code: def read_in_q_v_bias(state_dict, config):
      for i in range(config.vision_config.num_hidden_layers):
          # read in original q and v biases
          q_bias = state_dict.pop(
              f"visual_encoder.blocks.{i}.attn.q_bias"
          )
          v_bias = state_dict.pop(
              f"visual_encoder.blocks.{i}.attn.v_bias"
          )
  
          # next, set bias in the state dict
          qkv_bias = torch.cat(
              (
                  q_bias,
                  torch.zeros_like(v_bias, requires_grad=False),
                  v_bias,
              )
          )
          state_dict[
              f"vision_model.encoder.layers.{i}.self_attn.qkv.bias"
          ] = qkv_bias
  
file_path: transformers/src/transformers/models/instructblip/convert_instructblip_original_to_pytorch.py
imports: import argparse
  import requests
  import torch
  from lavis.models import load_model_and_preprocess
  from PIL import Image
  from transformers import AutoTokenizer, BlipImageProcessor, InstructBlipConfig, InstructBlipForConditionalGeneration, InstructBlipProcessor, InstructBlipQFormerConfig, InstructBlipVisionConfig, LlamaConfig, LlamaTokenizerFast, T5Config, T5TokenizerFast
  from transformers.utils.constants import OPENAI_CLIP_MEAN, OPENAI_CLIP_STD
label_desc: Use image node_id 4 for calling a global function with example usage: read_in_q_v_bias(state_dict, config) without return types