code: def fuse(self):
      bn, l = self._modules.values()
      w = bn.weight / (bn.running_var + bn.eps) ** 0.5
      b = (
          bn.bias
          - self.bn.running_mean
          * self.bn.weight
          / (bn.running_var + bn.eps) ** 0.5
      )
      w = l.weight * w[None, :]
      if l.bias is None:
          b = b @ self.l.weight.T
      else:
          b = (l.weight @ b[:, None]).view(-1) + self.l.bias
      m = nn.Linear(w.size(1), w.size(0), device=l.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 NormLinear obj's underlying member method code with example usage: obj.fuse() and returns: m