class_docstr: Gated positional self-attention. This is what makes a ConViT a ConViT.
  The "gating" refers to lambda, the parameter that controls the relative
  contribution (i.e. gating) of positional vs. content-based attention in
  each head.
class_docstr_tok: ["Gated","positional","self-attention",".","This","is","what","makes","a","ConViT","a","ConViT",".","The","``","gating","''","refers","to","lambda",",","the","parameter","that","controls","the","relative","contribution","(","i.e",".","gating",")","of","positional","vs.","content-based","attention","in","each","head","."]
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 copy
  import logging
  import math
  import torch
  import torch.nn
  from vissl.models.model_helpers import DropPath, to_2tuple, trunc_normal_
  from vissl.models.trunks import register_model_trunk
  from vissl.utils.hydra_config import AttrDict
label_desc: Use image node_id 2 for calling the GPSA obj's underlying member method code with example usage: obj._init_weights(m) without return types