code: def replace_pixel_module(
      self, dst_state_dict: StateDict, src_state_dict: StateDict
  ):
      dst_prefix: str = "pixel_level_module.decoder"
      src_prefix: str = "sem_seg_head.pixel_decoder"
  
      self.replace_backbone(dst_state_dict, src_state_dict, self.config)
  
      def rename_keys_for_conv(detectron_conv: str, mine_conv: str):
          return [
              (f"{detectron_conv}.weight", f"{mine_conv}.0.weight"),
              # 2 cuz the have act in the middle -> rename it
              (
                  f"{detectron_conv}.norm.weight",
                  f"{mine_conv}.1.weight",
              ),
              (f"{detectron_conv}.norm.bias", f"{mine_conv}.1.bias"),
          ]
  
      renamed_keys = [
          (
              f"{src_prefix}.mask_features.weight",
              f"{dst_prefix}.mask_projection.weight",
          ),
          (
              f"{src_prefix}.mask_features.bias",
              f"{dst_prefix}.mask_projection.bias",
          ),
          # the layers in the original one are in reverse order, stem is the last one!
      ]
  
      renamed_keys.extend(
          rename_keys_for_conv(
              f"{src_prefix}.layer_4", f"{dst_prefix}.fpn.stem"
          )
      )
  
      # add all the fpn layers (here we need some config parameters to know the size in advance)
      for src_i, dst_i in zip(range(3, 0, -1), range(0, 3)):
          renamed_keys.extend(
              rename_keys_for_conv(
                  f"{src_prefix}.adapter_{src_i}",
                  f"{dst_prefix}.fpn.layers.{dst_i}.proj",
              )
          )
          renamed_keys.extend(
              rename_keys_for_conv(
                  f"{src_prefix}.layer_{src_i}",
                  f"{dst_prefix}.fpn.layers.{dst_i}.block",
              )
          )
  
      self.pop_all(renamed_keys, dst_state_dict, src_state_dict)
  
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 4 for calling the OriginalMaskFormerCheckpointToOursConverter obj's underlying member method code with example usage: obj.replace_pixel_module(dst_state_dict, src_state_dict) and returns: list