class_docstr: Dataset used to parallelize the transformation of the dataset via a DataLoader
code: def __init__(self, input_path: str, output_path: str, train: bool):
      self.input_path = input_path
      self.output_path = output_path
      self.train = train
      self.image_folder = os.path.join(
          self.input_path, self.IMAGE_FOLDER
      )
      self.images = []
      self.targets = []
      self.labels = sorted(os.listdir(self.image_folder))
  
      # There is no train/val split in the EUROSAT dataset, so we have to create it
      for i, label in enumerate(self.labels):
          label_path = os.path.join(self.image_folder, label)
          files = sorted(os.listdir(label_path))
          if train:
              self.images.extend(files[: self.TRAIN_SAMPLES])
              self.targets.extend([i] * self.TRAIN_SAMPLES)
          else:
              self.images.extend(
                  files[
                      self.TRAIN_SAMPLES : self.TRAIN_SAMPLES
                      + self.VALID_SAMPLES
                  ]
              )
              self.targets.extend([i] * self.VALID_SAMPLES)
  
file_path: vissl/extra_scripts/datasets/create_euro_sat_data_files.py
imports: import argparse
  import os
  import shutil
  from torch.utils.data import DataLoader
  from tqdm import tqdm
  from vissl.utils.download import download_and_extract_archive
label_desc: Use image node_id 1 to create a new _EuroSAT object  with example: obj = _EuroSAT(input_path, output_path, train)