code: def test_triton_kernel_multi_kernel(self, grad):
      @triton.jit
      def mul2_and_add_and_zero_negatives_kernel(
          in_ptr0,
          in_ptr1,
          out_ptr,
          n_elements,
          BLOCK_SIZE: "tl.constexpr",
          ACTIVATION: "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
          indirection_kernel(
              in_ptr0,
              in_ptr0,
              n_elements,
              BLOCK_SIZE=BLOCK_SIZE,
              ACTIVATION="mul2_inplace_kernel",
          )
          indirection_kernel(
              in_ptr1,
              in_ptr1,
              n_elements,
              BLOCK_SIZE=BLOCK_SIZE,
              ACTIVATION="mul2_inplace_kernel",
          )
          x = tl.load(in_ptr0 + offsets, mask=mask)
          y = tl.load(in_ptr1 + offsets, mask=mask)
          output = x + y
          if ACTIVATION == "zero_negs":
              output = zero_negs(output)
          tl.store(out_ptr + offsets, output, mask=mask)
  
      @torch.compile
      def call_triton(
          x: torch.Tensor,
          y: torch.Tensor,
          xi: torch.Tensor,
          yi: torch.Tensor,
          output: torch.Tensor,
          outputi: torch.Tensor,
      ):
          n_elements = output.numel()
  
          grid = (x.numel(),)
          mul2_and_add_and_zero_negatives_kernel[grid](
              x,
              y,
              output,
              n_elements,
              BLOCK_SIZE=16,
              ACTIVATION="zero_negs",
          )
          mul2_and_add_and_zero_negatives_kernel[grid](
              xi,
              yi,
              outputi,
              n_elements,
              BLOCK_SIZE=16,
              ACTIVATION=None,
          )
  
          return (output, outputi)
  
      t1 = torch.tensor(
          [-2.0, -1.0, 0.0, 1.0, 2.0], device="cuda", requires_grad=grad
      )
      t2 = torch.tensor(
          [-2.0, -1.0, 0.0, 1.0, 2.0], device="cuda", requires_grad=grad
      )
      float_result = 2 * t1 + 2 * t2
      float_result = float_result.where(float_result >= 0, 0.0)
  
      t1i = torch.randint(-2, 2, (5,), device="cuda")
      t2i = torch.randint(-2, 2, (5,), device="cuda")
      o = torch.zeros_like(t1, requires_grad=grad)
      oi = torch.zeros_like(t1i)
      int_result = 2 * t1i + 2 * t2i
  
      (result, resulti) = call_triton(t1, t2, t1i, t2i, o, oi)
      self.assertEqual(float_result, result)
      self.assertEqual(int_result, resulti)
  
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 13 for calling the KernelTests obj's underlying member method code with example usage: obj.test_triton_kernel_multi_kernel(grad) and returns: output,  outputi