code: def testPmfBothZeroBatchesNontrivialN(self):
      # The probabilities of one vote falling into class k is the mean for class
      # k.
      with self.cached_session():
          # Both zero-batches.  No broadcast
          alpha = [1.0, 2]
          counts = [3.0, 2]
          dist = ds.DirichletMultinomial(5.0, alpha)
          pmf = dist.prob(counts)
          self.assertAllClose(1 / 7.0, self.evaluate(pmf))
          self.assertEqual((), pmf.get_shape())
  
imports: import numpy
  from tensorflow.python.eager import backprop
  from tensorflow.python.framework import constant_op
  from tensorflow.python.framework import dtypes
  from tensorflow.python.framework import tensor_shape
  from tensorflow.python.framework import test_util
  from tensorflow.python.ops import array_ops
  from tensorflow.python.ops import math_ops
  from tensorflow.python.ops.distributions import dirichlet_multinomial
  from tensorflow.python.platform import test
label_desc: Use image node_id 9 for calling the DirichletMultinomialTest obj's underlying member method code with example usage: obj.testPmfBothZeroBatchesNontrivialN() without return types