code: def test_chunked_higher_rank_running_variance(self):
      rng = test_util.test_np_rng()
      x = rng.rand(100, 2, 5)
      var = sample_stats.RunningVariance.from_shape((5,))
      var = consume(var, x, chunk_axis=0)
      final_mean, final_var = self.evaluate([var.mean, var.variance()])
      self.assertAllClose(
          np.mean(x.reshape(200, 5), axis=0), final_mean, rtol=1e-5
      )
      self.assertEqual(final_var.shape, (5,))
  
      # reshaping to be compatible with a check against numpy
      x_reshaped = x.reshape(200, 5)
      self.assertAllClose(
          np.var(x_reshaped, axis=0), final_var, rtol=1e-5
      )
  
imports: from absl.testing import parameterized
  import numpy
  import scipy.stats
  import tensorflow.compat.v2
  from tensorflow_probability.python.experimental.stats import sample_stats
  from tensorflow_probability.python.internal import test_util
  from tensorflow_probability.python.mcmc import diagnostic
label_desc: Use image node_id 4 for calling the RunningCovarianceTest obj's underlying member method code with example usage: obj.test_chunked_higher_rank_running_variance() without return types