reverted changes

Author: philschmid

src/sagemaker/huggingface/estimator.py

@@ -11,6 +11,7 @@
 # ANY KIND, either express or implied. See the License for the specific
 # language governing permissions and limitations under the License.
 """Placeholder docstring"""
+# commend to retrigger pipeeline
 from __future__ import absolute_import
 
 import logging
@@ -153,14 +154,10 @@ def __init__(
         self._validate_args(image_uri=image_uri)
 
         if distribution is not None:
-            instance_type = renamed_kwargs(
-                "train_instance_type", "instance_type", kwargs.get("instance_type"), kwargs
-            )
+            instance_type = renamed_kwargs("train_instance_type", "instance_type", kwargs.get("instance_type"), kwargs)
 
             base_framework_name = "tensorflow" if tensorflow_version is not None else "pytorch"
-            base_framework_version = (
-                tensorflow_version if tensorflow_version is not None else pytorch_version
-            )
+            base_framework_version = tensorflow_version if tensorflow_version is not None else pytorch_version
 
             validate_smdistributed(
                 instance_type=instance_type,
@@ -171,18 +168,14 @@ def __init__(
                 image_uri=image_uri,
             )
 
-            warn_if_parameter_server_with_multi_gpu(
-                training_instance_type=instance_type, distribution=distribution
-            )
+            warn_if_parameter_server_with_multi_gpu(training_instance_type=instance_type, distribution=distribution)
 
         if "enable_sagemaker_metrics" not in kwargs:
             kwargs["enable_sagemaker_metrics"] = True
 
         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)
         self.distribution = distribution or {}
 
     def _validate_args(self, image_uri):
@@ -220,9 +213,7 @@ def _validate_args(self, image_uri):
     def hyperparameters(self):
         """Return hyperparameters used by your custom PyTorch code during model training."""
         hyperparameters = super(HuggingFace, self).hyperparameters()
-        additional_hyperparameters = self._distribution_configuration(
-            distribution=self.distribution
-        )
+        additional_hyperparameters = self._distribution_configuration(distribution=self.distribution)
         hyperparameters.update(Framework._json_encode_hyperparameters(additional_hyperparameters))
         return hyperparameters
 
@@ -332,9 +323,7 @@ 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