old_contents: # coding=utf-8
  # Copyright (c) 2019 Uber Technologies, Inc.
  #
  # Licensed under the Apache License, Version 2.0 (the "License");
  # you may not use this file except in compliance with the License.
  # You may obtain a copy of the License at
  #
  #     http://www.apache.org/licenses/LICENSE-2.0
  #
  # Unless required by applicable law or agreed to in writing, software
  # distributed under the License is distributed on an "AS IS" BASIS,
  # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
  # See the License for the specific language governing permissions and
  # limitations under the License.
  # ==============================================================================
  
  import logging
  import os
  
  
  logger = logging.getLogger(__name__)
  
  
  class Wandb():
      "Class that defines the methods necessary to hook into process."
  
      @staticmethod
      def import_call(argv, *args, **kwargs):
          """
          Enable Third-party support from wandb.ai
          Allows experiment tracking, visualization, and
          management.
          """
          try:
              import wandb
              # Needed to call an attribute of wandb to make DeepSource not complain
              return Wandb() if wandb.__version__ else None
          except ImportError:
              logger.error(
                  "Ignored --wandb: Please install wandb; see https://docs.wandb.com")
              return None
  
      def train_model(self, model, *args, **kwargs):
          import wandb
          logger.info("wandb.train_model() called...")
          config = model.hyperparameters.copy()
          del config["input_features"]
          del config["output_features"]
          wandb.config.update(config)
  
      def train_init(self, experiment_directory, experiment_name, model_name,
                     resume, output_directory):
          import wandb
          logger.info("wandb.train_init() called...")
          wandb.init(project=os.getenv("WANDB_PROJECT", experiment_name),
                     sync_tensorboard=True, dir=output_directory)
          wandb.save(os.path.join(experiment_directory, "*"))
  
      def visualize_figure(self, fig):
          import wandb
          logger.info("wandb.visualize_figure() called...")
          if wandb.run:
              wandb.log({"figure": fig})
  
      def predict_end(self, stats, *args, **kwargs):
          import wandb
          logger.info("wandb.predict() called... %s", stats)
          if wandb.run:
              wandb.summary.update(dict(stats))
  
new_contents: # coding=utf-8
  # Copyright (c) 2019 Uber Technologies, Inc.
  #
  # Licensed under the Apache License, Version 2.0 (the "License");
  # you may not use this file except in compliance with the License.
  # You may obtain a copy of the License at
  #
  #     http://www.apache.org/licenses/LICENSE-2.0
  #
  # Unless required by applicable law or agreed to in writing, software
  # distributed under the License is distributed on an "AS IS" BASIS,
  # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
  # See the License for the specific language governing permissions and
  # limitations under the License.
  # ==============================================================================
  
  import logging
  import os
  
  
  logger = logging.getLogger(__name__)
  
  
  class Wandb():
      """
      Class that defines the methods necessary to hook into process.
      """
  
      @staticmethod
      def import_call(argv, *args, **kwargs):
          """
          Enable Third-party support from wandb.ai
          Allows experiment tracking, visualization, and
          management.
          """
          try:
              import wandb
              # Needed to call an attribute of wandb to make DeepSource not complain
              return Wandb() if wandb.__version__ else None
          except ImportError:
              logger.error(
                  "Ignored --wandb: Please install wandb; see https://docs.wandb.com")
              return None
  
      def train_model(self, model, *args, **kwargs):
          import wandb
          logger.info("wandb.train_model() called...")
          config = model.hyperparameters.copy()
          del config["input_features"]
          del config["output_features"]
          wandb.config.update(config)
  
      def train_init(self, experiment_directory, experiment_name, model_name,
                     resume, output_directory):
          import wandb
          logger.info("wandb.train_init() called...")
          wandb.init(project=os.getenv("WANDB_PROJECT", experiment_name),
                     sync_tensorboard=True, dir=output_directory)
          wandb.save(os.path.join(experiment_directory, "*"))
  
      def visualize_figure(self, fig):
          import wandb
          logger.info("wandb.visualize_figure() called...")
          if wandb.run:
              wandb.log({"figure": fig})
  
      def predict_end(self, stats, *args, **kwargs):
          import wandb
          logger.info("wandb.predict() called... %s", stats)
          if wandb.run:
              wandb.summary.update(dict(stats))
  
subject: fix formatting