code: def testMultiStepStackedBatchedSampling(self, batch_size):
      spec = specs.TensorSpec([], tf.int64, "action")
      replay_buffer = tf_uniform_replay_buffer.TFUniformReplayBuffer(
          spec, batch_size=batch_size
      )
  
      @common.function(autograph=True)
      def add_data():
          for i in tf.range(10, dtype=tf.int64):
              replay_buffer.add_batch(
                  tf.ones((batch_size,), dtype=tf.int64) * i
              )
  
      self.evaluate(tf.compat.v1.global_variables_initializer())
      add_data()
      self.evaluate_last_func_if_graph_mode()
  
      if tf.executing_eagerly():
          steps = lambda: replay_buffer._get_next(
              3,
              num_steps=2,
              time_stacked=True,  # pylint: disable=g-long-lambda
          )[0]
      else:
          steps, _ = replay_buffer._get_next(
              3, num_steps=2, time_stacked=True
          )
      self.assertEqual(self.evaluate(steps).shape, (3, 2))
  
      for _ in range(100):
          steps_ = self.evaluate(steps)
          self.assertAllEqual((steps_[:, 0] + 1) % 10, steps_[:, 1])
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  from absl.testing import parameterized
  import numpy
  import tensorflow
  from tf_agents import specs
  from tf_agents.replay_buffers import tf_uniform_replay_buffer
  from tf_agents.utils import common
  from tf_agents.utils import test_utils
label_desc: Use image node_id 12 for calling the TFUniformReplayBufferTest obj's underlying member method code with example usage: obj.testMultiStepStackedBatchedSampling(batch_size) without return types