code: def main():
      experiment.create(name="MNIST Keras")
  
      (x_train, y_train), (
          x_test,
          y_test,
      ) = tf.keras.datasets.mnist.load_data()
      x_train, x_test = x_train / 255.0, x_test / 255.0
  
      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"]
      )
  
      with experiment.start():
          model.fit(
              x_train,
              y_train,
              epochs=5,
              validation_data=(x_test, y_test),
              callbacks=[LabMLKerasCallback()],
              verbose=None,
          )
  
label_desc: Use image node_id 1 for calling a global function with example usage: main() without return types