code: def VerifyCovariance(self, dim):
      seed_stream = test_util.test_seed_stream()
      num_samples = int(5e4)
      mean_direction = tf.random.uniform(
          shape=[5, dim],
          minval=self.dtype(1.0),
          maxval=self.dtype(2.0),
          dtype=self.dtype,
          seed=seed_stream(),
      )
      mean_direction = tf.nn.l2_normalize(mean_direction, axis=-1)
      concentration = tf.math.log(
          tf.random.uniform(
              shape=[2, 1],
              minval=self.dtype(1.0),
              maxval=self.dtype(100.0),
              dtype=self.dtype,
              seed=seed_stream(),
          )
      )
  
      ps = power_spherical.PowerSpherical(
          mean_direction=mean_direction,
          concentration=concentration,
          validate_args=True,
          allow_nan_stats=False,
      )
      samples = ps.sample(num_samples, seed=test_util.test_seed())
      sample_cov = sample_stats.covariance(samples, sample_axis=0)
      true_cov, sample_cov = self.evaluate(
          [ps.covariance(), sample_cov]
      )
      self.assertAllClose(true_cov, sample_cov, rtol=0.15, atol=1.5e-3)
  
imports: import numpy
  from scipy import special
  import tensorflow.compat.v2
  from tensorflow_probability.python.distributions import kullback_leibler
  from tensorflow_probability.python.distributions import power_spherical
  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
  from tensorflow_probability.python.stats import sample_stats
label_desc: Use image node_id 13 for calling the _PowerSphericalTest obj's underlying member method code with example usage: obj.VerifyCovariance(dim) without return types