code: def rename_keys(state_dict):
      new_state_dict = OrderedDict()
      total_embed_found, patch_emb_offset = 0, 0
      for key, value in state_dict.items():
          if key.startswith("network"):
              key = key.replace("network", "poolformer.encoder")
          if "proj" in key:
              # Works for the first embedding as well as the internal embedding layers
              if key.endswith("bias") and "patch_embed" not in key:
                  patch_emb_offset += 1
              to_replace = key[: key.find("proj")]
              key = key.replace(
                  to_replace, f"patch_embeddings.{total_embed_found}."
              )
              key = key.replace("proj", "projection")
              if key.endswith("bias"):
                  total_embed_found += 1
          if "patch_embeddings" in key:
              key = "poolformer.encoder." + key
          if "mlp.fc1" in key:
              key = replace_key_with_offset(
                  key, patch_emb_offset, "mlp.fc1", "output.conv1"
              )
          if "mlp.fc2" in key:
              key = replace_key_with_offset(
                  key, patch_emb_offset, "mlp.fc2", "output.conv2"
              )
          if "norm1" in key:
              key = replace_key_with_offset(
                  key, patch_emb_offset, "norm1", "before_norm"
              )
          if "norm2" in key:
              key = replace_key_with_offset(
                  key, patch_emb_offset, "norm2", "after_norm"
              )
          if "layer_scale_1" in key:
              key = replace_key_with_offset(
                  key,
                  patch_emb_offset,
                  "layer_scale_1",
                  "layer_scale_1",
              )
          if "layer_scale_2" in key:
              key = replace_key_with_offset(
                  key,
                  patch_emb_offset,
                  "layer_scale_2",
                  "layer_scale_2",
              )
          if "head" in key:
              key = key.replace("head", "classifier")
          new_state_dict[key] = value
      return new_state_dict
  
file_path: transformers/src/transformers/models/poolformer/convert_poolformer_original_to_pytorch.py
imports: import argparse
  import json
  from collections import OrderedDict
  from pathlib import Path
  import requests
  import torch
  from huggingface_hub import hf_hub_download
  from PIL import Image
  from transformers import PoolFormerConfig, PoolFormerForImageClassification, PoolFormerImageProcessor
  from transformers.utils import logging
label_desc: Use image node_id 2 for calling a global function with example usage: rename_keys(state_dict) and returns: new_state_dict