code: def _fuse_conv_bn_(m: GraphModule) -> None:
      has_bn = any(_is_bn_node(n) for n in m.graph.nodes)
      if not has_bn:
          return
      for n in m.graph.nodes:
          if (
              n.op != "call_function"
              or n.target
              != torch.ops.aten._native_batch_norm_legit_no_training.default
          ):
              continue
          bn_node = n
          n = bn_node.args[0]
          if not _is_conv(n):
              continue
          conv_node = n
          conv_weight_node = conv_node.args[1]
          conv_bias_node = (
              conv_node.args[2] if len(conv_node.args) > 2 else None
          )
          fold_bn_weights_into_conv_node(
              conv_node, conv_weight_node, conv_bias_node, bn_node, m
          )
  
      m.graph.eliminate_dead_code()
      m.recompile()
  
imports: import operator
  import types
  import torch
  from torch._export import capture_pre_autograd_graph
  from torch.fx import GraphModule, Node
  from torch.nn.utils.fusion import fuse_conv_bn_weights
  from typing import Any, Callable, Dict, Optional, Tuple, List, Union
  from torch.utils._pytree import LeafSpec
  from torch.ao.quantization.fx._decomposed import quantized_decomposed_lib
  from torch.ao.quantization.quantizer import QuantizationAnnotation
label_desc: Use image node_id 13 for calling a global function with example usage: _fuse_conv_bn_(m) without return types