code: def test_log_prob_approaches_target_distribution(self):
      seed = test_util.test_seed(sampler_type="stateless")
      target = normal.Normal(loc=0.0, scale=2.0)
      proposal = student_t.StudentT(df=2, loc=-4.0, scale=5.0)
      resampled = importance_resample.ImportanceResample(
          proposal,
          target_log_prob_fn=target.log_prob,
          importance_sample_size=1000,
      )
  
      xs, target_lp = self.evaluate(
          target.experimental_sample_and_log_prob(100, seed=seed)
      )
      self.assertAllClose(
          target_lp, resampled.log_prob(xs, seed=seed), atol=0.17
      )
  
imports: from absl.testing import parameterized
  import numpy
  import tensorflow.compat.v1
  import tensorflow.compat.v2
  from tensorflow_probability.python.distributions import categorical
  from tensorflow_probability.python.distributions import independent
  from tensorflow_probability.python.distributions import joint_distribution_coroutine
  from tensorflow_probability.python.distributions import mixture_same_family
  from tensorflow_probability.python.distributions import mvn_diag
  from tensorflow_probability.python.distributions import mvn_tril
  from tensorflow_probability.python.distributions import normal
  from tensorflow_probability.python.distributions import student_t
  from tensorflow_probability.python.experimental.distributions import importance_resample
  from tensorflow_probability.python.internal import samplers
  from tensorflow_probability.python.internal import test_util
  from tensorflow_probability.python.vi import csiszar_divergence
label_desc: Use image node_id 5 for calling the ImportanceResampleTest obj's underlying member method code with example usage: obj.test_log_prob_approaches_target_distribution() without return types