code: def __init__(self, dataset_info, **kwargs) -> None:
      super().__init__()
  
      self.data_points: List[DataPair] = []
      self.split: List[int] = []
  
      def identity(x):
          return x
  
      preprocessor = identity
      if "lowercase" in kwargs and kwargs["lowercase"]:
          preprocessor = str.lower
  
      for image_info in dataset_info:
          image = Image(imageURL=image_info["url"])
          for caption in image_info["descriptions"]:
              # append Sentence-Image data point
              self.data_points.append(
                  DataPair(
                      Sentence(
                          preprocessor(caption), use_tokenizer=True
                      ),
                      image,
                  )
              )
              self.split.append(int(image_info["split"]))
  
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 FeideggerDataset object from inherited base classes: FlairDataset with example: obj = FeideggerDataset(dataset_info)