docstr: This function resets the weights of Transformer model as done in the CLIP paper
docstr_tok: ["This","function","resets","the","weights","of","Transformer","model","as","done","in","the","CLIP","paper"]
code: def reset_parameters_clip_style(self):
      """This function resets the weights of Transformer model as done in the CLIP paper"""
  
      # reset the weights of the embedding and positional embedding layers
      nn.init.normal_(self.embedding_layer.weight, mean=0.0, std=0.02)
      # if self.positional_embedding is not None and not getattr(
      #    self.opts, "model.text.transformer.sinusoidal_pos_emb", False
      # ):
      #    nn.init.normal_(
      #        self.positional_embedding.pos_embed.weight, mean=0.0, std=0.01
      #    )
  
      # compute standard deviation for different linear layers in transformer model
      attn_std = self.model_dim**-0.5
      proj_std = attn_std * ((2 * len(self.transformer)) ** -0.5)
      fc_std = (2 * self.model_dim) ** -0.5
  
      for block in self.transformer:
          # multi-head attention QKV projection layer
          nn.init.normal_(
              block.pre_norm_mha[1].qkv_proj.weight,
              mean=0.0,
              std=attn_std,
          )
          # multi-head attention output projection layer
          nn.init.normal_(
              block.pre_norm_mha[1].out_proj.weight,
              mean=0.0,
              std=proj_std,
          )
          # FFN expansion layer
          nn.init.normal_(
              block.pre_norm_ffn[1].weight, mean=0.0, std=fc_std
          )
          # FFN reduction layer
          nn.init.normal_(
              block.pre_norm_ffn[4].weight, mean=0.0, std=proj_std
          )
  
      nn.init.normal_(self.projection_layer, mean=0.0, std=attn_std)
  
imports: import argparse
  import math
  from typing import Optional, Sequence
  import torch
  from torch import Tensor, nn
  from torch.nn import functional
  from torch.utils.checkpoint import checkpoint
  from cvnets.layers import Dropout, Embedding, PositionalEmbedding, get_normalization_layer
  from cvnets.modules import TransformerEncoder
  from cvnets.text_encoders import TEXT_ENCODER_REGISTRY, BaseTextEncoder
  from utils import logger
label_desc: Use image node_id 2 for calling the TextTransformer obj's underlying member method code with example usage: obj.reset_parameters_clip_style() without return types