code: def __init__(self, root, opt):
      self.root = root
      self.opt = opt
      print(root)
      self.df = pd.read_csv(
          os.path.join(root, "labels.csv"),
          sep="^([^,]+),",
          engine="python",
          usecols=["filename", "words"],
          keep_default_na=False,
      )
      self.nSamples = len(self.df)
  
      if self.opt.data_filtering_off:
          self.filtered_index_list = [
              index + 1 for index in range(self.nSamples)
          ]
      else:
          self.filtered_index_list = []
          for index in range(self.nSamples):
              label = self.df.at[index, "words"]
              try:
                  if len(label) > self.opt.batch_max_length:
                      continue
              except:
                  print(label)
              out_of_char = f"[^{self.opt.character}]"
              if re.search(out_of_char, label.lower()):
                  continue
              self.filtered_index_list.append(index)
          self.nSamples = len(self.filtered_index_list)
  
imports: import os
  import sys
  import re
  import six
  import math
  import torch
  import pandas
  from natsort import natsorted
  from PIL import Image
  import numpy
  from torch.utils.data import Dataset, ConcatDataset, Subset
  from torch._utils import _accumulate
  import torchvision.transforms
label_desc: Use image node_id 1 to create a new OCRDataset object from inherited base classes: Dataset with example: obj = OCRDataset(root, opt)