code: def test_inflated(self):
      zinb = inflated.Inflated(
          negative_binomial.NegativeBinomial(5.0, probs=0.1),
          inflated_loc_probs=0.2,
      )
      samples = zinb.sample(sample_shape=10, seed=test_util.test_seed())
      self.assertEqual((10,), samples.shape)
  
      spike_and_slab = inflated.Inflated(
          normal.Normal(loc=1.0, scale=2.0), inflated_loc_probs=0.5
      )
      lprob = self.evaluate(spike_and_slab.log_prob(99.0))
      self.assertLess(lprob, 0.0)
  
      param_props = inflated.Inflated.parameter_properties(
          dtype=tf.float32
      )
      self.assertFalse(param_props["distribution"].is_tensor)
      self.assertTrue(param_props["inflated_loc_logits"].is_preferred)
      self.assertFalse(param_props["inflated_loc_probs"].is_preferred)
      self.assertTrue(param_props["inflated_loc"].is_tensor)
  
file_path: probability/tensorflow_probability/python/distributions/inflated_test.py
imports: import numpy
  import tensorflow.compat.v2
  from tensorflow_probability.python.distributions import gamma
  from tensorflow_probability.python.distributions import inflated
  from tensorflow_probability.python.distributions import lognormal
  from tensorflow_probability.python.distributions import negative_binomial
  from tensorflow_probability.python.distributions import normal
  from tensorflow_probability.python.experimental.util import trainable
  from tensorflow_probability.python.internal import test_util
  from tensorflow_probability.python.math import gradient
label_desc: Use image node_id 1 for calling the DistributionsTest obj's underlying member method code with example usage: obj.test_inflated() without return types