code: def testComplexShapes(self):
      alpha = np.random.rand(3, 2, 2)
      n = np.array([[3.0, 2], [4, 5], [6, 7]], dtype=np.float64)
      dist = dm.DirichletMultinomial(n, alpha, validate_args=True)
      self.assertEqual(2, self.evaluate(dist.event_shape_tensor()))
      self.assertAllEqual(
          [3, 2], self.evaluate(dist.batch_shape_tensor())
      )
      self.assertEqual(tf.TensorShape([2]), dist.event_shape)
      self.assertEqual(tf.TensorShape([3, 2]), dist.batch_shape)
      self.assertEqual(
          tf.TensorShape([3, 2, 2]),
          dist.sample(seed=test_util.test_seed()).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 3 for calling the DirichletMultinomialTest obj's underlying member method code with example usage: obj.testComplexShapes() without return types