code: def _createStackBidirectionalDynamicRNN(
      self,
      use_gpu,
      use_shape,
      use_state_tuple,
      initial_states_fw=None,
      initial_states_bw=None,
      scope=None,
  ):
      del use_gpu
      del use_state_tuple
      self.layers = [2, 3]
      input_size = 5
      batch_size = 2
      max_length = 8
  
      initializer = tf.random_uniform_initializer(
          -0.01, 0.01, seed=self._seed
      )
      sequence_length = tf.placeholder(tf.int64)
  
      self.cells_fw = [
          rnn_cell.LSTMCell(  # pylint:disable=g-complex-comprehension
              num_units,
              input_size,
              initializer=initializer,
              state_is_tuple=False,
          )
          for num_units in self.layers
      ]
      self.cells_bw = [
          rnn_cell.LSTMCell(  # pylint:disable=g-complex-comprehension
              num_units,
              input_size,
              initializer=initializer,
              state_is_tuple=False,
          )
          for num_units in self.layers
      ]
  
      inputs = max_length * [
          tf.placeholder(
              tf.float32,
              shape=(batch_size, input_size)
              if use_shape
              else (None, input_size),
          )
      ]
      inputs_c = tf.stack(inputs)
      inputs_c = tf.transpose(inputs_c, [1, 0, 2])
      (
          outputs,
          st_fw,
          st_bw,
      ) = contrib_rnn.stack_bidirectional_dynamic_rnn(
          self.cells_fw,
          self.cells_bw,
          inputs_c,
          initial_states_fw=initial_states_fw,
          initial_states_bw=initial_states_bw,
          dtype=tf.float32,
          sequence_length=sequence_length,
          scope=scope,
      )
  
      # Outputs has shape (batch_size, max_length, 2* layer[-1].
      output_shape = [None, max_length, 2 * self.layers[-1]]
      if use_shape:
          output_shape[0] = batch_size
  
      self.assertAllEqual(outputs.get_shape().as_list(), output_shape)
  
      input_value = np.random.randn(batch_size, input_size)
  
      return input_value, inputs, outputs, st_fw, st_bw, sequence_length
  
imports: import itertools
  from absl.testing import parameterized
  from magenta.contrib import rnn
  import numpy
  import tensorflow.compat.v1
label_desc: Use image node_id 2 for calling the StackBidirectionalRNNTest obj's underlying member method code with example usage: obj._createStackBidirectionalDynamicRNN(use_gpu, use_shape, use_state_tuple, initial_states_fw, initial_states_bw, scope) and returns: input_value,  inputs,  outputs,  st_fw,  st_bw,  sequence_length