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 _init_weights(self, m):
      if isinstance(m, nn.Linear):
          trunc_normal_(m.weight, std=0.02)
          if isinstance(m, nn.Linear) and m.bias is not None:
              nn.init.constant_(m.bias, 0)
      elif isinstance(m, nn.LayerNorm):
          nn.init.constant_(m.bias, 0)
          nn.init.constant_(m.weight, 1.0)
  
imports: from functools import partial
  from typing import List
  from typing import Tuple
  import torch
  import torch.hub
  import torch.nn
  from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
  from timm.layers import DropPath, to_2tuple, trunc_normal_, _assert
  from ._builder import build_model_with_cfg
  from ._features_fx import register_notrace_function
  from ._registry import register_model, generate_default_cfgs
  from .vision_transformer import Block
label_desc: Use image node_id 2 for calling the CrossVit obj's underlying member method code with example usage: obj._init_weights(m) without return types