code: def testSample(self, dist_name, data):
      if (dist_name in self.sample_blocklist) != FLAGS.blocklists_only:
          self.skipTest("Distribution currently broken.")
  
      def _sample(dist):
          return dist.sample(seed=random.PRNGKey(0))
  
      params_unconstrained, batch_shape = data.draw(
          dhps.base_distribution_unconstrained_params(
              enable_vars=False, dist_name=dist_name
          )
      )
  
      for (
          param_name,
          unconstrained_param,
          dist_func,
          func,
      ) in self._param_func_generator(
          data, dist_name, params_unconstrained, batch_shape, _sample
      ):
          dist = dist_func(param_name, unconstrained_param)
          if (
              dist.reparameterization_type
              != reparameterization.FULLY_REPARAMETERIZED
          ):
              # Skip distributions that don't support differentiable sampling.
              self.skipTest(
                  "{} is not reparameterized.".format(dist_name)
              )
          self._test_transformation(
              functools.partial(func, param_name),
              unconstrained_param,
              msg=param_name,
          )
  
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 3 for calling the _GradTest obj's underlying member method code with example usage: obj.testSample(dist_name, data) and returns: dist