code: def manual_testSampleCorrectMarginalsWithHypothesis(self, data):
      # You probably want --test_timeout=300 for this one
      dist = data.draw(dhps.distributions(dist_name="Multinomial"))
      special_class = data.draw(
          hps.sampled_from(
              range(dist._probs_parameter_no_checks().shape[-1])
          )
      )
      # TODO(axch): Drawing the test seed inside the property will interact poorly
      # with --vary_seed under Hypothesis, because the seed will change as
      # Hypothesis tries to shrink failing examples.  It would be better to
      # compute the test seed outside the test method somehow, so each example
      # gets the same one.
      # TODO(axch): Not sure how to think about the false positive rate of this
      # test.  Hypothesis will try an adversarial search to make the statistical
      # assertion fail, which seems like a multiple comparisons problem with a
      # combinatorially large space of alternatives?
      self.propSampleCorrectMarginals(
          dist, special_class, under_hypothesis=True
      )
  
imports: import unittest
  from absl.testing import parameterized
  import hypothesis
  from hypothesis import strategies
  import numpy
  import tensorflow.compat.v1
  import tensorflow.compat.v2
  from tensorflow_probability.python.bijectors import identity
  from tensorflow_probability.python.distributions import binomial
  from tensorflow_probability.python.distributions import hypothesis_testlib
  from tensorflow_probability.python.distributions import multinomial
  from tensorflow_probability.python.distributions.internal import statistical_testing
  from tensorflow_probability.python.internal import hypothesis_testlib
  from tensorflow_probability.python.internal import test_util
  from tensorflow_probability.python.math import gradient
  from tensorflow_probability.python.util import deferred_tensor
label_desc: Use image node_id 28 for calling the MultinomialTest obj's underlying member method code with example usage: obj.manual_testSampleCorrectMarginalsWithHypothesis(data) without return types