code: def test_triton_kernel_with_grad_option(self, grad_fn, backend):
      def call_triton(x: torch.Tensor):
          with grad_fn():
              output = torch.zeros_like(x)
              n_elements = output.numel()
              grid = lambda meta: (
                  triton.cdiv(n_elements, meta["BLOCK_SIZE"]),
              )
              mul2_kernel[grid](x, output, n_elements, BLOCK_SIZE=16)
              return output
  
      t = torch.rand(5, device="cuda")
      compiled_func = torch.compile(
          call_triton, backend=backend, fullgraph=True
      )
      self.assertEqual(2 * t, compiled_func(t))
  
imports: from unittest.mock import patch
  import torch
  import torch._dynamo.test_case
  import torch._dynamo.testing
  from torch._higher_order_ops.triton_kernel_wrap import triton_kernel_wrapper_functional, triton_kernel_wrapper_mutation
  from torch._inductor import metrics
  from torch.testing._internal import common_utils
  from torch.testing._internal.common_utils import skipIfRocm
  from torch.testing._internal.triton_utils import *
label_desc: Use image node_id 6 for calling the KernelTests obj's underlying member method code with example usage: obj.test_triton_kernel_with_grad_option(grad_fn, backend) and returns: output