Fixed error on Python 2

          feed_dict[initial_state] = zero_state

        yield feed_dict

  def __call__(self, *inputs, outputs=None):

  def __call__(self, *inputs, **kwargs):

    """Execute the model in eager mode to compute outputs as a function of inputs.

    This is very similar to predict_on_batch(), except that it returns the outputs

      may be tensors, numpy arrays, or anything else that can be converted to

      tensors of the correct shape.

    outputs: list of Layers

      the output layers to compute.  If this is None, self.outputs is used

      the output layers to compute.  If this is omitted, self.outputs is used

      (that is, all outputs that have been added by calling add_output()).

    Returns

    if len(inputs) != len(self.features):

      raise ValueError('Expected %d inputs, received %d' % len(self.features),

                       len(inputs))

    if outputs is None:

    # TODO Once we drop Python 2 support, turn outputs into a proper keyword arg

    # instead of using the **kwargs hack.

    if 'outputs' in kwargs:

      outputs = kwargs['outputs']

    else:

      outputs = self.outputs

    feed_dict = dict(zip(self.features, inputs))

    results = self._run_graph(outputs, feed_dict, False)