code: def _verifyCovariance(self, vmf):
      dim = tf.compat.dimension_value(vmf.event_shape[-1])
      nsamples = 10000
      samples = vmf.sample(nsamples, seed=test_util.test_seed())
      samples = tf.debugging.check_numerics(samples, "samples")
      cov = vmf.covariance()
      samples, cov = self.evaluate([samples, cov])
      batched_samples = np.reshape(samples, [nsamples, -1, dim])
      batch_size = batched_samples.shape[1]
      est_cov = np.zeros([batch_size, dim, dim], dtype=cov.dtype)
      for bi in range(batched_samples.shape[1]):
          est_cov[bi] = np.cov(batched_samples[:, bi], rowvar=False)
      self.assertAllClose(
          np.reshape(est_cov, cov.shape), cov, atol=0.015
      )
  
imports: from absl.testing import parameterized
  import numpy
  from scipy import special
  import tensorflow.compat.v2
  from tensorflow_probability.python.distributions import kullback_leibler
  from tensorflow_probability.python.distributions import spherical_uniform
  from tensorflow_probability.python.distributions import von_mises_fisher
  from tensorflow_probability.python.internal import dtype_util
  from tensorflow_probability.python.internal import tensorshape_util
  from tensorflow_probability.python.internal import test_util
label_desc: Use image node_id 6 for calling the VonMisesFisherTest obj's underlying member method code with example usage: obj._verifyCovariance(vmf) without return types