code: def fuse(self):
      c, bn = self._modules.values()
      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 = nn.Conv2d(
          w.size(1) * self.c.groups,
          w.size(0),
          w.shape[2:],
          stride=self.c.stride,
          padding=self.c.padding,
          dilation=self.c.dilation,
          groups=self.c.groups,
          device=c.weight.device,
      )
      m.weight.data.copy_(w)
      m.bias.data.copy_(b)
      return m
  
imports: import torch.nn
  from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
  from ._registry import register_model, generate_default_cfgs
  from ._builder import build_model_with_cfg
  from timm.layers import SqueezeExcite, trunc_normal_, to_ntuple, to_2tuple
  from ._manipulate import checkpoint_seq
  import torch
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