code: def testExcessiveConcretizationWithDefaultReinterpretedBatchNdims(
      self,
  ):
      loc = tfp_hps.defer_and_count_usage(
          tf.Variable(np.zeros((5, 2, 3)), shape=tf.TensorShape(None))
      )
      scale = tfp_hps.defer_and_count_usage(
          tf.Variable(np.ones([]), shape=tf.TensorShape(None))
      )
      dist = independent.Independent(
          logistic.Logistic(loc=loc, scale=scale, validate_args=True),
          reinterpreted_batch_ndims=None,
          validate_args=True,
      )
  
      for method in ("event_shape_tensor", "mean", "variance"):
          with tfp_hps.assert_no_excessive_var_usage(
              method, max_permissible=4
          ):
              getattr(dist, method)()
  
      with tfp_hps.assert_no_excessive_var_usage(
          "batch_shape_tensor", max_permissible=10
      ):
          # Automatic inference of batch shape requires additional concretizations.
          dist.batch_shape_tensor()
  
      with tfp_hps.assert_no_excessive_var_usage(
          "sample", max_permissible=6
      ):
          dist.sample(seed=test_util.test_seed())
  
      # In addition to the four reads of `loc`, `scale` described above in
      # `testExcessiveConcretizationOfParams`, the methods below have two more
      # reads of these parameters -- from computing a default value for
      # `reinterpreted_batch_ndims`, which requires calling
      # `dist.distribution.batch_shape_tensor()`.
  
      for method in ("log_prob", "log_cdf", "prob", "cdf"):
          with tfp_hps.assert_no_excessive_var_usage(
              method, max_permissible=6
          ):
              getattr(dist, method)(np.zeros((4, 5, 2, 3)))
  
      with tfp_hps.assert_no_excessive_var_usage(
          "entropy", max_permissible=6
      ):
          dist.entropy()
  
      # `Distribution.survival_function` and `Distribution.log_survival_function`
      # will call `Distribution.cdf` and `Distribution.log_cdf`, resulting in
      # one additional call to `Independent._parameter_control_dependencies`,
      # and thus two additional concretizations of the parameters.
  
      for method in ("survival_function", "log_survival_function"):
          with tfp_hps.assert_no_excessive_var_usage(
              method, max_permissible=8
          ):
              getattr(dist, method)(np.zeros((4, 5, 2, 3)))
  
imports: import functools
  import os
  from absl.testing import parameterized
  import numpy
  from scipy import stats
  import tensorflow.compat.v1
  import tensorflow.compat.v2
  from tensorflow_probability.python.distributions import bernoulli
  from tensorflow_probability.python.distributions import exponential
  from tensorflow_probability.python.distributions import independent
  from tensorflow_probability.python.distributions import kullback_leibler
  from tensorflow_probability.python.distributions import log_prob_ratio
  from tensorflow_probability.python.distributions import logistic
  from tensorflow_probability.python.distributions import lognormal
  from tensorflow_probability.python.distributions import mvn_diag
  from tensorflow_probability.python.distributions import normal
  from tensorflow_probability.python.distributions import poisson
  from tensorflow_probability.python.internal import hypothesis_testlib
  from tensorflow_probability.python.internal import tensorshape_util
  from tensorflow_probability.python.internal import test_util
label_desc: Use image node_id 20 for calling the IndependentDistributionTest obj's underlying member method code with example usage: obj.testExcessiveConcretizationWithDefaultReinterpretedBatchNdims() without return types