code: def fuse(self):
      if not self.num_classes > 0:
          return nn.Identity()
      head = self.head.fuse()
      if self.distillation:
          head_dist = self.head_dist.fuse()
          head.weight += head_dist.weight
          head.bias += head_dist.bias
          head.weight /= 2
          head.bias /= 2
          return head
      else:
          return head
  
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 3 for calling the RepVitClassifier obj's underlying member method code with example usage: obj.fuse() and returns: nn, head, head