code: def test_triton_kernel_inner_triton_function(self, backend):
      def f(x: torch.Tensor):
          @triton.jit
          def pow2_kernel(
              in_ptr0,
              out_ptr,
              n_elements,
              BLOCK_SIZE: "tl.constexpr",
          ):
              pid = tl.program_id(axis=0)
              block_start = pid * BLOCK_SIZE
              offsets = block_start + tl.arange(0, BLOCK_SIZE)
              mask = offsets < n_elements
              x = tl.load(in_ptr0 + offsets, mask=mask)
              output = x * x
              tl.store(out_ptr + offsets, output, mask=mask)
  
          output = torch.zeros_like(x)
          n_elements = output.numel()
          grid = lambda meta: (
              triton.cdiv(n_elements, meta["BLOCK_SIZE"]),
          )
          pow2_kernel[grid](x, output, n_elements, BLOCK_SIZE=16)
          return output
  
      t = torch.rand(5, device="cuda")
  
      compiled_func = torch.compile(f, backend=backend, fullgraph=True)
  
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 7 for calling the KernelTests obj's underlying member method code with example usage: obj.test_triton_kernel_inner_triton_function(backend) and returns: output