code: def __getitem__(self, index):
      index = self.filtered_index_list[index]
      img_fname = self.df.at[index, "filename"]
      img_fpath = os.path.join(self.root, img_fname)
      label = self.df.at[index, "words"]
  
      if self.opt.rgb:
          img = Image.open(img_fpath).convert("RGB")  # for color image
      else:
          img = Image.open(img_fpath).convert("L")
  
      if not self.opt.sensitive:
          label = label.lower()
  
      # We only train and evaluate on alphanumerics (or pre-defined character set in train.py)
      out_of_char = f"[^{self.opt.character}]"
      label = re.sub(out_of_char, "", label)
  
      return (img, label)
  
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 3 for calling the OCRDataset obj's underlying member method code with example usage: obj.__getitem__(index) and returns: img,  label