code: def test_mcmc_reducer(self, reduce_dims):
      shape = [6, 2, 15]
      loc = 2.0 * np.ones(shape, dtype=np.float32)
      stddev = 3.0
      observations = [2.0, 3.0, 6.0]
      model = normal.Normal(loc, stddev, validate_args=True)
      actual_mean = acquisition_function.MCMCReducer(
          predictive_distribution=model,
          observations=observations,
          reduce_dims=reduce_dims,
          acquisition_class=TestFunction,
      )()
      expected_mean = tf.reduce_mean(
          model.mean(), axis=list(range(reduce_dims))
      )
      self.assertAllClose(actual_mean, expected_mean)
  
file_path: probability/tensorflow_probability/python/experimental/bayesopt/acquisition/acquisition_function_test.py
imports: from absl.testing import parameterized
  import numpy
  import tensorflow.compat.v2
  from tensorflow_probability.python.distributions import normal
  from tensorflow_probability.python.experimental.bayesopt.acquisition import acquisition_function
  from tensorflow_probability.python.internal import test_util
label_desc: Use image node_id 2 for calling the AcquisitionFunctionTest obj's underlying member method code with example usage: obj.test_mcmc_reducer(reduce_dims) without return types