class_docstr: Vision Transformer with support for patch or hybrid CNN input stage
      
class_docstr_tok: ["Vision","Transformer","with","support","for","patch","or","hybrid","CNN","input","stage"]
code: def fix_init_weight(self):
      def rescale(param, layer_id):
          param.div_(math.sqrt(2.0 * layer_id))
  
      for layer_id, layer in enumerate(self.blocks):
          rescale(layer.attn.proj.weight.data, layer_id + 1)
          rescale(layer.mlp.fc2.weight.data, layer_id + 1)
  
imports: import math
  from functools import partial
  import torch
  import torch.nn
  import torch.nn.functional
  import torch.utils.checkpoint
  from timm.models.layers import drop_path, to_2tuple, trunc_normal_
  from timm.models.registry import register_model
  from lavis.common.dist_utils import download_cached_file
label_desc: Use image node_id 2 for calling the VisionTransformer obj's underlying member method code with example usage: obj.fix_init_weight() without return types