code: def testPmfBothZeroBatchesMultidimensionalN(self):
      # The probabilities of one vote falling into class k is the mean for class
      # k.
      with self.cached_session():
          alpha = [1.0, 2]
          counts = [3.0, 2]
          n = np.full([4, 3], 5.0, dtype=np.float32)
          dist = ds.DirichletMultinomial(n, alpha)
          pmf = dist.prob(counts)
          self.assertAllClose(
              [[1 / 7.0, 1 / 7.0, 1 / 7.0]] * 4, self.evaluate(pmf)
          )
          self.assertEqual((4, 3), 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 10 for calling the DirichletMultinomialTest obj's underlying member method code with example usage: obj.testPmfBothZeroBatchesMultidimensionalN() without return types