code: def rename_and_reshape_key(dct, old, new, config):
      val = dct.pop(old)
  
      if (
          "out_proj" in new
          or "v_proj" in new
          or "k_proj" in new
          or "q_proj" in new
      ) and "vision" in new:
          val = val.reshape(-1, config.vision_config.hidden_size)
      if (
          "out_proj" in new
          or "v_proj" in new
          or "k_proj" in new
          or "q_proj" in new
      ) and "text" in new:
          val = val.reshape(-1, config.text_config.hidden_size)
  
      if "patch_embedding" in new:
          print("Reshaping patch embedding... for", new)
          val = val.transpose(3, 2, 0, 1)
      elif (
          new.endswith("weight")
          and "position_embedding" not in new
          and "token_embedding" not in new
      ):
          val = val.T
  
      if new.endswith("bias"):
          val = val.reshape(-1)
  
      dct[new] = torch.from_numpy(np.array(val))
  
file_path: transformers/src/transformers/models/owlv2/convert_owlv2_to_hf.py
imports: import argparse
  import collections
  import os
  import jax
  import jax.numpy
  import numpy
  import torch
  from flax.training import checkpoints
  from huggingface_hub import hf_hub_download
  from PIL import Image
  from transformers import CLIPTokenizer, Owlv2Config, Owlv2ForObjectDetection, Owlv2ImageProcessor, Owlv2Processor, Owlv2TextConfig, Owlv2VisionConfig
  from transformers.utils import logging
label_desc: Use image node_id 4 for calling a global function with example usage: rename_and_reshape_key(dct, old, new, config) without return types