code: def test_all_distributions_either_work_or_raise_error(
      self, dist_name, data
  ):
      if not tf.executing_eagerly():
          self.skipTest(
              "No need to test every distribution in graph mode."
          )
      if dist_name in PRECONDITIONING_FAILS_DISTS:
          self.skipTest("Known failure.")
  
      dist = data.draw(
          dhps.base_distributions(
              dist_name=dist_name,
              enable_vars=False,
              param_strategy_fn=_constrained_zeros_fn,
          )
      )
      try:
          b = distribution_bijectors.make_distribution_bijector(dist)
      except NotImplementedError:
          # Okay to fail as long as explicit error is raised.
          self.skipTest("Bijector not implemented.")
      self.assertDistributionIsApproximatelyStandardNormal(
          invert.Invert(b)(dist)
      )
  
imports: from absl.testing import parameterized
  import hypothesis
  from hypothesis import strategies
  import tensorflow.compat.v2
  from tensorflow_probability.python.bijectors import invert
  from tensorflow_probability.python.distributions import exponential
  from tensorflow_probability.python.distributions import hypothesis_testlib
  from tensorflow_probability.python.distributions import independent
  from tensorflow_probability.python.distributions import joint_distribution_auto_batched
  from tensorflow_probability.python.distributions import markov_chain
  from tensorflow_probability.python.distributions import mvn_tril
  from tensorflow_probability.python.distributions import normal
  from tensorflow_probability.python.distributions import poisson
  from tensorflow_probability.python.distributions import uniform
  from tensorflow_probability.python.experimental.bijectors import distribution_bijectors
  from tensorflow_probability.python.experimental.vi import automatic_structured_vi
  from tensorflow_probability.python.internal import hypothesis_testlib
  from tensorflow_probability.python.internal import prefer_static
  from tensorflow_probability.python.internal import test_util
  from tensorflow_probability.python.internal import tf_keras
  from tensorflow_probability.python.math import gradient
  from tensorflow_probability.python.mcmc import dual_averaging_step_size_adaptation
  from tensorflow_probability.python.mcmc import nuts
  from tensorflow_probability.python.mcmc import sample
  from tensorflow_probability.python.mcmc import transformed_kernel
  from tensorflow_probability.python.vi import optimization
label_desc: Use image node_id 2 for calling the DistributionBijectorsTest obj's underlying member method code with example usage: obj.test_all_distributions_either_work_or_raise_error(dist_name, data) without return types