code: def testFindValidInitialPoints(self):
      seed = test_util.test_seed(sampler_type="stateless")
      model_dist = jds.JointDistributionSequential(
          [
              uniform.Uniform(0.0, 1.0),
              half_cauchy.HalfCauchy(0.0, 1.0),
              lambda sigma, mu: normal.Normal(mu, sigma),
          ]
      )
      # Intentionally bad bijectors lead to a ~25% chance to
      # initialize in the valid region
      bijectors = [
          identity.Identity(),
          identity.Identity(),
          identity.Identity(),
      ]
      init_dist = initialization.init_near_unconstrained_zero(
          model_dist, constraining_bijector=bijectors
      )
      # We expect to be able to fix it with rejection sampling
      init_states = initialization.retry_init(
          init_dist.sample,
          model_dist.log_prob,
          sample_shape=[20],
          seed=seed,
      )
      for s in self.evaluate(init_states):
          self.assertAllFinite(s)
          self.assertEqual((20,), s.shape)
      self.assertAllFinite(
          self.evaluate(model_dist.log_prob(init_states))
      )
  
imports: import tensorflow.compat.v2
  from tensorflow_probability.python.bijectors import exp
  from tensorflow_probability.python.bijectors import identity
  from tensorflow_probability.python.bijectors import pad
  from tensorflow_probability.python.bijectors import sigmoid
  from tensorflow_probability.python.distributions import half_cauchy
  from tensorflow_probability.python.distributions import joint_distribution_auto_batched
  from tensorflow_probability.python.distributions import joint_distribution_coroutine
  from tensorflow_probability.python.distributions import joint_distribution_named
  from tensorflow_probability.python.distributions import joint_distribution_sequential
  from tensorflow_probability.python.distributions import multinomial
  from tensorflow_probability.python.distributions import mvn_diag
  from tensorflow_probability.python.distributions import normal
  from tensorflow_probability.python.distributions import sample
  from tensorflow_probability.python.distributions import uniform
  from tensorflow_probability.python.experimental import distribute
  from tensorflow_probability.python.experimental.mcmc import initialization
  from tensorflow_probability.python.internal import distribute_test_lib
  from tensorflow_probability.python.internal import test_util
label_desc: Use image node_id 4 for calling the InitializationTest obj's underlying member method code with example usage: obj.testFindValidInitialPoints() without return types