code: def preprocess_train(self, examples):
      # image aug
      image = examples["pixel_values"]
      original_sizes = (image.height, image.width)
      image = self.train_resize(image)
      # crop_top_lefts=[]
      y1 = max(
          0, int(round((image.height - self.args.resolution) / 2.0))
      )
      x1 = max(
          0, int(round((image.width - self.args.resolution) / 2.0))
      )
      image = self.train_crop(image)
      if self.args.random_flip and random.random() < 0.5:
          # flip
          x1 = image.width - x1
          image = self.train_flip(image)
      crop_top_left = (y1, x1)
      crop_top_lefts = crop_top_left
      image = self.train_transforms(image)
      examples["original_sizes"] = original_sizes
      examples["crop_top_lefts"] = crop_top_lefts
      examples["pixel_values"] = image
      return examples
  
imports: import glob
  import logging
  import os
  import random
  import cv2
  import io
  import numpy
  import hashlib
  import lmdb
  import pickle
  import torch
  import torch.nn.functional
  from torch.utils.data import Dataset, IterableDataset, DataLoader, DistributedSampler, ConcatDataset
  import torchvision.transforms
  from torchvision.transforms import Compose, ToTensor, Normalize, RandomResizedCrop
  from PIL import Image
  from enum import Enum
  import csv
  import pandas
  import json
  import albumentations
label_desc: Use image node_id 2 for calling the InpaintingTextTrainDataset obj's underlying member method code with example usage: obj.preprocess_train(examples) and returns: examples