code: def test_inflated_batched(self):
      nb = negative_binomial.NegativeBinomial(
          total_count=np.array(
              [1.0, 2.0, 3.0, 4.0, 5.0], dtype=np.float32
          ),
          logits=np.array(
              [-2.0, -1.0, 0.0, 1.0, 2.0], dtype=np.float32
          ),
      )
      zinb = inflated.Inflated(
          nb,
          inflated_loc_probs=np.array(
              [0.2, 0.4, 0.6, 0.8, 1.0], dtype=np.float32
          ),
      )
  
      lprob = zinb.log_prob([0, 1, 2, 3, 4])
      self.assertEqual((5,), lprob.shape)
  
      samples = zinb.sample(seed=test_util.test_seed())
      self.assertEqual((5,), samples.shape)
  
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 2 for calling the DistributionsTest obj's underlying member method code with example usage: obj.test_inflated_batched() without return types