code: def testSampleBatchCorrectProbabilities(self, buffer_batch_size):
      spec = specs.TensorSpec([], tf.int32, "action")
      replay_buffer = tf_uniform_replay_buffer.TFUniformReplayBuffer(
          spec, batch_size=buffer_batch_size, max_length=4
      )
  
      actions = tf.stack(
          [tf.Variable(0).count_up_to(9)] * buffer_batch_size
      )
      sample_batch_size = 2
  
      @common.function
      def add(actions):
          replay_buffer.add_batch(actions)
  
      @common.function
      def probabilities():
          _, buffer_info = replay_buffer.get_next(
              sample_batch_size=sample_batch_size
          )
          return buffer_info.probabilities
  
      self.evaluate(tf.compat.v1.global_variables_initializer())
      num_adds = 3
      for i in range(1, num_adds):
          add(actions)
          self.evaluate_last_func_if_graph_mode()
          expected_probabilities = [
              1.0 / (i * buffer_batch_size)
          ] * sample_batch_size
          probabilities_ = self.evaluate(probabilities())
          self.assertAllClose(expected_probabilities, probabilities_)
  
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 16 for calling the TFUniformReplayBufferTest obj's underlying member method code with example usage: obj.testSampleBatchCorrectProbabilities(buffer_batch_size) and returns: buffer_info