class_docstr: Vision Transformer
  A PyTorch impl of : `An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale`  -
      https://arxiv.org/abs/2010.11929
class_docstr_tok: ["Vision","Transformer","A","PyTorch","impl","of",":","`","An","Image","is","Worth","16x16","Words",":","Transformers","for","Image","Recognition","at","Scale","`","-","https",":","\/\/arxiv.org\/abs\/2010.11929"]
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: import torch
  import torch.nn
  import torch.nn.functional
  from functools import partial
  from timm.models.vision_transformer import _cfg, PatchEmbed
  from timm.models.registry import register_model
  from timm.models.layers import trunc_normal_, DropPath
  from timm.models.helpers import named_apply, adapt_input_conv
  from fairscale.nn.checkpoint.checkpoint_activations import checkpoint_wrapper
label_desc: Use image node_id 2 for calling the VisionTransformer obj's underlying member method code with example usage: obj._init_weights(m) without return types