# 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

# 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

Format code using black and remove comments
Format code using black and remove comments