code: def test_cat_splitwithsizes(self):
      # good case
      def fn(a, b, c):
          cat = torch.ops.aten.cat.default([a, b, c], 1)
          split_with_sizes = torch.ops.aten.split_with_sizes.default(
              cat, [2, 3, 5], 1
          )
          return [s**2 for s in split_with_sizes]
  
      args = [
          torch.randn(2, 2, device="cuda"),
          torch.randn(2, 3, device="cuda"),
          torch.randn(2, 5, device="cuda"),
      ]
      self.common(fn, args, 1, 2)
  
      # cat node has other users
      def fn(a, b, c):
          cat = torch.ops.aten.cat.default([a, b, c], 1)
          split_with_sizes = torch.ops.aten.split_with_sizes.default(
              cat, [2, 3, 5], 1
          )
          return [s**2 for s in split_with_sizes] + [cat**3]
  
      args = [
          torch.randn(2, 2, device="cuda"),
          torch.randn(2, 3, device="cuda"),
          torch.randn(2, 5, device="cuda"),
      ]
      self.common(fn, args, 0, 0)
  
      # cat and split dims are different
      def fn(a, b, c):
          cat = torch.ops.aten.cat.default([a, b, c], 1)
          split_with_sizes = torch.ops.aten.split_with_sizes.default(
              cat, [2, 3, 5], 0
          )
          return [s**2 for s in split_with_sizes]
  
      args = [
          torch.randn(10, 2, device="cuda"),
          torch.randn(10, 3, device="cuda"),
          torch.randn(10, 5, device="cuda"),
      ]
      self.common(fn, args, 0, 0)
  
      # cat and split lenghts are different
      def fn(a, b, c):
          cat = torch.ops.aten.cat.default([a, b, c], 1)
          split_with_sizes = torch.ops.aten.split_with_sizes.default(
              cat, [5, 5], 1
          )
          return [s**2 for s in split_with_sizes]
  
      args = [
          torch.randn(2, 2, device="cuda"),
          torch.randn(2, 3, device="cuda"),
          torch.randn(2, 5, device="cuda"),
      ]
      self.common(fn, args, 0, 0)
  
      # cat input sizes and split sizes are different
      def fn(a, b, c):
          cat = torch.ops.aten.cat.default([a, b, c], 1)
          split_with_sizes = torch.ops.aten.split_with_sizes.default(
              cat, [2, 5, 3], 1
          )
          return [s**2 for s in split_with_sizes]
  
      args = [
          torch.randn(2, 2, device="cuda"),
          torch.randn(2, 3, device="cuda"),
          torch.randn(2, 5, device="cuda"),
      ]
      self.common(fn, args, 0, 0)
  
imports: import copy
  import unittest
  import torch
  import torch._dynamo.config
  import torch._inductor.config
  from torch._dynamo.test_case import run_tests, TestCase
  from torch._dynamo.utils import count_calls, counters
  from torch._higher_order_ops.out_dtype import out_dtype
  from torch._inductor.fx_passes import joint_graph
  from torch._inductor.fx_passes.serialized_patterns.central_index import get_serialized_pattern
  from torch._inductor.pattern_matcher import _TargetExpr, Arg, CallFunction, gen_pattern, KeywordArg, Match, PatternExpr, PatternMatcherPass, PatternPrettyPrinter, register_graph_pattern, stable_topological_sort
  from torch._inductor.utils import run_and_get_code
  from torch._inductor.virtualized import V
  from torch.testing import FileCheck
  from torch.testing._internal.common_cuda import SM80OrLater
  from torch.testing._internal.common_utils import IS_LINUX, skipIfRocm
  from torch.testing._internal.inductor_utils import HAS_CUDA
label_desc: Use image node_id 25 for calling the TestPatternMatcher obj's underlying member method code with example usage: obj.test_cat_splitwithsizes() and returns: unknown, unknown, unknown, unknown, unknown