code: def _testDynamicDecodeRNNWithTrainingHelperMatchesDynamicRNN(  # pylint:disable=invalid-name
      self, use_sequence_length
  ):
      sequence_length = [3, 4, 3, 1, 0]
      batch_size = 5
      max_time = 8
      input_depth = 7
      cell_depth = 10
      max_out = max(sequence_length)
  
      with self.session(use_gpu=True) as sess:
          inputs = np.random.randn(
              batch_size, max_time, input_depth
          ).astype(np.float32)
  
          cell = tf.nn.rnn_cell.LSTMCell(cell_depth)
          zero_state = cell.zero_state(
              dtype=tf.float32, batch_size=batch_size
          )
          helper = seq2seq.TrainingHelper(inputs, sequence_length)
          my_decoder = seq2seq.BasicDecoder(
              cell=cell, helper=helper, initial_state=zero_state
          )
  
          # Match the variable scope of dynamic_rnn below so we end up
          # using the same variables
          with tf.variable_scope("root") as scope:
              (
                  final_decoder_outputs,
                  final_decoder_state,
                  _,
              ) = seq2seq.dynamic_decode(
                  my_decoder,
                  # impute_finished=True ensures outputs and final state
                  # match those of dynamic_rnn called with sequence_length not None
                  impute_finished=use_sequence_length,
                  scope=scope,
              )
  
          with tf.variable_scope(scope, reuse=True) as scope:
              final_rnn_outputs, final_rnn_state = tf.nn.dynamic_rnn(
                  cell,
                  inputs,
                  sequence_length=sequence_length
                  if use_sequence_length
                  else None,
                  initial_state=zero_state,
                  scope=scope,
              )
  
          sess.run(tf.global_variables_initializer())
          sess_results = sess.run(
              {
                  "final_decoder_outputs": final_decoder_outputs,
                  "final_decoder_state": final_decoder_state,
                  "final_rnn_outputs": final_rnn_outputs,
                  "final_rnn_state": final_rnn_state,
              }
          )
  
          # Decoder only runs out to max_out; ensure values are identical
          # to dynamic_rnn, which also zeros out outputs and passes along state.
          self.assertAllClose(
              sess_results["final_decoder_outputs"].rnn_output,
              sess_results["final_rnn_outputs"][:, 0:max_out, :],
          )
          if use_sequence_length:
              self.assertAllClose(
                  sess_results["final_decoder_state"],
                  sess_results["final_rnn_state"],
              )
  
imports: from magenta.contrib import seq2seq
  import numpy
  import tensorflow.compat.v1
label_desc: Use image node_id 6 for calling the DynamicDecodeRNNTest obj's underlying member method code with example usage: obj._testDynamicDecodeRNNWithTrainingHelperMatchesDynamicRNN(use_sequence_length) without return types