make tox happy

Author: philschmid

src/sagemaker/huggingface/estimator.py

@@ -218,13 +218,17 @@ def __init__(
 
         kwargs["py_version"] = self.py_version
 
-        super(HuggingFace, self).__init__(entry_point, source_dir, hyperparameters, image_uri=image_uri, **kwargs)
+        super(HuggingFace, self).__init__(
+            entry_point, source_dir, hyperparameters, image_uri=image_uri, **kwargs
+        )
 
         if "entry_point" not in kwargs:
             kwargs["entry_point"] = entry_point
 
         self.base_framework_name = "tensorflow" if tensorflow_version is not None else "pytorch"
-        self.base_framework_version = tensorflow_version if tensorflow_version is not None else pytorch_version
+        self.base_framework_version = (
+            tensorflow_version if tensorflow_version is not None else pytorch_version
+        )
 
         if distribution is not None:
             distribution = validate_distribution(
@@ -323,12 +327,18 @@ def _huggingface_distribution_configuration(self, distribution):
     def hyperparameters(self):
         """Return hyperparameters used by your custom PyTorch code during model training."""
         hyperparameters = super(HuggingFace, self).hyperparameters()
-        additional_hyperparameters = self._huggingface_distribution_configuration(distribution=self.distribution)
-        hyperparameters.update(EstimatorBase._json_encode_hyperparameters(additional_hyperparameters))
+        additional_hyperparameters = self._huggingface_distribution_configuration(
+            distribution=self.distribution
+        )
+        hyperparameters.update(
+            EstimatorBase._json_encode_hyperparameters(additional_hyperparameters)
+        )
 
         if self.compiler_config:
             training_compiler_hyperparameters = self.compiler_config._to_hyperparameter_dict()
-            hyperparameters.update(EstimatorBase._json_encode_hyperparameters(training_compiler_hyperparameters))
+            hyperparameters.update(
+                EstimatorBase._json_encode_hyperparameters(training_compiler_hyperparameters)
+            )
 
         return hyperparameters
 
@@ -438,7 +448,9 @@ def _prepare_init_params_from_job_description(cls, job_details, model_channel_na
 
         if framework != cls._framework_name:
             raise ValueError(
-                "Training job: {} didn't use image for requested framework".format(job_details["TrainingJobName"])
+                "Training job: {} didn't use image for requested framework".format(
+                    job_details["TrainingJobName"]
+                )
             )
 
         return init_params