code: def testRngBitGeneratorIsDeterministic(self, algorithm):
      dtype = np.uint32
      key = np.array([1, 2], dtype=np.uint64)
      shape = (10, 12)
  
      def rng_fun_is_deterministic(k):
          res1 = xla.rng_bit_generator(algorithm, k, shape, dtype=dtype)
          res2 = xla.rng_bit_generator(algorithm, k, shape, dtype=dtype)
          return (res1[0] - res2[0], res1[1] - res2[1])
  
      self._assertOpOutputMatchesExpected(
          rng_fun_is_deterministic,
          args=(key,),
          expected=(
              np.zeros(key.shape, dtype=key.dtype),
              np.zeros(shape, dtype=dtype),
          ),
      )
  
imports: import functools
  from absl.testing import parameterized
  import numpy
  from tensorflow.compiler.tests import xla_test
  from tensorflow.compiler.tf2xla.ops import gen_xla_ops
  from tensorflow.compiler.tf2xla.python import xla
  from local_xla.xla import xla_data_pb2
  from tensorflow.python.eager import def_function
  from tensorflow.python.framework import dtypes
  from tensorflow.python.framework import errors
  from tensorflow.python.framework import function
  from tensorflow.python.framework import ops
  from tensorflow.python.framework import tensor_shape
  from tensorflow.python.framework import test_util
  from tensorflow.python.ops import array_ops
  from tensorflow.python.ops import array_ops_stack
  from tensorflow.python.ops import random_ops_util
  from tensorflow.python.platform import googletest
label_desc: Use image node_id 15 for calling the XlaOpsNumericalTest obj's underlying member method code with example usage: obj.testRngBitGeneratorIsDeterministic(algorithm) and returns: unknown,  unknown