code: def test_sample_and_log_prob(self):
      s = sample.Sample(
          independent.Independent(
              normal.Normal(loc=tf.zeros([3, 2]), scale=1), 1
          ),
          [5, 4],
          validate_args=True,
      )
      x, lp = s.experimental_sample_and_log_prob(
          [6, 1], seed=test_util.test_seed()
      )
      self.assertAllClose(lp, s.log_prob(x))
  
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 3 for calling the SampleDistributionTest obj's underlying member method code with example usage: obj.test_sample_and_log_prob() without return types