code: def testStatelessRNGOpsWithSingleDimensionShardedComparingWithNonDTensor(
      self, op, dtype, op_version, dim, shard_type
  ):
      shape = [128, 128]
      seed = [123, 321]
      sharding = self.shardings[shard_type]
      layout = sharding(self.mesh, dim, rank=2)
  
      # Raw rng Ops do not have inputs, so we need to place the Op DTensor device
      # explicitly.
      with ops.device_v2(api.device_name()):
          with api._dtensor_device()._default_layout(layout):
              b = _call_dtensor_op(
                  op=op,
                  seed=seed,
                  shape=shape,
                  dtype=dtype,
                  key=self.key,
                  counter=self.counter,
                  alg=self.alg,
                  minval=self.minval,
                  maxval=self.maxval,
                  op_version=op_version,
                  mesh=self.mesh,
              )
  
      api.check_layout(b, layout)
      b = [tensor.numpy() for tensor in api.unpack(b)]
  
      if dim == _MESH_DIM_X:
          if shard_type == "batch":
              local_shape = [64, 128]
          else:
              local_shape = [128, 64]
  
          def device_index_fn(x_cord, y_cord):
              # See todo of device_index_fn in 2d sharding case.
              del y_cord
              return x_cord
  
          for index, device_id in enumerate(_LOCAL_IDS):
              self.assertAllEqual(
                  b[index],
                  rng_op_spmd(
                      op,
                      device_id,
                      seed,
                      local_shape,
                      dtype,
                      key=self.key,
                      counter=self.counter,
                      alg=self.alg,
                      minval=self.minval,
                      maxval=self.maxval,
                      op_version=op_version,
                      device_index_fn=device_index_fn,
                      is_tpu=self.mesh.device_type().upper() == "TPU",
                  ),
              )
      elif dim == _MESH_DIM_Y:
          if shard_type == "batch":
              local_shape = [32, 128]
          else:
              local_shape = [128, 32]
  
          def device_index_fn(x_cord, y_cord):
              # See todo of device_index_fn in 2d sharding case. note this case is
              # particulary interesting as 2*y_cord is more natual.
              del x_cord
              return y_cord
  
          for index, device_id in enumerate(_LOCAL_IDS):
              self.assertAllEqual(
                  b[index],
                  rng_op_spmd(
                      op,
                      device_id,
                      seed,
                      local_shape,
                      dtype,
                      key=self.key,
                      counter=self.counter,
                      alg=self.alg,
                      minval=self.minval,
                      maxval=self.maxval,
                      op_version=op_version,
                      device_index_fn=device_index_fn,
                      is_tpu=self.mesh.device_type().upper() == "TPU",
                  ),
              )
  
      else:
          self.fail("should not reach here.")
  
imports: from absl.testing import parameterized
  from tensorflow.dtensor.python import api
  from tensorflow.dtensor.python import d_variable
  from tensorflow.dtensor.python import layout
  from tensorflow.dtensor.python import numpy_util
  from tensorflow.dtensor.python.tests import test_util
  from tensorflow.dtensor.python.tests import test_util_ops
  from tensorflow.python.distribute import tpu_strategy
  from tensorflow.python.distribute.cluster_resolver.tpu import tpu_cluster_resolver
  from tensorflow.python.eager import remote
  from tensorflow.python.eager.polymorphic_function import polymorphic_function
  from tensorflow.python.framework import constant_op
  from tensorflow.python.framework import dtypes
  from tensorflow.python.framework import ops
  from tensorflow.python.ops import gen_bitwise_ops
  from tensorflow.python.ops import gen_stateful_random_ops
  from tensorflow.python.ops import gen_stateless_random_ops_v2
  from tensorflow.python.ops import math_ops
  from tensorflow.python.ops import variables
  from tensorflow.python.platform import test
  from tensorflow.python.tpu import device_assignment
label_desc: Use image node_id 5 for calling the DTensorRNGTest obj's underlying member method code with example usage: obj.testStatelessRNGOpsWithSingleDimensionShardedComparingWithNonDTensor(op, dtype, op_version, dim, shard_type) and returns: x_cord, y_cord