class_docstr: Dataset used to parallelize the transformation of the dataset via a DataLoader
code: def __init__(self, input_path: str, output_path: str, split: str):
      self.input_path = input_path
      self.output_path = output_path
      self.split = split
      self.class_file = os.path.join(
          self.input_path, self.META_FOLDER, "classes.txt"
      )
      self.split_path = os.path.join(
          self.input_path, self.META_FOLDER, split.lower() + ".txt"
      )
      self.IMAGE_FOLDER = os.path.join(
          self.input_path, self.IMAGE_FOLDER
      )
      with open(self.class_file, "r") as f:
          self.classes = {line.strip() for line in f}
  
      self.targets = []
      self.images = []
      with open(self.split_path, "r") as f:
          for line in f:
              label, image_file_name = line.strip().split("/")
              assert label in self.classes, f"Invalid label: {label}"
              self.targets.append(label)
              self.images.append(
                  os.path.join(
                      self.input_path,
                      self.IMAGE_FOLDER,
                      label,
                      image_file_name + self.IMAGE_EXT,
                  )
              )
  
file_path: vissl/extra_scripts/datasets/create_food101_data_files.py
imports: import argparse
  import os
  from PIL import Image
  from torch.utils.data import DataLoader
  from tqdm import tqdm
  from vissl.utils.download import download_and_extract_archive
  from vissl.utils.io import cleanup_dir
label_desc: Use image node_id 1 to create a new Food101 object  with example: obj = Food101(input_path, output_path, split)