code: def testMeanHigherDimension(self, dtype):
      testee_dist = lkj.LKJ(
          dimension=6,
          concentration=dtype([1.0, 3.0, 5.0]),
          validate_args=True,
      )
      num_samples = 20000
      results = testee_dist.sample(
          sample_shape=[num_samples], seed=test_util.test_seed()
      )
      mean = testee_dist.mean()
      self.assertEqual(mean.shape, [3, 6, 6])
      # lkj.LKJ has some small numerical issues, so we allow for some amount of
      # numerical tolerance when testing means.
      numerical_tolerance = 1e-5
      check1 = st.assert_true_mean_in_interval_by_dkwm(
          samples=results,
          low=-1.0,
          high=1.0,
          expected_low=mean - numerical_tolerance,
          expected_high=mean + numerical_tolerance,
          false_fail_rate=1e-6,
      )
      check2 = assert_util.assert_less(
          st.min_discrepancy_of_true_means_detectable_by_dkwm(
              num_samples,
              low=-1.0,
              high=1.0,
              # Smaller false fail rate because of different batch sizes between
              # these two checks.
              false_fail_rate=1e-7,
              false_pass_rate=1e-6,
          ),
          # 4% relative error
          0.08,
      )
      self.evaluate([check1, check2])
  
imports: from absl.testing import parameterized
  import numpy
  import tensorflow.compat.v1
  import tensorflow.compat.v2
  from tensorflow_probability.python.distributions import lkj
  from tensorflow_probability.python.distributions.internal import statistical_testing
  from tensorflow_probability.python.internal import assert_util
  from tensorflow_probability.python.internal import dtype_util
  from tensorflow_probability.python.internal import test_util
label_desc: Use image node_id 14 for calling the LKJTest obj's underlying member method code with example usage: obj.testMeanHigherDimension(dtype) without return types