code: def test_splitwithsizes_cat(self):
      # Good case
      def fn(a):
          split_with_sizes = torch.ops.aten.split_with_sizes.default(
              a, [8, 24], 1
          )
          getitem = split_with_sizes[0]
          getitem_1 = split_with_sizes[1]
          cat = torch.ops.aten.cat.default([getitem, getitem_1], 1)
          return cat**2
  
      args = [
          torch.randn(2, 32, device="cuda"),
      ]
      self.common(fn, args, 1, 4)
  
      # Not all getitems are passed to cat
      def fn(a):
          split_with_sizes = torch.ops.aten.split_with_sizes.default(
              a, [8, 8, 16], 1
          )
          getitem = split_with_sizes[0]
          getitem_1 = split_with_sizes[1]
          getitem_2 = split_with_sizes[2]
          cat = torch.ops.aten.cat.default([getitem, getitem_1], 1)
          return cat**2 + getitem_2
  
      args = [
          torch.randn(2, 32, device="cuda"),
      ]
      self.common(fn, args, 0, 0)
  
      # Different dimensions  (TODO this case should be handled by replacing with a reshape)
      def fn(a):
          split_with_sizes = torch.ops.aten.split_with_sizes.default(
              a, [8, 8, 8, 8], 1
          )
          cat = torch.ops.aten.cat.default(split_with_sizes, 0)
          return cat**2
  
      args = [
          torch.randn(2, 32, device="cuda"),
      ]
      self.common(fn, args, 0, 0)
  
      # https://github.com/pytorch/pytorch/issues/99686.
      def fn(a):
          x = torch.ops.aten.split_with_sizes.default(
              a, [3, 2, 3], dim=1
          )
          cat = torch.ops.aten.cat.default([x[1], x[0], x[2]], dim=1)
          return cat
  
      args = [
          torch.randn(1, 8, 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 24 for calling the TestPatternMatcher obj's underlying member method code with example usage: obj.test_splitwithsizes_cat() and returns: unknown, unknown, unknown, cat