How to use the census.tf-keras.trainer.model.input_fn function in census

num_eval_examples = eval_x.shape[0]

  # Create the Keras Model
  keras_model = model.create_keras_model(
      input_dim=input_dim, learning_rate=args.learning_rate)

  # Pass a numpy array by passing DataFrame.values
  training_dataset = model.input_fn(
      features=train_x.values,
      labels=train_y,
      shuffle=True,
      num_epochs=args.num_epochs,
      batch_size=args.batch_size)

  # Pass a numpy array by passing DataFrame.values
  validation_dataset = model.input_fn(
      features=eval_x.values,
      labels=eval_y,
      shuffle=False,
      num_epochs=args.num_epochs,
      batch_size=num_eval_examples)

  # Setup Learning Rate decay.
  lr_decay_cb = tf.keras.callbacks.LearningRateScheduler(
      lambda epoch: args.learning_rate + 0.02 * (0.5 ** (1 + epoch)),
      verbose=True)

  # Setup TensorBoard callback.
  tensorboard_cb = tf.keras.callbacks.TensorBoard(
      os.path.join(args.job_dir, 'keras_tensorboard'),
      histogram_freq=1)

Args:
    args: dictionary of arguments - see get_args() for details
  """

  train_x, train_y, eval_x, eval_y = util.load_data()

  # dimensions
  num_train_examples, input_dim = train_x.shape
  num_eval_examples = eval_x.shape[0]

  # Create the Keras Model
  keras_model = model.create_keras_model(
      input_dim=input_dim, learning_rate=args.learning_rate)

  # Pass a numpy array by passing DataFrame.values
  training_dataset = model.input_fn(
      features=train_x.values,
      labels=train_y,
      shuffle=True,
      num_epochs=args.num_epochs,
      batch_size=args.batch_size)

  # Pass a numpy array by passing DataFrame.values
  validation_dataset = model.input_fn(
      features=eval_x.values,
      labels=eval_y,
      shuffle=False,
      num_epochs=args.num_epochs,
      batch_size=num_eval_examples)

  # Setup Learning Rate decay.
  lr_decay_cb = tf.keras.callbacks.LearningRateScheduler(