code: def testDeterministicAsDatasetWithNumStepsAndSampleBatch(self):
      max_length = 4
      buffer_batch_size = 6
      sample_batch_size = 3
      num_steps = 2
      unused_rb, rb_values = self._create_collect_rb_dataset(
          max_length,
          buffer_batch_size,
          num_adds=4,
          sample_batch_size=sample_batch_size,
          num_steps=num_steps,
      )
  
      # Expect to get 5 episodes per batch, 2 frames at a time when
      # num_steps=2, max_length=4, and sample_batch_size=5.
      # Once an episode batch row is finished, move on to the next episode in that
      # batch row.
      expected = np.asarray(
          [
              # First minibatch out, time steps t=0,1 for eps 0..2.
              [[0, 1], [10, 11], [20, 21]],
              # Second minibatch out, time steps t=2,3 for eps 0..2.
              [[2, 3], [12, 13], [22, 23]],
              # Third minibatch, time steps t=0,1 for eps 3..5
              [[30, 31], [40, 41], [50, 51]],
              # Fourth minibatch, time steps t=2,3 for eps 3..5
              [[32, 33], [42, 43], [52, 53]],
          ]
      )
      self.assertAllEqual(expected, rb_values)
  
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 23 for calling the TFUniformReplayBufferTest obj's underlying member method code with example usage: obj.testDeterministicAsDatasetWithNumStepsAndSampleBatch() without return types