def _build(self, inputs, prev_state):
    """Connects the `StandardDeepLSTM` module into the graph.

    Args:
      inputs: 2D `Tensor` ([batch_size, input_size]).
      prev_state: `DeepRNN` state.

    Returns:
      `Tensor` shaped as `inputs`.
    """
    # Adds preprocessing dimension and preprocess.
    inputs = self._preprocess(tf.expand_dims(inputs, -1))
    # Incorporates preprocessing into data dimension.
    inputs = tf.reshape(inputs, [inputs.get_shape().as_list()[0], -1])
    output, next_state = self._rnn(inputs, prev_state)
    return self._linear(output) * self._scale, next_state
Connects the `StandardDeepLSTM` module into the graph.

    Args:
      inputs: 2D `Tensor` ([batch_size, input_size]).
      prev_state: `DeepRNN` state.

    Returns:
      `Tensor` shaped as `inputs`.
    
networks.py