code: def __init__(self, model_cfg, image_size, train=True):
      self.mask_factor = model_cfg.mask_factor
      self.mask_type = model_cfg.mask_type
      self.image_size = image_size
      self.patch_size = model_cfg.patch_dim
      self.mask_size = int(
          self.mask_factor
          * (image_size[0] / self.patch_size)
          * (image_size[1] / self.patch_size)
      )
      self.train = train
      self.data_type = _to_torch_data_type(model_cfg.precision)
  
      if self.train:
          self.transform = T.Compose(
              [
                  T.RandomResizedCrop(self.image_size),
                  T.RandomHorizontalFlip(),
                  T.ColorJitter(0.4, 0.4, 0.4, 0.1),
                  ImageNetPolicy(),
                  T.ToTensor(),
                  T.ConvertImageDtype(self.data_type),
              ]
          )
      else:
          self.transform = T.Compose(
              [
                  T.Resize(self.image_size, interpolation=2),
                  T.CenterCrop(self.image_size),
                  T.ToTensor(),
                  T.ConvertImageDtype(self.data_type),
              ]
          )
  
imports: import random
  import numpy
  import torch
  from PIL import Image, ImageFilter, ImageOps
  from torch.utils.data import Dataset
  from nemo.collections.multimodal.data.common.data_samplers import SharedEpoch
  from nemo.collections.vision.data.megatron.autoaugment import ImageNetPolicy
  from nemo.collections.vision.data.megatron.image_folder import ImageFolder
label_desc: Use image node_id 1 to create a new InpaintingTransform object  with example: obj = InpaintingTransform(model_cfg, image_size, train)