code: def testExcessiveConcretizationOfParamsWithReparameterization(self):
      logits = tfp_hps.defer_and_count_usage(
          self._build_variable(
              np.zeros(5), name="logits", static_rank=True
          )
      )
      loc = tfp_hps.defer_and_count_usage(
          self._build_variable(
              np.zeros(5), name="loc", static_rank=True
          )
      )
      scale = tfp_hps.defer_and_count_usage(
          self._build_variable(1.0, name="scale", static_rank=True)
      )
      dist = mixture_same_family.MixtureSameFamily(
          mixture_distribution=categorical.Categorical(logits=logits),
          components_distribution=logistic.Logistic(
              loc=loc, scale=scale
          ),
          reparameterize=True,
          validate_args=True,
      )
  
      # TODO(b/140579567): With reparameterization, there are additional reads of
      # the parameters of the underlying mixture and components distributions when
      # sampling, from calls in `_distributional_transform` to:
      #
      #  - `self.mixture_distribution.logits_parameter`
      #  - `self.components_distribution.log_prob`
      #  - `self.components_distribution.cdf`
      #
      # NOTE: In the unlikely case that samples have a statically-known rank but
      # the rank of `self.components_distribution.event_shape` is not known
      # statically, there can be additional reads in `_distributional_transform`
      # from calling `self.components_distribution.is_scalar_event`.
  
      with tfp_hps.assert_no_excessive_var_usage(
          "sample", max_permissible=4
      ):
          dist.sample(seed=test_util.test_seed())
  
imports: import numpy
  import tensorflow.compat.v1
  import tensorflow.compat.v2
  from tensorflow_probability.python.distributions import bernoulli
  from tensorflow_probability.python.distributions import categorical
  from tensorflow_probability.python.distributions import dirichlet
  from tensorflow_probability.python.distributions import exponential
  from tensorflow_probability.python.distributions import independent
  from tensorflow_probability.python.distributions import logistic
  from tensorflow_probability.python.distributions import mixture_same_family
  from tensorflow_probability.python.distributions import mvn_diag
  from tensorflow_probability.python.distributions import normal
  from tensorflow_probability.python.internal import hypothesis_testlib
  from tensorflow_probability.python.internal import samplers
  from tensorflow_probability.python.internal import tensorshape_util
  from tensorflow_probability.python.internal import test_util
  from tensorflow_probability.python.math import gradient
label_desc: Use image node_id 25 for calling the _MixtureSameFamilyTest obj's underlying member method code with example usage: obj.testExcessiveConcretizationOfParamsWithReparameterization() without return types