code: def testComplexShapes(self):
      p = 0.5 * np.ones([3, 2, 2], dtype=np.float32)
      n = [[3.0, 2], [4, 5], [6, 7]]
      dist = multinomial.Multinomial(
          total_count=n, probs=p, 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([17, 3, 2, 2]),
          dist.sample(17, seed=test_util.test_seed()).shape,
      )
  
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 3 for calling the MultinomialTest obj's underlying member method code with example usage: obj.testComplexShapes() without return types