class_docstr: python -m pytorch_toolbelt.datasets.providers.inria_aerial inria_dataset
code: def __init__(self, root_dir: str):
      self.root_dir = root_dir
      self.train_dir = os.path.join(root_dir, "train")
      self.test_dir = os.path.join(root_dir, "test")
  
      if not os.path.isdir(self.train_dir):
          raise FileNotFoundError(
              f"Train directory {self.train_dir} does not exist"
          )
      if not os.path.isdir(self.test_dir):
          raise FileNotFoundError(
              f"Test directory {self.train_dir} does not exist"
          )
  
      self.train_images = fs.find_images_in_dir(
          os.path.join(self.train_dir, "images")
      )
      self.train_masks = fs.find_images_in_dir(
          os.path.join(self.train_dir, "gt")
      )
  
      if len(self.train_images) != 180 or len(self.train_masks) != 180:
          raise RuntimeError(
              "Number of train images and ground-truth masks must be 180"
          )
  
imports: import os
  import subprocess
  import warnings
  from pathlib import Path
  from typing import Union, Optional, Tuple
  import hashlib
  import numpy
  import pandas
  import torch
  import zipfile
  from sklearn.model_selection import GroupKFold
  from pytorch_toolbelt.utils import fs
label_desc: Use image node_id 6 to create a new InriaAerialImageDataset object  with example: obj = InriaAerialImageDataset(root_dir)