code: def test_cat_slice_cat_cuda(self):
      def fn(a, b):
          cat_1 = torch.ops.aten.cat.default([a, b], 1)
          slice_1 = torch.ops.aten.slice.Tensor(
              cat_1, 0, 0, 9223372036854775807
          )
          slice_2 = torch.ops.aten.slice.Tensor(slice_1, 1, 0, 19)
          return torch.ops.aten.cat.default([cat_1, slice_2], 1)
  
      args = [
          torch.randn(2, 32, device="cuda"),
          torch.randn(2, 16, device="cuda"),
      ]
      self.common(fn, args, 1, 3)
  
      args = [
          torch.randn(2, 8, device="cuda"),
          torch.randn(2, 16, device="cuda"),
      ]
      counters.clear()
      expected = fn(*args)
      actual = torch.compile(fn)(*args)
      torch.testing.assert_close(actual, expected)
      # We don't recompile for dynamic-shape cases.
      if dynamo_config.assume_static_by_default:
          self.assertEqual(
              counters["inductor"]["pattern_matcher_count"], 1
          )
          self.assertEqual(
              counters["inductor"]["pattern_matcher_nodes"], 3
          )
  
      # Verify we fallback to non-optimal path for negative `end`.
      def fn(a, b):
          cat_1 = torch.ops.aten.cat.default([a, b], 1)
          slice_1 = torch.ops.aten.slice.Tensor(
              cat_1, 0, 0, 9223372036854775807
          )
          slice_2 = torch.ops.aten.slice.Tensor(slice_1, 1, 0, -1)
          return torch.ops.aten.cat.default([cat_1, slice_2], 1)
  
      args = [
          torch.randn(2, 8, device="cuda"),
          torch.randn(2, 16, device="cuda"),
      ]
      self.common(fn, args, 1, 3)
  
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 21 for calling the TestPatternMatcher obj's underlying member method code with example usage: obj.test_cat_slice_cat_cuda() and returns: torch, torch