code: def test_sink_cat_after_pointwise(self):
      def test_kwarg(x, y):
          return torch.cat([x, y], dim=-1).view(-1).view(128).tanh()
  
      def test_arg(x, y):
          return torch.cat([x, y], -1).view(-1).view(128).tanh()
  
      def test_arg2(x, y):
          return torch.cat([x, y]).view(-1).view(128).tanh()
  
      def test_kwarg2(x, y):
          return torch.cat(tensors=[x, y], dim=0).tanh()
  
      def test_kwarg3(x, y):
          return torch.cat(tensors=[x, y], dim=0).view(128).tanh()
  
      trace_func = chain_passes(
          torch.fx.symbolic_trace, sink_cat_after_pointwise
      )
      inputs = [
          torch.randn(8, 8),
          torch.randn(8, 8),
      ]
      for f in [
          test_kwarg,
          test_arg,
          test_arg2,
          test_kwarg2,
          test_kwarg3,
      ]:
          traced = trace_func(f, inputs)
          self.assertTrue(torch.allclose(f(*inputs), traced(*inputs)))
          self.assertEqual(count_call_method(traced, "tanh"), 2)
  
file_path: pytorch/test/inductor/test_fx_fusion.py
imports: from typing import Any, Callable
  import torch
  from torch._inductor.fx_passes.pre_grad import linear_permute_fusion, linear_transpose, permute_linear_fusion, permute_matmul_fusion, sink_cat_after_pointwise, transpose_linear, transpose_matmul
  from torch.fx.passes.shape_prop import ShapeProp
  from torch.testing._internal.common_utils import run_tests, TestCase
label_desc: Use image node_id 1 for calling the TestFxFusion obj's underlying member method code with example usage: obj.test_sink_cat_after_pointwise() and returns: torch, torch, torch, torch, torch