code: def dynamic_rnn(
      cell,
      inputs,
      att_scores=None,
      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`.
  
      Performs fully dynamic unrolling of `inputs`.
  
      Example:
  
      ```python
      # create a BasicRNNCell
      rnn_cell = tf.nn.rnn_cell.BasicRNNCell(hidden_size)
  
      # 'outputs' is a tensor of shape [batch_size, max_time, cell_state_size]
  
      # defining initial state
      initial_state = rnn_cell.zero_state(batch_size, dtype=tf.float32)
  
      # 'state' is a tensor of shape [batch_size, cell_state_size]
      outputs, state = tf.nn.dynamic_rnn(rnn_cell, input_data,
                                         initial_state=initial_state,
                                         dtype=tf.float32)
      ```
  
      ```python
      # create 2 LSTMCells
      rnn_layers = [tf.nn.rnn_cell.LSTMCell(size) for size in [128, 256]]
  
      # create a RNN cell composed sequentially of a number of RNNCells
      multi_rnn_cell = tf.nn.rnn_cell.MultiRNNCell(rnn_layers)
  
      # 'outputs' is a tensor of shape [batch_size, max_time, 256]
      # 'state' is a N-tuple where N is the number of LSTMCells containing a
      # tf.contrib.rnn.LSTMStateTuple for each cell
      outputs, state = tf.nn.dynamic_rnn(cell=multi_rnn_cell,
                                         inputs=data,
                                         dtype=tf.float32)
      ```
  
  
      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]`.
          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.
        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. If cells are `LSTMCells`
          `state` will be a tuple containing a `LSTMStateTuple` for each cell.
  
      Raises:
        TypeError: If `cell` is not an instance of RNNCell.
        ValueError: If inputs is None or an empty list.
      """
      assert_like_rnncell(cell.name, cell)
  
      # By default, time_major==False and inputs are batch-major: shaped
      #   [batch, time, depth]
      # For internal calculations, we transpose to [time, batch, depth]
      flat_input = nest.flatten(inputs)
  
      if not time_major:
          # (B,T,D) => (T,B,D)
          flat_input = [
              ops.convert_to_tensor(input_) for input_ in flat_input
          ]
          flat_input = tuple(
              _transpose_batch_time(input_) 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):
              invalidInputError(
                  False,
                  "sequence_length must be a vector of length batch_size, "
                  "but saw shape: %s" % sequence_length.get_shape(),
              )
          sequence_length = (
              array_ops.identity(  # Just to find it in the graph.
                  sequence_length, name="sequence_length"
              )
          )
  
      # Create a new scope in which the caching device is either
      # determined by the parent scope, or is set to place the cached
      # Variable using the same placement as for the rest of the RNN.
      with vs.variable_scope(scope or "rnn") as varscope:
          if varscope.caching_device is None:
              varscope.set_caching_device(lambda op: op.device)
          batch_size = _best_effort_input_batch_size(flat_input)
  
          if initial_state is not None:
              state = initial_state
          else:
              if not dtype:
                  invalidInputError(
                      False,
                      "If there is no initial_state, you must give a dtype.",
                  )
              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:
              # Perform some shape validation
              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,
              att_scores=att_scores,
              sequence_length=sequence_length,
              dtype=dtype,
          )
  
          # Outputs of _dynamic_rnn_loop are always shaped [time, batch, depth].
          # If we are performing batch-major calculations, transpose output back
          # to shape [batch, time, depth]
          if not time_major:
              # (T,B,D) => (B,T,D)
              outputs = nest.map_structure(
                  _transpose_batch_time, outputs
              )
  
          return (outputs, final_state)
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  from tensorflow.python.framework import constant_op
  from tensorflow.python.framework import dtypes
  from tensorflow.python.framework import ops
  from tensorflow.python.framework import tensor_shape
  from tensorflow.python.ops import array_ops
  from tensorflow.python.ops import control_flow_ops
  from tensorflow.python.ops import math_ops
  from tensorflow.python.ops import rnn_cell_impl
  from tensorflow.python.ops import tensor_array_ops
  from tensorflow.python.ops import variable_scope
  from tensorflow.python.util import nest
  from bigdl.dllib.utils.log4Error import *
label_desc: Use image node_id 7 for calling a global function with example usage: dynamic_rnn(cell, inputs, att_scores, sequence_length, initial_state, dtype, parallel_iterations, swap_memory, time_major, scope) and returns: outputs,  final_state, control_flow_ops