code: def test_bijector_constant_underlying_ildj(self):
      d = scale_lib.Scale([2.0, 3.0])(normal.Normal([0.0, 0.0], 1.0))
      bij = sample.Sample(
          d, [3]
      ).experimental_default_event_space_bijector()
      ildj = bij.inverse_log_det_jacobian(
          tf.zeros([2, 3]), event_ndims=1
      )
      self.assertAllClose(-np.log([2.0, 3.0]) * 3, ildj)
      ildj = bij.inverse_log_det_jacobian(
          tf.zeros([2, 3]), event_ndims=2
      )
      self.assertAllClose(-np.log([2.0, 3.0]).sum() * 3, 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 19 for calling the SampleDistributionTest obj's underlying member method code with example usage: obj.test_bijector_constant_underlying_ildj() without return types