docstr: Tests that samples can be correctly transformed into gaussian samples.
docstr_tok: ["Tests","that","samples","can","be","correctly","transformed","into","gaussian","samples","."]
code: def testSampleMethod(self):
      """Tests that samples can be correctly transformed into gaussian samples."""
      tailweight = 0.1
      loc = 2.0
      scale = 3.0
      lwn = lambertw_f.LambertWNormal(
          loc=loc, scale=scale, tailweight=tailweight
      )
      samples = lwn.sample(100, seed=test_util.test_seed)
  
      self.assertAllEqual(samples.shape, [100])
      gaussianized_samples = lambertw_transform.LambertWTail(
          shift=loc, scale=scale, tailweight=tailweight
      ).inverse(samples)
      _, p = stats.normaltest(self.evaluate(gaussianized_samples))
      self.assertGreater(p, 0.05)
  
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 3 for calling the LambertWNormalTest obj's underlying member method code with example usage: obj.testSampleMethod() without return types