docstr: Use Anderson-Darling test to test distribution appears normal.
code: def testDistributionOfNormal(self, dtype):
      """Use Anderson-Darling test to test distribution appears normal."""
      if dtype == dtypes.bfloat16 and not test_util.is_gpu_available(
          cuda_only=True, min_cuda_compute_capability=(8, 0)
      ):
          self.skipTest("Bfloat16 requires compute capability 8.0")
      n = 1000
      gen = random.Generator.from_seed(1234)
      x = gen.normal(shape=[n], dtype=dtype).numpy()
      # The constant 2.492 is the 5% critical value for the Anderson-Darling
      # test where the mean and variance are known. This test is probabilistic
      # so to avoid flakiness the seed is fixed.
      self.assertLess(
          random_test_util.anderson_darling(x.astype(float)), 2.492
      )
  
imports: import os
  import re
  from absl.testing import parameterized
  import numpy
  from tensorflow.python.checkpoint import checkpoint
  from tensorflow.python.eager import context
  from tensorflow.python.eager import def_function
  from tensorflow.python.framework import config
  from tensorflow.python.framework import constant_op
  from tensorflow.python.framework import dtypes
  from tensorflow.python.framework import errors
  from tensorflow.python.framework import ops
  from tensorflow.python.framework import test_util
  from tensorflow.python.kernel_tests.random import util
  from tensorflow.python.ops import array_ops
  from tensorflow.python.ops import gen_random_ops
  from tensorflow.python.ops import gen_stateful_random_ops
  from tensorflow.python.ops import logging_ops
  from tensorflow.python.ops import random_ops_util
  from tensorflow.python.ops import stateful_random_ops
  from tensorflow.python.ops import variables
  from tensorflow.python.platform import test
label_desc: Use image node_id 30 for calling the StatefulRandomOpsTest obj's underlying member method code with example usage: obj.testDistributionOfNormal(dtype) without return types