Returns an input function that returns a dataset with zeroes.

  This is useful in debugging input pipeline performance, as it removes all
  elements of file reading and image preprocessing.

  Args:
    height: Integer height that will be used to create a fake image tensor.
    width: Integer width that will be used to create a fake image tensor.
    num_channels: Integer depth that will be used to create a fake image tensor.
    num_classes: Number of classes that should be represented in the fake labels
      tensor

  Returns:
    An input_fn that can be used in place of a real one to return a dataset
    that can be used for iteration.

def get_synth_input_fn(height, width, num_channels, num_classes):
  """Returns an input function that returns a dataset with zeroes.

  This is useful in debugging input pipeline performance, as it removes all
  elements of file reading and image preprocessing.

  Args:
    height: Integer height that will be used to create a fake image tensor.
    width: Integer width that will be used to create a fake image tensor.
    num_channels: Integer depth that will be used to create a fake image tensor.
    num_classes: Number of classes that should be represented in the fake labels
      tensor

  Returns:
    An input_fn that can be used in place of a real one to return a dataset
    that can be used for iteration.
  """
  def input_fn(is_training, data_dir, batch_size, *args, **kwargs):  # pylint: disable=unused-argument
    images = tf.zeros((batch_size, height, width, num_channels), tf.float32)
    labels = tf.zeros((batch_size, num_classes), tf.int32)
    return tf.data.Dataset.from_tensors((images, labels)).repeat()

  return input_fn