code: def __init__(self, model_cfg, image_size, train=True):
      self.data_type = _to_torch_data_type(model_cfg.precision)
      assert (
          TORCHVISION_AVAILABLE
      ), "Torchvision imports failed but they are required."
      if train:
          self.transform = T.Compose(
              [
                  T.RandomResizedCrop(image_size),
                  T.RandomHorizontalFlip(),
                  T.ColorJitter(0.4, 0.4, 0.4, 0.1),
                  ImageNetPolicy(),
                  T.ToTensor(),
                  T.Normalize(
                      (0.485, 0.456, 0.406), (0.229, 0.224, 0.225)
                  ),
                  T.ConvertImageDtype(self.data_type),
              ]
          )
      else:
          self.transform = T.Compose(
              [
                  T.Resize(image_size),
                  T.CenterCrop(image_size),
                  T.ToTensor(),
                  T.Normalize(
                      (0.485, 0.456, 0.406), (0.229, 0.224, 0.225)
                  ),
                  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 ClassificationTransform object  with example: obj = ClassificationTransform(model_cfg, image_size, train)