code: def _decode_input_tensor_to_features_dict(feature_map, hparams):
    
    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
docs: 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.