code: def test_triton_kernel_native(self, grad, dynamic, backend):
      def call_triton_add(
          x: torch.Tensor,
          y: torch.Tensor,
          output: torch.Tensor,
          grid_type: int,
          num=1,
          positional=False,
      ):
          n_elements = output.numel()
  
          def grid_fn(meta):
              return (triton.cdiv(num, meta["BLOCK_SIZE"]),)
  
          if grid_type == 0:
              grid = (x.numel(),)
          elif grid_type == 1:
              grid = lambda meta: (
                  triton.cdiv(n_elements, meta["BLOCK_SIZE"]),
              )
          else:
              grid = grid_fn
  
          if positional:
              add_kernel[grid](x, y, output, n_elements, 16)
          else:
              add_kernel[grid](x, y, output, n_elements, BLOCK_SIZE=16)
  
          return output
  
      t1 = torch.rand(5, device="cuda", requires_grad=grad)
      t2 = torch.rand(5, device="cuda", requires_grad=grad)
      o1 = torch.zeros_like(t1, requires_grad=grad)
  
      torch_add = t1 + t2
  
      # No Dynamo -- Make sure triton kernel works
      self.assertEqual(call_triton_add(t1, t2, o1, 1), torch_add)
      # No Dynamo -- Make sure triton kernel works (with positional BLOCK_SIZE)
      o2 = torch.zeros_like(t1, requires_grad=grad)
      self.assertEqual(call_triton_add(t1, t2, o2, 1, True), torch_add)
  
      # With Dynamo
      compiled_func = torch.compile(
          call_triton_add,
          backend=backend,
          fullgraph=True,
          dynamic=dynamic,
      )
      # With simple kernel
      o3 = torch.zeros_like(t1, requires_grad=grad)
      self.assertEqual(compiled_func(t1, t2, o3, 0), torch_add)
      # With lambda kernel
      o4 = torch.zeros_like(t1, requires_grad=grad)
      self.assertEqual(compiled_func(t1, t2, o4, 1), torch_add)
      # With lambda kernel (with positional BLOCK_SIZE)
      o5 = torch.zeros_like(t1, requires_grad=grad)
      self.assertEqual(compiled_func(t1, t2, o5, 1, 1, True), torch_add)
      # With user defined function kernel
      o6 = torch.zeros_like(t1, requires_grad=grad)
      self.assertEqual(compiled_func(t1, t2, o6, 2, 200), torch_add)
  
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 17 for calling the KernelTests obj's underlying member method code with example usage: obj.test_triton_kernel_native(grad, dynamic, backend) and returns: output, triton