code: def testLogProb(self, dist_name, data):
      if (dist_name in self.logprob_blocklist) != FLAGS.blocklists_only:
          self.skipTest("Distribution currently broken.")
      if dist_name in [
          "NegativeBinomial",
          "ZeroInflatedNegativeBinomial",
      ]:
          self.skipTest(
              "Skip never-terminating negative binomial vmap logprob."
          )
      dist = data.draw(
          dhps.distributions(
              enable_vars=False,
              dist_name=dist_name,
              eligibility_filter=lambda dname: dname
              not in self.logprob_blocklist,
          )
      )
      sample = dist.sample(
          seed=test_util.test_seed(), sample_shape=self.batch_size
      )
      result = self.map(dist.log_prob)(sample)
      if not FLAGS.execute_only:
          self.assertAllClose(
              result, dist.log_prob(sample), rtol=1e-6, atol=1e-6
          )
  
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 4 for calling the _MapTest obj's underlying member method code with example usage: obj.testLogProb(dist_name, data) without return types