class_docstr: Test cases for random-number generating operators.
code: def testDeterminismMultinomial(self):
      # Stateless values should be equal iff the seeds are equal (roughly)
      num_samples = 10
      with self.session(), self.test_scope():
          seed_t = array_ops.placeholder(dtypes.int32, shape=[2])
          seeds = [(x, y) for x in range(5) for y in range(5)] * 3
          for logits in (
              [[0.1, 0.25, 0.5, 0.15]],
              [[0.5, 0.5], [0.8, 0.2], [0.25, 0.75]],
          ):
              pure = stateless_random_ops.stateless_multinomial(
                  logits, num_samples, seed=seed_t
              )
              values = [
                  (seed, pure.eval(feed_dict={seed_t: seed}))
                  for seed in seeds
              ]
              for s0, v0 in values:
                  for s1, v1 in values:
                      self.assertEqual(s0 == s1, np.all(v0 == v1))
  
imports: import collections
  import numpy
  from tensorflow.compiler.tests import xla_test
  from tensorflow.python.framework import dtypes
  from tensorflow.python.framework import random_seed
  from tensorflow.python.ops import array_ops
  from tensorflow.python.ops import random_ops
  from tensorflow.python.ops import stateless_random_ops
  from tensorflow.python.platform import googletest
  from tensorflow.python.platform import test
label_desc: Use image node_id 9 for calling the CategoricalTest obj's underlying member method code with example usage: obj.testDeterminismMultinomial() without return types