code: def test_log_prob_independence_per_x(self):
      dist = importance_resample.ImportanceResample(
          proposal_distribution=normal.Normal(loc=0.0, scale=1.0),
          target_log_prob_fn=normal.Normal(
              loc=0.85, scale=0.1
          ).log_prob,
          importance_sample_size=20,
      )
  
      seed = test_util.test_seed(sampler_type="stateless")
      xs = np.linspace(0, 1.6, 100)
      aucs = []
      for s in samplers.split_seed(seed, n=30):
          aucs.append(
              np.trapz(
                  self.evaluate(dist.prob(xs, seed=s, sample_size=100)),
                  xs,
              )
          )
  
      self.assertAllClose(aucs, np.ones_like(aucs), atol=0.05)
  
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 10 for calling the ImportanceResampleTest obj's underlying member method code with example usage: obj.test_log_prob_independence_per_x() without return types