code: def fuse(model: torch.nn.Module, inplace=False) -> torch.nn.Module:
      """
      Fuses convolution/BN layers for inference purposes. Will deepcopy your
      model by default, but can modify the model inplace as well.
      """
      patterns = [
          (nn.Conv1d, nn.BatchNorm1d),
          (nn.Conv2d, nn.BatchNorm2d),
          (nn.Conv3d, nn.BatchNorm3d),
      ]
      if not inplace:
          model = copy.deepcopy(model)
      fx_model = fx.symbolic_trace(model)
      modules = dict(fx_model.named_modules())
      new_graph = copy.deepcopy(fx_model.graph)
  
      for pattern in patterns:
          for node in new_graph.nodes:
              if matches_module_pattern(pattern, node, modules):
                  if (
                      len(node.args[0].users) > 1
                  ):  # Output of conv is used by other nodes
                      continue
                  conv = modules[node.args[0].target]
                  bn = modules[node.target]
                  if not bn.track_running_stats:
                      continue
                  fused_conv = fuse_conv_bn_eval(conv, bn)
                  replace_node_module(node.args[0], modules, fused_conv)
                  node.replace_all_uses_with(node.args[0])
                  new_graph.erase_node(node)
      return fx.GraphModule(fx_model, new_graph)
  
imports: import torch.fx
  from torch.fx.node import Argument, Target
  from torch.nn.utils.fusion import fuse_conv_bn_eval
  from typing import Type, Dict, Any, Tuple, Iterable, Optional, List, cast
  import torch
  import torch.nn
  import torch.nn.functional
  from torch.fx.passes.shape_prop import ShapeProp
  import copy
  from collections import defaultdict
  import torch.utils.mkldnn
  import operator
  import time
  import logging
  from enum import Enum
label_desc: Use image node_id 4 for calling a global function with example usage: fuse(model, inplace) and returns: fx