def METHOD_NAME():
  max_pool = tf.keras.layers.MaxPooling2D((2, 2), (2, 2), padding="same")
  # The model consists of a sequential chain of layers, so tf.keras.Sequential
  # (a subclass of tf.keras.Model) makes for a compact description.
  return tf.keras.Sequential([
      tf.keras.layers.Reshape(
          target_shape=[28, 28, 1],
          input_shape=(28, 28,)),
      tf.keras.layers.Conv2D(2, 5, padding="same", activation=tf.nn.relu),
      max_pool,
      tf.keras.layers.Conv2D(4, 5, padding="same", activation=tf.nn.relu),
      max_pool,
      tf.keras.layers.Flatten(),
      tf.keras.layers.Dense(32, activation=tf.nn.relu),
      tf.keras.layers.Dropout(0.4),
      tf.keras.layers.Dense(10)])