code: def __init__(self, model_cfg, image_size, train=True):
      self.data_type = _to_torch_data_type(model_cfg.precision)
      flip_and_color_jitter = T.Compose(
          [
              T.RandomHorizontalFlip(p=0.5),
              T.RandomApply(
                  [
                      T.ColorJitter(
                          brightness=0.4,
                          contrast=0.4,
                          saturation=0.2,
                          hue=0.1,
                      )
                  ],
                  p=0.8,
              ),
              T.RandomGrayscale(p=0.2),
          ]
      )
  
      if model_cfg.precision in [16, "bf16"]:
          normalize = T.Compose(
              [
                  T.ToTensor(),
                  T.Normalize(
                      (0.485, 0.456, 0.406), (0.229, 0.224, 0.225)
                  ),
                  T.ConvertImageDtype(self.data_type),
              ]
          )
      else:
          normalize = T.Compose(
              [
                  T.ToTensor(),
                  T.Normalize(
                      (0.485, 0.456, 0.406), (0.229, 0.224, 0.225)
                  ),
              ]
          )
  
      # first global crop
      scale_const = 0.4
      self.global_transform1 = T.Compose(
          [
              T.RandomResizedCrop(
                  image_size,
                  scale=(scale_const, 1),
                  interpolation=Image.BICUBIC,
              ),
              flip_and_color_jitter,
              GaussianBlur(1.0),
              normalize,
          ]
      )
      # second global crop
      self.global_transform2 = T.Compose(
          [
              T.RandomResizedCrop(
                  image_size,
                  scale=(scale_const, 1),
                  interpolation=Image.BICUBIC,
              ),
              flip_and_color_jitter,
              GaussianBlur(0.1),
              Solarization(0.2),
              normalize,
          ]
      )
      # transformation for the local small crops
      self.local_crops_number = model_cfg.dino_local_crops_number
      self.local_transform = T.Compose(
          [
              T.RandomResizedCrop(
                  model_cfg.dino_local_img_size,
                  scale=(0.05, scale_const),
                  interpolation=Image.BICUBIC,
              ),
              flip_and_color_jitter,
              GaussianBlur(p=0.5),
              normalize,
          ]
      )
  
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 DinoTransform object from inherited base classes: object with example: obj = DinoTransform(model_cfg, image_size, train)