def _time_step(time, output_ta_t, state):
    'Take a time step of the dynamic RNN.\n\n    Args:\n      time: int32 scalar Tensor.\n      output_ta_t: List of `TensorArray`s that represent the output.\n      state: nested tuple of vector tensors that represent the state.\n\n    Returns:\n      The tuple (time + 1, output_ta_t with updated flow, new_state).\n    '
    input_t = tuple((ta.read(time) for ta in input_ta))
    for (input_, shape) in zip(input_t, inputs_got_shape):
        input_.set_shape(shape[1:])
    input_t = nest.pack_sequence_as(structure=inputs, flat_sequence=input_t)
    call_cell = (lambda : cell(input_t, state))
    if (sequence_length is not None):
        (output, new_state) = _rnn_step(time=time, sequence_length=sequence_length, min_sequence_length=min_sequence_length, max_sequence_length=max_sequence_length, zero_output=zero_output, state=state, call_cell=call_cell, state_size=state_size, skip_conditionals=True)
    else:
        (output, new_state) = call_cell()
    output = nest.flatten(output)
    output_ta_t = tuple((ta.write(time, out) for (ta, out) in zip(output_ta_t, output)))
    return ((time + 1), output_ta_t, new_state)
Take a time step of the dynamic RNN.

Args:
  time: int32 scalar Tensor.
  output_ta_t: List of `TensorArray`s that represent the output.
  state: nested tuple of vector tensors that represent the state.

Returns:
  The tuple (time + 1, output_ta_t with updated flow, new_state).
def _time_step(time, output_ta_t, state):
    'Take a time step of the dynamic RNN.\n\n    Args:\n      time: int32 scalar Tensor.\n      output_ta_t: List of `TensorArray`s that represent the output.\n      state: nested tuple of vector tensors that represent the state.\n\n    Returns:\n      The tuple (time + 1, output_ta_t with updated flow, new_state).\n    '
    input_t = tuple((ta.read(time) for ta in input_ta))
    for (input_, shape) in zip(input_t, inputs_got_shape):
        input_.set_shape(shape[1:])
    input_t = nest.pack_sequence_as(structure=inputs, flat_sequence=input_t)
    call_cell = (lambda : cell(input_t, state))
    if (sequence_length is not None):
        (output, new_state) = _rnn_step(time=time, sequence_length=sequence_length, min_sequence_length=min_sequence_length, max_sequence_length=max_sequence_length, zero_output=zero_output, state=state, call_cell=call_cell, state_size=state_size, skip_conditionals=True)
    else:
        (output, new_state) = call_cell()
    output = nest.flatten(output)
    output_ta_t = tuple((ta.write(time, out) for (ta, out) in zip(output_ta_t, output)))
    return ((time + 1), output_ta_t, new_state)