code: def testDeterministicAsDatasetWithSampleBatch(
      self, buffer_batch_size
  ):
      max_length = 3
      unused_rb, rb_values = self._create_collect_rb_dataset(
          max_length,
          buffer_batch_size,
          num_adds=3,
          sample_batch_size=buffer_batch_size,
      )
  
      # Expect to see batches of data in the form:
      #  [0, 10, 20, ..., 10 * (sample_batch_size - 1)]  # frames 0
      #  [1, 11, 21, ..., 1 + 10 * (sample_batch_size - 1)]  # frames 1
      #  [2, 12, 22, ..., 2 + 10 * (sample_batch_size - 1)]  # frames 2
      # because here, sample_batch_size == buffer_batch_size
      expected = np.vstack(
          [
              10 * np.arange(buffer_batch_size) + i
              for i in range(max_length)
          ]
      )
      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 22 for calling the TFUniformReplayBufferTest obj's underlying member method code with example usage: obj.testDeterministicAsDatasetWithSampleBatch(buffer_batch_size) without return types