code: def test_triton_kernel_autotune(
      self, grad, dynamic, backend, grid_type
  ):
      def call_triton(
          x: torch.Tensor, y: torch.Tensor, output: torch.Tensor
      ):
          n_elements = output.numel()
  
          def grid_fn(meta):
              return (triton.cdiv(n_elements, meta["BLOCK_SIZE"]),)
  
          if grid_type == 1:
              grid = (n_elements,)
          elif grid_type == 2:
              grid = lambda meta: (
                  triton.cdiv(n_elements, meta["BLOCK_SIZE"]),
              )
          elif grid_type == 3:
              grid = grid_fn
  
          add_kernel_autotuned[grid](x, y, output, n_elements)
          return output
  
      t1 = torch.rand(256, device="cuda", requires_grad=grad)
      t2 = torch.rand(256, device="cuda", requires_grad=grad)
      output = torch.zeros_like(t1, requires_grad=grad)
  
      torch_add = call_triton(t1, t2, output)
      compiled_func = torch.compile(
          call_triton, backend=backend, fullgraph=True, dynamic=dynamic
      )
  
      output2 = torch.zeros_like(t1, requires_grad=grad)
      self.assertEqual(compiled_func(t1, t2, output2), 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 15 for calling the KernelTests obj's underlying member method code with example usage: obj.test_triton_kernel_autotune(grad, dynamic, backend, grid_type) and returns: output, triton