code: def testLogProbSample(self, dist_name, data):
      if (
          dist_name in self.logprob_sample_blocklist
      ) != FLAGS.blocklists_only:
          self.skipTest("Distribution currently broken.")
  
      params, batch_shape = data.draw(
          dhps.base_distribution_unconstrained_params(
              enable_vars=False, dist_name=dist_name
          )
      )
      constrained_params = dhps.constrain_params(params, dist_name)
  
      dist = data.draw(
          dhps.base_distributions(
              batch_shape=batch_shape,
              enable_vars=False,
              dist_name=dist_name,
              params=constrained_params,
          )
      )
  
      sample = dist.sample(seed=random.PRNGKey(0))
      if np.issubdtype(sample.dtype, np.integer):
          self.skipTest(
              "{} has integer samples; no derivative.".format(dist_name)
          )
  
      def _log_prob(sample):
          return dist.log_prob(sample)
  
      self._test_transformation(_log_prob, sample)
  
imports: import functools
  import os
  from absl import flags
  from absl.testing import parameterized
  import hypothesis
  from hypothesis import strategies
  import jax
  from jax import random
  import jax.numpy
  from tensorflow_probability.python.internal import reparameterization
  from tensorflow_probability.python.internal.backend import jax
  from tensorflow_probability.substrates.jax.bijectors import bijector_test_util
  from tensorflow_probability.substrates.jax.distributions import hypothesis_testlib
  from tensorflow_probability.substrates.jax.distributions import normal
  from tensorflow_probability.substrates.jax.distributions import transformed_distribution
  from tensorflow_probability.substrates.jax.internal import hypothesis_testlib
  from tensorflow_probability.substrates.jax.internal import test_util
label_desc: Use image node_id 5 for calling the _GradTest obj's underlying member method code with example usage: obj.testLogProbSample(dist_name, data) and returns: dist