code: def testBatchSamplesAreIndependent(self):
      num_samples = 1000
      lwn = lambertw_f.LambertWNormal(
          loc=0.0, scale=1.0, tailweight=[0.0, 0.0]
      )
      xs = lwn.sample(num_samples, seed=test_util.test_seed())
      cov = 1.0 / num_samples * tf.matmul(xs, xs, transpose_a=True)
      self.assertAllClose(cov, tf.eye(2), atol=0.2)
  
imports: import math
  from absl.testing import parameterized
  import numpy
  from scipy import stats
  import tensorflow.compat.v2
  from tensorflow_probability.python.bijectors import lambertw_transform
  from tensorflow_probability.python.distributions import lambertw_f
  from tensorflow_probability.python.distributions import normal
  from tensorflow_probability.python.distributions import student_t
  from tensorflow_probability.python.distributions import transformed_distribution
  from tensorflow_probability.python.distributions import uniform
  from tensorflow_probability.python.internal import test_util
  from tensorflow_probability.python.internal import tf_keras
label_desc: Use image node_id 2 for calling the LambertWNormalTest obj's underlying member method code with example usage: obj.testBatchSamplesAreIndependent() without return types