docstr: Tests that the output of lambertw_distribution is of the correct type.
docstr_tok: ["Tests","that","the","output","of","lambertw_distribution","is","of","the","correct","type","."]
code: def testLambertWDistributionOutputType(
      self, distribution_constructor, distribution_kwargs
  ):
      """Tests that the output of lambertw_distribution is of the correct type."""
      shift = 1.0
      scale = 2.0
      tail = 1.0
  
      lambertw_distribution = lambertw_f.LambertWDistribution(
          distribution=distribution_constructor(**distribution_kwargs),
          shift=shift,
          scale=scale,
          tailweight=tail,
      )
      self.assertIsInstance(
          lambertw_distribution,
          transformed_distribution.TransformedDistribution,
      )
      samples = lambertw_distribution.sample(
          10, seed=test_util.test_seed()
      )
      self.assertAllEqual(samples.shape, [10])
  
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 1 for calling the LambertWDistributionTest obj's underlying member method code with example usage: obj.testLambertWDistributionOutputType(distribution_constructor, distribution_kwargs) without return types