code: def testPmfBothZeroBatchesNontrivialN(self):
      # Both zero-batches.  No broadcast
      p = [0.1, 0.9]
      counts = [3.0, 2]
      dist = multinomial.Multinomial(
          total_count=5.0, probs=p, validate_args=True
      )
      pmf = dist.prob(counts)
      # 5 choose 3 = 5 choose 2 = 10. 10 * (.9)^2 * (.1)^3 = 81/10000.
      self.assertAllClose(81.0 / 10000, self.evaluate(pmf))
      self.assertAllEqual([], pmf.shape)
  
imports: import unittest
  from absl.testing import parameterized
  import hypothesis
  from hypothesis import strategies
  import numpy
  import tensorflow.compat.v1
  import tensorflow.compat.v2
  from tensorflow_probability.python.bijectors import identity
  from tensorflow_probability.python.distributions import binomial
  from tensorflow_probability.python.distributions import hypothesis_testlib
  from tensorflow_probability.python.distributions import multinomial
  from tensorflow_probability.python.distributions.internal import statistical_testing
  from tensorflow_probability.python.internal import hypothesis_testlib
  from tensorflow_probability.python.internal import test_util
  from tensorflow_probability.python.math import gradient
  from tensorflow_probability.python.util import deferred_tensor
label_desc: Use image node_id 11 for calling the MultinomialTest obj's underlying member method code with example usage: obj.testPmfBothZeroBatchesNontrivialN() without return types