code: def testPmfBothZeroBatchesMultidimensionalN(self):
      # The probabilities of one vote falling into class k is the mean for class
      # k.
      alpha = [1.0, 2]
      counts = [3.0, 2]
      n = np.full([4, 3], 5.0, dtype=np.float32)
      dist = dm.DirichletMultinomial(n, alpha, validate_args=True)
      pmf = dist.prob(counts)
      self.assertAllClose(
          [[1 / 7.0, 1 / 7.0, 1 / 7.0]] * 4, self.evaluate(pmf)
      )
      self.assertEqual((4, 3), pmf.shape)
  
imports: import numpy
  import tensorflow.compat.v1
  import tensorflow.compat.v2
  from tensorflow_probability.python.distributions import dirichlet_multinomial
  from tensorflow_probability.python.internal import test_util
  from tensorflow_probability.python.math import gradient
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