Apply Black formatting

Signed-off-by: Beat Buesser <[email protected]>

Author: Beat Buesser

art/defences/preprocessor/pixel_defend.py

@@ -159,6 +159,7 @@ def _check_params(self) -> None:
 
         from art.estimators.classification.classifier import ClassifierMixin
         from art.estimators.estimator import NeuralNetworkMixin
+
         if hasattr(self, "pixel_cnn") and not (
             isinstance(self.pixel_cnn, ClassifierMixin) and isinstance(self.pixel_cnn, NeuralNetworkMixin)
         ):

art/defences/transformer/poison/neural_cleanse.py

@@ -106,6 +106,7 @@ def __call__(
         :param batch_size: The batch size for optimizations in the Neural Cleanse optimization
         """
         import keras
+
         if isinstance(transformed_classifier, KerasClassifier) and keras.__version__ == "2.2.4":
             transformed_classifier = KerasNeuralCleanse(
                 model=transformed_classifier.model,

art/estimators/speech_recognition/pytorch_deep_speech.py

@@ -333,7 +333,7 @@ def predict(
             )
 
             # Overwrite results
-            result_outputs[begin : end, : results[m].shape[1], : results[m].shape[-1]] = results[m].cpu().numpy()
+            result_outputs[begin:end, : results[m].shape[1], : results[m].shape[-1]] = results[m].cpu().numpy()
 
         # Rearrange to the original order
         result_output_sizes_ = result_output_sizes.copy()
@@ -514,7 +514,7 @@ def transform_model_input(
         y: Optional[np.ndarray] = None,
         compute_gradient: bool = False,
         tensor_input: bool = False,
-        real_lengths: Optional[np.ndarray] = None
+        real_lengths: Optional[np.ndarray] = None,
     ) -> Tuple["torch.Tensor", "torch.Tensor", "torch.Tensor", "torch.Tensor", List]:
         """
         Transform the user input space into the model input space.

tests/defences/test_neural_cleanse.py

@@ -56,7 +56,7 @@ def test_keras(self):
         Test with a KerasClassifier.
         :return:
         """
-        if keras.__version__ != '2.2.4':
+        if keras.__version__ != "2.2.4":
             self.assertRaises(NotImplementedError)
         else:
             # Build KerasClassifier