code: def fuse(self):
      c, bn = self.conv, self.bn
      w = bn.weight / (bn.running_var + bn.eps) ** 0.5
      w = c.weight * w[:, None, None, None]
      b = (
          bn.bias
          - bn.running_mean
          * bn.weight
          / (bn.running_var + bn.eps) ** 0.5
      )
      m = torch.nn.Conv2d(
          w.size(1) * self.conv.groups,
          w.size(0),
          w.shape[2:],
          stride=self.conv.stride,
          padding=self.conv.padding,
          dilation=self.conv.dilation,
          groups=self.conv.groups,
      )
      m.weight.data.copy_(w)
      m.bias.data.copy_(b)
      return m
  
imports: import itertools
  from collections import OrderedDict
  from typing import Dict
  import torch
  import torch.nn
  from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
  from timm.layers import SqueezeExcite, SelectAdaptivePool2d, trunc_normal_, _assert
  from ._builder import build_model_with_cfg
  from ._manipulate import checkpoint_seq
  from ._registry import register_model, generate_default_cfgs
label_desc: Use image node_id 2 for calling the ConvNorm obj's underlying member method code with example usage: obj.fuse() and returns: m