code: def copy_attn_layer(hf_attn_layer, pt_attn_layer):
      q_proj, k_proj, v_proj = pt_attn_layer.in_proj_weight.chunk(
          3, dim=0
      )
      (
          q_proj_bias,
          k_proj_bias,
          v_proj_bias,
      ) = pt_attn_layer.in_proj_bias.chunk(3, dim=0)
  
      out_proj_weights = pt_attn_layer.out_proj.weight
      out_proj_bias = pt_attn_layer.out_proj.bias
  
      hf_attn_layer.q_proj.weight.data = q_proj
      hf_attn_layer.q_proj.bias.data = q_proj_bias
  
      hf_attn_layer.k_proj.weight.data = k_proj
      hf_attn_layer.k_proj.bias.data = k_proj_bias
  
      hf_attn_layer.v_proj.weight.data = v_proj
      hf_attn_layer.v_proj.bias.data = v_proj_bias
  
      hf_attn_layer.out_proj.weight = out_proj_weights
      hf_attn_layer.out_proj.bias = out_proj_bias
  
file_path: H2O/h2o_flexgen/benchmark/third_party/transformers/src/transformers/models/clip/convert_clip_original_pytorch_to_hf.py
imports: import argparse
  import torch
  from clip import load
  from transformers import CLIPConfig, CLIPModel
label_desc: Use image node_id 1 for calling a global function with example usage: copy_attn_layer(hf_attn_layer, pt_attn_layer) without return types