code: def testMultiStepStackedSampling(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
              )
  
      if tf.executing_eagerly():
          steps = lambda: replay_buffer.get_next(num_steps=2)[0]
      else:
          steps, _ = replay_buffer.get_next(num_steps=2)
  
      self.evaluate(tf.compat.v1.global_variables_initializer())
      add_data()
      self.evaluate_last_func_if_graph_mode()
      for _ in range(100):
          steps_ = self.evaluate(steps)
          self.assertEqual((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 11 for calling the TFUniformReplayBufferTest obj's underlying member method code with example usage: obj.testMultiStepStackedSampling(batch_size) without return types