old_contents: # Copyright 2020 Google LLC. All Rights Reserved.
  #
  # 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 os
  
  import tensorflow as tf
  
  # Create the model
  def create_keras_model():
          
      model = tf.keras.Sequential(
          [
              tf.keras.layers.Conv2D(32, 3, activation="relu", input_shape=(28, 28, 1)),
              tf.keras.layers.MaxPooling2D(),
              tf.keras.layers.Flatten(),
              tf.keras.layers.Dense(64, activation="relu"),
              tf.keras.layers.Dense(10, activation="softmax"),
          ]
      )
  
      model.compile(
          loss="sparse_categorical_crossentropy",
          optimizer=tf.keras.optimizers.Adam(),
          metrics=["accuracy"],
      )
  
      return model
  
new_contents: # Copyright 2020 Google LLC. All Rights Reserved.
  #
  # 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 os
  
  import tensorflow as tf
  
  def create_keras_model():
          
      model = tf.keras.Sequential(
          [
              tf.keras.layers.Conv2D(32, 3, activation="relu", input_shape=(28, 28, 1)),
              tf.keras.layers.MaxPooling2D(),
              tf.keras.layers.Flatten(),
              tf.keras.layers.Dense(64, activation="relu"),
              tf.keras.layers.Dense(10, activation="softmax"),
          ]
      )
  
      model.compile(
          loss="sparse_categorical_crossentropy",
          optimizer=tf.keras.optimizers.Adam(),
          metrics=["sparse_categorical_accuracy"],
      )
  
      return model
  
subject: Format code using black and remove comments