def dynamic_rnn(cell, inputs, sequence_length=None, initial_state=None, dtype=None, parallel_iterations=None, swap_memory=False, time_major=False, scope=None):
    'Creates a recurrent neural network specified by RNNCell `cell`.\n\n  This function is functionally identical to the function `rnn` above, but\n  performs fully dynamic unrolling of `inputs`.\n\n  Unlike `rnn`, the input `inputs` is not a Python list of `Tensors`, one for\n  each frame.  Instead, `inputs` may be a single `Tensor` where\n  the maximum time is either the first or second dimension (see the parameter\n  `time_major`).  Alternatively, it may be a (possibly nested) tuple of\n  Tensors, each of them having matching batch and time dimensions.\n  The corresponding output is either a single `Tensor` having the same number\n  of time steps and batch size, or a (possibly nested) tuple of such tensors,\n  matching the nested structure of `cell.output_size`.\n\n  The parameter `sequence_length` is optional and is used to copy-through state\n  and zero-out outputs when past a batch element\'s sequence length. So it\'s more\n  for correctness than performance, unlike in rnn().\n\n  Args:\n    cell: An instance of RNNCell.\n    inputs: The RNN inputs.\n\n      If `time_major == False` (default), this must be a `Tensor` of shape:\n        `[batch_size, max_time, ...]`, or a nested tuple of such\n        elements.\n\n      If `time_major == True`, this must be a `Tensor` of shape:\n        `[max_time, batch_size, ...]`, or a nested tuple of such\n        elements.\n\n      This may also be a (possibly nested) tuple of Tensors satisfying\n      this property.  The first two dimensions must match across all the inputs,\n      but otherwise the ranks and other shape components may differ.\n      In this case, input to `cell` at each time-step will replicate the\n      structure of these tuples, except for the time dimension (from which the\n      time is taken).\n\n      The input to `cell` at each time step will be a `Tensor` or (possibly\n      nested) tuple of Tensors each with dimensions `[batch_size, ...]`.\n\n    sequence_length: (optional) An int32/int64 vector sized `[batch_size]`.\n    initial_state: (optional) An initial state for the RNN.\n      If `cell.state_size` is an integer, this must be\n      a `Tensor` of appropriate type and shape `[batch_size, cell.state_size]`.\n      If `cell.state_size` is a tuple, this should be a tuple of\n      tensors having shapes `[batch_size, s] for s in cell.state_size`.\n    dtype: (optional) The data type for the initial state and expected output.\n      Required if initial_state is not provided or RNN state has a heterogeneous\n      dtype.\n    parallel_iterations: (Default: 32).  The number of iterations to run in\n      parallel.  Those operations which do not have any temporal dependency\n      and can be run in parallel, will be.  This parameter trades off\n      time for space.  Values >> 1 use more memory but take less time,\n      while smaller values use less memory but computations take longer.\n    swap_memory: Transparently swap the tensors produced in forward inference\n      but needed for back prop from GPU to CPU.  This allows training RNNs\n      which would typically not fit on a single GPU, with very minimal (or no)\n      performance penalty.\n    time_major: The shape format of the `inputs` and `outputs` Tensors.\n      If true, these `Tensors` must be shaped `[max_time, batch_size, depth]`.\n      If false, these `Tensors` must be shaped `[batch_size, max_time, depth]`.\n      Using `time_major = True` is a bit more efficient because it avoids\n      transposes at the beginning and end of the RNN calculation.  However,\n      most TensorFlow data is batch-major, so by default this function\n      accepts input and emits output in batch-major form.\n    scope: VariableScope for the created subgraph; defaults to "rnn".\n\n  Returns:\n    A pair (outputs, state) where:\n\n      outputs: The RNN output `Tensor`.\n\n        If time_major == False (default), this will be a `Tensor` shaped:\n          `[batch_size, max_time, cell.output_size]`.\n\n        If time_major == True, this will be a `Tensor` shaped:\n          `[max_time, batch_size, cell.output_size]`.\n\n        Note, if `cell.output_size` is a (possibly nested) tuple of integers\n        or `TensorShape` objects, then `outputs` will be a tuple having the\n        same structure as `cell.output_size`, containing Tensors having shapes\n        corresponding to the shape data in `cell.output_size`.\n\n      state: The final state.  If `cell.state_size` is an int, this\n        will be shaped `[batch_size, cell.state_size]`.  If it is a\n        `TensorShape`, this will be shaped `[batch_size] + cell.state_size`.\n        If it is a (possibly nested) tuple of ints or `TensorShape`, this will\n        be a tuple having the corresponding shapes.\n\n  Raises:\n    TypeError: If `cell` is not an instance of RNNCell.\n    ValueError: If inputs is None or an empty list.\n  '
    if (not isinstance(cell, rnn_cell_impl._RNNCell)):
        raise TypeError('cell must be an instance of RNNCell')
    flat_input = nest.flatten(inputs)
    if (not time_major):
        flat_input = tuple((array_ops.transpose(input_, [1, 0, 2]) for input_ in flat_input))
    parallel_iterations = (parallel_iterations or 32)
    if (sequence_length is not None):
        sequence_length = math_ops.to_int32(sequence_length)
        if (sequence_length.get_shape().ndims not in (None, 1)):
            raise ValueError(('sequence_length must be a vector of length batch_size, but saw shape: %s' % sequence_length.get_shape()))
        sequence_length = array_ops.identity(sequence_length, name='sequence_length')
    with vs.variable_scope((scope or 'rnn')) as varscope:
        if (varscope.caching_device is None):
            varscope.set_caching_device((lambda op: op.device))
        input_shape = tuple((array_ops.shape(input_) for input_ in flat_input))
        batch_size = input_shape[0][1]
        for input_ in input_shape:
            if (input_[1].get_shape() != batch_size.get_shape()):
                raise ValueError('All inputs should have the same batch size')
        if (initial_state is not None):
            state = initial_state
        else:
            if (not dtype):
                raise ValueError('If no initial_state is provided, dtype must be.')
            state = cell.zero_state(batch_size, dtype)

        def _assert_has_shape(x, shape):
            x_shape = array_ops.shape(x)
            packed_shape = array_ops.stack(shape)
            return control_flow_ops.Assert(math_ops.reduce_all(math_ops.equal(x_shape, packed_shape)), [('Expected shape for Tensor %s is ' % x.name), packed_shape, ' but saw shape: ', x_shape])
        if (sequence_length is not None):
            with ops.control_dependencies([_assert_has_shape(sequence_length, [batch_size])]):
                sequence_length = array_ops.identity(sequence_length, name='CheckSeqLen')
        inputs = nest.pack_sequence_as(structure=inputs, flat_sequence=flat_input)
        (outputs, final_state) = _dynamic_rnn_loop(cell, inputs, state, parallel_iterations=parallel_iterations, swap_memory=swap_memory, sequence_length=sequence_length, dtype=dtype)
        if (not time_major):
            flat_output = nest.flatten(outputs)
            flat_output = [array_ops.transpose(output, [1, 0, 2]) for output in flat_output]
            outputs = nest.pack_sequence_as(structure=outputs, flat_sequence=flat_output)
        return (outputs, final_state)
Creates a recurrent neural network specified by RNNCell `cell`.

This function is functionally identical to the function `rnn` above, but
performs fully dynamic unrolling of `inputs`.

Unlike `rnn`, the input `inputs` is not a Python list of `Tensors`, one for
each frame.  Instead, `inputs` may be a single `Tensor` where
the maximum time is either the first or second dimension (see the parameter
`time_major`).  Alternatively, it may be a (possibly nested) tuple of
Tensors, each of them having matching batch and time dimensions.
The corresponding output is either a single `Tensor` having the same number
of time steps and batch size, or a (possibly nested) tuple of such tensors,
matching the nested structure of `cell.output_size`.

The parameter `sequence_length` is optional and is used to copy-through state
and zero-out outputs when past a batch element's sequence length. So it's more
for correctness than performance, unlike in rnn().

Args:
  cell: An instance of RNNCell.
  inputs: The RNN inputs.

    If `time_major == False` (default), this must be a `Tensor` of shape:
      `[batch_size, max_time, ...]`, or a nested tuple of such
      elements.

    If `time_major == True`, this must be a `Tensor` of shape:
      `[max_time, batch_size, ...]`, or a nested tuple of such
      elements.

    This may also be a (possibly nested) tuple of Tensors satisfying
    this property.  The first two dimensions must match across all the inputs,
    but otherwise the ranks and other shape components may differ.
    In this case, input to `cell` at each time-step will replicate the
    structure of these tuples, except for the time dimension (from which the
    time is taken).

    The input to `cell` at each time step will be a `Tensor` or (possibly
    nested) tuple of Tensors each with dimensions `[batch_size, ...]`.

  sequence_length: (optional) An int32/int64 vector sized `[batch_size]`.
  initial_state: (optional) An initial state for the RNN.
    If `cell.state_size` is an integer, this must be
    a `Tensor` of appropriate type and shape `[batch_size, cell.state_size]`.
    If `cell.state_size` is a tuple, this should be a tuple of
    tensors having shapes `[batch_size, s] for s in cell.state_size`.
  dtype: (optional) The data type for the initial state and expected output.
    Required if initial_state is not provided or RNN state has a heterogeneous
    dtype.
  parallel_iterations: (Default: 32).  The number of iterations to run in
    parallel.  Those operations which do not have any temporal dependency
    and can be run in parallel, will be.  This parameter trades off
    time for space.  Values >> 1 use more memory but take less time,
    while smaller values use less memory but computations take longer.
  swap_memory: Transparently swap the tensors produced in forward inference
    but needed for back prop from GPU to CPU.  This allows training RNNs
    which would typically not fit on a single GPU, with very minimal (or no)
    performance penalty.
  time_major: The shape format of the `inputs` and `outputs` Tensors.
    If true, these `Tensors` must be shaped `[max_time, batch_size, depth]`.
    If false, these `Tensors` must be shaped `[batch_size, max_time, depth]`.
    Using `time_major = True` is a bit more efficient because it avoids
    transposes at the beginning and end of the RNN calculation.  However,
    most TensorFlow data is batch-major, so by default this function
    accepts input and emits output in batch-major form.
  scope: VariableScope for the created subgraph; defaults to "rnn".

Returns:
  A pair (outputs, state) where:

    outputs: The RNN output `Tensor`.

      If time_major == False (default), this will be a `Tensor` shaped:
        `[batch_size, max_time, cell.output_size]`.

      If time_major == True, this will be a `Tensor` shaped:
        `[max_time, batch_size, cell.output_size]`.

      Note, if `cell.output_size` is a (possibly nested) tuple of integers
      or `TensorShape` objects, then `outputs` will be a tuple having the
      same structure as `cell.output_size`, containing Tensors having shapes
      corresponding to the shape data in `cell.output_size`.

    state: The final state.  If `cell.state_size` is an int, this
      will be shaped `[batch_size, cell.state_size]`.  If it is a
      `TensorShape`, this will be shaped `[batch_size] + cell.state_size`.
      If it is a (possibly nested) tuple of ints or `TensorShape`, this will
      be a tuple having the corresponding shapes.

Raises:
  TypeError: If `cell` is not an instance of RNNCell.
  ValueError: If inputs is None or an empty list.
def dynamic_rnn(cell, inputs, sequence_length=None, initial_state=None, dtype=None, parallel_iterations=None, swap_memory=False, time_major=False, scope=None):
    'Creates a recurrent neural network specified by RNNCell `cell`.\n\n  This function is functionally identical to the function `rnn` above, but\n  performs fully dynamic unrolling of `inputs`.\n\n  Unlike `rnn`, the input `inputs` is not a Python list of `Tensors`, one for\n  each frame.  Instead, `inputs` may be a single `Tensor` where\n  the maximum time is either the first or second dimension (see the parameter\n  `time_major`).  Alternatively, it may be a (possibly nested) tuple of\n  Tensors, each of them having matching batch and time dimensions.\n  The corresponding output is either a single `Tensor` having the same number\n  of time steps and batch size, or a (possibly nested) tuple of such tensors,\n  matching the nested structure of `cell.output_size`.\n\n  The parameter `sequence_length` is optional and is used to copy-through state\n  and zero-out outputs when past a batch element\'s sequence length. So it\'s more\n  for correctness than performance, unlike in rnn().\n\n  Args:\n    cell: An instance of RNNCell.\n    inputs: The RNN inputs.\n\n      If `time_major == False` (default), this must be a `Tensor` of shape:\n        `[batch_size, max_time, ...]`, or a nested tuple of such\n        elements.\n\n      If `time_major == True`, this must be a `Tensor` of shape:\n        `[max_time, batch_size, ...]`, or a nested tuple of such\n        elements.\n\n      This may also be a (possibly nested) tuple of Tensors satisfying\n      this property.  The first two dimensions must match across all the inputs,\n      but otherwise the ranks and other shape components may differ.\n      In this case, input to `cell` at each time-step will replicate the\n      structure of these tuples, except for the time dimension (from which the\n      time is taken).\n\n      The input to `cell` at each time step will be a `Tensor` or (possibly\n      nested) tuple of Tensors each with dimensions `[batch_size, ...]`.\n\n    sequence_length: (optional) An int32/int64 vector sized `[batch_size]`.\n    initial_state: (optional) An initial state for the RNN.\n      If `cell.state_size` is an integer, this must be\n      a `Tensor` of appropriate type and shape `[batch_size, cell.state_size]`.\n      If `cell.state_size` is a tuple, this should be a tuple of\n      tensors having shapes `[batch_size, s] for s in cell.state_size`.\n    dtype: (optional) The data type for the initial state and expected output.\n      Required if initial_state is not provided or RNN state has a heterogeneous\n      dtype.\n    parallel_iterations: (Default: 32).  The number of iterations to run in\n      parallel.  Those operations which do not have any temporal dependency\n      and can be run in parallel, will be.  This parameter trades off\n      time for space.  Values >> 1 use more memory but take less time,\n      while smaller values use less memory but computations take longer.\n    swap_memory: Transparently swap the tensors produced in forward inference\n      but needed for back prop from GPU to CPU.  This allows training RNNs\n      which would typically not fit on a single GPU, with very minimal (or no)\n      performance penalty.\n    time_major: The shape format of the `inputs` and `outputs` Tensors.\n      If true, these `Tensors` must be shaped `[max_time, batch_size, depth]`.\n      If false, these `Tensors` must be shaped `[batch_size, max_time, depth]`.\n      Using `time_major = True` is a bit more efficient because it avoids\n      transposes at the beginning and end of the RNN calculation.  However,\n      most TensorFlow data is batch-major, so by default this function\n      accepts input and emits output in batch-major form.\n    scope: VariableScope for the created subgraph; defaults to "rnn".\n\n  Returns:\n    A pair (outputs, state) where:\n\n      outputs: The RNN output `Tensor`.\n\n        If time_major == False (default), this will be a `Tensor` shaped:\n          `[batch_size, max_time, cell.output_size]`.\n\n        If time_major == True, this will be a `Tensor` shaped:\n          `[max_time, batch_size, cell.output_size]`.\n\n        Note, if `cell.output_size` is a (possibly nested) tuple of integers\n        or `TensorShape` objects, then `outputs` will be a tuple having the\n        same structure as `cell.output_size`, containing Tensors having shapes\n        corresponding to the shape data in `cell.output_size`.\n\n      state: The final state.  If `cell.state_size` is an int, this\n        will be shaped `[batch_size, cell.state_size]`.  If it is a\n        `TensorShape`, this will be shaped `[batch_size] + cell.state_size`.\n        If it is a (possibly nested) tuple of ints or `TensorShape`, this will\n        be a tuple having the corresponding shapes.\n\n  Raises:\n    TypeError: If `cell` is not an instance of RNNCell.\n    ValueError: If inputs is None or an empty list.\n  '
    if (not isinstance(cell, rnn_cell_impl._RNNCell)):
        raise TypeError('cell must be an instance of RNNCell')
    flat_input = nest.flatten(inputs)
    if (not time_major):
        flat_input = tuple((array_ops.transpose(input_, [1, 0, 2]) for input_ in flat_input))
    parallel_iterations = (parallel_iterations or 32)
    if (sequence_length is not None):
        sequence_length = math_ops.to_int32(sequence_length)
        if (sequence_length.get_shape().ndims not in (None, 1)):
            raise ValueError(('sequence_length must be a vector of length batch_size, but saw shape: %s' % sequence_length.get_shape()))
        sequence_length = array_ops.identity(sequence_length, name='sequence_length')
    with vs.variable_scope((scope or 'rnn')) as varscope:
        if (varscope.caching_device is None):
            varscope.set_caching_device((lambda op: op.device))
        input_shape = tuple((array_ops.shape(input_) for input_ in flat_input))
        batch_size = input_shape[0][1]
        for input_ in input_shape:
            if (input_[1].get_shape() != batch_size.get_shape()):
                raise ValueError('All inputs should have the same batch size')
        if (initial_state is not None):
            state = initial_state
        else:
            if (not dtype):
                raise ValueError('If no initial_state is provided, dtype must be.')
            state = cell.zero_state(batch_size, dtype)

        def _assert_has_shape(x, shape):
            x_shape = array_ops.shape(x)
            packed_shape = array_ops.stack(shape)
            return control_flow_ops.Assert(math_ops.reduce_all(math_ops.equal(x_shape, packed_shape)), [('Expected shape for Tensor %s is ' % x.name), packed_shape, ' but saw shape: ', x_shape])
        if (sequence_length is not None):
            with ops.control_dependencies([_assert_has_shape(sequence_length, [batch_size])]):
                sequence_length = array_ops.identity(sequence_length, name='CheckSeqLen')
        inputs = nest.pack_sequence_as(structure=inputs, flat_sequence=flat_input)
        (outputs, final_state) = _dynamic_rnn_loop(cell, inputs, state, parallel_iterations=parallel_iterations, swap_memory=swap_memory, sequence_length=sequence_length, dtype=dtype)
        if (not time_major):
            flat_output = nest.flatten(outputs)
            flat_output = [array_ops.transpose(output, [1, 0, 2]) for output in flat_output]
            outputs = nest.pack_sequence_as(structure=outputs, flat_sequence=flat_output)
        return (outputs, final_state)