code: def main(autodist):
      d = autodist
  
      mnist = tf.keras.datasets.mnist
  
      (x_train, y_train), (x_test, y_test) = mnist.load_data()
      x_train, x_test = x_train / 255.0, x_test / 255.0
  
      with tf.Graph().as_default(), d.scope():
          model = tf.keras.models.Sequential(
              [
                  tf.keras.layers.Flatten(input_shape=(28, 28)),
                  tf.keras.layers.Dense(128, activation="relu"),
                  tf.keras.layers.Dropout(0.2),
                  tf.keras.layers.Dense(10),
              ]
          )
          loss_fn = tf.keras.losses.SparseCategoricalCrossentropy(
              from_logits=True
          )
          model.compile(
              optimizer="adam", loss=loss_fn, metrics=["accuracy"]
          )
          model.fit(x_train, y_train, epochs=1)
          print(model.evaluate(x_test, y_test, verbose=2))
  
label_desc: Use image node_id 1 for calling a global function with example usage: main(autodist) without return types