code: def test_bijector_scalar_underlying_ildj(self):
      d = normal.Normal(0.0, 1.0)  # Uses Identity bijector, ildj=0.
      bij = sample.Sample(
          d, [1]
      ).experimental_default_event_space_bijector()
      ildj = bij.inverse_log_det_jacobian(
          tf.zeros([1, 1]), event_ndims=1
      )
      self.assertAllEqual(0.0, ildj)
      ildj = bij.inverse_log_det_jacobian(
          tf.zeros([1, 1]), event_ndims=2
      )
      self.assertAllEqual(0.0, ildj)
  
imports: import functools
  import os
  from absl.testing import parameterized
  import numpy
  import tensorflow.compat.v2
  from tensorflow_probability.python.bijectors import exp
  from tensorflow_probability.python.bijectors import scale
  from tensorflow_probability.python.bijectors import scale_matvec_tril
  from tensorflow_probability.python.distributions import cholesky_lkj
  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.distributions import sample
  from tensorflow_probability.python.distributions import transformed_distribution
  from tensorflow_probability.python.distributions import uniform
  from tensorflow_probability.python.internal import test_util
label_desc: Use image node_id 18 for calling the SampleDistributionTest obj's underlying member method code with example usage: obj.test_bijector_scalar_underlying_ildj() without return types