code: def test_abi_compatible(self):
      from test_aot_inductor import AOTIRunnerUtil
  
      f, args = self._generate(device="cuda")
      constraints: List[torch.export.Constraint] = [
          torch._export.dynamic_dim(args[0], 0) >= 1,
          torch._export.dynamic_dim(args[0], 0) <= 2048,
      ]
      with config.patch("aot_inductor.abi_compatible", True):
          result, code = run_and_get_cpp_code(
              lambda: AOTIRunnerUtil.run(
                  "cuda", f, args, constraints=constraints
              )
          )
  
      FileCheck().check(
          "int64_t int_array_2[] = {24L + (align(12L*s0)), };"
      ).check_next("int64_t int_array_3[] = {1L, };").check_next(
          "AtenTensorHandle pool1_handle;"
      ).check_next(
          "aoti_torch_empty_strided(1, int_array_2, int_array_3,"
      ).check_next(
          "RAIIAtenTensorHandle pool1(pool1_handle);"
      ).check_next(
          "int64_t int_array_4[] = {s0, 3L};"
      ).check_next(
          "int64_t int_array_5[] = {3L, 1L};"
      ).check_next(
          "AtenTensorHandle tmp_tensor_handle_1;"
      ).check_next(
          "aoti_torch__alloc_from_pool(pool1, 0"
      ).run(
          code
      )
      self.assertTrue(same(f(*args), result))
  
file_path: pytorch/test/inductor/test_memory_planning.py
imports: import sys
  from torch.testing._internal.common_utils import IS_CI, IS_WINDOWS, skipIfRocm
  import unittest
  from typing import List
  import torch
  from test_torchinductor import run_and_get_cpp_code
  from torch._C import FileCheck
  from torch._dynamo.test_case import run_tests, TestCase
  from torch._dynamo.utils import same
  from torch._inductor import config
  from torch.utils._triton import has_triton
label_desc: Use image node_id 4 for calling the TestMemoryPlanning obj's underlying member method code with example usage: obj.test_abi_compatible() without return types