code: def sink_cat_after_pointwise(
      module: torch.fx.GraphModule,
  ) -> torch.fx.GraphModule:
      def one_user(node):
          users = list(node.users)
          return users[0] if len(users) == 1 else None
  
      def is_view(node):
          view = {"view"}
          return node.op == "call_method" and node.target in view
  
      def is_pointwise_unary(node):
          pointwise = {torch.relu, torch.tanh, "relu", "tanh"}
          return (
              node.op in {"call_function", "call_method"}
              and node.target in pointwise
          )
  
      g = module.graph
      for node in g.nodes:
          if node.op != "call_function" or node.target != torch.cat:
              continue
  
          cat_or_view = node
          while True:
              user = one_user(cat_or_view)
              if not user or not is_view(user):
                  break
              cat_or_view = user
  
          if user and is_pointwise_unary(user):
              with g.inserting_before(node):
  
                  def cat_args(tensors, dim=0):
                      return tensors, dim
  
                  tensors, dim = cat_args(*node.args, **node.kwargs)
                  new_tensors = [
                      g.create_node(
                          user.op,
                          user.target,
                          args=(arg,),
                          kwargs=user.kwargs,
                      )
                      for arg in tensors
                  ]
                  new_cat = g.create_node(
                      "call_function",
                      torch.cat,
                      args=(new_tensors, dim),
                  )
                  user.replace_all_uses_with(cat_or_view)
                  node.replace_all_uses_with(new_cat)
                  g.erase_node(user)
                  g.erase_node(node)
      g.lint()
      module.recompile()
      return module
  
imports: import copy
  import logging
  from typing import List, Optional
  import torch
  import torch.nn
  from torch._dynamo.utils import detect_fake_mode
  from torch._utils_internal import print_graph
  from torch.fx.experimental.optimization import matches_module_pattern, replace_node_module
  from torch.fx.passes.shape_prop import ShapeProp
  from torch.nn import functional
  from torch.nn.utils.fusion import fuse_conv_bn_eval, fuse_conv_bn_weights
  from ..None import config
  from ..fx_utils import matches_module_function_pattern
  from ..pattern_matcher import init_once_fakemode, PatternMatcherPass, stable_topological_sort
  from ..utils import is_cpu_device
  from .group_batch_fusion import group_batch_fusion_passes
  from .misc_patterns import numpy_compat_normalization
label_desc: Use image node_id 8 for calling a global function with example usage: sink_cat_after_pointwise(module) and returns: module, unknown, unknown, unknown, tensors,  dim