code: def __init__(self, **kwargs) -> None:
      dataset = "feidegger"
  
      # cache Feidegger config file
      json_link = "https://raw.githubusercontent.com/zalandoresearch/feidegger/master/data/FEIDEGGER_release_1.1.json"
      json_local_path = cached_path(
          json_link, Path("datasets") / dataset
      )
  
      # cache Feidegger images
      with json_local_path.open(encoding="utf-8") as fin:
          dataset_info = json.load(fin)
      images_cache_folder = os.path.join(
          os.path.dirname(json_local_path), "images"
      )
      if not os.path.isdir(images_cache_folder):
          os.mkdir(images_cache_folder)
      for image_info in tqdm(dataset_info):
          name = os.path.basename(image_info["url"])
          filename = os.path.join(images_cache_folder, name)
          if not os.path.isfile(filename):
              urllib.request.urlretrieve(image_info["url"], filename)
          # replace image URL with local cached file
          image_info["url"] = filename
  
      feidegger_dataset: Dataset = FeideggerDataset(
          dataset_info, **kwargs
      )
  
      train_indices = list(np.where(np.in1d(feidegger_dataset.split, list(range(8))))[0])  # type: ignore[attr-defined]
      train = torch.utils.data.dataset.Subset(
          feidegger_dataset, train_indices
      )
  
      dev_indices = list(np.where(np.in1d(feidegger_dataset.split, [8]))[0])  # type: ignore[attr-defined]
      dev = torch.utils.data.dataset.Subset(
          feidegger_dataset, dev_indices
      )
  
      test_indices = list(np.where(np.in1d(feidegger_dataset.split, [9]))[0])  # type: ignore[attr-defined]
      test = torch.utils.data.dataset.Subset(
          feidegger_dataset, test_indices
      )
  
      super().__init__(train, dev, test, name="feidegger")
  
imports: import json
  import logging
  import os
  import urllib
  from pathlib import Path
  from typing import List
  import numpy
  import torch.utils.data.dataloader
  from torch.utils.data import Dataset
  from tqdm import tqdm
  from flair.data import Corpus, DataPair, FlairDataset, Image, Sentence
  from flair.file_utils import cached_path
label_desc: Use image node_id 1 to create a new FeideggerCorpus object from inherited base classes: Corpus with example: obj = FeideggerCorpus()