code: def testSamplesHaveCorrectTotalCounts(self):
      seed_stream = test_util.test_seed_stream()
      concentration = 1.0 + 2.0 * tf.random.uniform(
          shape=[4], dtype=np.float32, seed=seed_stream()
      )
      total_count = tf.constant(list(range(int(1e4))), dtype=np.float32)
      dist = dm.DirichletMultinomial(
          total_count=total_count,
          concentration=concentration,
          validate_args=True,
      )
      x = dist.sample(seed=seed_stream())
      self.assertAllEqual(tf.reduce_sum(x, axis=-1), total_count)
  
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 28 for calling the DirichletMultinomialTest obj's underlying member method code with example usage: obj.testSamplesHaveCorrectTotalCounts() without return types