diff --git a/benchmarks/glm.py b/benchmarks/glm.py
index b3b2302e24..09defe5222 100644
--- a/benchmarks/glm.py
+++ b/benchmarks/glm.py
@@ -113,20 +113,14 @@ def get_preprocessor() -> ColumnTransformer:
 
 def get_train_test_data(data: pandas.DataFrame) -> Tuple[pandas.DataFrame, pandas.DataFrame]:
     """Split the data into a train and test set."""
-    (
-        train_data,
-        test_data,
-    ) = train_test_split(
+    (train_data, test_data,) = train_test_split(
         data,
         test_size=0.2,
         random_state=0,
     )
 
     # The test set is reduced for faster FHE runs.
-    (
-        _,
-        test_data,
-    ) = train_test_split(
+    (_, test_data,) = train_test_split(
         test_data,
         test_size=500,
         random_state=0,
diff --git a/src/concrete/ml/pytest/torch_models.py b/src/concrete/ml/pytest/torch_models.py
index cb87f4bf6b..d2b41e4d9f 100644
--- a/src/concrete/ml/pytest/torch_models.py
+++ b/src/concrete/ml/pytest/torch_models.py
@@ -867,7 +867,9 @@ def __init__(self, input_output, activation_function, n_bits=2, disable_bit_chec
         n_bits_weights = n_bits
 
         # Generate the pattern 0, 1, ..., 2^N-1, 0, 1, .. 2^N-1, 0, 1..
-        all_weights = numpy.mod(numpy.arange(numpy.prod(self.fc1.weight.shape)), 2**n_bits_weights)
+        all_weights = numpy.mod(
+            numpy.arange(numpy.prod(self.fc1.weight.shape)), 2**n_bits_weights
+        )
 
         # Shuffle the pattern and reshape to weight shape
         numpy.random.shuffle(all_weights)
diff --git a/src/concrete/ml/quantization/base_quantized_op.py b/src/concrete/ml/quantization/base_quantized_op.py
index 903fc4767f..31c3df90b7 100644
--- a/src/concrete/ml/quantization/base_quantized_op.py
+++ b/src/concrete/ml/quantization/base_quantized_op.py
@@ -237,14 +237,14 @@ def dump_dict(self) -> Dict:
         metadata["_input_idx_to_params_name"] = self._input_idx_to_params_name
         metadata["_params_that_are_onnx_inputs"] = self._params_that_are_onnx_inputs
         metadata["_params_that_are_onnx_var_inputs"] = self._params_that_are_onnx_var_inputs
-        metadata["_params_that_are_required_onnx_inputs"] = (
-            self._params_that_are_required_onnx_inputs
-        )
+        metadata[
+            "_params_that_are_required_onnx_inputs"
+        ] = self._params_that_are_required_onnx_inputs
         metadata["_has_attr"] = self._has_attr
         metadata["_inputs_not_quantized"] = self._inputs_not_quantized
-        metadata["quantize_inputs_with_model_outputs_precision"] = (
-            self.quantize_inputs_with_model_outputs_precision
-        )
+        metadata[
+            "quantize_inputs_with_model_outputs_precision"
+        ] = self.quantize_inputs_with_model_outputs_precision
         metadata["produces_graph_output"] = self.produces_graph_output
         metadata["produces_raw_output"] = self.produces_raw_output
         metadata["error_tracker"] = self.error_tracker