Builds the default start state for a DeepRNN.

    Args:
      batch_size: An int, float or scalar Tensor representing the batch size.
      dtype: The data type to use for the state.
      trainable: Boolean that indicates whether to learn the initial state.
      trainable_initializers: An initializer function or nested structure of
          functions with same structure as the `state_size` property of the
          core, to be used as initializers of the initial state variable.
      trainable_regularizers: Optional regularizer function or nested structure
        of functions with the same structure as the `state_size` property of the
        core, to be used as regularizers of the initial state variable. A
        regularizer should be a function that takes a single `Tensor` as an
        input and returns a scalar `Tensor` output, e.g. the L1 and L2
        regularizers in `tf.contrib.layers`.
      name: Optional string used to prefix the initial state variable names, in
          the case of a trainable initial state. If not provided, defaults to
          the name of the module.

    Returns:
      A tensor or nested tuple of tensors with same structure and shape as the
      `state_size` property of the core.

    Raises:
      ValueError: if the number of passed initializers is not the same as the
          number of recurrent cores.

def initial_state(self, batch_size, dtype=tf.float32, trainable=False,
                    trainable_initializers=None, trainable_regularizers=None,
                    name=None):
    """Builds the default start state for a DeepRNN.

    Args:
      batch_size: An int, float or scalar Tensor representing the batch size.
      dtype: The data type to use for the state.
      trainable: Boolean that indicates whether to learn the initial state.
      trainable_initializers: An initializer function or nested structure of
          functions with same structure as the `state_size` property of the
          core, to be used as initializers of the initial state variable.
      trainable_regularizers: Optional regularizer function or nested structure
        of functions with the same structure as the `state_size` property of the
        core, to be used as regularizers of the initial state variable. A
        regularizer should be a function that takes a single `Tensor` as an
        input and returns a scalar `Tensor` output, e.g. the L1 and L2
        regularizers in `tf.contrib.layers`.
      name: Optional string used to prefix the initial state variable names, in
          the case of a trainable initial state. If not provided, defaults to
          the name of the module.

    Returns:
      A tensor or nested tuple of tensors with same structure and shape as the
      `state_size` property of the core.

    Raises:
      ValueError: if the number of passed initializers is not the same as the
          number of recurrent cores.
    """
    initial_state = []
    if trainable_initializers is None:
      trainable_initializers = [None] * self._num_recurrent
    if trainable_regularizers is None:
      trainable_regularizers = [None] * self._num_recurrent

    num_initializers = len(trainable_initializers)

    if num_initializers != self._num_recurrent:
      raise ValueError("The number of initializers and recurrent cores should "
                       "be the same. Received %d initializers for %d specified "
                       "recurrent cores."
                       % (num_initializers, self._num_recurrent))

    with tf.name_scope(self._initial_state_scope(name)):
      recurrent_idx = 0
      for is_recurrent, core in zip(self._is_recurrent_list, self._cores):
        if is_recurrent:
          core_initial_state = core.initial_state(
              batch_size, dtype=dtype, trainable=trainable,
              trainable_initializers=trainable_initializers[recurrent_idx],
              trainable_regularizers=trainable_regularizers[recurrent_idx])
          initial_state.append(core_initial_state)
          recurrent_idx += 1
    return tuple(initial_state)