diff --git a/smash/core/simulation/_standardize.py b/smash/core/simulation/_standardize.py
index 2562c049..83c57bf1 100644
--- a/smash/core/simulation/_standardize.py
+++ b/smash/core/simulation/_standardize.py
@@ -206,7 +206,7 @@ def _standardize_simulation_optimize_options_bounds(
                         f"Choices: {parameters}"
                     )
         else:
-            TypeError("bounds optimize_options must be a dictionary")
+            raise TypeError("bounds optimize_options must be a dictionary")
 
     parameters_bounds = dict(
         **model.get_rr_parameters_bounds(),
@@ -269,7 +269,7 @@ def _standardize_simulation_optimize_options_control_tfm(
                     f"Choices: {F90_OPTIMIZER_CONTROL_TFM[optimizer]}"
                 )
         else:
-            TypeError("control_tfm optimize_options must be a str")
+            raise TypeError("control_tfm optimize_options must be a str")
 
     return control_tfm
 
diff --git a/smash/io/handler/_hdf5_handler.py b/smash/io/handler/_hdf5_handler.py
index ac15a723..c7614166 100644
--- a/smash/io/handler/_hdf5_handler.py
+++ b/smash/io/handler/_hdf5_handler.py
@@ -148,8 +148,7 @@ def _load_hdf5_to_dict(h5: h5py.File | h5py.Group) -> dict[str, Any]:
         elif isinstance(value, h5py.Dataset):
             dct[key] = _load_hdf5_dataset_to_npndarray(value)
 
-    for key, value in h5.attrs.items():
-        dct[key] = value
+    dct.update(dict(h5.attrs.items()))
 
     return dct
 
diff --git a/smash/tests/core/simulation/test_bayesian_optimize.py b/smash/tests/core/simulation/test_bayesian_optimize.py
index 9580b0df..92089ad9 100644
--- a/smash/tests/core/simulation/test_bayesian_optimize.py
+++ b/smash/tests/core/simulation/test_bayesian_optimize.py
@@ -72,8 +72,8 @@ def generic_custom_bayesian_optimize(model: smash.Model, **kwargs) -> dict:
         },
     ]
 
-    for i, kwargs in enumerate(custom_sets):
-        instance = smash.bayesian_optimize(model, **kwargs)
+    for i, inner_kwargs in enumerate(custom_sets):
+        instance = smash.bayesian_optimize(model, **inner_kwargs)
 
         qsim = instance.response.q[:].flatten()
         qsim = qsim[::10]  # extract values at every 10th position
diff --git a/smash/tests/core/simulation/test_optimize.py b/smash/tests/core/simulation/test_optimize.py
index df876711..343ba910 100644
--- a/smash/tests/core/simulation/test_optimize.py
+++ b/smash/tests/core/simulation/test_optimize.py
@@ -220,8 +220,8 @@ def generic_custom_optimize(model: smash.Model, **kwargs) -> dict:
         },
     ]
 
-    for i, kwargs in enumerate(custom_sets):
-        instance = smash.optimize(model, **kwargs)
+    for i, inner_kwargs in enumerate(custom_sets):
+        instance = smash.optimize(model, **inner_kwargs)
 
         qsim = instance.response.q[:].flatten()
         qsim = qsim[::10]  # extract values at every 10th position