def _decode_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

  x = inputs
  p_hparams = hparams.problem_hparams
  # Add a third empty dimension
  x = tf.expand_dims(x, axis=[2])
  x = tf.to_int32(x)
  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 tf.shape(x)[1] + 50)
  features["inputs"] = x
  return features