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