code: def _load_example(self, file_name: str) -> Sentence:
      data_path = self.base_path / "tagged" / f"{file_name}.json"
  
      with data_path.open("r", encoding=self.encoding) as f:
          data = json.load(f)
      sentence = Sentence(text=data["words"])
  
      img_path = self.base_path / "images" / f"{file_name}.jpg"
      with PIL.Image.open(img_path) as img:
          width, height = img.size
          if self.load_images:
              img.load()
              sentence.add_metadata("image", img.convert("RGB"))
          sentence.add_metadata("img_width", width)
          sentence.add_metadata("img_height", height)
  
      for token, (left, top, right, bottom) in zip(
          sentence, data["bbox"]
      ):
          if self.normalize_coords_to_thousands:
              left = int(1000 * left / width)
              top = int(1000 * top / height)
              right = int(1000 * right / width)
              bottom = int(1000 * bottom / height)
  
          token.add_metadata(
              "bbox",
              BoundingBox(
                  left=left, top=top, right=right, bottom=bottom
              ),
          )
  
      for span_indices, score, label in get_spans_from_bio(
          data["labels"]
      ):
          span = sentence[span_indices[0] : span_indices[-1] + 1]
          value = self._remap_label(label)
          if value != "O":
              span.add_label(self.label_type, value=value, score=score)
  
      return sentence
  
imports: import json
  from pathlib import Path
  from typing import Dict, Optional, Union
  import gdown.download_folder
  import PIL
  from torch.utils.data import Dataset
  import flair
  from flair.data import BoundingBox, Corpus, FlairDataset, Sentence, get_spans_from_bio
  from flair.datasets.base import find_train_dev_test_files
label_desc: Use image node_id 3 for calling the OcrJsonDataset obj's underlying member method code with example usage: obj._load_example(file_name) and returns: sentence