code: def fuse_fx(
      gm: torch.fx.GraphModule, example_inputs
  ) -> torch.fx.GraphModule:
      is_cpu = is_cpu_device(example_inputs)
  
      fake_mode = detect_fake_mode(example_inputs)
  
      gm = sink_cat_after_pointwise(gm)
      if config.permute_fusion and not is_cpu:
          # For linear permute fusion, we need to check input info to identify
          # and perform proper permutation/transpose
          ShapeProp(gm, fake_mode=fake_mode).propagate(*example_inputs)
          gm = linear_permute_fusion(gm)
          gm = permute_linear_fusion(gm)
          gm = permute_matmul_fusion(gm)
  
      # make sure the autograd is disabled.
      if torch.is_grad_enabled() or not is_cpu:
          return gm
      if config.freezing:
          gm = remove_identity(gm)
          gm = fuse_conv_bn(gm)
      return gm
  
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 3 for calling a global function with example usage: fuse_fx(gm, example_inputs) and returns: gm, gm