJ0LLNAEcediMNPshqEQx4JFwJYchI1CE4/btGksaD9pgdYdgzNBfaENyJ1eSfqd9oCzTaOpdW43E9wPRr6cDyxYmNru+fRD1mbPuYLBgTFZRW+YnVHs6k59Pdk4QTSa+sFCF/kyVS1J2T50dN8uS7XdPD4SuwhiN0BROO5/gOPDj4CLL2T4zfT34Fz1f4gfciHkPecU2rwhy/cPZrj5BoZl3wF33cPBi6oQ/sXz4EWV8L5+AYSmdYU2kSCIAUQwGMS6deuwbp16b6itrcW6detQV1cHAHjggQdw3XXXGeX/8Y9/4JNPPsH27duxceNG3HXXXVi0aBHOPPPMnPepJ9zt/nTqAwB98BAPF9KIXqyK47vl6qAUAKDI6r+BgNbGzCQORdX0KujsLIRB+bld6Z5d5nGT0LQWYsPqlM306KyMA9yBGh2ax4xvkpT/ucsCTUA8kn73eQxM+8WzK816Bu26YkNwSJyhTaK9NI9G3n3XLUWt8OgTHPfoNsyzn6s98hzrYXt99XXqB4tcZqH8/Etgzdoe7To3ujmL5RC8sgmCMMM5x4N/43jnPeD8cxnOOnYr3B/fCnncwYjNvqzQ5g15jj6K4dI/MHz6GXDfAyEovuEIn/4PcHcJPK9fANa+vdAmEgQxQKiursbJJ5+Mk08+GQBw99134+STT8bDDz8MAGhubkZ9fb1RPh6P495778VJJ52Es846C+vXr8fzzz+P2bNn57xPW7GLc4g7FgPhvsspNJBpaODo6OjuwKE31A+ODpNw5Nj4ARybPuyFenuBXghN6XeURHxeXJuUzyxgsTRiohHGaBpjSRJHZ+fAGYSnJ3cb4/YTFWZErF0CccfCtOv7bQbLHroYMa1zORsgan88AkR7Kww7c5v0RBvp/raDM4wReYQ0p0cXu3r74sjvGgiF0pdrabH+nS2UWxe1Gxrs1g6MMEZK0kMQQxjOOR55lOPNt4CzzgTO+XUYnlf+CO4sQuT4eZSnq5/49RkMLa0cL/4rgpJiht/8ajTCpz0L76tnwvvf8xH+1cvgxSMKbSZBEAXmkEMOwYYNG9Kuv+eeeyx/X3DBBbjgggt6tE9bsSseAgu1QKwPQZ74kx7V3z/07qBo9Rr151FH5mNCL9iQSUjqYf2c80Si315hAA1Es5KwVZZ58qKUMjrMxrNrzVqgqRn48Y+4JSF9bzmh9Fo/5WFQd3N1sXj65OrddeCpr+coK0MeScCz7ChLWzKu3QCFHP0/4iEIzRugjD4wt/J54qj5FAAg7XuCuiDSBXAZ8HZjNkLOM+oNPREkc3BSsoXlJU5mL7N+A8fYMeps6H2J7uUp93nOrp7Um33jXbs41q4HDvoeR0V5apslV8GyhHLry/vlG0g324Y8uwhiiMI5xxNPcbz2H+CMXwIXng94PrsDQtsWRI+fR+JKP3PpxQwnHu/CY09wfPARB6/YE5FTnwKLdMDz398DkYLEfxAEsZsjZQxjLLwXTyTC4ff3n7BiiCGFJIsJ0SjHx59yY8aqnKrstcPK8ZxQZDjWv2vJ7VUwzF5c2h8pHgM2DWTn2eX3qz/lPogqbW7m+OQzIBDolcRAOZeU8vXsyuFkykdIMVdXvVadGTBvupmgnkEfrefm2SU0VEPoqgMLtdqu37K1d+8fjm3z4di+oFvbsmBzr9piR28d7eYajq8X5JdELBrl2FkLLF/RS0ZkwO5ekA+KwrF+vaLlDExfSbbrRmjemDrJVR4Con7/CubqPJgllNsQuzIpSj14+FhzdlEYI0EQJp77B8dLrwCnnQJc9gcG59o34Fz7JmKzL4M84ZBCm7fbIQgMd8wtxsEHAXffy7FoMYcycioiP38MQsd2eN+8pMD5ZgiC2F3YtEkVS4DEoN3RjSiemi1qPX05C9/8b4BF3Rn8dhM9/1T+aG3QK5+4M7dne4f60t/QaLNlmr7o9S7KVqGsKihCq/0sd5LEsWRpfoJdt+xIt1nKgtSBlDEDm7lwmu7tjbC91qYYykPV6OzoFfeRlCWsY6ft5DgxTezK1bkpl0G13i25XA3JXSjlIyRm7f8cPbtyFLuYzW86ssxRsyWnavqFbBN2pCSoz6PubM3O/I0QN39qc13Zu4Rt3QaEB/ArMOuhZ1dzixoeX9+A3O5ZaU5boXUTBH990tLcwyONhPNp1icvN3J2ZfHsEvrouxjNxkgQhC0vvsTx/AvASScCf7ycQWzZCPend0DaYw7ih1xUaPN2W1xOhrvmMuw9CbjlLxxr13HI47+PyAkPQKhfAc87fzIGCARBEH3Fth2J33uSoH6blnJw4OQRTrxxixs/7JZnQ0TLue12aTUGW/rxvpw1QUrWGjgHHLIfgtJt1S4zhpiXoydNGvGvvR3o6AQ222thvYzJVq0NLYM3zmErDumrbQZ6fXHOFwU2oDi6HWKgrueV2dgnNqyC0Fmbspy112Jc+7twsBzP8zw8u/o+vClHr5a0Cep1Q/Pcrc2BDZz7YG7kY29TkzVXHU/5xYrQtBZMigBS0iQG2TbsRWIxjmg0+35CIY7m5szl7Dy78pqEQQEArtbTV2GMOZCvh5pgJ/ib4Jlu87198ZNnF0EQAPCvlzmefJrj2GOAa69iEOJBeN7+I7i3HJHj7h2aM84MIoqKGObdy1A5DLjuBo4dOznkvX+K6E9vh2PrV3B/dHMfJK8kCIKwR8qYryfzy6p+qyr4IM/OK0eRIDRvzLuqiKYRud0A5BjEnYshJoeN2NrQm41gGlRmqFdftWNHYrDGOTCq6yuM6vqyj8zLM5dSmgGPwgFwnhAbum1N4sDEHYsgNK7JXD6tnphbzq5cZifrLvrAkmec/jFXcvc0dHSq7khOnn52Rdu6M5XoZs6uvOlu8igNw7NLY8cOjobGDHXlcD32JeEwRyzWOzvKycEo0AShYTVWrga+XZpHHdkzm/eOgUh/in85X51hMBsLFwMrVmUuY4g+xjNPDTneuCk3GxUtfVrfiF25XwPZuiVd+bY2oN1m0pbeELU551DS3PN645qiUS9BDCFeekXN03XM0cCN1zEIjMPz4Y1gnbWInPAgUDSs0CYSACoqGB68n4EJwNXXcrS2ckjTf4HoYVfDue5tuL6aV2gTCYLYDVAU3qPcQ4XWuLKTv4V6/iKn07R5zJ97BXm+9XPOsXUbt+YKM73h5xLGsWFTYrCmlxd4Dl460YCaV8tvEw+ZjWyjECOuz749uAJUBpdheNP7+e87DSzUCsEuR5jJVnu/NG7btAnxybSPNN27fAWwbn3ProiEkNYLV1YeH80U7dxjucYi5ZOzK4cqE3pVd44793xFduhiq94GGzYB61YGMuRR1dqqfTt4Uuxqf4hdXy8ANuSv4duSi7li7RIIHQlX4KYmvbO0HxyorbXzoErX8dyy9tPPOb5bnmtQXd+QLQRZkrgRWquYjhsAdmqOkpEIR1eG3JLOtk0oC69Vw/16WzDNI2eXnYBvrct+8ZatwFKbqNiMYleOdtVsAZbl8D2puxcYiV0EMUR46RWOx5/kOOYo4KbrGUSRwbnocTg2f4LYj2+AMnZWoU0kTIwdq3p4dXQC11yv5i2JH/x7xGadDdey5+H87p+FNpEgiCGOoiTCGAszDX3PWbqMY3nawVL+2B5LLl/Mu7m/ujo1lG/bdmQPjeqtRL8aLNKh/gzkIXblcKCSxCEbiZfSiF0c8MYbez4FQpo22bFDzSenegyYxK50ng0ZxCG7gWHyADkUBmp35WBvBnSxqTfyf4VDCsLhHL1OtP0pvPszIMoyRyikCRmtmyE2LAdgyuWjyIAUs68tjWNKKMTR1pZjeGLaayPzgFv37OIchnfJqK4v4diWziVILSP464GWTbamDBa6E0SwcrW2rXas4TCwbgOwujqpYPoLzbJcUYDWtvztsK26j1i4CAhoCd3TeTPP/ybzxAquzo1wS+0AS/UmrK1NTMKSs8eV3QQJOWwsdNOzKx36vdEuaCge55Ck7DuKRNLnyqQE9QRBAABeflUVuo7+KXDTDarQJW7+FO6Ff0d86qmIzziz0CYSNkyZzPDXOxi2bgNu+rPqmh47/HrEJx8L1xf3QNz4QaFNJAhiCMN5mlnl+nvUpkjd2qe49SsodWvRoeeSSX4x70ad/T1e1RMeW8NJ03l2qWQagPQkaig/0tv4+ZfA/K8zx7cYAlKaY2FtW8A681GPrBVt1fLJpYbpqjvesRPazGj6otQGyuTZpXBg+QqemzAV9atiTxaYVnlPPLtYsBls7XtYtSKecz403asp593alKteA3yzUBWMxOYNcPitecfEnYvh2PxxbtVpDf7NQmDZ8symMKhCW1dXd9tMF124ka8vmWg0Te4n2Xpy9bvY1cMd9sYHDv3+FU9xJM2WHCqHnWcxsL/aO2ISYuQksStXR169Gew8u9ZtSEzCkmu94o5FdnvJaocrtBOueGuviV36cdkVW7+BY916ZA275TzXviTPLoLYLXn5VY7HnuA46qfAzTeqQhdr3QzP+9dBHnUAokf+pT8yhBLd5OCDGG6+kWHZd8Cdd3MonCF67D1Qxn4Pnvevg1C7pNAmEgQxRFGUxEtmIAhrKB3Qb88Ox8YPbWeJywaL+lES3QrzS3A4nCEpcY4J3m2WZjcmi4dJMGj/ldveASKdkNRd+3uT3BJzJ3JxWcs3NqkeV7pOkC5yTmxaB7F+RQ72pOtrbe9ctgpN2v7CYWDtOlNhm4OwDfkxxUG22DhXpNTRvg2OrV+Bde7MWlbQGqMnObuE1s0IBRU4FS30NpeQQ23E2hOPsvZ2WOpIDm9i4fasdXAOFEe2YFzH+4Bs7wVmt9HOncCmzWlyWWUJpTI8uxSOUNhqs85XX5tyPyW1p2P9u2CtmwH0jkdefvRQ7OolK6yVcghN6wBZU4iSxa48Qu6ylcl2apeG16M8tDqH/eROsmdX3r6QNkn/XFIbBCWSvDhvmLZxezvHrjr7inwdqzAisCjnjyK5enbZzeSqC9ChLLNsmt9DMtpDYYwEsfvxyv+pQtdPjwRu1jy6EOmE961LwV0+RH72COBwFdpMIgtHHclw+aUMn30OPPwoBxddCP/871DK94D3rcsgJLnKEwRBdBfevDnxu2mMv7MWWJklSW/aOvV30FgwT2+cBEKXul1DA08V3QBAihqDymTMSco31wAbtVtmLKagZkt+L8i24lIvjAoXLFLzOiVjjCUsL/Vp7El62bd79++JZ9fOWo6GhtwPNhpV+8puC8btPbu2bFV/hkJ6wZx3lxf68TpqPoVjqzVZv6BEIMrBJG+U9Mdt55ySq7DBtNxPTMo+O6YeCtRTwTIWV9vfIQK5nLyKpHt2dd+1K13UWk56OVdzQdXWAr6YJgrmPAMqRzQGiEoEwq7vcvKgs9in5+ziiVAqhyOLsUm/i80b8tpnJiSJY3U1R21tzz2fsm/f/ep4yi8qLNwGoW1L4nxPuXgyfxTIx6BsXlClkRoUR3fYr7StL7tNcpKYm3PdZs8u8wIAI/wLMdKvqql59Wi4A4gFUz60LP3OLORbSd59tmPOKnZpl5sr3gzH+neBaCDtPrtDvh977CCxiyAGKS+/yvHo4xxHHgHcciODw8EAKQbP/64A89cjctLD4MUjCm0mkSNnnM7wm18B//kv8NIrADxliJz6FLjDA88bF3UvgTBBEEQS3JSfSVGsr4+J3CndE4jEbV/n6I1jT1sbx+o1sA2/EupXqoPKcAcANezI8OBK88K+dSvQuXopojv1wag6kNy+I8PxdftLcvayHTY5rw2RIE1d6VMRKRlChLrHrl1AfUPu5b/6GlixMmFGNJrIe5TOswsAwBW4AtvUtT2d3jDNdsbiJNGEgWNM56cY3fWFdd829ejeCrKihuJEozz/2RiNUWWavopHVO+gjp0JT7IezcbINBGPa6JNDmKXtj87jzJJUj3xdpivGXtXPssqRQFEJQzAXnwSWjaiMrDEKLtyNbDRrGVnOLebmhL5wfTjc8oBCP56CE1rwTrMXnRpjj8eBrhieHaxUBtcbevNh2KPuRkUaxhjb3h26QJsXX0upXsmdvXI3nTfAZLERl30liQtgXtenl1ZTDBVkTbUtJv15VrGF92uzgCrHacsczQ2pVaUsiTp/BaVqGVxLgKRY/s3cGz5wlR7Dgdg8lZdv4HDn6RN5StW6+WLourHKhY1P+Ry+1jEeY5nA3l2EcTuAecczz6vGB5df75JE7q4AvdHN8NR+y2ix9wNZcyMQptK5MnFFzIccxTwxFMc73/AwUtGI3LqU2BRPzxvXKTm/SAIguglOIf9W2Y3XyqZkpIgCQi3Q9i1LCeXIz2/kl3uHJYkGqxdl/DgShyE9c1cljm88UY42jZZ9tXVlf4Y0kgn6TdIKZpn21lEgmxDHF1IYhjpnw9p1YdZ86HkYxbPqZxVYGxrT2wTCJoSVacbtXGgJFqDos41ltXi5k8g1nye3cgMSLIaviNJ9t5m6bG/EPSB3uYa4Mv5qriXEKRyrVpvLwWyzLFho2Zf82Yg3AEWDwIAhK46GFJaD3UAyazvpevQSKfhhaGLXGr/W8vHtGjCmi3Axk1cW6+Kxg2NibJ2IuDozs9Q5U/K2q2dF0LLJnjjTcZ+E/XoapnNvURj5WpTMnDOLV5lQscOiA2r1ATecVP8lEXEVuCo+QxC/Upjf0JnLVxdNSlFUzGt1WJxuSAa++8RimycV3ZhYammdH+HDOqRFEe2YFz7uwDP7BFXHNkKTyyhhKeN2k5eoPX3ylVqnyW0rlxsz82zC0gKNe0muZikGB5R6s/yUDWE9m1wKuq1tGEjsGo1UnLIGdtl2VnPzqHcN25s1Ly5s2QPyOrZpR2XyLTrlaW6RXJFsl6LdnWkez2QJeMcYt38uENiF0EMIjjn+PtjHM+/AJx0oknoAuD6+iE417+D6GFXQ9r3hAJbSnQHQWC48XqG7x8M3HMfx8JFHMrwKYj87O8Q2rbA878rcs9jQRAEYYfpbTp9rgx78SiHKlMQdy2D4G/IcO/K9QVdtUV/4W1rS16TvuZ8Uo9lDJuI+u1nwbLdq7nO9MdoG/5leqlPJ6o45QDAFVSvSd5XFvOyFM4q4hiDXJ68CAAQ2rUDrngrGKyzMcoyxzcLOAJBQFDixkZMAFiwBUyOgcVD6AktLep5sWuXaTCeYn9iUN/WDmyu0QTEPEeZOZfW+1KR0dCgJsav2QLw+tVwbP/GUqMudvXMsysRxpiQsJJt4nBs+9oI70x446VPuC/JwPYdQGdnos7m5sT6ZBFQ/+mWkqbasxuwaiZ6Y7vgkFXxj2UJRzQm+0xIhBbEHYsgbp1vv7HW1+ospNxYlJv+YiqkC3Ja/GmPxS6ep9iVrwhvansmqJsXR7cBAESe+d2yPLwWVcFl+dvEFcRiHG3tuhnpVLL8MWropVDofPQ3JVnL19pW/1CTPPFLioOn9kvys6FbrZJLm0oxINJl7CCqdbfdLIr5YMzGqF8LYkLsMj5kBBrgqPlMDXNMMyNrOlx13wKN61Jm1c0HErsIYpAgyxz3PcDxf/8GzjgduO5qLUcXAOd3L8K15GnEDvwN4gedX2BLiZ7gcDDceTvDPvsAf76NY81aDnnCIYgeezccOxfB/eHN9i+LBEEQeZI+RC6xYuEijuUr8nzJzCv5i8k7JNMsg0lT4+U0REgZA5jKpbuPKnG44y3aNoYbAgDAsfWrNLNgZbABmZvD4hFj0wDcPOObTT2xpLFDT3J2pcnTnloIVkHGvE1FaDVGBBYlxCPtmIIhU6JilsiyxqDO1NebmLsuuZtHtn9h+XvrNqQIAUY9Nm1hiJM5e3bpo1zZdkxqlWr0BPU9ecYzRKNqGKnqhGVzEEmiohG+yHluHoA29iWLtha53CxcZWi4yuCKxB82YtfGTRxLlyWLKRmEZCVudiVKXZ8kKqdtr3QYYlf3PbssYoeiGDmhcvPsyn0/4vZv1MT/GgLT7U2TbM1kn534mt6zKzkhvYJvFthsiHT18tSy6R4MPdfL8kZJc9zZ9DZjFkd9gT4xQkr75WaHJHG0t+e+sbj9Gzi2zU8pkTxBSMr6LOeh3t2GZ5dlpY09UqqHV0ahOR7GqmURfPZ5mvpygMQughgESBLHnXdzvP0OcO7vgMsuYcYU1c4VL8H9xV8h7XMMYj+5iWZeHAIUFTHcfw9DVSVw3Q1qrgxp3xMQPewaONe/A9f8BwttIkEQgxWzR04kgBEtH2i5dTTkOMyvvIGgNutczgmjkacgn0kkMq+zDsrMuxAU++TfmUJsHBveBws0pWzj6dqE4YHFcMQ7Eh4gaV6yWbDFpo7ML+TJX/LtHtnm/blrPsxYt15UULTcT8HmlDLmcroNduGPnOfi2aWJXZoWIejxUEn7SfbsSmtPT19ZbMRSRVZg5xzV2clNHkG54XYBvqLE37YzNGYgOfxW/T3xq9i83rRQ9+zKz0YzoTDXwoHTe3swLe8dF5zq/mTD3SRl33Y5fGztS/LsMg6XAZDjkGUtp5LNxgmBzDQs5ZJpvVpg+w6gvcNm43xDS01tk3ptZxP8zOKUfhGI2v45RnV+Bm8s90k6rB6dslElA8fKVRz1eUwYkQl9ooRhwe/gjdWr/WgR2O33s7MWWLPWxu6UX/S/U8VI8zVnDmO06y/z5izrvTTd8u61WU5Cb/JlZdy/FDAupa0kOfzReI5x+3LZqK0FandBy1OW/lrXSec1m03Msns+tbQkcuYZnohchqJwyFKWA+BqGHckkrA102yMUJREWLpWKBZT8wj6/bn1M4ldBDHAiUQ4brqF4+NPgEsuZjj/XMEQuhwrX4X7szsh7X0UIsffbzxwicFPRQXDA/cziCJw1bUcLS0c8YPOQ2zmWXAtfRbO5f8qtIkEQQxCzAMBsWsHGJfh1XKxFEVr4dj0EZg+o5L2rBGUiLq8bUuue8nHIOPX5Bdry4u/EW+hh38kVqULr7FLuG3GThjiUJ+jnnBtxm0B1SNJrF0Ci0E2b+3mRVLyB3Bbxwp7ryk7dJ3CJauDWbFze+YNoOY6+3I+Uma95DwXEUcf5Kg/BSGNB5SeXyzUmhK+wsGMQ+z297lIlxqmZsrtpFdVXvu+7SY7dtouRrrBoiu0C15XzHZAmK9nF5dlNOmnm/mcD7cnivLMXja5EAypdRQXpfdogqyJw6ImdilqP7Acd225rmJBdRZWfZ0hXFm2wJYt6nnHuYyWZqs7or6Noolvqo1SynozjMc1sSmbwTZ9a1WY1H1r8z1446YZGhQpNWzZ4omlfQDQPLugKHAoYVSEVme2yFSH+R7Hgi3G9dzUzNHUrIbmZqgp435S7AVQFKtHZfA7MM2zS/csTEwoYaXTZlIN8671UyEQBOrreeqFkZILLHFeZhO7smFXdt16jk8+y72OfPed7jYv8DjGdnyIoqD97OlKQk/WfqY+x8yrs6ELiGq+q1SjiqK1ELd8kbJd8v7ELMNGuzZZvhJYqDnj6v0vII5164GvF6ReW8lVLFkKzP/GuswoE+mEY/27cErqiadOAmG9l+k5NxclpQNMB4ldBDGA6ezk+ONVHIu+Ba6/huE3v0q8FTpWvQbPp7dDmnQkIifMM15aiKHD2DEM8+5j6PID11yv5jqJHX49pH2Ogevzv0Lc+GH2SgiCIEwoctJAS/u675Q64Yup07SzpGSyAo8jGuXo3GY/jXsmD6rspBeHzIMhnqQK2ec1MotEiSTliXqS3VRSX4MVTeyyerL1zLsi7TjbVLU5hMr8xdo6LtcVItMiIzxGyxuURq36ZiGwYqW6rlkbQBvCm6Y4cVN9yQcg7vxWFQeN9ldXCYJJIOPpw9WEqHVWgHQtyrrSe8V0dXEj8TOL+sGiXQmPBZNqxgGURDbrpueBWjge5wh2hlHSuQLl/uX2JdPUmywg6gV37ZJNs53aj/JTz9duoFUiiunFVyYnEvZEoxyyzOF0qhunDOI7d1r6UhVJEoUcW76AY0tiZstQGFhdzREJJ8oDHBFNX9u+lWP18hgCAdM5rv1UmOk91nQu2bXH2I6PINR8AXCeki+sJ1QGE/0t1K2AsH0hBMUszpkaSE/MpKmhiTxO9icH89eDtW9Pez8QG6tNuZ5yV15aWzl27OSIxzPfE63GWFd1N/m32f7qtTb7S6o3cX/iQDSgipZp6svm/ml3ZLXa7aM7ee9yuVekeGhBvR5ERU3W5Y7YT6Op32sTh6+JXcl2an9KMvDVfHsPXCDdR4JE2WGhlWCxYFY1LUXIz7HZkj04Bcjpr7+kDzmBYGpdhoeyNvO8R1I9phlXEkKsbQqD7JDYRRADlPp6jkuv4Ni0GfjrHQwnnWgSula8DM8nf4E08SeInPggILoKaCnRl0zeh+HuOxm2bQduvIUjGhcQOe5eKGNnwfP+dRB0rwKCIIgcSB4EcACMyxjp/xpuSfUy4aIjqYyATZuBHdvsQxmziV3r1nGsW5/mTdhcNKNnV0LskmVuL+qYnS4U66A8GuWpb8k2YpdtzpicFJMMnjSW+u3/tgwa1+T+Oq8kiV12uY50mluAYJAjrAkRKU3I0xwql8GCzeqsmvoifWBvGjQL3OxllWSHxfOcpR0YiXUr0tq/eIn6z2KAeTBt0kPLwhsAruQodlk7Ydl3wLeLZYRCgDPeYRlYZgtjTDkuzb541Kzc2m+sL+3sVEN9uoOiNYIABZbkZWY0IZdz1TuCgaO8XC1r8TTy18PRuAqlmnCYyX69XZq0qF5d2GNJ5UMhBYzHE6FtnCdy+JvELvPMrinekBqb1oVhksGzYLJZm9lan1lSN5EnFWVRv+r1lnwu6yR5dhlyZZquE3d9B7Gx2rLMcrsRnaYwxlzEJ476eo7vVqizANqFG6ajwr8cnvqFgNmzK49TLu2t0SZnl9XixC+OrV+iKpDpHTbLvTQ5SbyJdOdMT7Ect0n4Z2k8tVLsSTrJMoUxRmNAa5r5UKwCb2pnJJ4HmRsiWTQLR2C59+TqWcy0ez8zjotnDUO13YdWD4fDJHhbn635Or6S2EUQA5DvlsdxwR84WtuAhx5gmPPDxBuc6+uH4PnsDtWj68SHSOjaDfjeLIY/38SwYiVwx185ZOZC+OePQikfD+9bl0FosXebJgiCSMYsEimy+uKY/HU9GT28SVByFLuSXnIlWQtxsd/afp/t26AE1BGzL7oNrEMNK4xGZHz2hWlgmQZFgeFAE4mo09Lv2pWidqXu1ybEKVfCYY5QML9trKJJtm1T1xvplowZ4RKjJbvcNQvM+fVthDdbEUqf+t0UNmaEr4iJ/mdmsStlkGtt65wHLOF2CLVLAVO4n8V4fUBk8vDRVzmUJBeCdCR5W/gD6rGog1jJqNcldcAb2mYuaiDKIYxrfxc81AEACAQ4lrxdjdoaNRzH3Da2nl3giTBGcMtso/lgeFow/QpJL3ZJEkcsBng8gMcNpORR0soJ3JQTj6Xpu3QRmPpyI/m+DGZIcoC5LRRmep81DdLnf2PvqaNOzsAz7Dt1mFtb04G2ZQuM8swUFm14lCaJ5nZhj2YbOUv2qsxd8LZU7fAmrucsFwjnXM2F1rwdZaG1qAwsQziSZWcmvLE6CJE2k8dsfp5d3RW7jMZVJHAO4wOLalM9HDWf2tscDQBarrlkG+yQbL3cEvj9HG1tSfeoHO5J1nNEMXLYMegijXbOmW53isINsUvfRUIcS/341H1MIhWz//iRXL9dGOPyFabyKc+IJHuTBMeqwBIg3GER4bK1q0WT125AnDGISkR7P0nK7k9iF0EMbt7/kOO8C7pQVgo8/TjDAdO1W4gch/ujm+H69knEDzgDkZMeAhwkdO0uHHkEwxWXMXzxJfDw3zm4uxSRU58Gd3rhef1CMH9D9koIgtjtUTiHS2oHuGy8MwomsUtRuGmAYlUPGBQIa99JGrjbkMY7qssuoWyaN2GheSOEzp1wSp2oCK0xlkfCimGLTWWm40j8FdXG6q1tSfuyiwXJy5vLyuYa4LsV6asEAMeOBRC3fGlnMuwWWsWn1ML6WMYYtJoGN3aH4JS7UKQl0FYUjpZWjpXfdqJr03pjwC80bwTippGzjSCnh+sJifRbEEweMCn9YxEeWM7NK3TughBohNTWaF1huAMlkuEk96ZTToROZkpa3dTEDS8fvZzuzcNY4jQZ4f8GJQH1XEzWXvScaaxZ/fgUDgPF0W1GziOL+JfWFpMElEF74JynPR5FE8wYSxogmveihzFqNlVW6ntPDWO0zatkIzwl5+xKMthoRCnOLW3BTLNUWhUC6yA9XXuYbUkZmDMxZeG2jUE0NgKSHm5qo2OZo+f0311SO1hnbVJ5XeXVwhj1P/MYjfOka0tJEg8Ae6GPc0BsWA1vRzVKolvhjTekcerMYAtP7Ck3T7IEkiSjKFqb0i8syQjGtRi1pNxdTImn9FdFqBqQIgmPOeNerMCx9Us4tlsTPdkdmT67oBTPfDyLvgUWLo5rCd5VjGOJdKW9Rs25t/Q2s3h22Ugr5hlzjVPfEHCSxKNMp45ppXn2UyOvnGVjffbiJLErh+u7I02eNqfUmbq9jb1C6yarx1kW9FNErVB9t6gIrcHori+05YnzQP/LFW81nmPZILGLIAYIisLxxFMK7rqb4+DvOfHEowzjxml3s3A7PG9cCOeaNxA99ApEj/wLIDgyV0gMOU4/jeHMXwP/fQP418sALxmNyClPgcWD8Lx+ofqAJgiCyEQ0hBH+BerAQvfOMYldli+pNig8/eyHCTja2jiamzk21ySWLf4W2LEz+wsw59pgSFbDK/VlgGkgaTcq4MkDF276XfV20ZFljpiNoxpH4oU6L8HL+OqctE2oDY4tn6sCI9SE5CwWsJrMOZqaFKxfrwstaY7Rxh7TuFxbYPbsSpQb0/ERqgKLMbJrPoYFVxjrQ0HAEeuA3LgZUOIQ410QWjdBrF9u2okleZr6QxMLmGD6up/JsyvNoXR2Aus3JIk35t+1ga8uctoYkviTWdbAoYRsc+wkU1fP0dGhbqgPTJkl/5jduWb9U9a8klhYdclKHgRaQ+Hs6+MmcTlTyqEFC4EvvrRfp5sqMMU6UISa86ypiSfCGBVdONZz92SbiVCtThdf7MI7022kH5ssKRZhxZo6iiMmlkESisBlq6Cezq64KadRqmdXwqjODnUGOP3aWrfOdEDa/3aXsN6GI/wLINavhG3facvEYOrsrnaY66+rg3Hdg5vbP1Fo61YYs99Z6pCjlrpkO7FLK2AnjnLA8ABiKYnk01Nfz1EeXo9hoZUQI8kJ/FM9u6qCSzCu4wOrHTzNuZbHLdeurJ6DSoqnzx9oJh5NegiEO+DYNh+sfWvGXasCFze8nvX2swuqjZoel4nu5eYfNgVsSMqdp28vNm9I2ZgbcY5JH6ayP1Js17vjLeqzuM06AUoijD7B5g1xU37C7F1qdZxM7Sv9mo1GFSiKGvY8IrDIeI5lg8QughgAhEIcN9/K8a+XgVNPBh5/tAQlJVrehZaNKHr5DIi7vkPk2HsR/8Efsr1VEEOYiy9kOPYY4MmnOd59n0MZPhmRn/0dQsc2eN+6FJCyDUIJgtid0QeRLrlDDZnhSErADLOypC9IrFIsKwCoyc8XLDSPEBUsWw6sWAUEtfzhI/0LwJQYAgEkkVw3EOyKo6GBQ5GV1FJ6SF2GQSegh5ZoYpdN0U2bgSXfJV6DJYnjy/kc/i7z4DDNa7rp7VyWuSbEGENl1b5AExAPQWxaBxYPGV5G5hd75m8Aiwcwwv81xnW8D38guf58vC20bRR7sUvgcXji1qndZAUJgUgBRCUKmWtxLZL5nDCH3FgHaaJpNkZjwMcEG9ttButQPaDicVM4ZnJZbbCmhyUlTknr135wJTEANMS3DEKICQau7Z8jqh22Of+YiERbpMvZZeSq0eKVkvdnEf/sY0Utgm6mqLJQODEbWzIK17zRbDxHtu8AGptgCIhmAcQQCZLHxly1CACcUgc8m9+FEGlP2a958J16aAklUpGVJGFFMR23As4EcCZAke09UhxywCKqRqI8/b4F0eiXbTuAJd9KlpDt5OhEuzZPzcGWeoCMc/WjdPOK1AqyULMV2Gk4qCTOgeJoQlQIhYGaLTZmcG65VGzFrhxDyfLx7AqFAVHREv9lm32RK/DEtWlIObd4qaX9XmGnOiajyHA1LoegJDxQy0OrMUybmdeS8y5FeFfXuWLNcNd8BLd2X+QcxoQXLGLv3mQVdxTD+0//YKTwVGlFCiZ93NBtUiS4Nn+QZFvaIzaOQ5Y54nGb8ub7HQT1OG1OakEADj8MWl0Z9ifFIEpqfjuHoh4Dj1gf4Hb2+rsktLcj+ZGYdgPLk9ZOdNWOYekyBbvq8tNDARK7CKLg1NVzXHI5x4IFwJV/ZLjqTwIcDvXJ7djwPryv/BqQYwif8RKk/X9WYGuJQsMYww3XMvzgEOC++zm+WcAhj/8+osfeC2HXMnjeuyZrQkqCIAYmS5YswcUXX4w5c+ZgypQp+OSTT7Jus3jxYpxyyimYNm0ajjrqKLz++usZy+vZchxyCEyRwJHk2QWkvCCzJBHJ7oOLLmqlQ+AxeKXm1BU2nkixqITmFkCKJe5liYTzpjiSlLqS7Ez5PbE+HocltK6rS/XqiYQ1USXDqIMFE8exZQsw/xuzp4rm5VG7BOK2b4zcNHb2iruWwVf3JVxGuJ21DIOS9wxzPEsYo6Ws2fkBAJOjaWZWVBdWrwE2bNAGXJpdnZ0cTVpz6OcRhwjGlSRvEvPvqYnjLWeU2YNBz4uULheQTeyWkRw9W7ituTqu1hmN6PYk9lcc3JBSPrVfuGV5yhiTZ/PsMnm68MyeXZnQIwaZ7UhTQ/Ps0gUxxgCHQ90mZqt7czAeh0dqAedA7bpUD6aMYpd6lwEATcBOnNeMy6ZBse5VKaaoiVwT5Ud1fYmK0Gpjed0ujmAwsZfWNo6dtbp6ljirGOeoavrQEhINmETKJLt1UcYsmqeFczApmvV6Mwg0Y1Tn53YniVFHaWSTxajk3Ep6v5h3aRYuuDZDu2PTx9ZzK6UOLYwxj5xdsqxNgAAgkWdOI/nCMB8Dj6RblbRNhnu8Bgs0QgzUoTy8ziQQ7kBRXE3pkcmzSzfRFW+DogDeuBoiHY8DW7daBbkU00wau95mipLID2jkytLK+Wtr4dimumEKpg8D4AoQj+QUFpi8823b1TyUqeVNYhcTNQ/pJA9J7afLxVDsyxwuLW6bj/LmrwAknoe55F4UuGSIcblQ7F+HEV1fg3OeEgYLJK4/BqCjI/tzLcWe/IoTBNGbLFzMcf6FHI1NwLz7GE47RUvgGQ3C/eHN8Lx7FZQR+yH8m9egjJpWYGuJgYLDwXDHbQyTpwC33s5RvYZDmnIcYj+5GY7Nn8D9yW35Pw0Igig4oVAIU6ZMwV/+8pecyu/cuRMXXXQRDjnkELz11lv43e9+h1tuuQXz589Pu40RfgYFjsBOy1fpRKH0A5acxJdu3n/0rQzRxORmsmGDOq28MUDPMgA1hzHqg8DkbeznceOJ/9Mch2iaBTcSBYTkr9Gahy2TY0Zb2oln8ThHfdJM9fosVozH0wpu9r7dplGYRra+UvOzJTYTlYglgXSi6kRFbVreMyHUjLLwOozreA9F2z+GoESSPLuSms9kC+NKqqhi+cNUWA9nUxLCjFqHdh4bg6PU3mQmUS29x5G6LdfOF/2U0z2AGAM84Z0Q5aBlk3SnuN7mRv4mzSjB3ACco71DQWMTR1gXV6NdKOpclyiSp8gJqGGKspzIWwQOMCmqehmaMATExNR/UCdg5Yi2NMDfaR0gF0d3YGzHRwAYgiHA77cZkKYTu7i6UA+rikUVMK5Y8ltZPNpg79llnhXRG0vkKI0m5UMKBpHwHtVydqUTL+xMNdPYxBCOJHs92Z5IyDbzp1C7FIqi2iI2rYVDCcGheUcJSlRtOJMHXPK+lM5GNDbxlDXWkEuznaZ6bL1lNLONnF25hzHKMiAKujCZdNUl7UuRJaOMqESyPktMd9/MmGajUBTAoV2f+mI5Zr3eLDaZPn4oippbrySyGVu2Ag21IXR0ADt3qknsU+wzXf/m54kxk3GSZ1f1yijq6tTfzYLl6tWK/ay72qKRXV+iLJQ8vaa6MmT6sGTpcq5A0SbIMPo/OUG9JoZrB5BRXGNSQpzUzw+OZNUVKWKqwOOQJJOQbK4zqW/Fms/gC28xch7a5xiz3nRTBNYskNhFEAVAUTief4Hjuhs4Ro8Cnn2K4eCDtLDFhmpIjx8Fx9o3EZ19GcKn/wO8eESBLSYGGl4vw/13M4wYAVx3I8f27RzxmWciOvsyOKv/C9fXDxbaRIIg8uTwww/HlVdeiaOOOiqn8q+++irGjRuHG264AZMmTcJvf/tbHHPMMfjHP/6RdhvLbH1aYliL2KW9vCZtZfyWOjuZ3U7SjNSzbGcMerUwKzkpVkvNq6TnaLIPBbPYqf9uTNiXvH9mLEsMvs1xKhnsNc0MxiBbhBPE1UEsF11QJG1WP9OsbwAgbvwAdUlCl77eoQQwtuMj+GI7UlemQ19nn6XatjznCc8QDnXQzcFTv7Dz1ME+50BJRI2tEngMvlitKUG9CAYFfj+wYQNHRwdP+KBwDqcSyHSKqQnydRQJ8ThHa0MIVYFvwZCcvDoxG2PyuNuc/Dy9x5HpN84TwmhSfpzkBOypnmnaeaRvFkueGo8n2pEr2LlDRlMT0NxsKWF4ZRlhnVE/xI0fALHMM0vGYhyLlwAtLczSDpEIB6+rtt9Iz70FwCEyeKQ2hNYvw/qv1qu7jlpnZ9S9sOy8gNJl10gd7qo5u9paEyWq15pKMwEcaiI4d7wZJRE14b8Rtger+GQJVYV+f9JXmkXX9MKC/mvyHAJ19UB9fVKOQlsXKfVGal5THNkKQG3/hkYOIdCITZuBNRb9goNxCWM6P4HQsFoVK0ydxyAbAolYuxSrVpu21O/Dpp1aPBnNE0LYeHbJClDfAKPj7D4epBMJZRlwiEpKGYccgNBZaynLZQ5FUPPZiUrI5h5sKW06ONPftqgWM63d9RlDDbHL/OywEbuq/IvgjWw3zm9vrAGu0C6UhTdAkoGGRuC75dY9inIIvq61pr623tOBxAQRqnbJLSG75pDvrk4ZzdaocktdTjmAkujWpJWp1525i5gch7j1a7ikdi2MEanPA/02yRV1cpFcRXX9OZkkHZnbwbDD8OxKGOf3c6xZyyFJSWW1Z6VqErcX5wzPrtT2zgUSuwiin+nyc1x/E8ezz3Mcdyzw+N8ZxoxW7zjOJc/A+6oWtvjLFxGffSkloifSUl7O8MB9DE4HcNV1HC0tHPEfXILYjDPhWvIMnEueLbSJBEH0IStWrMDs2bMty+bMmYMVK1ak3cYye5n205yfSA/nUmGm/03rs355T7c+sXx1NceGjdz65qr9KmjTuCtJYpdDNHn0ZNmteRCgV2OXn0ROXmeOU8mAeWYwxhWYZSPji7jDjfXrZWzcBCQPjJgip85kpq10S23az9asszEa20L1GjELEZkHBQqS9SymRBKeNpaRv/n31EXJ5Zioph0PBoFYXAtx1YwpC69L5PAxW+PwGr8LHaYkyEocO2uBeEcrPPFmI0zJdmCcIkCZ2sLGXMt6TTxIiF1Js8fpng26J0OaMEZdpPLt+jSxRgsp0/MccZ4Qs8x2GV4XpsNqrN6EliYpMeNaGszniSBAzSUENTfdpp3FqeUr90kRHlxizLBz6zaOpcu4xXOKcQmNTZnzO4XC9sv1WeoYVz27IlFYc4tBv44EdRZFRcHwwLcoC280xJpkQcbtBhwO0/2Mq+3Q4jvIJPbw9HcjS+MnSXKmQ3QoYZOYZ1ebKmSa6ysPq6rWt0uANaui4NwUImrySjI+NLTvAJOiUHjCc4ZxBRUVQFmZ+ve49ncNcdcfSJ3sg0Ex3WDMnl085d6XsDXphDORnL/NvFz37DI3lB7KZ9lPxA9REwtFJWYVhmxOI1lSJzFIMtIGZtaOjetVP2zrbIzq762t6sQpsgx4pFaISsQ0+6UMZ7xdqzCxe7OYNyy0AkWhrUC0yxLGaHmEMbPYlXiWAYBgmhzUIQcwqutLy7YtrannqkVvtOkjzjVB2/B60kRFJkCWgcZ6m3hCBoDLEITs3r/6/c4QmmzCGNXzzlw9R1yylmloVPelpwlQlydsBgBEOsGifhsjrOJnnloXaBRNEP3Ixk0ct/yFo7kZuPZqhp+dCDDGwNq2wPPxrRB3LUN8ynHwnvYQlEg3fNiJ3Y4xoxnm3Qdc9keOq6/j+PvfGEp+chNYpBPu+fPAveWQpp1WaDMJgugDWlpaUFVVZVlWVVWFQCCASCQCj8eTsg3ngNvtBgB43ALcLsXy8ujzOeAsKQEkH+Argc/ngzMehTumbeMR4fMVQRZ9KXX7fOoyVlYGn69ILR9xQR8XFRV5UVJSgooKJ/wBNXSnbNRmQNuutLQMPp8Eb8QBd9wNt9MFtzvxLPS4GYp9XoxRNoJBMo5D3bcTXHDA7VZfxt1uAR63A4Kihmj5fD4UFTGIpm28Xi9KSirg8TAEgrK6b8UNN3ND8HpRXlYG7vNBkjl8ZeWAz4dYjMPpVJ/dnHO43RJ8RW4UeXxwu6Mo8haho0XAaFcRnOWVcApRwC2iyOOB4vOhyFeEIq+gtaXqtWT0jduLoiIfPC5N7HOXwlsUgs+nCQXl5eA+H4IhxTh2vc1dsRCKinwAONxudeBXWlYBn08dYbtDieMGgOIiL4p8PjCPB9Eoh9sjoIi7obiLUFTkgygyCBUV4JyjpjaGCe4iuN0Sirxu+Hw+uN2ypW+8XlWscituMLcHDB643B643RweN0NJSRl8Ph/Ko11wuK22AICvuAwuLqYsB2MQRQFuN9fOoSJUVFSAR4rBIz7A4wVECXD7EPEqxrGr7emC11sEn0+AI6r2lWWfRUXgghOeiBtuj4Dy8jK0+Ivg88nwcQdc8MHrVevzCW64o264XAw+XxGKS5zw+RL7cke96jnrFlFe7EXUWwS3W/XMKa6ogqexFaUKR8zlg88rQYJ6HXo8DD6fOhzzemSEvQokpwfFRW6UO4LYUteKINwY46uCp6JC63NVPKioSLRjOMzh88Xgkb3wwoMiL0OnNAyOWABy3IGKigrjnCkqckAoL0ewvQi+iBc+n9pGZaVuKJIb3F2E+gYfvN4iuFxeo58VjwNuuCGKLjhktS632wGHg6FELEFUCx0zX5duN0N5WSk8Hi9EWcEIvh0h715wK244HICvyIO4Uz2HvTEXJNGr9Y0It1tVzsrcEkpLh8NXJMMdTdQ9cqQACA401qvXR5FXRCSiwOUeBt/oSUA0AFZWjpjXB7eHQVCsPh4+n8M4J7xeEbKiGOeZ1+uAx+2GQ47D5xHh5W74fE71A7QpxNfn8wFFxWClJYhofa6zcVMxHE4FRTwMr9dn7MtXXAq3OwZfkReciXBH3CgqckAUGdweEaVFUfgDHMVFHngdXnjcQEQbExQXueEOu9HWBiiCC8UViWujuMiN8ooKcJ8PcPuAqHq8W9p8EPweo5wFlxduwY0irxulZWWoqEhcg8EQt/SlfryhMDDC50Yk7oa7qAg+pvafO+Yz7kc6jmjiPl3kdaG4uBg+SY0zjcXV69VXVAQwBnfEjZ21AiRHEQ75UQU4C4B3WusTtGuAixFIRUXweD2QinwQWQDuuBteL4PP54PHHYWg7be8rAzMXYwFi9Tr5kdznIlnoEftM4cgwlNUhLhbvRd4vB7IRT6Ul7uMe6g37oZb8KC8rAwhpRRFRXF4mQder2D0u+L1wefyoazMidJShiKPC26uX3cMsqyed+Wieh8sKhKNbTdvEDHT8wkanQcb9pWUJPqERxzgPh/c7oSAJQoCduxUUFHBsOceDsRiHD6vBx7mRUtrGHVwY9TUMqNf3B4ZnhhHeVkZSkoECAKHYHPbrahwQ/H54PFIKHa7UcTccHM3ikvKjboqKtwoLo6juCgET8yDmOlZ5nb74PZ64OZu07MSKCsrNbYvL3cB2vHIMlAmtULQ7dSOsahIhMfthM/ng5d54VbckOKC5dmTDRK7CKKfeOddjgcf4iivAB59mGH//Rggx+Bc8gxci58AnD5Ejr0X0n4nochbBtjMdkMQduyzN8PddwJXX8dx4y0cD9zHgGP+qgpeH98K7i6FvE9uYVEEQQxtFA5EtbnQgyE155SZYDAKsasDQjAIrrgQDAbhkoIo1rYJBIFQMAhJtElSr2WKljraEQyqL77RSBQCVwcLYTEIv9+P9naGYJDDJbUhWJcIWetwdCIYBFjUD080iqA/jmg08VIrikCgtR5Cp/p8NJseDMagMBHRqKT9DUQjTtVzIgoEXUE45YBxHLo9ra3tKCpiaGtVPZHc4RCEWBQIhdDR0QFHMIjV1RyhtnbMcgawfgMwvAoYNYohHueIRoFwMICamgCi0SjCYgjr18TAy4OoFDuNtg6zIIIIIuQOgStq24XD3DItfUQJIRAMGstCkBAKheByajPZbVsFIRgE4E30od7m8SCCwSBCocRU960t7Uby7mjU2tGhYABdnX7EQxH1GEJAOBJCSAogEAyqkVmtbWhtBdZVdyLuUO2KIIBt2wNoaLBUh7AQBjiHKxoFgwMRxYEg1LqDIUDp6EAwKKIkEoasJJ10AAKhENw2niGA6rmge8VEwiG0t7dD6NTO0RgHi4fAYxyhcMjSnlEliGAwBIeDIRq1trXaBn4wLiEaiSAciqGjvR3tHRKCQcAZDoLFJUQ901DasQxh7kc0qiYhD7IAOjpEo23BFTjDOxCNRhEMAJ31WxAMqrYIAuCPKohEIkB4C6Juji4hjiKtT8IhIBhMnA+xCBCT/RDWvw5/lxPRqDroq69rR7mzSOtz9dpqb08k7wmFOIIBGb5QO6KOCMIRwB9h4LwYrmg72tvbjXMgEIiC+4OIBUMIBf0IO9Q2kmIhxKNRhHkEQTEIREMI8bDRbjGlA0I8CllgRh8uXRZFRQXQVuy3Pdc4Bzo7OhCOxuCUowCiiCntRluGggHEnOrAvjgSRlx0AGAI+P0IxdUZQksaP0J767EIhYKWuiMRgDPBuEcEg2o+o0A8BL8/ABb1QynqgBwMIhpxWDxYASAQjCautbB6Deh/B4NRRCMcshJFDB0Ix6IIBmOq65DmiuLz+RAMBsElEYrQjlDIev7V7lC9VHi0FV3+xHUdCKjHEQ52gUNENBpFa2sUPh9DKOwGc0W0tvEj4gqDI2FXKJhoA4UpCIXixrpwoAvtbRzOYBA8DjAt9HXl+k6M7kwOq9XaUAkD8SgiLIDOjk60Fyfu634/T7lvBAMBgDHElBCi0SiUziZEoiJkwQtZuwcBqkcUY2ofGfYJIQS6/AAPanWp12Qo2AnOnNqzIo7GOj/a20Wwrk6IQes9QWpX7/0s0IVgKIRIOAy/Iwh31I+iaBSCAARZEK1KF1zajtvbWsE8ceO6aWtTz1G3221cpwqToQSDUKJRhEJAOByFXwyirS1onNdFkSgUKYI3/9eO0XtxhEN+RGIR9VmjHSPrakBp7C10tP4InLsQCQUgxtSVbrc6CUowyBCNyJCVqGVbqbMWISUE1rERUW2bjo5OlJZo+e66uuDqCljOMb9f3b6hARhepd7nwiE/EI8hGI8gIvjR2tqhPTcSz5yO9jYEgx74/bB4Yem0t4fgCAYRiXCEpADESAiOaBSdXQGjj9vbQ+jq4ggFwwjLEYtdHR1BlIbDcEYTOcsA9V4QDLq1fgjCHQwiHFY9N7u6uuDU6o5G1b5Sn2duBINBCOEQXNEoIlGWfdIIExTGSBB9TDTKcfd9Cu65n2PGDOC5p1ShS6hbAe+/ToN7wSOQ9jkWwXPeVWdbTJf4gCAyMGsmw59vZli5CrhtLofEHYic9Dcoo2fA897VEHcsLrSJBEH0MlVVVWhpsSb+aGlpQXFxsa1XFwBLDIBt+pl0KzQiYa0U5xjh/wZum5C09NtzyyNOVJJm59JzCBk5u6xfbx0OGDPJ2dZu2q8atmPdPikqEsy0LJGYXA+V4Ni8WVFDLQG0tSdCPvxapIURAsJlNDYnYl8Y1BBFFk58tDLP3JUJc6SKYJ6pDoDQvk0rZN6A2/2q2mmdJT55T6khrYoCT7wZXAHWbwBWV6sDIYZEQnEBUorQpaOHugiCFtJnSqelB+ikS4TNmY17ge1Okmd/0zrOLo+UKRTR7pQsC6/H6K4vIPCYMaGBOYyRQ4QserU8Won+Y1AsYWHl4XUojqqhl7ICsGjA6CPGAAiqR5AvuhOjur6CLKeGEieMBgSmnT9KUixQBhQFqAoug0vqMHKMyRAgC+6UWfAUrhqmmEKxAMAb15PIMUu9Oonr1WpLezvgalgCO8y5tpLrEQRraKL6u5azS5EhC4nQVh4Lp/QxA8CSoqAVRQ0lU8OtlDQhp1p50/K2Vuug3xIWqon1Tc0cUduoC26EWtrBoFht0PNkcdm4HrZsVQUiBYLRf4zLgDUFW1J4rXWfDDKiEW78ZXugybbp4X82Seztwr71vheYWqfPvw5VgW+1OhL7WbNWzXlmDoVkSW1kWJqyb24tkA49hE6RjPyG+u0hZtboks4d2dQclllB9WpNUZRKJGiEH5tpaYF2r7ee3qIUgKhEIYRbLZMqJB+aXSiwwNUwT4W5jGUO/y7j9wULFWzZYt1GTqqGc2seO5b0DDHn7GIsdXtju44difogJ+5/Scnh1f3ZzxqcLg+cO96CkvCm1LyHUqogq9efrr5cILGLIPqQXbs4Lr6U4733gfPOYbj/HobyohBcn/8V3ld/AxYPIXzKU4gefx9QNKzQ5hKDnCN+zHD1lQzzvwHuvJtDFjwIn/wYlIq94HnrEggNaZLUEgQxKJkxYwYWLVpkWbZgwQLMmDEj7TaWwYbNCypXgPbtdWquEpsp1ppb1JdOgcfgkjowLLQypQ4l3Rt0EvogMrGhJpZoeWySX6AFQU3CmxaTeKPIqS/HipT0ssy5MdlfQghTy8SiHHV1PGXWQLNdPM3AhXElZRDhUIKwn2kqxSRTPVIaQTJp9AJYZrXTMZJh2wlBUKAoiW24Anhju1AW2Wgsa2pWB3tVgSWmWRDTJPIBS4hdTNHqTxyTkWstjSKQMssXEuKlnuDaulKx/rQ5fnNL2e3WLbWaSqqFFEUXYWRwJoBpMT7GYFgTb2QZcEltKIruhENJqIqyDECOWffLFXCWCKZJd33o2wjMJnePjfJgziekcBi50IzLFiJk5lEHi6brhiswclqN6fw4MWOkMSrMLHbZCSPeeFPK+Z2oQ7HUqYsH6v6sNyQOBs4EcEWBzEwhdFI4dbDLrNceVzSxCwycM7BYCMyflOPNhPmciESts9yZZ3MVFNULrbER2FWHFCRJgRyX7XZh7FuWgYB7T8t+GRRLWyoKwLkqdunHpv+eOGTrtW+9X8gI67qMKUF9pk/oevvZCQl2Ypcu3shyIl+VU9bPf00Q0fbY1mYVu0ojmyAEGxPW6wK6cU9J3MvUPzM8R4wk8RzlodXwxNWPPvq5HDF7BNcugVC/CqISxtiO98HDXca6RM6uxIcY/bLiAJxbv8Dozs+S2iAhepuPw3QI6GqLoqHBeq0wZhb4Uj9+CEoMkmQVuzytK6AoHLW16iy9yd6pyc/ItjZNnNLqF7VzF1A99eKSdgCa2GVu4vJQtZoXDoDYkJgNgXE5cRxJkxboYpj52rGsTF4kcwwPLEZZZGNqsVBbmiqMu6lqT56iF4ldBNFHfP0Nx/kXcjQ2AfPuZTjvHAbXjq9R9MJJcC7/F+KzzkLo7P9B3uuwQptKDCFO/hnDZZcwfPIpcP8DHIqrFJFTnwYvGgbvGxeCtW0ttIkEQaQhGAxi3bp1WLduHQCgtrYW69atQ50WB/DAAw/guuuuM8r/6le/ws6dO3HfffehpqYGL730Et5//32cc845afdhfle1+xq7YydQV6fNfKiR8nLJuTFAsRUpAq1wyl0py5MHXYJiFa70mSJ1scs2qXJGz67E77JiXeCON6d4dpk9eeQkscsOvb2Sxa7hgW9NAz4OJHn+AKoYUBn8Lm3dyfsANLHLpox1DJEYCJidaBymRCW2CcW5kuLZpfez2fbWOjWsJCF2ZZjtUdu5g0fgkjot7ZQYVKXZPsmzKxjkqF4DBJRyxMUyc0HLvph5NsakxhK4hOZmNXQnbnPaWAQoTdxwdW6CjzdropwqvJj3o2ieEbEYMMK/EMNCqywdoopdcYt3CMCNegBY292s9Wj6sgg5ZZzPbS5W8yJzeUPsYg4o+jGavMT0IWNyeV3sMiehNt8v9OvSbjZGICF6mLfXk8YDgEvN0w9RmzlPFJLEACgAY+o9RduHLKiCF4uF4JLaLftjDNZJB6Hvi2HjZob2dg6hc6cqYNqct1mc5YzrQTSJ8oLNyLm6mmPFivSJ8AFuiHCW+rlsmQlXltXZ7gTTtcYA42/VJnN7WWfLFLiEkJ4AXOtUVRDN7tlVHN0Gz7aPEOnoNNal3i8TYrWieSdGTM44DEBbO4esJK5luzp09PYXlTAExdTGjANyHGLDqvQbm1rbLFqnVK79ygJN8MYawLgC0TRjpMJTN7HzPisLr4PbOP8U7T6g3gzsyjfWRbBxs3XyF8a0kMNGbvSjeUZekccQi8Hq5cqB2lpg3QZ1v0VF1sNMvi10dunXp3YvVkJQuPrhYNt2IGA8phQIgvWWqXunWuCa6KqVTN6fLubnimxq8FAY2LbdpvGSELiEse3vGxO35AuJXQTRy0gSxxNPKbjhZo7x49WwxUOmd8D9wQ3wvnEhuMuH8K9fQezHNwKu1AS/BNFTfvVLhvPPZXjnPeDhRzkU33CET3sWXBDh/e/5YH6b+e4Jgig41dXVOPnkk3HyyScDAO6++26cfPLJePjhhwEAzc3NqK9PXL/jx4/Hk08+iQULFuDnP/85nn/+edx555047LAMH1FMYyE7sUsfvFg/4Ca/hXLDK8s8iNdhLZswsmt+yvLy0BowOTE6EpM8u9zta1EeqjYGgOHk6BFuP+hPrLeKDmaRbnjg25TZ5wCbMEZtYOiW2lAVsIZm6ZtnclxjUGdE1Gc6lAW3MWD3xptSxI2kA7AsE7iUSXuz2Auo4ZV6/40aaa7WzrNLG4DrnggmbTIRnqmg06+FXOmCSFrPLpOXCFO99ixhjJzDF91mKzoAqYeph4r6gwzMpDAY7WNVk2xqUO2JRIDNNcD2Han7VExilx5u5+nciOGBb6GKh6rwojrXaGKXJoqZPSw82mDbuKaSPLvqW30wi0s8lnbKQnVfTEm94pTU4zNfCubVujcQZwIUpipMrU2Ja40rAAQh5fxLFnIccjBNeFI6savLKGFGDdvkqBhXCQDG7HyqB521LnU2RgFcu4Bk5gZnAsTO7SgPr0uyw0pCWGPo9DPU7kJGMt1KLJ5dpvuUy8bJEJwjHk8fLsmg3o90ESPh3SOneHYpPNEJhtesZTrcpPZK8uyKJIUxynJ6cVItlVjX3BDH0oWJHFn2szFqHklJN0FRCaM8VI1du4CNmxPHkG5GR9V49cfwwLcY0/mxsdjpUADZ+myQZY6aGg6/3yTuc3WmTNGUA1AXX+y8f0qiNWpd2jUB2J8DdiJySSQRP8i4Fpaqz8ZoPiT9Y4PuBWlqX70bm5oFo6BZLBR4FIqihlcbtnB1RlsAKPXJqVHcJlujUQ5JAqpcDYYw51BCUOSkjygMKedRMrrXKIcuiOpebKntmpyOQC2YxnvVFMK9YUPiPm/5GGCzDwYFbqnNVmzOBiWoJ4hepKOD4y9zOZZ9B5xyMnD5HwDv9g/hfvNOsEgXorMvQ/z7FwCi3dOSIHqPc84GwhHg5VcAr4fjogsmIHLqM/C+dja8/z4X4V/+E7x4RKHNJAjCxCGHHIINGzakXX/PPffYbvPmm2/mvA99UCwKKREJFiQpQz4PJMLZuM1300zv0UX+DQBmAFBf7s04A7UojgJRh31YfybdZ3U1x6hRib9t880kvUQzKEYYozHoMOc7MQ1URnTNBy+3FrEfLKvTtOttF3KOQVG8zlhjfpG38yyxDEog2x9z8uhKSxxjTgRsHhTYDfwYFMvAw84GgUtG/9rltkk1ilv2bXh2Kap0ZB7EpW6dGMXJMken5mDCBDX/kr420WbJ3oap54dDtk94r2NuFzV/lWyEMULmABNUoY0lCwap4USA6k0XCABSNOHZFReLsapxb+zvNHVOTI35SR64cdUoCExJuYZCQRmKwiGY3Hw2bgKmTdW2tZw3WsgUcxhiV3W1BMsTn6WKXcZAmjGURGpQEt0KnsfgUuQ2jcJNA1ynCxMmMOzYoQqJgsjVdtZ3yxXtfBPAuKJ5eqmCnRDzp1TNBFiclnSBWT2XEoZny5NnhzV8M3FcDtvUcqr3lN3VNPNAYNNiTVjWBvSJMEbZomPJMtTwS0HfryaYWDy7zEpMktgFKWE3S4hd+pUhiqn3xeRrRK8/EuFYuw4Yl3Q8+nXgdAKSqbuHBZcbQnhMEg2BIVnssvOCSsYh8pSbo9+vegJ1bQf2mKAg3iJD4Kn2c54qoIZCQElJHKL2wJO4ek0IAiwen/oeu7oAuDMFf3LICuCwCWNM3DtVsc4c9q3PesiZYPvRQFSiqeeqJuA6RMAppLqnmvtTDyUsdfnRoh2PqESgyErSc0Xtx3TCUVlorXEfVEPQE6KsRXSXY6is+wgum9us2RvMjKN9U8LeMOAV3BCVKDo9+4JzNe4+0znicMA2tUAmyLOLIHqJTZs4fn8Rx+pq4JabGK75fQtKP7gC3neuBC8di/Bv/4v47EtJ6CL6BcYY/nAhwyknAy++BPzzXxzK8CkIn/oUWLAZnv+eZxsfTxDE0Eb/YsuElPGEBUkClq9Qf9dfWhMDPbNnV+roz/rCnixKWAe3sCnKuGTxujFvmknwMidOz3WAW9+gfknu0sbS6bxWXHKXpc5wmCMas4o0OuaEvZwJpjxVmSegSQ1LiucQapXY1oxV7Eo9Jm+sAQ1N3NbzQh9ACTyeNGgGXHJn6gYAwHkikbCe+0YP++QApBgELiPsHJlm80TbNDQmvBkEQbB89VcSGyTXYG9XBswhZFz3yOKqKOCNN6niJHNoCcNN4UhcsRWKvVo+9a72xGgs6hgGMGbx2NFVAsF0Dcbi3AgdFkwip6JdX3V1HIu/TcxSBqjnro7Fs8sIRxSMUE3zsar9YsrpZBPG6I3VmcrmhpHsXBcBtLNSltXfBIGhtMIJl1MV4gQBqAyuMNWgbccEQ3zUtkxvB0sI7rJJ6LG7LpPJdGzpJmFobAI6O61udGruLcX2WnU61fZQ+8c67Ba4jLLQWuNvWVY9uyyWsySxiysoLwdGjgSSPUEZN7lqmq5b4/6ddEt1u4ExY6zL9D5sts57YjJHrWuPCdbllmvJdL2aw+GBJCEjTfs7RA4oEiIRjpYWdYOodkn5ioBdH72Hpu9WWLyiLHWawvgAYNt2a347/V7jdLJEWB8AVzg16iGdZ6Oas4tbjkkSU6N1zB8HRgwHKirsn5mAKkwlt4miiV2CoE4OkuztbBa7dC9lZ1I/K9Fw4nkEbcYDLWeXHSXRrRYR0BNvUnPyAVDMHRiPpL3rloXXw6NtY0afIRRQRSvGOQLuPQ1RPhtijvOYmCGxiyB6gY8+4bj4MjVe//FHgBPHvomiF06EuH0Boj++AeFfvQylap9Cm0nsZjDGcOUVDMcfCzz1DMdr/+FQRh+I8ClPQuisg/e/5wPhjkKbSRBEf6K9nQpC+sGeKAKS5SOy1WMH4BC1wY3Zs0uSOIJBnnEQaZ19LTmESbONxyELidkkJaEoUSBHTSP56685GXbINdrYY3sHEAyqbeHO8i3K/J6/uQbYtQuWvE9mIxNeJiIMiSYl7ip1H2ZvupSZtOytSl8ZAHe8BcOCy1OWl0Y2QVQitl/JdRsYl9LOIpksRpq/5AtJx8k5IGgzbYVcSaNrw/rERnGzTUz19DEV1H6mukBkEwaTMeeMkxUAkjoroyVHkqB6CSV7dtkxYri2Nh4ziU9qZZJsypGleXWYBclt29SfigyILHENMXBwCGBQEO4KYulS+7gwxTTo1XOaK0w0+inFk8Q00rVLUO9QVDeRTN6fyRh5zbQE2yHXWDW0UFY9dUSRAYJTFdqZmNifafICzgTjnqLWpybmsjODMd2DTRO7zJ5dpuNLdz+yO1/0nEjmmRmTRYEdO9V7HZAQLpidayFUsQvgljBGHbfUYrkfyop6P2VCYp8sWeyCArc7IWiYxQ/GZdMxaeedBONaSRa7nE6gOEmfKQtvwK6P3kFb2m+hCoq8qXWZrw+7PI52pAqSmgeaoCbu7+hUBV1F4Yaw5TTdo2WbS0HPqZfcGZbcXNoJsdeeAnym47eb/MRW7EqaHMPI9WUjq5ifcQ4HQ2mJutQOUYmkXm+a2KWGhssZn616ewhJza/Iqsdf0D0ezSU/gG60fl65481wJeXDsoZpm2ZcluJgXEJRdCfEHdbJccwUR7cb9xCLLSnXiBYubro27BL+63QnjJHELoLoAYqi5ueaeyfH/vsBz/2tAwdu+BM8H94EecT+CJ39FuKzfpd65yGIfkIQGK6/luGInwAP/53j7Xc4lHEHIXLyoxDatsD7+oVANDU8gCCIoYnxsimk95ZwOqzhHfrbp/Eo4xxMFwpMX/G3bFX/hdKkJAKsX9jBFduwPoFLhtjl85nCf2A/QE2mvDx1mcORGGIEXeMBJAaMevhHUVHm/DZ2+07JWcbV8KSEl4lg9XTJGMbD0WT6GK56tliJRjnqGxI22oaOQRsUcHXmK3dSYm+jDLfJ2g5zKE7csDI1LDQ12bYu6qU0CYcxsUDaL/gmzy7zgEbhSMrnAsTjHAG/tWW4oiCS4bwzoydKZ0nJwZkchSybwo30YZIgJCVSt0fUzicuxVNFB9nkSWUWu7Ry5sElg0m04Lp3oIzRXV/A12zNI6djnq3R2NQUxpji2WUTxmgW+XRxLFXkdCb9nVA9jLxmghNh5yj4PZMAJIQhQWRG4ymaZ5f2l8nyhCefGsareWnZXHu6qKf/n7BVC33N4txlJ+QJguZ9w2F4ZNoNsNfpEbmMQVBiKI5uswlB4xBFQEBSgnqtoEOxnrB6gnotKtlynIk6FUMAMwtlnAkQICUmxGSJ8GP9/pMcgmnXPAKPIRAAOtrtb7TM8AhK8iQ1ial2eRyNdeb7n433rlFISVwD0Wgip6D59mwXZq9uoxihvMZyS7ir2khuD9PESG25RWRR/6izTXGr3ef089ZW7NIFXKsiJ4rpPbvMM9jqtLapM4AKourZBQAtxQfbbq97dom6h6bWvjweh6IAcbEUCnMaObsSz4hvMcK/0FKX8RGEW88zZ2cNxnZ8iGGhVWBKds/jZMz9ICgRw3uYM9FW0E6u3zY9QRZI7CKIbhKJcNx6G8e/Xlbzcz1y2dcY/dbPIW79EtHDr0fkF8+Bl48vtJkEAVFk+PNNDIfOBu57gOOd9zjkCbMROelhCM3r4X3jYiCWObcJQRBDBK57Cwi2YSCAGo4ViSLFpUEf9Ik8AqeixX+Y3l71AXu7vbaSUp5Bsf2KK/A4ZKaKXcOrEp4C2cIYdZJnrAJ0TwTT4BAMTlH9W09krXthpMM2mbGNeMN4YjZGzkRTlQzbdwCNTYnwF7MHG5AseqR6K9XWWv/WJwKYtBfH8KrEcrWvlIzhIenELt12xiVjQJd67EliF1LDGHUUBeBawul09pgP0yJ2KYJlECkqEaxe5seGjQoURU1YzTlHfZ06E1kyAfeElGUjRiRs1pFloLUxinjcFCrDNMGEidb8bYGFKIkkcs+Y7daT1Cf3W9zk2aW3uyimnm2qZ4qCYFANldUFIPPECe64TYyZaRSoC9VqGKMTo0cBE8aYwsw0AS1bgnogu0efok2+AAAiS8xIoA7omeHZxaBo7aruV2ZuY38MaoMxcHDGjDIClzTRxioSh50j4fdMtCTiB8yePsySkyj5GHSx0+4+xZjVg1VfllbAYYJxHiWLZwwSBEHtZ0VJiCF68v3kfFOypAmblhBk6/Uk6LMxJntPwmERx7k+G6PJpmRvrHSqraIAsbi96M8go7JzAZhiVR2YJYwx/Qd+S34rCLbtyrkCKJLxqAiaHIScLWssdgJWkYkrejinknQfMf0u6x6oVkHUKjCmfw4Y97mkfrcciy6UJuU4VGdATK/CpvW2FtT7hsLElGeGjixbz19Ze26629ebxFYtTJbLcMS7UBlcZqljn73VSRj0sFEOQJQTomy+4lYy5vNxTOen2j4EKEjcDDPtwnaCiCyQ2EUQ3aC1lePyP3F89TVw1aUR3HjAX1H8v4vAvcMQ/s2/Ef/eOamfNgmigDidDHfcxvCDQ4B77uN44y0OeeLhiJzwAIT6lfC8dSkQTzPyJQhiyKBw7WU4g8fx8OHWv43wNO2xVhlcgSItp49dPqi4vYYCAEmJtLnx8qsoHK2mGeT1GQwFIXkQkb5uo16bsYR1oKeOkh0OtbJWLYJDFO2Px9i1nWdX0qu06lOifqGXZa4NMLixFgCamlTvJCBpYGizg+S2tMux5Y43w9u6wjpI1jw/0olLjFlnwzQfhz6O9UitxoA8+Ys6T2nkRJ6c5DBG8ESIkMKcaCg9HBGn9SRTBHsvD4Vb47jKwuvhrf0K4Ar8fjUfT3MLEAxZ204/f3LNBcM5EA2p7VFVqS/VAqIE0eLZ5RKiKAtvTKlDYNrEDyaxSx/YSpJJ7NIEC4HZn1NyXF2/ZSsMAcjIh8WA4YHFcGneerpnpDmXjh4GypkDYAwurwPjx0iYNDFxrHB6wUWnUaf5p0NJiDDJg+9kbxyZJUafhtilHbdukaQlSRdFlng3ZgJilWp2fdWDKnGNcCZqnlWK4dllbqf2omno9EyBnn4o4T2n28jUrdJoCvp9zJyvSccqdiWWpQ3NM12/yW2lC25MC9XSj908o6zCnIaAqObJEqwCV0rOLtnk0ZZAYaIW5ma911g8u5JDDzN4vqW7D4pKFK54qkpo8TJLuid2efYxrkcz6scLh2FLQkQCoMjGs8EcUi92bDN+N3ICCqbZFbl+D+YZxC7FELTN9yqrU29C5E8WWIxZZ/XZcm29DhXobnZFRYncaKrYlX58mE7sYkyd8ZIzR9rtJSnh1aXWpV0d0U4jRDaRs4vDHdmVklfL42FwOBLPGT3hf1wsUf9Ovh/wzMejM1qbPKax0fboAO2aVys11Z9UcngVMHGvrLuzQKNxgsiTmi3/n70zj5OjqPv/u7rnntl7k02yue+bJCBnuEEFPDg9EFBAQYWfqAheeIAKCOrjgScKKnI8iOKjAvp4oIKCeIAI4gPInQTIsdljdnaO7vr90dM93TM9s7vJJptNvu/XK9mZnuqq6q7q6q5Pf7/f0pzzbs0zz8CXPvosb8m9mehDN1HY+yxyp/4Qe9LC8a6iIIQSjys+c5ni4LXw+f/S/PcPNdaCo8kf81nMF/5C4qf/D0qjXOZEEIQJhXYtu2oUiQq1Fh713XnCVpXyVrQLK79qXXf34XnjpspkFUD7RApXEBqpZVdY2dWWXQDRSPB7+EprFULdZmqsGDSphJNwcNCZJCuCE1CAf/9f7UQhbILZ0xN0/SyGiF2pwvqyuFXBdTVqJPZEzXBrDLeJMvlnaM09BgzvPuKtnkdFzyhEWulLLKBkQSlfsewqmRlKRjIw2bbS3dgtztpv/vNsayN0MqXQXn/p66u4nAH0x+d4loF1zVdCGOxzzOricTfqj1G2HjJIFyomdfX6tiuUhE1YLTvoxqh8uo8frcEqBl0INaZnDebF13Jj5rmimm9Vw1I51JUbOwvDxNjytLevK3YNzHhlIE+XqFU/tEGNZZdP7PKsjtz+XrYes8sNapTFrohZPrfeiaz0HWdC7sbsKjkHUhOzywBlUGqeXRainAOo9FEFVCx7aladbDD7VYzOsstvpVMrdjmWXSgbdGUUeGlD5WKqttLRqBrR2t+FnRUqa9tMKxNDlSrHWk7gWToRYtnVAPd66q4JYF8awSUVTOAXxqsNed3+5LxoKAu3tg26cix+gd9/j3BFef8Y57SBo0DVFbtKVsWq0LX+U2ZwkQetmd5zR+i9zK2naefIFuO+YP5VltBlsat7GnS0O785/WH08ku68Bzx/IvllUHDG8CyKtalgBeIX9vOudLKrPRjbWPaQ1hmiq3JJYF8TLP2pYr38sAvRJVvyBsz+5KLNl7dvdEhOzG7gq7qLtUve5SCdHrkYzqI2CUIo+K+P2vedb5G23DjR37N2sfegJHdyNBJ36Zw6EUQkZUWhV2bWMyx8DricPjKVzU33KgpLT6O/Cs/Q+TZe0nc8X4vtoogCLsf2g1M28DVJGQvIPjW2MUv0PgnYHWFI/9qjL4YJY3cpTyrCj0yN4qwR+FIpFK2+3a7evJnRioTQ6idUIYJPq6li11+4949DebNcfLIZoOT5GprKK1rxQMIrpCmdfUEo7YOnjuif1JsQNvgI9hG/eeSmOEXuyr1DBP1aql2Y7S9c+dODktGisGYI2C9tN5dvTMaur+Ngd0x3/kcELsc15ua0rXlua0VCiEiqN80pt5PVWT7izVWTgB2JBjFu77YpTyxy4th7cWbq+xk6CLRqMINfVQdt67Wis7wYv94YpddFg/d66dcYCIB07uddK4IYGemBvb1BJpqEai82ZlQ16Oq3XxCg4m7GqMOnECrHLPLjDgqTWsbgUD0pp33jgdleC6WzthSttPxa+ReRWOgKoK735rOb9lT417oHX7ogOZb8c0ndtWx7Aq40FW7heoSqmxl5rpnAvT3VRq4EGkN5heiCgRE7Kp+UNkvQkRZtGz6U2Avd9XAjnbIZOrnW4tzwqNVWnk91+dGOSv/irQBtasicgSuKdtRZ9w29wsv/mvD8sRgI2T12fpil1WqCKFeX1CR0FWEbbt2BUB3pdKIla0snkJQ+HTcumvbajg3xnrEilt8/TacfN4x2HbTuQK7G/fLeaFheDG7TCtHyUjWWN1FzNoxyFZOGl11L9JAIdrB5jpxxFwaxs9TynFjDOHll2uSjhoRuwRhhPzods0HP6yZPb3ID8++mrl/vQC7Yz6Dp/0Ya9aB4109QRgxkYji4x9VvOqV8M1rNV/7hk1x6esZOvITRP7zG+J3XQx2+IpPgiBMbCqWXSN5BFQYdp6O7ENA+CTfH4A38FBfNWGslO8XuyoxqWo933yrM7nBluu4jNTUKeSBOCBsObNXMkP/YXI55pWbpp54B87b8YH4rMDkJzAJVjClSxONaCJmWYSpEhU1Bn2J+ZTMdCCOj7N7uBvgY485Qb7tOkvjmfZQTX0VjsVXdVwgPxGzcqz+42i04pf/SPw4bnbOtngMmjLQlFF0dJYtGnTBEUZcixN30uXlVjFXsS1nYh6JlGN2hTSoqfMUyvNuw6gVGtwJZdi+9eaarsWVl4dy6mhHgkHgGl06Vqy53K71C1Pa9lyjNCMRF5Un6FRcDQeZ0ns3autzzoZy35g3F9rayuexLERZLTMD+3oWH9sQf6e6P/vFLkMFZ8huGzz7nGMpYxiORVZHu2LBfINJk53fu/rvZWrf3d4+RSNTEa7Kpk0Bwde9ZiJRxxKrRoBRuKvN5fM6sGIh+AWOEGFJVa4/93psbNnlbC8ZyZrrJqqdYFMGGq2Vdz6c2FrO54HYTGIxmDnDzc+suY4DQo4XID6IjUmitBGjNBg4SNuGlNHLtGmqRrRppLlUC9cuhi4OKzi411zFeiooCFpE2PCiZtPmSiWC5WiUXYk/VqwjdlX6iBk4tqjVj6GLgTwt3wIBucGK2OW2dbUFrD9WYfU5iJd6SBbWEbEHsXxiV8B8SevKvcR3woZzY6yHVpGAlObSm6zvTWRXW9hRbdk1iGUkPStYb2zxuTG6jeCKegHReYQvn4I1rsWJLei73zdIvS1i1ygMGgVhz0Rrzbe+o7nhB/DKg3v59PL3EfvXnymsPp3CIR9w3iwJwgQjElF89EPQ1KS56Rbo6dF88KI3oqw88d9dCWaM/Kuv2CZza0EQdl1csavRjN3/QJnJP0vEdNxHwsJ8mXaeZGE9uejUwH41EysX34xQad+qczWWCpVJUED4qlvr8Pq7RHxuMk5xTtyWmNVXSVPl6ljtklayoGRmWLpA8cyzmv5+xzKruxueX1/eR2nQFii8OCm+mpXzd95k2zbogHIWdAP009tXX4SKWb3BOD++z6auH4uxWthxcSeULc3hQd+rmTIFIm2ap56ouLvOnq2wWwyeGlTkcKxc/FYQNeVTsXyxbIjGnImopVWNwAKOeNbfX8knMOkKdICRz44Mu+iZAThZhJh51X4N0NexH4OFHpK554GXfOUHd4rFnAml1v7A6g5TpsCLL1a+a2XWCDrN5QD5xoZ/omIRbLultm5+Cygq/crtRlUebyMK/lw9Ufe3jd+NUQciSwVjcgG0thnBvl+pNJaZxo7EgTzu6ozBSbV7XPVcdBXu4heP164j4BM4YjVu2GFDo1HHsmvTJptSvCzEqyh21eqKMe0EBVNGWWD0VpnU5KJT2JLeC60idHdCJllzdACUUl1Ei5VAR/GSE9iwug+qsttmtchq2dCcf4qw66BxzK5a1/WZM+BfLw8vdrkXkTt+KrOyQ27yPjz+nEVq84Pgs3IKDoMa7JI33gUsu0Lfw6pAPdvdlzM+SzbLci0kLfJDNs1VBqau2BWPOxZS/pc4bt6pJHR344h0PU4Zg3Usu/BZdhmRCOCK1WqEL5qqKOXBgFx0iuMOXCYf6QgmaxQvU5neSwZlW5h2npJKYle50pqRskWxrSuCo+mcTLtK7BopjfuMcgQvV7zcBhG+ETKLEYQGWJbm6i84QtdZr3uWK+acSvTFBxk65moKh39EhC5hQmMYigvOV5zzdsVdv4SPXKLpX3oG+bXvJ/rYT4n/6hPURKMUBGFCo23XsCn4CNibWMhLzQc7XwIPpnZF5KrzENqRfRCq4k3VF7uKDL64nuk9d2DaQ97Dc41bTohl14iUrhCU55oUnHC7q7K5T9fVrpfVcxLbAlU+sIp1iOlNnl33KXclsJrg3uXfnRglbmG1ll1h84L162FTyCJ81cfpyywQ9Dk0faBulXq49a4WN3PRKXXzam3SJON2VT2UFyQZgsKIVkag/If/aTA4VJ6gW+UYPobjxmjrMDdG25sEFwrB2C5eO1Dbz51aVX33LHnsKqs6Z7W2YioYuKjRZMyIRMhFurzJcz0rhWikshJctcvQpE4VdGVFBSbfAIm4+xuo7CYvFp4qZxroCmVfNE96qrbsKv+QSCi6uuofG1AToN4vklbELq9kAFoH/+WIdUpVLO2UARh0TQ6KbG4fsSPN5eNzRLNAN3bLjDdhGNSIqChnNcZ67eTuvjW1lL7Egpoqe9e97/yEWXY9/4LFv5/wi13O9s3pVVhGgrjdT+Tfd5AaehatVKAvaGV459IR2CrCasBAyAhaesVLW2qEbXAWm/CLvv1ZxfMvaLZu9Vmq1QhkjUQIu+Z3wyhb0Q0jdrmLKVSsp1yR34mNlStf535BNDDWOoHeho3Z5SVXZng8SZ+Ylh8CMMvXklVj2eW1hfeSoBDIxyWRUIH7RNAF0G97ZXvnoVqsNyK1le3urq1/W6tvHztPKdHJ5szelb5SVSY45yfMTdexTjR9infREWFVBKvK1d29d5esSn8qlcUu/yVVnLKGrZlVtRUPo1GfUapqXGlg2eU7dT2p5SMqWsQuQahDoaD55Kc0P/0ZXHLqX/h/iTej8n3kTvkupSWvGe/qCcKYoJTijNMUH/yA4v4H4IL3a16a/3YK+59H9JHbiN19+di/ZhEEYdzQ4AXdDmw3ohRNZ4IZeJTWtvdwX8+yKB9pozq4elh8LwBll/i/Pz/nfS/Whpsqb/C5vpSFL1uPTH+vmQga4ZO6uDdPKVsxmEFxqNqd0LJxXLF8ZWhlBsQdx3hLYyg3gLJ/AlTx2fTEkMBEqPFYG7YSoy9zqqdAw1lgBCbVvnpYljNR8guWtoqyJb1XvaJBWzV9AKVqRKh6FdZA34Dhle+2ma0NdIjY1ZjRWXYlEtDWFkzuBj5XgBVroTexkETZAKKh2GUoLMsXJyqk+IgJmabKD2Gx4IJWd7UB6pUBRbMJO5IpCwP1Vx9UykAbEa+9XbHL1x09KiJa+DmvdvcKD1Dv5Fo5Bbb3yb8aI0oxebIKxJLygs27ba4cq4+wsUcnmhmcfgh9ifkhNdV128mL5aVi9FW5gSm/2BWI2dXYjdE24l4dLSNNyUgRs3qc8nAC1AfGghGK0EoZte0a0s6GPRQQuzZuUmzdWj7Oev21kWWXtgPxn7xyfCLQQHw2W1PLauuiyzHf3NU+A0seGr5xoBKzK2jZZaOGtoa2n3uO/YJTdcwurx4GvNh8KABDedda2BHeAportf3asIuBfJxygvtA7QIDfrwFO8ygQGyE3BybMrXnuqur4t5qWjm0WXY39J3PMDE9cD8q094GqbRZOQDbxjScc6JVNNC/3er5reiKhhO30IsRqEyszFRy8aBKZ5rOsdTUCVgQdpm6x+Cz3nTPs2XE2ZJaWZNPZb+R3RdE7BKEEAYHNR/8iObu38FXzrydkwbfgZ2eRO7UW7GnrR7v6gnCmPPa1ygu/5TiP0/BOe/SPNb1bgqveDuxh24k9oerRfAShN0E17KrOiBsuAWKE3fEtfCpji3kLkdeMtI4MUoqhLk8AihriESxYqI05HrZVc1WNAaFSKtT1/Kb50IB/pk7oGGcEgiZoJnV2TsJot483RnfYv3PBtzFqo05bCdeNnbrTK8Mm4gvb1W2pvK7MSovCH1lglux7PJbjKgqsWvU8Umqj3s4sav8N5UMil22DRgVy6tEHNa3HBU6ucjGpmNHk6C1F4/NP9kqWb5KBATMqkm8MkCZjuuMBiszpWwdp7DqBC9ueGTuYgQhJzHMwsUNxF2x8qp8UeX6uXHfGgmukagjdnmCUsh1tWSJormpfH51uIhZPanziyNeLDEVYcPLJmgL26oSu6oscjBjXn9Yt17x4EO6ochmG5XJf9xnvOIXBV5qOghLVX50Y3Y5wchV4ER3dZUzVxURq+LWFzxW/98Z0xXKqL4yfIcYawq1urKMZH3LrvDN3m9erKlAzK7wyD+uoFEy0r7VMx0RJELBy9MmaNmlKPkz8cqpyd+MhG+v7sNaB2LXWb7rzl21sGafsvYzFO0MOS4dOn74RaCimaFo1CobEdedM+qIM35LpHwx+OYhYsKkTuhor+xvWllUvr/uyxWlqu8vqq7YZRlJ8pE28nnKsaEUCqvGotjtb54VnK4Vu/zlu9iqnmWX9lxkjSqTYaOOG6O7OZmEhQsgGi0fl7Yw7CHsaKqcrr6YP3tW5XPA6tIEVT5pCo2x9dmywOjs77/e3XHOtmEw1s0LbcfhLmhTaRMjKOqX6eyEWbOoQalGbtKVYyhZ2uu7uehUBmPdbE6vDu2jIw2zImKXIFTR26u54ELNg3+3+e+z/ouDN1+CNXN/cm+6Cd0SYmcqCLsJaw9SfO0rzoP6O8+H35nvo7D6dGJ/u57Yn7483tUTBGEMsHU9SwXfg7pfnyjHtoJay66i2UTRzKDQtAwFg+NETCd2SnVMnOoJaM4TuyoP29OmOQ/qGzP7Uph1KHmzzfutEGnHUklGg2EEJywa5w10xHTFjPJKdlseqdnPj22DYSrsKSsqLmGqOqC0E7XXMMoWO0rxUtNaSmbaN8FVnrVaIBaQDlqSjGSSWym32iKE6nlQ7T6+yVU8HhS7lG822dpWTuwX5sp1zUfaseOtjluMG3PM03kUad9ChgE5NKiFeC5NrptSNOL8btkGtq4fYjger91WY0E2AmpSudeIbzLsTQIbWnY57T6Ud/tPI/OZsjASIjqFibMuRcNx39MqwuYeE7tYwraD4kSheU5lbwWYUQxDMb0bUmnFps2Vay+sT/nFHf/iDraKeue8aDYzFJ1MNjadgfjMGjdGb7VFEyZPKndIn0lNaNB3dzEK97pQCqVUXeHDOU9Bdy2ArcnF5M2WBvtAaNuokJhdRkUM3ppcEvgtYjkB4YOulApLxSsWbar8nz9QecjCHt3TnHyCbp2144Cits02Nh0QFGF87sNhrn9uPnZmSpVg4+WAUWURq1QwQL0mvA2N8mqeurywg5PEuWgGhyqrcDoum4opU1SgnOTQC04NVHhMNrPm5YWmd8oRbMrsE6yH7/orldyKKBLFTRWxq5w2ajkBAHOpWc7YWxW7MZCvr66WirG+5WjWtxwdPBdae26M1ZZdplnb75SvexgGxOPK+xy1+kGDHW2urU9VR2hqqpzLgJWyUdlRKV2uR+UM+Bfn8ARvG6pl5kqWzlwhTIVWIQOKYzHpbM/GZwTzLPeHYvtifv/CATz5lCpvdwTxXGwaRcN5sRY4xSMc20XsEgQfmzdrzr9As+6ZQe444/0s2vRtCqvewtDxX4N403hXTxB2OAsXKK79hmLWTPjQJXDDxg9RWPlGYn/+BtH7vz7e1RMEYXupEru65zjCkV1luTCpk/IDu+1NbuN138za3oqALoYJqcK6mpRDVfHSBwbgP09p+nOV8jvaywFrVRTiIf4dozR5MsvuOH7Hqqj/cF0FLp4O7FddjGX53Bhd15YqN0avDDOGbYOlElhmmly0y5t8ODG7nArYPosqvxugZSZDDzNSX/cJTATsGfuC4SQumC3korXBmHzVrpm0VgdRro5n5s9FG3GwClTHbQOYNVMxb25NidROQVQgWLtumYphwGB0KsUGYlcmxGWmnEP5/9qpTmj3Ce1SlY1aqYDFQz1M01k9zl3Fre6EzIx5bmdhYkS1G6PL5tRqimaTt3CDVibaLgfX8e2Tb1sazKvcF9raFJ1d8eBx+EWSpCMs2741zILurBHmzvVNuJVBT3ovLCPllFNeldMfj6myv8+yi3DfYs8tULsTc0dkrWtNV9V3vbKUSS7SFX69eGJNyE8hYpcq1+uFtuMYSMwN/OaW7Vq5uplYRiIQK8ux9POLxbXmfG1tiv0PiBDd65jKRmNka8kVIq3YkRSFQtk6xlaeS2qlWj5rLxVhsHkRpckrwl3hynEHA/p0WezybSFMSvDGwIhzwwiIsMXwlyw65TPtKu++JbM3uehkb7MXZ6vq5YXCRsWSWEbwJYhTf+WzFqysalpt2dWfmIutYgw2LSIX66rNh8ptIujGGMc2YmXrY/951JWXG1Vilwrz8VehlwOG4SyiogE72lTe5huXGrox6uA2o1p0q+yfjc0MlAnOOap+OeUK/Vopb4GN8LLrMxQJWmm51rcv6XkUIu1459GXmXeN++bi4sYoCKNk40bN+e/VlLa+zB0nvo3JPb8hf/glFI64ZMQ3G0HYHejsVFzzJcVhh8JXvgaX/u0ShhYfT/xPXyb61+vGu3qCIGwHdlWAeqtpBi82H0IuOtVLoxRMmaLKIpcjds2fB5Mnh+WoylY+dsCSol7MrjAGB6F/oEpNCYgvwSfo4Zyqa9wYvXl1ZU93/rFwgS/3aHCyFCZ2Jdwg7F6iipuRVsrzGRrMzGd9y1FYZnnJ9oArXsW1Mbi9YtlVMpKhMkndwP8Ez5IylOfrY6sIQ9HaxvN7lFEVRNlvfaCA/faF1hBDGa2UI9xYIZZdOKJZMummDboxhtXetXIy081smXYcBZXB1k4A6pbmqrJRIxCuwmdfk6q8YsL0VH/+GtM7967AVhMYnWD7TOmqXz6xFArH4mTjxpBD8PpUxRKmpQWGyGCrqOfGqDHBtmosuwKyooHX2DqWIj9lX4Aa6xan3LL4EBBhK2hlEDHdscEvnphlMSQYt8sr3/vqm8h6HTCYP1TELkcfq2/Z5QpRLhETDj8UZs108oiGGAdVzlNt21RbM4FrEBTejluTS9mcXk0xErw4bCNeOVTKAre/HB2+bJ5pBi0oMZy+X4i0eivi+a2AAvVUToy8xx8Hy1ahx+7LmHzzfFQk/A1GR/avmKpUc4b8FkDVQfddXm46kK3JJd614BemiqWKCAoVsUXHMgzGppW3OZR0JBgTrryb+/KiUierLNyEqJTgS1xrAeimGYpOYn3r0eU4Y8FjamTZ5R83g26qdsWyqjyHtNtmY01bhRkSoF756uQ/NsNwLOW0Bl2+PwUD3FcSd9UM8b7zrCsvMLyz4RsXelNLebnl0ED5YWJXJa6WESp2uUybBnPn+GriF37rjP1P/if4PdRC1y8cjvCll4hdggC8+KLmvAs0nYXHuO3oN5HJP8PQ8V+nuPot4101QRgXEgnFJz+mOOttijt/YXDGjy6jd+ZxxP9wNdEHbxzv6gmCsI24Aerdh1QjYlIym+o8ODqWXQpIJhV65r4hKYxyOgvL9sUIGcETpr9Ii+BLJf8Dce2Eqv5DbtHMBPJNxB03PSf2iftqvhLjxXEXcQUsi3ykjYG4E3TEndRsaD7M25aOOy46FcuuitiltMboc6zZDFM5E173GKr8gSwj4bjphLgGghNrJuww61l21U6AFdp9UaeMwDkcjE0lm5hVmQsSYtlV5WqTSinm+wIMtzTrsouiQrsWHFVB1N3cVfXM0k3kmyg5E+eKlVOsHK8mO+iIOtO7oblK7LKN+LCOLO55b6kS6qZMqax46C3aEKi1s9EvOikFixY57mYAJbNW7PJbwEWjIRm7VAW1q7bCMQzYmlrGi02HelaAhgJbm9hGFEM5gpStImjLoliw68bJc+sPYLfOgpgTSylUQCrXt9rS08vHdb+tOmkas2w9WevbVHHLq7LUcGN2BU6R68ZYMeMxTVU3vliNq6iCSESV41c5gctTKTzBFap09JA8w/pCPQs9W8XIxapW6yy7MfpFPjtg1RYUu4KXS1UFwkR/BVbrHKpxd7Msx/3XMJ0XFP44Tl4dVR2h2KufRbK4IbiPpkqyUzXjBkAh0sZAYm7ALc/tEwXLrAhcVKyulFJsSa8GnH65cZOmYEUwlK88n9hVbdnljIvBA6oedyKxygUyY3ogy4r1l658dvtt9Ysbt+ye1Apv2/61t0bPjdEVxHS8Gd3cjRHixujUN/jX/eyu4OsGpg+6CTqfVyx3Fnvwszm9prLwhj9f33n0t6bbLu7xbd5SG6fTtVhFGeWFTKqOofy3o12RTjtiqP+erPyqXs1e5XqE3C/8/cSa7FitWqr+4gB+ROwS9njWrXeErr3iv+XbB55ONG6Se9NNWHMOGe+qCcK4YhiO2PX5qxQvbzJ5zfWXs6H1lcTv/jSRh28d7+oJgrANuAHqXeEnLFiu+zDtvIP3BcaOpUOeU92poI2tTKIdU+joqBOMdvZ+ga9+KxiLqtWwTMWM7npagfvQH9zquHYFj2fWLOiarAJujNUWQe52Q5fK8WvK+bvuHCrKUHQSAIlM0lcDKEUrfnT+lcqq3QD9Flwax23kxebDqibRTj3icedBvrW19sjrujGGWXsYblyw4A+WSjiBxf1zimrLLqN2IuN3/Wxq8r2lN53G1pqA25GTd+2Exflcm7/WPsuuqPL6h1Ym6Qw14l+pjiDoHFJwOcSZMxSpZLBK7vEUi+Hzr6olFwBXhHO2W0aSTem9A7sFVpXTtcfYl5iH1b23v2oARKqscJRy3H0sM+UJCk7sKANbRR0LJGznu2XR36dJpYL7ezX3WXBARcStduVyPlfcDCd1OhZwgQmxClfUtDKdblR2Y0Q5k11wLdyqUBUfueAVUBbbcINpKyJRg2LZI6ylpcp1VQWFWvfYjLLApF237VrNJHRwCbPsahh7rY5poWt95yXRQSsov9tYI0tEVV5F03F9NispfB1tfcuRNTWwbEfw65qsaGoKr/twQnHc6gu609ZY8Sg8cTKkX4SJN6VygPr2djfP2kRaw4svOoKd35An4Mbo77OeZZezMZNxxkk37pU77kSizvdUqvJbpVjnQzaLJy7Pm4snsjc3O5/99chHKq6XsViwHyqtMSiVRd3gmQ5bjdGvcdZqWc6CD+7moBtjLUqVxxkzxeTJTt1aW2vzr4535x6Ye3y5XG3eWjvnVlOOr1hP7fK+arakVgXqVituhctRgXu5r+66fQ4vtB5bY01ZDxG7hD2a557XnP8em+M7r+fyFe+ByQvJvfm/sTsbr/QkCHsS+75Ccd21ilmzI7z2hs/yhDqU+K8/SeRfPxnvqgmCMEq8APWeaVejle40aNv3RtismdtplOeyoTGxuvema/XK0DmgjgYtYQJB46ssSQ49JMLixao8wap6OEYxf17IpFQ5k+7AxNZ7sHdEuertTobOrN8RuyLepMmfZijaxYvNhxFpn1LOoLyrGf52WVWd10BMn3K8ItuIsXSxf/Lu1CMWU+x3YIzWruYa8c4VaDxRoE4QZ8fsxQtgFBAt3f0CMYVqYnYF1ECn7EBRFSs5zIpZUtFspjR5WTmJJth2tZ8LkVYG4jOd+GwaimW9MBY3vP4RiZlETFUzObeMYFyz6mNIp8MnTC6uIFsq1Zv4V8oMm5DZRGomXJ5AF1ASKxTMVnTTlNDSAiX7xDZPKC2Li7osdqE1GpN1z1sUS3hiXnWxQWuKitAbai1VPulamUyZ4gQP98fLqo6L5KKV6Qiu2vIsUdraHIsTV1gIzIzr+OK559l/DUai5fhAKKLz9wlYzzmpKm3jrrDqCkRl3S28ziGtbiiwp60KbmygCoVbfKmawPL+GGYlI8XWZFVMtarC+jLL6EssQEcTNdZHzj6Vz7bhjEGBmH12+AqFVdWsi2FAxB6s4y5Zrk/AYtRplLDYfo4w61p2OWJxc3k49I3INfvZmAGrKr8bY41ll1npO00ZWLJY1dzaotFwkclffr4A/fE55CNtmKYTR00pxayZimSy9j5UlVvgV/f6DGyDcDfG8uWwKb13IJdoBDrabMcSzb2X+S3DQsYlf5uZ01ewaKEiFq1s9ItmYYJ+dfB/P7bttrFjbVltHRpuuRUU2kZsqR3YHHTBHKkLY2VPQdgDefoZzfvem+e9cz7Bu+Z9jtKiY8id/F10OmR5U0HYw5k82YnjddLJMd7ys//i4YEDiP/io0T+787xrpogCKOhPPnzrJkaBNdypmd+sUuFLEevQOuymKI8K5JotFocARUL+mpVB74OJvbPZqpfFzsTD787ScFsqYgBdeKI+K19wuIbKV0KiEeG77gBSma6EofGe3h3RJn+xLxAWdWuKvnoJC/+jP9hv7XN8FYs9LsIKQXFWQfXBEtOeFpDUOxSVImHKFTZjVGr4OpiCl12oazkVG2xE9Yt/O3pHr/GQEd87poYhK3G5dQjKHZp7QTP31p2ByqVoKfHEf/c8wpg1DFnc1a8Cx6z74iYNpWGE6+A9WFtFwtsD3OZ0cqsESNrY6oFM16+zDfp9P2UrNKQ/NZEnmWXqggMyrPsMskOWChsEnW8epwJqOFVxy03zLLLra4diEXkWIq81HwwpXLcqGqLjqAbYzD/SiK/2GVQbU3o5FO2FLJNb0skUu4rkTbsdJdnYZNM1k6e3cU0jPJY4AbUntbtj/VT+XjI2mD5SlFjlen09XqT6+G3Oy8XKoKzc+3Vm+A723PJ2fQlF1LtSuslqx2IAzWxRiB2Vf+ej7QzGHMWhoiYYBp1lnH0l+i5vVYE2XSVd29wXDKIRPyx8IKurP6FNLSqiF35SJt3gDVil7aIRH1joec+6gq3rmVXmNusQ1tbZaNlptjYdCD4g+aH4bsuyxt8P2pcy8vqPhLmxqjK7TwUnYz2vTxQSrFwAaQylRdNtSJqSNXKv+i2ig+re878t9bJk1QlFqNr0d3AxVXb7juUcsyusDTVApwvP2dMGKlQVUk30mD0YYjYJeyRPPkfzUc+0MOVy8/luKk/orD/eeSP/RxER+b/Kwh7IpGI4v+dZ3DZpxJc/OCX+fuWvYndcTHmE78a76oJgjBCbO0Gsi6vDNjQsovA8uXl2WwAR0ixvQd7dwUs01QsXqQCbneGafJS00GV7wHLrqp6lH/0u6f4KuJVB9zfdY27FtVfvWNRge0d2b/S3XMnhi46FmbuQ7+XplK+K2b0T1nLhubDQSnyC46jP+ELaAU1ll3OMQYVlCotrcZPKCwYfTJRlZdSASuRQHne4jqK4HJ2mpKZCViHhLqE6uBvRphCCOhYGmvKCvpTi8jGZwTFrkDGYdOOyjHnhpyYXU58LeVNdE0zfJJq1cTMCibw3CyryE9aUXM87qeB5LyQnKAYaWGwY02w5j7LFldoqnVjrOTUlIH2ttramiZMnlRTTe+APatHV8zFsRpS2i67Dzox8/ziXX03xooIUmlff3u5llU+sUsDGBTNqqBpPtw6Ku0G8lEhorPfedWnugXEtrLYVXZvM7A9kcIZa2DqVMXRRypiMVVjlehaDKnyMdu2k30qbXiuc35rnuprLGwMcWo+crFLl9PXiBLlE2IZDeYaqroPBxdJcJOoUJmhgq3Dxa4Vy5UXyFxVFTcY66Y3ucQRuyJgKivUjdE/7rrupoOxbu+3fV8Baw+EXHouJSNFKdnlE7GMQIB577yWK7sltdJ38CamWekv7j6m4SS3VYyB+Ew2pfdxLF6r7iHVMbvMSH3h2zCcRVj2WhHyI6BjzlhjN3dXtrlji6q49nm7lu+JwYHSfcEUfs/1XPyqq1m2kPVb4vr2Cs0Lrb3xwOpajt2xIFQsW7zY8GIX+jtMPaHUtQw3IgZPP1NrHepetYGq+OoYi4VdS+F9OWiVqxqmbYSIXcIex/89rrnqI8/wtdVvYa+2Bxk65moKB55f/y2LIAgB1h6k+Na3U3y/9FX+sXkl0Z++n9w/fz/e1RIEYQR4XoHuM2MDsUspHbDUwGdt49vo/K8tbxJeLwK0E3OoUt5ILLviccU+eysm+cSAWjdDhUKTTDoPyEEXF//ncKeZqDXgCAaKUDfGSZNqxS7iLSFii6+sENOoyuSo/tv4wPeqZKmUfxJScffyTzQDO3sxuwz8bowKm2Kkhd7px/gqFyw7LHYRQG9yIVuTS1HKjX/mtLlunUk2Nd9zqXKPmMA3FfrZpVB2YTTLgqnPC7P2+ICSSozcSMBfj1SlM82ZY7BgfqXLWkYSa9Licr46UKYVr1h6ZDIQjUa8a8Jdeczf92rmtHXq6rRrlfWHUTlHbr9xkhjkox3oZBu9yYWV4PW6VLOCpvfZMVeqqUeYG6Nbj8DKmXa1VR5UGWl6QpTSDSy77OBYUrEk8eXjWnbpym9+d858JSyeb1+nDZYtxXM1cyy7qPWkDe4dLnYN8wIA8Kwxw6fezvn2iws2imKkha3JJfT4BZ2a6tUKtp4I4t+GTX9iLhszlcjo/ku4ehysV00/bsB4Q8H06TB9Winwu2vZmfQucYU2YqxrOZq+xEKvbpGIY3lrRzO82HI4KhpjS2oVG1qOAKWIRl0hTvnG2ipht0wlbl/FYlcZlaD3W1MrKEZaAi9VaoQ0v0kYwf7mpdUwZ3ZtkHcAq3sN1tzDKS06BnvqXhWx2HefME3Fvq8InEzKSyDWDFxhboxeXZSqvX06KwME+lNPahn98TmBRVC8/E1nfFruepO3zcKeVAnPMzDpwNoTAA1ffri4qznX7VuKkB8rQuWsWY6Lac3x+VNrnyDuJsFr1FEjYpewR/HovzQ3XPYnvrn3m5ja2sfQG75HaclrxrtagjDh6OhQfPqKDP/e+xs83reI5l9cwGN33Tfe1RKECc+NN97IEUccwYoVKzjllFN4+OGH66b98Y9/zKJFiwL/VqxYUTc9ALYzEezscGaiiUR9tUChMXQxKCoFkmtH7NC289lzOwl/vHSCnoe/PdbVsad8D95NTeETt+rYIlOmwKLFRiC2SrC+ZWs251A8Zs9yxArHSqQyazJNWLgAli2tFbsaeVlCHfdQz2omuF+9FqgOjD1vblC6c6nOr5JBpPJDYCbszhp9SQlObE0TJk2CjnZoa61s708sYCAxx+fmVxtLq8bXNcykwDFTCgh8eWehS2fi6hNW652fWCo+rJVDmGWXX2hLNxkkEr7YXBhVE8Dw/OfMVixb7lhZRaN4QpN/0p1OKxbN157VVk371BHxKtucH+bOM+noKHcfpdAqytDUAymZTRU3MB3uclbtiotn2VmZuIYUHLDssqsumNLMA5i610JWr6rsZuPE7DK81RhDLLuqN7irMfqTuMKWrmzxhF0VoaXKuMwvhgUs9crjkLcgh6qUUyME+jAMAm5k3g5V6SZ1+n+sQjkijv/Uu4HYBxJzsY3q1Tvqz+DriQ4Ki97kEvLRinAbOL2qvhujLudQEdLcfSousvG4IhYJir3JlMH8eXgvHry2MmL1K0q5DyrDi/cWcOOrDlAfEnfPPWL3NBlGTbKgi3XVO4WKKFV7QtwYb5NCI9i49fT5/yln4YUpU2DmLDOYKqCcll37lYHdNhsdy6CbppaPqXL8qRRMnerWNyj8+fMq+y17x5WNz6Y3FW7RaxiK5Uuhvb1WOAXQyVbfRp8oOwLLLjcfo45g5+YZtg84C3ykUtXybr24A/57nOF9Gi0idgl7DP/4h809X7yRz68+l1jnFPKn3Yo9bfV4V0sQJixKKV57YjPqtGvZUJzF8n+ex39/4a8MDW3DqxdBELjzzju54oorOO+887j99ttZvHgxZ599Nps3b667TyaT4d577/X+3X333Q3LsMuWDpM6HJeWsKC9FTSGXQgKG/55ijch1uUA9YbznFvnSdkwq8QkXzKbessMekX7vzn5Vc0JDOUIY/XobA+P2dXU5ExgHMuuoFVHPB60/qiO2eWu9lZT35BzUC9oeaiaUzff8s+eKGEEJizBtJXVGLVRG8i+Mhkk6K5aLidiKqZNUwERoaNs3BSNeKaBIzaMD1hzqfIUxzdDd612TNP53d8/rBn7eeUMxrqZdMjR7L232dgqxiD0JAYnk0EXyeoYQoFsqw0SIqmajdXNPmmy8ibidXSl0Ob3i11Tp5lMmxoUFd1y3P66ZEEpZH9/3V0TGbuyb8itWhUHgSoXUV0lGqbaMboW0tmhOGSt0yf8boxugPoay67qiWqYu2C5npbtTvw13dMdQWCfV0Robq6dwIctHmAazvbKMVYLYdAZInAYdfpMXTfGummNwOF5qw6G5hHcu7ZOFdfDyi616XTV53qChSPI6vAVc/1jiV0MXCtaOS8SVEUdCezan5jLQNOysCwDuPEWddnZD8IEHieeWkuLezyVtnQti/zt7h+jK2NztZCmAl/BEV+WLIGZMxu1T+1vkzor907/y6DKJ+eeqJSCWBpr7qEQcU54OmPQXfaGTCSgs6N8PyuLgDWlVZkn+tt18iS/IDgy/PHe/KJs4OVHg8cCQxG6irOXRT1VHyjNXltjJervuf5FNoLX3AhvMiGI2CXsEfztLwVe+u6lfGDxZ8jPPJTiaTehW7qH31EQhGGZsaCN1vO+w2B0Km8svpPLL3iYfz0mgpcgjJbrr7+eN7zhDZx00knMnz+fSy+9lEQiwY9+9KO6+yilmDRpkvevM2wG50NrdzLguqHVf4g07XzVpKoidg1FO+lNLvHF7LLQynBil4QIK1A7QWkYsyt4kKHWH0HPrLIVQlU+fqFm4XzbidtTz41QOZP2WnfC2jqHucIEyg0N/F+lQAzz/B42nwiRbnyWLVXbPXcsg4HYLHLRyd4e/npr7VrkVKgTVobVq+DwQyGVdMU2f0yhKmrcUYzwn8u4ll2O2GUEzrVOd6LjTkAmrQwyzTEiUdPL2ulbVZPNkDLdOlcSBifAXt+qqnn1flb33ujMpJrGDztvRlhm/q/lD1OnOlaGbn0qcdmcTP3t6zatu+hBW3owNPNqyy6/qGlblerb6UnY7XMxi30AFHzxuaotu/zE44p4HG/1Qb+FWa1lV/XSbc4BmQGd29lWLDl/o6bGjJh0dihUyMl13RhrtpetSN2YXdXttGhRxc2rNr9Q+TFkW73xU1X1xYpbZhgjmcZ3+lyplQLdHDKHCZxvI2QxEYf2Npg9o0j3tDChznfd2RUB1RFsa93T/PQmlzCYml2TZ/X4mErXHYJr9vMvKuK37Co1TQ/EKvRbdlULvdpnGRhGJCRgfCCDeveL6hNcFooDVkihg3idmF1eUC7nj902u5yNFaiDvzp7rVTMnTMyIUj7zl9YZoYCbcYCadpadI3VmzLCXTG3pPYi37G85vg0vnt1oiXccq1MW5v/u19o9d2sRknj12iCsBvw13u3kLnrvRw64y/0rjwX88j3hL5NEgRh24m0dBJ9x/UYN5zGpfPO4e0fup79XruYM9/qxGcQBKExhUKBRx99lHPPPdfbZhgGBx54IA8++GDd/QYHBzn88MOxbZulS5fy/ve/nwULFjQsK5lKksmkQQ2h2tpI+14NxwfjpNPOzCEVK6GsOKmUSTptYLS381IygW1r+lvXkIm3ktBJoqUCEatIKZlm8qRWWltT6E1OnolEiWLReUBta2shlS4Qz8XL9TCIx50JcLR5CvFSnHnzymU5T70A6EKcQipFPG6RTqeJFXKk02mSyRKlkiaeaSVBnHQazJYWKG0hHi8CeMcCoFqayTUnSeXTpGJJ0unaScesWS2UeodgY/C402lHienocOqezVqk0yWiUWhri5FOFwLnriXZSnpTMKZXvJAiWiqQTKUx4mlMA1qam71jc0mno6jmZlR7O4lEgriKe9ttWwMl4ok0pgVGNEk6nSGVTpGIK+JxZ4La2tZGuilPPh6nlEySzmTQ0YXE+3rR8RildJqWlgh2PE4yYZC2ohQHK+5bmaY06TSoWftBU1dNwH37xRSJRJFUJE1rayttbQaZdAHD0LQ0txAvpKG5CaOtDTuTIR4fcupR7msJM0XCjlOKJSj4+p/Vsojm5heItLUxlHfOcVOToq0thpFpIh4vYCXTdHS0ESVOsXzuIhEw7TiGDclkkrhKkE5DMpUmXnDaJZkoYVma1tY20oPlMqNJKOawbZt43MJKN9HSHCeRTJDSCZqbm7FsSKctmlsU6ayznzFzCdFB7bS7lSCddqZUyc420ukCSZ0inU6jWlpoGWrGTiZJJhO0tLSgWtqwt5TrGbdJJhXpdKTGQiOdzjiue20WuidNIqFJv+gk6miPkk4XMSyDeDFOU0yjfBm0tLSQTpeIRaGtLY4utqJzaVRTE7EWp47RKCQSzrkxVr4KgKFClni8h3hTl9dW8XiJeLwFnXC+t7UF3fxaW0v09pZIkiRFjLgdJ5lKkUg3kU76DspZZhNKaVRLM6qtDTudJpXSpJKa51+wSGec1R5jRppoPE5La4ZMcwY9mEa1tKF84wJAoaBJpbMkCpVrz2hro1CwSKWyxItxEklFJtNEMqmIx21SSZO2VgPV3o6KpYnHff0+EyHR1hbYlkoZJEspLPd8DMZJpU3AGY/mLkjz5H8s4oPudZpCqwgp7fSLZKpErpAinQo3wUlnMiSizvWvWlpRTW1kMgUsW9PaUiJdSmNGm0kkIW7Eic17BW3ds0mngwHM4qUUVvn6T6bSpNMp0unw+U4m45ynYlGXr5c4yWQSlWgiNVTpzwBLlzh9NJpMQCnvbT/ssBb++GDwmJrL1ypAU1ORdNqmpdkkna6Mb1OnKDIvpkkkEpgRZ0yIt7ZU+lv5PHZ0tJHvyRCPx9HxJDE7gTI0mbSJMfcQjGyJxd0GK1dGy+c9T0GtIpN5nHTaWSU2nUuT1EnixMk0tzIUj5MqX2/OPmmvz3jtUR7rM00ZUDlUaxsqU/ndLu/T0lR9jXUTj28im5xLOvcUKhYhZqZoq+qzWmUpJJ1xK5EwvPtQJqNI22nizXNIp59HtbShCxudxjJLNCec8lpaTHq2OuezrS3u1cdDGahZK1DJlsDmpqYi2UGbttYIacPZR7W2kaPZGWcziuZ934C97iGSG57AsjQtLc44PpizvXZJJQ10JEXaqh6wFpGeESGT3QoR574ejxdJpzNk0mknVltbG5ESvFy+voqRFsyW2aTLrr0tLSZD8RhKGyRTaSiPOXuvbKFzMI2RzmC0tXltNBJE7BJ2ax6+8+8sfvBC2tt62HL454itPm68qyQIuy06Mxn7LdeTuuV0vnPIOzjjJ9fzx/vmc8mHYcF8EbwEoRE9PT1YlkVHR0dge0dHB0899VToPnPmzOHyyy9n0aJF9Pf3c9111/GmN72JO+64gylTptQta2gox0B/HyqXpbS1l2y28ls+nyebLWA3TSGf30A+D4ODjgVZqaeHfH6IfB6yuSGwskSG8uhSFsvOM2jkGRzcylYTIuVM83lN0dGdyA32kh10ygAoFivWPH1DimJqXxYa95LNQqmnp1Kp4hC5oUGn3GyWYilHNpsln9esi76CoWKGxNAgg4NDMJDFKP8GkM1WJoR271Zygzmy+UGGrBzZbO24NHP2EC88Okh/Pk9uELJZ57izWV1uJ+ftfX+/JpuFaAS2bs2SzVbOHcCAHvD2cUnlC9ilPC0tOZ7enMU0obevj8HcoHceCmYz2Ww/JTsBPT0MDeUxCnnvWByxK04uX6S7I8/zW/JkI4PkIoPYduV8bt26lVx+iGI+T07lyaosViFHKp9nyM4xEM3S3+fUOZeDQbuAna9MIIqFLNlsFqs/i7b7as5TJJsll7PJmjl6e7cSiSiiUU12E2SzfZSyWexIP3ZPD2Z2kHy+6NUDwC7kSA3lnXYwKh0wZ86jf+Y86OlhYKByjnt6FLls1qkvQ/T19WDaeXLlc2dZkLcda8SckcMoDDE4mGUwl/PaJTekyedha2+v1z91VKOKg+Ryzm/ZwRy9fVmGhobI2Tn6+vuwSpDNQl+fTXbI2a/U08PgoFO/oaEhry8V+3rIZiGWc/qo1dvLwEAz2bxmyBiitz+LtntoAXJDOfJ5GBoitC8OZAdBmWzt6yeSzaKNCIl4ls1bYMsWvOu2qWAxuOXFgOVUX9y5rosxp8+q/gHMbBa7t4ccTh2d2EdzGMg9hVW+3vJty3g+NkRhcJC+tqWYL/yFXE7TDxSsbOAacMlmNdlByBl5hgb7ieXz5IxBCn39ZO1K29pGr2Oxl81i9fah6fHaIZl0xops+XvEztGUzzOY7cNWJYxsFrt/ADveEyi7WNQMDFrkhirXXqmnh75+TXZwiHQ+z9AQDGQNhvLO+R7Mla/rrb0QLXjjEUAul2do69aqbTCYGyKLO6blGcwCxMlmcwwMWN7175yPHLaKMGQ4/WJoSDOYz5PVvoHWx2B2ACvqtJ3VuxVdinp9v7+/FzObJR9NMJSLoIp5BgYGUFt7AuM2QM7OeXUYNAcZGhoMLFpQTamnh1JJk8vlMAp5cpEcg1aWfGko0B9N01k8Im/bqNKQt13rgZp+GzGdaxUqY2Rff6WvxqKg7T4GB7MMDUUoRIbIZrMU4v1e27vH0NfXQ2lgwLnmdYGoNUTJcq6X3l6nf/f2VcrLZjVZusmaG1DZlxnI22SzWWKDA5iFPP05p61z5estnU57ZfrvN6tWOmPBwEAWlc1ibd2KLlYkE7fP9uPUoRApXxORScx+9SH8+X9fIJLPk7cGsaKKnp5gn2UwT25wsObaH8pHyBaybO6cTduUqaj+F51rVvWiCln68r3lPlE5nz09g159vHZdfBwM2TAULLevT/v2Lx93by+9vc52pZxzafQPUCwOleuXpd/oD/bvQRiKD5HN1/bn3l7I5LOoXOX+nx0cJJvNEo0699LBvj4vrxdSr4BcEXAeEgYGwBrKobDJRgrkymNOX/8A6cIg2jaxfPdjqA3QX42Ytwi7J9rm2Vu+zb7/OgMiMfpOvlGELkHYCejmbobe8F1SGZObX/V2UkPP8o53ar53g6ZUEtdGQRhLVq9ezfHHH8+SJUvYd999+cpXvkJ7ezu33HJLw/2UAh0pB8cww10O/QRWN3S9CVTEi7viukZpTMedxOem4LqXdE2mZqWreMwJDN+UKedZjmlidVUF3q1yY1xYFs8NBUNRN5iN68boVLbqZXo5ia44TNSbAxoRvFQNNHrXQDwSDfdymTTJYNECmDUzsBfgWDbUbnWYtaDJmazEwq1A3LKKZoZJnTAU7aq4qAXSKC9gsutWUwmMrD1Pka2pZeST0+jPLGVrcolXjhvPJyweUpBKjJ8li+HA/SES9ZXjq1lYsPhq58doVFXc26qSu3HQNOXg276gzf5ynEQ6ELy8Kiffx+Dv2o1mHvpz+TjMYLCjkpnxpVfMmwvLfC5yhgFbk4sZaF2FTnf60tbUJryerluQYTJvrrO1pQUWLyqXb6RrXQTdsqvcGNG2/yPZ1Dysha+slBhNUIi0Or9nyteWBkv5V9msKsOLI2dieG6MITG76tQxjGLZgy5iaNDOF23U2mjUc2N0g6K7aXTLDCZPgo4O/4ILIWe+ZUq4m5kvaSoVXI2xeiVALwC8b1c33pS7WEFdqt20fN8rbq1GaIyrkZxeHcsMm6ae+2ONV0xoPLyQ3crbkgk49BBFJBKWrrLBVhEsI45SlRVZNSoQs6th/d3+6Lkkl/tk0F/WsVoNIZVStLX5Cwl/dnXHJy8unFIY0bh3LEpboUHxiVYCU/lL8eJgKQOi/ph5diDlSGMkVuNfzbKCqgwz/ipG3DrV5mNbYIa66btZVrsxVi1s0+AAHNfV8vOELx+tt12yErFL2P3I9dB/7btYtv7z/G3olZjn3kZidohjviAIOwTdOpPcydcTjVhcf8hZnHj4eq79juZd52uefVYEL0EIo62tDdM0a4LRb968edg4XC7RaJQlS5bw3HPPNUynFNhTVmB1r6krqvgna8GtDjamF4NLuZNRpQKxU8ARe2bMCApdGzP7efVIp30TGiNBaeGr0W1z6hcMTJ0WFuvHERrcJ/np3SokJo8uTwirV5X0YdQJVlWHdCr82T0SNZk5U3lCnlN6eRJkBMUpl1QKJ6ZYA9xJbsnIUFpwNP2Jeb7fAgkxyzMWV2Tae43BQr+Hq4KB+GwG2lajjRgDibneT1471jtR2o35VkliGKosqFXto2o+1KYpk/QFKG4Q57jc1pU+GpygB6aQtfkFgr0Z5brXpq9ZtVIprCkrsWYfFMhvU9uhgWrOnaNIuyuO6UosuWJ6dLFi29sUixb66mtEaGlRHHWkIh5XtJcFXTskFlHNoXoB6rVPDKC2qUKaxdZgGcOLXSgTQzsWGlMmw4rl1Ql911YjIZlKnzVNjaea1VvlLUx0MYKF2O1zUJ1zmDY1uOCCnxdaX43uXlMzw3eCZlf2mTHDFwtQKU8YCNRHKV8jVGJ2tbeHiEmNxItqsTIkeVsrgTq4VAsVdqYLa8a+DQoL369S8PAVbyTEeLqdEUEpvNUZgUD8q/UtR7Oh+YhgfkoFYk41KkdVBcnz4shVXyepsDcivvo2/NUnqlWLjN7LBzv8ZEYSoXl7C0d4+bn11w3bf6S45y+gU/lXY/Rl7N5bw86zZVetqFlNg9UYnZ8bi10evvHCXcjAPYiOYe6TfkTsEnYrjBf+ivrWCbT33s8Pej/B/A98jmRr03hXSxD2OHTHPIZOvg7TyvGhKWfxuY++zIYNcOY7NP/9Q112xREEwSUWi7Fs2TLuu+8+b5tt29x3332sXr16RHlYlsXjjz/OpEmNzQeUAsyotxT6aKisBhjxJpTem1hMIhHnodSa4QhakYiitaXyBNvWCvlop1cPOz0p+LAdKjapUGuDKV2Vh17lii/KL25U7eTNSsKFPOenSEXjaPA8n8s5f9P1gi37rEqqt9XMf+oKNfWxVQQVqVgY+Q2SvPNZNjkxFOy7j78ywQD1/s+56BRKZhrTm8wPM1UYYQzUuXOoWYXLOc/Be4G7+lqgiKoJv9fGwRUKqqy4XMuuWnRw5lVdWqDMmi7UOiNodTECmpudNm+qehwN1QB97L23wcwZyuswOhmcnFesLOsLtJUyKjNz/zEVClXpq608lFFeDK5+O0e8YPlRDO1kOGeuQSpVKciavAS7axl268zysdSfrSoDCmaLU5eWbidAN4SODdXtY3Xv7ST1tb0rPehIlctT9c7KdBaWqLIgi8XggP2Vd5zBvRRRn6Gff5zy6WHeyoCqNgOsKatqjquS+/DPSvvsrTjsUBVYXdNbsMMto3sN9vR9AlZFbt2qGWpdgt0+J6Qy9S275paTh8Zjry4jlkZPX8OW9Ep/qmA53vjpiupGQOxyQ6qlQi5F10IqXtZnjfLLmJq3IyOVjeoERTfM8J/dr1FrIPwE17uxlDuMXblNlQladrlUr9A5UgLN6A9Qb1QSuIKYZdUeu22DYeVC89a6dpyvrnujRXH8/ceM+q/34L1rzWrF0UeOrP1E7BJ2D0p5Yr+/iuStZ7CxN8l/9d/MsR99I/GEdHFBGC/sSYvInfhtVK6HI144ixu/vpn994OvfFXznvdp1q0XwUsQ/Jx55pnceuut3H777fznP//hk5/8JLlcjhNPPBGAiy++mM9//vNe+muuuYZ7772X559/nkcffZSLLrqI9evXc8oppzQsp96y4QH8lgn+zd4Hd7WvcOFApzvRIS6Sq/YKZmtP34etU4/2igyvS7Vo5HwxTcX8+V6J5d/MQJoAWvtcTuqUNULLrqlToLnJsVxrJHYFPOvKj93VqyE2emRfso8jXL7YfEhgu1aRGjEvqAkpb7IwucugpUU5Ykmylb7kokB6v+HA5szevNh8WEiu4WiqXQn9P1bG+HTasUaq7OezWvDRXFkEsGaaX3EJqghx3mkOUxHKaYL1rRY4guJWtdvmtlpR+OnsUBx5uGLGdH9n0AFx0u5ssKhENEVp5gHYU1YENodao+G4qlWs7cobXdfLSKyxVUyVyFeaexj56Wvr7+Aro0QMwy4EM3Dr1D7XqUOq3XHTdUWXkMoYCiwzxUuTj0M3dVVE6mEsRgB00xRf0uAFptvnYk2pI7AABx3gbq7qA8rZpv3f3fJQxKqGuWp3XYWvv1ePZeU62031Yyw6+1V2qqc7VosuYS64AHbLDKzJS6hHoWUuOtEaUonh3RhHim6ehlZRz6XUzbqpysuyIh4GRZm2NsXeq2HO7Nq8XaujWMzgiMNAaSt8leDh6j/M764gVPsk61c8wxsr37Kwdq9y2mrLLrSN37IN4LBDwlcTbYSbb417eHW6jnmomKMmWhahREoD9QvyPwfgvIUJvlipf179vyxZ7HtxNZJnljpIgHphwmO89CjxX3wIc/OT3PL0m3lkyvv5wEfS3ttlQRDGD3vKcnInfovkj97OlF+/nc98+Lv86pBWvvAlzdvO0pz3Lnj96xrf/ARhT+HYY49ly5YtfPnLX2bjxo0sWbKEb3/7254b44YNGwJCVV9fHx/72MfYuHEjLS0tLFu2jFtuuYX5FQUonFE+OPofsqs9wNxJ3JQp0F4Vk8uaeQAq34+5vrKaZCSiaGrSvGgdxrRZBnFloMoT8frDgKqqcljCoDmWVqbnXllJYnvhqhq6MY5Ah4/HFfuVPYJ02Jv/MMuucr0N1SC4TlXFUtOms661C13lglPtuuafRLuGfZGyb5NlV9zgijMPovi8I49UGXoBsNeKsnXVuuBxNKjw8JvK+du2Aa6WGNIAa1YF3TgzZQ9bd0IbT5q0tkLLpIrC5Ql8VXkq6sXsqha7gu1k+2LYDIcnkDX6cYTYLTMwNj1RP0Gq1hIqTKArzT4YogmUG5vaTZOZjDVtFbppKoZS9Wrt5ektHBhNsmzvBPOG4L4/h1fN8Cy7YpheEPaRHb82IiirGMzPAKyKa57dNhuj/0V0qtadWynFrJmaOVWnx4nZ5RMylXIEq9YZ8OLDoXXxW6IFCwkeT/U1Hakzmw5qsxXhLawUu2s5RJPodNAq14sPF88AQzX7Bax7Ak2qg2Omr4/YU/2CXy2mCeFxy6pr7hMj6iWhrmEUh6yF+BYFvY6QfdQR0D8Af36gtkj/deweSnt7eHtVXOwUpqmYMc2iJQEbal5kDNdHG/9e/zbqH4fCExVaFgCPB/MrH2yN2GVXuUMqtmmV81CLZV+/8Avjpckr4bm/hLbdjBmwqa9BrM9ynqXZa1n/fKKmzEbClf9209pucvih8Nzz0NWl4DkY0c25ChG7hIlLKU/sgW8RfeBb9JY6+eD932bOEQdw8Tvr++MLgrDzsaetZuj4r5P48Tmkbn8HrzzpOlavauLKqzWf+y/NH+6FD14EXZPluhWE0047jdNOOy30txtuuCHw/SMf+Qgf+chHRl2GGkVcKsd9pCLO+B9aDVWZaDc3QaQzH3wUjTeh403gE7vcPEpmGqJufWrzrtmhznf3LbXrSlnJLOSBWtvlOEUN3Bihopv5NsWiEKuz8JMKEw/C4giZBm1pvFlHPVe5apYvjziV8S2uZavayYZhKC9OUkkpjEh50mHVm6g7+MW/aJSABdZwhLoJZqZgN0/DnrQ4kMovytjlKYhlVE5qR0ew3GhUcfSRle9GJMKM6Qq702vtClXWNlDnJUrNpvoTeCeP2izGioBl3wjdQf1UYm/59k2UTePK7l/+x2HdPHzMMMNQrN5LB1wuIxFFJuOU1KgeFlEWzC4EyrSm7gXV7oOBnaMQJnbhC/yebKO06Ji6WSxcoIj8O9hQzuIZriVlHUbZtuGiTW2cQjdjvwDUyLILgEgce/LSms063oI1c3/saCvwj3IeTkWOOiKYtmsKrPMW7tVVokb4wYZtNgzQTV3YrbMwtj7rS1xr2bV4EWQHqGh5Ifm57dg9Lbg9HleewOPsq4jHwsdR/9hhKGqv/8AuwXpO6rRRRVXjnjrcNadbumHgJece5t+eaodirq7YFRTmws97Ign9VFwt/Wkrbow+yy5GF0uyEcHbvwq9D7W3g9UOHd2wtb+yPZMBc+7+DKxLwfN1CnDPqxlHGzGUXaiyTKzTFwmOV8qIEIkox0U2t+2WXeLjJUxIzGf/ROr7ryd2/9e4b+A4XvuL29n75AM5/92GCF2CsAtizdiXoddfg7HpcZK3n8uklkE+91nFxR9Q/PMReOuZmrt+qcMtJARBGFtqYpdUGIp0lC0onIfg6vgbKV/IF8PvKkTFhWg4qh+ujeHErgaWXW5A84LZWv7JdWOsI3aNcEWvag49RHHAfqN/vvDvsddKxfTpqmLZpaidgIXMwKdMUUzpCm4vmiHBraow43HykTbyRp207lxKV2KfubFwRnyCws6zYWJPWw3RYFBzfyDnQrSDLelVgRUgR1qWDkyQffmHnLsVyxUzZ/jTVE/YDf8fZ1IddcyaSmamoSASizluV0sWjfwQ6rMNfSvEsquabfH+6exUoxI8XcG5pGKkU5pksmK+pFum11grBSvo9P/SrAN5ofXVQMWVNTP8woH1s60am8LP7yjOuX8SXiUiqZpiVGAf5dsptBbDVEOnOsodNJhQKRUQEdpafTEMa/Id2bFGTGfVRAC7pVocre5Mjmvu4sXhgsnU8u2go90R5ubOHb4OsVjVhpBnwmHneVWVsaatxs50gRnn5cz+gXTWjP2chVpC0E1Tgy63ZayZB2DNO6K+x7vveozEwi/Azg5HxOnwWad5Y1DNIdtjorp7Mc/qWHb5MQzFtGlBIddWMUcITXdCeTXnmTNCrtPqt1cjrHuNEBxY0KKuz+iwiNglTChUdhPxOy8m+aOzsWzFp56/jv/328t5z0UtnPomEbkEYVfGmr2WoeP+C+OlR0j85N2o0hCve43i+9cpFiyAz1yh+cglmp4eEbwEYUehoGE8y01N+2PN3M/31Bl8om9vV0wrv6H3x0my2+eMOnC3SyUwbr1K11+63J34bMrsQ2nWQZU8Qmb5Rt/68gN/g9UY8T1Pj8FjRaj7RnnWEY04YkCxeeao8nyp8zhso3pWWIsZMdjYdCB5sy28Pr7Ps2YpDlnbwJWrDjXiUZ1UAKWq5eMHY92EukvVI8Rqr147um6ekyc58crAcfmKRKpWyHTFrkqO6MxketoPIhtv3C6Godh/P1VjkRakzv0sUtV+29DXKi5etXHqdtQTcSpZu80sF28xfJ+sxrOaUabXF9zJc9hiBfWwuvdxXDjLONZU7pfG+7a1hogsbv2U4dlpzZlVydtP7YITKphuJJZdI6AvMZ+C2RzqzukV7bP2U5EGrmY+epOLGIxNYzA6hYPXQldX+YdkG3ZHZbXXRha27ke/S2dbmxNEPJUKinKNaJRuxMJttdiSbHOC8ytFIdoRvNbTndu0UAvUxr5ycfvdtGnOyqz1cFau9VfbybASJ8v3NmIMJBt/gP9KobUvncBxLwYgkvR+39B8KFu7HHPC7m5HxKyOmebcXqvfXtWKtKGoKu0tIHa5+4x+fiBilzAx0DaRh28l9d3jiDzxSzYuO59Tfn87P//Xflx5ueKYV4nQJQgTAWv+keSP+Szmur+S+Ol7oFRg6lTFl76geM/5ij//BU4/U3PPvSJ4CcKOYMpUg3hyZFEswiy7AC8gs+GL2WWYo3ezGLmVVWNxCkCrKCRbqZhThNdH63KdG+TnPvSP1lA8YDVQxl9vO9UBgJluYvEiWL3KSVBqnh2+Qx0arWblz8e1evPH9fFn3zXZcS2aN9f57rfmsVvK5lDVokxtQcNHvnEbWm/ns1p1gPqQurhsSu+DNWlRoP7ptGLpUhWM6VoVs8sVv6xYa2D7aLHLE+h6woSavoZ8ekal2tvjxtjgvAwnEBzSOPZ8gCMPhwNqu3jFjVH7xZWRnTh7ygqsaasr7pc4iz4sWQTTRqFB6KauQB5+y67h3KOnT1csWhieqHtGtBwfSTF3riPeVE/W61kaeVP9amuv+ppRXRzX7wwvNx+MEa1zTZrRQNb2olf5rOrqx2izjQRb0qtBOavp+o/PGwcIvxd4+bhVGDtvu5oS5s+D2bNGt0/gW3X1t9Naqt7uSxZXrLYi5sifZdNpRUszLHBDbvrdGNVI1uVsjB16v60VLAFItWNNW4XtW8hAGzHMuHNTiccVa1YrYrGQk1A1HtUuCmKEbjeqb8vVAUK3EYnZJezyGC/8lfjvr8J86Z+UZh7A36d+jAuvnIVpwpe+oFi+TIQuQZhIlBYdC6UCiV9+mMQd72foNf+FYUZ5w8mw7yvgU5/RfPgSzbHHaC44X9W8/RIEYduZ0mUyoEfy+KeY3g2xrUbo8u5QZSVU7zU3zoTfTnfU7Bf6prlOQcG30cExYcUyyA5W7xOeaUsz0G94rjoupdlrPYFseremJzc6yxJwXPNq6+4vvJtS8ySIxJkxvV6ikVCbvnbi5UxEjjxcBybj/nSmqVhax4tQt8+h1DZ7+AnhiCaMTkPbDd6xx0diFDSC1fhcLDON7ghbqKGO0uBtrhK/GgUHaoS76mAdVCROoWkO8HxQDBkFlVhxrntnrcXbcM0zGnfFeqKOa4CRi3b5to4wX8NENweDOZmmI0BtD0rhHbyCOieifhktLdDbC+2dESgUtk0Y8XUt7etX26K5BK/b8DTWtL2x1v8V2IoVdSzmtFuNsQgRUROzq/LdzX3UYlfIwc+bE55PPK4qbtYhxKJQKFK/8wcEbT1ia7N61DvWzk4DMz/6AcQ0DfZ9hb9OlTw0ML0b+vtHK/j5qBOg3q5TxUqMP2fHZBLmzQtL6PuoodYCt9qCrZ7FVwNNq/qhYRSI2CXssqieZ4jf83kiT/4au2kqQ8d+jtufOIbPfdzxEb7qCsXUqTIJFoSJSGnZ8QxZeRK//iTxuy4mf+zVYESYPUvxza/B927QfP8G+PvfNR/9MKxeJde6IIwVeoQB6qNRxbRuE1UIXn/uxM0w8ALDGw3ELmvmfuH1KP/1hKwGz7GN4s9MmaJqE9Z5ak7v80qONo2agNbEm7x9TFMxeRsXzLBm7g/FXJ2aEh6suzbozxhQtrjbnjimDSaDdtts9FNPjyq7es37ir0hna7zY6A+5X6rrcomXxVHZvEW3i/cPd0YNfPmweAgNDVBfvMI6rYt6JB+u03Z1BcBxyDUz7C4l76louh4Myrft+MLHQa/1WldGpycmTMUerp2Aug7iWvSpJKN8w/LfqTNkU5DX39FUPHnVfflQDTBQNdBrB/IY7qraQzTAUIX16ibuFKwNWPfgJuZ63pXb2XK0RCM7TVyceOgA8v16KsjspS/bu04iOmTNm5XHWFkqzGOyu2ujjiH1qAci1R3AZJtIXw1xorF2HBjxYL5I1wFcpgVZ1yRMR9tr9reyJp6BA8JdRCxS9j1yPUQu//rRP9xM0Ti5Ne+n6G9TuPr34lzy62OCfUnPybWHoIw0SmtfCP50hDx313pXOuvuhyUQSSiOPtMxQH7az51ueY979O88RTNO84eXdBcQRDqMBKxq45oVJp7GNktEXi0LHaVhQdlmCN+DK0Ov+E+4NZ7wxzYx/k2gkLqHKNZx4RoO9wk/OhUiHWXW0Sdao9ekBiJZddYlRWO3bWMlycvhd4RJPbexoef41RqpJOosqWObddzygIcN7hnqy39qvKoxjQV06Zp5qxy+k1zk+KgA53fNm3eUW71quZ/ALt1JmrgpRHn4sbsUiFWJNsSoH60BKzghl1adST5bX8ndURety7bXg8vdlFInebPN2kqewmuPRBij4Nf/1W+D26MvbCYXWGHu2Sx48bpxtDzi1KN2tRxS4wTcdOUA4mPaMyvl2Hli/epetGBUsn5u+PcGIcnElFlsa2O5VD5rx1rRneM0mw3hPrt4Dd1GoVpaK2fpfO/thsuQqHNGMoqDJt9vdVEt3XRlnr52pmusuulY0EXZk37UvPBlIwqk/GQa8O/z7YiYpew61AqEH3oRmJ//gYUshRXvoHiAefRZ7XzqU9o/nQ/vOFkOO9dCtOUCa8g7A4U17wVSnni9/4XoMi/8lPe6kxLlyiuvxa+8S3NLbfCnx/QfOyjzjLjgiBsB9sSFNwllqZjsmZmnxOc9h9Pj/5JuXsabOnBc4904yhXAvMOU42GZTm/aTV8LKmdQcDVs67YtQNqWvet+k4rqoZ4XJELmXCNVJCpWCQ2nkB2dyumtdedNTk5ZLowqgSljnZFKbnzwhnXk9DsKSuAFSPPJyx4807s/K7AYdvsHFOyETK8ddvwddWGWSNGuphm5cV7MqmIxINWUv7yLRX3Po/kFJmmor09/LfhxC5/be3JS9DJtoYi/IhpIGRuv2VXfUF56lToGlX1d45ZozMXDam3/63NqPyg66igjopcdy9rzqFg5Yk8/YeGue+1El5YV1nB2CujrFaN2cuYRDO2L35edQZKQdFsphpFo3dO7rkQyy5hIqI1kcd/QezeL2D0vkBp7uHkD74Q3TGP/zzlrM720stw0YWK179217mJCoIwNhT3PQe0Jv7HL6Ly/Qwd93nP1SeRULz3PYqDDtRcfqXmnHdpznobnPomgkGGBUEYOSN6yx9u2QWO1cSihe638sP8KAS0KVMUU6ZUvrsTpFIjsavBt/AdGqfRyTZUrqfOj2NnyTOiiYLfd2O0AXx2RH1GyIzpsLW3avLUgL1WGQwAf3twG+vkJrQrHaVRLLfwPMpZdO+NrW2MFx+u+r22v7eU52VNTSOs5wix/ZZH2+PG6K7GGNJvd4Zllyt2pVJQL07PeKC248W4Traiclt9mVU+luYeRuSp3w1fvk948lZPVdScmtE2fcMVC6t1HsNEt3TXTT+C0nwf3dhwtWP9jrTs6uxQWNPbsbf2Ywy8PPwOO1HoD8XvZj2qe0m4ZZfzsUHlI7ERLCQCmYxi8aLa7dtr2dXSUomZOZLYi41ENUPBS01rMXSRQDjJ7Wg7EbuEccVY/yDx338Wc8M/sCYvIXfy9U68C+DXv9FcebUmk4FrviSB6AVhd6a437noRAvx31xG4sfnMPT6r0K8sj78K/ZRfO96+OKXNN/6tuZP98ElH97+ILaCsMcRz2C3zh4+nc+N0e6YB4VwvzDPdWo73ACj5afRRpZdhgH98TnlshrlVnFhKc1ei9H7AkbPM4BvOXXAmnkAoIn83111c7K6lqNbZ9T9vZpo2FN1YL5SZxLmvbVunL81dS+MzU8wc4ZJIT9cbRqPjfFEw59HRLVoWQ9tmCjbIh43iKVrrSFGLnbVF3XAH6OpUYY+6xRlDpPWYfJkxSFr9Zi70Y8oxtgIaG4Jidnl/t0Jt8hIRLHPGu2IgRu2341xrHCvq/qGXQ2sZWbsB1YR46VHvdw8YiMJMOcTfhTYrmVXSH3G8lSNfay2EMGlkWXXWItdvmvdnrIS48lfD79LXTVlrCo1XAX8ll0jF7sarXa5I3FXaRxOGK93KEsWO7G08wVHmByOeoc5ZYrzGFGMhLiYlsd+nWwdNv9qROwSxgXVu47YvZ8n+n93YWe6GHrVFZSWvg6UQamkPbelVXvBZZ9QtNc1RxcEYXehtNebINFM/K4PkvzhWxk68VsB0/vmJsXHL1GsXav53Bc0b3u75vx3wetft4PcgARhN8RYdDT01LFoCkErA3vS4rq/K8+yaztiaozgadRa8hp613uhdBuk9L2mTrRA/4uVn3xiV3m5tsaFNlweKsiB+4cfx4jOygjPnW6ZjtUynRVtEXp6hqt7vaIUK5ZpWltHVOSYYE/dCzW4BZ0IL3T0Yle4KjoQn41C+1YR257CguyYeJFlMWY7sj78UDCzBmyoynnneHJ5tLU5BVXch8f/nqy2Z3EGIxIyXoycxYugaaDSvpaqxOyaPx8eebRxjMJtZiR67zbnXb3KXgV3ddtGqyVuX9kjGK+9tOX61VFndvg1EXBd3P6YXc7HHWii6d4yt3F3w1A0NcFIDV+VUkzp0tg2vFxeJ+DwQx1xeMuWeoWYzorJIxSa/YjYJexc8gPEHvgW0b9/D5RJ/sD/R3HvMyHq2MBv2qT55Kc0D/3Dic/17ncqcVUShD2I0qJj0bEmEj+7gOTNbyZ3wjfQ7XMDaY44TLFyBVx5leZz/6W554/w4Yuhs1PGCkEYc4Z5yB4Ly65Rx3lpOFtpIIiZ0dptY8R2LZozBrOv0WQRWL1yJ6CbpqKbptb9faQvK3TMCfKm45V4L/5dLSPJy00HQcPnxu30IWtUv8jozOW03n5lIhJRKNO5gLSvf7uHVc9aY/VeOyiY+I6clI+W6gBW25PHCJnSBc8MOnEJebyy3XVjjESgvV3RNRl+9Rs3VtLY9UFX39senS+AChFcQtp4wQKYPNlxldthjDjrnTe+zeh23Pj86OZudO86dCSO3bWs7r617rCNxqbhj8nuXFD3hULD/YZ5h9TeBi++BOlU+O+N2G9fKBSAqsUvVyx3CnOvAXeubxgNLOES27aogIhdws7BLhF55EfE/vhlVK6H0rITKBx0AToz2Uvy179pLv20ZmgIPvExxdFHysRVEPZErDkHk3vD90j85N2kbn4zQ6/7irPMtY/ODsXVV8L//Ayu+ZrmjLM0F74Pjjxcxg1BGFOGie/Vm1yEoQvodOc2FzH6oMYNrvNGAUhGLHaNxIJsZIwkZpLyJxqzie9uOBYm2yjNPhji4TYEixcporVxj4PsILOO0sJXj5/rnltupDZ4Wr0a7bCXQ8oXh2ycUWNgOefPbSS0tSmOPiCYdsCYglYRJk+C5uYde2K8rrAjZvgNzAUbBdQfGY3WVw3/Vpd6yXbAoqqLF4cUFoljzTl4+J2rrZQaCcUj6MR258Jh04QxXMyu7m5FZ+e2uXE3N5UtPremsNtnD5s+lYJMxhHYxopdSH4XdlfMZ/9I8oYTSfz6k9gd88mddhv5V33GE7osS/Od623e9wFNext855sidAnCno49ZQW5U2/BznSR+NHbiTz6k5o0SimOf53iu99WzJoJn7hU88lP2fT176hl4gVhD8J78m18Py6ZGTY2HRB0+Rklo15hudGDv64vVNmZ8ABTY7JS2fYwogUDGlPrAbObPkclmgPHVmqZ5X3u6hpB2IsddV4Mc9RWTV7Mru2tU7nP64jPh8zNeg+e6Y3UjdHuXIDte/lelcuIy7OmrMDOdNVsb1q2N00ZJ7bRDmfMxa7K8WuvM41TpxphZ1ZWEQC9DS5v40r1ODBKy67tLbaRpef2unFb8w5Ht80ZNl08rjhgP8WihWN3vGLZJeww1NbniP/uCiJP/Q67dSa5112DNe+IwMX74ouaT1/huC2+5lh473sUicRu+oAmCMKo0M3d5N50I4mfv4/ELz9MYfMTFNa+r2ZSPX264povwU23wHeu1zz4kOY958MRh0ksL0HYbnbJ2fII3Birrn27bTa6vfZhuzT/aDB34OPwCIYgpVRZ+NAj22GsCt4NKE1ajq3Wo1RxhKJReFwca9JidHrSmNevEWMVoN4jJHjzTr8FDhMvqRHTpsL6DcOnG3FVRujFODKLmOGPR7fORLfOrNne3KTYf78RFDEGGGMudvlo4Ma441Ej7sx2ZgrkB7AnhSw9uIthN0/D6FvvfKk5r34X0h13IXdPg3weZs8aPu1ERMQuYewp5py4XH/9Dphx8od+kOKqU8EMrkf6v7/WfOG/NBr4+CWKVx61ZzyYCYIwCuJNDB3/dWK//yyxv16H8eIj5I/7XM2kxDQVp78F9t8Prvqc5hOXau64Ey58r2OCLQjCaHGDv+yIwD7hNI88wm3wrx/PJ8OdOLhWL3XiKYUs2a6bpsDW59DJ7felGHmYGVfsGn3+GrCT7eiYjcr3bZPQMFFxDnUUbqf1Jo1mzLEa24nEYooCdVbxHAU6Mxmrew3aZ7no2WWOm9g1+ujry5Yqli0dw6psf0i0CYcbf2lbxK4ZYes6BIyLhgkEty24c0NjJC7mI2zIRDN295ptrtLOxJ66CsyYs2Jwtdi1Ay/e0tzDUMUc4ASYnz9vhxU17ojYJYwdWmM+8Sviv78So38DxaXHUzj4wpo4Hv39ms9/UfPr38BeK+FjH1E7PViqIAgTCDNK4YhLsKeuIv6rj5P8wUkMvea/sLv3rkm6YL7iG191Ynl981rN6WdqTjsVTn0TYjUqCNvCdrgnjgbHErNxmnQ5ngeA3dyNbp0RkqqO8DGKiYNOT6K0+LgRp2/ESIpVCjQGYI96gqOUI/gUpx9ANKUwH/8lSpe2rbI7mUQchvLbl8foVx3cdSwVp09XZNdDSyuEry85cqoXANgRusSI2A6xa6xxj327NAN35yoBuTTvcFRTBobqH2dp7mFQ2s4OPkqs8qW/LWJXaPypMMbQssvumIc2Y/VXUPWf9+0Uf3bJdwBKVYS+Rud1rCsfS+8Sbp5rVu14QV7ELmFMUFueIn73Z4g8+yesyUsYPO7z2NNW16R78CHHbXHTJjj3HYpT37QNsToEQdgjKS15DfakRSR+9h6St76VwkEXUNznrBrLE9NUnHg8HHowXPN1zXXf1fzs53DuOfDKo5y3WIIgDINVAEa/wty2MpJngQN9gZ/taavCEw0XbXcnM1KxCwxHphvtRLLKIMyaeQBG//qdapG3rey3LxSL25dHJqOYPBk6Wql/spVREV8axsXZuShl0Nqq0DtQkdrpR1dHHBoPdmjTRlOoZAsM9dRPE0vXBiHfwZRcsWvMLn/fSfTadAz7qzLQbaPzn7O20z1xF7k11FJVsYCbsz0xXmCMlo6OHd8Yu87rDWFiUsgS+8PVpL7/eswXH2XoyI+TO/WHNUJXLqf58jU273mfJh6Db35NcfpblAhdgiCMCrtzAYOn/pDSwlcTv/cLJP/7Laiep0PTdnQoPnGJwTe+6kyGPn255h3v1Dz0j/F/CBeEXR1VHHI+RGtXeNu1GX+LklHjn0+O0pLOqLZsSjRjT9oZkbC3n1hMkU5v/3PglC6IRRX1pJ3SvCMdKxsaOYqOw31hB/rZeRZv42XZNR7nswrPsmt8q7FTcd0Yo7Xe2dtPWTDW46EWlcssLT4O3TF/55e/I/GGgQZujHp7bT/3XMSyS9g2tCbyf3cS+/1VqOxGSitOJr/2fRAS3+LvD2quvFqzYQOcdCK88x0ShF4QhO0gniF/3Oew5h9J/DeXkbrhRAoHvofimtNDXa6WL3NcG3/zW/j6tzTnX6A58ADNWW9VIzfbF4Q9jZIjdu0sy64xw/Ni3DWu7ZFXozrW2Ojy30UOd1xQw1nzRWJAefbfKAj0OKOTbWO+MujO7xe7nhvj2DD+4t3smdA0TFzD2bPAtp2g42OCvwO51qLR1BhlLgSp32GVLWLXtiJilzBqjE2PE//tpzFf+AtW1wqGXn8N9pQVNekGBzVf+6bmJ/8DM2bANV9S7LVy13moEARhYlNadAzW9H2I//pS4n+4ishjPyV/5CdC3ZuUUhx1JBy8Fm77Mdx0s+bt79QcuL/mzLcplojoJQhB3Fgz0cZi114rILVLzX22TTTa0Qw/8XbrPTr/oz1Z5Bo1qnZFN2cxgmfHZDGCba6TuyLkrAPHNlsqln87jXLBStvjLg9519x2nYNd5wJbsGD4ukQiioULxrLUSpk62YE1ZSW6eayUtJEwdr1ovPtjXeq8oFF+wXg3dWPcGexaTwLCrk2+n9jdl5O84USMzU8ydPSnyJ16S6jQ9cBfnMDQP/2ZExj6u98WoUsQhLFHpycx9PpryL3+q6ihXpK3nEr8V5+A3NbQ9PG44i1vVvzwFsU7z1E8+i94xzs1F7zf5r77Nba9yz4OCXsIN954I0cccQQrVqzglFNO4eGHH26Y/q677uLVr341K1as4LWvfS2///3vx6Qe9rRVjpWJ2dgfZvJkRSaz69zfddNUtBnFbikHr98FYgeBE4x9RIzWHEUsu6gIhSM5CVViV7rTWYwgPtKlQMeaHdtwO1vz1btgzK7t07rc49ne2uwGKOUsCjIB4gE2YtcbK8srBldXzG/NJZZd24yIXcLwaJvIv35C6vpjiT50I8WVbyT7tjsprTi55i66caPm45favP8iTTIJ3/iq4t3vNIjHd7mRRRCE3Qhr3hEMvu3nFPc5i8ijPyZ93auIPnAtuHGHqkilFKed6ohe736n4vnn4aIPac44U/PTn2vyeXmyFXY+d955J1dccQXnnXcet99+O4sXL+bss89m8+bNoen//ve/c+GFF3LyySfzk5/8hCOPPJLzzjuPxx9/fLvrojOTsWbuv9357HSiSawFrwwJDD0+zyFmeV7YXWexMRflxhobpWWXrLfhZ8SrAewyaGXs0K658wPUl/vvLuzGqBMtO7ciE5nA9bJrXTu7D3Wskf2C8QRZYXdXRMQuoSHGy4+R/O/TSPziw+jWGeTechuFIz8GydZAulJJc/N/a049Q/PHP8E5b1dc9y3F0iUyMAqCsJOIpigc8gEGT78da/oriN/7BVLXv5rIP2+rawKeSilOfZPi1psVn/yYIpGAqz6nOekNmmu+ZvPssyJ6CTuP66+/nje84Q2cdNJJzJ8/n0svvZREIsGPfvSj0PTf//73Ofjgg3n729/OvHnzeO9738vSpUv5wQ9+sJNrLtQjHlccdgjMnlX/eUhBxXJklFYTu5AhzQRgV3wm3TF1Gq8A9TozGQC7aerOLTiEevHsrJkHUJp/9ChzkwtsPNG75LU7RtRZKVZnJmN3LgTAbp+3s2u12yAxu4RQVHYTsfuuIfLPH6KT7Qy9+gpKS14f+kbs7w9q/utLmqefgYMPgvecr5g6dTcelARB2KXRHfMZev01GOv+Tvyez5P41cewH7iWwivOprT0+HKw4iCRiBPT68gj4OF/wm0/1tz2Y7jlVs3KFZrXHqc4/DBkcQ1hh1EoFHj00Uc599xzvW2GYXDggQfy4IMPhu7z0EMP8ba3vS2wbe3atfz617/ekVUVRkk0OvJxQ4/SsosJKnaZY+kJ5R38SCy7drH3/Mr7b0yJRiGTgabMmGfdmFjacQvdBajrEWyYIxaVddNU6H0BnWgds3pNLHx9c1ysIscwZteuOkbWG7+Uwu5cgN05pkHY9jhE7BKCFLJE//ZdYn+9DqwixdWnUzjgvNBYBs8+q/n6tzT3/hGmToWrrlAceIBMBAVB2DWwu9eQe+MPMJ/+HbH7v0Hi15/Avu+rFNecTnH5yTUWquAEst9rJey1UtGzVfPL/4Wf/VzzmSs1X/wKHHmE5ugjFStXgGnKeCeMHT09PViWRUdHcDW2jo4OnnrqqdB9Nm3aRGdnZ036TZs2NSyrrS0YjDudzlf9PtIAU7s+eqgZnU+jmptRVce9q2CaNhsTMRJxaG1rR6WGr6fbhs1NBQxD09IaI52aGGPS616jMUyIjUIEbISdzgAa1daGarCggp3JQHMzxi7SD9ra2rAzzvP1jqjTsa8e8ywnFC0tRYrxOImkWTPmjZi2NpixqMHP295u7rg73uNto3pordFpxyVctbai4jtXPbW3NIHKo1pbUOntu0YGshbpdImmJoO2tiiwfe03VtiDGSikUa1tu+w9aiIjYpfgYBWJPHIbsfu+hjG4ieKiYygc9F5068yapFu2aK77nuZnP4NkEt79TsVJJyBxuQRB2PVQCmvu4eTmHIb5/J+JPvAt4vd8nth9X6W05HUU93oT9uQlobu2tSre9AZ44ynwz0cc0etXv4Kf/kzT0QFHHKY58gjF2oN21deFghBOT09P4Hs2W+nD8Rj09Azu7CrtMIy+XoxsFquvDx3pGX6HcaC/X5MfGsJQsLWvH/KN07e1tXltOGOG5sknYSiXpZCfWM9h2THKJ5IdAKC0tRciubrpzOwg2hzA7hn/fuC2oZl1zoK1C9RpdyM7oNH5PNksRHbA+fVfh9uCO+6O93g7XD0i5T5a2roVYsWdVS0AzIF+1GDWubYL22cOmohr0imYPAl6etR2t99YYfT1Ofeo3j60Gv/6TCRGIlaK2LWnYxWIPPYzYn/+Fkbvc5Sm78vQ8V8LXWFx82bNTbdofvJTKJXgxBPgbWcoWlom1sOVIAh7IEphzdwfa+b+GJseJ/rgjUQe+ynRf96KNWkJpWUnUFzyGghZfl4px5Jr5QrFhe/T3Hc//Oa3mv/5GfzwR5pp07Zy2KE2Rx2hWDDfSS8Io6WtrQ3TNGuC0W/evLnGesuls7OzxoqrUfrhOOgAx/1JGEdG6WbX1qp4xT47qC4TjREtxriLuTGikHhQOwZlOGd2l3VfE0bOGDxXRSKO5f4uhxeza1cbm3YPROzaUynliTz6Y2IPXIvRvwGrawW5w7+BNeeQmgFl02bNjTc5EzurBK96Fbz1NEV3t0zoBEGYeNidC8kffSn5g99P5P/uJPrI7cR/dzmxP1yNNe9wiktehzV7LURqTfoTCSd21+GHKQYHHTfu399jcusPbW66WdM9DQ5eqznkYMWypeLqKIycWCzGsmXLuO+++zjqqKMAsG2b++67j9NOOy10n1WrVnH//fcH4nb96U9/YtWqVdtUh9QEcYPbJibKoRnyaL7DUMauN6FUSrSuHYRhgI2IXWPGOLzIs9tmYw5ugWj16rq7EZ7YNb7V2F2RO+qexlAf0Ud/TPSv12NkX8bq3pvc0Z/CmnVgzSD21FOa227X/OKXYFlwzKvg9NMU3dPkahQEYTcg0UJprzdT2uvNGJueIPLoT4g89lOST/wvOpamNO8ISgtfjTVrbWhQ+1RK8cqj4Y1vaObZ57bwh3vgD3/Q/Ph2J7B9exscdJAjfO29GmIxGTuFxpx55pl88IMfZPny5axcuZLvfe975HI5TjzxRAAuvvhiurq6uPDCCwE444wzOP3007nuuus49NBDufPOO3nkkUe47LLLxvMwhO1hVxNjJhSNx1ire2+IpnZSXUaKWHbtKNwA9SJ2jRU7/xlGN03dZRY82OHI2L9DELFrD0FtfpLYQzcS+ddPUcVBSjP3Z+jYq7Fn7BtIVyo5Ljq3/Vjzt79DIuEEuHzLm2WFRUEQdl/szgUUDr2IwsHvw3zhL0T+7xdEnvwV0cd+ho5lKM05BGvOoZRmr4VUe83+zU2K1xwLrznWsfi6/wG4517Nb+92Yn2lUnDAfo7wtf9+kE7LeCrUcuyxx7Jlyxa+/OUvs3HjRpYsWcK3v/1tzy1xw4YNGL4lxtasWcPnPvc5vvjFL/KFL3yB2bNn89WvfpWFCxeO1yEI20CsrKU3NTHiVeKEEIazPAkZu8edHbQao1DpDiJ2Cbs0rmWXjAM7BBG7dmfyA0Se+F8ij/2UyPN/RptxSkteS3H1adiTgiuLPPWU5q5fav73V7B5C0zpcgLPv+Y4ZxInCIKwR2BEsGYegDXzAPJHfgzz+QeIPP5LzKd+R/T/7kSjsKes8MQvu2tpTRaplOKIw+CIwxTFoubvDzrC1z33wm/u1kSjsPcazcFrFQcfBO3tMsYKFU477bS6bos33HBDzbZjjjmGY445ZkdXa+IyAWa68bhiyWIwTbDk7f52MBHH0olY54lBWxu8CKSS412T3QXpqzsE9x4lY/8OQcSu3Q2riPnsH4k89jMiT/4GZeWx22aTX/t+iitO9oIva6155lm451743R80jz8OkQgcdCAc82rF/vs6gfwEQRD2WIwI1qwDHTdvrTE2/hvz6T8QefoPxO7/Guq+a9CJVkpzDyLatQprxr7YHfMDDyzRqGK/fWG/fRXvf6/mX4/BH+7V/OEeuPrzms99AZYvc4SvQw9GYiEKwh6Kmrkatj4/3tWY2EzExUGUAdoa71rslrS1KjqWgmEoSuNdGUGohxezawKOXxMAEbt2B4Z6iTxzL+ZTvyPy9D2ofC92sp3iilMoLX0ddtdyUIpcTvOPP2v+8jfNH/8EL7zgXFfLl8H7LlAcdQSysqIgCEIYSmFPXoI9eQnF/c6FXA+RZ/+E+dx9RNf/jfi/7gBAJ9uwpu+L1b031tS9sCcvBtPxUTIMxfJlsHyZ4l3nOC8c/nAP/OEezde+ofnaN2DuHM0hB8MhB8vKjoKwJ6Gbu7Gau8e7GhOciTheqolZ7QmCYcjJHTPkeWSHoEI+CWOHiF0TkVIB46V/Yj38L5KP/S/G+gdR2sJOdVKafySlBUdjzTqI3oEI//o3/OsuzYMPaR55FEolJzbEmtVw6psUaw8UFxpBEIRRk2yjtPg4SouPI9XWxtZnH8V84QHM5/+C+fyfiTzxSwC0GcPuWoY1dS9H/Opajm7uRinFnNkwZza89XTFSy87bo733Ku54Qfw3e9rpnRVVnZcsVysbYUdRywGhcJ412LHoBOtzodY07jWQ9gJTMTJuKzGKOziaCOCssU2bochll07FBG7dnVsC7X1WcyXH8N4+V+YG/6B8eI/UVYBG2DyUvKvOIeXmg/j3/3LeOY5gydv0vzrX7BuvXP3NA1YsADe/EbYZ2/HsiAelwtKEARhrNDN0ygtPZ7S0uMBUAMvY2z4B+aLD2Nu+AfRf/w3sb9910kby2BPWoTVuQh78mLsSYvp6pjHySemOPlERW+v5k/3Oe6OP/05/PBHmpbm8sqOaxWv2EfGcGFs2X/f3VjsaummlGyF2G68dL0wgVGIRYcwnixdAqkGi5TqzGRU33qkn+4onPm6lphdOwQRu3YFtI0aeAlj6/Oorc9h9L6A6n0OY+vzGJv/gyrlALCNGP2ZpWxoPZ3H83vz0OZX8I+7Uzz/HBSKXmZ0dTkD1/GvVyxdAosWQiIhA5QgCMLOQmcmYy04GmvB0c4Gu4Sx6XGMlx/DePnfmBv/TfTfP0P94yZvHzs9Cd06i3jbLF7XPovXnDGLoXO7+dsTk/nNn9u4516DO+/SJBKw376aA/Z3hK+uyTK+C9tHPK6Ix8e7FjsQEbqEXRUZvoVxpnta405oT90Lu20ORGI7qUZ7FnZ6EubgFogkxrsquyUidm0L2gar6Pyzi6hSAewiWAWUu90qoIqDUBhA5QdQhQEoZFH5flSuBzW42fmX3eR89wWntIiwVU3n5eIMnhp4Iw+/vJi/rlvCMwOzsbTTZIYB06cbzJhus/++MHuWYtYsmDVTlrQXBEHY5TAi2JOXYk/2rd6oNapvPcbGf2NseQpj67MYPc9iPn0P0eyPAEgCRwFHJqPo102iX03hhf7JPLG+gxf/p4XbfthCtKmFqXNbmL24lQUrWki0tUC8CczouByqIAiCMFLEskvYxVEGJFvHuxa7Lbp9HqWWmSIm7iAmpNhlbPgHRs/TYNvOCibadtz9dPl79fayKOWJUOW/2MXKZ6sY2B4UroK/bavfsq0VQzpNf6mNzYUONuWm8XJ2BZvzHbyUm8LzgzN4ITuTl3JdxBMmkyZBVxdMmQeHH6jomux87+qCyZNg8uQ2enp6xvjsCoIgCDsFpdAt3Vgt3VgcGfytOIix9TlU33rH8rf/JdTAS6QHXmSJ+X8sjW+BoT6UG+zFBv5V/lfGVhF0NIWKpyCaRkdT6GgSYil0NAXRFDpW/htNQTTp/O6mK293Pie9fTCjEltCEIRdBm1Gnef0iYiMpTsUHUuhCoPjXQ1BqI9SInTtQCak2JX4+fsw+jeMOL2Nia2iWCqGTRRLRbGIOtuIYRGlRJSSjlHSSQp2jJIdpWhHKdgxinaEfCnKUClGrhglV4iRK0TJFaIM5mMUyumC+0QZLKXIltIM6QxGMkMsnSTTZNDUBM3N0NwObW2KtjaY3QqvboO2VmhthWRSbn6CIAh7LNEU9qTFMGlx/TTahvwAaqgXa2Arzz/Ry3OPb2XLul76Ng4Q0YMkzRxtqUEmtQ7SnhmkJZEjHdtMnOdQpZwzCSgOouyRTxS1Mn3iV/lvLIWOJCuCWSyFjpR/j/kEs4ibNg5GpPzPBCOCNqLeZ+d75TdnQqicN8xm1PkrCIIAWLPXoob6xrsa24gSwWsHYs05FLSsACAIeyoTUuwaPOOnqKFex5dPmaAMtOH8RZne9mJJ8dqTDAaywz8UKwXRKMSiEI05n6MR53MsCvE4JJPOv0RT+XMCMklFIgGpJCTKvzdlymJWEzQ1QSIhy8cLgiAIY4wyINGMTjRjtM5g1nSYdbjzk2Vpnn4GHnkU/viY5sn/wNNPQ7GsaUUiMHUqTO92/k2bVmRyS47OlhwdTTna0o5Qpko5Rwwr5squ+c5nPKGsvL2Uc9z1sxsxiu4+5d/HeKmx0uyDGTrxW2OapyAIExjXOnVCIvODHYoydtlT3NEOljV8OkEQtp0JKXYRz6DjmWGTRSNw+22aoTwY5RfChiprYVV/TVMEKUEQBGH3wDQV8+fB/Hlw/Ouce1uppHnueRzh6xnN+nXwwjp4+J+QzUaBKNDs5ZFIQCZTedGTcl/4JByxzDSdf+491DTA1s5LdG076wtpW2PqIaI6R8TOEVODRO0cJkMY2sLQJRQlYmaJIw+z6Gi1wLaccAK25YQNcEMHaBu0xu5aWnO8giAIExIlMbv2VNaslnYXhB3NxBS7RkEyqUgmx7sWgiAIgjC+RCKKuXNg7hzwT6601mSzsGULbNrs/N28BTZv1gxkYSgHuRwM5qC3F1580Xkb7f9n22DZ5RdI5bmb85JJoVQSQyV925x0ispLp2gMlp2kaFkoD/+CIOw5aKVE6hIEQdhB7PZilyAIgiAI9VFKkck4VlwzZwZ+Ga8qCYIg7BHoZBtE8+NdDUEQhN0SEbsEQRAEQRAEQRB2Mrpj/hhHNRQEQRBcZDkjQRAEQRAEQRAEQRAEYbdBxC5BEARBEARBEARBEARht0HELkEQBEEQBEEQBEEQBGG3QcQuQRAEQRAEQRAEQRAEYbdBxC5BEARBEARBEARBEARht0FprWUREEEQBEEQBEEQBEEQBGG3QCy7BEEQBEEQBEEQBEEQhN0GEbsEQRAEQRAEQRAEQRCE3QYRuwRBEARBEARBEARBEITdBhG7BEEQBEEQBEEQBEEQhN0GEbsEQRAEQRAEQRAEQRCE3QYRuwRBEARBEARBEARBEITdBhG7dmFuvPFGjjjiCFasWMEpp5zCww8/3DD9XXfdxatf/WpWrFjBa1/7Wn7/+9/vpJoKo2U0bfvjH/+YRYsWBf6tWLFiJ9ZWGI6//OUvvPOd72Tt2rUsWrSIX//618Pu8+c//5kTTjiB5cuXc/TRR/PjH/94J9RUGC2jbds///nPNdfrokWL2Lhx406qsSCEM9pnCmHn8c1vfpOTTjqJ1atXc8ABB/Dud7+bp556KpAmn89z6aWXst9++7F69Wr+3//7f2zatCmQZv369ZxzzjnstddeHHDAAXz2s5+lVCrtzEMRgG9961ssWrSIz3zmM942ab9dn5deeokPfOAD7LfffqxcuZLXvva1/POf//R+11rzpS99ibVr17Jy5Ure9ra38cwzzwTy2Lp1KxdeeCFr1qxhn3324SMf+QjZbHYnH8meiWVZfPGLX+SII45g5cqVHHXUUXz1q19Fa+2lkTbc8xCxaxflzjvv5IorruC8887j9ttvZ/HixZx99tls3rw5NP3f//53LrzwQk4++WR+8pOfcOSRR3Leeefx+OOP7+SaC8Mx2rYFyGQy3Hvvvd6/u+++eyfWWBiOwcFBFi1axCc+8YkRpX/++ec599xz2W+//fif//kf3vrWt3LJJZdwzz337OCaCqNltG3r8otf/CJwzXZ0dOygGgrC8GzLfUfYeTzwwAO85S1v4dZbb+X666+nVCpx9tlnMzg46KW5/PLLufvuu/niF7/IDTfcwMsvv8z555/v/W5ZFueeey7FYpFbbrmFK6+8kttvv50vf/nL43FIeywPP/wwt9xyC4sWLQpsl/bbtent7eXNb34z0WiUa6+9ljvuuIMPfvCDtLS0eGmuvfZabrjhBj75yU9y6623kkwmOfvss8nn816aD3zgAzz55JNcf/31fOMb3+Cvf/0rH//4x8fjkPY4rr32Wm6++WY+/vGPc+edd/KBD3yAb3/729xwww2BNNKGexha2CU5+eST9aWXXup9tyxLr127Vn/zm98MTX/BBRfoc845J7DtlFNO0R/72Md2aD2F0TPatv3Rj36k9957751VPWE7Wbhwof7Vr37VMM1VV12ljzvuuMC29773vfqss87akVUTtpORtO3999+vFy5cqHt7e3dSrQRheEZ73xHGl82bN+uFCxfqBx54QGutdV9fn162bJm+6667vDRPPvmkXrhwoX7wwQe11lr/7ne/04sXL9YbN2700tx00016zZo1Op/P79T676kMDAzoV77ylfqPf/yjPu200/SnP/1prbW030Tg6quv1m9+85vr/m7btj7ooIP0t7/9bW9bX1+fXr58uf75z3+uta606cMPP+yl+f3vf68XLVqkX3zxxR1XeUFrrfU555yjP/zhDwe2nX/++frCCy/UWksb7qmIZdcuSKFQ4NFHH+XAAw/0thmGwYEHHsiDDz4Yus9DDz3EAQccENi2du1aHnrooR1ZVWGUbEvbgmNdcvjhh3PooYfyrne9iyeeeGJnVFfYQcj1uvtz/PHHs3btWs4880z+9re/jXd1hD2Ybb3vCONHf38/gGdV8sgjj1AsFgNtOG/ePKZNm+bdNx566CEWLlxIZ2enl2bt2rUMDAzw5JNP7rzK78FcdtllHHrooYF2Amm/icBvf/tbli9fznve8x4OOOAAjj/+eG699Vbv9xdeeIGNGzcG2rCpqYm99trLG0cffPBBmpubA6FGDjzwQAzDELfxncDq1au5//77efrppwH497//zd/+9jcOOeQQQNpwTyUy3hUQaunp6cGyrBq3l46OjpoYDi6bNm0K3CDd9NXxAITxZVvads6cOVx++eUsWrSI/v5+rrvuOt70pjdxxx13MGXKlJ1RbWGMCbteOzs7GRgYYGhoiEQiMU41E7aXSZMmcemll7J8+XIKhQI//OEPOeOMM7j11ltZtmzZeFdP2APZlvuOMH7Yts3ll1/OmjVrWLhwIeDcM6LRKM3NzYG0HR0dXjzAevcVQGIG7gTuuOMO/vWvf3HbbbfV/Cbtt+vz/PPPc/PNN3PmmWfyzne+k3/+8598+tOfJhqNcsIJJ3htEDaOunOtTZs20d7eHvg9EonQ0tIibbgTOOeccxgYGOCYY47BNE0sy+J973sfr3vd6wCkDfdQROwShF2c1atXs3r16sD3Y489lltuuYX3vve941cxQRBqmDt3LnPnzvW+r1mzhueff57vfve7XH311eNYM0EQJgKXXnopTzzxBDfddNN4V0UYIRs2bOAzn/kM1113HfF4fLyrI2wDWmuWL1/O+9//fgCWLl3KE088wS233MIJJ5wwzrUTRsJdd93Fz372Mz7/+c8zf/58HnvsMa644gomT54sbbgHI26MuyBtbW2YplkTOHbz5s01b31cOjs7a6y4GqUXxodtadtqotEoS5Ys4bnnntsRVRR2AmHX66ZNm8hkMmLVtRuyYsUKuV6FcWMs7jvCzuGyyy7jd7/7Hd/73vcCltudnZ0Ui0X6+voC6Tdv3sykSZO8NGH3FcBLI+wYHn30UTZv3syJJ57I0qVLWbp0KQ888AA33HADS5culfabAEyaNIl58+YFts2dO5f169d7vwMNx9HOzk62bNkS+L1UKtHb2yttuBO46qqrOOecczjuuONYtGgRxx9/PG9961v55je/CUgb7qmI2LULEovFWLZsGffdd5+3zbZt7rvvvoCFj59Vq1Zx//33B7b96U9/YtWqVTuyqsIo2Za2rcayLB5//HEZdCcwcr3uWfz73/+W61UYN8biviPsWLTWXHbZZfzqV7/ie9/7HjNmzAj8vnz5cqLRaKANn3rqKdavX+/dN1atWsXjjz8emMj96U9/IpPJMH/+/J1yHHsq+++/Pz/72c/4yU9+4v1bvnw5r33ta73P0n67NmvWrPFiPbk888wzdHd3AzB9+nQmTZoUaMOBgQH+8Y9/eOPo6tWr6evr45FHHvHS3H///di2zcqVK3fCUezZDA0NoZQKbDNNE601IG24pyJujLsoZ555Jh/84AdZvnw5K1eu5Hvf+x65XI4TTzwRgIsvvpiuri4uvPBCAM444wxOP/10rrvuOg499FDuvPNOHnnkES677LLxPAwhhNG27TXXXMOqVauYNWsWfX19fOc732H9+vWccsop43kYgo9sNhuw3HnhhRd47LHHaGlpYdq0aXz+85/npZde4qqrrgLgTW96EzfeeCNXXXUVJ510Evfffz933XWX9/ZJ2HUYbdt+97vfZfr06SxYsIB8Ps8Pf/hD7r//fq677rrxOgRBGPa+I4wvl156KT//+c/52te+Rjqd9mLDNDU1kUgkaGpq4qSTTuLKK6+kpaWFTCbDpz/9aVavXu2JJWvXrmX+/PlcfPHFXHTRRWzcuJEvfvGLvOUtbyEWi43j0e3+ZDIZL76aSyqVorW11dsu7bdr89a3vpU3v/nNfOMb3+CYY47h4Ycf5tZbb/XmUUopzjjjDL7+9a8za9Yspk+fzpe+9CUmT57MUUcdBTiLDhx88MF87GMf49JLL6VYLPKpT32K4447jq6urvE8vD2Cww8/nG984xtMmzbNc2O8/vrrOemkkwBpwz0VpV25U9jl+MEPfsB3vvMdNm7cyJIlS7jkkkvYa6+9ADj99NPp7u7myiuv9NLfddddfPGLX2TdunXMnj2biy66iEMPPXS8qi80YDRte/nll/OrX/2KjRs30tLSwrJly3jve9/L0qVLx/MQBB9//vOfOeOMM2q2n3DCCVx55ZV86EMfYt26ddxwww2Bfa644gqefPJJpkyZwrvf/W6ZeO6CjLZtr732Wm699VZeeuklkskkCxcu5LzzzmP//fff2VUXhACN7jvC+LJo0aLQ7VdccYV3X8jn81x55ZXccccdFAoF1q5dyyc+8YmA1ei6dev45Cc/yQMPPEAymeSEE07gwgsvJBKRd9s7m9NPP53Fixfz0Y9+FJD2mwjcfffdfOELX+CZZ55h+vTpnHnmmbzhDW/wftda8+Uvf5lbb72Vvr4+9t57bz7xiU8wZ84cL83WrVv51Kc+xW9/+1sMw+CVr3wll1xyCel0ejwOaY9iYGCAL33pS/z6179m8+bNTJ48meOOO47zzjvPE4ylDfc8ROwSBEEQBEEQBEEQBEEQdhskZpcgCIIgCIIgCIIgCIKw2yBilyAIgiAIgiAIgiAIgrDbIGKXIAiCIAiCIAiCIAiCsNsgYpcgCIIgCIIgCIIgCIKw2yBilyAIgiAIgiAIgiAIgrDbIGKXIAiCIAiCIAiCIAiCsNsgYpcgCIIgCIIgCIIgCIKw2yBilyAIgiAIgiAIgiAIgrDbIGKXIAiCIAiCIAiCIAiCsNsgYpcgCIIgCIIgCIIgCIKw2yBilyAIuyVHHHEEH/rQh8a7GoIgCIIgCEIV8pwmCMKORsQuQRAEQRAEQRAEQRAEYbdBxC5BEMadQqEw3lUQBEEQBEEQQpDnNEEQJiIidgmCsFP5yle+wqJFi3jyySd529vexurVq/ngBz/Ibbfdxlve8hb2339/Vq9ezQknnMBPfvKTmv0XLVrEV77yFa677joOO+ww1qxZw9vf/nbWr1/fsNxischFF13Evvvuy9///vcddHSCIAiCIAgTF3lOEwRhdyEy3hUQBGHP5N3vfjcnn3wy73znO4lEItxzzz285jWvYebMmQA88MADfOQjHyGXy/HmN785sO/tt9/OnDlz+NjHPkaxWOSqq67iAx/4ADfddFNoWYODg7znPe/h8ccf58Ybb2TBggU7/PgEQRAEQRAmKvKcJgjCREfELkEQxoUzzzwz8HC0zz77eJ9t22a//fZj06ZN3HzzzTUPUbFYjG9+85tEIpUh7IILLuCll16iq6srkLanp4dzzjmH/v5+br75Zrq7u3fQEQmCIAiCIOweyHOaIAgTHRG7BEEYF4488sjA92eeeYYvf/nL/OUvf2HTpk3Ytg04D0zVrF27NvAAtXDhQgA2bNgQeIh68cUXOfXUU0mlUtx00020t7fviEMRBEEQBEHYrZDnNEEQJjoidgmCMC5MmjTJ+5zNZjnrrLPo6OjgQx/6EFOnTiUajXLzzTfzox/9qGbflpaWwHf3QSufzwe2P/bYY2zdupUPfehD8gAlCIIgCIIwQuQ5TRCEiY6IXYIgjAtKKe/zQw89xLp167jxxhuZOnWqt/373//+dpVx+OGHM2vWLD772c8Sj8c59dRTtys/QRAEQRCEPQF5ThMEYaIjYpcgCONOLpcDCJi89/T08Nvf/na7837Xu96FYRhcdtllAPIgJQiCIAiCMArkOU0QhImIiF2CIIw7a9asIZPJcNFFF3H22WfT29vL17/+ddrb2xkYGNju/M8991yUUvIgJQiCIAiCMErkOU0QhImIiF2CIIw77e3tXHPNNXz2s5/lvPPOY/LkyZxxxhn09vZyzTXXjEkZ55xzjrw5FARBEARBGCXynCYIwkREaa31eFdCEARBEARBEARBEARBEMYCY7wrIAiCIAiCIAiCIAiCIAhjhYhdgiAIgiAIgiAIgiAIwm6DiF2CIAiCIAiCIAiCIAjCboOIXYIgCIIgCIIgCIIgCMJug4hdgiAIgiAIgiAIgiAIwm6DiF2CIAiCIAiCIAiCIAjCboOIXYIgCIIgCIIgCIIgCMJuQ2SsM+zp6RnrLMedlpYWent7x7sa446cBzkHLnIe5By4yHmQc+Cyu52Htra28a7CDsG27d2qnfZEdrdrbU9E2nDiI204sZH2m/iM5DlNLLtGgGHIaQI5DyDnwEXOg5wDFzkPcg5c5DxMDKSdJj7ShhMfacOJj7ThxEbab89AWlkQBEEQBEEQBEEQBEHYbRCxSxAEQRAEQRAEQRAEQdhtELFLEARBEARBEARBEARB2G0Y8wD1giAIY45VROX7wCoEtxtRdDQJkTgYMpwJgiAIgiAIVdgWKAOUGu+aCIKwE5HZoSAI40NxCNW/AaNvHapvvfM3uwk11OsIW0N93mdVHBw2Ox1vxs50oZumoDNd2G2zsSctwu5ciM5M3gkHJAiCIAiCIOxqRB7/BXZzN/a0VeNdFUEQdiIidgmCsGMoDjoiVu86VH/5b986jL71zvbBTYHk2oyiU53oRCs60QytM7ETzeh4CzrR7GyLJHw7aLBLqFIOijnU4BaMgZdQ/S9ibPw30Udu85LazdOwpr8Ca/q+WHMORqcn7ayzIAiCIAiCIIwzRt86EbsEYQ9DxC5BELYNrVHZjaieZzC2PovR8yxG7/MVK61cTzC5GUe3dGM3TcOev4Ricze6eRp28zR0czc63emYmI8VQ30Ym5/AfPnfGOv/hvnMH4n+63/QKOzuNZTmH0Vp/tHolu6xK1MQBEEQdlVyPZBsG+9aCIIgCMJOQcQuQRDqozVqcFNZ0HrOEbS2Pkux7wXSm592rKrcpGYcu3U6unk6xSkr0M3d2C3d6KZp6JZudLJ958ZKSDRjd++N3b03rH4LaI2x+UnM//yWyBP/S/z3nyX++89ide9NcfnJlBa+EqKpnVc/QRAEQdhJqN51mBsewupeg26aOt7V2WYsS/Pb38G8uTB3jsRfEgRBEOojYpcgCB6qfwPmur9jvPwYxsbHMF7+N0Zui/e7NmNYzTMYTM/j5Un7splZvFicxQuDM3lxsIvCSwaF56FYgEIRikWwbTBNiEQ0pqkxzfJ3E8wIwe++bc53RTQu6z14AAD7cklEQVQK8TjEYpV/cf/neHCbP61h+B6ElcLuXIDduYDifueietcRefwXRB/9MYlffhh992coLj+R4urT0S3Tx+HsC4KwJ7N161Y+9alPcffdd2MYBq985Sv56Ec/Sjqdrpv+K1/5Cvfeey8bNmygvb2do446igsuuICmpqadXHthV0cVs87f/AB6AncP23b+Pv8CzJ0zvnURBEEQdm1E7BKEPRjV+wLm8w9grvsr5gt/weh9AXBELbtzIdnpR/JsfhGPvjybvz09i4ef7eKljSZaB/OJRiGTcQSmaNQRnqJR559hQD4PlhX8VyqNYJutQ2o9cqJRHSqSJZPQ0T6Vzkln0tl+JovnPcSy3K10PnQT0Qd/gDX/aAr7vwt70qLtKl8QBGGkfOADH2Djxo1cf/31FItFPvKRj/Dxj3+cz3/+86HpX375ZV5++WU++MEPMn/+fNatW8cnP/lJXn75Zb785S/v5NoLuzraDRNQfQOfoGh7vGsgCIIg7OqI2CUIexLaxnjxn0T+81vM//wGc/N/ALBTHdjd+zC01xk83Ls3dz86j/t/GeXZ55zdYlGYPx9Wr4bp0xWLF6Vpbs7S1grNzY54pHaAi6Jta4pFKBScf/mC73O+/udKOh26z+AgPPsc/O1B6O8HWAWsojP+Pk5beBMnl24h88QveTp9LFtWns+MvWaTSom7hCAIO4b//Oc/3HPPPdx2222sWLECgEsuuYRzzjmHiy++mK6urpp9Fi5cyFe+8hXv+8yZM3nve9/LRRddRKlUIhKRRzzBhxcTc2KrRK5Wt5todoIgCLs9PT2aRx+DA/ZzvHZ2JvIkJAh7AKrnGaKP/JjIv/4HI/sy2ohgzdiX/Mo3U5q5P397dg6/+i387luO+JNIwOpV8PrXKlasgPnzIBqtDE5tbXF6egZ3eL0NQxGPO66J28bwA+rQkObljbBuPaxfN5l16y/go8+dxT6F73LitO8z44+/5Of/fTy/Kr6T6cumsWqlYuUKaJMYv4IgjBEPPvggzc3NntAFcOCBB2IYBg8//DBHH330iPIZGBggk8mI0CXU4r6QmuAmUa7ItZ2G34IgCMJO4oknIZeDbNYxktiZyNOQIOyuaBvz6d8T/et3ibzwgCNwzTmUwqJjKM0+mJxu4pf/C7d9UfPMs5BKwaEHw5FHKFavgnh8z7BkSiQUM2fAzBnuFgW0ABewZd1p2Pdcy2uMmzlO/5QfPHoml/7oHQxZKRYv2soB+9scdKBi0cIdY9kmCMKewaZNm2hvbw9si0QitLS0sHHjxhHlsWXLFr72ta/xxje+cdi0baLWT3hG24ba3ooeSKMyGdQEbv+hIU06XUDhvHibyAzXhlprens1ra1juFL1HojWGl2OfWiMcd/fXcdSnc+iH/8VasGRqMQEDvI3DLtr++1qNDUVKFmalpboTh/PROwShN0N2yLy7zuI/uVazM1PYjd3kz/4A5SWvh6d7qRnq+bm72l++jPNwAAsWQIf/6ji0EP2HIFrpLR1d8CbPkS+/0xif/oSZ6pvceqyn/K7+MXc9tixfO8GuP57ms5OOOwQzatfJcKXIAgVPve5z3Httdc2THPnnXdudzkDAwOce+65zJs3j/PPP3/Y9D09PdtdpjB+tLW1jboNVV8/ZjaLHe3FTk3c9h8a0mSdWPs7xcK8EaWSxrK27dlpJG34xJPOy8j994WmJnmu2Ga0JlLuNKUxHPu25TqcKKjNT2IODGA/9+huG792d26/XY2BAWfc7ukBrcduLBuJWClilyDsRpjP/JHYH67G3PR/WJMWM3Ts5ygtfBUYEQYHNbd8V3Pzf2sKeTj8cDjlJMWypfIANRy6qYv8qy6nuOIU4r/9NK96+f28eu2PefH8i7n38fnc80fNT38Ot/1YM2c2vOY4OPbV8nAqCHs6Z511FieccELDNDNmzKCzs5MtW7YEtpdKJXp7e5k0aVLD/QcGBnj7299OOp3mq1/9KtFodLvrLeyGeAHqdw83xl2B+x9wXHOOPnLH5O/EFHVWthYEQZioeF7041C2iF2CsBugel8g/pvLiDxzD3bLTHKv+SLWgleCUpRKmp/8RPPd72u2boUjD4e3n62YMV2EmNFiT1tN7tRbiTzyIxJ//CKTnzmR164+jVdd8v8YyCe5+3dw5y80X/mq5trvwKuO1px4gmLeXDnXgrAn0t7eXuOeGMbq1avp6+vjkUceYfny5QDcf//92LbNypUr6+43MDDA2WefTSwW4+tf/zrxbQ9wKAD9/Zr+AZg2dTccs3ezmF27ArnceNdAGBm7UKeZcOyGY6Gw0/EcXsbhUhSxSxAmMtom+tDNxO79AiiD/GEfprjXm8CMAfDQPzRf+KLmqafhFfvAue9QLF4kN67twjAprXwDkVecQu6OS4n+7XtEnvw15tGf5rWv2Y/Xvkbx5H80P7pd84v/hf/5mWbVXpq3nKrYf19xcRQEoZZ58+Zx8MEH87GPfYxLL72UYrHIpz71KY477jhvJcaXXnqJt771rVx11VWsXLmSgYEBzjrrLHK5HFdffTUDAwMMDAwAjshmmuZ4HtKE5P4HnL/Tpo5vPXYInmWXTPwFQRCEnYf3rkXELkEQRorq30Dizosx1/2V0pxDyB91KbppCgCbN2u+9g3NL38FU6fCZy9XHHSgiCxjiUq1kz/qkxSXvp7ELz9K8ra3UVz5RvKHXMT8eWn+P3vvHW+5UZ6PPzPS6efWLd7qsusOrhhwIAQIJYQSmmkBYgPGhB8twXRsBzBgig0kIXxNsykJIaEXGwdj3Puu13W9vbe7e/s9TedIM78/RiPNSKNzzr1b7u5az+dj7z3SaOad0cxI76O3fPwjBO99D8eNfwB+8SuOj36c48wzgPe8GzjrzPRepEiRQsfVV1+NK6+8EhdeeCEopXjpS1+Kyy67LDjfarWwefNm1H1zkieeeAKPPPIIAMSyNd5yyy1YsmTJoRM+xRGA1LIrBQDmAs0akD/EKdEOMRjjGB0D5s4h6aRJkeIpjJTsSpHiCIS16Tbkb/oEwFw0XvYluKf9HUAIOOe46f+Af/2miMv1jguBt/09SQPPH0SwReeg9rZfInvvN5FZeT2szXfAecnn4B3/l+jtIXjzG4ELXgfc8AfgBz/keN8HOc5/Nse730VwysnpfUmRIoVAf38/rrnmmsTzS5Yswdq1a4Pfz372s7XfKY4ucM4PsCWwUPhJSnY9pWHtWAlSG4Z7yssV36KjD+vWA9t3AM9+JkdvebalOYRgnrDi3M97S9KFluIAYjYtu9JctilSHElgHrJ3fBWFX78XrG8Jam/7JdzTXw0QguERjo9/iuMLX+I46UTgR9cTvOsdNCW6DgUyeTT/6iOov/m/wbNFFH75buT+79NAYxIAYNsEr34VwU//i+AD7yNYswZ41yUcn7+KYWwsfaFIkSLFkQsysQOoj8+2GEcV6nWOP/0Z2LX7IDwfUrLrKQ1S9xNhHOHzoBNqfqLOp1pwf3vdTbC23TvbYqRIoWE2A9SnZFeKFEcKnAryv3kfsiuuQ/Pst6H+pp+A9x8Lzjn+eDPH2y/ieGgV8M8fJPjXrxEsSQPQH3KwhWei/tZfovns98Be/RsUf/R3sLbdF5zP5Qje9AaC//1vggvfDtzyZ+Atb+f45a85PC99g0+RIsWRB2v3I7C33j3bYhxVqPqK+t69B7JWHvn3yMSRLf3McHAMsI7ukdTH7MjsK58hs0vqYwdYkhQp9g+zGaA+JbtSpDgCQCZ2ovDTv4e19W40XvI5NP/604CdxcgIx6cu4/jcFziWnQD88PsEr38dAaUp0TVrsLNoPvefUP/7/wGyZeR//k5k7/wa4IWfF4tFgne/i+JH1xOcfhrwtW9wXPJejvXrj8wXshQpUqRIcQDhPwoOCslxpFv0pI/JA4MjfR50CQ5MyxzwT7dw3P/A7E+y4RGO224HXHcasjxF7ulhjdT0tC1SN8YUKVLEQPetReG/3wxa2YvG674H94w3AADuvofjwndyPLAC+OD7Cf79GwSLF6ck1+ECdszTUHvrz+Ce+UZkH/wuCj99K8jYVq3M0iUE13yF4MrPEAyPABf/I8cPfsSn93KTIkWKFLOFVLk6KDioCgE7su/ZES7+YQCu/XO4YGycY+/eAyfUTIliDmBy6oCJMWM06oDrAZ43jYvc5oEXJFUrugYdegL22htnW4zDGinZlSJFCg101yoU/vcfACuD2lt+Au/YZ8NxOL7x7wwf/xTH/GOA679L8MYLUmuuwxKZApwXfwb1v/t30IltKP7n62A/8WtttyeE4IUvIPjxDwhe/NfA967jeO/7ObZsPczeRFOkSJEiCs+dbQmOfjSrsLbeo1kHzwjyuXOEE5Tpk3FmIBM7QMa3hQcOs3mwYiXwyGOzLcXhh2mRu55Pdh3FiQcOZ9Bx/4N2at0VQxqzK0WKFDFYW+5C4efvBC/NRf3N/wU+uAxbtnK85//j+PkvgDe/Efj2fxAce2z6UDvc4Z34YtTe/ht4C85A/v8+idyNHwWaVa1Mbw/B5Z+m+MLnCHbvAd55Mcevf8tnHLMhRYoUKQ46eEp2dYLnTd9alytujMSZFDF4WvUDJNER/kyZDfFdJyQSDiEO5OPf2v0IrD0qm3SEz4Np4cjraxBhbxqiE88R11jZAy/QUxDr1s/M2nD7DoY9Q0fenDuYSLMxpkiRQoO15W7kf/P/gQ0uQ+2NPwYrL8Bvf8/xrks4RkaBa75C8P7/jyKTSYmuIwW85xg0Xv99OH/5YdjrbkLxJ28CGdkYK/f8vyL48fUE554LXP01js9fxdFopA/NFClSHIZgwseGU3uWBTl8cdc9wK23T+8a844/w2DVY1sFWRNUM816Wo0ZtXuwMBvKkr3hT7A2/vnQN3wwcZhZdh1ozGZA7AOJac136cZ4QMiuI3zgDgC2bpuutaGYdGvWcjz2+EER6YhFGqA+RYoUAaxt9yP/m/eBzTkJ9Quux6Q3gMs/w/GVqznOOlMEoX/2s1KS64gEtdB61rtRf8P1gDOB4k/eaPTvHxgg+MpVBO9+F8HNfwIueS/Hzp3pi0eKFCkOMzDfsot0fp3cvOXIcc+OWdQ6UzOOh9Oc7mXMC4gIQqC4H85g7JpVWEOPw9q1CmGspu6DAJHJXbA33gLURqff9kGCOgyH0vKZsOkETzqMsT/zCZi2hVulMrsf7PZnijz2OMfIyOzILuWelhsj812d048PswLuMzoERzeRvD9ILbtSpHiKg+5cifyv3ws2cBzqr/8eHl3fg3dczHHX3cD7/z+Cq79MMDiYEl1HOtiSZ6L+1l+AzT8N+RsuRfbWL8ZeICkluPDtBF/7KsHICPDu93KsevjIUBRTpEjx1ACRZFcXytWGjcD6DQdZoA7gnMPzpr+P2pvvgLXljoMgkaGtdTehsPNOANHQOzPY/6X1jvp8mYZFD6mPiX+dyem3fZCgjkLq5b8/mP7gkeo+2OtvBqnu6/qae+8H7rx72k0dWMxwnuwZAlY9fEAlmTam6QAt/kljdh0yTE1x3HEnR7PJEUTzTzemGGZzSqZkV4oUhwno7kdR+NV7wHsXovra7+P6/+3H+z/EkckA1/4HwZvfmAahP5rAy/NRv+B6NJ/xTmRX/RiFn10EMjUUK3feMwi+cy3B4CDwT5dy/O736UM0RYoUhwmmYdl1OGDDBuDPtwGMtd9HTboKUV0BDzJos6JK4/9zgPb+GVVzGL17HM6PQKfSucxMcCCGP7pGZ+DGSOrj/r9jB0CgFG3BI/9O55qDuF6nptJYsiq2bgOcJjCqGL8eRrvlYYNuAtQ3GgcnG/2R8XaSIsVRDjKyEYVfXQJenINtL7gOH7xsEN+/nuNlLwWu+w7BqaekW+dRCSuD5vM/ivor/xV0eB0K/3UB6O5HYsUWLyL49n8QPPM84MtXc1z3g/RlI0WKFIcBpGvXkeA2wxl27BDk3IHYPunQEyIe1kEANxhokP1leY6SR4buxjh7ckRBKnthb74dZHLXbItiBI+aVszy4E03ccNDqzgefWz6MotuzuS6w2NyMQY95l5bHASZWw2Qyl4AwOQUx30PAFsOzrZ3FOAoCRR3ENBNgPo77wZWPnTg207JrhQpZhlkagiFX74b3MrgtqXfx9s/MA/rNwD/cjnBpz5BUSymRNfRDu/kl6L29z8DskUU/vcfYK+5IVamVCL48hcJXvly4LofcPz7f/CO1gkpUqRIcVARuDFasytHF7C2348FI/8HoLOe342eS8e2wBo6BFGIZ5KWrasKj0zsF9nVrILuWnVwgrP7GZZJYyKxSKPBccutHJXKob8HJDZY+yHDARD/gQenl7hhZBQY2rv/7XaL2ea6gvabVdgb/gQytnkaFx040IntsHY8CABo+Alhp6YOeDNHB4j8J43ZFUWgyXaYopMHYW6lZFeKFLOJxiTyv7oEcKZwbeXb+PAXFuHYpcD13yN4yYtSkuupBD54Ampv+Sm8hWcjf+NHkL3n32MvLpZF8PGPErzpjcD//hz40lcOjslvihQpUnQFn+xyGcWqhzkc5/Ddj8g0gqzPtqIrIb6Gz8SfyYSZxPw6/GIA7c8oWHseBZ3cBbQhpGaMwHQhWdF1HGGpU693V+VBnYczqDxmHbYfqFRnfm29zrF2XXsL98NhyjLGcd/9HKOj+3EjmzUAAKl0EydNtnNwOn+YbIuHPw6XB8jhhFkMZ5aSXSlSzBa8FvK/+xDI6GZc8eS/4bu/PxVvfxvwH/9GsGjhYfCUTnHoURhA4/XfReuMNyB737eQu+HDsbTvhBC8/70EF7+T4MabgH/5HEerlT5YU6RIsf+4/wGOjZu630+ktcjIKMHwiIhfkmL/YbwDXWoJrZZJuT563in2S1kKUtt1OR6NCZCRjd2VlTGx2ggYZNg7DB7ZZJrWbStWcuwdOviCd+M++PgTwLbt3VkYCS/Gmbo/7h8aDWCqAqxZK36vWcvx8CPdVRyluLuZsXHrvf1Am6oOByKxGzgOx823cOzde6gWXOrG2AmzMTIp2ZUixSwhe/uXYW+/D/+y6vO4b+jZ+MY1BO+5mMK2j5CnSIqDAysL58WfhfOCT8Be/0cUfnYhEAkGSwjBRf9A8E8fJLj9DuAzV6YWXilSpNh/TE4Bm7rwlgGAWo1jcsJXmI+w7edI+fBOCEJhuxT64UeAjXc/Bja2Qz/Bo+rzEYr9cWOUBE+XCRXsLXfB2remu7oDsqsziXR4zL/pCTE2DmwM9oaD1wF2mHiAHch7JMmh7TuAfcPTlWM6OsHBnViHx7ztHhU/X8TOQxFGjyC40akWF0c3MbsOFlKyK0WKWUDzgZ8j+/B/4fr178To4lfiB98jeMa56faYwgchaJ17IRqv/ibo8DoUf/pWkMmdsWIXvI7gn33C68ovpoRXihQpDh3uvhd44smjhECJYNaVOuk9qP7ocoyrVaDsbIO16+EDL9dhgP2J2cW5J1xtD0b20C7cGIM76RdxHI4HV3A0m4diwkXamPVJbsaBEku3PpqdvgZ9mcHrfcBxs8iBthdNv52jGbSzsWUcrQbQ6tLPOBGdGdtHH+N4cs30b1ilwjE2br6OVIeD2IGHG8gsGr2lZFeKFIcYG257CD13fBZ3730erBf/M770BYL+/pToShGHt+yFqF9wHUhtFIWfvhV0eF2szOtfR/CB9xHc8mfgqq+kQetTpEhx6BAE4j1MFeckHO7imsXr0v2pQ7HpuTod3BhAM8H+POKG9nCsWw/UawfWfIhzlUBrI2DEjXH7DmB8ogvLk4MxXyOkHN21CmRkQ4eLxDzYu4/joVWdhbLX3IC+2urpidUNpzON8Thga70+BjIR/+jYTdv7s3qmJ/5B/PigDOSR4sbYTZwouvtRWNvuDX7bG2+BvfHP+9VgN5lzh/YCO6Y3nQAA994PrFhpPmdtvx/2ptumX+khgJwzrgesfpKj0Th0D+GU7EqR4hDBdTl+8p2dWHLPBzHUXIrei76K173eBjlinhopZgNs0Tmov+nHAIDC/7wddGc8L++b3kDwnncT/N8fgW9+q33Q1hQpUqQ4YIi4xh1uT7OkvfBAZGM8JJiBG6NEPFZRd9czxuF5sz8Ak5Mc1apBjv2w7KpVPAAwJlKYnOTYtXtm/RZyTMON8bBw1dP7Sid3wdq31liSTO4EZc3g98ZNHCNd5nvoceJ+0XT7A5hTWWEsr7kxNqsgtZHkyg/ihhPNzWBvvQfW7ocPXoMJ7U/PjTG4+oDKEtQ5+9vCtED9oWvnGksntk8reUlbHEBfPcc5NN4aXHm+0KEnDrpl2OioIPfXmLeag4KU7EqR4hBg506OD32wjufu+BDKOQeld3wTJ5zaN9tipThCwOaehPqbfwJemovCz98Ja9OtsTJvf2uYpfG//2cWhEyRIsVRAzK6CXTXqs7lpqn9ZN0x0H3rYK+5YaaidY0nVnP8aYYf6Geb7DInQZyuz97M2r7nXo6VN64GWrWZVXCAcP+DwD33xY93ujeuy3Hv/RxTU6aC4hg3mIfd/yDwhDRCajVgrb95egJ348aYEKC+0zfPAzIfo5V0y7i1GrB2PYwFk7eB8mbn8l2AVveh0BoynlPFtDfdBmubYRJ0gQPtxjg6On3L+QORzDQcD/EHYxzr1iclJjqIG9ch2hTJ1B6gMXlg6jrkX18OnK/eHXcB985s6k8Lf/qzcKlEYwJ0bAus3Y8clHZIhHg8lPcmJbtSpDiI4JzjD//HcdHFDG8tX4HT+1eDv/ZqZBYum23RUhxh4L2LUHvTf4LNOwX5334Q1ro/xsq87x8JXvpi4FvXinmXIkWKFDOBtfdJ0MluovpGrI7avMDmWsOYP3UP6Mh6/ZqDhF27E07MApNVcrZprjIdwQ0/ph2fKnJRl/12KxPocTaLmF8HQls/wOjUi7ExEZjalGiBEubXoY8Lqe7TypHqXhCve2JHHVrSTYT1w+Hx3K0M3APnHJS30NPYdFBFAjoHqJ+a4piYDhfCARq1WHMqIFNmsi24TBmfffs4du4SrmfTgayCzkTbDohRAsY4Wr6Vz54hkfV2o+lWzNAKtFuBDsV2YO1cCXvLnQekrijBcjBgGurpfgRKQsM5INV0xNBeYN+QG3HHTsamzRy33c6xZu30++mlZFeKFEcPKhWOz36e4wtXcfzzedfhpQtuQPN5H4a37PmzLVqKIxWFAdQvuA5s4VnI3/Bh2Gtv1E5TSvDJjxM865nAl77SfYrrFClSPLXguhzV8SrIyAbxJX3G6D5GjMUaCdceavBDzncN1B6blqtMEClrP6xT4m6MXdTjTOGYqbun1V6txo1ugQcLnQLUS2XKRDAEMeYUCx0ythnW9geQb+0NK6XWfkg4fcuuWcE0fCmj43yglPlu2gKguVbd/8D06qPNiRhxb2++HdZOsxtlFIwDdT9euetOr+0DM0wEm7cAjz4qrRL9qo11H5mWXWRqjzEJ04HCIdvvg/36cFjg08OTaxiGRwDeBdm1cRPQckXMwW7geeEzlwlP8pkRwDNESnalSHEQ8OhjHBe9i+PW24Av/MMdeMPA19E69ZVonfeu2RYtxZGObAn1130H3pLzkLvxo7BX/1Y7nckQXPkZgqVLgcuu4Ng9wxgkKVKkOHqx8iFg259uhbVvLaydCdFufaxfz7Ftm3kfIe01r/bYDw1k9+7u45lEXY9IF5myDgmYm+iuow+NHw+tPjY9hXAGw0sq7a1dTLj7XuFyc8jQgexyfE7VSHb581WN5Uaawl3TYg301teIj0jEnp5IWmi0ztkYWZfLRp4fGwf27j3Qz/Iu6zvEzLDJCsfefEcwrtOWZobyq5etW9eaUR37Y1EUzBUO1GrqEQGjYUwkhuKBBU8g4fcf1s6VbbPHjo9z3HwLx8TE/t/LsfHuYvN1G/u2PL4KCyZuBbwmuoqIj/gz6XAA4R48F9Mn+p1K2/42mxx/vg3YulX8Tt0YU6Q4wuG6HN+/nuH9H+KwbeAHX96Cl9c/BnbM6XBecuVh5QqQ4ghGpojGa/4fvGPPR+6mT8B+4lfa6VKJ4MtfIPAY8InLOGq1w+/BmiJFitnD5FT3ZbdsA9auPwhCzDBK9+Qkx+Oruw9wG1c4O1t26eTFwdk/6c6HhLuOaRxUr1D/bzq+ra1CGKsi6sao/l0fE8lOuu7b4fPu0s6ya98+jnV+QkGzZZdvHZMQuD9w05vJu9o0XMjillLtsWkz8MhjyedXrDRnR3Rdjno9QZ79sOw6eBcl0DScgcCbWX0zXL7qvmGxmcUqa0cO0aEnQMa2dq4jEqA+6M9BX5K87c/9Rdu5GcGobxTbbVKEKNQ5sGKlEpuvDch453sDALn6LtisBsurh9d2GCyvzVQm49uNiagOBtR9kPKWkLoLyy4Jy6vC3ny7MVO8hOO7YkaJ/pTsSpHiCMS+fRwf+jDH9T8EXvY3wPXfnMLTHnsfuJVD4+++CWTysy1iiqMJmQIar/4WvBOeh9z/fRr26t9opxcvFhZeWzYDn79q+oFVU6RIkULFfffH9xACBs75DPWgmV0lFYVml/on5wA4C/dAPk2y6wCBc65VTBrj4g8mYiI9+lgYVH1/mu94Leewdj4EOrXbt0YIQWIdP3TPjYkJjsnJLoiiNuemFBKXUuE+s3OnckUHw4sD4p7XjkTqng+bFsbGzUTAipXAXfd0EKajQIZznKPkbBFWHdO5rguwBCKAtCGFjeX306VMJbsIj/gvMg9072pwr71foxTZNK/o2BZYQ493FmQ6nFNAuBqO7TcObMyuVQ+3m5sHBoGd2wyGgO423BvO22Qr5Og2QH07ssva86jYmxUMD3PcceeB99RQ5aByjpPuLbssP2FF24yp0Talm3lKdqVIcWTh/gc43nExx7r1wL9cTvCpj3IM3PpRkImdaPzdv4H3LJhtEVMcjbBzaLzq3+Ad9xzk/u9TsQxn5z2D4IPvJ7jjTuC6H6RkV4oUhzPGx8dx6aWX4txzz8V5552HT33qU6hWu0sDzjnHxRdfjFNOOQV/+tOfptVut1/X3dGdwvVObxmch8pcWyUoenKmW5ISdLgbV0bGAFIdVi7nwdfmJJjcCPcXknQLBfFfwTlDpSKCBEuLA11n3Q/XHS5riMbV8u9XZQhk4uDFypkOHlghMiJ2QjvF1Va8D22vgo0bPaxeIz5GAsoU7PTxZ5pWh5oVXRfZGNtVv207x4MrDoxF9lRbLkolu9oRdGYye6D2BKytB95/NanXhMcZgoPpYamRXRHXZzKxDXR0M7Av2aIFCKfZ/H03dpXdVkWn+G7mvVZ3Y6T71gq33Bla0RoFOkAYnzig1bXFTEW3vQqoYtVH9z0Je9NtQKseLzyNMW5HdpkwOQk4zQM/Zuq4UO676kYsu+jOh9qQWdPPPhmQ2dHXgYO4mFOyK0WK/YDrcnz7ewyXfoxjzlzg+98heMmLCLJ3fR32ljvhvOgKsEXnzLaYKY5m2MJy0FvyLOT+8HFY627STr/utcDfvQr4wY+AW25NCa8UKQ5XfOQjH8GGDRtw/fXX49prr8WKFStwxRVXdHXtD3/4Q5DpfG73XyzHxjg2bASGR9rvDbZXwZzqw7A23gq44cs/kVZSHV5USW0Eg9WH9WMziA+lYmwcuPX2zoQXGd8Ka8eDiogcKx5CYEXVEQfoJTxOdvlf0A1K/IGwAFJJyN17CFY+BLRaeoXWnsdg7X4YqI/pDR/GaOfGaFnhiYF9tyM3JNyBpHIZqGaGgeWq9jUjskv/Ya2/OdEdqR3XtnWrUGonDeHcOimE1SrHjh3dzmulj6Y5GJ5MnhbtLJuSZO0wtkmnTbH2ulof01hE6vi2jcPGE47HKgz/7C67bRzSIjV0wW3Xnn6SjG0xHp8Z2syDAwTH4RgdPYCNSMJwhlzfgsnbcczUHcHvIMmIIVMr4SxgIAn3UHK2Jo6712naRK7jEM/g0t77O6zV9rjrHo6du8K6o9aLsWeU1wKd2g26I4zrqa2RDu2R6jBIY0w7Jq/Z7wD1je6Zv5TsSpFihhge5vinSzl+/J+CTPjOtwiOXUpgP/l7ZFd8H82z3wb3jAtmW8wUTwVk8mi85j/AFp2D/I0fhbX+5uAUIQT//EGCs84Ervoyx5ath78ykyLFUw0bN27EnXfeic9//vM466yzcN555+Gyyy7DDTfcgKGh9qTQk08+ieuuuw5f/OIXp9GieKGt+8G8ax0MyCQRQLxmJIuZsPropIST8W2xY9aeR6f1whrUFfnd8St5Q++cVBprho/zBx3qOBHFRC1aTBbxHNCJeMqryUmWbPljuBdjo5Gmokqxm2DqdlADXs8QbUSxbKUQAbJNYdEXjXPU2at/Jv3VzbaI14y5I3Vj2SWVwG3b45Ycneb6irtG8eSa7pRh0o41VNGF22/ChebDMevQyOkkSyYw5Fr7UHLCvaSdWKSDy2ontLPsUljTrutIREId0aWXdceF9b5vGtPWsuugWMmEdR4sD7SHVgE7dwn3Y7MITPvY0i2mMxqMibAfcggtZtobTSPAg+Pl5jYM1B4PycYIOq3j6O3jHOirrwGtDyPnzixwGecc9Tqw+snwmDo/A8sujQSPp7bV57RcZOY2re33I7fD7KuqzV/XmdaUJZW9sLfcZXyvMCElu1KkmAEeeJDjoos51q4Tbosfu5QilyOgex5H7o+XwV16PprP/9hsi5niqYRMEfXXXgu24Ezkb7gU1qZbw1MZgs/9C0GhAFzxGY5G4zBSXFKkSIFVq1aht7cXZ5xxRnDsOc95DiilePTRRxOvq9fruPTSS3HFFVdg3rx5XbcXjSEzLd1Ic+HQv/Z3Y1ymxmVKJFm6QMabRH/tsUTho/poaNjVWQM9KG6M0bp8dxF78+0xSwEpYnnkQRAnnk3gzrtbuPvebhoV7TVlOJbE+9Ohj4c4I187GIyoAkTjNJGILiZ/k04sxDT7O91sjBwAbzWQn9wQKyOToU0YLLvcNjwRZQ7mT92LwdqjyWSBJoxKdnWw7IpgJlZGATqMfaJlF2eYV3kAAzUlUv8BnpZJ/J9Kdllb7lavaFufJO7abouuA845mk1zXSxKTEuyq23L3ck3LcyY9OweMg5jUjOlfQ/A3nBzwlkBMrkL8ARxMxOufu1a4Mk1hhPMA9xG/HjEyo9zDuoTZISZs3gmkl1RuWXVHOCEwvOU2FqAT8p1R3CaTutz3NP6IRrw21IyNAZx27irvEdMf2IEe/HIRtgb/gRUu4/7hZbIoGt6Npowvdy6KVI8xcE5x4/+k+O73+dYtgy48jPCmgsASHUf8r/9AHh5Hhqv/BpgZWZZ2hRPOWRLqL/22yj84l3I/+6f0Hjdd+EtfRYAYM4cgis+DXz4oxz/+u8cH//o4ZNdK0WKpzqGh4cxODioHbNtG319fdi3b1/idVdddRXOOeccvPjFL55We6ViEbl6DoUCRS7H0NPbi4EBG6WSeEkvlUoAADowgFKxgXJtK3K5HEqlDFAcFMdLDgpuFsViCUVWQAkl9PZaGBiIv1qyqV7kcsLKZs8QsHCheD6S3h6QvoFpyU4IQ6nUQrG2G73OEPp6zkehnIv3seSAAyiVyyhZJbguRy7nolQqwrNK6O21MTBgxa4BgP7+DMplQUZxrwWujAcg3gWix5JQKjnI1XIoFW1k+npBskUxJsO9gO0rFpkWSqUBlHsISqUS8gUPJVJCifDgXgBCuRlnfQBaKJVKGBgw9LtYR66SQ6FgodzXBz5aQi6bB8vnUSyWkMsRwMoCXviOQvr6QPoGwJ1e5HKiznK5B8iVAW9cnE/opxwzkyzTQbSepHp7elyUSkIx6+vLoLc3/G5fb3golVwQ1kIhUwC1KUqkhJ27gN5eG4VCAbkcQ6lcxoDfH1YtA60SisUicp6Y46S3B3yyhCii9zqUMYtsvQY+XgKsDOjAAJhhfjQcIV+5RMF3PICB1m705Y5HX/+cYC729TYTLSXL5Qx6euJ2CqWSA8sDco0cLMtBb98AshmijSEr6f0hvT3BPeVOJnE+84wLp1hCLuf6bWVQKOSRQw6lkg1bKc92PARSngfSvxTcbYKXSsjlxFzdvIXCcTjOPycbHi8WMTCQ18ayp9dGf7+/9hSZB63RYG7KNdHXl8HAgNluo6enhVKJiX8bYT3qvSH9/Zr7t+dxlEpN//oMSiVBPuScPHKtHAoFgrLVAunpAa+UAM6DeWRCvS7ud6GZR6lkB3Kr94P0FPDY+hK272R4+cuygTxynpfLLXC/38WijZ5iGaVSBr198b2WTZUBtwTkS6KfxSLAmeinNT2Vnzu94Mq4kf5+TDYKKJVcY9tJmJxk4AD6evX7FO612eA+l0ol5HI5lEs2Msq49vaKseilNZRKpcQ9l7sO+PZ1ID0lkIHjg2eFbSfvK/KeTJI+rF3nwbZzsACUSnYw5wcGBsA23w3kbCBngwwMgOR7AQCjhTwaDkdvTxll3gNmuSjks7DdHHp7e437ptwHCPT9jTemwEviWaXOlZ6yi2ahjAzPo5TNgubF3n/DHxz09xE89zlZcMaMa5gzBoxuhtt3PEolN6gTAGybBXO84OVQyBfQUw7Hl9epqDPfGxxrtcQaWbDvN+H4Fcz3hJVKIJSh5MT30n5/DrFdQ0CphEIph94cQ291Nfr7ngtCkwPlczYGXimB9PYmllGRkl0pUnSJep3j81+s4KY/cvzNS4GPXUrESyMAuE3kf/chEGcS9Tf/N1CY3st7ihQHDLmyILx+diHyv34v6m/4AdgCYS3yzPMILnw7xw9+BJxzDsdLX5wSXilSHExcffXV+O53v9u2zI033jijum+55Rbcd999+NWvfjXta6vVChzHQb0mUoNPTExibIygWuX+eeH65wyPAmNbkKk9AQdAtdoEoz1gY2OoVjkK9Toq1SrqNYJqtYrJSWBsLL6v0KlJOEpU+GpVKJTe+Cg4K0xL9slJjmoVoI0Kco6D0dExFFtxoqVW42AcmJqsAM0qWi0RmL5WrcK1CCbGgUJel1X2f2wMaLX8c14Ltj8e7pgff4Sz+LEEVKscjuOgUnFAxkaBrBgHq1oFqYs6Jq1xVKslUAJUaRX1OkeVV9GglUAmANg3zLFiwzhKpRKq1SrGxmqx9ipVF72Og2oV8MbHYFWraDQs1Fkd1WoVrkvArSaIF1odeOPj4KwIOhnep0plCtzxQKtVeBPj4MSsWIRjFpdlOojW05isghMLY2N6JuuJCXH/AWB8HPC88B5OjItzhDVRz9XR9CxUWRXgHu67l+FMWofjAFNTkxjz7xudnAKtVlGr1ZB3HFSrzWDc2JzloCMbg/qj9zqUuYq8MwGrWgUIhTs2Zpwf4758E5MAii4a9TrqTgWV4TrGyRR472JMTYX9i2J4GHDd+PqqVjksr4Zex4Fr2RgZHkM+T7QxtSOVsolxsJwvmzOVOJ9JdRxupRokdahWm6jXa7AcB9WqA6aUt7eLDAvuqa8A3CbsahWOw1GtVrHBH8ZTj68ox6cwNlbXxnJ8HFi1CshmgWcXq2CMo9UC7PEnIXcQuT+NjwNUSesmSUJCCCoVMY6TE5Poc8O+6/dmVAvI7brh2I+NIfibN/39sg5UqwTexDisahWlORzjbdb/mH+/G14D1SoJ5Fbvhzc6jHXrGRgHRkerQX/kPjelrUkHU60xVKsl415LpyZBq1WgWgV/+AaQRhXgDM7wCNZvzuDE5YBtd/fet2fNOIrVCgYHSTBu4xNNMaYJ+/yu3RzZLDB3Tnju5lvEPXnJi8x77ehoFdlqFeMTHGOVKkqOg0rVETfAhxyLeqaOapVoc5QxDtcFslkCtOqwq1XxXCF9wXqjVIw55+r9rWFgYCC4J3ffM45qDVgix7rqBHN+bGwM9p7NQZvu+DiQE4R7o96A4wBjIxOgqCDrVtFoEGQ8B5OTU2DZ+PwYH/PlIvq+SarDsKriWVWtVABCMDZWw8SEi2ZtDFXWQN2toOqJvb9aFfWMjRGAecY1TMa2wBp6As7gOKrVE4K+q+MKAPl6HXW7jsrkBDx5fW0UdrUK7mWCY82muEbOyWq1Ce5SeP47AedAuSzutV31n2WGDW1ySshtVasgXhPNsXFkR54AWsOY2LUQvDQ3dk3Qpwmx17LsJPoWJRYLkJJdKVJ0gT17OD55GcfGTU184H0Eb7wA4dcgzpH78+dg7VqF+qv+DWzeybMrbIoUhX40Xv89FH76NhR++W7U3/gjsLliXr7jQoKHH+H46jUcp56CwDIxRYoUBx7vfOc78drXvrZtmaVLl2Lu3LkYHdVjcbiui4mJiUT3xPvuuw/btm3DM5/5TO34Bz7wAZx33nn48Y9/nNhmdNUnxpLmAI26Ytgh+UB8N8YZu7fMNHIwwphDPDnID8CFy8jEBEexKE/o5V2XY3gYWLAgaS+M1888hkaDI5+f5v6p+Y0oX64jY8yZlF/+IdBNFq+oi6pSaxu3ng7uKKrcrgNry13wlj5bWH4dJCyclK74r0wUJSpt4GIDNYIOcMzUXch4FfBBWS55wopz/tyypmOtJt2JOs9pOe2lFOU9d8Fqcbi9i9te1/b+K++k2rLi6kgY5AWUFGmmYvH+dOXGmFhd6IZlDDrPgEoVQBVoZTh27wEmEkL7Rdu/4y4AHHj+Xyll2rlYiSwOAOegux8G6VsGQJC62rYSGYPADXYGrmMxMC9oi7F44O64S5sfE67D1sOroyCWqGzPELB9h7jmlKiKwrlwn7Oz2uGt24HeBhAaHPNgGJLaltlkX/Ki9rKZsH07AMVOoFbjKBY7769PrgF27QZe/Nc83Pv8QaP1YeRbHprZYwD464d7WDJ+E8j4WYBijUT2M7DTho0cux2Cc0/2g9UDePQxjqe/sM1F0e75bpJr1gLoD9dtaeQhsNYwXOhZSY+ZvANNux/AWcltyAlkyCCpP37j+xdp48ZoamPtOvHnM85NFkcimOfBvuWhq+yObhN0n8nPNBkp2ZUiRQc8/gTHJz4tvhxc+x89OP00naHOPPwTZB7/BZy/eD+8k14yS1KmSKGDl+ahfsF1KPzPW5H/xcWov+k/wfuPhWURfOZy4KKLOa74DMe3v4XQQjFFihQHFIODgzH3RBPOOeccTE5O4vHHH8fTn/50AILMYozhzDPPNF5zySWX4A1veIN27FWvehU++clP4oUvbPeGDSCqZHIzAWBUrjVNR4/jop3hHJs3A/39QGVLEoky88xSsg8sgeySYu7aA3hDwMIF/vEgtor4/eQaoQyWSmE9nZTUdRsAvgE45RTedfBbrrQtBAmvDKysJIGXJEdX5IIoxFi8AjUjpQo6ugVeofM8BQAytQfEbYCObwWbezIKzSHUs+1JmkR4TaFoZ3U3l/ZklHK5J+6/tIZR7qDGE2a8CgCg5Q/zpk0cg8fpZKUM0KxlF+0yw2n8PiXNd+W0WrcU1muC8+QQGG7b0Fphm+q6XTJ+I+jQCR2E1uX1PI5qDejtSeh/d2kQjUdZKxQuIKyV+uRynlN5EE5faF3VTQvNaccu92vwmqCTu0DsXkiyS1uqsf2y/b41Ps7x4EpgmWHYY0iI6xQ0xfR9iUc+EDDGsX07sHQpQP1xdByOdevFsf4+QPbTtJ/TvatBx7bAPeml7cOv8La0YWfUx0And4Ed87RotQGKznYAQGUKuPde4Iyncyw4Rp+DMk6VNDjYMySPA5a07GMeOIDcrvsxtwLsGHgFABHzTgacp6MbgBPCZ2tsprftrGFdKM8VOV8qNfP6SVw+bkNZC2GhTCMMZSBja3HOkfGmkPGmAJyVTLJTQfVww83Xn90Gst6f57wjE5g8v5JAon9xhuBp2mZ/oUOPBXHrukUaoD5Fija4626OD32Yo1wGvnut8ItWYW27D9nbroJ74kvQOv+9syRlihRm8L7FqF9wHQhzUfjFu0CmxFvB3LkEl3+KYMNG4N+/tV+vLylSpDgAWL58OZ73vOfh8ssvx6OPPoqVK1fiyiuvxCte8Qocc4z4Kj00NISXvexlQcD6efPm4eSTT9b+A4BFixZh6dKlbduTL7aOohwaA9iaiCSlYEDemDIAjgEbNwMrVwH7hhME4RyTUzwgrMbHu9+PQuIogezy/5WuKGFwb718w485rOqQWglD9RNjorDXPrGcBlFWUVwVsoN40kErYjETaVu9Hf21J2BtfyDe0AzMbUhjHNaOB43nVEunKOjQ45hTfRgZzxBNvQtYm++E9+St2LhJr7+dwZ/avRUrgTvvNp3j2Dcs3NxUSLKLANi1Sz+X9RTzocCEpTs1KTZECYkXAvm8FtAIx4xb/rtlq9FWxzYFqA/J3vDfQOmUFi6mrHDqOo4oj48/Adz/gLB6BGeduLuEk+aznqtYjsAT8e8MvFuhtReuK1y9kttIPhUmqEguNLSX4eZbOGpVX6lXxsFTya4okeD/Ht7nYcXKeP07t7fQW1+H5rbVIKzZPpi80qa2B0nuQW0WoWWP3D527BDk+/btYWm5p034yUB8A6/4umo1wrnRkUDYvwD11o4HRVvRZBxKnYM18WybEtw0HEM8+CdW+5ZPUel40g8E90sjYyJForz2uvXxNtpBJ0R5cNQEDqDkbEXR0TPtklZDsU5NeK759z++FyQ8q6VVluFh5WnzwUB2yTnRybJL1ufNwFibEHgexwMPKDenTSPEm342zpTsSpEiAb/6DcenLuc4cTlw7TcJli7RNy0ythX53/0T2JzlaLzsqv23gU2R4iCADy5D/XXfBamPI//LdwH1MQDAs59F8Na3AL/+DXDPvSnhlSLFbOPqq6/GsmXLcOGFF+KSSy7Bueeei8997nPB+Varhc2bN6Nej7sjTBv+y2TFVyqSdoCo1VQ8yxvXLWHiTQTlTHDqLdz/gFAstu8Q1hDDI+33o1AZ8C273PaWXbJ8NDOaelWxuROZfY+D8GmwV20wMcGxbr0u14aNiJiLUGzZyjE8zAEvQo4kDYFyvOxsAakmJy+IXkBianMEXit2fHKC4/EngEolYYx915gYGWCC14qRQMRtYNNmYNPmiNTtpkDknGbNE1EUo5ZQDUV5JlQQIa5PvmTd8bBcnWFqioN3mfMuBrf9Gu0ZfTDIvAYAnAqyi7iNtgSOaZhvuVX8KyUlUN0Yu6wsUvG4z/uJeuIkR7KrrNaA8ajXUpVahvvuB7ZsVc4rorhu+1drzgG4HZTfNqIO7REnqxW/UYXwGVGSw8Utu8TvdetdjI3H6y1OrUFvYz2K1c3oa6xvnzpRcevs+MGBI8YmyDkuXPQSSGmqlFFgb7yljWAhPI+Dc9bWjbFjBlAqrMbqU46I48U9WJ45zp9c0znpMR/JqrpzR2RMAGzYAGzdKsdGHyOb1XwZk8UjvjurRHsrJe63yVBvSBF5+H9p5ZVEdnFgoPY4BqqPAI1JEBkX0HOM1reaBZZfd0sxCGy14mNPh56AvfZGNJvAxk0c7tRYjFzjJkLXlI1Rcbk3k1k8ONeJEM214l++PE+271votnueTOcLk49UO0+RIgLOOa79DsM1X+d47nOAf/0aQX9/ZMNyplD4zfvAqYXGa74VM8FPkeJwAjvmaai/5v+BTuxE4ZeXAI7QcC9+J8FJJwJf+grH2DQsKlKkSHHg0d/fj2uuuQarVq3CypUrcdVVV2mZ+JYsWYK1a9fi2c9+dmIda9eu7TIzo77eOTe/pHou1xTb1U9Ctwhp87W/3Y4iSTTma2pj40DVJ94adWDfPo4HHlTSqkdkFW1LhYJj61aORx7Vy0bJrlAg3Y0RAAarD8Oe3IqMO2EQ3tST9vvlAyuArdvix6tTItaXEJBiagrYvQeKZsW12qOtJIUnUyHfVhgD6LguhFRUiPGmxY9N+MZHU1OdGg7Puy43upZam24VKeYjiCqVMUufCNq58oX3ugP5xhkoATZuAh5aBbgeh8UbQR1r13Bs2QpMTk3HjVFZF67BJEUpYTfH9OM+2SXcmNq0o/ztOBxjYyYCM052ma2bVMLAPF6EJJ2Lmx2NjXnY7pMQtRrHxATHlq3xdlVShICjUhX3QUK1WHHdDp6kzSrsDTeDjG6OnwsWQnjIcThuvY2jGSUHAkY8nFxDe9XzkTFg+nqNEj0y1hHz50W0D9wLO0aYWXkPRIpae8mYXeardDn835Yl54GhAR9Ok8Opm2VZ/SSwdo0ywwzTyWR1qMF3kRwZEmtjfuU+LJy81Wg93HSAkrMtHPeY0VGcCNq+E9i0Wb+P3Hfhs5nwhW1ndSRuR/fvwZxzbN7CQ7Jdk7XT/hP+aW+5E5aMQcU8ZQ9T12f4N4UYaHW8m8246NJib88ehloNGNtbx2DtEa2MPvRykJUN1hCzq11/GGs/giVnG+ZV7kehuSsoV695qNd9K09/TTzxRHItUxMtPPY4h+NwoNnGx1lBSnalSKGAMY5rvsHxnz8BXvsa4POfJfEAtMxD/oZLQca3ofF3/wbeIaBoihSHA9iS89B41b+B7luLwq/fC7TqyGQILv80QaUCfOVqs2KZIkWKow+mAPVGq4KOewL364u83DcmQZuTWDjxJ/TVVqPY3B1rDwAodwHugbdCcuDJtcDDjwLVsSnQTXdqFjBhi2El3ONYtwHYm2DkFNPVE/pESNgPa2ontq4ZxaZNCQOjYMNGjmbTXCY6fisfYrj7Xtmg+gouFViDUqpV2FaUWCHd8qvDxYZGickMrkM9t94OPGDwiCSeOTZRtNl21gGOIwKWJ0FeJsWm3AuUtz4lmSTxCQjpysgZtG7VG+J+bNlKjVYTHZFgbWTuFwcLLLucWBmL1bFw4k+wvSr2DjHU66LAXfcAKx7S65H3SxKCbS2w2lh2afJGqnjs8fCgKuvDD3OMj4v36LvvBVY+xDE1Fa/TUywxTQHqVfKz1YHsor4FHa3sTS6kYGRUjE0gl3TzJEwKZ7wuybKLJsQqIkQdn3gHrLU3KWxW2KaJjImSSIR5mFu5H/07btCOZytbOo6DVn9Lt6q6/84pbLrhJpApfa+WGNobktCcA2hMwNpwi8iAuOYGsHHzdQF8yy7qOQBngSUlN7j8uh4wUHsM2fEN4UElZpgcXxUD1UfQX1+tCAhwIsgu6lvs5rbcggWTtyWKaJqPRnAeWD2q13IQ30opuhPFLk9oX/2YoxB6ahkuNsiWE046YeWV8FyTVmaGrkU9V3lEuNC9Wc14amrFXweRfTRa3mJivUpLOwBYv45j23bdsqvdx/epCXEvJ6cA2tGyWSAlu1Kk8OF5HF/6CsevfwO8/W3Ahz9EYFnxjSp759Wwt9wJ50VXgC1+xixImiLFzOCd8Dw0Xv5V0F0PIf+7DwFuE8tOIPjH9xDceRdw402zLWGKFCkODbpT4D2jKZFumcF53BLJ3nInCjvuhMUc9Dhxq4vw67WHuZUHMW847k7T29ggXDxqEbeHQBnw3RjbaA6F5m6UGps12cJAvKZLxMHM0MOoPX4PNhoMRtRy1RqweYsIcq/L6MFidYPyqlrdKAqEHOcgYLf2U7m6C8g6NIFj9GbwV2BphjixJ/uZG1sN0iWhAIQxd2aC1U+2I7vEvwP95vMmF6D5U4Jd1N3huIgH70WO+f+XCuJEhWKbwUKvQ+PoZNmReF85i93jQnM3LOZgweRtKG7+A+6/XyZm0MsR8HhspkgyBhV0chfI1J6YQK7LI66h8ZhdUpnfp+maMVY53igAplp2+fL11teg7GwBoPNNnSy7ulkP2v6g/DkxwdFqRsj6hMDzURJEWmNR32JKJaQY42j54yf2xvbWgaQ+Flv3qqwqIcEBcM6QN7iCFcefCMsl3C89ZpVeKOvH3Ute5yHZla3thLXncZGoQsb8GtmScJ1/tQx+7zmgXJlgTbMrIwBwLwyyR+AqbpRxIqjU3BGOS5JlZ6uRSABnWAUDtcfb9kGRDJwb4nQR4sf6aj8zQ9JOPcg1Qk+dObobo4vB2sPIbQpf2E3JSMJqPK1NFerjXY4LadVB964W9RmsDo3WcX7bzDPsbdryCwPQh8flhx4vPN9m/PIZIZNjDotoREp2pUgB8XD/3Oc5brwJePe7CN5zMQ0yfahgK3+C7MofoHnuhXDPuGAWJE2RYv/gnfw3cF76Bdhb7kT+xo8AzMUFrxOpgr/xbxw7d83gK3aKFCmOKESVt6T3ZBEPq93ba3LMrnYIyBzPRd4dMb5ABy++jAHOVMxVKUjBnpSNEcCc6kMG4ihZVllnx3hjgUWI+BnNAjenugoLJ/5siHVkVsKk9dBUFahW27iGdmF4ECXz1OxeUReZapVj/QY/ThoHnEZEqQ8TZYFMbNfOjYwKizqtsQOAPUPx6bRtO0e1yoN4TgMDCRcb7rEMPK+/0nGQVh2Wn+WMQx+30KCNotk+WZ4oF+1+kr9UZC7y6D0ykS2RoFW0MZooBLWEMkz9ZDSdnDCtnStj7a5YoajbHEayS3ZkZFS4LAII1mHgKpvQpm6p5JNdjY3orwmyZkgh0BjrRHYZTQ9F+wSJ51wX2LY9DOAeWokmkF0xN0bRCfk9XL3syTXAxJRWbURofTxJfRyl5vbE8pqLJEdb8iIQL4FM1aZlpE/BWAZukpKAC8eIc4CwJspjD4M0xsVx32XXJQUAYk9cu853M1Mh3TY9RyOcSKsN2aVUQVkzwdbJdKHet8HqwyAjGyMuoXod/RMrUWzubF9vVDbVCkr9O/giMw3LLs6wfSfDdp9cz3iVcOlrpBRDsblLm3NRKzO17egzreSTyuF1caHo6GagWVHGsdPIh2Rq6H7LMTrKRTzGSLA37f5Lsku5Zz2NzbDW3wzU4nudtMJMya4UKaaBZpPj8n/huOVW4APvI7jw7ebNie5cCe93H4d7/PPQ/KuPHGIpU6Q4cHCf9ho4f30Z7A03I/d/nwYlHJ/+BIFlAZ//Iu8caDRFihRHNgxv22Y3RuPFAHMxd+q+wB1BU967IL6CIsxspQIAnFi+W4UHOrkL1t7VEZmkcp1AdkVidiV+cdYUo25TSUllRvwTjSOVbwmNnbmeVj/xY0WJZsPjvFGD7U0BXCgHCj9larU7CQ39jo6BJOkadVH7KiWkS6WCtmzJ+g0kcAdrG6CeM5DaiH6sNhrEjoyX98AVaw/OOdauAx5cEVq5WAkhZAIXIIM8alY/AqCw607Mq/jZLGNjJOPB0UTeavOW6N0If993n4d9w8lrLJEcYyxGaEaL5lxdAbS8GggXyQUoFa6b+b0rAGfKcHUClEanKtEOd1uHKBcqoebrmKevBxUWq6PpkySUiipnmo0xKpeKvdJ4iTPkWsOwGsO+PL7FVqxNM9kVJLpT1v/ISEh8CM8sAuF5lrCoAVDeiokq/3SV9zHOEUTwb7fNxs5Jyxv1eGyNKJ3mHLan7+3SitfiTa0HxHfZbaEQtLFtu8iaqMsgSY2I+U8byy7wUKaMNxWITDveeEnUhUcaW540EteMcWzbxuEkuKKbq5eWXVpNiB7prz0R3/tC8TSQ6jDGdk2g5ue2mFe5P7CA04uHxJKEZyK7YCa7BmpPBC6s+hQwfGSRFoEuE89ZzpEZejSefZeLMChckWLjRmDnLilJ1B0y/uGFcC94aGe9CRCvCeLE/aDlZe0TCOhIya4UT2k4DscnL+O4827gI/9M8KY3mJ+qZGIn8r/9IDBwPBqvuAbwgx6mSHGkonX2W+H85YeRefK3yP35SsyfJ9bAY48DP/npbEuXIkWKg4vI63PCe76RSOIcpLoPeXckvFZV4rtIDc65qHvH9vBte3vkozoH9WMpeVoDofIllb7uyK7geEBk+L99BZdzoaSWnKjfGsfefcKyKN4R8U8syLr/SZ4zT3nRFxdks8Du3TxIbe9aJXDXwYLJO2JuKzHdwx+uOWO3x2UJCnHjtYAImr9xU5JSpx9fux6gRK+Lu82E8U5WFOnweljb7tOO2dvuhb3Z3Ic51VXIbr41+C3H1mMhJ2Qiu6am2luaaQZSnOvZECPFpTUYSDLZtcFPoJZ1xzSLIKEUMuxpE1ssfmPlDfcMa1F/L8239gKNyeC6hZO3Ym7lQRCEWfcYg0/IxJV+szwKAaUQO8nkdYSAUeropITqH9M4KAv3i4UTf0ZvQywM2/LXZNtsjF0kEFD2MBMdMK9yP7Jj68R5X3gaaTPRsovGiYdo0xwEE5PA9h0QxC/irqrMjy1ltOyKEDSsC/PO6JwNrG20rSjJ+pCDjqxHobUnIpPYeymLxJXzM8l6vFMQc+kqqlIiAJrJmUvVZrLehJJRt8OEjgajgsj4alqT9bpIxNGMWqK1hXwOhdckEf5039quarR2rghiiwXXtvn4olkHVvbFJg8PrKji1srM9YDGhPZ8j1odD+3hGNorjq18iGPlQwDcOujEdsyprIxKE1sDNeW2BuSvTzsR+JHseZgAp9Dag0JT3CA7ULGNJt9Bf3bs6O6epWRXiqcsWi2OKz7L8cCDwCc/TvCaVyc8NOvjKPzqPSDMg/22HwK5nkMraIoUBwmtZ70bzWe9B5lHforsndfgxX8NvPhFwPeu41i3fjoP/hQpUhxJ6DpAvdHKUy8srlXYmS7ILtcF1m8ApqbUgDTRl3VfeWKqdZSqXIhrk11TdAuIMI6ROLBmrbDsloqr+FLPMFB7TLheKHINDUE7FiXMonGtpezc8zT3lmJzJ0pkDGvXAZOTHK5VQouWY9Zqajc2bxFkW3CWc2T84M5m6EyZalDCOVBLMKQwZ2j0L2UAndqDNb/+I9Y8aSCS2lnERTNmdVDWC60hrf9SiaIkMGoxkl2rn9TriMkYcWNULVaiCIJnd4q3xF3Mn7oHmV0PApxj126OJ1YrLrYAyNRQoPDKlphyTwAO5jGRIVAGPmdN2F7FL6KrallvAnREsHoyy5yQVwSoJwSJpMDkpDlTppYRUBl86R4VJ4zjdfBYfDDzfNJjdnlBFkyJvH/vqCWqaJuN0cfISBi4PyYXhDuVm7SX6cKFbSuIzW9/vGTsKNWyk+gGUpC77cQEsGDyNrHO5NrM9/v1cxDeAh3fHLkWaCkB/TkPZUziexyHh6ROsAeIPxgDyPg22GtuSMwYCnCQahgTzI1MDYs7etvB/hnZvyPyTU14PtHJ9FXFkv2EVSI1402Fa1aJ2ZVtjeCYyTsiF+r7c7u6o5lLu0WM7JrG9V0YPwMAqG9iqJVXrQZ95PcIC9Vt2zhGRmVh3S1VrWP1ihHYW+5CZkJ90OnYveoxDK/fEfwenwAQkFXxyPZyn454LOrlk1MkAwgtHLNZRMppTQEQ83JsPFF8vd7uiqVIcXTBdTk++3mOu+8BPvJhglf8bcLT1HVQ+O37QSa2o/6a/wCZs+zQCpoixUFG87kfQvPstyG74vvIPPBtfPifCOYMAp/7giHmQooUKY4SmJTVeCkRoN6gxEQILanocACkC7Jrx07hQqcrCFGySzirEDU9nxLAW17rNsO3fpX4IpH4R/IlPKu4YGzaHCEmEgJUJ0G2F82YJhUNYSkSClVo7cWciXv8mFMcHET002SJ4ctSqQBDQ8rxDkiOYtQJ8SskMaGeGR+LBy0mnOHmWwTZE0PEEr5WcbFrd3L2X5nRTCIgu6zw7yQ3RkAQP72NDbHjuhsjV8Zcj6GkSRUxK/I8PfOmtOagdWHlWK/L+sMOWDtXBORUVMeT5OXQXo61axHM7wWTt2HB5O1GGYBwjUmXxqbdD5Xs4hywhtdr87lS4di6LZ611F5zA8h4aM0YU9qZhy1boxJECBjlujBmV+cZSMAxf+ruyDF/TKm/n7Qhu5hPtG/ZCmF5okroN1+rcuzcBezcaVo/0b6a3RijVjsksOwSv+OWXSFprZKVNqtDJQ95eR445yDcRX9tNTL7VmtEU6xWjmCAOQSxFbXiGhtTuhMluzhAx0V8MBIloSH7wlGthB2SFqgEHOPjHiwWCZakWWwlY/165mfdizxTDFZYYd3in9LwiggJHl6f80aR8XR3t3bEfVCDX8Sbxnx1nDDMh/g4orF+iM2npLa7KgXjZh7EUlNb5kKgickww2zoxhh+zJGojYt7T5rhszDa/5wrJlK1Kqyveurrg33M9JxkkWmgWUdGyEcCPxB/u3sPgDBPuIE6lRhJ3y1hCKRkV4qnIDyP46ovc9x2O/ChDxC8+lUJT1LOkLvpE6A7H0Ljb7+SZl5McXSCEDRf+Em0nvZa5O7+V8xZ/2N86hMEW7YA134nJbtSpDg6EX9xvOMuQymTFcjkLlhDYeYvxhQ3BQMRZkJLfsxXlEiL1TGnssKPPwRwWL6YqukWC0X3r1XJB8YQuhtG3oZlV3ob6wEDwSWUlziRoyHiXyjFjwWElpZdzDO6t+hkDVGuD8eRGlzaotYECUK2+dXuMh5TeJhhKKlhjCRBoboJ7djBsWkzx849lmZ1s24dw8hIxMLMbyDjToBAV+BVy652boySFJHzJ3Ze0XgIb4WkTDQAu0Z86WrSQ6uA2+9ErDBjAGdMEE2goCbSVFkX0XENxp15AA8tHCLiKHWJ8zKmDyM5//6FZBep7kPZz4TKEY5j0xDYmbjqwbDFSkUo+DHFso1rY0joJZGZOkEQXR/SlStj+/1oa9kVupM5CduO7LcbenUGIIrMrsdDy66YdqySexz1mk/I+RZGXoJll9pG+FtxEyWW7z7NQHnLJ7k7xORi4X51x10iG6wKS+GWuTLPSs5W9EypgbT0PklsWM+xbl18/uZbQ+jfcRPyrSG9RwarW5P8BAxNR7bVHdklbcCoV9fqzLsjsNfcIKxGI+3s3MXB5frrYvObjmXXuvXAli1CkKiLb9u4hRF0S9SYiK1gnSnNmQPUi3+oIemKzJCsiaxYHKoQFs0cfY11oONb/fmq37OREYa77hF/u57YM7SxCeZ8JOJ+0kAoe8j4w/difMVtuOVWYM8ers3pbpGSXSmeUuCc4+qvc/zfzcB730PwhtcnP0Wzd1yDzLqb0Hz+x+Cd/DeHUMoUKQ4xCIXzkivROvllyN12Fc7P/RJvvAD42S+AB1dM44mSIkWKIwLRF3PNMkl1YzL6NsaPaUd895h2L6Ohkh/K0dvYgEJrCMXmLr2cms+cx2N2uYqbz8aNwD33+YRXRIlS5QkyQJFozK5Oll1c+zepi5Ik4Z4XV3ZJlLQiMdJDyOLGGzA1yDygPhYEfA+s7CI66HTcbMKqdXIPCMdI9wqMK3pPrgU2bgK27bSxcVN4n7IZSVJqFQAAjpkSjKtnILsIVQLUt9FekvqpkhC9jY2Kq58uu27lpTc0PhGtVRTetBlYt84T95ZY5nnUakSnD8B9ef1GY8QbAKOqFrh+hRURcBDq3xf/sIl0M43OvmEeEJLq3vDIY0C1Er9CHWNr7xMAD0mrhHwRRgEIxBh7NB8e4wwnnxgGqO/kixZYkqgTslUHGMOcygqUJgXBYyTN/Iu3bgOefBJt3BjF8fFxUXbDRqBe5wEppu2fsXYiHVDXCiHgoH795iCD0fkcJLZIGBeVCGbKHjBQexzF+mY0mzwINh7UqdQ1VTETN/mWsDiTsRqj/eGdyB7Ogsmp9akN2SVjshGmWydlajtRq3GQ2khsfEZHgfHx7ve6pJh8KlTLx1odQbB+9Sao67gTosV27uKYmpLPlHACtc9EmlyflEicTCZPmRbzru1C09qKjvnISPjb8wQJq8UDCyy7ZBwxFhs/Xa7gL+zaBezeLX4N7e0gZgJSsivFUwrf+R7H734PvONC4K1vSd5FMit/gOzK69A85+1oPeOiQydgihSzBWrB+dsvwz3h+cj98XJ84AU34fjjgS9+iWNycgZPlxQpUhzG0Nc0azrIuBPxc55B2YrqbgDAGSyvBtqcBKtNYWLCYA1igHwJnjOokg6R9ORccQ+JZDYEgExtD/J+YNth4dUlsm5FlC/dxVF5EVfiFUUDBIsmtaBg/vVurE7tGsWyyxRkV5BZHKKXRCGmQsXEGMjaoCDQPY/C3noP7M23a5m/NGUoooFXDASGsR8sTuoFZBfRlRcTnnEOsOwkG5yHpFXZFqRcSzPAisxHRf8NrLmoT4JxjtzYapG9chpQFcd8XiFcFQu66DjxdtHRoa+NsREvuCYW0wbQYiS5HjAxIe8vD8fPSHbFwVpNKbAqrRQqfownK86cixhPGzdFrvFhDmcQHrMntgCNSY2oNdWjNKj8LQK2u7QYHJLWlYQmGpsolyvkt2LZZ2/8M4oTT6DQGgJRAuB3oOkBzjBYfQg99Q3oq4VWUCr5IzOQui7CmF3+tkEmdwHcC/YH0Z+IC6Tfar3Occ+9QNO1QzKgDQEgz3ci5NXbbApU//gTwu1T3R91yyBunL/ReoL2ZJbFDiynOse1CtqRXcEfUcKGYOMm+BZ28XbdZmd3dFlXaOHJ0dcLDPTHy1p7HosdYyyy1LrO5AtNZNflGB0VGSyFICrZFV1TiiWyEhuLMz3LKSBIS8/jwTOt03ertnEX23WFcy2GWqG5C+BM+wgV1C3302DD7YIgTRJ4GkjJrhRPGfzvzzl+/F/Aa18DvPOiZKLLfuznyN3+ZbRO+Vs0n//xQydgihSzDSuLxiu/AW/ps1D+08dxzbtuw9g4cM03UrIrRYqjBaZYSX377gysatSXXqNlV7Q+X3dZOHkr+vbeia3rprBtO+AY3KWiCCysaPzrb6ARMFd7OY5admUrWzG3KrJDyUDxloW2ZBc0y672Mbv08RJ/zxm/O+h7rE+VvUEfTJZdok5RF4cIsiT1RIow6D6JEG/NptD8o/V5IzvDr+jNakg6MGDvvnhMHyDu+iSHPN/aqx2W9ar9NBGCSdZUdgawIkGWyyMimLLTDMc21ieTZZfvxpjlNWQmNmOw+khQZnIqfo9iMiqvfZSqrmcKMRtjhDqoSZobk69UIsGyy21outqmzXqWTvFPnOA09Wf3TrEm5LgF68g/H3OrVciu6JIOiBpJFoFj3tQ9KDrbfZHiE0i9XyFBw2QXNFlivVEECOY7yQbHCBfuoKqFmqGSoC7OAYs1UGpu9a8Xdebqu4z9TeoHIJJmFJu70VNbG7h6iXJmpVy4B25DbawCUhuBtWsVemtr9fIxARjWrvWzeRKCiYogRzkhyliaSUPPC+8HB3DM5B0oNzZBhbYSePQPgWoVOtnF1TnEO1u4GhrpFKA+IMUihB5nya7jgWWXwRUPkNaiJhane+JGdWMkFMaJ63kcrZZK0kpnXHX9x93ARdH2K1rG+cvl5DllzzGsPct3XZZ1UOq7UTthDLbm5CQ2bSbYsRMGolDrRqJcKtSPQ7FruLynQK41jDnVVehrrEVDTRhhitnFw+MjpbMxXjg9LkhsTpn2x85Iya4UTwncfAvHv32T4wXPB/7pAwQk4ROXvfYPyN18BdwTng/nZV+O2zKnSHG0I5NH49X/ATb/dCx76J9xxd/fh1v+DPzxTynhlSLF0QDxRTqqNQhmKtfaB6oEIOYRdxcg4YU4NCCBV68ml0u4kJiu8BVZ0qorDagRf3jQHwDoq60GbwizC0oQUxTUn/Mq92NOZaWIcaRYaZmInGE1XjTnQRBrIE4qkNoIrB0PBsF9uZZJMqwi6ioXWhkpll0RhXPturhl17ZtHGvWAtuDpFmBjyTGxkVg+70BfxUhlAzZ6Wymp2r03KhKJ1y6tu/gKDU2B/0EZ8i19iFX36lZ0thWONcqUhfjsu5wHPKtvbC9Cnp7xW9jzC5fqcvwqiC+aCYoc/8D7V1+hNzK30QhZZRxVu8FAHAiiTp9rEIXHZW48e8dofq98+t4ZEVVEBxaPYFtn7jWcw1KaXiA+RaDngc/NlloLaO65iomMdHKxCFlsCTZZfuvuxarI+eOYbD2qD/f22vJQmkNJ3U0/lwUqpWUXHsezUYKif9k1eZOABs3cVSrIvB7f/VxZOV8BICIRUvoPqzXE3VVBQwxuwzkiSSmBmqPIb/9TvCWsNyjrKndf5U0A+IugoIcZQAItmwFnlyTvHNu2Qp4bkiIZ7wp9Nef1Aspl7dawJ4hbrwTTAk0xqOWXR3IIlN9nHPMqaxAX201ehrrMTh6h04GS2sxHs3G2M6NURPQYOhjJpjkJExKgiHrZkz/GCBj3kWxbp3I3qtc7Q8C14+Z2kloW6LWEA3mA0/ecPJlMvHyJDIWMvsq3fFQeN3WO8BBUKuF4250Y4xZO4u2VausaC9M9RAi1n+xuROA2ENqNRbsKZS3YHlVZa6ySGU02Gu11qKJIbq0fI3C7lwkRYojGw+u4PjCVRzPOBe44tMElmV+I7I23Y7cHz4GtuQ8NF75DcDKGMulSHHUI1tC/XXfQeFnF+FV4+/Hw+d9D1/7+tk460zgmPld5AFPkSLFYQtTMFv50juv8oB/xKzkcxAM7+MoFFTKiQb1cQ7AbYZ/d0CQzY6a3Rg5B0h9LAzubXBjlO30OJtRaO0BQJDZkQP4abrsijwZr4KMVwEUy64k97PVT3Ico/RWBgcP6tTCnkQ63dayS/aVKMSLEj8MnV/sJ/xkWpOT6lGdBPQS9FbXEBIsCqZkHgsk5i7Gx6X0vnWdN4H++pMoA6i3lmIifyoYzSKTAZp+md27obm7eIo305zqKgAA7ffPKbdhn082Uj9mVxbCDdKjBbPQXUw8QtQsbOo4R8guGXuN60owY4DtVWGz0JrCpv48gqXPI79Ct1oBK+tyeB6EWYQkijwW0Z91YoATG+AeOAMobSmWXVxOGt+91CcCEqxKVEJOkl3UEteohKfNqjBTJfoxorhihhYb8esogVaflIGRnF6fklWyPbvOAxKVEEGaVnPHaiVVhd5xgEG2SqmG+zHWIkp1LMh8Mtkl2mBoOZ5QqqnVwSVMp+sZxL2QLrPDwyIT9uhovDwAcM9M/ERL9/eLBAP79gG9S+ODuPoJD2cuhS+/Yr3DuVn+Tmwy5yi0hoKfNgFIZQ+ABeJyzv0q9Jsq3MjNdXOfvFezSWrnvciXg/BEe1kBrF0PjI83sST4tsODDKBRuIbqoh+NSEI2RtN9UoY6mJ8y5hz3F3A2q4wKV8lauc/411Fxn+fNDevftMmXiAGEtSG7tGPcn4MenlwTLwtAhCcw9IWAo8fZiFJTfHURVm5AoSjcfrPeJAZrjwUJNQjnsBsjsLau8PtMASV+WCBrLO7mDJgupJZdKY5yrF/P8anLOU44HvjilQTZrHlDpTseRP53HwKbdxrqr/4WkMkby6VI8ZRBvg+N138PvLwAnz7uH7G89CS++CUeS/+bIkWKIwsccesCL0E3iGdjJNg3rMQXAWLWMPJteGwUHRGQAgT6p2q1IiAIeg+oAer12CUSNquB1sdiCk9SfK2Q7LJjroMAUHR2hD8UMk+t02J1kLGtsWu5186yS2gwHEQJlh7KTHncyocxtCVzGk6ofrWzEAJ8sosbTwXwDAHqTdZvMqmAcOvajgWTtwIAbFsnuCYmgL37/L4Y5pwMPK+eq21ZA8qawvWQAVnfsstkkQMku1TG3BgDgjEku3hMoY+/MxLWBKaGsGDyNsytPBgct6m4XzHLLohxzBhijHH/fkpy4fHHPeFSFrgUMm3+cPix4OBnbFRuoDm2Xtwiw/NEoHXpltWSll1+9kOL1YNrMt6U8ZmvtsUYYG27T5DHfp+SyB5C9eUg5xKLWHb5hl1iPE31BCRfSA4SIkjsqNuWXKOVCtBoQEuA0Wmu2N4UCHeNlk6MhcSTaFDuI5ZxjZjbJODcgrB0CefaAw8KN99ic2cQGB4Qd1nGZlJvy9yp+8Iy/vGFC4F580JZFUEBAI1aOEcZA3oaGw3ydQ9TgHrSUFl4mdhCJ4Xa8Re7dgF/uqUVFPe4vuaTyI8gSUc7gZ0aeiqPolJBKBcxrXhjA75hl8qMd+86qV+nyyrXOCXAVIXjkUe5ZowZZD72m2s2hQWf3FcBP2YlIZo7fPxZEo2rydvEKBQFt21HbFA5AJCoVbAgNCWBl/EmAqJLni+OPxYScQj9R7UtOPrsZAkbQgekZFeKoxZ793J89JMcvb3AV79MUColEF27H0Hh1+8F61+K+uu+DeTKxnIpUjzVwItzUL/g+yCFHnz7ue/G6LqN+PkvZluqFClS7A90lxWBKBE0Nsaxe0+c3I6TAUDLDf2DPM9/0Yb5a3hcGHEdJSYFWf36H7rARNkcXUTl63BUdsNLsozZxQkFIiQFBwVlTsQNiYN4SsBrv865lQdgDT0OeJFAZdyL9Ssk68KYXaE7X6jYEu4ZCbokZcxxONbdvTaM6dJBKWgl6+MBpNWPWhXlLWNZaGX8IOOEBPLuLZ+PajWUy/Xieot0H1M5hN7GRuRbe4M4WxbxlWZ5rziPEJtJrKbeThCYGvo918dcjzcGAHNqq2DtWBGvn4VkR/Set2hZKINRkjmwDvHJG7+hkO9lenwsXxllTMTukfeCKJZU0gJEt1ALf9frQmndsRPw+o/D7r2CaCJ2xneNU6zVvEoCSxyfXP11EdCdcSAWhNwHjQTiCtwYie5JQcCheOS2aZ9H5qYTI2vaZ9sLx16T05+Hx0zdhbKz1Ujeie1F9beVwc9sY51hk2pdBMzfd9RYhQ1/GxmsPoxSc0cYb43YoWWX0oSaITHcX8J55MXc0iLznqv7b2c2wbiXRo7V68DG9S3lPPcJTH1yttunOIhGNDKuh5bhjJnJOXlNB/6p7GxGWVH5aFdMl9qEMpeT3BhN4qkEdtQ1UxJO/lrZu0/UIWULLCihE0N6dlso88knRyPtMAbwSDZGj3X+gBDrjyTnY1+8eBCPPqNYwMrzjKjOhSSQV3XDVpPHDNQe1Yj46SAlu1IclahWBdHVqANf/RLB3DkJRNfOlSj84l3gxblovP77QGHgEEuaIsXhDd6zEPULrkc2b+P6F1yM3//nFmzaPLMvfylSpJh9cI7A3UAi+iK8Y6eIVdWKvECrr43yRXvPPhoQTro7XWcEFho0/FvuLoSzuKKqudpwo+wB2sSC0ev0wGGDwwoCZvsnYhY6t97OUJtSYpr54gQEUNTtIsFkTrW64QjJrrKzJShDELfsEtWZ999aTcQuGqw+rMmWpEy6bntjBAIexG6JujFOB4QIUo9R3WLeYPQGy9d/pLVFKIuYC0N7Q6tEqfQN1h7GkvGbAiGT4g1FY3YFge+5GqA+YqUoY3YphzJexXgHmOf5lkhWPLkAk2SOfqW8n6G7YZjpUu2jRGDNxoG5lQeVdawQZv7PiNFGTOZKBai0SnCYb1VFBdllswoYseHRPGxWM1h36hCuWLoyrMYhA4Q13KLxm5FzR7XJFFhnIhIfl4RWITU9jJxeDAq5QwQBGGb9E/+0J7vMVmihFQ0D4S1h/RexemFMr1zGwBKWfcmNxi3wdHLUpK1I8o2RDBgLY3apGNorMjyq4yH7wZhav1wnumVXJ2iZSo0ETtTlDBgdbgU/VDIt3OuTaYjwHnjB84XFLLuShGEYGeHYtq393D1mPsUJx5OQYOmS7Go6DPV69F4mkF3Gg/F9VXdtl6796sZrrCJA0jpJSjbAWDxm18SUhb37TBJzw1+6HOo7hVz/MmOvGgdUnGdgCsEtPzYBgtQLthMWkqUlZzvKk0/MyO4wJbtSHHVwXY7LP8OxdSvwhSsJlp2QEKNr+wMo/PIS8PIC1N/4I/Dy/EMsaYoURwZ4/7GoX3Adeoouvn3+O/C9r27WM9OkSJHiiAEb2Yp8a1/bMlLRGBuNakGm5ymJuUEmu0MktAe0Z15kucDlTFEyNUOJ0BIn7+qZBY31EUHeMGKBE6KRFJzYgbVKcIxxDA/F3RiVAnr9zlRcmeaqOyIBQM1ZHQ3BeHULDB07RGxgPO2UVjzANuJKtmewrIpBUcqka1A7Fy29Pf9fIvsZVfLj42dRYM6gSflmGPHdYhsNn+zylbhCczcAwK4P+e12nke6BYcXEoMK8agLKyzngp+GJjJNISAnFFTGw/I41qzlmJyScej0CxkT2f+C7KCSyFPIFn3cxIlYYgSw0JJHxuxS3Ak5j8os6mm5JCCaPJL1LbsqaFk9voUa1xIyhFfr5Jaa3ZMF7YZlLN6EhSZsNgk1ABpRXP+KRbX+cENxTdMtuGGKGyMAyhpd7SNaPxIt0PwynAFgMUJOrGElk6tPQvXU1oLyFmrZhQmtqsQBgQdqznCqHAtIK8Wyy0RQ7dodVk+IEgfKVa0xRYGcG/qZq/t3N26MphLENE/8PpT9TJkg+pxu95ygvmeb6qoadWMUbtYGaRj394sOfdH2AZ9g7YLwuu9BYPWasH5J2pmtzAwVRDJhquWCuhQmt9GIW53JZZQUSi2wgkpgMj0vOvXFR4mOH6wi/Vmz1s/uGS3EmYjRR+z4/OYcnKrWnDSs1u+P63LUKvp1dmu8o8WyCSnZleKoAucc13yD44EHgY9/lOAZ5yYQXVvvQf5X7wHrW4L6G3+YEl0pUnQAn3MinDf+AL1lF5ctuQi/+v7GzhelSJHisAPZ9VDHMhnfwmZg/F5NQVfdGAPXGtCYERUj00vwQhQ3xoAkAQOnkTxKfupxzd3QaGUgvgRHMVI6N1ZOuDFaYDx0Y2xavaC8pQVclu1bPBKgHtI6iMcsu6zqEGxP/+Qe+zIOYkx4119/MsGDLFmZt23AtomeeS5BOUjwNNPk0hTzLpQMtUzGNxgigqGDawgoH+sfAXKGkKlqoPXjj2X+fPHvld0vTvjZ8LqN2RXWrczvmBujf5wD69brv00QBGJIilQrIp7O7iFqvJAxoNDaE7p9goMq2SxVF0dV9mj7UdIm/DPZIgMAXI8GhINHsgHp0bJ6wf3A6fBi5p16W9CtSjhH3JKDe77lVQucc0xWxFiHSjCBrSx1ykMXKBPCPYJrSjLlHqjfRlfgZjc4tW1BJMYtuzxPJ3h4ZO27tJQge8SNUVp2BRuqTyor80DOV07s0IIsYd+Thwkh4Zz3FOLMsH9oLucJYydjsgHA3uZCbKB/hT17xL63azfH0O64e7Mku2SWPpFUQpk7xEpcS5plFwvLq9i4MSEbI+Tm1n4eUPV5Nh2boYjZrPhQYt5QjfdJvQVc+yeYZ2Ew/0BAY72nnmIWURLuk5FQgRnl0axnouRxC8toY0hOeBItTyDWkYnQJOAi2YYsjpBllFZ2GzcB69YamO6U7ErxVMfPfgH87vfAOy4E/vZvEoiuzXcg/+v3gg2cgPobfgBenHOIpUyR4sgEm3sS3Lf+CIU88HejF2HdPetmW6QUKVIcBKgvtGV7ErkgWZr+XM1mxctsND6X/iLb+VVTc9kINAHDy7fvcqYqJiZPwSQFKqosceZbUMEKlXtDOQnhmhE26Cg6PeehdYd6LEfrsWPErSOM2dWGSDIcb6eU9ffLMoYqDESLWlWUIFBd0ab7Nb1YAJ52mqyHiTlALIwXTtfKTUSsCAgAyzj0oQB9vcy3EDEkIOAeeuvm55JKdulkRsSNMdZyfP62JbuUuRNajEnLj2RIpbCv/qRCxrHYNTQS5D1sSDQmlUVTbJ2YvIwGsjHfsgsIyS6Ag3imGG1hZdsjnDLnQI/ijgv4hBEBKGth716OzVuodg85sfT7Y+i3uf1QjiCoPKtrpE87JAbSV//mnu/GqBPvYsiVORhzm07ogeYTTMCIDcrillcWj5NdjGRCa70EUpYrvFlgEaaYx+mBxAUULgxZb8IstyahhY1bs9g3DIyOASMj5lh+NqthoPYosu44JvPL0cr0R/aZ5LusuvIqdzkmSRLBVBh/vKOVZyzrJk22lNLKybUWSBZmJI7JAgD1MdhrbgCa1VBAH/JDR9SNURD6IZIsu2y7vcBRq6vFi/x2GcJJFJB21Li3qc+cPXvaNqeUFxSW+cOXHrNL/B32m0PEIKMG19iZICW7Uhw1eOBBjm9+i+MFzwfecaF58dtr/4D8b98PNvcU1N9wfRqjK0WKaYIPLkPrLT8EqI1ld74D1U2Pz7ZIKVKkmAa6yaiqEkg5bxxz/bTmquLPGGBbAAxujAzhi+xo6eyO7QkFXVp2hYoEBwGnKvuhx2uKKiYyrkwi2RV57d03DNRrLjixsG0HCcmdJLKLu7CUNtUv40+sBpirW7RwCHeSrJ5sDr2Tj4qzRLgxOpG49kH9RsXDrMDNmQMsXBBoiB3BuKJowUSOhBkEuVmnjNfpizZnLjB3rq+8UEWgyA0bGdF+CuXNMPSUN5FvCi3LJhGXP/+eMcbQ42zqKoB+bN4E40BiJIJH83ECLGEsXBdwrTDiNYsosO0omGp2KQBoWctM1iKUJrkxhmSX6wFbtuqCcq0OIY/Hwznv0kJw/zyaE7GnYLbsake4VnPHodjcqVlFEu75Drvy3hBBLgcxu2jknngd5nBIdgXWlbJv3GCJloCkfmiWf4qMmoQedILL061QTMk8RH2qJSHQsvtBeVMkA1BkoqwRlFMtuyRBaNob1Hhd6r8sIVvI5i3+vZ9eGD5Bivh/y27rhJ0gOWxWDyxsW1YvAOoHqvcJu+ladgUx6/Q9IC4gkKtsaxs7TW0j+I2utk69IcBfK2ZLMs4BOiEs20hNbnhMO6+V9589hADzp+7GMZO3i98RxsY3mJ021LnNgweY3w/QhC1quiyTGAtKzR8LRCw8oFIR9XpWCWpMuNBiOj4xZ8J3pWRXiqMC27ZxXPEZjuXLgE9/ggQPchWZlT9A/oYPw1t4DuoXfB/I982CpClSHPnILjwBO178QzTcLFrXvQFkaHXni1KkSHHEgClkD1FeQ6MuCdQSL7PRF3b1q2038btUN8bgdZZLyyft7dx/jxZlcxESKWg/SQeKkFjVGkDhiVg4yitxkjsH4R4458n1D28NZSBZcA5YrBFJpw4l8C7RXvKjmMwuM8hgblyLMxRT4uIqgmrZZVI4u7HwiELG1NFD4fDgSGAHQYHeXrMbo8myawHZiLnVlbC9KihavlKqZGMEwD3WdTyxpKxrprk8UTg5njGtjcZVyR4XKxeugeQLPZpD0+4XNFDAWcYVaJMbIyLl6nVhdajF1lL6IF2ZPJcE5IlLQrKLkUxo6cjaW3ZFMVk4GS4touxsjZW3eMuXSdxBeb9i+4pvCdYJWnKB4JjXtQWIah3YUkhKVZxARgPZpQbvJ864+DeQ27zvqe6AAMGOyXkAgJw75h8Tdc6trgxLKTG75PoPXfvCdhwHmJgIywckWUKyDpkIopvMrDrCgOLSCpjwONmlghPbX3ghc94xQD33Lbsi60gP6D9Dcx9xcfvfiYi2SQzHBDY8OgYyucsvRjE+ztFqGsiuqBWoL4ucL9H14DihvHbE219CHmckXoAxA9lFLPMHloQg90kg4GKe+jG74hD71eYtwL7ys/yLRGe055gh3mUasyvFUxKTUxwf/zRHNgtc9QWCQiH6yY4he9uXkLv9y2id8rdovO67QK5ndoRNkeIowYnnHYc/Lv4RJuoFZP77HaBDT8y2SClSpOgC3bwrTuWXB3+3S8duJcTkmJEbI9ddQwi471cSsh+ex7FhY/gSnEkIDZbUR/NXZmHZpVoEmV/QgTm1hzG6YUsszbsJHs2CcwLLq/uqUEj4aMpDGzKwZYhzhSTXK80aJQTnQNYdR94d1sqrrl6dLLdqNWDDBvM5xx4M/va4uCGaSxrhipVLaHlnGYPoxxW3cjkkwBZVbgPxXYEI9wJrGKCzxWKSG6OUR56I1lLPLobTd7J2rG32OqWhkGBqE8AaYRkGS8yNoAoWzHWpFFISb18N+q0rxb4Sy/U1IcfYYxQUgunwSD4gLjixgzXBDUF6SJvJwjhB09a9JmSfCFoAxHwQWQtDq6lmYZFWvh3vEI5jXI7pKOZybMeLT8NQ7/PD4+pcSSDkKhVBEgUWSDGrTvO67m2s134zktN+m+aI3IMZsaEmHQAAlxa1ss1WnLPhbSes2RW8HbhC0kvLrqjcMbJL2k0pcdLkxwfT2g12DIW8DKyetIysBmuqNrKrlk3RZ1s3BKtsodjcibmVFbpcCS0TnzBmHHhwJbBPyQ8TWpUGpacpC3DicvPxrH8LXKuM4dIzAEQtu/RnLgxkvzivz5+OH7B8t2pC4h+YAMDiDsAZPJqDk5kXtCL/EftVfAB48L/pISW7UhzRcF2Of/ksx549wBc/T7DgmMjicB3kbvwIsg/9EM1nvAPOy68G7IRPwSlSpJgWXv32Jfhm9b+wr9KL3P++A3T3o7MtUooUKTqgm2RlnGSg8hNqMHoVloET6umJKjrdvrXr8UMADs6JMB8D0Gxy3HsvE8qc34moe2Agf5eWXYCvTMHSYy0lvMxbimtRpGYAwO49CGLScFBwmhXBuklYpyCWOAb7GM4+K1QzKrnjDLXGBzjJsosk/hCQLokS4ss+QpkMdU7mTwz+biZ4BzoKseHx0AUnFEWtOTxhyhgJascsu4hymXqNzepYMHk7MmwqaKedlZzWTMz9Vf5rDlAfJTdNCqGpPzHrmzYkEQcJ3NQCyy6FxHLsuaIUBVhMfVNDPIfQgu8rTdsZn6hgwv2MkDAbIyDXL4XFmgkit7FQYzS2fqQVnsVa2rWBmxKhqM05B8PlZ/rHdTdGTiialuKNoRDjIVEp/qG8JexGulGK/fGR82bEd7nWkhn4JLi6P8jMka0Wj8aVV6xgu5mLYnPV9lUZeEstpbgxqsWix9RqAcWyq0NUcc9Ntg4yQYxFOIdMYDRKdvnWYJprrSBX1m0wM9+CUlMsuyBJpQ5kV5t7r+4vxnvX5eNqsPpwSDyCoBsLMxZkk1TGIHLZdDIZB9ZfGYLTTo3fQzsjWyNwrR4sVBKEajG7FHdMI9kVmY+m5xIiJeR+ZLIqs5iDjLMP6mDLNSiTq5jGM43ZleIpiW/+P44HVwAfu5Tg6U/TdyhS2YvC//4D7LU3wXnBJ9B8/sfafkFNkSLF9GBZBB/7/Cn458d+gL2VAeR/8S7QHStmW6wUKVJMEw1bT9TCSCZwESSaFq0/Zy1DTI5iMaKUJTx3m1Zv8LcIUK/H4wJEpjUGik2bOdauU8uIt95MAtk1NWU+brK2oH42xtHSOcEx1vFlPkSuFX6mn5iAlrrdo0JAoZQoWhbzQChg2XGKok/V6U3kXBeWXd0gatll0tXkvTv5JP24qjCqlimMhX2xNt0KNCZ9eaVVWyikmeyy4mEoFLJVXqNb3sjx6D4+T/JYEaNCFbh5+QMVNUSZyi8LrChUBJZSiCu58aZpGCcrMHJg4SXSzYcCXswaKIztpRONSuY+leySll2eIJwIAVxkgxBUjNjghILyZgJxbB7rhj0HjFuxdUa4n0EzcGMUYxL+bfnWj+I6GglQz2GhlluiHZFyBIStv+cQeF1bfwSusJK48a2k1CkYujGGk76nHJ+L8XnTeUEG1p5aVkAWizsXEmjxfcm0p0lLrcD9sYPplscS1mMCRJtEayteJiKrjP6uTEZJrjgtA6lPRC2Eu/Hg7fI50CZmV9IU0Po5QzfGmOUcaHyRKZCWa2GiijgJHU1mMR05eLYI2yboiTgthZbPYqWp85ZDxDmcP3knFo//MWjbbNkVCRTf4WETkJBEXzdaGbce6Wtni7ZuE09EkWr+KY5Y/Pb3HD//BfCWNwF/+zJ9ddDdj6LwXxeAjm5E4zX/gda5F86SlClSHN2YN5fiHz+yCBfe/gOMNOej8Mt3w9p0+2yLlSJFigSYXmbPPLcARkLmiBE7yMCoZoWKxdahAIkcsyzdBcH08l7NLcVE4bTgt4jPorsmEc7gugTNlhVklCKKkgskuzHKLH9RBc4ceF6QXZ7iMjidr+vzKg9ov8fG5V8kIM0IARrSXYOr5IT67iL+njMIHH8c0Ndr/iqeZLamWaOoVjEJ2gGLxD43lxPjoFrQLV4EZBSxJKEH6LwMadZARzZoJIYqpHGILXFDT3paGcVjTwzrktLIawwKUc4dRW9jo6kTGjiIMbMZkGzZ4HmK3Jxh5079PCO2sT/SxaurbIwQiiH1ySdxkCFq4UAJtLUadsDkCqZa0Qj09wP9PqHqeUSQKz6BFgYCD2PYmZMkmCfVcM/5YNxAgDd3ggCw0ArnvTJg3L/J4bpz25K3QUZBriRZ8NcKleRIF1oxCSy7fKtLg8ItrTnVPaFUAvJ5xMqKA1K0LvaQ4OJwX6LMwaKJP+nFAoLC5DJuOMYBTu2QiPMS3OskGaaQXV2RXiRkoJPIrujeFdr6KGQXLDQc8z4n6XHCmcFCMiS4TS61I6PA6KhZrrZujDCTLbH1FhOWot2EixFZajZGvysySUkYlyxaSZv2rZxWRrYjn4+cCKu6aPhL6jWR9cKvM5xYRsvWKKnYsOe2ESZsQLgxtvlwpLp8Kz1mxEbLiocbMuTr6Aop2ZXiiMTDj3Bc83WOvzgf+MdL9C3BXv1bFP737YBdQO0tP4W37IWzJGWKFE8NPOuZBC993TF4w00/wkT2ROR/+37Ya2+cbbFSpDiiMD4+jksvvRTnnnsuzjvvPHzqU59CNZo33IBVq1bhH/7hH3D22Wfj3HPPxVvf+lY0GknuduaXxUyGYqj3L4PfjGQCZa5DuBdYdoTsomhrRiMyNBGNeCJEUaAVAbftoFi9RiGogixcXthWAopFoBAJeWVSDCn3YpZcSdkYu0FgVea7J8kU8aPFM9C0ekUvGQOl3KhZEQL09EjlPy4HTbCq0RS3LiwUWq1QUe2UvZKY0pb5iMYcEuX9f5kLQsJ+qs0kuTECQC5HQDOqv5H4J7Qoi18cBvjWreMIAU47DdqB5GyM1MglSgutrDcOyluGNRGvEwhJV4W9Cs5RCixfFpImAA3cGIPxU9yk1Os8Glzkl/Mz3JHIlPI7s2GjyHxJCLB0CUHBd8PzPJEt1c32g4OGLmkkXJ/G9W+YMBN5EdeMeYjN67w7AhCx1giYGGeNELf8S/z5xnXLrmKJ4Kwzwt+SYCYIk0U0fOsgUwa3JIRZAUlwBHHxAxklCoWQSEiyROnOjVGWDeezxeOpWQPSyrAfJFnOgFohSZYQoH6seBYY48EeBbSP0Rhtk4MmW3ZFZfUtu4hitccJxd69CaQ+kfM6no1Rt+yaHvuhxxM0iGi4pmn36Qc414jHTm6MUestTWb/z9BV2vxQM9UeHPP3zeBzkX/jwz2W+DIqxCmHFvNQXN+dZddk4VQ0F55nlFO2JmNEsqRnKdf3NXkzCAF29f8NqtklsUtabuf3ERNSsivFEYdduzkuu4Jj6VLgM5cTWDIPOHORveOryN/0cXiLz0Xt7/8HfM6J7StLkSLFAcHF7yRYctIA3njj91Gfey5yN3wE9qP/M9tipUhxxOAjH/kINmzYgOuvvx7XXnstVqxYgSuuuKLtNatWrcLFF1+Mv/zLv8TPfvYz/PznP8db3/pW0Daf542v5JG4MYxkAmse9eUyZllAAGrp6kEnyy5qUNz04OqKhRch8BRlzmY1UOaAgGNRGM86dm0tu8hIhMUUQxkgO/JCnqhAJsIQXwQELKiHAMSCa/UIZYIxvZ9qHxRrC5McttXZsqsbuG7ocsk5MLQ3XsaosBOdqPKoQnbF0nIKt7VwPpCgmHGKKkHgeKYYXBFVxHnfYnOnfBy7lGBw0K/SAmwrJKM4aLL1CiFGSyYpf8nZZrwsyYpHVdKlW5ZEJkNQLJKAwOMgYMSKkF3SCkYnuxg1WJoY3RjDueJ58XH0PKCWXYzxweeCcfNal7d0zqBab3yQqrmlQZ8TrZoIUGzuCn/IdogYHznOpqyaajyiY5dKwcKYXU1Pkl2eYjvUHqHFihxwv33T3uFvUstOACgNrQPlmI5PRC/qykTKr7v9fpMUM1GtIwZqhaSSgSGgFPCUGHXBnOtCbB5O0DZujDT2m4MKU7wgrqFPqJK4ia609iWchUSQT1TrAeqnB/XetuHwNTjWAPb2PDf4nfXGdVdECMuuJMIr3FP8NaqUi+43XBLncq12cz9odP74ZJc2b2hA5k3lTjCSRokWzZxrcpx/PsGxJ4bZS8cLp8fK9zY2JAaoDxH/QiPvSSW/zCQGuvj+F0NKdqU4olCrcXziUyJewpe/QFAq+YujsheFn70D2RXXoXn220TGxUL/7AqbIsVTCLZN8JnLCRoo4123XYvm8S9A/k+fQeaB7862aClSHPbYuHEj7rzzTnz+85/HWWedhfPOOw+XXXYZbrjhBgwNDSVed9VVV+Htb387LrnkEpx00klYtmwZXv7ylyObFLkdSGK7EMaPES/GUglnipIc1Q4IAWiEVRIvxWb3BEAqU0QjcvQSoSIjLMDC+svOVuHiwxkG+uOKgO2JN2FGbN/F0tTP8CKaQHZNJ519Pp98jnPdMiocC2ndkMxQMWZWFGzamezSrXuS5VPLrPGeFwToDhFRWAmNufq0s+yC51t2hWfMMgbNhf11i4uwt+c5YHYh5sbIrSz29vxFmw7FR1a1jom5t6ruP4bxYp4kYVpoWWXs6X1BpHKaSDZmMqJei4p51VtfmyAwBSeWsHwJWQoI0jcsJlwO4+s7an0BIOZ+pK5jQgCXheQOZxGySyZUYGG77RG6PRpj46l94G6wFqSVmlCM/fXCmrH2VD2cUmBOdSV6nE2BfJJYpvC6SsIBqGOmr892ll3EF0zKI8sOj9kYG1PcRgnF8ccBdhtdnyO6L+j3jCu7xM7+l8Kx52jkcnR/1DsXutYyQxR5uU9LsqqvT7hnHjM/XlU046O810kJHQDE3NcEiUMA1Y3RF9AzZp31u6HM64AICiy7PBQLB86yKwkezaNllYLfZWdrpNL2iyP0uox/4EgKUB+NU2iyuFKzyALhNhG37BItUiKKThRPRz0f/2CQGF8LHAsWiL8H+oFiyQJRFnQtuyhS3gvkkbHbWCSOp5gFVLsK0O+Jel6uI7d7w80AKdmV4ogBYxyf+wLH1m3A5z9LsHixWBHWlrtR+PFrQfeuRuPlV6P5158OTDpTpEhx6LBgAcFnryBYuymHyx7/Olqnvgq5u76G7B1Xm5/UKVKkACAstHp7e3HGGaGvznOe8xxQSvHoo48arxkZGcEjjzyCOXPm4M1vfjOe85zn4G1vextWrGifJCKaWU5CEiscItZLEMSatbcsiLoxxsiuKJFExHkWcWMM/tbcGYnxBTxwY0lQVhjJglpAKzdPP0FIYJVQLCJweYq30f1+NTCQfC54wVcUgzCekBfrQKjMAa5HjOPdjWWXrjB0BvNdSupZXQEyWWdE62ckA8ceFEo4iSgs3POV97hSb3ZjlAeF1E17AGrQLsmFETA07cEYkWpCzGURBmJKIbtMll3yGOVNMJKBa5U0a4Z2cmSzQghqAeAcmYjrULi2SKAYqm6MhLPYuHGTJUxg7aIcixBgYb/FPJPebdQ3uBkrn42pnqf78lha3zsNtVSGIw2pwhgPuD7RoboxZsfW6UV5PIteoTUk4vpJV1wSd2PsFHtPunGF5dqQXQG7pXcnsMQkFlotfa/s6emOTdGJf4XcIZJg89ug2Vgso0QrOmqFlj0Jll0cJCAPMjaw7ARizHAbi9XIpKtldP9SXPuiY0+k6ypHNMC8ieySru06YUsFYaNkETz91ANHdiURuo3M/ORxhr+n8NBiLXbePywD1SdlSfWl0OMvdsPU+DrvggXA3LnKvAnmZiYg5GXVrmeY64aJ39MDPO10oOTzncJFnCCbjVtlzfNDeakJFgJ3aGXP4rAFAajF7IqL4GTCxDkx47VpIGUEUhwx+O73Oe66G7j0nwnOPYcIt8V7v4nM/d8Bm3sy6q/6OvjACbMtZooUT2k88zyCf7wE+Na1GZx00lW46Ow+ZFd8H8SZgvOiK/bviZUixVGK4eFhDEq/Kx+2baOvrw/79u0zXrN9+3YAwDe/+U187GMfw2mnnYZf//rXuOiii/D73/8exx9/vPm6HR5yOd0ap7e3F6VSD3L1HDyrhONP6EUPSsjlXBAAxaKFXM4DzxWRUywLigUL8/rLGKuEx8plG4VCETkujhVLZeQa4fl8niBPCnCLvcHxUtlGLie0rhzfCZJdjkJekCcuLSJHcigUCOp18UpcKuRQKpdg2xy5nFAO582j2LdPKBHZ8hyQZacANY5c5Zag7VKphFyjAMpayGYJyvkcck4O+VIPSK6EXM2XOZ9DjsUtlkwoFS3kcwSER0i9TAG5fBmtbA6FYg4lu4Q8E+NHLdGHnp5eFAtF5Lwc3EIBOZJHqUSQz1NkslyMnaPLke8pgFcQu4elsoVSSWhGhbwLzw9Knc+RQMlKgnCpK8GzwzEAgEKxhJyXQ6mUQS7XAqM5lEouWi2GVotjtO98lLJl1MovBgDMHb0FtueiVLJQKFDAysDLlZHLczH2dgk5V9RfLlnBvZPo6elDyZsCcmX0Wb0olTwUkYPNOWo1hlKJolSywHp6UCqVkG2UQZnO3lYLy1AqbUex6KFSZchmCUolG5Qy5HIeLKuIcpkG8w0A8gWKXI7BLRaRy+WRy/nzrCSsOYrFHrBcDpZF4VlllEolFGgpmCOFYhkF5OG6HCcc34PBTB7jExyZDEEhD+RZCUWWRzFXRAHZYA2VSiUU8i5aLY5CsQzKs8ixHIpFgkaDo1jIwfI85EkepVIJ+WYepZKLHOuLzU+WIygUCmCMo+H495sQTZvOZMRYkEVnorB+JSqlhSh5OfT1URDCQHKnINsDlOocBSLWsLTFKRUpJnPJJlPZ/n5MVIWmmielmHyFvE94cg5GM7CsIrKtBlh+EKVSCX19FoZLFDknJ+4xQzA/mgufh/7+WjDny+X43JF7Eyc2ikULhTxD1S3F4hKpyKEGWDkUiiVY2RIsjyPn5FDuiddfytnI8RxK5TyKOS+YX4V8HpxzWHYZuTzgehyuy1EslsScz7dgJVijFIsl2NkScq0Ssi0RZ5FkMsj688OjeVisgWKBosTK4MRG3i0hR/3AgISA5UrI+daVTnY+WvYAyrW1KPf2gRNHzPNsFgU7p1lhFQoExUwJuVwRuZyHcrB/sFjfLbsA2w0JjGzPPJRyJeQaeVCmuL/TbLAe88UebQ4US2UUMiUUvAKKPI+clwOyFnIkh2x5DnJcdxEuFAjydgGEe8hB1OMWCsi7eVgtMaepnUVPTxG53CS6RbFI4Pjrg1CgWCghl2v5MloA4cgp83x44AUo2H0AZ8jVzc8Enisi16wIq1HDc6NQtGBRgieeLIh9q1lFriXKWbZO5OaLJeStHAr+nlQoEBDCkcsR8AgzFqznvn7w1ggAQUaNPZ6H7Yp9OJfz4BZ6USz1oFwug3GO0lRJPJtyOdh2aC2VL5Zj67anTNDTI+iiwUEOQgjI4Bz0lOrYk5PP+CL68vMx2F/B5FQ4d0plC3mnBzkvB2r3IuP65KZVQC5PkWPFYI+1XRe5pnjOlzLiWKPwl8jUnkSxvhn5PAElfBqfoUKkZFeKIwJ/vJnjx/8FvPY1wGtfTUAmdiJ/0ydg7VyB1hlvhPOCTwKZNr4EKVKkOGR4y5uAteuAa79LcNKXP4nnnt+L7H3fApxJOH/7ZcDqkNkmRYqjBFdffTW++932rrw33jizZA7M11ze9KY34fWvfz0A4PTTT8e9996LX/ziF7j00ksTr3UcPQjy5NQUqrUaGk4Tx52URem4Kpx11SBDVK0mskU1WAOkFV5bqwMDi+rI1x1s8T076o0Wao06Mv7F1Vpda49SoM4aqMBBv3+8VnWgikTHVqPB6uDEgkttWI6DxYuATePifMOaQq1aRbMZZrHq7wOKBWDdeqBmNTDVAmqVGjKOE+j81WoVDacFiznwPKDuTcFxHFTrTThuNZCzTqrIOA5aVjmwxEkKQtxoAA3HjsUZopk6JqZyaDkO7AZQJVVkGg1YjgNKgUbNxdRUGfU6Rc5xUEcDVtNBrcbgeQTVWgZVUovdK9SnkDPcw0ZdxKYCgIbDg3EhBIhWEUWrBdRIHa5FtXprdgMFx0G12oTjcHiUoFZvoOGIOiecLFgrDKLS06jDYw5qNYAx4TPjtjJoOE1Uq1Wc+bQGtt/lBOMWlWvKYSBuE6x/KSaGJ1GtAg1ag83F/HAcoFolcO0JVKtV1JsMtqdXsqe4DNXqGjQaYgzEfSfB76aVQ71e1dqW87tGHNS9RnBOJoiYpBW0HAeMMNQzBXHcaSAn56/dQIOI63hzCtRy4DhcjH0TqLkOmmig7lWQqVcB10E+lxfz0ZerVm+A8BbyjhOMTYNWYDEHtCnGr9RowGnWUGkw5CODR51NaNjCFTHpfnPmj1/LxtaeF6NZccGYi1JRJFao14XbZbUKkEYTWb8iQsS5dvOoVZtCteq7EjadYGwkLEvcC8cBGHHRsjLgroMpQlClVUxNAtWaI9ZjNSxbyy5Gy8lhknnh+jTI4rCKvzeJ6+sNoNoqI6/sV6pir6JWq8NpVWF5NfQ6Duo1YIwvhMUc5FyR1m/SysJyxPrkrotGgwPIwWmK++54Lmq1enDvqvVGcH+T4lrVajU4rSoKDQfcFXI2vVrwt2vZsD0HDQeoNGsAsZBzmkBTnOegqHMHlj8YDdZA0xJ771StAVqrwXE4HK8KizuaHJYF1Nw6JqdqYq41AMsSHxSiY9t0m8j662xX30vA3CzgVtFwmrBYWFjKCwA1S58DlWod5WwDZV5HvVZFznHQ9CYA18FUJj6fGw2g0ayBcheW3986GiiyBpo+79Zya6hMefE9sg3kPgIABEVUq9WA/KrX4nOrWqvDtWyA88R26rwJ0hR7RSYDzJkD7NkTnq9Midhm9VoNdbcKt1VDya+LtnT34Tqto9B00PDlsCzxL0F8znueWM8sXwNVglnVGxaynhP0pUo9VFEL9rNqtYopt46m44AzBONZsxraPcvnRfbWalW3+HLHxwHXCcajWq1h1+Az0Ve7QZOxXhP7WsFx0GBesBZbVgY12kCj0Qhksr06evy9r9oM+1LlS9GHBuY6W+A0Z+YkkpJdKQ57rH6S40tf4Tj3HOBD7wPsx36G3G1fAghF42+/Cve0V862iClSpFBACMEnPgps2crxL1cC3/1/78fx+V7kbvsSSLOKxqv+Fcgkx2hIkeJowTvf+U689rWvbVtm6dKlmDt3LkYjudJd18XExATmzZtnvE4eX758uXZ8+fLl2LVrl+kSDYwoBE3gN0VAbRuATP6iB72OuqZwBlBC0NND0LJKyHgVEItAc2OMuH8QGfRJ8VeIu5qRILC5vF515yg3Nsf6QwgJ4kNxIgJ/M6OBi+8+5SpujL6rxVjxaSg2dwVuJpXccRioPeGXyYDwuA+ocA0JtRVGsjhucRN7pgBPxuwS8YuDtjn3r2nnxsgoYPIdU97283mhFEo5ZorkuDvRmF0i6LWMn5KUXVGdL2qA+qgrWgyWDe+El4rzI0rtkfg1knSMZdFs42rULmYXlLlh0qWYH9vq9JNbGLczWLFLtJXLCWVSZjqT7QgXH45SEb6bnQyKzxENqh1G8SGA74oXBD/nrl9CHKjmjkW59Dgsrx8T3snoq0fc/WCcMRgtnoUMm8IctikQktLwvks3Rs8T7r1inCI1dZhflhK7jxpS+kVdlaWrE5OWoiR0B6XUQEplS9hXfhbmVR4wunYR7gp3MjBIj7Koe/IpJwNPrI5fy0HRUwZqE6FrV9PqR4EJxmJvz3NBfHmjsZRUVzF9HXVekGGcRAuLF4vsshXPg9UE9g2Hccj0mqKuxeHZuYMcuyZk86rLMQ9i7cntQ7oxeoora6KcRLfeSuqj6qoWy8JHKBgj/mz2LfasXmTdcTC7hCgCubmnH9Sa7C4VwYnLgeERYHxcjx0Xm+Ikfiz0W02+n7G4lG1KivPt3BiJdn3oFhtiyWJgx061UDy2onotI9ngB5t7MshOoOEQEbbeAtCSbej3bHAQYRK4GCJ97jDddTdGS6xPTW6zCzGnWYwXn4b5jS1RQ9WukcbsSnFYY98+jk9exjFvHvCFj+5D+Xf/iPzNV8BbeBZqF/42JbpSpDhMUSgQfPFKkbHoo5/gGDnxH9D4my/C2no3Cr+4GGhMdK4kRYojHIODg1i+fHnb/7LZLM455xxMTk7i8ccfD6697777wBjDmWeeaax7yZIlmD9/PjZv1omfLVu2YPHi9tnqBOIvmhwUnGaCN84FC0T2MTXWTxS8dyG8RWdjrCBi/VAajTUVUdVo+xhHgE8++end1Rf3Hj9cjc1qIL7CrkGRU3B2ofIc1K3IJpUO+ZJfzR2PfT3PCV7M1eDrnikDnmxCeQOfzJ+IclkQOy1Xxk4JqBDRHg9plZM0rjJU5pKIG1VRKpXCAPntsoxFYYzLY7g2HrPLJ60kx6QoK4sN2TGhEJDRyqOJDcRBGi/KWaCgBmSZT0aySBBsdVolJhYjloFclf8SoxWORuplMn49BP398jwNdWIAvb0Ep58GLFpE/Pnuk11cUd4jpAkHFUqpci7jTfnBsIUE1dxxaJ3yCvQNZtG0+uOCEju2RDkIarklmCicpq3jWl3pkx+g3nXDWH1aHKkuiFTLVshrA3OiJ00IyS4ZbF8EqDeHOehGv6W8ZYjRFd17Ejoi9xKNgKfBxwBB3EhlXLnRqoxEuF5G4xtGlXPHVtzVg/YoigWgr49g0QIWrOkgyDsBXvIiGRQ+bNiOTP8FCxTyx4/jRCnEnCP6HiH/9lz9t+lmJ+1F0X1cIzRiAeqpIFA5RHwrAJP5U7Cn9/kYmB8NgO+LIlak1p5GmnLWFfuRyQAFf0yzipce6WZid0NadogNF5KgajzK2J9+CZ3QM4a/C/ZGc/vR2IpyjbmnvgJs7kngAKo1+WxUPkxF62k3tAZGLiorK8xBPbMAldyxaNhh5gNOqF+3VomxDvU0JcBoMXwfquSObyNgiJTsSnHYotEQRFe9zvH/3nsDFvzq1bB2rEDjRVeg8frvg/csnG0RU6RI0QaLFxFc9QWCPXuAT13OUTvpNWi88hugQ4+j8D9vB5lKzjKXIsVTCcuXL8fznvc8XH755Xj00UexcuVKXHnllXjFK16BY445BgAwNDSEl73sZUHAekII3vWud+HHP/4xbrrpJmzduhXf+MY3sGnTJlxwwQUd29QsN0hoYUCkvxGAeXPDrMeAQbnhAKc2eO9iwFdUKY1aJHX+AmwKIh6quOGL+5IIh5dkfMKDbJKGF2hVoY1YdklM5ZdjtHgW6tmFkKGxGMkYSSIZSFk9QIggZlwmLTN8izNlLIQYBPO07GfhV32heITl+3qBxYuhWzokIGb8EEEmA/T2ir/lv/KquXPUkvoAy+DulhX+ljBabQWkXlyxIrm4gqsqnyE9GHYgsHjyw1ZEA7VLedzjnxdc79k94JmiViZKegTxogkCsquoiCePEQoQOxMUDg0ilaDSRFoWigPZrPgtCc5ocgVbIQ6lUirFK7T2it9KwGdAWACp96boG0pnc8SwvqJzLg6LhjYyPpenKb7UaPESqUNdQglmQuN95/kScVCmW3aJPisBzoPbzmNzWCOvfTkt1tSuh2I5UipFAugDmMqdoNYoyDa5X/hLT2YhZKaEALE9S5BdPIGAkPBoIchAGZDf6vxhXkh4RwKNR39EyS6qEcvhvmlK6CHH0I2SXRkD8ZRI5uj7q5ZxL5r0g1BwLgr2NtaLMoTCtcqYNw/wH3Ph9Rw6Oey3pxIwol/hgUWLErLjEuFaeOLyMNC6KrcEpUCHaZ4A9QMKYpVIEjTcIiLWnZHxreWWxslwdQ10ELKnRye7TCRy8BFJX7jtK9ZAcMrJMkGLv79F5HIHlwOEYLx4BlpWWWnb9i0vlfUuJUjmowEC1DPHBJbF48WndSVpSnalOCzBOcdVX+HYu2UUv3rjh3Hsgx8Dm3siav/wa7hnvaW7z0wpUqSYdZzxdIJPf4rg4UeAL32Fwz3xxWi87rugU7tR+J+/BxmNuyOlSPFUxNVXX41ly5bhwgsvxCWXXIJzzz0Xn/vc54LzrVYLmzdvRr0emmRcdNFFeM973oOrrroKr371q3Hffffhuuuuw7HHHjvN1sUzlZEseKaQ/Iw1msSQ8E+/TCaT/GmaGrSB6Bd24f4msvhJUk5yaJplVuJXYJJMdvnIZgRxdOxSgzJAKGq5JQCAXA6YMyjGRhIBTbsf1ewSY93cP2ZRoMWosUwgWexE+LtcppqOXyoDgwMEhVq4Z5qU10g1iVYxsnzUooMYiITwt7CKsoPhihMu+mHhTxYoVirZZcUVMLPlDQ9kohTwFp0NNvdkAACjURLCL5jvRSsvWLuRvvPhLX2WSbgAI6PhOUlsLV9GcL5/mVTSCQD4ZJea1TFuqajcx5KQS8Sy4aDcxby5wAnHi/6HxCEJSZA2r7iECNcjOaaUEixfTnDC8cDcuQS1vjMiF+jXyj+edV54XL0VgWWXapnTxSu3RnYayC5uFwHlfkmFnymWXeqEVpX7qAuyOi09Isaskjs22CvU8rv6XoRSSRD3mjyEomn1ib8RzyJLLYrR4pkYLj0DjObDexwQnJH+ERu8gxtjsWi2atUspxgLSkjySBLj0kpQws7ogvB+Zd/XLLsYOMlgbOD84LQkTOt1aAR2a+n5GCmdmyyf1kWi1KVnVVT3VJHBD75bulpvuLfPUQzeALE/CwnVQSUaWRUlQucMmsheKSpBoRB97pjLRlpAuRw/ftyxIYGq7pP13EI0Bk7Xyoa8rfhLjXMGAJO5EzGZD018K8WTgr/l2JYUT0/lW4KxIyefTPF0hQcyW0z6a0lZuNFytukypc1slqBQ8O+r6UGjWfcp8xTUUL79zaDEf3cgFHace26LNGZXisMSP/wRQ3n9r3HD316D/EQVzvM/jtY5b08zuaVIcQTiRS8k2L0buPY7HIsWcVz8zmeh/qYfI//Ld6P4079H/bXfBltodtVKkeKpgv7+flxzzTWJ55csWYK1a9fGjl9yySW45JJLpt3eqadSrA/i1wiLor3l83H8oAXYNrzF54KObQOpDQfXxN3aEL5oyxd+QvHMZxJU1gHbtie13knL8F12CA0sBOSXcdcqBkHjTTFXpJyWpStToczi2PITgWMHXRQmCNhk+3cLDsDJzEU2OySCZ4OiZfUmlCaglMCiHK50Y5RqiYHc4qX5AJ5ALbsYxdauYBiXLydYuohg5G5gbMw8YkQKFz1uKLzsBKDlAn4Sz6CMVKYCqxat32b3mEA/8itZugRYvJhAckaqWoMkyy4D2aW7kcm6QnqAUggrQr/uWMwu5fr63HOxb3QCFskCxAvGtb3LEYHrhv0L5pNy+4gtY0wp1jiUhlYbETK3UAAWLKRY1AJ2jgjiYcECglyOaG6DwmzMYMkTAaU6McNoFmzOUpSxEZwQOOVjATwWjkkCOawm86QGskuSSEG/k0UCAGSUW2EZXFQn5pwPbgjw7ZGA1QjkJYRplnUSwXpWyVxCsXPgFeAAik0Rq5D7HAgnFIzmE2QnIZnEPW1MqU+8cZpFI7sgcpX/b4zsolCTngbxuJRjmQwAj4j4YJNhbSrJoFoyUcvCvLnA4ADAIO+NQsjRcHWMF04D71sMQDwjOLXB5p4Mun4tCDyAUHi5OdjZ9xIsnrg52BMbjiCjgrVp51HPLgTCGOGJa0bbXz3AseegB5tifRroBxYWAWcoSnKEZFl0PBcuBEqUY+Nq3Y1x0SJg7lxg/Qb4sRVFhdLFPXGflH+r5G+kW9J6SD9IA9JnR//LsGT8JgCCAOspDWBV7axg3gHCCrDVewKAMDjc2Ji0mJVkl+JDLAVULdYsAmmkZ9nAySeJuTM2Fu+DEZYNQgiInUE9MxCQuvFGdXdy1YJx6VKgv69dQ/qHJBknT0OCzs6JIWaX+rHFYLwsrS05DDEXOyC17Epx2OH+3z+Jv1z/Nnz2nMuQWbgctbf9Eq1nXJQSXSlSHMF461uAv3sV8IMfAb/6DQebdyrqb/oJeL4XhZ+/A9aWu2dbxBQpnlLQrGjkCyvNBs9a3rMQ3FasLtSCPtS4G4FS7WsMfX1hTKMoOsfsoqH7m+qOQ3TrAdmH4fIzhauaIqdFzcqxhG0R5OBnWozGflJAINzcGvZcZBYcH1PCk9zGqAV4krTzM9Bq/ZZ/ZkvYMfAKtOw+SNJR9I1GLOQSRTTKLCEVENsOiS0QGroEUv0qPWZwVBukQV0qjvFdMUO3Pv9fznzjC6kYqWRXfMxNwZA1N8aIOCb3srD+LJzMPMiQ0ApFoJdTiRPfQiFKdnmeUki6UEJxY6Q05HqJXikhBCedSFEsyr7ompxUpI85hmL+PGHN05EKJvpc4lSOJY/Nx9BlLlKHIX4TELoxuqqVTshXJiKjuPjambh6SSmJzycgcEUNXa7EuGYyBCcuV9pXCiWS3JH6KYsnlFDhWpIh8a2mlPqJkQmJz/GgH7CghZAy9DWbAc4+i2DZMrlnymsV4pSz4G9KgQULiD92BBlbv+8kws4Qn/0O3Ap7FggO1fcxI8Tf46HHTlStMtV+LQri8LWnC+T8UePIRd0YCfFjIJrILiPvTZDNQrfsImIe5fNSSDHgTztdWFopVSZC9m937wu1oh7NJZJI0f2OkHA+e1YpmU3zsY2diZYb7mU2qwcusiah1d2KAMjliP6hwCCne9xzlAr8506ujJHyeQGJriK0mA4r8/wYlZR2IrpCIaSltinJCVFNw1QLRJ/sUvtt+tgSbU6S0PvmvgQ7+17SXj4FKdmV4vCBU8Hkz6/C89e8ASf0bUf1JV9C440/Ap+zvPO1KVKkOKxBCMGHP0TwV88Drvk6xx9u4uD9S1F/03+BDRyP/K//EfaTv59tMVOkeOrAkAlJ/yssE3XXCpQgrpxU/XtI+xfXM89IOBFUS4X7m2LXI6ttUeFTwrNqHCYilETld7sv+BJ0Ynv7AkF9AEAAu4D5MjlmguIt5RUKkq902FLWiMISdd+MkmGKXMaxJECv/9FeUxj9wg17jmb9pFq+qa6BiF8alEPkNyGGAPf+RVGlUHy+Z0EBVeeiBh8ZbTiCv3mirDzixkgIxTPOidSVoFQFcmh1Eu1YQBpoll2SuKTGVUNiHYFyH3mQ6VNCxhgqFsU1QZD6BEg3Rm3Oyo/BPK5xqkGcVZdVdb6of0sLLa4kZIjGyjNBtewq9xAcMz9+P03ujbrSDCBmicZjxoskVrHyN8J5q8YJMjSM019wKkZKZ6NpD2oWeZSatoTIvImue2JFLLtMhB+Qzcb3XU70YFGEAHt6/ypwn5WQSTeWnQCcflpUOkFynXOmyDqp708eOKXafVYD8idlSR3ol30JDwb7n4Jaz8nY3ffXWgZG9W+5dzcy8zAx+OxYY8JakSCaV4UgGrMrvOb004CnnyYsRykl6C7YvF4Piax9VpwLbue0YhwkIICDfSwy3zoxbIzmfHLHt+ziDbiWHkcwJlsXXdAsqQoDYL3+g1m6OCsx3P7qL6NX63sdIAg/cV0XjUctu6LyAIAWwy2slCEesytY9wlta7yuldX2p05Iya4Usw/OYa+5AfnvvxzHbP1P3DTyJtTecQP4Ga8209cpUqQ4ImHbBJ+5nODZzwKu+grHn2/j4KW5qL/hh/CWnIf8Hz6KzEM/mm0xU6R4SiDBcCFaynyt/6/6bvsXfwHfEiN8K03ikEplcaKRmaelRQ/rZ+KLPgkzO0qFhtEcdvS/DN4Jzwe3wmDhmqJPRPY71f0sqU9s4HizkEpxWTWhMgNk5zRV+TxAuXDbYplCQqn40SQruigqueMACOXztNPE/hpcKRUQYoXxpogyDlRXRAV5Gb9nplhUhAgLrDOeTiLB7IHly0QgaFUWdax0yy5D0O+4V0uARmYu2IKna8eibowDgxSDg3o/uEbIxivWeZO48g8ALBhDAipNmLSg9ArxpZAmEjIAOeEMJDJ38nmCk08Cjj1OCCwVzsRsnES3yhEHfXc85sUUTk/JKBr2S89kqpKUpiQMdGBBjPCOIqPcTitDMX8+0Y6pLneAsMacKJyq9CH2h7BEgoHFBYKYRCqxLV3nWKaM4XkvwWT+ZKOs8rpSj41GdrEvn26BGLPsiogQd2O0EHjtRmQ1iK9VwmEZSsd9+2xbzAvGVCvIcDemFMgX1JiJJIjZBZkR1Ica0zCJ7JJWP6rFXH8fcHw0JKRl+xa3BvIC4d7DOODaPfGe+u33RTzDCdHJ4TOfDnAr58ssXMWjE76T2ujOOQVDPX8p5o1ynIPCXfJsNPtOjVwR35PDcYyTXYOR2GOybtENjqw7how7CZcqQbiill1G0j/hvAI250TwbBm8NC9WLpeL7EkmcolY2N37gk7ffjQhpPuvgWePuKqHDckA9bo8AokB6pVzhITZNbtBSnalmFWQ0c3I/+KdyN/4EWwaWYD3rPwfnPT+y9Az1+RfnCJFiiMd2SzBFz5HcOYZwGevFIQXcmU0XvNttE5+GXK3XYXsnV8zfCJKkSLFgQRVNB811pH+op2scAOSSBBl8ln4AYBDVqWd3mFbwHD5WdjV/xJDSaE1chBzWnViiXZ9woQTwWypCnnUUiMJPBdXvmJl/P/UAOuJO5QvRE8ZsPqPgUdzaJZPMJRDXGshimIlNcSEhqrZpegpC8Xcjrj/KXcTXLoBRQg/ucWK4NAkCMKsF4uMfUReGtGX8nmCRQujLABTAtSrpiVxyy6zG6P4b6x4JjB4nHaO0QxyOSCTkxaI4fVUnaMqIRKZa6o+xgHs6X1BcEz2jzE182JYT3iM6CSIwbJLkF1urH+AUERlRkfpppvkWkuIdEdVmQulbFSJVCxsdK+iUMYexQBKDYQ9WjwTo6WzgSXnoDF4ll+feTGphJllx8uIMQoFaGTmY0oJym3iI3muB+OF04wEkpSfEyu814plF6fZYP62pab9a6MB6klM6/b3kgSyi8HSuK54sPzQDUuu84AsNrl3Km1KCBdqfQzibo06RIZB7hN44XErF7KaJjdCXZaIS2JE3GDdqq5qkToIEQdNZKkhxJu4JuL2WypTwIqysXGWZXfvC+JtS7kyhdBlXHOtownPCRI7ru3RCPt0+mnA0sVhz2XQfU58gpI5mD91DwgYGpl5hgr9Pkeex9583Ywvfpv9A7keeMueH7jNt4XckyN3Srhldk8Pyf2Oc6C29EXY1fciDJeegXrmGKOrOiDcz0V2WhapqF1D0PbYvzgfeMFfdSdjSnalmB206sje9Q0Uf/Rq0KHV+OHYv+Btd/wE7/jE07B4cYcJnyJFiiMa+TzBV64iOOPpwGc+x3HjHzhgZ+G84ho0z34rsg9+F7k/XgYws2KQIkWK/Qdbcq7yK6KsB4cjCpvmGgM97oaMS9W7KDxmgk9CvfAFgvQ+9ZQ4iUI4A2VNEeMl6kqnaWzypZ7E3F10Sw3leoO10rlny6x5ZghXM4JsTq0rXreoPwSzCtjd92LwbCl21vRuzzUFTHm7N+CsM4FSqc1ncPjEBIurzYIc1OUwKXocFLv7/jpecaQP0b54i88F6/eJKS3gk7D2WbgQQbY4vT5V6QvR0wMsXmzFrI4YbCxdAljSh84Uhw4QxBoBpnInxPqgK/oErlUKlG99ngPcyiiugEo2RkojY2AiuwioKfKyVka1xDKXJURm5VMP+nH2qA3GgaHe52HRImFpqZFdwZDr8qnZ5tTxqOWWopZdDDtD4QZymZkRlai0TWQXDYmyUglYHuF/TcPHBpeB02w4/oZ2ObFjll2cJGSHS2zQkCU2RrJHLo+ueyIybtbrwL7ys3Daae0tF5/+NIKnP12ScdH5IwpH+3DSicCy5QQ9PaZ1LwdJjXSukuehG+POvpfAOe6FkP1NsuwCgLzu1ScsC4O1Ja1uDeIYLCg5F6RgFFG34fAaYdk1lTsBU7kTwAdOiJNd0WBRlGpxHeNiqfurdsa/Xj+uhauLPv8idVkWMc5jaY3X29gQnGrYc7U24oKKf8YXvRRc7qXdQlpdkeRFwKEnqVDRNgB8gmUs5wCz82A0j0Z2AUbK5yXuiYxkwBgiz2x/bJPatrI6WWtFsj63QUp2pTjksDb+GcUfvhLZB76N1mmvwpeqN+Drd74Rn/i4hbPOTImuFCmeCigWCa7+MsEzzwO++GWOX/yKA4Si+cJPw3nOB5F54pfI//aDQKveubIUKVJMGyRXxlR+mf8jiTSJvvhLdsP/rbqI2Vm4J70UbO5Jod6Q9JbpX3PMfIKlS+Kkjs1qIOBwadFgSaJ8jfdjNhHuwjvm6SH54sevaRegXpVlzhyCgQHx89RTgMWLtNOYPw945jOAcimulCZ+ZVf+Mg2v0K3bahVtv3a3+xAenqPgRq0/wfIGoeIlx09XHPVGk5RUbhfCmGrcC68jBKeeQjB3jrlvPG+26s/lCE451YpZrjTtAbQGT0YrM0frA6Dccw6AWnCXvwQTxdMT+6D2rydi7OdJyy5iBWKrSrs6g4mJo5RWFAmWXeGFvmUXAZr2oE/OGYrSsE6N3Mj3g3OgZfXGCAnAEFMtaJrguGOB004xzyvLInBa/vxQCtSy5mBe1GCqQ2joklwuA/PmRc8jqD9owY+fFHbRMGcQxruS9e/eEyGqOnsd+2OqzB+DlaEoaD6uWj/1LZoX+3A/mV9u2IfkmPp9UGIkir7obLJlEcyfT/XL1XluqJsG4xpaLnGaBSw7uKQd2bVsGWLrLrxX4kLzWJnJLtPek2RZJt1+Gc2i75TTAWoFSVMCRD5ytDJzY23rcuj7XFCNiXCUghuJUQAgeNELoQ9axJ9v3lwAvmWXxI6BV0C1chRzx0y0EoOFZ5SEjxegWrmEQjj5JLGvDZfPw75yPJaaCm77iTkiHylC61dDC+qwKMeZ78ao7oemAPXq3xPzn4+xuc9v16FEpGRXikMGMrED+V+/F4XfvA8824Pam/4L3xn6PP73xkG88yKCl744JbpSpHgqIZ8nuOrzBM//K+Dr/8rxnz8RgWhb578XjRd/Ftbm21H4xcVAY2K2RU2R4qhE0/KDpCiufLpll5lo6imLDF4iC5/6uTWjlUv+Qtz+eW97Ikuia5WMQdIDBOQIAe9fCpT8r+VBRjOTHGa2SipcnintOSHo6Y3G0Enqg0J26bpqUrEAWt/aaCpO9piY+4lWTxD3iWgaUWAhQ8KrA5ImookTRflWQU0iGskdqZExo+tSlMTc0f9yIBua18V1d1PKNgpv8CTFjY+qp0Q9sqKokiyviMQoO+PpQJ+fiSwgtpiv4NKQMGAkY7y/cZUtLBALth3pCyFALbsQnGYwUTgVkwVzzKmesj5XeGEArHwMvIVnxuLUSXfI5ScgzI5qmFsnn0SwZAlJnHaOE7/Z44XTjWUz2XglqhujkfyNHGO9i4CyYMTkLTz/2WGGRhlrjxM13pUYk0pNt4oqGg192pGeJiHDOcGLc+H16EQfN81PvQVDlZJ4EX2QRILcu6IWr+KkSoQQ3/oVYEHAbn3lBMua6O546jxtR3ZZFkE+Mn5WhOwyuR+ra97zRL0swY0xa5gvQjrXr8vCKSf7ZaKWXVx3hav2P73t3kkS/FADMtBASCZMBX9Mozsx1+ZeX5+QX5JBI6WzYzJF90KNXPQzcernY1UYBSRtLbtoULKROQZOJrQ0Mz63ZeD+BLLL1FI7yy4AoBr578uc8AgkmSx4tpwsXxukZFeKgw+3icz916L4g1fC2vEgnBd8AvW3/Ry/WXUOvncdx8tfBrzjwtkWMkWKFLOBbJbgs1cQ/M1LgWu/w/H1f+VwXQ73zDei8cpvgA49jsL/vA1kami2RU2R4qhDPbsYe3pfEAS0jcP8lm9ZBMuXExH0NulrOIxhmfzzZhJNKs8Z5pNdtBiXQbmWzT0Zw+Xzghd1XhQWPozm9Bg8GkeV1CfxK0p2ybgr0oyik6JhUuYC/Up1Y1T+L8PnnHRieG3iR3sCjPU+q60MDRkjTHHn0uVWyAD/eKlMNFmFDL6blX8j1b7x4mB7yz1ZlCsxaDTtJSkQTps6kw5LSwZF6Y5aPwS/oxYnyt/LllEsOMZQR1BQcV0koUtjT6/SXwKwwUgW8cCyqz3ZBQCM5jG15KVoWb2atVBZi6tFcN4zdJKZLTkPyBRi91Xew2XLSBBwvB3ZTCkR1igR9PsE4IIFBPPnJ3dDiBO3LKFWqLQTxG9ndInywmBYxu9TPk9QKBBwmglcg7l2T0K3QAAYHBBZ6Hp7O7PN0blMo2ZYigubd+yz0Trm7MT61L5JglHPEBsnWkSjSvY6QswWbZGBq2aXYqR0NqrZaNR42Y/gLy02FlFugtpX8zIzj5Wcn7Gx8q+xn/5iAMJdjhAkb2oJbcv1opJB0WyJhIVrylt8HrhtSAZiJF30xlpWn9i2DNbMUbni+17y3kR9913PJ7tM2VajMdHiHxQ6slvm9hOOn/8s4Oyzkykg0xzgCWRXcJ4BhcjQ69+GlGeHtMhG3I1Rr0D/szPJZ0ZKdqU4qLC23o3ij1+N3N3/CvfEF6F20Y1onXsh7rzHwle/xvGc84GPfWQGKWNTpEhx1MC2CT79CYK3/T3wy18Dn/g0R63G4Z30EtRf913QqT0o/PQtIKObZlvUFCmOOriWUBrbPYaj7hJBinNxpeEKqUSJX0M9z9VPR1+Y/So8mhUWM/7Xekay4IRqgbV5RPFr/P/tnXl8FPX9/1+f2SubTTbZXIQjJARIgNwBEgj3rYK3Fi22nsW79eqhP7UeWFuVqlWreLeKWr9S2wpSFe8qKCoCcqjcNwTIfe7ufH5/zM7uzO7MXtlNAnk/Hw9IMvM53vOZz3zm83nP+/3+mPr55EofhkP2qXAZktQLbUEnv6cMwBe8W6nsKh4luVz70iqtM6RGkRVs3roUWpagll3SGQBAzXhgyiTAljcsQC5/lJsC6JZq8C36nYZkZVXScWXMLs+xguFS7LKEBEX1nnrcQ6biSPIEr0xiyiC4B4/3XZ/mdXmUA9ytaHP9RbseblNS0POSxYh8D31l+tz4glenakq/fh6gnBAMiphdDCYzkD8EGDnCb4GcmgNX4WnKkqTsCO3GqHdsWD4wa4bvb9kaJsCuRHFfGQvH4iiQ8rJAWfoPFDByJNCvn+ANrM4h7T6X4ud9ajQF1snAAhRxqvOqhbF0QFZA5wzyS6Tom1zxTHIInrhuDHX10jH/XehkApSe/sl0NfXa18CZAFHD5WzQQE9geaZvkeJV2CmVXUo3Rr2KJe0M2swDtbWHjPn6tyAEWM2UlwL9+vll8xTTphhXc3L8lF2eppGVUHpujAMHW1BcJG/AIPVN/3fJ6AqNrHIJnveAarMGDWWWL0NotYZ34wBFOzZYC1BvHanlsQh4lF2ydaDBoN7QQU7jqyBwd0g5Zpe+TMq+pooSplJKqo4FI0SC5GSGpCTdwVt7TW6QlV3aijnOAXsyw8Qa34cjPTE0Lbs8ia0JksV4oZ9RK1MYVkeqMiBlFxEXWNNhWJbfKLkggaHtvOfRMXcxeFIWNmzk+P09HCNHAPfcxfy2qCYIoi8iCAxXLRTwu18zrP0KuPo6jv37OcScKrTNfwkQnUh87acQ9n/d06ISxEmJVx2hfCX7WaJ4dztLyVFkDK3schpT1acDFpKye5JvgcfB5Jk+jCZ1Wv2LYF7lnTJAfdCYXUEsu9RBwP1XQp6zRt8e6KNGAuPGaVhJeH4mJ/kFqPccNxoZzGYGntwf7n7F6kx6BLGSYN4kAo4kjUOrub+6NCbA6gmpJQd9FwQpdlmA7ABgSoDTmBqgtNKL2SW1ldKPUEPZFeL65DVjU+Z4uIYEbrslB85WKruYwnTFW73oS6f8xWwKlEIMogARPAoW5ULQaJCsrIxGIXAhprJikxV/wTddMWrE2deSBYDGboESSks2XWVXBNNuZYBy/10/wRgGDmAY7KcMsSZZvDF+tOSVZdNDVuMJAsOsGQz5+f79hync1xQxuxQKMCBITCMNAnbc8wskpWcR6KtDwCH7FEkprEzndR/0/NToF957pBoXfQOEyp1R6cYY7II8KXy6dxYQsy3VISArU7vPH0sa442hl+ZgyJCt/RRKO9kiScuNEYzBbAbE7GK4+40CgzRkKZU+Q/KAtDRlv/Arx2O1pbIotQQqv0X7ALgzC8FtGWEYiMrjvS9hp8EB34cM3/E2U7b3+NgxwJjRwKiRDFlZ6krUfUNL2cW81+3vlq88lpioyhTkGjz5giiPtWTxKyXgSJbHwFtTKSuHKPBT6Mrd0bMPimdHZrWckiS+P2SlsGy5p9yswmAASkukMUW13wjTVxaHgpRdRGxxO2H6+kUkvngajDs+QsfEG9H6s3/BPXg8AGDbdo7f3MrRPxt44H6GhARSdBEE4WPeXIY/P8hw9Chw+UKO/33OIWaOQNsFr4EnZsD6xqUwbl3R02ISxEmLarHgmaF7F0l+i0rpmMZUUl5Q6M0yNawmrFaGzCwBnDEkJACFBQxFo6RKlbvw+cc3GT5M+qclgv9iQHvGoa/s0korV+8ty2j2BhI3GPQ/4M2awdA/O6BabcGVP/3wKkSCzeCViwzBjE5jmuq002hHehrD8GHKHR2Dz8fkhaDS4ktXVKUmT1m2zqI9KAazKqaczLhqSbmYmMjg5p5Ft6LtvZZdXuWPQokKYOhQabdCpSJVFPWUXR5FJ/O3agi8rmBuYEHdGCHJMzQfXjdBs1LJG6DsMkBkRtQnquNmBbptRm7ZpWSiV3ejfY2u3Alw5dYE5HMPnYbWBN8ucpkKpYpWEwX2If1xRflwcyYoyhNgMincb4Os9f2f0wCZQlh2+cvLwSAKCeg0pgbUm90PsCcHunn5FPtqyy5JKSZoWkRyW5Z/dj9B/GJ2KZQRgwL2EwgsQNvTRr27ZUDMLkG7XxuNDNyRB+4Yojm+Kqty9ysKLEBWdimfO7OGpadgAk8fFlCmrx5F+8nfKBTKLu6NJedTDraa++NY0mhftWYGR6rOGKnsqxrtz5kvZpe2gAaIggXWBMmaOTC2WqT4a8NCyAzAZPSz6tVJz/0tu+SqlKHT5J+qchTtDSNcghV1idI9998QRFNcFuTjSghI2UXEDGH/17AuPQ+Wj/8E9+BxaL14OZxVC71BQXfu4rjhJo7ERGDxg8wbAJQgCELJ6EqG555myMkBfncbx5JnRDhtA9B6wStwDxiNhLdvgemLJZF9tiUIInI8M1iLhSEvF/ApLZTTR30zFC2FDGMI2NFJTj+iUEBZqYDhwxjMCQYkJUmLSKWblP+X8bxchrzcQBm0Aqlr7vrl/dIv/amv7JLMUQJqYvDs8udXWWAVqi/V1gRPZl3khbCagQOA4iIjkrNSICZnQ7QPgCtvoiqNwGT3H9mdUVrEWZMtaMqsQf+yEQCg/uDoJ3etY6q2VAxB769XduUCU9ONMcQSJMTwbjJJVkUA4BZlCxM/OfVkg7QQt1oZsjJ9CkT/vuVvhMPDMC0IouvyC8gciNHIkD/Et+AOuuhlDAdS56DNMkh1OOC1GMTyMhy8igKlC6GyHGsqYHUAkPrmqBHyKQFuQVo95w8BkpJ8ChPRZNNQbvmJHFRE5rU6Vcbsyspi0sI5jNVwVZVXG6i6PC8BsYnUCUweRaTFY/nmMiRCD6uVoahIGTNNRq5cshjUDEjvjykBrvypWiJp4LOOEbkUYzFV6XIaqdbAg/ycyc+LUQj00SsvVf8tP2MunUeAO/LADSoTXq8lZFKS4sHWUkKGVDb7MJulHRJLSrXGJ2jeg+CWwf5DVaBlFxSWXVqqF84ENJtz0ewoR4s5J7R1WqhhKKz7ygKS+h51z7vHmuqz0PQOSn6WXerHSPW7v7IrKQnIzgZSUxkOpUyX3G+hP5X3ly1aN0adTWgJIgJaj8Py6UMwbXoTon0g2s78K9xDp6mS7NkrKbqMJuCxh5kqAChBEIQ/2dkMT/wF+MsTHC8tBTZt5rj91mRknbMElvd+D8tnj0Bo2IuOGb9X7ABHEES0aE4kFZYoNhugqezSmHlyi2dFlegA6jUq01tIMsFXNmOwJzNMqGGw7o98zsAYA/fMor3zdIOiLj/55UWrxexzydAo1Xe52pHfFeXzgFPJyUBmJmBLlBSI7mCKCL9zPoUdQ+5gA+rqBIgDR0MLpnTxgm9RKggcVVM8Vl4HNbPCmTESzftaIRptCAwxpZYt6OJDcXBUkQFiJtRNH+VCWwsXN8ICwMB8/VVrEeY5o/pLct/kOHwYEP2soKRFH5eUhUCQVaaGElSjzlCWXbFAt1uqxJFODs4BmpvDLVnjOfc7NmpkEA2WR7YDKTOROkCA9t6Yijyh4tJxn0JXvtYUhwHsaHiWXXrVev/U1Iz7SExkGDbSDnTWYiPmqs5p16tQ+PrV5VOyqk/4u9VGhvoZFcVo7FuUCk6GdlMGbFC4MTIDcgcDZnugBivTzz1StqDsdAaUrg/nKCwA+BD1vXANnwXWehyGKMJaMCb4YtI1eapRWo5FohvW8CNknKsGHUFgYEyQjdS0d6aFAWAMif0HIqlTsvDs/CbINeifkjAlQrQPkDbK2KlXCAsoS1bgyuJLsTkFGA5/B9E+EMLRHyE6cpWlgDGGvMHSe02nCtXfmRkMO1pDXUBgfuWmlKTsIroP0Q3jxv+D5X+PAK5WdFZfhc6qhYBJbae7fz/Hr26UtmJ97M8MAweSoosgiNCYzQy33MhQUsTx0MMcP7+M46YbTJg1+z6IqTmwfP4YWNNBtM97RNPNhSCILsLVX+x5mAtRGM1wDZ8Nd6sIHNA4r+MixJmgmH1Lv5jM6rq0Yp6Ewqso0swqnbTbGcpLOdLSgO9/0EumXKjKijS/L+QaVgbyEYMBfh/74jQfkhU9PqE8B0JndaXkoz4RsPqLxgLvf3BDJ18BdrsAMZkBbuWn+vDuYzgt5ObSckZpYaIfDDywRF8MHNlVx+T1SpBkEOFyB5FZwzpCqwL17mP6yFYwuYOBlkPS7wExpXQaJixll0fgwgL9RAySFUZghfq7CgYWEnheFCz+3qCa2bTuk++BEiDaMgH8gHZjJizew4Inr76yq2C4tuyMSQtqeSdKphFX0B97xTTUH9kfoDgOFoxcC6+yRVdxrt3OITf3YsyrwHC6ujbW1A08DUdFIB3qAPV2O0MnQl+wSbbsUii79LpPmykbPNEJ1nocZjODy3/DA4NZ/cEkAssurQRZWQIOHNMXKbRyRZFA0enko4IB0vihV5hnXDGagPHV0vmjGsntdl87BheHQRxQIQukm8Yfu2ca3d7uuwLuyIXLo+ByjZgbkAcAhg/3e5YUVaSnAceOq6UIdAFWoJzL+42r0cbsImUXERXCoe9gef8eGA5vhGvweHTMuAPcMSQg3f4DHL+6icPpBP7yCMPgwaToIggiMubMZigpARb9geOeRRz/+wy45carkWYfBMu7tyPx1QvRdubj4I68nhaVIHo9bkMigI6w0iq3dVdNUMP5tGowwWDWm2hruzFKk34/hZrB4pc2/Bmv/3d3ZdD6gLrhs0ZgTEtu6dOyb/mpthrz1RnBPEc7wIxK6mhmTYJ38Snl5t5YNOGbuujVK7vlACHce5RujN7FvLb7q6a1QwRWOS6PsksZO0ivi3aY0gH8qCmq6JHTXTBbfZ6LknurX99zDR4P1tkC1bUEseoRuFPrZAAGA8OsGdLvW75UWy/5y+x/3GYDjtdJCrOuGM/NnOGfWalQYIioL2kUFXA9qvQMQXs+A2B1YJ9DWnx7Q+EzwbORgKffa4go787o9ld2AZgxncG41XOv/IYZzRHBaPG4cGrviMkTM8Baj8oXpf6pSOjv5uuzHtWoVJEg9O1lMFvkoqTU/bKA+gaPJWtIZVnAL2BMEbPLo0YQwlDiygpcfatZH8cTSwC2XiFHoHaU64wloS5Ja6wZNUrAyCAakcjKDLxA9aYnGu8vj/Yw1P3M9ayhXe4AX+UQOTUICD4pKdMApbxdW7MzBlRWMKzfwFF7JHRZB+1TMSTH7LWvVY0JtBsj0W20Hofl3TtgfeUnYM2H0T73z2g/9zlNRdeOHRzXXM/R3g48/JAUh4AgCCIaBvRneOwRhquvZPjkU+Bnl3C8d+R0tJ37HNBWh8RX5sOw89OeFpMgej21junYnzo74LjWRFJM6qc4r1iAhjLN8KDrCaRnNaF0Y5RXm0YzXMNmencGi0SZJC/KvG6M/ruh6ZQ30BPIOT3N7wRTWrXobIUViZWBEMQF25M5OxtIS/MtRELhzqn2Kru4YtnAGFO4N+oj3zPJbVWN1/JOXmz7ta974GiIKYM8O1QqP8sHRhbmVl/jcn/lp5Iwbrds2SXHKvOrSkWnMQ3unGp1FbKyi2svixjcnk0lpcYpLwXGVQFITANPzQlDaeBpryjcGPenzMKBlJkBj4zPGk19vGC4tGucxcJCaSwjw99KMOB3/TxairpoLHBCaUADLLtCVBGsuq4oCmUx3YOrgyeU0/tZdgVaiQZXbpjNilhpfmn8LYEGD2aYPlVW+qnLcRWc4ieYtrzy0Nwhb3yRlKGdUIH/TpBA8DZWxc7SC/6o8Xs0/YoJRm88Na3skXUFqdEOJ0+EyLTGd4bJE4HRFYpDsrJLqQcNUoNKtZaQqt4dOWwpA/uWycRQMAzIy4vtel3+QOJzqfWTRX5eDDZVWBI5fYJF2uSBAtQT8UV0wf3F87C9cCqMm/8N55jL0Hrp23AVnqrZ6zZv4bjuBg6BAU/8hQWYOBIEQUSKwcCw4EKGZ55iyMwE7riL4zdPjcbeU16HmDIQCf+6Cqa1z1HgeoIIBjOAa07CA+GOXLgK5ijyKpRSYRC2ssu7uBOUf/jOGy2IdMkBACXF0s52cjyuYAHqldiTGWbNYF4rEF86prEolg7U2mvg8otXGqQKj0D6kYvk6zUapUDsWjv/acFtGV6rM6+Fi3e6H3pstNkYKsqBkX6LZ8YA5lkiyVYVskjetWiCHWL/Mm9bqTIrrkm6MDP2OeaiIaEAR5LHh5QrGPJW9sqYXcFcXvx39JSlEjWWRRXl8LWnp9DMTIbk5AiUPrLFThTKLi6YwZkpZJBsGUGQdo1z5U+Fc/CkiOuLBLM5QjWA3IzBlBwIp59rn2OMeaw3vaZHQYpgKpn8i1QGcs8ZpF+nFsopiJgxPOCge8BoKR6SKdEjZqTLcXUb9cuCdogY5huflO1pMGg8j4BqXFbu3MiZejdG+dlyGlPgKjwNSFLsEKlDpMoudcLwdxSVizySXIND9inhtW0IQSJSrngaymlMQUP/Wer3J6T+bTL5b6Ci3t1WXbkir6e/KNO5+xUF3Tm0XxY8m8v4l6t9Ubm50sYdwdKEoqwUyMpU9jO/TQCCbRKpUeWY0ZIiLloFNCm7iJAI+76C9eXzIC6/De7sErT+/N/onHwLYNb47Afgm3WS66I9GfjrYwy5GrskEQRBRMvwYQxPPcFw/bUMX30NXHhtfyy1vAzn8FNh+fQhWN7+NeBs62kxCaJXMn2qWbJK8UdvfquIjZKcnYEBxblhbwrBGMOkCeHE2lBUrqdQi2IqYbUy9MuSFmqdhhSPHNHOSZjHssvvi7hn5d5pdHgWrxEsio0ayq6QQZfCKV+27JItRXTMgHTISGcBikGmyi+Vl5kJFAyDWinozaBUdmnHJKoZBwytHg6XITDmYiSfLCoqjMjKQshgSUYDMCQvUA6vxYFGdJdEq0JJFcaiW9uLUbYa0Tgb5gpO7xnSzW62AQl65oDRWHapzSoGDQTGjgmZCYDPSkfZtfUtqXQUMaoC/OP4eRAMHj2rnyIriGyB9UtkZzMkekIQ5wwCjKboFABiRoFfrCMmKYUHVPjkZFqOW/qWhr54gdJfyr4hpg+HmDIIPMW3S2fBcKC0JPwl/6wZDCNHqGVJTZV+JiX5fTQIsz2Uyi63IH19CKFiUvyqIbvyQ4E7MEC+IdkBlyEJh+xT4B40VrtcGYWyKJTCSU/SQItfSO8GwYgqxXMiuySrhscwNNmuIVPgzpsQMp0/pSUMw4fpX5T29WpvmBAuaWkMZYrdLv3rkJ9Li1n9M0AMv0cuI12yts4KrVtVQTG7CF1Y8xGYP3kQpq3LIdoHwnDhC2jOrg46sK16n+MPf+TIyQH+/CBDejopugiCiD1GI8P884HJE4E/P8rx5ycS8M+8P+GhM0dg6A8PQ6jbgfYzHgO3DwxdGEH0IaxWf6sUNcEWiMWVyQBKIqovIYHBZIxAdRGh9Vg4iCJwxD4RGQKQPwRIVBrYRBQDTPmZ3ZPdvykViqUAgyb/xjX6xSNTFqyz0PC3StJCjjnmtWqIoi31pnqSMkEqz2xmyNWyGvAvQKd+m43BZgNGFnLYkiKTQ0mKwwRjFoNy91At5dC0qZ7CdL6FiDywMqMRvo0agjVKlNNd17BZYSkh/auWry89PYpKozKRUOdxOBhciVFedBjZNPu52QYxbQjEVO1OF06A+mhFGlcFGLaFV5a2zjWYMKH8s3SDd0m5lU1lNEvWlQDglgSxWBhEqxAYRj580ypk92NwpHJYLL5dbiNBskyV8h20TwODiOH+iQxmAJ2B2tCA3XsBmG1wZ5fCcGgDIPpi4bV7wlHmDwE2bZZiVHKbYuM0rWtW9DXN0yGaqWY8h1HeiIUH3i31WORxKVfEhQzrNlh8A6QqfbTmTsHeCTF89yoK9f4md5/8IZK7vMPh6xtaWeT0djtDZUXkNZOyiwjE3QnTN3+Hec2TgOhGx/jr4BxzORxZ/YG6Os0snHP87SXg2ec5KsqB++6VtgwnCIKIJ/37MzxwP/D5auCxvwLnPXoZLp9cgGuMv0biS+ei/ZT74dZxLSIIontQelm0J+ZppGCBv3claI4fVitgaJGsehzNAoRmjfr0YEyabWuYo6SkaImp/0U7AC03Rn/LLiZAvSumdqGu3AmQFwz+Aeqj2cUqqGFZWLdGuVgNLsCgQYEFRrSe9m4PFzpmlxby2k7Lssto9FnecF2308CFXGAShvwhGifDtJL0b0JBYJhYw70uunFHM2ZXqDzah/UMWczK5tXROohZowKPy80qCJ64fGHE7NKx7tNLajYJMFo0bfMCxYlUF+QdW/ytbcJTfoXr4qpRcZjJpHSyBWekGxTI5A4GWluBhgYDOp2GgDZ351Sjbuthj5u9UlmuI6f8scDtU3a1tko/bYmqC9D5PTxCDuMCfG7mXAzIoHx25Q8mSus4rUcruKufsv2jVXYxVW5VfUydpqvIMbtk5E0KBIOs6NLJpyVbFJCyi1Bh2PU/WD68D0LdLriGzUTHlN+BpwS3jOjo4HhgMcc77wKnzgF+cwuDyRSbB4QgCCIUjDFMqAGqxgLL3gRe+NtErBJex5PTb8aAf1+DztGXonPijWEvKgiiL6IbXysWZSsm+y0pRQHnxcxCGA6skwKbM21rJC6YoJ6eh4/RyDBdtupp9jsZ0YReSltWArBkDuNxBpe8aNBYIGiEy1GjZbHgt4h0FZ4KVr8HhkMblSIEYk0NKEN2H9N0kwlQovmdZsH+Dt1massc3+9i+nCISZkh8+vJoYlGO0ai4EtKAhISgMwhgZkEgaHZkgcwAVzHoig8IRlsNgYuGAEx0O0qZG6NKryxdSLIEz2RF8ahvaDWc2McVwWIW6Ovz2fZJSsegqZWJ4lhW4UIo6+dhxkA5om11toJ1CqeX//VvmeH2raUAqA9iCGOyroyvuuyUSOBFDuw97/6aQo88Zv/9zkHnAhsCpMVLRbPMya7eAaZt8nnmOJ5avNYbSYqlV0Rab6lH9YEKSj6ocORGjoF3n0tSyxVzK4oDami+YjhX6l203TNjVG7Pt9Oxn4RAPyTaf7eFUjZRQAAWN1OWD5+EMYdH0J0DEHbOc+G5Ru8/wDHHb/n+HEbsPAKhp8tkDXOBEEQ3YvJxHDBT4A5s4Bnn8/BOStexk0li/ETvAC27xt0nr6Y3BoJQofKcmliH27QaTG5P4Smg2GlDTUp58nZ0oY3AHwTbHUmsX85eOM+OPenoGv4X1/w6+XMCMadqmOStY8nt2fOk+XV34QwEVH+1K4xqDzhLEB89ifygibwBnDBAOYOpuwKtN5gAeqLYEJoW3aJmQWh8yLCr/mMgSekqNzbgjaxn4WW0cAwfBjgStLJxASPwiv6+S1nDIwDMFnhGlABHPkaXKWgDE6XFraxIKpL17EG01TcSZZDhmQGdCKqtpaVXV5FVhTe0678qdpK6DDkMRoAl1tbhxygsPKQ5gCO10Eyc2GCFGuNy4HrdRAMcI2Yi45tHktOXdFCWDSF3cShEw4cEKe1X7Ada+VzCsuu8jLgwEF4jC40WjBIMHfAN3Yy5vsApH/rud9PaHY65Yck2cXWYAAarIUwuRt1o9TJ8b20GDTQo9CLx5rb+56KfdGAz803lJKvrBTYu1caG7oCKbv6Ou0NMK95EqZvlwLGBHRM/g2cFQtC7BAk8flqjnvuk2JSPPBHhvHVpOQiCKLncTgYfn0zw/zzLXj62VuxZu0Y3FNxB0zPnwPXKfeAj5gTuhCC6AOUlvhchxITGfKHaKfjglH19RwAxIGVgTFgdIhooe4N2uw3pzCawdPykWTjaI3l/hMhFgvu3PEQmg5JM3NVWt8OfZMnAiZ5TaZRnnxEzCgABIP0U2+m7/VO0VmohmEGwE1SfBeXYPVk0VroGiCZV4SHJE6U/iRxiQGjxp03UV1lsNtqtsGVNxGGw5vA2rTDc/gTasEVes0pLyANgCUZbMRsuJtaw6obiLEralSEo6jVFkBWICgdr5TFTBgvxfbTrC8MvPoFQZAMFmW306C7Mer8qbH5VqhLtiUCdru0693qLyJTslVWSOnF9gpwk38nC6GID7WXhaoorUShMupfSFmJn/VUFOjJLQUs95wMZpHv2eCDW3wbXKSns+Bx7MLc2ZExn7tdZI9RCMsuj7JLEICmhGEAgCSNZ7shawoOukwI3LpDQnb/c0XtxhhsQNFTv0VZlV8x3sc1hGWXzcYwYkRgmkghZVdfRXTBtOEfMH/+ONDRCFfJ+eio+SWQmBYya2srx+NPcvznLaCwAFh0N0P//qToIgiidzF4MMOiexg2b5mNe54biZ9Zb0HJ2zdgz2dzkfKT/wdDsqOnRSSIHqVfVnjvbvfQ6Yha0YHIXCS57O6gMxkfP66L841INQCWZIgWjSWHYkWruRsheGBVBhPEzFCzd+0d57yEIb8rbRgOJ2fCaUz1FKWRJ4SFgz8DBgD1DVL1AYpITTl98ocTVF+3mCjzhVQOJWgGXNNk2pTwu42+ooOpBGNmGyB0hlco4u6FFhcBvBskyH3apx9WoTk+RLWpggDJnsfPRVE7tV/eiKvzUjNeytzaqlbqhVGtJ1g5wBO1NDRhKrvCcWOERqJwL1ojWVaY7w5NgiiRJoz3fDio83yF0XT19mAwS7EKLTq7W2gRYmBgXgVtBMpErv7D/3uFqkrPQfWOloH1uI1JEOP6jSCYBXJsK/aPScbDtOyKFT1tFEv0AIZd/4P1pbNg+WAR3Fkj0XbRm+iYeVdYiq6vvub4+aUcy1cAF/0U+OtjpOgiCKJ3M2okwx0P5qB23lK8euRX6F/3DsS/no51r62C09nFyJcEESPq6+tx8803o7KyEmPGjMFtt92GlpaWoHlqa2vx61//GhMmTEB5eTnOPvtsvPPOO7EXzmAKy+JbD1V8krAtYLppihpNPRzgsgWIKbR5Q0QLaa60f/EcUlibcHPohV1qKvMquoBgll3hM3wYw9jR3BeIOSSRWaMF0MWhOayQGmGa4BiNDAZD8PLC7daRtrs3ezRuffGw7AILGkdJnUVPYRuGbBEIr7TskpQI8qo6iDx+ShSt6iLtgvI4FyQUHsLoKRqyaEviVRqELFEvUe9bvyUmSnGfuTFBOhBq7LCmBleI+RMyrc+NUWMoDqP8wPekln5N+TwbQ7pLauCNbRmtZVcQV8Ww3O2jR27XUJZdsYIsu/oQwuFNMH+6GMY9qyGm5qLtrL/CPWRqWD2rvp7j0cea8X/LOHIHA089wTBqZO8bJAmCILRgjKGq2gRedSXWvj8N/b+8DZMOXI9Vd56O2spbMfuMVB3rDILoHm655RbU1tbihRdegNPpxG233YY777wTixcv1s3z29/+Fo2NjXjyySfhcDjw1ltv4YYbbsCyZcswapTGrmU9RGRujF2cxIeAW9OApkPKCqMrJ3UwXJZkwKpvIRrdJWissBLT4MqbBObuAE9IDVmCNJYptrbXugERWnYBysVJOJZdXVR2RVBV9HTnxw6PtVEU7d4rUCyO3XmTwZzBFfEAwKF9rQzBrD2jv+HclASkDkRJPwe+/DZQl+kqOAXG79+W0vopPYwxWBHLTaSlQxXThgCudulnuOWEsuyS0+s+Xkzn90iJ7UMY0iIN8AbhB3fHtO5QY5HySsO27JLdKY0JcA8cDRxQn1Z+IBikETa2pBjYuw9I1jAg1quaMwEsuFY1BEzxv/+pOLsxhnP/YwhZdvUBWN1OWJbfiMSl50Go/R4dU29D68X/gTt/Wsgn2Onk+Mf/cVywgOPNf3fgpxcCzz9Dii6CIE5MGGMonVmIjN+8ip15v8TUjJU4ZdtpePGG1/Hqa26vGwJBdCfbt2/Hp59+ikWLFqGsrAxjxozB7bffjhUrVuDw4cO6+datW4eLLroIpaWlyMnJwTXXXAO73Y5NmzZ1o/ShyQp/8z3Fojo2U1R3dgncOVXev3naELhyawLri4Ygiq5oi/YGsvbPm2AHt2WGvavstCnS7mgAtK2x5LhGiek6LlTBhAznwrqm7JLjAXU1LlBQurqnvWaZOsdlJVd3rfBijuJ+mhLC6zN+/cRr0SGoLVtipddmBgPEARVg5gRVfZr4KbvC72f6wvqUVNr1idklYVkh+dojeMPIgb7DMbbUdD1WHBNTB4cuJEbIsbAMwR4FjwIphJlc7FHoefSGYn+4LRPunCrJ3d+UEDTtyBGBpSUkMAwfxsKy3nTnVMM9oDwMqUIg91WPBZ26z8ZnjPJXBodtJNxFyLLrJIY1H4F59RMwfrcMMFrQMf46OEdfohl80R9R5Pj0f8CTT3Ps2wfUjANuuzUVqSmN8RecIAgizjCjGZnnXI3OozMhvLUIN1vuwneb3sAdb92OUTNLcPaZDGlppNQnuod169bBbrejpKTEe6ympgaCIGDDhg2YNWuWZr6KigqsXLkSU6dOhd1ux8qVK9HR0YGqqirN9D1FdjaDdQBw4EDotF6iVAoUj1JbaXCtRZzKFbC3PefR+M4EIrneeVxyNFYVXDCAARDT8sGTsmIvWxctu7KzGaxWjpSUON4fhTYkHhZX7oGVgVuPRenGGA0xNY6MJmaXX6D4iAKqh8HkiVKZX32jLlcuPqgq0+9+B9uFVrnDI4wW/SLDcmOMgGCmYgijPfU2ufA/xpikiNMXJMg5NUeTxgbsXuuP6DHWCmbxy2XLLrGblV0KEj37BZgVnoncmgo428Fc7erxw+b7ohMTHbpOGdyWIf1yeLP0M9qHyWCGu3852lvSgeN+Msfassvv72CWXeTGSIRHewPMa5+Fad3LAHfDWbEAnVVXhhWTy+3m+OAj4KWXOXbsBPLygMUPMFRXMTgcBtSFt2kNQRDECYGYMRyGS15E+/dvo+D9B/Bk6oVYvuEMXPPP61A6cQDmn88wNL+3LYaJk42jR48iLU39jjYajUhJSUFtba1uvkceeQQ33ngjqqurYTQakZCQgMcffxy5ublB63M4glskxQO3LREWixum5GQ4HPrWSWJzKuBuALOngEUhZzhZuCiC26QPf8zhADOGF4+MtzEpX2ISBJ2KeIdZSmNJQoopGY1NIux2IxyO8JQcvMMO3mYDSwl+/eHcw+RkJzo6RSQnG2DzXK8st9iUCvBmsGQbWKp2WTZbh6cuaeEpNtsBlw0sxR7y3vBOs6KN08BCWDxo0dVu6i+/P2KtDTC6wYZPBxLsUcXFkmmzWmGxiLDb7XA4PMsrxQWI9lSgDWApqd62C+cehrqGYIgih83W6c0v+vWBiMuzJQE2e9j5bUmdMFsssNkMsDkcSErqBBM4Uh1m2BKZ79rSzDCbpLYXk5KADoClpoKFGXg8ydYJQeBITTXDamUwmUTYbE4YBL/rTkvz/s7SM8GMlqDt618uN04Ckvqp+rLyHsrtnZEuBB3jQuF2S+UkGBI9zy3TbPOkJCdsNhGpDv3xxXu9OuOJaLMBJqtm+eLRJMDgku5FmPfckCqZyAXrr1ZrB0QOpKWb4HBoa7y4MwG81gakDoi6vyrvrVbfF/cCNptNXX5jM45aLDAnJqJojAN5eRxZWQoZHfPAmw6D7/wcSE7WlO3YcRdsNjdSUgzescBisXhk8aUP1veS7S7Y6t1ISdW+t2JSMuBqB0t1gJmjNH91ONBx2A2bTdrT0TvON9gBtEr33a7f9uGOJ/K1WN0JsNmMSEy0wS0CaQ4TkpOltvW1hTlkfMRIIWXXyURnC0zfvgLz2meBzma4Rp2JzvHXgts1HIT9aGzkWP428Oa/OQ4eBIbmA3fdwTBtKmLe6QiCIHoVjME1Yi6QPxX4YgnmGf+O03JW4rVdC3Ddlb9AYWkK5p/PUF2l4w5EEDo89NBDeOaZZ4Kmefvtt6Mu/9FHH0VjYyNefPFFOBwOrFq1CjfccAOWLl2KwsJC3Xx1PfDlqrW1FR0dQFtTE+rq9J8joakJQksLREszxDjKafQE/3fV14ftGgjRBWNrG9yOUeB6sjnbYWxpgchsaGxqQksL0NAgBV4OB6GxAUJLC9yNjeAm7TocDkdY97C5maOlBWg1t6OFea5XzmfKgqFjF9xOE/S+ZLa0SJ/g6+paA2VDiPqdbb42bmgADG0h5Y01/vL7Y2hqAutsgauhEWjvmgVJW1sbOjqAhsZGzf5taGkFa2uBaGmBWFcX9j0MdQ3BEEXp/sv5vfcjyufK2NoCLprgDjN/c0sbbB0daGkBOuvq0NzM0doGNNS3oLODea+tob4FRqMnhlDSEAitW+Bu6QBag1sI+ZCus7GxBe3tDK2t0t8CC7xu7++NzQBrRX4eBxO027e5haOtDaivl8oF7EBzGwCpL2vdw6KRHDYbgo5xoZDvm9jZ5t2kROueNTRI6ZoagQSdmKPy9bqbmsCNgWUw+zDwxDTNMcDQ3AzW3gJ3QwM4C++eh9Nfm5qlNE1NgDHYGjO9FEiw645Pkcii1fdTB49BQ3ObaixvbGhER0cHxLZWNDTUw6QxPLLmehhaWiCiSfMd1dgo90dfP+jo6PDI4ksfrK2a5DIagLokrfGkBczVLr2/TB3hNIcmTU3qMQIAhKZmaZyvrwd36ystwx1P5DraeDtaWhiam1sgcql9XC7p2uS2qK9viWieHc4HA1J2nQy0N8L07VKYv/kbWHsDXENnoHPCryBmDA+aze3m+HY98M67HO9/CHR0AJUVwK+uY6gZT4s6giD6GGYbOifdBGf5T2H+/DEsYH/DeUOW4bU9F+Pu2xcgfUAyzj8XmDMbsFppfCRCc9lll+Hss88OmiYnJwcZGRk4fvy46rjL5UJDQwMyM7UDXu3Zswcvv/wyli9fjuHDpff9iBEj8NVXX2Hp0qW45557YnMR3U2YVlYxIxIXO8EIV+GpwdOYEuAeNBbc6gBrkg5F5tUSu+i9Pm8UjfEqwQ73sJkRlhjBlSjl70bXPSUCA/r1C5LAaAY6EbfNEFTIbXCiujFKJUZWqF8f9jrBBimCJ6bDnTcxIqmKi6SFs+yKGML7TyVbdnZsGym2brfhlRXeLdFOxFMG6WeR/Q3j1GeDxuwCwvJI6gosNQecqxU1YY1wIRo8MwPYth3QeXWHRUg5YhT7T9N721t2bGMayq0mx2zrjmEXIGXXiU1bHczf/A2mdUvBOpslJVf1VRCzi3WzuFwcGzYCn33O8cGHQO1RIMkGnHoKcM5ZDPlDaAFHEETfhidno2POfXCOvgTmzx7FJfwxLMh9EW/VLsCjj/8MTz6dilNmc5x5OkM+uTgSQUhLSwtwT9SioqICjY2N+O6771BcLL3D16xZA1EUUVpaqpmnrU2yLhD8Ap8YDAbweATe7ia4HE/LFf3X6oiIw4xbjoHFmHQfIrkd3JoGYDt4iOD34SBfmhBzC/1wyutazK5YMGN6cDndAyrBmg6FFcs2JCGahDMGBoBHtDVp70IzyHkwArZh0z7cVYxGBuUwGzRQ/AlAuO3jDQfXBWVXUOQxOEicsq7QGx+FWMSVS0pimDUjNvLo4u3kXbNI1VQ4hlm2K38qILpC1qHXllr3n2J2EQAA1nQQpm/+DtP6fwCudrgKT4Wz6kqImQWa6ZubOb74Evjf5xyr1wDNzVKwveqxwC89VlwWHfNXgiCIvoqYMRztZz4OofZ7mL54CmeLS3DmvL/jy86zsPijn2LZm0NQWsKx4KcdGDuaBw1wSxDBGDp0KCZNmoQ77rgDd999N5xOJ+69917MnTsX/TymKYcPH8bFF1+MBx54AKWlpcjPz0dubi7uvPNO/Pa3v0VqaipWrVqFzz77DEuWLOnhK4oe7lE8sM7I3baioxue20iUXUlZcBXMCWvXtnDpSiwqnQLDSBNh+p7AaAF3BI9vF3O61bKLIbYqH4ZInhcO9bXqKX17onuGw+BBwPc/qje86A7k+8ZDtLWsLAhrb4UoGoWJkhupvGNfOFSUA64wvU97pbJL/iVYe8nK+xiO0fp1hTjRxQ9b2vdArjRE2WF+JNBrSrLsIgIQDm2E6esXYfzhHU+MmXnorFoInjZEla6jg2PTZuCbdRzfrAM2bQbcbiDNAUydDEycwDBmtLTVKUEQBBEcMbMQHfMeRuex7TB/9RzGbX0Db0x6BfsSJuK5TQvwu1snIiVFwGmnStZeAwfS2EpEzkMPPYR7770XF198MQRBwOzZs3H77bd7zzudTuzcudNr0WUymfD0009j8eLFuOqqq9Da2orBgwfjj3/8I6ZMmdJTl6FLa78q1DYwZIR6PDwTaB5t0N1IieOM27tkiHQ9EqNFlHd3upD+QmEimOQCw6ldLcRJjs3TXdP0DPK8N6P7V/jmmHkGR3YveZjX2muVXYMZBmts5tpthLiQkSOA5GTAkRqhpWWkRLC5REZ6+PUYesa7OTg84JfAJNY0iBkFEFO7piifMB7o7Iwur7t/KQy1W4Euvic1lV1h+QFHgV/X0Lbsiv37gpRdvR3RBcP2D2D+5u8w7P8a3GKHc8ylcJYvAE/OBgB0dnJs3gJ8s05ScG3eDHQ6JdPEESOABRcCE2oYRo6gOFwEQRDRwtOHomPOH9Ax6RaYNv4fBqx/Db8fejV+W5qHVcfPwmP/OROvvJaFinKO005hmDqFYnsR4ZOamorFixfrnh80aBC+//571bG8vDw89thj8RYtJritmegIJw68wQxX3sTYuJb1NL3k8RditIAQs0aAW2xeN82gGEwQ04dCtAeJCXQSYbMxFBdxuB0h2ro7rEEUFI8CUlNjVBjz/hcWBqN6NZudDezaHX8lRzCLIffA0QB3x1eAmBG8rc1mhvwhQZMoiurCGBCnPtudyi5uMIEnZYdOF44bI2Mh42KHQ2IiQ6KOriqknsnqgHvw+C7LoK3sEsIUIjz0mpIsu/o4rPkIjN+9AdOG1yE0H4aYkoOOabfDWXQWnCwRW7ZKiq1133Js/E7SDAsCUFgAnHceUFnOUFoS/u4/BEEQRJgkpsFZfSWcYy+HYdv7SNz0Oua1PIK5s/6CPeZJeGXL2fjTn6bgz4+aMX2apPgqLYnPFyuCOFGQJ9VhuQQlpMRVlu6mp2IH+YyJWGyEEIzgjnBX14CYOSIGlZ44hDXGd7PvVv/+Ppm4ObGL7sGRvcOKiwW0tEoffVwAhg0F8ocE7vLena9G2VDgRCCUG2NE9ML5R3c+Cu7hs8NKxz2qEc7C3KH3BCeoG2MX44H5F+ffBZV1FwwDftgWm+r8IWVXb4KLMOz9EsYN/4Bx2ypAdMOdNxGt036PLa5J+HqdAd+8wrFxI0dbu9Rphg8HzjkLqKxgKCuVviwRBEEQ3YBghLtgDozVF6B+5waYNr2JnM3/wq35N+CWQgfWds7Fc2vn4dq3izFoIMOppwCzZgID+tM4TfQ9+mUBHcOAQX3D0AdA/LxBIq1fMDAgdBxhIq7I435kK3yrFbDEyA3RnTfJt8NeNLDIdmO0JBhgy/KlZ4x1izVPb4wFFQ0p9liWFvm8wzV0Rlyt4Hqjt5E7MRP1iUWwxdgiNT3LDB5uMDMF8dZRBnVjjPNnGuXHgdxchtw4hU8kZVcvgNXvhWnzv2Hc/C8IjfshWh04kn8JPm45Hx9/NQjfPge0tAAAx9ChwLy5HuVWGWBP7n0DBUEQRF+Dp+agc8Iv0Tn+Whj2rIFx0zKM3/Y6aqpeRrM5Bx8dPRUvvD4Xzzw3DEWjOGbNZJg+FUhLozGc6BswFr/JbG+lp40pZMVCtIv/zAwpJhDRc0ysiWEnEoxddEmLT4eOtdVzTz93sWDCeMBsSAF2xqjAaBolglhdJwvJyUCzJQ/DB8a23KxJM2NbYIzQUj7zxHSgbhe4JTba1p5WPpOyq6fobIHxx3dh2vQmDPvWgjMBtckT8L77Jvzt/Wk4dFTa5jV3sGQJMLqCobw83CCEBEEQRI8gGODOmwB33gR0dDTBuO19JGxdgbnO5zBv2tM4bhyGDw7NxP+9OBN/eXwExoxmmDWDYdJEabtqgiBOQnrIsivBs1aN1rKsvIzGJEJBjIPrj64ADhyMaZEA4rELZfcjhaGxwJ01KkbuZPQsh4PVyjBrRhwKjvDZkY3e4m38pvWs8ORsuIbNAoyxMSmVFWqx8oqMFFJ2dSfONhh2fgLT9yth2PkxmKsddYYheK/uRjz/9ek41NoPdjtQNRa4olraMTGSXS0IgiCIXoQlGa6is+AqOgtoPQ7jj+/C/sN/ca77GZw39Sk0CAPx4cEZ+PfTM/HQ4nJUVBowaSLDpAlk8UUQJwNR78YYIyzSd1N0dCBS7zki1nSTa1BciVHoN5m0NIa0tBgWqAO3ZYK11Ma/ojjA08KPkRccmlOcSAwbBhhNQL9+PSRAjBRdgM+yS+yhoY+UXXGGtR6DYeenMO74CIadn4C52tCETKw6eB7+ue00bKwrRUEBw5xzgfHjpB0T/QM3EgRBECc4iWlwlV0AV9kFQFsdjNs/hG3bKpzJXsNZ/f6OFqThf0en4a2XZuLRR8ahcKQZkycxTJ4EDBxA7wSCOCHp4UfX6rHs6ugAYO1RUfo8PCkbaDoEbjmR/UIji9nVW3APGgO4+3jQuhPwvvVljEaGYUN7WorYIFt2OY0pEFNSUJMjh2fqHkjZFWs4h3D0exh2fATjjo8gHNwABo46dzbe338m3t5zKr5vr8TYMQJOX8iwqApIJ+stgiCIvoPVAVfxOXAVnwN0tsCw6zOYt63C7J3vYk7GMnTCivVN4/Hftyfhly9MQmJ2f1RXAdVV0kYkZjO9MwjiRMAboL6H6k9Kkn5mZQJo7iEhCAAATxkIV3I2IHRDhPa4cYK+e5gQU0uVE5JepOyqHgt0Rh6rnThBMXgsu46lTkRufwYbAJut++onZVcMYM1HYNj7hfRv12cQmg+Bg+HH1lK8u/t6fHJ4ClqSCjGhhuHnP5MWKyZT7xl0CIIgiB7CbIO7YDbcBbPR4e6EYe9aGLZ/gNG7PsHY5A8AAPucBXh/0yS8/P4k3N5ajrIyE0aPZigr8Zi6G+l9QhC9mZ5yYzSbGWZO51Jclq09IwOh4IRWdMGjMIn8fcOtqTEXJRyMJ3hzx5beM0+w23uPLET8kS273PHb2DMopOyKFC6C1e+G4eAGGPZ/A8PeLyDU7wYAtPAUrD1ajff3XY/VtZOQMyIdNTMZ7hoP5OTEfrcRgiAI4iTCYPYGt+/kHKxuF4w7P0b2zk/wc8vfcPHQ59COJHxdV4N3/zMZrz47ES0sE0VFHGWl0oeUUSOBhAR61xBEb8Br2dWDYZpo7tk9uHJrAHaSa1cMFsAQmYWUq/C0HrEqGlMJWMl11weNA0QP4Y3ZRQHqeyFcBGs6CPHwWpi3fQbh0EYYDn0H1tEIAGiDHd80jMEney/A2qNVqDMXYOwYAdWzGK4ZCyQn08BCEARBRAFj4GlD4EwbAufoSyR3x71fwLjzE4zf+QkmON4FABxCIb45PhYfvDMGy5aOQbPoQP4QjuHDgYLhDAXDgWFDpR2GCILoXuip60NYHT0tQdxxD6yMfEfGHlKyOBz09CnhNBoRPYRs2UXKrp6Ci2DNtWBNByA0HgBr2Afh+HYIx7ZDOL4TzNUGNwAjjDiIQqw/ehpW7y3Bd3UlOOwaguISAdWnMtw+BhiSR1/QCIIgiDhgtsE9dDrcQ6dLsSGP/QjDzk+QsWcNTjX9E6elvQwAOCoMx8amMfjkx7F4cdUY1HWmgzEgZxBHXh6QMwjIyWEYnCNZHKem0HuL6Ju48iaBudrjWkd2NrBjJ5DdUztqEUQsMZh6WgIiWug9T+gweaLP+ioeyN7bblJ2xRDRDeMP/5W2mXU7AXcnmLsTcLWDtTeAtdWDtdeDtR4HazoEJqqj5DUbB+KAayh+qKvCt/vysbVuOH5oLITZakFpKVA+l+H0MqBgOMVKIQiCILoZxiBmFEDMKIBz7BWA2wnhyGYY9n6J1H1rMfXAfzDN9ip+PxJoSRiM/WIRtjQU4euDo/D2V6NQ1+bbDSwpSVKE9csCMjKBrEyGjAwpoHVmJpCRDlgs9J4jTkIS7OCwx7WKxESGmTPiWgVBEIQuXDB51rn0Hie0ifccT46dJ1LMrtjBWo7C8u7tqi923GCGaEiA05iCdpaKVp6CBtdgHOjojx3H+mPLwQHYUTcAh9r6o91tRXKypMwqrAYuLk/CwIEtGDQQEAQaLAiCIIhehMEEsX8ZxP5lcFb9AhBdEI5sgWHfWlgObsCwIxtR0LkSZ+YDyAc6k3NRl1CE/c7h2N44BN8dzseWfTn46hszmpsDgwtZEzhSUgB7imQJZrcD9mQpHorVypBoBRITgYyMDogi9xwHLBbAZASMRsBokiY8JpPnbyNgMND7lCC6C9fg8YBwUk77CYLQw2AERCdZdhE9BrkxRsG773Hs3MXhdkuR/d2i9NPlAtragNaWTDjb/oe2VhENLSY0NJvQ2Bj4kBsEycR80CAgZyxQOoghZxAwOEc6Lrt2OBwW1NW1dvdlEgRBEETkCEaI2SUQs0vgtVtuq4fhyGYIhzfBcHgTMo9sQL+Gt1EJ4PxMgGcZwFMGwZWcgxZjfzQgG7Wd/XGwtT8OtfXDgaYM1DYkorGR4eAhoKkRaGsHOjqUyrHmiMRkjEMQpDn4r29mmHsqTcYJIm4kpvW0BARBdDek4CZ6GNlFktwYI+D1Nzh27JA0hf7/EhPlf1akZQM5iYDNBqSlMaSlAelp8P5MTSU3RIIgCKIPYE2FO7cG7twanwLM2QqhbjeE4zsgHN8BdnwHDI0HkHL8ezhaapGnzG8CeJYJPDcFPMEBWFPBE1IhmmxwMSuczApmdqChXUCHaEWny4hOlwCXaIBLNMDt+el0S8dEN4fbxcEBMHBUOADDjxzggOc/AByMc+lvxXZ27sxC8PRhcW8ygiAIgjihkeOs8R7SNBB9HrLsioJnl8QxihpBEARB9AVMiRCzRkLMGhl4ztUJ1nwYrOkghKaDYG11UrzLtjqw9nqgrR6sfjeMzlYYO1thdbaBudqij4D0efhJ3QMq0HbBK9HWRBAEQRB9Am5MAAPAuIjAIAUEEX+kEFA91/tOSGUXQRAEQRBxxGgGT80BT81BuB/jUlNSUH/0IOBsBxNdAHdLn/K4W4pMyt1gnp8A88QQka2rmedXBq48Lv/uOQcw8KTMGF4oQRAEQZyciP2KwRNSwRPTe1oUog9jMQN5uT1TNym7CIIgCILoMkwQAFMiYErU/YZHX5YJgiAIopswmMDThvS0FEQfZ/KkngsbRcougiAIgiAIgiAIgiBOCvpnA52dPS0F0dOQsosgCIIgCIIgCIIgiJOC4iLahI4AKNI7QRAEQRAEQRAEQRAEcdJAyi6CIAiCIAiCIAiCIAjipIGUXQRBEARBEARBEARBEMRJAym7CIIgCIIgCIIgCIIgiJMGUnYRBEEQBEEQBEEQBEEQJw2Mc857WgiCIAiCIAiCIAiCIAiCiAVk2UUQBEEQBEEQBEEQBEGcNJCyiyAIgiAIgiAIgiAIgjhpIGUXQRAEQRAEQRAEQRAEcdJAyi6CIAiCIAiCIAiCIAjipIGUXQRBEARBEARBEARBEMRJAym7CIIgCIIgCIIgCIIgiJMGUnZpUF9fj5tvvhmVlZUYM2YMbrvtNrS0tATNs2fPHlx77bUYN24cKisr8atf/QpHjx7tJoljTzRtUFtbi1//+teYMGECysvLcfbZZ+Odd97pJonjQ6TtsG/fPhQWFmr+W7lyZTdKHjui6QsAsG7dOvz85z9HeXk5KisrsWDBArS3t3eDxPEhmnb42c9+FtAP7rzzzm6SOPZE2xcAgHOOK664AoWFhVi1alWcJY0v0bTDnXfeiZkzZ6K0tBTjxo3D1Vdfje3bt3eTxLEn0jaor6/Hvffeizlz5qC0tBRTp07FokWL0NTU1I1S922WLl2K6dOno6SkBOeffz42bNjQ0yIRHpYsWYJzzz0XFRUVGD9+PK655hrs2LFDlaajowN33303qqurUVFRgeuvvz5gnnngwAEsXLgQZWVlGD9+PP70pz/B5XJ156UQAJ5++mkUFhbivvvu8x6j+9f7OXz4MG655RZUV1ejtLQUp59+OjZu3Og9zznHo48+iokTJ6K0tBSXXHIJdu3apSqjK/Mkomu43W488sgjmD59OkpLSzFz5kw88cQT4Jx709A97INwIoDLL7+cn3HGGfzbb7/la9eu5bNmzeI33XSTbvqWlhY+Y8YMfu211/KtW7fyrVu38quvvpqfe+653O12d6PksSPSNuCc80svvZSfe+65fP369XzPnj38iSee4CNGjOCbNm3qJqljT6Tt4HK5+JEjR1T/HnvsMV5eXs6bm5u7UfLYEU1f+Oabb3hlZSVfsmQJ/+GHH/j27dv5ihUreEdHRzdJHXuiaYeLLrqI33777ar+0NTU1E0Sx55o2kDmhRde4FdccQUvKCjg7733XpwljS/RtMNrr73Gv/zyS753717+3Xff8SuvvJJPmTKFu1yubpI6tkTaBt9//z2/7rrr+Pvvv893797NP//8cz579mx+/fXXd6PUfZcVK1bwoqIi/sYbb/Aff/yR33777XzMmDH86NGjPS0awTm/7LLL+LJly/gPP/zAt2zZwn/xi1/wqVOn8paWFm+aO++8k0+ZMoV//vnnfOPGjfwnP/kJnz9/vve8y+Xi8+bN45dccgnfvHkz/+ijj3h1dTVfvHhxT1xSn2X9+vV82rRp/PTTT+eLFi3yHqf717upr6/n06ZN47/73e+865hPP/2U796925tmyZIlfPTo0fy9997jW7Zs4VdddRWfPn06b29v96bpyjyJ6BpPPvkkr6qq4h9++CHfu3cvX7lyJS8vL+d/+9vfvGnoHvY9SNnlx7Zt23hBQQHfsGGD99jHH3/MCwsL+aFDhzTzfPrpp3zEiBGqRWxjYyMvLCzkn332WdxljjXRtAHnnJeXl/M333xTdayqqoq//vrr8RI1rkTbDv6ceeaZ/NZbb42HiHEn2jY4//zz+cMPP9wNEnYP0bbDRRddpJrsnsh05XnYvHkznzRpEj9y5MgJr+yK1biwZcsWXlBQoJpInyjEqg3efvttXlRUxJ1OZzzEJBScd955/O677/b+7Xa7+cSJE/mSJUt6UCpCj2PHjvGCggL+5Zdfcs6lOWVRURFfuXKlN438HK5bt45zzvlHH33ER4wYwWtra71pXnnlFV5ZWXlCf2g6kWhubuazZ8/mn332mer9T/ev9/Pggw/yCy+8UPe8KIp8woQJ/Nlnn/Uea2xs5MXFxXz58uWc89i9G4noWLhwYcB667rrruM333wz55zuYV+F3Bj9WLduHex2O0pKSrzHampqIAiCrsl/Z2cnGGMwm83eYxaLBYIg4Ouvv467zLEmmjYAgIqKCqxcuRL19fUQRRErVqxAR0cHqqqqukPsmBNtOyj57rvvsGXLFpx33nnxEjOuRNMGx44dw/r165Geno4LLrgANTU1uOiii/DVV191l9gxpyt94a233kJ1dTXmzZuHxYsXo62tLd7ixoVo26CtrQ0333wz7rzzTmRmZnaHqHElFuNCa2sr/vnPf2LQoEHIzs6Ol6hxIxZtAADNzc1ISkqC0WiMh5iEh87OTmzatAk1NTXeY4IgoKamBuvWretByQg9ZPfelJQUANJcwul0qu7h0KFDMWDAAHz77bcAgG+//RYFBQXIyMjwppk4cSKam5uxbdu27hO+D3PPPfdgypQpqvsE0P07Efjggw9QXFyMX/7ylxg/fjzOOussvP76697z+/btQ21treoeJicno6yszDuOxurdSERHRUUF1qxZg507dwIAtm7diq+//hqTJ08GQPewr0IzTD+OHj2KtLQ01TGj0YiUlBTU1tZq5ikvL4fVasWDDz6Im266CZxzLF68GG63WzdPbyaaNgCARx55BDfeeCOqq6thNBqRkJCAxx9/HLm5ufEWOS5E2w5K3njjDQwdOhSVlZXxEDHuRNMGe/fuBQA8/vjj+M1vfoORI0fiX//6Fy655BIsX74ceXl58RY75kTbF+bNm4cBAwYgKysL33//PR566CHs3LkTjz/+eLxFjjnRtsH999+PiooKzJw5M94idgtdGReWLl2Khx56CK2trRgyZAheeOEF1UeSE4VYjI3Hjx/HX//6V8yfPz8eIhIK6urq4Ha7kZ6erjqenp4eEBeK6HlEUcQf/vAHVFZWoqCgAID0zJlMJtjtdlXa9PR07zN39OhRlaIEgPfvE3EueqKxYsUKbN68GW+88UbAObp/vZ+9e/fi1VdfxaWXXoqrrroKGzduxKJFi2AymXD22Wd774HWOCrHXovFu5GInoULF6K5uRmnnnoqDAYD3G43brzxRpxxxhkAQPewj9JnlF0PPfQQnnnmmaBp3n777ajKTktLw6OPPoq77roLL730EgRBwNy5c1FUVATGWFRlxoN4tgEAPProo2hsbMSLL74Ih8OBVatW4YYbbsDSpUtRWFgYdbmxJt7tINPe3o7ly5fjmmuu6XJZsSaebSCKIgBg/vz5OPfccwEAo0aNwurVq7Fs2TLcfPPNUZUbD+LdF5QL+cLCQmRmZuKSSy7Bnj17MHjw4KjLjSXxbIP3338fa9aswZtvvhlV/u6kO8aFM844AxMmTEBtbS2ee+453HDDDXj11VdhsVi6VG6s6K6xsbm5GVdeeSWGDh2K6667rsvlEcTJxN13340ff/wRr7zySk+LQoTJwYMHcd999+H555/vNeM5ERmccxQXF+Omm24CIM1bf/zxR7z22ms4++yze1g6IhxWrlyJt956C4sXL8awYcOwZcsW3H///cjKyqJ72IfpM8quyy67LGRHz8nJQUZGBo4fP6467nK50NDQENQFZ+LEiVi1ahWOHz8Oo9EIu92OCRMm4LTTTouJ/LEgnm2wZ88evPzyy1i+fDmGDx8OABgxYgS++uorLF26FPfcc09sLiIGxLsvyPz3v/9Fe3s7zjrrrK6IGxfi2Qby8aFDh6qODx06FAcOHOiC1LGnu/qCTFlZGQBg9+7dvUbZFc82WLNmDfbs2YOxY8eqjl9//fUYM2YMXnrppa4JH0O6oy8kJycjOTkZeXl5KCsrQ1VVFd577z3Mmzevy/LHgu5og+bmZlxxxRWw2Wx44oknYDKZuiw3ERyHwwGDwYBjx46pjh87dizAkoToWe655x589NFHePnll1UuzhkZGXA6nWhsbFRZBx07dsz7zGVkZAS42cjWCieDC3lvZtOmTTh27BjOOecc7zG32421a9di6dKleO655+j+9XIyMzMD5q35+fneXeXle3Ds2DFkZWV50xw7dgwjRowAgJjNFYnoeOCBB7Bw4ULMnTsXgPSR+cCBA1iyZAnOPvtsuod9lD6j7EpLSwswS9SioqICjY2N+O6771BcXAxAWrCJoojS0tKw6gGA1atX49ixY5g+fXrXBI8h8WwDOQ6RIKjDwBkMBtWWr72B7uoLy5Ytw/Tp08Oqq7uJZxsMGjQIWVlZXp95mV27dnn95nsL3dUXZLZs2QKgd01c49kGCxcuxPnnn686dvrpp+PWW2/FtGnTui58DOnuvgBIX5I7OzujkjcexLsNmpubcfnll8NsNuPJJ58kC4huwmw2o6ioCKtXr/a6E4uiiNWrV+Oiiy7qYekIQBoL7r33Xrz33nt46aWXkJOTozpfXFwMk8mE1atXY86cOQCAHTt24MCBAygvLwcghdR46qmncOzYMa+bzueff46kpCQMGzasW6+nrzFu3Di89dZbqmO33nor8vPz8Ytf/AL9+/en+9fLqays1Jy3Dhw4EIA0t83MzMTq1asxcuRIANI7bf369bjwwgsBxHZ+QEROe3t7gEeVch1K97CP0pPR8Xsrl19+OT/rrLP4+vXr+VdffcVnz56t2nL00KFDfM6cOXz9+vXeY2+88QZft24d3717N//Xv/7Fq6qq+P33398T4seESNugs7OTz5o1i//0pz/l69ev57t37+bPPfccLyws5B999FFPXUaXiaYvcM75rl27eGFhIf/444+7W+SYE00bvPDCC7yyspKvXLmS79q1iz/88MO8pKTkhNx5TibSdti9ezd//PHH+caNG/nevXv5qlWr+IwZM/iCBQt66hK6TLTPg5ITfTdGziNvhz179vCnnnqKb9y4ke/fv59//fXX/Morr+RVVVX86NGjPXUZXSLSNmhqauLnn38+nzdvHt+9ezc/cuSI95/L5eqpy+gzrFixghcXF/N//vOffNu2bfyOO+7gY8aMUe38RvQcv//97/no0aP5F198oXo22travGnuvPNOPnXqVL569Wq+ceNGPn/+fD5//nzveZfLxefNm8cvu+wyvmXLFv7JJ5/wcePG8cWLF/fEJfV5/HdjpvvXu1m/fj0fNWoUf/LJJ/muXbv4f/7zH15WVsb//e9/e9MsWbKEjxkzhq9atYpv3bqVX3311Xz69Om8vb3dmybUu5GIH7/97W/5pEmT+Icffsj37t3L3333XV5dXc0feOABbxq6h30PUnZpUFdXx2+66SZeXl7OKysr+e9+9zve3NzsPb93715eUFDA16xZ4z324IMP8pqaGl5UVMRnz57Nn3/+eS6KYk+IHxOiaYOdO3fy6667jo8fP56XlZXx008/nb/55ps9IH3siKYdOOd88eLFfMqUKdztdne3yDEn2jZYsmQJnzx5Mi8rK+Pz58/na9eu7W7RY0qk7XDgwAG+YMECXlVVxYuLi/msWbP4n/70J97U1NRTl9Blou0LSk4GZVek7XDo0CF+xRVX8PHjx/OioiI+efJkftNNN/Ht27f31CV0mUjbYM2aNbygoEDz3969e3vqMvoUL730Ep86dSovKiri5513Hv/22297WiTCg96zsWzZMm+a9vZ2ftddd/GxY8fysrIyfu211/IjR46oytm3bx+/4ooreGlpKa+uruZ//OMfudPp7O7LIXigsovuX+/ngw8+4PPmzePFxcX8lFNO4f/4xz9U50VR5I888givqanhxcXF/OKLL+Y7duxQpQn1biTiR1NTE1+0aBGfOnUqLykp4TNmzOB//vOfeUdHhzcN3cO+B+O8l/mYEQRBEARBEARBEARBEESUCKGTEARBEARBEARBEARBEMSJASm7CIIgCIIgCIIgCIIgiJMGUnYRBEEQBEEQBEEQBEEQJw2k7CIIgiAIgiAIgiAIgiBOGkjZRRAEQRAEQRAEQRAEQZw0kLKLIAiCIAiCIAiCIAiCOGkgZRdBEARBEARBEARBEARx0kDKLoIgCIIgCIIgCIIgCOKkgZRdBEEQBEEQBEEQBEEQxEkDKbsIgiAIgiAIgiAIgiCIkwZSdhEEQRAEQRAEQRAEQRAnDf8fg9QaY5CCumYAAAAASUVORK5CYII=", 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" ] @@ -1126,16 +1267,16 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "More details on this plot are available in the [ArviZ documentation](https://arviz-devs.github.io/arviz/_modules/arviz/plots/traceplot.html).\n", + "More details on this plot are available in the [ArviZ documentation](https://python.arviz.org/en/latest/api/generated/arviz.plot_trace.html).\n", "\n", "## Summarizing\n", "\n", - "If you prefer numerical summaries of the posterior estimates, you can use the ``az.summary()`` function from [ArviZ](https://arviz-devs.github.io/arviz/generated/arviz.summary.html#arviz.summary) which provides a pandas DataFrame with some key summary and diagnostics info on the model parameters, such as the 94% highest posterior density intervals" + "If you prefer numerical summaries of the posterior estimates, you can use the ``az.summary()`` function from [ArviZ](https://python.arviz.org/en/latest/api/generated/arviz.summary.html) which provides a pandas DataFrame with some key summary and diagnostics info on the model parameters, such as the 94% highest posterior density intervals" ] }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 19, "metadata": {}, "outputs": [ { @@ -1173,50 +1314,50 @@ " \n", " \n", " Intercept\n", - " -3.510\n", - " 1.172\n", - " -5.663\n", - " -1.414\n", - " 0.018\n", - " 0.014\n", - " 4173.0\n", - " 1944.0\n", + " -2.129\n", + " 1.139\n", + " -4.216\n", + " 0.117\n", + " 0.024\n", + " 0.017\n", + " 2245.0\n", + " 1512.0\n", " 1.0\n", " \n", " \n", - " gre\n", + " scale(gre)\n", + " 0.268\n", + " 0.126\n", + " 0.024\n", + " 0.496\n", + " 0.003\n", " 0.002\n", - " 0.001\n", - " 0.000\n", - " 0.004\n", - " 0.000\n", - " 0.000\n", - " 2012.0\n", - " 1462.0\n", + " 2321.0\n", + " 1658.0\n", " 1.0\n", " \n", " \n", " gpa\n", - " 0.793\n", - " 0.328\n", - " 0.181\n", - " 1.406\n", - " 0.006\n", + " 0.787\n", + " 0.323\n", + " 0.131\n", + " 1.335\n", + " 0.007\n", " 0.005\n", - " 2769.0\n", - " 1954.0\n", + " 2119.0\n", + " 1439.0\n", " 1.0\n", " \n", " \n", " rank\n", - " -0.567\n", + " -0.566\n", " 0.129\n", - " -0.815\n", - " -0.340\n", + " -0.813\n", + " -0.332\n", " 0.003\n", " 0.002\n", - " 2125.0\n", - " 1646.0\n", + " 2584.0\n", + " 1778.0\n", " 1.0\n", " \n", " \n", @@ -1224,20 +1365,20 @@ "" ], "text/plain": [ - " mean sd hdi_3% hdi_97% mcse_mean mcse_sd ess_bulk \\\n", - "Intercept -3.510 1.172 -5.663 -1.414 0.018 0.014 4173.0 \n", - "gre 0.002 0.001 0.000 0.004 0.000 0.000 2012.0 \n", - "gpa 0.793 0.328 0.181 1.406 0.006 0.005 2769.0 \n", - "rank -0.567 0.129 -0.815 -0.340 0.003 0.002 2125.0 \n", + " mean sd hdi_3% hdi_97% mcse_mean mcse_sd ess_bulk \\\n", + "Intercept -2.129 1.139 -4.216 0.117 0.024 0.017 2245.0 \n", + "scale(gre) 0.268 0.126 0.024 0.496 0.003 0.002 2321.0 \n", + "gpa 0.787 0.323 0.131 1.335 0.007 0.005 2119.0 \n", + "rank -0.566 0.129 -0.813 -0.332 0.003 0.002 2584.0 \n", "\n", - " ess_tail r_hat \n", - "Intercept 1944.0 1.0 \n", - "gre 1462.0 1.0 \n", - "gpa 1954.0 1.0 \n", - "rank 1646.0 1.0 " + " ess_tail r_hat \n", + "Intercept 1512.0 1.0 \n", + "scale(gre) 1658.0 1.0 \n", + "gpa 1439.0 1.0 \n", + "rank 1778.0 1.0 " ] }, - "execution_count": 17, + "execution_count": 19, "metadata": {}, "output_type": "execute_result" } @@ -1256,16 +1397,16 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 20, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "['Intercept', 'gre', 'gpa', 'rank']" + "['Intercept', 'scale(gre)', 'gpa', 'rank']" ] }, - "execution_count": 18, + "execution_count": 20, "metadata": {}, "output_type": "execute_result" } @@ -1289,16 +1430,16 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 21, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "pymc.model.Model" + "pymc.model.core.Model" ] }, - "execution_count": 19, + "execution_count": 21, "metadata": {}, "output_type": "execute_result" } @@ -1309,7 +1450,7 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 22, "metadata": {}, "outputs": [ { @@ -1317,19 +1458,19 @@ "text/latex": [ "$$\n", " \\begin{array}{rcl}\n", - " \\text{Intercept} &\\sim & \\operatorname{N}(0,~26.6)\\\\\\text{gre} &\\sim & \\operatorname{N}(0,~0.0217)\\\\\\text{gpa} &\\sim & \\operatorname{N}(0,~6.58)\\\\\\text{rank} &\\sim & \\operatorname{N}(0,~2.65)\\\\\\text{admit} &\\sim & \\operatorname{Bern}(f(\\text{Intercept},~\\text{rank},~\\text{gpa},~\\text{gre}))\n", + " \\text{Intercept} &\\sim & \\operatorname{Normal}(0,~23.4)\\\\\\text{scale(gre)} &\\sim & \\operatorname{Normal}(0,~2.5)\\\\\\text{gpa} &\\sim & \\operatorname{Normal}(0,~6.58)\\\\\\text{rank} &\\sim & \\operatorname{Normal}(0,~2.65)\\\\\\text{admit} &\\sim & \\operatorname{Bernoulli}(f(\\text{Intercept},~\\text{rank},~\\text{gpa},~\\text{scale(gre)}))\n", " \\end{array}\n", " $$" ], "text/plain": [ - "Intercept ~ N(0, 26.6)\n", - " gre ~ N(0, 0.0217)\n", - " gpa ~ N(0, 6.58)\n", - " rank ~ N(0, 2.65)\n", - " admit ~ Bern(f(Intercept, rank, gpa, gre))" + " Intercept ~ Normal(0, 23.4)\n", + "scale(gre) ~ Normal(0, 2.5)\n", + " gpa ~ Normal(0, 6.58)\n", + " rank ~ Normal(0, 2.65)\n", + " admit ~ Bernoulli(f(Intercept, rank, gpa, scale(gre)))" ] }, - "execution_count": 20, + "execution_count": 22, "metadata": {}, "output_type": "execute_result" } @@ -1340,16 +1481,16 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 23, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "[admit ~ Bern(f(Intercept, rank, gpa, gre))]" + "[admit ~ Bernoulli(f(Intercept, rank, gpa, scale(gre)))]" ] }, - "execution_count": 21, + "execution_count": 23, "metadata": {}, "output_type": "execute_result" } @@ -1360,19 +1501,19 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 24, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "[Intercept ~ N(0, 26.6),\n", - " gre ~ N(0, 0.0217),\n", - " gpa ~ N(0, 6.58),\n", - " rank ~ N(0, 2.65)]" + "[Intercept ~ Normal(0, 23.4),\n", + " scale(gre) ~ Normal(0, 2.5),\n", + " gpa ~ Normal(0, 6.58),\n", + " rank ~ Normal(0, 2.65)]" ] }, - "execution_count": 22, + "execution_count": 24, "metadata": {}, "output_type": "execute_result" } @@ -1380,6 +1521,42 @@ "source": [ "model.backend.model.unobserved_RVs" ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Last updated: Sat Nov 11 2023\n", + "\n", + "Python implementation: CPython\n", + "Python version : 3.10.6\n", + "IPython version : 8.5.0\n", + "\n", + "arviz : 0.16.1\n", + "scipy : 1.11.2\n", + "bambi : 0.13.1.dev7+gc6e5dbb5.d20231111\n", + "numpy : 1.23.4\n", + "pandas: 2.1.0\n", + "\n", + "Watermark: 2.4.3\n", + "\n" + ] + } + ], + "source": [ + "%load_ext watermark\n", + "%watermark -n -u -v -iv -w" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [] } ], "metadata": { @@ -1398,7 +1575,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.4" + "version": "3.10.6" }, "vscode": { "interpreter": { From dcd879bfef1f68280d60f8ce81fdc3576cc0257b Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Tom=C3=A1s=20Capretto?= Date: Thu, 16 Nov 2023 19:10:11 -0300 Subject: [PATCH 2/3] Add link to talk and remove incorrect link from quarto.yml (#759) --- docs/_quarto.yml | 1 - docs/notebooks/hsgp_1d.ipynb | 243 +++++++++- docs/notebooks/hsgp_2d.ipynb | 917 ++++++++++++++++++++++++++++++++++- 3 files changed, 1149 insertions(+), 12 deletions(-) diff --git a/docs/_quarto.yml b/docs/_quarto.yml index 5328aaa6b..4a0f4430b 100644 --- a/docs/_quarto.yml +++ b/docs/_quarto.yml @@ -88,7 +88,6 @@ website: - notebooks/plot_predictions.ipynb - notebooks/plot_comparisons.ipynb - notebooks/plot_slopes.ipynb - - notebooks/survival_models.ipynb quartodoc: style: pkgdown diff --git a/docs/notebooks/hsgp_1d.ipynb b/docs/notebooks/hsgp_1d.ipynb index 59e2731de..8f8e1f9a4 100644 --- a/docs/notebooks/hsgp_1d.ipynb +++ b/docs/notebooks/hsgp_1d.ipynb @@ -11,7 +11,9 @@ "\n", "HSGP is a framework that falls under the class of low-rank approximations that are based on forming a basis function approximation with $m$ basis functions, where $m$ is usually much less smaller than $n$, the number of observations.\n", "\n", - "For references see [Hilbert Space Methods for Reduced-Rank Gaussian Process Regression](https://arxiv.org/abs/1401.5508) and [Practical Hilbert Space Approximate Bayesian Gaussian Processes for Probabilistic Programming](https://arxiv.org/abs/2004.11408)." + "For references see [Hilbert Space Methods for Reduced-Rank Gaussian Process Regression](https://arxiv.org/abs/1401.5508) and [Practical Hilbert Space Approximate Bayesian Gaussian Processes for Probabilistic Programming](https://arxiv.org/abs/2004.11408).\n", + "\n", + "If you prefer a video format, have a look at [Introduction to Hilbert Space GPs in PyMC](https://www.youtube.com/watch?v=ri5sJAdcYHk&ab_channel=PyMCDevelopers) given by Bill Engels." ] }, { @@ -890,7 +892,122 @@ "outputs": [ { "data": { - "image/svg+xml": "\n\n\n\n\n\n\n\nclusterhsgp_weights_dim (12) x hsgp_by (3)\n\nhsgp_weights_dim (12) x hsgp_by (3)\n\n\nclustery_obs (300)\n\ny_obs (300)\n\n\n\nhsgp_sigma\n\nhsgp_sigma\n~\nExponential\n\n\n\nhsgp_weights\n\nhsgp_weights\n~\nDeterministic\n\n\n\nhsgp_sigma->hsgp_weights\n\n\n\n\n\ny_sigma\n\ny_sigma\n~\nHalfNormal\n\n\n\ny\n\ny\n~\nNormal\n\n\n\ny_sigma->y\n\n\n\n\n\nhsgp_ell\n\nhsgp_ell\n~\nExponential\n\n\n\nhsgp_ell->hsgp_weights\n\n\n\n\n\nhsgp_weights_raw\n\nhsgp_weights_raw\n~\nNormal\n\n\n\nhsgp_weights_raw->hsgp_weights\n\n\n\n\n\nhsgp\n\nhsgp\n~\nDeterministic\n\n\n\nhsgp_weights->hsgp\n\n\n\n\n\nhsgp->y\n\n\n\n\n\n", + "image/svg+xml": [ + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "clusterhsgp_weights_dim (12) x hsgp_by (3)\n", + "\n", + "hsgp_weights_dim (12) x hsgp_by (3)\n", + "\n", + "\n", + "clustery_obs (300)\n", + "\n", + "y_obs (300)\n", + "\n", + "\n", + "\n", + "hsgp_sigma\n", + "\n", + "hsgp_sigma\n", + "~\n", + "Exponential\n", + "\n", + "\n", + "\n", + "hsgp_weights\n", + "\n", + "hsgp_weights\n", + "~\n", + "Deterministic\n", + "\n", + "\n", + "\n", + "hsgp_sigma->hsgp_weights\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "y_sigma\n", + "\n", + "y_sigma\n", + "~\n", + "HalfNormal\n", + "\n", + "\n", + "\n", + "y\n", + "\n", + "y\n", + "~\n", + "Normal\n", + "\n", + "\n", + "\n", + "y_sigma->y\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "hsgp_ell\n", + "\n", + "hsgp_ell\n", + "~\n", + "Exponential\n", + "\n", + "\n", + "\n", + "hsgp_ell->hsgp_weights\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "hsgp_weights_raw\n", + "\n", + "hsgp_weights_raw\n", + "~\n", + "Normal\n", + "\n", + "\n", + "\n", + "hsgp_weights_raw->hsgp_weights\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "hsgp\n", + "\n", + "hsgp\n", + "~\n", + "Deterministic\n", + "\n", + "\n", + "\n", + "hsgp_weights->hsgp\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "hsgp->y\n", + "\n", + "\n", + "\n", + "\n", + "\n" + ], "text/plain": [ "" ] @@ -1124,7 +1241,127 @@ "outputs": [ { "data": { - 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Bambi supports Gaussian Processes through the low-rank approximation known as Hilbert Space Gaussian Processes. For references see [Hilbert Space Methods for Reduced-Rank Gaussian Process Regression](https://arxiv.org/abs/1401.5508) and [Practical Hilbert Space Approximate Bayesian Gaussian Processes for Probabilistic Programming](https://arxiv.org/abs/2004.11408).\n", "\n", - "For a demonstration of Gaussian Processes in 1D together with a more in depth explanation see **To Do**." + "If you prefer a video format, have a look at [Introduction to Hilbert Space GPs in PyMC](https://www.youtube.com/watch?v=ri5sJAdcYHk&ab_channel=PyMCDevelopers) given by Bill Engels." ] }, { @@ -211,7 +211,122 @@ "outputs": [ { "data": { - "image/svg+xml": "\n\n\n\n\n\n\n\nclusterhsgp_weights_dim (100)\n\nhsgp_weights_dim (100)\n\n\nclusteroutcome_obs (144)\n\noutcome_obs 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Sep 17 00:00:00 2001 From: =?UTF-8?q?Tom=C3=A1s=20Capretto?= Date: Fri, 17 Nov 2023 07:06:24 -0300 Subject: [PATCH 3/3] Improve tests (#757) * start moving tests * modifications * Replace numpy.round_ with numpy.round * Keep porting tests.. * Add testcategorical * Finalize test migration * fixes --- bambi/priors/prior.py | 2 +- tests/test_aliases.py | 38 + tests/test_alternative_samplers.py | 78 ++ tests/test_built_models.py | 1075 ---------------------- tests/test_interpret_messages.py | 2 - tests/test_model_construction.py | 111 ++- tests/test_models.py | 1336 ++++++++++++++++++++++++++++ tests/test_predict.py | 429 --------- 8 files changed, 1563 insertions(+), 1508 deletions(-) create mode 100644 tests/test_alternative_samplers.py delete mode 100644 tests/test_built_models.py create mode 100644 tests/test_models.py delete mode 100644 tests/test_predict.py diff --git a/bambi/priors/prior.py b/bambi/priors/prior.py index fd5470d57..d4c2da420 100644 --- a/bambi/priors/prior.py +++ b/bambi/priors/prior.py @@ -65,7 +65,7 @@ def __repr__(self): def format_arg(value, decimals): try: - outcome = np.round_(value, decimals) + outcome = np.round(value, decimals) except: # pylint: disable = bare-except try: outcome = value.name diff --git a/tests/test_aliases.py b/tests/test_aliases.py index e332d7ad7..81a098039 100644 --- a/tests/test_aliases.py +++ b/tests/test_aliases.py @@ -1,6 +1,8 @@ import pytest import bambi as bmb +import numpy as np +import pandas as pd @pytest.fixture(scope="module") @@ -13,6 +15,26 @@ def anes(): return bmb.load_data("ANES") +@pytest.fixture(scope="module") +def data_100(): + size = 100 + rng = np.random.default_rng(121195) + data = pd.DataFrame( + { + "n1": rng.normal(size=size), + "n2": rng.normal(size=size), + "n3": rng.normal(size=size), + "b0": rng.binomial(n=1, p=0.5, size=size), + "b1": rng.choice(["a", "b"], size=size), + "count1": rng.poisson(lam=2, size=size), + "count2": rng.poisson(lam=2, size=size), + "cat1": rng.choice(list("MNOP"), size=size), + "cat2": rng.choice(list("FGHIJK"), size=size), + } + ) + return data + + def test_non_distributional_model(my_data): # Plain model formula = bmb.Formula("y ~ x") @@ -126,3 +148,19 @@ def test_set_alias_warnings(my_data): print(model.constant_components) assert len(record) == 1 assert str(record[0].message) == expected_warning + + +# FIXME: Move somewhere +def test_set_alias(data_100): + model = bmb.Model("n1 ~ n2 + (n2|cat1)", data_100) + aliases = { + "Intercept": "α", + "n2": "β", + "1|cat1": "α_group", + "n2|cat1": "β_group", + "sigma": "σ", + } + model.set_alias(aliases) + model.build() + new_names = set(["α", "β", "α_group", "α_group_σ", "β_group", "β_group_σ", "σ"]) + assert new_names.issubset(set(model.backend.model.named_vars)) diff --git a/tests/test_alternative_samplers.py b/tests/test_alternative_samplers.py new file mode 100644 index 000000000..a16134762 --- /dev/null +++ b/tests/test_alternative_samplers.py @@ -0,0 +1,78 @@ +import bambi as bmb +import numpy as np +import pandas as pd + +import pytest + + +@pytest.fixture(scope="module") +def data_n100(): + size = 100 + rng = np.random.default_rng(121195) + data = pd.DataFrame( + { + "b1": rng.binomial(n=1, p=0.5, size=size), + "n1": rng.poisson(lam=2, size=size), + "n2": rng.poisson(lam=2, size=size), + "y1": rng.normal(size=size), + "y2": rng.normal(size=size), + "y3": rng.normal(size=size), + "cat2": rng.choice(["a", "b"], size=size), + "cat4": rng.choice(list("MNOP"), size=size), + "cat5": rng.choice(list("FGHIJK"), size=size), + } + ) + return data + + +def test_laplace(): + data = pd.DataFrame(np.repeat((0, 1), (30, 60)), columns=["w"]) + priors = {"Intercept": bmb.Prior("Uniform", lower=0, upper=1)} + model = bmb.Model("w ~ 1", data=data, family="bernoulli", priors=priors, link="identity") + results = model.fit(inference_method="laplace") + mode_n = results.posterior["Intercept"].mean().item() + std_n = results.posterior["Intercept"].std().item() + mode_a = data.mean() + std_a = data.std() / len(data) ** 0.5 + np.testing.assert_array_almost_equal((mode_n, std_n), (mode_a.item(), std_a.item()), decimal=2) + + +def test_vi(): + data = pd.DataFrame(np.repeat((0, 1), (30, 60)), columns=["w"]) + priors = {"Intercept": bmb.Prior("Uniform", lower=0, upper=1)} + model = bmb.Model("w ~ 1", data=data, family="bernoulli", priors=priors, link="identity") + results = model.fit(inference_method="vi", method="advi") + samples = results.sample(1000).posterior["Intercept"] + mode_n = samples.mean() + std_n = samples.std() + mode_a = data.mean() + std_a = data.std() / len(data) ** 0.5 + np.testing.assert_array_almost_equal( + (mode_n.item(), std_n.item()), (mode_a.item(), std_a.item()), decimal=2 + ) + + +@pytest.mark.parametrize( + "args", + [ + ("mcmc", {}), + ("nuts_numpyro", {"chain_method": "vectorized"}), + ("nuts_blackjax", {"chain_method": "vectorized"}), + ], +) +def test_logistic_regression_categoric_alternative_samplers(data_n100, args): + model = bmb.Model("b1 ~ n1", data_n100, family="bernoulli") + model.fit(tune=50, draws=50, inference_method=args[0], **args[1]) + + +@pytest.mark.parametrize( + "args", + [ + ("mcmc", {}), + ("nuts_numpyro", {"chain_method": "vectorized"}), + ("nuts_blackjax", {"chain_method": "vectorized"}), + ], +) +def test_regression_alternative_samplers(data_n100, args): + model = bmb.Model("n1 ~ n2", data_n100) + model.fit(tune=50, draws=50, inference_method=args[0], **args[1]) diff --git a/tests/test_built_models.py b/tests/test_built_models.py deleted file mode 100644 index 5e58466eb..000000000 --- a/tests/test_built_models.py +++ /dev/null @@ -1,1075 +0,0 @@ -from os.path import dirname, join - -import logging -import re - -import pytest - -import numpy as np -import pandas as pd -import pymc as pm - -from scipy.special import expit - -import bambi as bmb -from bambi.terms import GroupSpecificTerm - - -@pytest.fixture(scope="module") -def crossed_data(): - """ - Group specific effects: - 10 subjects, 12 items, 5 sites - Subjects crossed with items, nested in sites - Items crossed with sites - - common effects: - A continuous predictor, a numeric dummy, and a three-level category - (levels a,b,c) - - Structure: - Subjects nested in dummy (e.g., gender), crossed with threecats - Items crossed with dummy, nested in threecats - Sites partially crossed with dummy (4/5 see a single dummy, 1/5 sees both - dummies) - Sites crossed with threecats - """ - - data_dir = join(dirname(__file__), "data") - data = pd.read_csv(join(data_dir, "crossed_random.csv")) - data["subj"] = data["subj"].astype(str) - return data - - -@pytest.fixture(scope="module") -def dm(): - """ - Data obtained from https://github.com/jswesner/nps_emergence/tree/v2_nps_emerge - and used in Gamma GLM - """ - data_dir = join(dirname(__file__), "data") - data = pd.read_csv(join(data_dir, "dm.csv")) - return data - - -@pytest.fixture(scope="module") -def init_data(): - """ - Data used to test initialization method - """ - data_dir = join(dirname(__file__), "data") - data = pd.read_csv(join(data_dir, "obs.csv")) - return data - - -@pytest.fixture(scope="module") -def inhaler(): - data_dir = join(dirname(__file__), "data") - data = pd.read_csv(join(data_dir, "inhaler.csv")) - data["rating"] = pd.Categorical(data["rating"], categories=[1, 2, 3, 4]) - data["treat"] = pd.Categorical(data["treat"]) - return data - - -@pytest.fixture(scope="module") -def categorical_family_categorical_predictor(): - data_dir = join(dirname(__file__), "data") - data = pd.read_csv(join(data_dir, "categorical_family_categorical_predictor.csv")) - return data - - -@pytest.fixture(scope="module") -def data_100(): - size = 100 - rng = np.random.default_rng(121195) - data = pd.DataFrame( - { - "n1": rng.normal(size=size), - "n2": rng.normal(size=size), - "n3": rng.normal(size=size), - "b0": rng.binomial(n=1, p=0.5, size=size), - "b1": rng.choice(["a", "b"], size=size), - "count1": rng.poisson(lam=2, size=size), - "count2": rng.poisson(lam=2, size=size), - "cat1": rng.choice(list("MNOP"), size=size), - "cat2": rng.choice(list("FGHIJK"), size=size), - } - ) - return data - - -@pytest.fixture(scope="module") -def data_1000(): - size = 1000 - rng = np.random.default_rng(121195) - data = pd.DataFrame( - { - "n1": rng.normal(size=size), - "n2": rng.normal(size=size), - "n3": rng.normal(size=size), - "b0": rng.binomial(n=1, p=0.5, size=size), - "b1": rng.choice(["a", "b"], size=size), - "count1": rng.poisson(lam=2, size=size), - "count2": rng.poisson(lam=2, size=size), - "cat1": rng.choice(list("MNOP"), size=size), - "cat2": rng.choice(list("FGHIJK"), size=size), - } - ) - return data - - -@pytest.fixture(scope="module") -def sleepstudy(): - return bmb.load_data("sleepstudy") - - -def test_empty_model(crossed_data): - model0 = bmb.Model("Y ~ 0", crossed_data) - model0.fit(tune=0, draws=1) - - -def test_intercept_only_model(crossed_data): - model0 = bmb.Model("Y ~ 1", crossed_data) - model0.fit(tune=0, draws=1) - - -def test_slope_only_model(crossed_data): - model0 = bmb.Model("Y ~ 0 + continuous", crossed_data) - model0.fit(tune=0, draws=1) - - -def test_cell_means_parameterization(crossed_data): - model0 = bmb.Model("Y ~ 0 + threecats", crossed_data) - model0.fit(tune=0, draws=1) - - -def test_3x4_common_anova(crossed_data): - # add a four-level category that's perfectly crossed with threecats - crossed_data["fourcats"] = sum([[x] * 10 for x in ["a", "b", "c", "d"]], list()) * 3 - - # with intercept - model0 = bmb.Model("Y ~ threecats*fourcats", crossed_data) - fitted0 = model0.fit(tune=0, draws=1) - assert len(fitted0.posterior.data_vars) == 5 - - # without intercept (i.e., 2-factor cell means model) - model1 = bmb.Model("Y ~ 0 + threecats*fourcats", crossed_data) - fitted1 = model1.fit(tune=0, draws=1) - assert len(fitted1.posterior.data_vars) == 4 - - -def test_cell_means_with_covariate(crossed_data): - model = bmb.Model("Y ~ 0 + threecats + continuous", crossed_data) - model.build() - # check that threecats priors have finite variance - assert not np.isinf(model.response_component.terms["threecats"].prior.args["sigma"]).all() - - -def test_many_common_many_group_specific(crossed_data): - # This test is kind of a mess, but it is very important, it checks lots of things. - # delete a few values to also test dropna=True functionality - crossed_data_missing = crossed_data.copy() - crossed_data_missing.loc[0, "Y"] = np.nan - crossed_data_missing.loc[1, "continuous"] = np.nan - crossed_data_missing.loc[2, "threecats"] = np.nan - - # Here I'm comparing implicit/explicit intercepts for group specific effects work the same way. - model0 = bmb.Model( - "Y ~ continuous + dummy + threecats + (threecats|subj) + (1|item) + (0+continuous|item) + (dummy|item) + (threecats|site)", - crossed_data_missing, - dropna=True, - ) - model0.fit( - tune=10, - draws=10, - chains=2, - ) - - model1 = bmb.Model( - "Y ~ continuous + dummy + threecats + (threecats|subj) + (continuous|item) + (dummy|item) + (threecats|site)", - crossed_data_missing, - dropna=True, - ) - model1.fit( - tune=10, - draws=10, - chains=2, - ) - # Check that the group specific effects design matrices have the same shape - X0 = pd.concat( - [pd.DataFrame(t.data) for t in model0.response_component.group_specific_terms.values()], - axis=1, - ) - X1 = pd.concat( - [pd.DataFrame(t.data) for t in model1.response_component.group_specific_terms.values()], - axis=1, - ) - assert X0.shape == X1.shape - - # check that the group specific effect design matrix contain the same columns, - # even if term names / columns names / order of columns is different - X0_set = set(tuple(X0.iloc[:, i]) for i in range(len(X0.columns))) - X1_set = set(tuple(X1.iloc[:, i]) for i in range(len(X1.columns))) - assert X0_set == X1_set - - # check that common effect design matrices are the same, - # even if term names / level names / order of columns is different - X0_list = [] - X1_list = [] - for term in model0.response_component.common_terms.values(): - if term.levels is not None: - for level_idx in range(len(term.levels)): - X0_list.append(tuple(term.data[:, level_idx])) - else: - X0_list.append(tuple(term.data)) - - for term in model1.response_component.common_terms.values(): - if term.levels is not None: - for level_idx in range(len(term.levels)): - X1_list.append(tuple(term.data[:, level_idx])) - else: - X1_list.append(tuple(term.data)) - - assert set(X0_list) == set(X1_list) - - # check that models have same priors for common effects - priors0 = { - x.name: x.prior.args - for x in model0.response_component.terms.values() - if not isinstance(x, GroupSpecificTerm) - } - priors1 = { - x.name: x.prior.args - for x in model1.response_component.terms.values() - if not isinstance(x, GroupSpecificTerm) - } - - # check dictionary keys - assert set(priors0) == set(priors1) - - # check dictionary values - def dicts_close(a, b): - if set(a) != set(b): - return False - else: - return [np.allclose(a[x], b[x], atol=0, rtol=0.01) for x in a.keys()] - - assert all([dicts_close(priors0[x], priors1[x]) for x in priors0.keys()]) - - # check that fit and add models have same priors for group specific effects - priors0 = { - x.name: x.prior.args["sigma"].args - for x in model0.response_component.group_specific_terms.values() - } - priors1 = { - x.name: x.prior.args["sigma"].args - for x in model1.response_component.group_specific_terms.values() - } - - # check dictionary keys - assert set(priors0) == set(priors1) - - # check dictionary values - def dicts_close(a, b): - if set(a) != set(b): - return False - else: - return [np.allclose(a[x], b[x], atol=0, rtol=0.01) for x in a.keys()] - - assert all([dicts_close(priors0[x], priors1[x]) for x in priors0.keys()]) - - -def test_cell_means_with_many_group_specific_effects(crossed_data): - # Group specific intercepts are added in different way, but the final result should be the same. - formula = "Y ~" + "+".join( - [ - "0", - "threecats", - "(threecats|subj)", - "(1|subj)", - "(0 + continuous|item)", - "(dummy|item)", - "(0 + threecats|site)", - "(1|site)", - ] - ) - model0 = bmb.Model(formula, crossed_data) - model0.fit(tune=0, draws=1) - - formula = "Y ~" + "+".join( - [ - "0", - "threecats", - "(threecats|subj)", - "(continuous|item)", - "(dummy|item)", - "(threecats|site)", - ] - ) - model1 = bmb.Model(formula, crossed_data) - model1.fit(tune=0, draws=1) - - # check that the group specific effects design matrices have the same shape - X0 = pd.concat( - [ - pd.DataFrame(t.data) - if not isinstance(t.data, dict) - else pd.concat([pd.DataFrame(t.data[x]) for x in t.data.keys()], axis=1) - for t in model0.response_component.group_specific_terms.values() - ], - axis=1, - ) - X1 = pd.concat( - [ - pd.DataFrame(t.data) - if not isinstance(t.data, dict) - else pd.concat([pd.DataFrame(t.data[x]) for x in t.data.keys()], axis=1) - for t in model0.response_component.group_specific_terms.values() - ], - axis=1, - ) - assert X0.shape == X1.shape - - # check that the group specific effect design matrix contain the same columns, - # even if term names / columns names / order of columns is different - X0_set = set(tuple(X0.iloc[:, i]) for i in range(len(X0.columns))) - X1_set = set(tuple(X1.iloc[:, i]) for i in range(len(X1.columns))) - assert X0_set == X1_set - - # check that common effect design matrices are the same, - # even if term names / level names / order of columns is different - X0 = set( - [ - tuple(t.data[:, lev]) - for t in model0.response_component.common_terms.values() - for lev in range(len(t.levels)) - ] - ) - X1 = set( - [ - tuple(t.data[:, lev]) - for t in model1.response_component.common_terms.values() - for lev in range(len(t.levels)) - ] - ) - assert X0 == X1 - - # check that fit and add models have same priors for common effects - priors0 = { - x.name: x.prior.args - for x in model0.response_component.terms.values() - if not isinstance(x, GroupSpecificTerm) - } - priors1 = { - x.name: x.prior.args - for x in model1.response_component.terms.values() - if not isinstance(x, GroupSpecificTerm) - } - assert set(priors0) == set(priors1) - - # check that fit and add models have same priors for group specific effects - priors0 = { - x.name: x.prior.args["sigma"].args - for x in model0.response_component.terms.values() - if isinstance(x, GroupSpecificTerm) - } - priors1 = { - x.name: x.prior.args["sigma"].args - for x in model1.response_component.terms.values() - if isinstance(x, GroupSpecificTerm) - } - assert set(priors0) == set(priors1) - - -def test_group_specific_categorical_interaction(crossed_data): - crossed_data["fourcats"] = sum([[x] * 10 for x in ["a", "b", "c", "d"]], list()) * 3 - model = bmb.Model("Y ~ continuous + (threecats:fourcats|site)", crossed_data) - model.fit(tune=10, draws=10) - - -def test_logistic_regression_empty_index(data_100): - model = bmb.Model("b1 ~ n1", data_100, family="bernoulli") - model.fit() - - -def test_logistic_regression_good_numeric(data_100): - model = bmb.Model("b0 ~ n1", data_100, family="bernoulli") - model.fit() - - -def test_logistic_regression_bad_numeric(): - error_msg = "Numeric response must be all 0 and 1 for 'bernoulli' family" - rng = np.random.default_rng(1234) - data = pd.DataFrame({"y": rng.choice([1, 2], 50), "x": rng.normal(size=50)}) - with pytest.raises(ValueError, match=error_msg): - model = bmb.Model("y ~ x", data, family="bernoulli") - model.fit() - - -@pytest.mark.parametrize( - "args", - [ - ("mcmc", {}), - ("nuts_numpyro", {"chain_method": "vectorized"}), - ("nuts_blackjax", {"chain_method": "vectorized"}), - ], -) -def test_logistic_regression_categoric_alternative_samplers(data_100, args): - model = bmb.Model("b1 ~ n1", data_100, family="bernoulli") - model.fit(tune=50, draws=50, inference_method=args[0], **args[1]) - - -@pytest.mark.parametrize( - "args", - [ - ("mcmc", {}), - ("nuts_numpyro", {"chain_method": "vectorized"}), - ("nuts_blackjax", {"chain_method": "vectorized"}), - ], -) -def test_regression_alternative_samplers(data_100, args): - model = bmb.Model("n1 ~ n2", data_100) - model.fit(tune=50, draws=50, inference_method=args[0], **args[1]) - - -def test_laplace_regression(data_100): - bmb_model = bmb.Model("n1 ~ n2", data_100, family="laplace") - bmb_model.fit() - - -def test_poisson_regression(crossed_data): - # build model using fit and pymc - crossed_data["count"] = (crossed_data["Y"] - crossed_data["Y"].min()).round() - model0 = bmb.Model("count ~ dummy + continuous + threecats", crossed_data, family="poisson") - model0.fit(tune=0, draws=1) - - # build model using add - model1 = bmb.Model("count ~ threecats + continuous + dummy", crossed_data, family="poisson") - model1.fit(tune=0, draws=1) - - # check that term names agree - assert set(model0.response_component.terms) == set(model1.response_component.terms) - - # check that common effect design matrices are the same, - # even if term names / level names / order of columns is different - - X0_list = [] - X1_list = [] - for term in model0.response_component.common_terms.values(): - if term.levels is not None: - for level_idx in range(len(term.levels)): - X0_list.append(tuple(term.data[:, level_idx])) - else: - X0_list.append(tuple(term.data)) - - for term in model1.response_component.common_terms.values(): - if term.levels is not None: - for level_idx in range(len(term.levels)): - X1_list.append(tuple(term.data[:, level_idx])) - else: - X1_list.append(tuple(term.data)) - - assert set(X0_list) == set(X1_list) - - # check that models have same priors for common effects - priors0 = { - x.name: x.prior.args - for x in model0.response_component.terms.values() - if not isinstance(x, GroupSpecificTerm) - } - priors1 = { - x.name: x.prior.args - for x in model1.response_component.terms.values() - if not isinstance(x, GroupSpecificTerm) - } - # check dictionary keys - assert set(priors0) == set(priors1) - # check dictionary values - def dicts_close(a, b): - if set(a) != set(b): - return False - else: - return [np.allclose(a[x], b[x], atol=0, rtol=0.01) for x in a.keys()] - - assert all([dicts_close(priors0[x], priors1[x]) for x in priors0.keys()]) - - -def test_laplace(): - data = pd.DataFrame(np.repeat((0, 1), (30, 60)), columns=["w"]) - priors = {"Intercept": bmb.Prior("Uniform", lower=0, upper=1)} - model = bmb.Model("w ~ 1", data=data, family="bernoulli", priors=priors, link="identity") - results = model.fit(inference_method="laplace") - mode_n = results.posterior["Intercept"].mean().item() - std_n = results.posterior["Intercept"].std().item() - mode_a = data.mean() - std_a = data.std() / len(data) ** 0.5 - np.testing.assert_array_almost_equal((mode_n, std_n), (mode_a.item(), std_a.item()), decimal=2) - - -def test_vi(): - data = pd.DataFrame(np.repeat((0, 1), (30, 60)), columns=["w"]) - priors = {"Intercept": bmb.Prior("Uniform", lower=0, upper=1)} - model = bmb.Model("w ~ 1", data=data, family="bernoulli", priors=priors, link="identity") - results = model.fit(inference_method="vi", method="advi") - samples = results.sample(1000).posterior["Intercept"] - mode_n = samples.mean() - std_n = samples.std() - mode_a = data.mean() - std_a = data.std() / len(data) ** 0.5 - np.testing.assert_array_almost_equal( - (mode_n.item(), std_n.item()), (mode_a.item(), std_a.item()), decimal=2 - ) - - -def test_prior_predictive(crossed_data): - crossed_data["count"] = (crossed_data["Y"] - crossed_data["Y"].min()).round() - # New default priors are too wide for this case... something to keep investigating - model = bmb.Model( - "count ~ threecats + continuous + dummy", - crossed_data, - family="poisson", - ) - model.build() - pps = model.prior_predictive(draws=500, random_seed=1234) - - keys = ["Intercept", "threecats", "continuous", "dummy"] - shapes = [(1, 500), (1, 500, 2), (1, 500), (1, 500)] - - for key, shape in zip(keys, shapes): - assert pps.prior[key].shape == shape - - assert pps.prior_predictive["count"].shape == (1, 500, 120) - assert pps.observed_data["count"].shape == (120,) - - pps = model.prior_predictive(draws=500, var_names=["count"], random_seed=1234) - assert pps.groups() == ["prior_predictive", "observed_data"] - - pps = model.prior_predictive(draws=500, var_names=["Intercept"], random_seed=1234) - assert pps.groups() == ["prior", "observed_data"] - - -def test_posterior_predictive(crossed_data): - crossed_data["count"] = (crossed_data["Y"] - crossed_data["Y"].min()).round() - model = bmb.Model("count ~ threecats + continuous + dummy", crossed_data, family="poisson") - fitted = model.fit(tune=0, draws=10, chains=2) - pps = model.predict(fitted, kind="pps", inplace=False) - - assert pps.posterior_predictive["count"].shape == (2, 10, 120) - - pps = model.predict(fitted, kind="pps", inplace=True) - - assert pps is None - assert fitted.posterior_predictive["count"].shape == (2, 10, 120) - - -def test_gamma_regression(dm): - # simulated data - rng = np.random.default_rng(1234) - a, b, N, shape_true = 0.5, 1.2, 100, 10 # alpha - x = rng.uniform(-1, 1, N) - y_true = np.exp(a + b * x) - - y = rng.gamma(shape_true, y_true / shape_true, N) - data = pd.DataFrame({"x": x, "y": y}) - model = bmb.Model("y ~ x", data, family="gamma", link="log") - model.fit(draws=10, tune=10) - - # Real data, categorical predictor. - data = dm[["order", "ind_mg_dry"]] - model = bmb.Model("ind_mg_dry ~ order", data, family="gamma", link="log") - model.fit(draws=10, tune=10) - - -def test_beta_regression(): - data_dir = join(dirname(__file__), "data") - data = pd.read_csv(join(data_dir, "gasoline.csv")) - model = bmb.Model("yield ~ temp + batch", data, family="beta", categorical="batch") - model.fit(draws=10, tune=10, target_accept=0.9) - - -def test_t_regression(data_100): - bmb.Model("n1 ~ n2", data_100, family="t").fit(draws=10, tune=10) - - -def test_vonmises_regression(): - rng = np.random.default_rng(1234) - data = pd.DataFrame({"y": rng.vonmises(0, 1, size=100), "x": rng.normal(size=100)}) - bmb.Model("y ~ x", data, family="vonmises").fit(draws=10, tune=10) - - -def test_quantile_regression(): - rng = np.random.default_rng(1234) - x = rng.uniform(2, 10, 100) - y = 2 * x + rng.normal(0, 0.6 * x**0.75) - data = pd.DataFrame({"x": x, "y": y}) - bmb_model0 = bmb.Model("y ~ x", data, family="asymmetriclaplace", priors={"kappa": 9}) - idata0 = bmb_model0.fit() - bmb_model0.predict(idata0) - - bmb_model1 = bmb.Model("y ~ x", data, family="asymmetriclaplace", priors={"kappa": 0.1}) - idata1 = bmb_model1.fit() - bmb_model1.predict(idata1) - - assert np.all( - idata0.posterior["y_mean"].mean(("chain", "draw")) - > idata1.posterior["y_mean"].mean(("chain", "draw")) - ) - - -def test_plot_priors(crossed_data): - model = bmb.Model("Y ~ 0 + threecats", crossed_data) - with pytest.raises(ValueError, match="Model is not built yet"): - model.plot_priors() - model.build() - model.plot_priors() - - -def test_model_graph(crossed_data): - model = bmb.Model("Y ~ 0 + threecats", crossed_data) - with pytest.raises(ValueError, match="Model is not built yet"): - model.graph() - model.build() - model.graph() - - -def test_potentials(): - data = pd.DataFrame(np.repeat((0, 1), (18, 20)), columns=["w"]) - priors = {"Intercept": bmb.Prior("Uniform", lower=0, upper=1)} - - potentials = [ - (("Intercept", "Intercept"), lambda x, y: bmb.math.switch(x < 0.45, y, -np.inf)), - ("Intercept", lambda x: bmb.math.switch(x > 0.55, 0, -np.inf)), - ] - - model = bmb.Model( - "w ~ 1", - data, - family="bernoulli", - link="identity", - priors=priors, - potentials=potentials, - ) - model.build() - assert len(model.backend.model.potentials) == 2 - - pot0 = model.backend.model.potentials[0].get_parents()[0] - pot1 = model.backend.model.potentials[1].get_parents()[0] - assert pot0.__str__() == "Switch(Lt.0, Intercept, -inf)" - assert pot1.__str__() == "Switch(Gt.0, 0, -inf)" - - -def test_binomial_regression(): - data = pd.DataFrame( - { - "x": np.array([1.6907, 1.7242, 1.7552, 1.7842, 1.8113, 1.8369, 1.8610, 1.8839]), - "n": np.array([59, 60, 62, 56, 63, 59, 62, 60]), - "y": np.array([6, 13, 18, 28, 52, 53, 61, 60]), - } - ) - - model = bmb.Model("prop(y, n) ~ x", data, family="binomial") - model.fit(draws=10, tune=10) - - # Using constant instead of variable in data frame - model = bmb.Model("prop(y, 62) ~ x", data, family="binomial") - model.fit(draws=10, tune=10) - - -@pytest.mark.skip(reason="this example no longer trigger the fallback to adapt_diag") -def test_init_fallback(init_data, caplog): - model = bmb.Model("od ~ temp + (1|source) + 0", init_data) - with caplog.at_level(logging.INFO): - model.fit(draws=100, init="auto") - assert "Initializing NUTS using jitter+adapt_diag..." in caplog.text - assert "The default initialization" in caplog.text - assert "Initializing NUTS using adapt_diag..." in caplog.text - - -def test_categorical_family(inhaler): - model = bmb.Model("rating ~ period + carry + treat", inhaler, family="categorical") - model.fit(draws=10, tune=10) - - -def test_categorical_family_varying_intercept(inhaler): - model = bmb.Model( - "rating ~ period + carry + treat + (1|subject)", inhaler, family="categorical" - ) - model.fit(draws=10, tune=10) - - -def test_categorical_family_categorical_predictors(categorical_family_categorical_predictor): - formula = "response ~ group + city" - model = bmb.Model(formula, categorical_family_categorical_predictor, family="categorical") - model.fit(draws=10, tune=10) - - -def test_set_alias(data_100): - model = bmb.Model("n1 ~ n2 + (n2|cat1)", data_100) - aliases = { - "Intercept": "α", - "n2": "β", - "1|cat1": "α_group", - "n2|cat1": "β_group", - "sigma": "σ", - } - model.set_alias(aliases) - model.build() - new_names = set(["α", "β", "α_group", "α_group_σ", "β_group", "β_group_σ", "σ"]) - assert new_names.issubset(set(model.backend.model.named_vars)) - - -def test_fit_include_mean(crossed_data): - draws = 500 - model = bmb.Model("Y ~ continuous * threecats", crossed_data) - idata = model.fit(tune=draws, draws=draws, include_mean=True) - assert idata.posterior["Y_mean"].shape[1:] == (draws, 120) - - # Compare with the mean obtained with `model.predict()` - mean = idata.posterior["Y_mean"].stack(sample=("chain", "draw")).values.mean(1) - - model.predict(idata) - predicted_mean = idata.posterior["Y_mean"].stack(sample=("chain", "draw")).values.mean(1) - - assert np.array_equal(mean, predicted_mean) - - -def test_group_specific_splines(): - x_check = pd.DataFrame( - { - "x": [ - 82.0, - 143.0, - 426.0, - 641.0, - 1156.0, - 986.0, - 365.0, - 187.0, - 254.0, - 550.0, - 101.0, - 661.0, - 327.0, - 119.0, - ], - "day": ["Sun", "Mon", "Tue", "Wed", "Thu", "Fri", "Sat"] * 2, - "y": [ - 571.0, - 684.0, - 1652.0, - 2130.0, - 2455.0, - 1874.0, - 1288.0, - 1011.0, - 1004.0, - 1993.0, - 593.0, - 1986.0, - 1503.0, - 711.0, - ], - } - ) - knots = np.array([191.0, 297.0, 512.5]) - - model = bmb.Model("y ~ (bs(x, knots=knots, intercept=False, degree=1)|day)", data=x_check) - model.build() - - -def test_2d_response_no_shape(): - """ - This tests whether a model where there's a single linear predictor and a response with - response.ndim > 1 works well, without Bambi causing any shape problems. - See https://github.com/bambinos/bambi/pull/629 - Updated https://github.com/bambinos/bambi/pull/632 - """ - - def fn(name, p, observed, **kwargs): - y = observed[:, 0].flatten() - n = observed[:, 1].flatten() - # It's the users' responsibility to take only the first dim - kwargs["dims"] = kwargs.get("dims")[0] - return pm.Binomial(name, p=p, n=n, observed=y, **kwargs) - - likelihood = bmb.Likelihood("CustomBinomial", params=["p"], parent="p", dist=fn) - link = bmb.Link("logit") - family = bmb.Family("custom-binomial", likelihood, link) - - data = pd.DataFrame( - { - "x": np.array([1.6907, 1.7242, 1.7552, 1.7842, 1.8113, 1.8369, 1.8610, 1.8839]), - "n": np.array([59, 60, 62, 56, 63, 59, 62, 60]), - "y": np.array([6, 13, 18, 28, 52, 53, 61, 60]), - } - ) - - model = bmb.Model("prop(y, n) ~ x", data, family=family) - model.fit(draws=10, tune=10) - - -def test_zero_inflated_poisson(): - rng = np.random.default_rng(121195) - - # Basic intercept-only model - x = np.concatenate([np.zeros(250), rng.poisson(lam=3, size=750)]) - df = pd.DataFrame({"response": x}) - - model = bmb.Model("response ~ 1", df, family="zero_inflated_poisson") - idata = model.fit(chains=2, tune=200, draws=200, random_seed=121195) - model.predict(idata, kind="pps") - - # Distributional model - x = np.sort(rng.uniform(0.2, 3, size=1000)) - - b0, b1 = 0.2, 0.9 - a0, a1 = 2.5, -0.7 - mu = np.exp(b0 + b1 * x) - psi = expit(a0 + a1 * x) - - y = pm.draw(pm.ZeroInflatedPoisson.dist(mu=mu, psi=psi)) - df = pd.DataFrame({"y": y, "x": x}) - - formula = bmb.Formula("y ~ x", "psi ~ x") - model = bmb.Model(formula, df, family="zero_inflated_poisson") - idata = model.fit(chains=2, tune=200, draws=200, random_seed=121195) - model.predict(idata, kind="pps") - - -def test_zero_inflated_binomial(): - rng = np.random.default_rng(121195) - - # Basic intercept-only model - y = pm.draw(pm.ZeroInflatedBinomial.dist(p=0.5, n=30, psi=0.7), draws=500, random_seed=1234) - df = pd.DataFrame({"y": y}) - model = bmb.Model("p(y, 30) ~ 1", df, family="zero_inflated_binomial") - idata = model.fit(chains=2, tune=200, draws=200, random_seed=121195) - model.predict(idata, kind="pps") - - # Distributional model - x = np.sort(rng.uniform(0.2, 3, size=500)) - b0, b1 = -0.5, 0.5 - a0, a1 = 2, -0.7 - p = expit(b0 + b1 * x) - psi = expit(a0 + a1 * x) - - y = pm.draw(pm.ZeroInflatedBinomial.dist(p=p, psi=psi, n=30)) - df = pd.DataFrame({"y": y, "x": x}) - - formula = bmb.Formula("prop(y, 30) ~ x", "psi ~ x") - model = bmb.Model(formula, df, family="zero_inflated_binomial") - idata = model.fit(chains=2, tune=200, draws=200, random_seed=121195) - model.predict(idata, kind="pps") - - -def test_zero_inflated_negativebinomial(): - rng = np.random.default_rng(121195) - - # Basic intercept-only model - y = pm.draw( - pm.ZeroInflatedNegativeBinomial.dist(mu=5, alpha=30, psi=0.7), draws=500, random_seed=1234 - ) - df = pd.DataFrame({"y": y}) - priors = {"alpha": bmb.Prior("HalfNormal", sigma=20)} - model = bmb.Model("y ~ 1", df, family="zero_inflated_negativebinomial", priors=priors) - idata = model.fit(chains=2, tune=200, draws=200, random_seed=121195) - model.predict(idata, kind="pps") - - # Distributional model - x = np.sort(rng.uniform(0.2, 3, size=500)) - b0, b1 = 0.5, 0.35 - a0, a1 = 2, -0.7 - mu = np.exp(b0 + b1 * x) - psi = expit(a0 + a1 * x) - - y = pm.draw(pm.ZeroInflatedNegativeBinomial.dist(mu=mu, alpha=30, psi=psi)) - df = pd.DataFrame({"y": y, "x": x}) - - priors = {"alpha": bmb.Prior("HalfNormal", sigma=20)} - formula = bmb.Formula("y ~ x", "psi ~ x") - model = bmb.Model(formula, df, family="zero_inflated_negativebinomial", priors=priors) - idata = model.fit(chains=2, tune=200, draws=200, random_seed=121195) - model.predict(idata, kind="pps") - - -def test_hurlde_families(): - df = pd.DataFrame({"y": pm.draw(pm.HurdlePoisson.dist(0.5, mu=3.5), 1000)}) - model = bmb.Model("y ~ 1", df, family="hurdle_poisson") - idata = model.fit() - model.predict(idata, kind="pps") - - df = pd.DataFrame({"y": pm.draw(pm.HurdleNegativeBinomial.dist(0.6, 5, 3), 1000)}) - model = bmb.Model("y ~ 1", df, family="hurdle_negativebinomial") - idata = model.fit() - model.predict(idata, kind="pps") - - df = pd.DataFrame({"y": pm.draw(pm.HurdleGamma.dist(0.8, alpha=10, beta=1), 1000)}) - model = bmb.Model("y ~ 1", df, family="hurdle_gamma") - idata = model.fit() - model.predict(idata, kind="pps") - - df = pd.DataFrame({"y": pm.draw(pm.HurdleLogNormal.dist(0.7, mu=0, sigma=0.2), 1000)}) - model = bmb.Model("y ~ 1", df, family="hurdle_lognormal") - idata = model.fit() - model.predict(idata, kind="pps") - - -@pytest.mark.parametrize( - "family, link", - [ - ("cumulative", "logit"), - ("cumulative", "probit"), - ("cumulative", "cloglog"), - ("sratio", "logit"), - ("sratio", "probit"), - ("sratio", "cloglog"), - ], -) -def test_ordinal_families(inhaler, family, link): - data = inhaler.copy() - data["carry"] = pd.Categorical(data["carry"]) # To have both numeric and categoric predictors - model = bmb.Model("rating ~ period + carry + treat", data, family=family, link=link) - idata = model.fit(tune=100, draws=100) - model.predict(idata, kind="pps") - assert np.allclose(idata.posterior["rating_mean"].sum("rating_dim").to_numpy(), 1) - assert np.all(np.unique(idata.posterior_predictive["rating"]) == np.array([0, 1, 2, 3])) - - -def test_cumulative_family_priors(inhaler): - priors = { - "threshold": bmb.Prior( - "Normal", - mu=[-0.5, 0, 0.5], - sigma=1.5, - transform=pm.distributions.transforms.ordered, - ) - } - model = bmb.Model( - "rating ~ 0 + period + carry + treat", inhaler, family="cumulative", priors=priors - ) - model.fit(tune=100, draws=100) - - -def test_predict_new_groups_fail(sleepstudy): - model = bmb.Model("Reaction ~ 1 + Days + (1 + Days | Subject)", sleepstudy) - idata = model.fit(tune=20, draws=20) - - df_new = sleepstudy.head(10).reset_index(drop=True) - df_new["Subject"] = "xxx" - to_match = "There are new groups for the factors ('Subject',) and 'sample_new_groups' is False." - with pytest.raises(ValueError, match=re.escape(to_match)): - model.predict(idata, data=df_new) - - -@pytest.mark.parametrize( - "data,formula,family,df_new", - [ - ( - "sleepstudy", - "Reaction ~ 1 + Days + (1 + Days | Subject)", - "gaussian", - pd.DataFrame({"Days": [1, 2, 3], "Subject": ["x", "y", "z"]}), - ), - ( - "inhaler", - "rating ~ 1 + period + treat + (1 + treat|subject)", - "categorical", - pd.DataFrame( - { - "subject": [1, 999], - "rating": [1, 1], - "treat": [0.5, 0.5], - "period": [0.5, 0.5], - "carry": [0, 0], - } - ), - ), - ( - "crossed_data", - "Y ~ 0 + threecats + (0 + threecats | subj)", - "gaussian", - pd.DataFrame({"threecats": ["a", "a"], "subj": ["0", "11"]}), - ), - ], -) -def test_predict_new_groups(data, formula, family, df_new, request): - data = request.getfixturevalue(data) - model = bmb.Model(formula, data, family=family) - idata = model.fit(tune=100, draws=100) - model.predict(idata, data=df_new, sample_new_groups=True) - - -def test_censored_response(): - data = bmb.load_data("kidney") - data["status"] = np.where(data["censored"] == 0, "none", "right") - - # Model 1, with intercept - priors = { - "Intercept": bmb.Prior("Normal", mu=0, sigma=1), - "sex": bmb.Prior("Normal", mu=0, sigma=2), - "age": bmb.Prior("Normal", mu=0, sigma=1), - "alpha": bmb.Prior("Gamma", alpha=3, beta=5), - } - model = bmb.Model( - "censored(time, status) ~ 1 + sex + age", data, family="weibull", link="log", priors=priors - ) - idata = model.fit(tune=100, draws=100, random_seed=121195) - model.predict(idata, kind="pps") - model.predict(idata, data=data, kind="pps") - - # Model 2, without intercept - priors = { - "sex": bmb.Prior("Normal", mu=0, sigma=2), - "age": bmb.Prior("Normal", mu=0, sigma=1), - "alpha": bmb.Prior("Gamma", alpha=3, beta=5), - } - model = bmb.Model( - "censored(time, status) ~ 0 + sex + age", data, family="weibull", link="log", priors=priors - ) - idata = model.fit(tune=100, draws=100, random_seed=121195) - model.predict(idata, kind="pps") - model.predict(idata, data=data, kind="pps") - - # Model 3, with group-specific effects - priors = { - "alpha": bmb.Prior("Gamma", alpha=3, beta=5), - "sex": bmb.Prior("Normal", mu=0, sigma=1), - "age": bmb.Prior("Normal", mu=0, sigma=1), - "1|patient": bmb.Prior("Normal", mu=0, sigma=bmb.Prior("InverseGamma", alpha=5, beta=10)), - } - model = bmb.Model( - "censored(time, status) ~ 1 + sex + age + (1|patient)", - data, - family="weibull", - link="log", - priors=priors, - ) - idata = model.fit(tune=100, draws=100, random_seed=121195) - model.predict(idata, kind="pps") - model.predict(idata, data=data, kind="pps") - - -def test_truncated_response(): - rng = np.random.default_rng(12345) - slope, intercept, sigma, N = 1, 0, 2, 200 - x = rng.uniform(-10, 10, N) - y = rng.normal(loc=slope * x + intercept, scale=sigma) - - def truncate_y(x, y, bounds): - - return (x[keep], y[keep]) - - bounds = [-5, 5] - keep = (y >= bounds[0]) & (y <= bounds[1]) - xt = x[keep] - yt = y[keep] - - df = pd.DataFrame({"x": xt, "y": yt}) - priors = { - "Intercept": bmb.Prior("Normal", mu=0, sigma=1), - "x": bmb.Prior("Normal", mu=0, sigma=1), - "sigma": bmb.Prior("HalfNormal", sigma=1), - } - model = bmb.Model("truncated(y, -5, 5) ~ x", df, priors=priors) - idata = model.fit(tune=100, draws=100, random_seed=1234) - model.predict(idata, kind="pps") diff --git a/tests/test_interpret_messages.py b/tests/test_interpret_messages.py index 1fe27eecb..a1a875471 100644 --- a/tests/test_interpret_messages.py +++ b/tests/test_interpret_messages.py @@ -16,8 +16,6 @@ def mtcars(): # Use caplog fixture to capture log messages generated by the interpret logger - - def test_predictions_list(mtcars, caplog): model, idata = mtcars caplog.set_level("INFO", logger="__bambi_interpret__") diff --git a/tests/test_model_construction.py b/tests/test_model_construction.py index 6a2942e7d..a79d1a5c1 100644 --- a/tests/test_model_construction.py +++ b/tests/test_model_construction.py @@ -1,3 +1,5 @@ +import logging + from functools import reduce from operator import add from os.path import dirname, join @@ -60,6 +62,16 @@ def crossed_data(): return data +@pytest.fixture(scope="module") +def init_data(): + """ + Data used to test initialization method + """ + data_dir = join(dirname(__file__), "data") + data = pd.read_csv(join(data_dir, "obs.csv")) + return data + + def test_term_init(diabetes_data): design = design_matrices("BMI", diabetes_data) term = design.common.terms["BMI"] @@ -207,7 +219,7 @@ def test_model_term_classes(): def test_one_shot_formula_fit(diabetes_data): model = bmb.Model("S3 ~ S1 + S2", diabetes_data) - model.fit(draws=50) + model.fit(tune=100, draws=100) named_vars = model.backend.model.named_vars targets = ["S3", "S1", "Intercept"] assert len(set(named_vars.keys()) & set(targets)) == 3 @@ -467,3 +479,100 @@ def test_extra_namespace(): model = bmb.Model(formula, data, family="poisson", link="log", extra_namespace=extra_namespace) term = model.response_component.terms["C(veh_body, levels=levels)"] assert (np.asarray(term.levels) == data["veh_body"].unique()).all() + + +def test_drop_na(crossed_data, caplog): + crossed_data_missing = crossed_data.copy() + crossed_data_missing.loc[0, "Y"] = np.nan + crossed_data_missing.loc[1, "continuous"] = np.nan + crossed_data_missing.loc[2, "threecats"] = np.nan + + with caplog.at_level(logging.INFO): + bmb.Model("Y ~ continuous + threecats", crossed_data_missing, dropna=True) + assert "Automatically removing 3/120 rows from the dataset." in caplog.text + + with pytest.raises(ValueError, match="'data' contains 3 incomplete rows"): + bmb.Model("Y ~ continuous + threecats", crossed_data_missing) + + +def test_plot_priors(crossed_data): + model = bmb.Model("Y ~ 0 + threecats", crossed_data) + with pytest.raises(ValueError, match="Model is not built yet"): + model.plot_priors() + model.build() + model.plot_priors() + + +def test_model_graph(crossed_data): + model = bmb.Model("Y ~ 0 + threecats", crossed_data) + with pytest.raises(ValueError, match="Model is not built yet"): + model.graph() + model.build() + model.graph() + + +def test_potentials(): + data = pd.DataFrame(np.repeat((0, 1), (18, 20)), columns=["w"]) + priors = {"Intercept": bmb.Prior("Uniform", lower=0, upper=1)} + + potentials = [ + (("Intercept", "Intercept"), lambda x, y: bmb.math.switch(x < 0.45, y, -np.inf)), + ("Intercept", lambda x: bmb.math.switch(x > 0.55, 0, -np.inf)), + ] + + model = bmb.Model( + "w ~ 1", + data, + family="bernoulli", + link="identity", + priors=priors, + potentials=potentials, + ) + model.build() + assert len(model.backend.model.potentials) == 2 + + pot0 = model.backend.model.potentials[0].get_parents()[0] + pot1 = model.backend.model.potentials[1].get_parents()[0] + assert pot0.__str__() == "Switch(Lt.0, Intercept, -inf)" + assert pot1.__str__() == "Switch(Gt.0, 0, -inf)" + + +@pytest.mark.skip(reason="this example no longer trigger the fallback to adapt_diag") +def test_init_fallback(init_data, caplog): + model = bmb.Model("od ~ temp + (1|source) + 0", init_data) + with caplog.at_level(logging.INFO): + model.fit(draws=100, init="auto") + assert "Initializing NUTS using jitter+adapt_diag..." in caplog.text + assert "The default initialization" in caplog.text + assert "Initializing NUTS using adapt_diag..." in caplog.text + + +def test_2d_response_no_shape(): + """ + This tests whether a model where there's a single linear predictor and a response with + response.ndim > 1 works well, without Bambi causing any shape problems. + See https://github.com/bambinos/bambi/pull/629 + Updated https://github.com/bambinos/bambi/pull/632 + """ + + def fn(name, p, observed, **kwargs): + y = observed[:, 0].flatten() + n = observed[:, 1].flatten() + # It's the users' responsibility to take only the first dim + kwargs["dims"] = kwargs.get("dims")[0] + return pm.Binomial(name, p=p, n=n, observed=y, **kwargs) + + likelihood = bmb.Likelihood("CustomBinomial", params=["p"], parent="p", dist=fn) + link = bmb.Link("logit") + family = bmb.Family("custom-binomial", likelihood, link) + + data = pd.DataFrame( + { + "x": np.array([1.6907, 1.7242, 1.7552, 1.7842, 1.8113, 1.8369, 1.8610, 1.8839]), + "n": np.array([59, 60, 62, 56, 63, 59, 62, 60]), + "y": np.array([6, 13, 18, 28, 52, 53, 61, 60]), + } + ) + + model = bmb.Model("prop(y, n) ~ x", data, family=family) + model.fit(draws=10, tune=10) diff --git a/tests/test_models.py b/tests/test_models.py new file mode 100644 index 000000000..386a08b46 --- /dev/null +++ b/tests/test_models.py @@ -0,0 +1,1336 @@ +import re + +from os.path import dirname, join + +import pytest + +import bambi as bmb +import numpy as np +import pandas as pd +import pymc as pm + +from bambi.terms import GroupSpecificTerm + +TUNE = 50 +DRAWS = 50 + + +@pytest.fixture(scope="module") +def crossed_data(): + """ + Group specific effects: + 10 subjects, 12 items, 5 sites + Subjects crossed with items, nested in sites + Items crossed with sites + + common effects: + A continuous predictor, a numeric dummy, and a three-level category + (levels a,b,c) + + Structure: + Subjects nested in dummy (e.g., gender), crossed with threecats + Items crossed with dummy, nested in threecats + Sites partially crossed with dummy (4/5 see a single dummy, 1/5 sees both + dummies) + Sites crossed with threecats + """ + + data_dir = join(dirname(__file__), "data") + data = pd.read_csv(join(data_dir, "crossed_random.csv")) + data["subj"] = data["subj"].astype(str) + data["fourcats"] = sum([[x] * 10 for x in ["a", "b", "c", "d"]], list()) * 3 + return data + + +@pytest.fixture(scope="module") +def data_n100(): + size = 100 + rng = np.random.default_rng(121195) + data = pd.DataFrame( + { + "b1": rng.binomial(n=1, p=0.5, size=size), + "n1": rng.poisson(lam=2, size=size), + "n2": rng.poisson(lam=2, size=size), + "y1": rng.normal(size=size), + "y2": rng.normal(size=size), + "y3": rng.normal(size=size), + "cat2": rng.choice(["a", "b"], size=size), + "cat4": rng.choice(list("MNOP"), size=size), + "cat5": rng.choice(list("FGHIJK"), size=size), + } + ) + return data + + +@pytest.fixture(scope="module") +def beetle_data(): + return pd.DataFrame( + { + "x": np.array([1.6907, 1.7242, 1.7552, 1.7842, 1.8113, 1.8369, 1.8610, 1.8839]), + "n": np.array([59, 60, 62, 56, 63, 59, 62, 60]), + "y": np.array([6, 13, 18, 28, 52, 53, 61, 60]), + } + ) + + +@pytest.fixture(scope="module") +def gasoline_data(): + data_dir = join(dirname(__file__), "data") + return pd.read_csv(join(data_dir, "gasoline.csv")) + + +@pytest.fixture(scope="module") +def inhaler_data(): + data_dir = join(dirname(__file__), "data") + data = pd.read_csv(join(data_dir, "inhaler.csv")) + data["rating"] = pd.Categorical(data["rating"], categories=[1, 2, 3, 4]) + return data + + +@pytest.fixture(scope="module") +def cat_response_cat_preds_data(): + data_dir = join(dirname(__file__), "data") + data = pd.read_csv(join(data_dir, "categorical_family_categorical_predictor.csv")) + return data + + +@pytest.fixture(scope="module") +def zi_count_data(): + rng = np.random.default_rng(1234) + n1, n2 = 30, 70 + y = np.concatenate([np.zeros(n1), rng.poisson(3, size=n2)]) + x = np.concatenate( + [rng.normal(loc=-1, scale=0.25, size=n1), rng.normal(loc=0.5, scale=0.5, size=n2)] + ) + return pd.DataFrame({"x": x, "y": y}) + + +@pytest.fixture(scope="module") +def zi_bounded_count_data(): + rng = np.random.default_rng(1234) + n1, n2 = 40, 60 + y = np.concatenate([np.zeros(n1), rng.binomial(n=30, p=0.6, size=n2)]) + x = np.concatenate( + [rng.normal(loc=-1, scale=0.25, size=n1), rng.normal(loc=0.5, scale=0.5, size=n2)] + ) + return pd.DataFrame({"x": x, "y": y}) + + +@pytest.fixture(scope="module") +def zi_continuous_data(): + rng = np.random.default_rng(1234) + n1, n2 = 40, 60 + y = np.concatenate([np.zeros(n1), rng.gamma(shape=2, scale=3, size=n2)]) + x = np.concatenate( + [rng.normal(loc=-1, scale=0.25, size=n1), rng.normal(loc=0.5, scale=0.5, size=n2)] + ) + return pd.DataFrame({"x": x, "y": y}) + + +@pytest.fixture(scope="module") +def kidney_data(): + data = bmb.load_data("kidney") + data["status"] = np.where(data["censored"] == 0, "none", "right") + return data + + +@pytest.fixture(scope="module") +def truncated_data(): + rng = np.random.default_rng(12345) + slope, intercept, sigma, N = 1, 0, 2, 200 + x = rng.uniform(-10, 10, N) + y = rng.normal(loc=slope * x + intercept, scale=sigma) + bounds = [-5, 5] + keep = (y >= bounds[0]) & (y <= bounds[1]) + xt = x[keep] + yt = y[keep] + + return pd.DataFrame({"x": xt, "y": yt}) + + +@pytest.fixture(scope="module") +def multinomial_data(inhaler_data): + df = inhaler_data.groupby(["treat", "carry", "rating"], as_index=False).size() + df = df.pivot(index=["treat", "carry"], columns="rating", values="size").reset_index() + df.columns = ["treat", "carry", "y1", "y2", "y3", "y4"] + return df + + +@pytest.fixture(scope="module") +def sleepstudy(): + return bmb.load_data("sleepstudy") + + +class FitPredictParent: + def fit(self, model, **kwargs): + return model.fit(tune=TUNE, draws=DRAWS, **kwargs) + + def predict_oos(self, model, idata, data=None): + # Reuse the original data + if data is None: + data = model.data.head() + return model.predict(idata, kind="pps", data=data, inplace=False) + + +class TestGaussian(FitPredictParent): + def test_intercept_only_model(self, crossed_data): + model = bmb.Model("Y ~ 1", crossed_data) + idata = self.fit(model) + self.predict_oos(model, idata) + + def test_slope_only_model(self, crossed_data): + model = bmb.Model("Y ~ 0 + continuous", crossed_data) + idata = self.fit(model) + self.predict_oos(model, idata) + + def test_cell_means_parameterization(self, crossed_data): + model = bmb.Model("Y ~ 0 + threecats", crossed_data) + idata = self.fit(model) + assert list(idata.posterior["threecats_dim"]) == ["a", "b", "c"] + self.predict_oos(model, idata) + + def test_2_factors_saturated(self, crossed_data): + model = bmb.Model("Y ~ threecats*fourcats", crossed_data) + idata = self.fit(model) + assert list(idata.posterior.data_vars) == [ + "Intercept", + "threecats", + "fourcats", + "threecats:fourcats", + "Y_sigma", + ] + assert list(idata.posterior["threecats_dim"].values) == ["b", "c"] + assert list(idata.posterior["fourcats_dim"].values) == ["b", "c", "d"] + assert list(idata.posterior["threecats:fourcats_dim"].values) == [ + "b, b", + "b, c", + "b, d", + "c, b", + "c, c", + "c, d", + ] + self.predict_oos(model, idata) + + def test_2_factors_no_intercept(self, crossed_data): + model = bmb.Model("Y ~ 0 + threecats*fourcats", crossed_data) + idata = self.fit(model) + assert list(idata.posterior.data_vars) == [ + "threecats", + "fourcats", + "threecats:fourcats", + "Y_sigma", + ] + assert list(idata.posterior["threecats_dim"].values) == ["a", "b", "c"] + assert list(idata.posterior["fourcats_dim"].values) == ["b", "c", "d"] + assert list(idata.posterior["threecats:fourcats_dim"].values) == [ + "b, b", + "b, c", + "b, d", + "c, b", + "c, c", + "c, d", + ] + self.predict_oos(model, idata) + + def test_2_factors_cell_means(self, crossed_data): + model = bmb.Model("Y ~ 0 + threecats:fourcats", crossed_data) + idata = self.fit(model) + assert list(idata.posterior.data_vars) == ["threecats:fourcats", "Y_sigma"] + assert list(idata.posterior["threecats:fourcats_dim"].values) == [ + "a, a", + "a, b", + "a, c", + "a, d", + "b, a", + "b, b", + "b, c", + "b, d", + "c, a", + "c, b", + "c, c", + "c, d", + ] + self.predict_oos(model, idata) + + def test_cell_means_with_covariate(self, crossed_data): + model = bmb.Model("Y ~ 0 + threecats + continuous", crossed_data) + idata = self.fit(model) + assert list(idata.posterior.data_vars) == ["threecats", "continuous", "Y_sigma"] + assert list(idata.posterior["threecats_dim"].values) == ["a", "b", "c"] + self.predict_oos(model, idata) + + def test_many_common_many_group_specific(self, crossed_data): + # Comparing implicit/explicit intercepts for group specific effects work the same way. + terms0 = [ + "continuous", + "dummy", + "threecats", + "(threecats|subj)", + "(1|item)", + "(0 + continuous|item)", + "(dummy|item)", + "(threecats|site)", + ] + terms1 = [ + "continuous", + "dummy", + "threecats", + "(threecats|subj)", + "(continuous|item)", + "(dummy|item)", + "(threecats|site)", + ] + + model0 = bmb.Model("Y ~ " + " + ".join(terms0), crossed_data) + idata0 = self.fit(model0) + self.predict_oos(model0, idata0) + + model1 = bmb.Model("Y ~ " + " + ".join(terms1), crossed_data) + idata1 = self.fit(model1) + self.predict_oos(model1, idata1) + + # Check that the group specific effects design matrices have the same shape + X0 = pd.concat( + [pd.DataFrame(t.data) for t in model0.response_component.group_specific_terms.values()], + axis=1, + ) + X1 = pd.concat( + [pd.DataFrame(t.data) for t in model1.response_component.group_specific_terms.values()], + axis=1, + ) + assert X0.shape == X1.shape + + # check that the group specific effect design matrix contain the same columns, + # even if term names / columns names / order of columns is different + X0_set = set(tuple(X0.iloc[:, i]) for i in range(len(X0.columns))) + X1_set = set(tuple(X1.iloc[:, i]) for i in range(len(X1.columns))) + assert X0_set == X1_set + + # check that common effect design matrices are the same, + # even if term names / level names / order of columns is different + X0_list = [] + X1_list = [] + for term in model0.response_component.common_terms.values(): + if term.levels is not None: + for level_idx in range(len(term.levels)): + X0_list.append(tuple(term.data[:, level_idx])) + else: + X0_list.append(tuple(term.data)) + + for term in model1.response_component.common_terms.values(): + if term.levels is not None: + for level_idx in range(len(term.levels)): + X1_list.append(tuple(term.data[:, level_idx])) + else: + X1_list.append(tuple(term.data)) + + assert set(X0_list) == set(X1_list) + + # check that models have same priors for common effects + priors0 = { + x.name: x.prior.args + for x in model0.response_component.terms.values() + if not isinstance(x, GroupSpecificTerm) + } + priors1 = { + x.name: x.prior.args + for x in model1.response_component.terms.values() + if not isinstance(x, GroupSpecificTerm) + } + + # check dictionary keys + assert set(priors0) == set(priors1) + + # check dictionary values + def dicts_close(a, b): + if set(a) != set(b): + return False + else: + return [np.allclose(a[x], b[x], atol=0, rtol=0.01) for x in a.keys()] + + assert all([dicts_close(priors0[x], priors1[x]) for x in priors0.keys()]) + + # check that fit and add models have same priors for group specific effects + priors0 = { + x.name: x.prior.args["sigma"].args + for x in model0.response_component.group_specific_terms.values() + } + priors1 = { + x.name: x.prior.args["sigma"].args + for x in model1.response_component.group_specific_terms.values() + } + + # check dictionary keys + assert set(priors0) == set(priors1) + + # check dictionary values + def dicts_close(a, b): + if set(a) != set(b): + return False + else: + return [np.allclose(a[x], b[x], atol=0, rtol=0.01) for x in a.keys()] + + assert all([dicts_close(priors0[x], priors1[x]) for x in priors0.keys()]) + + def test_cell_means_with_many_group_specific_effects(self, crossed_data): + # Group specific intercepts are added in different way, but the final result should be the same. + terms0 = [ + "0", + "threecats", + "(threecats|subj)", + "(1|subj)", + "(0 + continuous|item)", + "(dummy|item)", + "(0 + threecats|site)", + "(1|site)", + ] + + terms1 = [ + "0", + "threecats", + "(threecats|subj)", + "(continuous|item)", + "(dummy|item)", + "(threecats|site)", + ] + model0 = bmb.Model("Y ~ " + " + ".join(terms0), crossed_data) + idata0 = self.fit(model0) + self.predict_oos(model0, idata0) + + model1 = bmb.Model("Y ~ " + " + ".join(terms1), crossed_data) + idata1 = self.fit(model1) + self.predict_oos(model1, idata1) + + # check that the group specific effects design matrices have the same shape + X0 = pd.concat( + [ + pd.DataFrame(t.data) + if not isinstance(t.data, dict) + else pd.concat([pd.DataFrame(t.data[x]) for x in t.data.keys()], axis=1) + for t in model0.response_component.group_specific_terms.values() + ], + axis=1, + ) + X1 = pd.concat( + [ + pd.DataFrame(t.data) + if not isinstance(t.data, dict) + else pd.concat([pd.DataFrame(t.data[x]) for x in t.data.keys()], axis=1) + for t in model0.response_component.group_specific_terms.values() + ], + axis=1, + ) + assert X0.shape == X1.shape + + # check that the group specific effect design matrix contain the same columns, + # even if term names / columns names / order of columns is different + X0_set = set(tuple(X0.iloc[:, i]) for i in range(len(X0.columns))) + X1_set = set(tuple(X1.iloc[:, i]) for i in range(len(X1.columns))) + assert X0_set == X1_set + + # check that common effect design matrices are the same, + # even if term names / level names / order of columns is different + X0 = set( + [ + tuple(t.data[:, lev]) + for t in model0.response_component.common_terms.values() + for lev in range(len(t.levels)) + ] + ) + X1 = set( + [ + tuple(t.data[:, lev]) + for t in model1.response_component.common_terms.values() + for lev in range(len(t.levels)) + ] + ) + assert X0 == X1 + + # check that fit and add models have same priors for common effects + priors0 = { + x.name: x.prior.args + for x in model0.response_component.terms.values() + if not isinstance(x, GroupSpecificTerm) + } + priors1 = { + x.name: x.prior.args + for x in model1.response_component.terms.values() + if not isinstance(x, GroupSpecificTerm) + } + assert set(priors0) == set(priors1) + + # check that fit and add models have same priors for group specific effects + priors0 = { + x.name: x.prior.args["sigma"].args + for x in model0.response_component.terms.values() + if isinstance(x, GroupSpecificTerm) + } + priors1 = { + x.name: x.prior.args["sigma"].args + for x in model1.response_component.terms.values() + if isinstance(x, GroupSpecificTerm) + } + assert set(priors0) == set(priors1) + + def test_group_specific_categorical_interaction(self, crossed_data): + model = bmb.Model("Y ~ continuous + (threecats:fourcats|site)", crossed_data) + idata = self.fit(model) + self.predict_oos(model, idata) + + assert list(idata.posterior.data_vars) == [ + "Intercept", + "continuous", + "Y_sigma", + "1|site_sigma", + "threecats:fourcats|site_sigma", + "1|site", + "threecats:fourcats|site", + ] + assert list(idata.posterior["threecats:fourcats|site"].coords) == [ + "chain", + "draw", + "site__factor_dim", + "threecats:fourcats__expr_dim", + ] + assert list(idata.posterior["1|site"].coords) == ["chain", "draw", "site__factor_dim"] + assert list(idata.posterior["1|site_sigma"].coords) == ["chain", "draw"] + assert list(idata.posterior["threecats:fourcats|site_sigma"].coords) == [ + "chain", + "draw", + "threecats:fourcats__expr_dim", + ] + + assert list(idata.posterior["threecats:fourcats__expr_dim"].values) == [ + "b, b", + "b, c", + "b, d", + "c, b", + "c, c", + "c, d", + ] + assert list(idata.posterior["site__factor_dim"].values) == ["0", "1", "2", "3", "4"] + + def test_fit_include_mean(self, crossed_data): + draws = 100 + model = bmb.Model("Y ~ continuous * threecats", crossed_data) + idata = model.fit(tune=draws, draws=draws, include_mean=True) + assert idata.posterior["Y_mean"].shape[1:] == (draws, 120) + + # Compare with the mean obtained with `model.predict()` + mean = idata.posterior["Y_mean"].stack(sample=("chain", "draw")).values.mean(1) + + model.predict(idata) + predicted_mean = idata.posterior["Y_mean"].stack(sample=("chain", "draw")).values.mean(1) + + assert np.array_equal(mean, predicted_mean) + + def test_group_specific_splines(self): + x_check = pd.DataFrame( + { + "x": [ + 82.0, + 143.0, + 426.0, + 641.0, + 1156.0, + 986.0, + 365.0, + 187.0, + 254.0, + 550.0, + 101.0, + 661.0, + 327.0, + 119.0, + ], + "day": ["Sun", "Mon", "Tue", "Wed", "Thu", "Fri", "Sat"] * 2, + "y": [ + 571.0, + 684.0, + 1652.0, + 2130.0, + 2455.0, + 1874.0, + 1288.0, + 1011.0, + 1004.0, + 1993.0, + 593.0, + 1986.0, + 1503.0, + 711.0, + ], + } + ) + knots = np.array([191.0, 297.0, 512.5]) + + model = bmb.Model("y ~ (bs(x, knots=knots, intercept=False, degree=1)|day)", data=x_check) + idata = self.fit(model) + self.predict_oos(model, idata) + + +class TestBernoulli(FitPredictParent): + def assert_posterior_predictive_range(self, model, idata): + y_name = model.response_component.response_term.name + y_posterior_predictive = idata.posterior_predictive[y_name].to_numpy() + assert set(np.unique(y_posterior_predictive)) == {0, 1} + + def assert_mean_range(self, model, idata): + y_mean_name = model.response_component.response_term.name + "_mean" + y_mean_posterior = idata.posterior[y_mean_name].to_numpy() + assert ((0 < y_mean_posterior) & (y_mean_posterior < 1)).all() + + def test_bernoulli_empty_index(self, data_n100): + model = bmb.Model("b1 ~ 1 + y1", data_n100, family="bernoulli") + idata = self.fit(model) + model.predict(idata, kind="pps") + self.assert_mean_range(model, idata) + self.assert_posterior_predictive_range(model, idata) + + # out of sample prediction + idata = self.predict_oos(model, idata) + self.assert_mean_range(model, idata) + self.assert_posterior_predictive_range(model, idata) + + def test_bernoulli_good_numeric(self, data_n100): + model = bmb.Model("b1 ~ y1", data_n100, family="bernoulli") + idata = self.fit(model) + model.predict(idata, kind="pps") + self.assert_mean_range(model, idata) + self.assert_posterior_predictive_range(model, idata) + + def test_bernoulli_bad_numeric(self, data_n100): + error_msg = "Numeric response must be all 0 and 1 for 'bernoulli' family" + with pytest.raises(ValueError, match=error_msg): + model = bmb.Model("y1 ~ y2", data_n100, family="bernoulli") + self.fit(model) + + def test_categorical_group_specific(self, data_n100): + # see https://github.com/bambinos/bambi/issues/447 + + formula = "b1 ~ 0 + cat2 + y2 + (0 + cat2|cat5) + (0 + y2| cat4 + cat5)" + model = bmb.Model(formula, data_n100, family="bernoulli") + idata = self.fit(model, chains=2) + idata = self.predict_oos(model, idata) + + self.assert_mean_range(model, idata) + self.assert_posterior_predictive_range(model, idata) + + +class TestBinomial(FitPredictParent): + def assert_mean_range(self, model, idata): + y_mean_name = model.response_component.response_term.name + "_mean" + y_mean_posterior = idata.posterior[y_mean_name].to_numpy() + assert ((0 < y_mean_posterior) & (y_mean_posterior < 1)).all() + + def test_binomial_regression(self, beetle_data): + model = bmb.Model("prop(y, n) ~ x", beetle_data, family="binomial") + idata = self.fit(model) + model.predict(idata, kind="pps") + self.assert_mean_range(model, idata) + y_reshaped = beetle_data["n"].to_numpy()[None, None, :] + + assert (idata.posterior_predictive["prop(y, n)"].to_numpy() <= y_reshaped).all() + assert (0 <= idata.posterior_predictive["prop(y, n)"].to_numpy()).all() + + y_reshaped = beetle_data["n"].to_numpy()[None, None, :3] + idata = self.predict_oos(model, idata, data=model.data.head(3)) + self.assert_mean_range(model, idata) + assert (idata.posterior_predictive["prop(y, n)"].to_numpy() <= y_reshaped).all() + + def test_binomial_regression_constant(self, beetle_data): + # Uses a constant instead of variable in data frame + model = bmb.Model("prop(y, 62) ~ x", beetle_data, family="binomial") + idata = self.fit(model) + model.predict(idata, kind="pps") + self.assert_mean_range(model, idata) + assert (idata.posterior_predictive["prop(y, 62)"].to_numpy() <= 62).all() + assert (0 <= idata.posterior_predictive["prop(y, 62)"].to_numpy()).all() + + # Out of sample prediction + idata = self.predict_oos(model, idata) + self.assert_mean_range(model, idata) + assert (idata.posterior_predictive["prop(y, 62)"].to_numpy() <= 62).all() + + +class TestPoisson(FitPredictParent): + def assert_mean_range(self, model, idata): + y_mean_name = model.response_component.response_term.name + "_mean" + y_mean_posterior = idata.posterior[y_mean_name].to_numpy() + assert (y_mean_posterior > 0).all() + + def test_poisson_regression(self, crossed_data): + crossed_data["count"] = (crossed_data["Y"] - crossed_data["Y"].min()).round() + model0 = bmb.Model("count ~ dummy + continuous + threecats", crossed_data, family="poisson") + idata0 = self.fit(model0) + idata0 = self.predict_oos(model0, idata0) + self.assert_mean_range(model0, idata0) + + # build model using add + model1 = bmb.Model("count ~ threecats + continuous + dummy", crossed_data, family="poisson") + idata1 = self.fit(model1) + idata1 = self.predict_oos(model1, idata1) + self.assert_mean_range(model1, idata1) + + # check that term names agree + assert set(model0.response_component.terms) == set(model1.response_component.terms) + + # check that common effect design matrices are the same, + # even if term names / level names / order of columns is different + + X0_list = [] + X1_list = [] + for term in model0.response_component.common_terms.values(): + if term.levels is not None: + for level_idx in range(len(term.levels)): + X0_list.append(tuple(term.data[:, level_idx])) + else: + X0_list.append(tuple(term.data)) + + for term in model1.response_component.common_terms.values(): + if term.levels is not None: + for level_idx in range(len(term.levels)): + X1_list.append(tuple(term.data[:, level_idx])) + else: + X1_list.append(tuple(term.data)) + + assert set(X0_list) == set(X1_list) + + # check that models have same priors for common effects + priors0 = { + x.name: x.prior.args + for x in model0.response_component.terms.values() + if not isinstance(x, GroupSpecificTerm) + } + priors1 = { + x.name: x.prior.args + for x in model1.response_component.terms.values() + if not isinstance(x, GroupSpecificTerm) + } + # check dictionary keys + assert set(priors0) == set(priors1) + # check dictionary values + def dicts_close(a, b): + if set(a) != set(b): + return False + else: + return [np.allclose(a[x], b[x], atol=0, rtol=0.01) for x in a.keys()] + + assert all([dicts_close(priors0[x], priors1[x]) for x in priors0.keys()]) + + # Now test prior predictive + pps = model1.prior_predictive(draws=200, random_seed=1234) + + keys = ["Intercept", "threecats", "continuous", "dummy"] + shapes = [(1, 200), (1, 200, 2), (1, 200), (1, 200)] + + for key, shape in zip(keys, shapes): + assert pps.prior[key].shape == shape + + assert pps.prior_predictive["count"].shape == (1, 200, 120) + assert pps.observed_data["count"].shape == (120,) + + pps = model1.prior_predictive(draws=200, var_names=["count"], random_seed=1234) + assert pps.groups() == ["prior_predictive", "observed_data"] + + pps = model1.prior_predictive(draws=200, var_names=["Intercept"], random_seed=1234) + assert pps.groups() == ["prior", "observed_data"] + + # Now test posterior predictive + # Fit again to make sure we fix the number of chainS + idata = model1.fit(tune=50, draws=50, chains=2) + pps = model1.predict(idata, kind="pps", inplace=False) + assert pps.posterior_predictive["count"].shape == (2, 50, 120) + + pps = model1.predict(idata, kind="pps", inplace=True) + assert pps is None + assert idata.posterior_predictive["count"].shape == (2, 50, 120) + + +class TestNegativeBinomial(FitPredictParent): + # To Do: Could be modified to follow the same format than the others + def test_predict_negativebinomial(self, data_n100): + + model = bmb.Model("n1 ~ y1", data_n100, family="negativebinomial") + idata = self.fit(model) + + model.predict(idata, kind="mean") + assert (0 < idata.posterior["n1_mean"]).all() + + model.predict(idata, kind="pps") + assert (np.equal(np.mod(idata.posterior_predictive["n1"].values, 1), 0)).all() + + model.predict(idata, kind="mean", data=data_n100.iloc[:20, :]) + assert (0 < idata.posterior["n1_mean"]).all() + + model.predict(idata, kind="pps", data=data_n100.iloc[:20, :]) + assert (np.equal(np.mod(idata.posterior_predictive["n1"].values, 1), 0)).all() + + +class TestLaplace(FitPredictParent): + def test_laplace_regression(self, data_n100): + model = bmb.Model("y1 ~ y2", data_n100, family="laplace") + idata = self.fit(model) + assert set(idata.posterior.data_vars) == {"Intercept", "y2", "y1_b"} + assert (idata.posterior["y1_b"] > 0).all().item() + + idata = self.predict_oos(model, idata) + assert "y1_mean" in idata.posterior + + +class TestGamma(FitPredictParent): + def test_gamma_regression(self, data_n100): + # Construct a positive variable + data_n100["o"] = np.exp(data_n100["y1"]) + model = bmb.Model("o ~ y2 + y3 + n1 + cat4", data_n100, family="gamma", link="log") + idata = self.fit(model) + assert set(idata.posterior.data_vars) == {"Intercept", "y2", "y3", "n1", "cat4", "o_alpha"} + idata = self.predict_oos(model, idata) + assert (idata.posterior_predictive["o"] > 0).all().item() + + def test_gamma_regression_categoric(self, data_n100): + data_n100["o"] = np.exp(data_n100["y1"]) + model = bmb.Model("o ~ 0 + cat2:cat4", data_n100, family="gamma", link="log") + idata = self.fit(model) + assert set(idata.posterior.data_vars) == {"cat2:cat4", "o_alpha"} + idata = self.predict_oos(model, idata) + assert (idata.posterior_predictive["o"] > 0).all().item() + + +class TestBeta(FitPredictParent): + def test_beta_regression(self, gasoline_data): + model = bmb.Model( + "yield ~ temp + batch", gasoline_data, family="beta", categorical="batch" + ) + idata = self.fit(model, target_accept=0.9, random_seed=1234) + + # To Do: Could be adjusted but this is what we had before + model.predict(idata, kind="mean") + model.predict(idata, kind="pps") + + assert (0 < idata.posterior["yield_mean"]).all() & (idata.posterior["yield_mean"] < 1).all() + assert (0 < idata.posterior_predictive["yield"]).all() & ( + idata.posterior_predictive["yield"] < 1 + ).all() + + model.predict(idata, kind="mean", data=gasoline_data.iloc[:20, :]) + model.predict(idata, kind="pps", data=gasoline_data.iloc[:20, :]) + + assert (0 < idata.posterior["yield_mean"]).all() & (idata.posterior["yield_mean"] < 1).all() + assert (0 < idata.posterior_predictive["yield"]).all() & ( + idata.posterior_predictive["yield"] < 1 + ).all() + + +class TestStudentT(FitPredictParent): + def test_t_regression(self, data_n100): + model = bmb.Model("y1 ~ y2", data_n100, family="t") + idata = self.fit(model) + assert set(idata.posterior.data_vars) == {"Intercept", "y2", "y1_nu", "y1_sigma"} + self.predict_oos(model, idata) + + +class TestVonMises(FitPredictParent): + def test_vonmises_regression(self): + rng = np.random.default_rng(1234) + data = pd.DataFrame({"y": rng.vonmises(0, 1, size=100), "x": rng.normal(size=100)}) + model = bmb.Model("y ~ x", data, family="vonmises") + idata = self.fit(model) + assert set(idata.posterior.data_vars) == {"Intercept", "x", "y_kappa"} + idata = self.predict_oos(model, idata) + assert (idata.posterior_predictive["y"].min() >= -np.pi).item() and ( + idata.posterior_predictive["y"].max() <= np.pi + ).item() + + +class TestAsymmetricLaplace(FitPredictParent): + # This test doesn't follow the previous pattern but it works... + def test_quantile_regression(self): + rng = np.random.default_rng(1234) + x = rng.uniform(2, 10, 100) + y = 2 * x + rng.normal(0, 0.6 * x**0.75) + data = pd.DataFrame({"x": x, "y": y}) + bmb_model0 = bmb.Model("y ~ x", data, family="asymmetriclaplace", priors={"kappa": 9}) + idata0 = bmb_model0.fit() + bmb_model0.predict(idata0) + + bmb_model1 = bmb.Model("y ~ x", data, family="asymmetriclaplace", priors={"kappa": 0.1}) + idata1 = bmb_model1.fit() + bmb_model1.predict(idata1) + + assert np.all( + idata0.posterior["y_mean"].mean(("chain", "draw")) + > idata1.posterior["y_mean"].mean(("chain", "draw")) + ) + + +class TestCategorical(FitPredictParent): + # assert pps.shape[-1] == inhaler.shape[0] + def assert_mean_sum(self, model, idata): + y_mean_name = model.response_component.response_term.name + "_mean" + y_dim = model.response_component.response_term.name + "_dim" + y_mean_posterior = idata.posterior[y_mean_name] + assert np.allclose(y_mean_posterior.sum(y_dim).to_numpy(), 1) + + def assert_mean_range(self, model, idata): + y_mean_name = model.response_component.response_term.name + "_mean" + y_mean_posterior = idata.posterior[y_mean_name].to_numpy() + assert ((0 < y_mean_posterior) & (y_mean_posterior < 1)).all() + + def assert_posterior_predictive_range(self, model, idata, n): + y_name = model.response_component.response_term.name + y_posterior_predictive = idata.posterior_predictive[y_name].to_numpy() + assert set(np.unique(y_posterior_predictive)).issubset(set(range(n))) + + def test_basic(self, inhaler_data): + model = bmb.Model("rating ~ period + carry + treat", inhaler_data, family="categorical") + idata = self.fit(model) + + for name in ["Intercept", "period", "carry", "treat"]: + assert list(idata.posterior[name].coords) == ["chain", "draw", "rating_reduced_dim"] + + assert list(idata.posterior.coords["rating_reduced_dim"].values) == ["2", "3", "4"] + + idata = self.predict_oos(model, idata) + assert list(idata.posterior["rating_mean"].coords) == [ + "chain", + "draw", + "rating_obs", + "rating_dim", + ] + assert list(idata.posterior.coords["rating_dim"].values) == ["1", "2", "3", "4"] + self.assert_mean_range(model, idata) + self.assert_mean_sum(model, idata) + self.assert_posterior_predictive_range(model, idata, len(np.unique(inhaler_data["rating"]))) + + def test_varying_intercept(self, inhaler_data): + formula = "rating ~ period + carry + treat + (1|subject)" + model = bmb.Model(formula, inhaler_data, family="categorical") + idata = self.fit(model) + + for name in ["Intercept", "period", "carry", "treat"]: + assert set(idata.posterior[name].coords) == {"chain", "draw", "rating_reduced_dim"} + + assert set(idata.posterior["1|subject"].coords) == { + "chain", + "draw", + "rating_reduced_dim", + "subject__factor_dim", + } + + assert ( + idata.posterior["subject__factor_dim"].values + == np.unique(inhaler_data["subject"]).astype(str) + ).all() + + assert list(idata.posterior.coords["rating_reduced_dim"].values) == ["2", "3", "4"] + + idata = self.predict_oos(model, idata) + assert set(idata.posterior["rating_mean"].coords) == { + "chain", + "draw", + "rating_obs", + "rating_dim", + } + assert list(idata.posterior.coords["rating_dim"].values) == ["1", "2", "3", "4"] + self.assert_mean_range(model, idata) + self.assert_mean_sum(model, idata) + self.assert_posterior_predictive_range(model, idata, len(np.unique(inhaler_data["rating"]))) + + def test_categorical_predictors(self, cat_response_cat_preds_data): + formula = "response ~ group + city" + model = bmb.Model(formula, cat_response_cat_preds_data, family="categorical") + idata = self.fit(model) + + assert set(idata.posterior["group"].coords) == { + "chain", + "draw", + "response_reduced_dim", + "group_dim", + } + assert set(idata.posterior["city"].coords) == { + "chain", + "draw", + "response_reduced_dim", + "city_dim", + } + assert list(idata.posterior["group_dim"].values) == ["group 2", "group 3"] + assert list(idata.posterior["city_dim"].values) == ["Rosario", "San Luis"] + assert list(idata.posterior["response_reduced_dim"].values) == ["B", "C", "D"] + + idata = self.predict_oos(model, idata) + assert list(idata.posterior["response_dim"].values) == ["A", "B", "C", "D"] + self.assert_mean_range(model, idata) + self.assert_mean_sum(model, idata) + self.assert_posterior_predictive_range(model, idata, 4) + + +class TestZeroInflatedFamilies(FitPredictParent): + @pytest.mark.parametrize( + "formula, data_name, family, priors", + [ # Zero Inflated Poisson + (bmb.Formula("y ~ x"), "zi_count_data", "zero_inflated_poisson", None), + (bmb.Formula("y ~ x", "psi ~ x"), "zi_count_data", "zero_inflated_poisson", None), + # Zero Inflated Negative Binomial + ( + bmb.Formula("y ~ x"), + "zi_count_data", + "zero_inflated_negativebinomial", + {"alpha": bmb.Prior("HalfNormal", sigma=20)}, + ), + ( + bmb.Formula("y ~ x", "psi ~ x"), + "zi_count_data", + "zero_inflated_negativebinomial", + {"alpha": bmb.Prior("HalfNormal", sigma=20)}, + ), + # Zero Inflated Binomial + (bmb.Formula("p(y, 30) ~ 1"), "zi_bounded_count_data", "zero_inflated_binomial", None), + ( + bmb.Formula("p(y, 30) ~ 1", "psi ~ x"), + "zi_bounded_count_data", + "zero_inflated_binomial", + None, + ), + ], + ) + def test_family(self, formula, data_name, family, priors, request): + data = request.getfixturevalue(data_name) + model = bmb.Model(formula, data, priors=priors, family=family) + idata = self.fit(model) + self.predict_oos(model, idata) + + +class TestHurdle(FitPredictParent): + @pytest.mark.parametrize( + "data_name, family", + [ + ("zi_count_data", "hurdle_poisson"), + ("zi_count_data", "hurdle_negativebinomial"), + ("zi_continuous_data", "hurdle_gamma"), + ("zi_continuous_data", "hurdle_lognormal"), + ], + ) + def test_hurlde_families(self, data_name, family, request): + # To access 'data' which is a fixture + data = request.getfixturevalue(data_name) + model = bmb.Model("y ~ 1", data, family=family) + idata = self.fit(model, random_seed=1234) + self.predict_oos(model, idata) + + +class TestOrdinal(FitPredictParent): + @pytest.mark.parametrize( + "family, link", + [ + ("cumulative", "logit"), + ("cumulative", "probit"), + ("cumulative", "cloglog"), + ("sratio", "logit"), + ("sratio", "probit"), + ("sratio", "cloglog"), + ], + ) + def test_ordinal_families(self, inhaler_data, family, link): + # To have both numeric and categoric predictors + inhaler_data["carry"] = pd.Categorical(inhaler_data["carry"]) + model = bmb.Model("rating ~ period + carry + treat", inhaler_data, family=family, link=link) + idata = self.fit(model, random_seed=1234) + idata = self.predict_oos(model, idata) + + assert np.allclose(idata.posterior["rating_mean"].sum("rating_dim").to_numpy(), 1) + assert set(np.unique(idata.posterior_predictive["rating"])).issubset({0, 1, 2, 3}) + + def test_cumulative_family_priors(self, inhaler_data): + priors = { + "threshold": bmb.Prior( + "Normal", + mu=[-0.5, 0, 0.5], + sigma=1.5, + transform=pm.distributions.transforms.ordered, + ) + } + model = bmb.Model( + "rating ~ 0 + period + carry + treat", inhaler_data, family="cumulative", priors=priors + ) + idata = self.fit(model, random_seed=1234) + self.predict_oos(model, idata) + + +class TestCensoredResponses(FitPredictParent): + def test_model_with_intercept(self, kidney_data): + priors = { + "Intercept": bmb.Prior("Normal", mu=0, sigma=1), + "sex": bmb.Prior("Normal", mu=0, sigma=2), + "age": bmb.Prior("Normal", mu=0, sigma=1), + "alpha": bmb.Prior("Gamma", alpha=3, beta=5), + } + model = bmb.Model( + "censored(time, status) ~ 1 + sex + age", + kidney_data, + family="weibull", + link="log", + priors=priors, + ) + idata = self.fit(model, random_seed=121195) + self.predict_oos(model, idata) + # Assert response is censored + assert isinstance(model.backend.model.observed_RVs[0]._owner.op, pm.Censored.rv_type) + + def test_model_without_intercept(self, kidney_data): + priors = { + "sex": bmb.Prior("Normal", mu=0, sigma=2), + "age": bmb.Prior("Normal", mu=0, sigma=1), + "alpha": bmb.Prior("Gamma", alpha=3, beta=5), + } + model = bmb.Model( + "censored(time, status) ~ 0 + sex + age", + kidney_data, + family="weibull", + link="log", + priors=priors, + ) + idata = self.fit(model, random_seed=121195) + self.predict_oos(model, idata) + # Assert response is censored + assert isinstance(model.backend.model.observed_RVs[0]._owner.op, pm.Censored.rv_type) + + def test_model_with_group_specific_effects(self, kidney_data): + # Model 3, with group-specific effects + priors = { + "alpha": bmb.Prior("Gamma", alpha=3, beta=5), + "sex": bmb.Prior("Normal", mu=0, sigma=1), + "age": bmb.Prior("Normal", mu=0, sigma=1), + "1|patient": bmb.Prior( + "Normal", mu=0, sigma=bmb.Prior("InverseGamma", alpha=5, beta=10) + ), + } + model = bmb.Model( + "censored(time, status) ~ 1 + sex + age + (1|patient)", + kidney_data, + family="weibull", + link="log", + priors=priors, + ) + idata = self.fit(model, random_seed=121195) + self.predict_oos(model, idata) + # Assert response is censored + assert isinstance(model.backend.model.observed_RVs[0]._owner.op, pm.Censored.rv_type) + + +class TestTruncatedResponse(FitPredictParent): + def test_truncated_response(self, truncated_data): + priors = { + "Intercept": bmb.Prior("Normal", mu=0, sigma=1), + "x": bmb.Prior("Normal", mu=0, sigma=1), + "sigma": bmb.Prior("HalfNormal", sigma=1), + } + model = bmb.Model("truncated(y, -5, 5) ~ x", truncated_data, priors=priors) + idata = self.fit(model, random_seed=121195) + self.predict_oos(model, idata) + # PyMC seems to automatically dispatch to TruncatedNormal + assert isinstance(model.backend.model.observed_RVs[0]._owner.op, pm.TruncatedNormal.rv_type) + + +class TestMultinomial(FitPredictParent): + def assert_posterior_predictive(self, model, idata): + y_name = model.response_component.response_term.name + y_posterior_predictive = idata.posterior_predictive[y_name].to_numpy() + assert (y_posterior_predictive.sum(-1).var((0, 1)) == 0).all() + + def test_intercept_only(self, multinomial_data): + model = bmb.Model("c(y1, y2, y3, y4) ~ 1", multinomial_data, family="multinomial") + idata = self.fit(model, random_seed=121195) + idata = self.predict_oos(model, idata, data=model.data) + self.assert_posterior_predictive(model, idata) + + def test_numerical_predictors(self, multinomial_data): + model = bmb.Model( + "c(y1, y2, y3, y4) ~ treat + carry", multinomial_data, family="multinomial" + ) + idata = self.fit(model, random_seed=121195) + idata = self.predict_oos(model, idata, data=model.data) + self.assert_posterior_predictive(model, idata) + + def test_categorical_predictors(self, multinomial_data): + multinomial_data["treat"] = multinomial_data["treat"].replace({-0.5: "A", 0.5: "B"}) + multinomial_data["carry"] = multinomial_data["carry"].replace({-1: "a", 0: "b", 1: "c"}) + + model = bmb.Model( + "c(y1, y2, y3, y4) ~ treat + carry", multinomial_data, family="multinomial" + ) + idata = self.fit(model, random_seed=121195) + idata = self.predict_oos(model, idata, data=model.data) + self.assert_posterior_predictive(model, idata) + + def test_group_specific_effects(self): + data = pd.DataFrame( + { + "state": ["A", "B", "C"], + "y1": [35298, 1885, 5775], + "y2": [167328, 20731, 21564], + "y3": [212682, 37716, 20222], + "y4": [37966, 5196, 3277], + } + ) + + model = bmb.Model( + "c(y1, y2, y3, y4) ~ 1 + (1 | state)", data, family="multinomial", noncentered=False + ) + idata = self.fit(model, random_seed=121195) + idata = self.predict_oos(model, idata, data=model.data) + self.assert_posterior_predictive(model, idata) + + +class TestDirichletMultinomial(FitPredictParent): + def assert_posterior_predictive(self, model, idata): + y_name = model.response_component.response_term.name + y_posterior_predictive = idata.posterior_predictive[y_name].to_numpy() + assert (y_posterior_predictive.sum(-1).var((0, 1)) == 0).all() + + def test_intercept_only(self, multinomial_data): + model = bmb.Model("c(y1, y2, y3, y4) ~ 1", multinomial_data, family="dirichlet_multinomial") + idata = self.fit(model) + idata = self.predict_oos(model, idata, model.data) + self.assert_posterior_predictive(model, idata) + + def test_predictor(self, multinomial_data): + model = bmb.Model( + "c(y1, y2, y3, y4) ~ 0 + treat", multinomial_data, family="dirichlet_multinomial" + ) + idata = self.fit(model) + idata = self.predict_oos(model, idata, model.data) + self.assert_posterior_predictive(model, idata) + + +class TestBetaBinomial(FitPredictParent): + def test_basic(self, beetle_data): + model = bmb.Model("prop(y, n) ~ x", beetle_data, family="beta_binomial") + idata = model.fit(draws=100, tune=100) + idata = self.fit(model) + idata = self.predict_oos(model, idata, model.data) + n = beetle_data["n"].to_numpy() + assert np.all( + idata.posterior_predictive["prop(y, n)"].values <= n[np.newaxis, np.newaxis, :] + ) + + +def test_wald_family(data_n100): + data_n100["y"] = np.exp(data_n100["y1"]) + priors = {"common": bmb.Prior("Normal", mu=0, sigma=1)} + model = bmb.Model("y ~ y2", data_n100, family="wald", link="log", priors=priors) + idata = model.fit(tune=DRAWS, draws=DRAWS, random_seed=1234) + + model.predict(idata, kind="mean") + model.predict(idata, kind="pps") + + assert (0 < idata.posterior["y_mean"]).all() + assert (0 < idata.posterior_predictive["y"]).all() + + model.predict(idata, kind="mean", data=data_n100.iloc[:20, :]) + model.predict(idata, kind="pps", data=data_n100.iloc[:20, :]) + + assert (0 < idata.posterior["y_mean"]).all() + assert (0 < idata.posterior_predictive["y"]).all() + + +def test_predict_include_group_specific(): + rng = np.random.default_rng(1234) + size = 100 + + data = pd.DataFrame( + { + "y": rng.choice([0, 1], size=size), + "x1": rng.choice(list("abcd"), size=size), + } + ) + + model = bmb.Model("y ~ 1 + (1|x1)", data, family="bernoulli") + idata = model.fit(tune=DRAWS, draws=DRAWS, random_seed=1234) + idata_1 = model.predict(idata, data=data, inplace=False, include_group_specific=True) + idata_2 = model.predict(idata, data=data, inplace=False, include_group_specific=False) + + assert not np.isclose( + idata_1.posterior["y_mean"].values, + idata_2.posterior["y_mean"].values, + ).all() + + # Since it's an intercept-only model, predictions are the same for all observations if + # we drop group-specific terms. + assert (idata_2.posterior["y_mean"] == idata_2.posterior["y_mean"][:, :, 0]).all() + + # When we include group-specific terms, these predictions are different + assert not (idata_1.posterior["y_mean"] == idata_1.posterior["y_mean"][:, :, 0]).all() + + +def test_predict_offset(): + # Simple case + data = bmb.load_data("carclaims") + model = bmb.Model("numclaims ~ offset(np.log(exposure))", data, family="poisson", link="log") + idata = model.fit(tune=DRAWS, draws=DRAWS, random_seed=1234) + model.predict(idata) + model.predict(idata, kind="pps") + + # More complex case + rng = np.random.default_rng(121195) + data = pd.DataFrame( + { + "y": rng.poisson(20, size=100), + "x": rng.normal(size=100), + "group": np.tile(np.arange(10), 10), + } + ) + data["time"] = data["y"] - rng.normal(loc=1, size=100) + model = bmb.Model("y ~ offset(np.log(time)) + x + (1 | group)", data, family="poisson") + idata = model.fit(tune=DRAWS, draws=DRAWS, target_accept=0.9, random_seed=1234) + model.predict(idata) + model.predict(idata, kind="pps") + + +def test_predict_new_groups_fail(sleepstudy): + model = bmb.Model("Reaction ~ 1 + Days + (1 + Days | Subject)", sleepstudy) + idata = model.fit(tune=20, draws=20) + + df_new = sleepstudy.head(10).reset_index(drop=True) + df_new["Subject"] = "xxx" + to_match = "There are new groups for the factors ('Subject',) and 'sample_new_groups' is False." + with pytest.raises(ValueError, match=re.escape(to_match)): + model.predict(idata, data=df_new) + + +@pytest.mark.parametrize( + "data,formula,family,df_new", + [ + ( + "sleepstudy", + "Reaction ~ 1 + Days + (1 + Days | Subject)", + "gaussian", + pd.DataFrame({"Days": [1, 2, 3], "Subject": ["x", "y", "z"]}), + ), + ( + "inhaler_data", + "rating ~ 1 + period + treat + (1 + treat|subject)", + "categorical", + pd.DataFrame( + { + "subject": [1, 999], + "rating": [1, 1], + "treat": [0.5, 0.5], + "period": [0.5, 0.5], + "carry": [0, 0], + } + ), + ), + ( + "crossed_data", + "Y ~ 0 + threecats + (0 + threecats | subj)", + "gaussian", + pd.DataFrame({"threecats": ["a", "a"], "subj": ["0", "11"]}), + ), + ], +) +def test_predict_new_groups(data, formula, family, df_new, request): + data = request.getfixturevalue(data) + model = bmb.Model(formula, data, family=family) + idata = model.fit(tune=100, draws=100) + model.predict(idata, data=df_new, sample_new_groups=True) diff --git a/tests/test_predict.py b/tests/test_predict.py deleted file mode 100644 index b3e401b13..000000000 --- a/tests/test_predict.py +++ /dev/null @@ -1,429 +0,0 @@ -from os.path import dirname, join - -import numpy as np -import pandas as pd -import pytest - -import bambi as bmb - - -@pytest.fixture(scope="module") -def data_numeric_xy(): - rng = np.random.default_rng(121195) - x = rng.uniform(size=100) - y = x + rng.normal(scale=0.5, size=100) - data = pd.DataFrame({"y": y, "x": x}) - return data - - -@pytest.fixture(scope="module") -def data_bernoulli(): - # Taken from https://juanitorduz.github.io/glm_pymc3/ - rng = np.random.default_rng(121195) - n = 250 - x1 = rng.normal(loc=0.0, scale=2.0, size=n) - x2 = rng.normal(loc=0.0, scale=2.0, size=n) - intercept = -0.5 - beta_x1 = 1 - beta_x2 = -1 - beta_interaction = 2 - z = intercept + beta_x1 * x1 + beta_x2 * x2 + beta_interaction * x1 * x2 - p = 1 / (1 + np.exp(-z)) - y = rng.binomial(n=1, p=p, size=n) - df = pd.DataFrame(dict(x1=x1, x2=x2, y=y)) - return df - - -@pytest.fixture(scope="module") -def data_beta(): - return pd.read_csv(join(dirname(__file__), "data", "gasoline.csv")) - - -@pytest.fixture(scope="module") -def data_gamma(): - rng = np.random.default_rng(121195) - N = 200 - a, b, shape = 0.5, 1.1, 10 - x = rng.uniform(-1, 1, N) - y = rng.gamma(shape, np.exp(a + b * x) / shape, N) - data = pd.DataFrame({"x": x, "y": y}) - return data - - -@pytest.fixture(scope="module") -def data_count(): - rng = np.random.default_rng(121195) - data = pd.DataFrame({"y": rng.poisson(list(range(10)) * 10), "x": rng.uniform(size=100)}) - return data - - -@pytest.fixture(scope="module") -def inhaler(): - data_dir = join(dirname(__file__), "data") - data = pd.read_csv(join(data_dir, "inhaler.csv")) - data["rating"] = pd.Categorical(data["rating"], categories=[1, 2, 3, 4]) - return data - - -def test_predict_bernoulli(data_bernoulli): - data = data_bernoulli - model = bmb.Model("y ~ x1*x2", data, family="bernoulli") - idata = model.fit(tune=100, draws=100, target_accept=0.9) - - # In sample prediction - model.predict(idata, kind="mean") - model.predict(idata, kind="pps") - - assert (0 < idata.posterior["y_mean"]).all() & (idata.posterior["y_mean"] < 1).all() - assert (idata.posterior_predictive["y"].isin([0, 1])).all() - - # Out of sample prediction - model.predict(idata, kind="mean", data=data.iloc[:20, :]) - model.predict(idata, kind="pps", data=data.iloc[:20, :]) - - assert (0 < idata.posterior["y_mean"]).all() & (idata.posterior["y_mean"] < 1).all() - assert (idata.posterior_predictive["y"].isin([0, 1])).all() - - -def test_predict_beta(data_beta): - data = data_beta - data["batch"] = pd.Categorical(data["batch"], [10, 1, 2, 3, 4, 5, 6, 7, 8, 9], ordered=True) - model = bmb.Model("yield ~ temp + batch", data, family="beta") - idata = model.fit(tune=100, draws=100, target_accept=0.90) - - model.predict(idata, kind="mean") - model.predict(idata, kind="pps") - - assert (0 < idata.posterior["yield_mean"]).all() & (idata.posterior["yield_mean"] < 1).all() - assert (0 < idata.posterior_predictive["yield"]).all() & ( - idata.posterior_predictive["yield"] < 1 - ).all() - - model.predict(idata, kind="mean", data=data.iloc[:20, :]) - model.predict(idata, kind="pps", data=data.iloc[:20, :]) - - assert (0 < idata.posterior["yield_mean"]).all() & (idata.posterior["yield_mean"] < 1).all() - assert (0 < idata.posterior_predictive["yield"]).all() & ( - idata.posterior_predictive["yield"] < 1 - ).all() - - -def test_predict_gamma(data_gamma): - data = data_gamma - - model = bmb.Model("y ~ x", data, family="gamma", link="log") - idata = model.fit(tune=100, draws=100) - - model.predict(idata, kind="mean") - model.predict(idata, kind="pps") - - assert (0 < idata.posterior["y_mean"]).all() - assert (0 < idata.posterior_predictive["y"]).all() - - model.predict(idata, kind="mean", data=data.iloc[:20, :]) - model.predict(idata, kind="pps", data=data.iloc[:20, :]) - - assert (0 < idata.posterior["y_mean"]).all() - assert (0 < idata.posterior_predictive["y"]).all() - - -def test_predict_gaussian(data_numeric_xy): - data = data_numeric_xy - model = bmb.Model("y ~ x", data, family="gaussian") - idata = model.fit(tune=100, draws=100) - - model.predict(idata, kind="mean") - model.predict(idata, kind="pps") - - model.predict(idata, kind="mean", data=data.iloc[:20, :]) - model.predict(idata, kind="pps", data=data.iloc[:20, :]) - - -def test_predict_negativebinomial(data_count): - data = data_count - - model = bmb.Model("y ~ x", data, family="negativebinomial") - idata = model.fit(tune=100, draws=100) - - model.predict(idata, kind="mean") - model.predict(idata, kind="pps") - - assert (0 < idata.posterior["y_mean"]).all() - assert (np.equal(np.mod(idata.posterior_predictive["y"].values, 1), 0)).all() - - model.predict(idata, kind="mean", data=data.iloc[:20, :]) - model.predict(idata, kind="pps", data=data.iloc[:20, :]) - - assert (0 < idata.posterior["y_mean"]).all() - assert (np.equal(np.mod(idata.posterior_predictive["y"].values, 1), 0)).all() - - -def test_predict_poisson(data_count): - data = data_count - - model = bmb.Model("y ~ x", data, family="negativebinomial") - idata = model.fit(tune=100, draws=100) - - model.predict(idata, kind="mean") - model.predict(idata, kind="pps") - - assert (0 < idata.posterior["y_mean"]).all() - assert (np.equal(np.mod(idata.posterior_predictive["y"].values, 1), 0)).all() - - model.predict(idata, kind="mean", data=data.iloc[:20, :]) - model.predict(idata, kind="pps", data=data.iloc[:20, :]) - - assert (0 < idata.posterior["y_mean"]).all() - assert (np.equal(np.mod(idata.posterior_predictive["y"].values, 1), 0)).all() - - -def test_predict_t(data_numeric_xy): - data = data_numeric_xy - model = bmb.Model("y ~ x", data, family="t") - idata = model.fit(tune=100, draws=100) - - model.predict(idata, kind="mean") - model.predict(idata, kind="pps") - - model.predict(idata, kind="mean", data=data.iloc[:20, :]) - model.predict(idata, kind="pps", data=data.iloc[:20, :]) - - # A case where the prior for one of the parameters is constant - model = bmb.Model("y ~ x", data, family="t", priors={"nu": 4}) - idata = model.fit(tune=100, draws=100) - - model.predict(idata, kind="mean") - model.predict(idata, kind="pps") - - model.predict(idata, kind="mean", data=data.iloc[:20, :]) - model.predict(idata, kind="pps", data=data.iloc[:20, :]) - - -def test_predict_wald(data_gamma): - data = data_gamma - - model = bmb.Model("y ~ x", data, family="wald", link="log") - idata = model.fit(tune=100, draws=100) - - model.predict(idata, kind="mean") - model.predict(idata, kind="pps") - - assert (0 < idata.posterior["y_mean"]).all() - assert (0 < idata.posterior_predictive["y"]).all() - - model.predict(idata, kind="mean", data=data.iloc[:20, :]) - model.predict(idata, kind="pps", data=data.iloc[:20, :]) - - assert (0 < idata.posterior["y_mean"]).all() - assert (0 < idata.posterior_predictive["y"]).all() - - -def test_predict_categorical(inhaler): - model = bmb.Model("rating ~ period + carry + treat", inhaler, family="categorical") - idata = model.fit(tune=100, draws=100) - - model.predict(idata) - assert np.allclose(idata.posterior["rating_mean"].values.sum(-1), 1) - - model.predict(idata, data=inhaler.iloc[:20, :]) - assert np.allclose(idata.posterior["rating_mean"].values.sum(-1), 1) - - model = bmb.Model( - "rating ~ period + carry + treat + (1|subject)", inhaler, family="categorical" - ) - idata = model.fit(tune=100, draws=100) - - model.predict(idata) - assert np.allclose(idata.posterior["rating_mean"].values.sum(-1), 1) - - model.predict(idata, data=inhaler.iloc[:20, :]) - assert np.allclose(idata.posterior["rating_mean"].values.sum(-1), 1) - - -def test_posterior_predictive_categorical(inhaler): - model = bmb.Model("rating ~ period", data=inhaler, family="categorical") - idata = model.fit(tune=100, draws=100) - model.predict(idata, kind="pps") - pps = idata.posterior_predictive["rating"].to_numpy() - - assert pps.shape[-1] == inhaler.shape[0] - assert (np.unique(pps) == [0, 1, 2, 3]).all() - - -def test_predict_categorical_group_specific(): - # see https://github.com/bambinos/bambi/issues/447 - rng = np.random.default_rng(1234) - size = 100 - - data = pd.DataFrame( - { - "y": rng.choice([0, 1], size=size), - "x1": rng.choice(list("abcd"), size=size), - "x2": rng.choice(list("XY"), size=size), - "x3": rng.normal(size=size), - } - ) - - model = bmb.Model("y ~ x1 + (0 + x2|x1) + (0 + x3|x1 + x2)", data, family="bernoulli") - - idata = model.fit(tune=100, draws=100, chains=2) - - model.predict(idata, data=data) - - assert idata.posterior.y_mean.values.shape == (2, 100, 100) - assert (idata.posterior.y_mean.values > 0).all() and (idata.posterior.y_mean.values < 1).all() - - -def test_predict_multinomial(inhaler): - df = inhaler.groupby(["treat", "carry", "rating"], as_index=False).size() - df = df.pivot(index=["treat", "carry"], columns="rating", values="size").reset_index() - df.columns = ["treat", "carry", "y1", "y2", "y3", "y4"] - - # Intercept only - model = bmb.Model("c(y1, y2, y3, y4) ~ 1", df, family="multinomial") - idata = model.fit(tune=100, draws=100) - - model.predict(idata) - model.predict(idata, data=df.iloc[:3, :]) - - # Numerical predictors - model = bmb.Model("c(y1, y2, y3, y4) ~ treat + carry", df, family="multinomial") - idata = model.fit(tune=100, draws=100) - - model.predict(idata) - model.predict(idata, data=df.iloc[:3, :]) - - # Categorical predictors - df["treat"] = df["treat"].replace({-0.5: "A", 0.5: "B"}) - df["carry"] = df["carry"].replace({-1: "a", 0: "b", 1: "c"}) - - model = bmb.Model("c(y1, y2, y3, y4) ~ treat + carry", df, family="multinomial") - idata = model.fit(tune=100, draws=100) - - model.predict(idata) - model.predict(idata, data=df.iloc[:3, :]) - - data = pd.DataFrame( - { - "state": ["A", "B", "C"], - "y1": [35298, 1885, 5775], - "y2": [167328, 20731, 21564], - "y3": [212682, 37716, 20222], - "y4": [37966, 5196, 3277], - } - ) - - # Contains group-specific effect - model = bmb.Model( - "c(y1, y2, y3, y4) ~ 1 + (1 | state)", data, family="multinomial", noncentered=False - ) - idata = model.fit(tune=100, draws=100, random_seed=0) - - model.predict(idata) - model.predict(idata, kind="pps") - - -def test_posterior_predictive_multinomial(inhaler): - df = inhaler.groupby(["treat", "carry", "rating"], as_index=False).size() - df = df.pivot(index=["treat", "carry"], columns="rating", values="size").reset_index() - df.columns = ["treat", "carry", "y1", "y2", "y3", "y4"] - - # Intercept only - model = bmb.Model("c(y1, y2, y3, y4) ~ 1", df, family="multinomial") - idata = model.fit(tune=100, draws=100) - - # The sum across the columns of the response is the same for all the chain and draws. - model.predict(idata, kind="pps") - assert np.all(idata.posterior_predictive["c(y1, y2, y3, y4)"].values.sum(-1).var((0, 1)) == 0) - - -def test_predict_include_group_specific(): - rng = np.random.default_rng(1234) - size = 100 - - data = pd.DataFrame( - { - "y": rng.choice([0, 1], size=size), - "x1": rng.choice(list("abcd"), size=size), - } - ) - - model = bmb.Model("y ~ 1 + (1|x1)", data, family="bernoulli") - idata = model.fit(tune=100, draws=100, chains=2, random_seed=1234) - idata_1 = model.predict(idata, data=data, inplace=False, include_group_specific=True) - idata_2 = model.predict(idata, data=data, inplace=False, include_group_specific=False) - - assert not np.isclose( - idata_1.posterior["y_mean"].values, - idata_2.posterior["y_mean"].values, - ).all() - - # Since it's an intercept-only model, predictions are the same for all observations if - # we drop group-specific terms. - assert (idata_2.posterior["y_mean"] == idata_2.posterior["y_mean"][:, :, 0]).all() - - # When we include group-specific terms, these predictions are different - assert not (idata_1.posterior["y_mean"] == idata_1.posterior["y_mean"][:, :, 0]).all() - - -def test_predict_offset(): - # Simple case - - data = bmb.load_data("carclaims") - model = bmb.Model("numclaims ~ offset(np.log(exposure))", data, family="poisson", link="log") - idata = model.fit(tune=100, draws=100, chains=2, random_seed=1234) - model.predict(idata) - model.predict(idata, kind="pps") - - # More complex case - rng = np.random.default_rng(121195) - data = pd.DataFrame( - { - "y": rng.poisson(20, size=100), - "x": rng.normal(size=100), - "group": np.tile(np.arange(10), 10), - } - ) - data["time"] = data["y"] - rng.normal(loc=1, size=100) - model = bmb.Model("y ~ offset(np.log(time)) + x + (1 | group)", data, family="poisson") - idata = model.fit(tune=100, draws=100, chains=2, target_accept=0.9, random_seed=1234) - model.predict(idata, kind="pps") - - -def test_posterior_predictive_dirichlet_multinomial(inhaler): - df = inhaler.groupby(["treat", "rating"], as_index=False).size() - df = df.pivot(index=["treat"], columns="rating", values="size").reset_index() - df.columns = ["treat", "y1", "y2", "y3", "y4"] - - # Intercept only - model = bmb.Model("c(y1, y2, y3, y4) ~ 1", df, family="dirichlet_multinomial") - idata = model.fit(tune=100, draws=100) - - # The sum across the columns of the response is the same for all the chain and draws. - model.predict(idata, kind="pps") - assert np.all(idata.posterior_predictive["c(y1, y2, y3, y4)"].values.sum(-1).var((0, 1)) == 0) - - # With predictor only - model = bmb.Model("c(y1, y2, y3, y4) ~ 0 + treat", df, family="dirichlet_multinomial") - idata = model.fit(tune=100, draws=100) - - # The sum across the columns of the response is the same for all the chain and draws. - model.predict(idata, kind="pps") - assert np.all(idata.posterior_predictive["c(y1, y2, y3, y4)"].values.sum(-1).var((0, 1)) == 0) - - -def test_posterior_predictive_beta_binomial(): - data = pd.DataFrame( - { - "x": np.array([1.6907, 1.7242, 1.7552, 1.7842, 1.8113, 1.8369, 1.8610, 1.8839]), - "n": np.array([59, 60, 62, 56, 63, 59, 62, 60]), - "y": np.array([6, 13, 18, 28, 52, 53, 61, 60]), - } - ) - - model = bmb.Model("prop(y, n) ~ x", data, family="beta_binomial") - idata = model.fit(draws=100, tune=100) - model.predict(idata, kind="pps") - - n = data["n"].to_numpy() - assert np.all(idata.posterior_predictive["prop(y, n)"].values <= n[np.newaxis, np.newaxis, :])