code: def testLargeLogProbDiff(self):
      b = 15
      n = 5_000
      d0 = independent.Independent(
          normal.Normal(tf.fill([b, n], 0.0), tf.fill([n], 0.1)),
          reinterpreted_batch_ndims=1,
          experimental_use_kahan_sum=True,
      )
      d1 = independent.Independent(
          normal.Normal(tf.fill([b, n], 1e-5), tf.fill([n], 0.1)),
          reinterpreted_batch_ndims=1,
          experimental_use_kahan_sum=True,
      )
      strm = test_util.test_seed_stream()
      x0 = self.evaluate(  # overdispersed
          normal.Normal(0, 2).sample([b, n], seed=strm())
      )
      x1 = self.evaluate(  # overdispersed, perturbed
          x0 + normal.Normal(0, 1e-6).sample(x0.shape, seed=strm())
      )
      d0_64 = d0.copy(
          distribution=normal.Normal(
              tf.cast(d0.distribution.loc, tf.float64),
              tf.cast(d0.distribution.scale, tf.float64),
          )
      )
      d1_64 = d1.copy(
          distribution=normal.Normal(
              tf.cast(d1.distribution.loc, tf.float64),
              tf.cast(d1.distribution.scale, tf.float64),
          )
      )
      self.assertNotAllZero(d0.log_prob(x0) < -1_000_000)
      self.assertAllClose(
          d0_64.log_prob(tf.cast(x0, tf.float64))
          - d1_64.log_prob(tf.cast(x1, tf.float64)),
          log_prob_ratio.log_prob_ratio(d0, x0, d1, x1),
          rtol=0.0,
          atol=0.01,
      )
  
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 24 for calling the IndependentDistributionTest obj's underlying member method code with example usage: obj.testLargeLogProbDiff() without return types