code: def rename_keys_in_detr_decoder(
      self, dst_state_dict: StateDict, src_state_dict: StateDict
  ):
      dst_prefix: str = "transformer_module.decoder"
      src_prefix: str = "sem_seg_head.predictor.transformer.decoder"
      # not sure why we are not popping direcetly here!
      # here we list all keys to be renamed (original name on the left, our name on the right)
      rename_keys = []
      for i in range(self.config.decoder_config.decoder_layers):
          # decoder layers: 2 times output projection, 2 feedforward neural networks and 3 layernorms
          rename_keys.append(
              (
                  f"{src_prefix}.layers.{i}.self_attn.out_proj.weight",
                  f"{dst_prefix}.layers.{i}.self_attn.out_proj.weight",
              )
          )
          rename_keys.append(
              (
                  f"{src_prefix}.layers.{i}.self_attn.out_proj.bias",
                  f"{dst_prefix}.layers.{i}.self_attn.out_proj.bias",
              )
          )
          rename_keys.append(
              (
                  f"{src_prefix}.layers.{i}.multihead_attn.out_proj.weight",
                  f"{dst_prefix}.layers.{i}.encoder_attn.out_proj.weight",
              )
          )
          rename_keys.append(
              (
                  f"{src_prefix}.layers.{i}.multihead_attn.out_proj.bias",
                  f"{dst_prefix}.layers.{i}.encoder_attn.out_proj.bias",
              )
          )
          rename_keys.append(
              (
                  f"{src_prefix}.layers.{i}.linear1.weight",
                  f"{dst_prefix}.layers.{i}.fc1.weight",
              )
          )
          rename_keys.append(
              (
                  f"{src_prefix}.layers.{i}.linear1.bias",
                  f"{dst_prefix}.layers.{i}.fc1.bias",
              )
          )
          rename_keys.append(
              (
                  f"{src_prefix}.layers.{i}.linear2.weight",
                  f"{dst_prefix}.layers.{i}.fc2.weight",
              )
          )
          rename_keys.append(
              (
                  f"{src_prefix}.layers.{i}.linear2.bias",
                  f"{dst_prefix}.layers.{i}.fc2.bias",
              )
          )
          rename_keys.append(
              (
                  f"{src_prefix}.layers.{i}.norm1.weight",
                  f"{dst_prefix}.layers.{i}.self_attn_layer_norm.weight",
              )
          )
          rename_keys.append(
              (
                  f"{src_prefix}.layers.{i}.norm1.bias",
                  f"{dst_prefix}.layers.{i}.self_attn_layer_norm.bias",
              )
          )
          rename_keys.append(
              (
                  f"{src_prefix}.layers.{i}.norm2.weight",
                  f"{dst_prefix}.layers.{i}.encoder_attn_layer_norm.weight",
              )
          )
          rename_keys.append(
              (
                  f"{src_prefix}.layers.{i}.norm2.bias",
                  f"{dst_prefix}.layers.{i}.encoder_attn_layer_norm.bias",
              )
          )
          rename_keys.append(
              (
                  f"{src_prefix}.layers.{i}.norm3.weight",
                  f"{dst_prefix}.layers.{i}.final_layer_norm.weight",
              )
          )
          rename_keys.append(
              (
                  f"{src_prefix}.layers.{i}.norm3.bias",
                  f"{dst_prefix}.layers.{i}.final_layer_norm.bias",
              )
          )
  
      return rename_keys
  
imports: import sys
  from argparse import ArgumentParser
  from dataclasses import dataclass
  from pathlib import Path
  from pprint import pformat
  from typing import Any, Dict, Iterator, List, Set, Tuple
  import requests
  import torch
  import torchvision.transforms
  from detectron2.checkpoint import DetectionCheckpointer
  from detectron2.config import get_cfg
  from detectron2.data import MetadataCatalog
  from detectron2.projects.deeplab import add_deeplab_config
  from PIL import Image
  from torch import Tensor, nn
  from transformers.models.maskformer.feature_extraction_maskformer import MaskFormerImageProcessor
  from transformers.models.maskformer.modeling_maskformer import MaskFormerConfig, MaskFormerForInstanceSegmentation, MaskFormerForInstanceSegmentationOutput, MaskFormerModel, MaskFormerModelOutput
  from transformers.utils import logging
label_desc: Use image node_id 5 for calling the OriginalMaskFormerCheckpointToOursConverter obj's underlying member method code with example usage: obj.rename_keys_in_detr_decoder(dst_state_dict, src_state_dict) and returns: rename_keys