code: def test_rrelu_with_noise(self, device):
      # rrelu_with_noise behavior depends on a) whether elements in the input
      # are <= 0, and b) whether we're in training mode. Cover all cases:
      dtype = torch.float64
      x = torch.tensor(
          [-3.0, -2.0, -1.0, 0.0, 1.0, 2.0],
          dtype=dtype,
          device=device,
      )
      lower = 1.0
      upper = 4.0
      training = False
  
      torch.manual_seed(123)
      noise_ref = torch.zeros(x.shape, dtype=dtype, device=device)
      ref = torch.ops.aten.rrelu_with_noise(
          x, noise_ref, lower, upper, training
      )
  
      torch.manual_seed(123)
      noise_res = torch.zeros(x.shape, dtype=dtype, device=device)
      res = torch._decomp.decompositions.rrelu_with_noise(
          x,
          noise_res,
          lower,
          upper,
          training,
      )
      self.assertEqual(ref, res)
      self.assertEqual(noise_ref, noise_res)
  
      # Now with training=True:
      training = True
  
      torch.manual_seed(123)
      noise_ref = torch.zeros(x.shape, dtype=dtype, device=device)
      ref = torch.ops.aten.rrelu_with_noise(
          x, noise_ref, lower, upper, training
      )
  
      torch.manual_seed(123)
      noise_res = torch.zeros(x.shape, dtype=dtype, device=device)
      res = torch._decomp.decompositions.rrelu_with_noise(
          x,
          noise_res,
          lower,
          upper,
          training,
      )
      self.assertEqual(ref, res)
      self.assertEqual(noise_ref, noise_res)
  
imports: from collections import defaultdict
  from torch import Tensor
  import torch.autograd
  from torch._decomp import core_aten_decompositions, decomposition_table
  from torch.utils._python_dispatch import TorchDispatchMode
  from torch.utils._pytree import tree_map, tree_flatten, tree_unflatten
  from torch.utils import _pytree
  from torch.testing import make_tensor
  from torch.testing._internal.common_cuda import tf32_off
  from torch.testing._internal.common_utils import unMarkDynamoStrictTest
  from torch.testing._internal.common_utils import is_iterable_of_tensors, IS_WINDOWS, TestCase, skipIfCrossRef, suppress_warnings, TEST_WITH_ASAN, TEST_WITH_SLOW, run_tests, skipIfTorchDynamo
  from torch.testing._internal.common_modules import module_db, modules
  from torch.testing._internal.common_device_type import onlyNativeDeviceTypes, ops, instantiate_device_type_tests, onlyCPU, onlyCUDA
  from torch.testing._internal.common_methods_invocations import op_db, skip, skipOps, xfail
  from torch._dispatch.python import enable_python_dispatcher
  from torch._ops import DispatchKey
  import itertools
  import functools
  from functools import partial
  import unittest
label_desc: Use image node_id 6 for calling the TestDecomp obj's underlying member method code with example usage: obj.test_rrelu_with_noise(device) without return types