code: def testKumaraswamySample(self):
      a = 1.0
      b = 2.0
      dist = kumaraswamy.Kumaraswamy(a, b, validate_args=True)
      n = tf.constant(100000)
      samples = dist.sample(n, seed=test_util.test_seed())
      sample_values = self.evaluate(samples)
      self.assertEqual(sample_values.shape, (100000,))
      self.assertFalse(np.any(sample_values < 0.0))
      self.assertLess(
          sp_stats.kstest(
              # Kumaraswamy is a univariate distribution.
              sample_values,
              lambda x: _kumaraswamy_cdf(1.0, 2.0, x),
          )[0],
          0.01,
      )
      # The standard error of the sample mean is 1 / (sqrt(18 * n))
      expected_mean = _kumaraswamy_moment(a, b, 1)
      self.assertAllClose(
          sample_values.mean(axis=0), expected_mean, atol=1e-2
      )
      expected_variance = (
          _kumaraswamy_moment(a, b, 2)
          - _kumaraswamy_moment(a, b, 1) ** 2
      )
      self.assertAllClose(
          np.cov(sample_values, rowvar=0), expected_variance, atol=1e-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 kumaraswamy
  from tensorflow_probability.python.internal import test_util
label_desc: Use image node_id 20 for calling the KumaraswamyTest obj's underlying member method code with example usage: obj.testKumaraswamySample() without return types