new_contents: #
  # Copyright 2016 The BigDL Authors.
  #
  # 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.
  #
  #
  # this file is adapted from the comments in https://github.com/apple/tensorflow_macos/issues/25
  
  import tensorflow.compat.v2 as tf
  import tensorflow_datasets as tfds
  from tensorflow.keras import Sequential
  
  tf.enable_v2_behavior()
  
  from tensorflow.python.framework.ops import disable_eager_execution
  disable_eager_execution()
  
  # from tensorflow.python.compiler.mlcompute import mlcompute
  # `mlcompute.set_mlc_device(device_name='gpu')
  
  
  (ds_train, ds_test), ds_info = tfds.load(
      'mnist',
      split=['train', 'test'],
      shuffle_files=True,
      as_supervised=True,
      with_info=True,
  )
  
  def normalize_img(image, label):
    """Normalizes images: `uint8` -> `float32`."""
    return tf.cast(image, tf.float32) / 255., label
  
  batch_size = 128
  
  ds_train = ds_train.map(
      normalize_img, num_parallel_calls=tf.data.experimental.AUTOTUNE)
  ds_train = ds_train.cache()
  ds_train = ds_train.shuffle(ds_info.splits['train'].num_examples)
  ds_train = ds_train.batch(batch_size)
  ds_train = ds_train.prefetch(tf.data.experimental.AUTOTUNE)
  
  
  ds_test = ds_test.map(
      normalize_img, num_parallel_calls=tf.data.experimental.AUTOTUNE)
  ds_test = ds_test.batch(batch_size)
  ds_test = ds_test.cache()
  ds_test = ds_test.prefetch(tf.data.experimental.AUTOTUNE)
  
  
  model = Sequential([
    tf.keras.layers.Conv2D(32, kernel_size=(3, 3),
                   activation='relu'),
    tf.keras.layers.Conv2D(64, kernel_size=(3, 3),
                   activation='relu'),
    tf.keras.layers.MaxPooling2D(pool_size=(2, 2)),
  #   tf.keras.layers.Dropout(0.25),
    tf.keras.layers.Flatten(),
    tf.keras.layers.Dense(128, activation='relu'),
  #   tf.keras.layers.Dropout(0.5),
    tf.keras.layers.Dense(10, activation='softmax')
  ])
  model.compile(
      loss='sparse_categorical_crossentropy',
      optimizer=tf.keras.optimizers.Adam(0.001),
      metrics=['accuracy'],
  )
  
  model.fit(
      ds_train,
      epochs=12,
      validation_data=ds_test,
  )
subject: Add Original Code of Lenet TensorFlow Example (#4645)