class_docstr: You need to to download and convert the [India Driving Dataset](https://idd.insaan.iiit.ac.in/evaluation/autonue21/#bm5)
  following the instructions of Segmentation Track. IDD_Dectection dataset also need for pseudo-labeling.
  
  And then, you need to organize data following the below structure.
  
  IDD_Segmentation
  |
  |--leftImg8bit
  |  |--train
  |  |--val
  |  |--test
  |
  |--gtFine
  |  |--train
  |  |--val
  |  |--test
  
  Args:
      transforms (list): Transforms for image.
      dataset_root (str): Cityscapes dataset directory.
      mode (str, optional): Which part of dataset to use. it is one of ('train', 'val', 'test'). Default: 'train'.
      coarse_multiple (float|int, optional): Multiple of the amount of coarse data relative to fine data. Default: 1
      add_val (bool, optional): Whether to add val set in training. Default: False
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code: def __len__(self):
      return self.total_num_files
  
file_path: PaddleSeg/contrib/AutoNUE/datasets/auto_nue_autolabel.py
imports: import os
  import glob
  import random
  import paddle
  import numpy
  from PIL import Image
  from paddleseg.cvlibs import manager
  from paddleseg.transforms import Compose
label_desc: Use image node_id 3 for calling the AutoNueAutolabel obj's underlying member method code with example usage: obj.__len__() and returns: self