code: def testVonMisesSampleVarianceUniform(self):
      dist = von_mises.VonMises(
          self.make_tensor(1.0),
          self.make_tensor(0.0),
          validate_args=True,
      )
  
      n = 10000
      samples = dist.sample(n, seed=test_util.test_seed())
  
      # For circular uniform distribution, the mean is not well-defined,
      # so only checking the variance.
      expected_variance = 1.0
      standardized_samples = samples - tf.expand_dims(dist.mean(), 0)
      variance_samples = 1.0 - tf.cos(standardized_samples)
  
      self.assertAllMeansClose(
          self.evaluate(variance_samples),
          expected_variance,
          axis=0,
          rtol=0.1,
      )
  
imports: import numpy
  from scipy import special
  from scipy import stats
  import tensorflow.compat.v1
  import tensorflow.compat.v2
  from tensorflow_probability.python.distributions import kullback_leibler
  from tensorflow_probability.python.distributions import von_mises
  from tensorflow_probability.python.internal import dtype_util
  from tensorflow_probability.python.internal import test_util
  from tensorflow_probability.python.math import gradient
label_desc: Use image node_id 20 for calling the _VonMisesTest obj's underlying member method code with example usage: obj.testVonMisesSampleVarianceUniform() without return types