Convert the interactive input format (see above) to a dictionary.

  Args:
    feature_map: dict with inputs.
    hparams: model hyperparameters

  Returns:
    a features dictionary, as expected by the decoder.

def _interactive_input_tensor_to_features_dict(feature_map, hparams):
  """Convert the interactive input format (see above) to a dictionary.

  Args:
    feature_map: dict with inputs.
    hparams: model hyperparameters

  Returns:
    a features dictionary, as expected by the decoder.
  """
  inputs = tf.convert_to_tensor(feature_map["inputs"])
  input_is_image = False if len(inputs.get_shape()) < 3 else True

  x = inputs
  if input_is_image:
    x = tf.image.resize_images(x, [299, 299])
    x = tf.reshape(x, [1, 299, 299, -1])
    x = tf.to_int32(x)
  else:
    # Remove the batch dimension.
    num_samples = x[0]
    length = x[2]
    x = tf.slice(x, [3], tf.to_int32([length]))
    x = tf.reshape(x, [1, -1, 1, 1])
    # Transform into a batch of size num_samples to get that many random
    # decodes.
    x = tf.tile(x, tf.to_int32([num_samples, 1, 1, 1]))

  p_hparams = hparams.problem_hparams
  input_space_id = tf.constant(p_hparams.input_space_id)
  target_space_id = tf.constant(p_hparams.target_space_id)

  features = {}
  features["input_space_id"] = input_space_id
  features["target_space_id"] = target_space_id
  features["decode_length"] = (
      IMAGE_DECODE_LENGTH if input_is_image else inputs[1])
  features["inputs"] = x
  return features