code: def _testStepWithTrainingHelper(
      self, use_output_layer
  ):  # pylint:disable=invalid-name
      sequence_length = [3, 4, 3, 1, 0]
      batch_size = 5
      max_time = 8
      input_depth = 7
      cell_depth = 10
      output_layer_depth = 3
  
      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)
          helper = seq2seq.TrainingHelper(
              inputs, sequence_length, time_major=False
          )
          if use_output_layer:
              output_layer = tf.layers.Dense(
                  output_layer_depth, use_bias=False
              )
              expected_output_depth = output_layer_depth
          else:
              output_layer = None
              expected_output_depth = cell_depth
          my_decoder = seq2seq.BasicDecoder(
              cell=cell,
              helper=helper,
              initial_state=cell.zero_state(
                  dtype=tf.float32, batch_size=batch_size
              ),
              output_layer=output_layer,
          )
          output_size = my_decoder.output_size
          output_dtype = my_decoder.output_dtype
          self.assertEqual(
              seq2seq.BasicDecoderOutput(
                  expected_output_depth, tf.TensorShape([])
              ),
              output_size,
          )
          self.assertEqual(
              seq2seq.BasicDecoderOutput(tf.float32, tf.int32),
              output_dtype,
          )
  
          (
              first_finished,
              first_inputs,
              first_state,
          ) = my_decoder.initialize()
          (
              step_outputs,
              step_state,
              step_next_inputs,
              step_finished,
          ) = my_decoder.step(tf.constant(0), first_inputs, first_state)
          batch_size_t = my_decoder.batch_size
  
          self.assertIsInstance(
              first_state, tf.nn.rnn_cell.LSTMStateTuple
          )
          self.assertIsInstance(
              step_state, tf.nn.rnn_cell.LSTMStateTuple
          )
          self.assertIsInstance(
              step_outputs, seq2seq.BasicDecoderOutput
          )
          self.assertEqual(
              (batch_size, expected_output_depth),
              step_outputs[0].get_shape(),
          )
          self.assertEqual((batch_size,), step_outputs[1].get_shape())
          self.assertEqual(
              (batch_size, cell_depth), first_state[0].get_shape()
          )
          self.assertEqual(
              (batch_size, cell_depth), first_state[1].get_shape()
          )
          self.assertEqual(
              (batch_size, cell_depth), step_state[0].get_shape()
          )
          self.assertEqual(
              (batch_size, cell_depth), step_state[1].get_shape()
          )
  
          if use_output_layer:
              # The output layer was accessed
              self.assertEqual(len(output_layer.variables), 1)
  
          sess.run(tf.global_variables_initializer())
          sess_results = sess.run(
              {
                  "batch_size": batch_size_t,
                  "first_finished": first_finished,
                  "first_inputs": first_inputs,
                  "first_state": first_state,
                  "step_outputs": step_outputs,
                  "step_state": step_state,
                  "step_next_inputs": step_next_inputs,
                  "step_finished": step_finished,
              }
          )
  
          self.assertAllEqual(
              [False, False, False, False, True],
              sess_results["first_finished"],
          )
          self.assertAllEqual(
              [False, False, False, True, True],
              sess_results["step_finished"],
          )
          self.assertEqual(
              output_dtype.sample_id,
              sess_results["step_outputs"].sample_id.dtype,
          )
          self.assertAllEqual(
              np.argmax(sess_results["step_outputs"].rnn_output, -1),
              sess_results["step_outputs"].sample_id,
          )
  
imports: from magenta.contrib import seq2seq
  import numpy
  import tensorflow.compat.v1
label_desc: Use image node_id 1 for calling the BasicDecoderTest obj's underlying member method code with example usage: obj._testStepWithTrainingHelper(use_output_layer) without return types