code: def testPmfNonIntegerCounts(self):
      p = [[0.1, 0.2, 0.7]]
      n = [[5.0]]
      # No errors with integer n.
      multinom = multinomial.Multinomial(
          total_count=n, probs=p, validate_args=True
      )
      self.evaluate(multinom.prob([2.0, 1, 2]))
      self.evaluate(multinom.prob([3.0, 0, 2]))
      # Counts don't sum to n.
      with self.assertRaisesOpError(
          "counts must sum to `self.total_count`"
      ):
          self.evaluate(multinom.prob([2.0, 3, 2]))
      # Counts are non-integers.
      x = tf1.placeholder_with_default([1.0, 2.5, 1.5], shape=None)
      with self.assertRaisesOpError(
          "cannot contain fractional components."
      ):
          self.evaluate(multinom.prob(x))
  
      multinom = multinomial.Multinomial(
          total_count=n, probs=p, validate_args=False
      )
      self.evaluate(multinom.prob([1.0, 2.0, 2.0]))
      # Non-integer arguments work.
      self.evaluate(multinom.prob([1.0, 2.5, 1.5]))
  
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 9 for calling the MultinomialTest obj's underlying member method code with example usage: obj.testPmfNonIntegerCounts() without return types