code: def __call__(self, original_config: object) -> MaskFormerConfig:
      model = original_config.MODEL
      mask_former = model.MASK_FORMER
      swin = model.SWIN
  
      dataset_catalog = MetadataCatalog.get(
          original_config.DATASETS.TEST[0]
      )
      id2label = {
          idx: label
          for idx, label in enumerate(dataset_catalog.stuff_classes)
      }
      label2id = {label: idx for idx, label in id2label.items()}
  
      config: MaskFormerConfig = MaskFormerConfig(
          fpn_feature_size=model.SEM_SEG_HEAD.CONVS_DIM,
          mask_feature_size=model.SEM_SEG_HEAD.MASK_DIM,
          num_labels=model.SEM_SEG_HEAD.NUM_CLASSES,
          no_object_weight=mask_former.NO_OBJECT_WEIGHT,
          num_queries=mask_former.NUM_OBJECT_QUERIES,
          backbone_config=dict(
              pretrain_img_size=swin.PRETRAIN_IMG_SIZE,
              image_size=swin.PRETRAIN_IMG_SIZE,
              in_channels=3,
              patch_size=swin.PATCH_SIZE,
              embed_dim=swin.EMBED_DIM,
              depths=swin.DEPTHS,
              num_heads=swin.NUM_HEADS,
              window_size=swin.WINDOW_SIZE,
              drop_path_rate=swin.DROP_PATH_RATE,
              model_type="swin",
          ),
          dice_weight=mask_former.DICE_WEIGHT,
          ce_weight=1.0,
          mask_weight=mask_former.MASK_WEIGHT,
          decoder_config=dict(
              model_type="detr",
              max_position_embeddings=1024,
              encoder_layers=6,
              encoder_ffn_dim=2048,
              encoder_attention_heads=8,
              decoder_layers=mask_former.DEC_LAYERS,
              decoder_ffn_dim=mask_former.DIM_FEEDFORWARD,
              decoder_attention_heads=mask_former.NHEADS,
              encoder_layerdrop=0.0,
              decoder_layerdrop=0.0,
              d_model=mask_former.HIDDEN_DIM,
              dropout=mask_former.DROPOUT,
              attention_dropout=0.0,
              activation_dropout=0.0,
              init_std=0.02,
              init_xavier_std=1.0,
              scale_embedding=False,
              auxiliary_loss=False,
              dilation=False,
              # default pretrained config values
          ),
          id2label=id2label,
          label2id=label2id,
      )
  
      return config
  
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 torch
  import torchvision.transforms
  from PIL import Image
  from torch import Tensor, nn
  import requests
  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 transformers.models.maskformer.feature_extraction_maskformer import MaskFormerFeatureExtractor
  from transformers.models.maskformer.modeling_maskformer import MaskFormerConfig, MaskFormerForInstanceSegmentation, MaskFormerForInstanceSegmentationOutput, MaskFormerModel, MaskFormerModelOutput
  from transformers.utils import logging
label_desc: Use image node_id 1 for calling the OriginalMaskFormerConfigToOursConverter obj's underlying member method code with example usage: obj.__call__(original_config) and returns: config