code: def make_classification_train_transform(
      *,
      crop_size: int = 224,
      interpolation=transforms.InterpolationMode.BICUBIC,
      hflip_prob: float = 0.5,
      mean: Sequence[float] = IMAGENET_DEFAULT_MEAN,
      std: Sequence[float] = IMAGENET_DEFAULT_STD,
  ):
      transforms_list = [
          transforms.RandomResizedCrop(
              crop_size, interpolation=interpolation
          )
      ]
      if hflip_prob > 0.0:
          transforms_list.append(
              transforms.RandomHorizontalFlip(hflip_prob)
          )
      transforms_list.extend(
          [
              MaybeToTensor(),
              make_normalize_transform(mean=mean, std=std),
          ]
      )
      return transforms.Compose(transforms_list)
  
file_path: dinov2/dinov2/data/transforms.py
imports: from typing import Sequence
  import torch
  from torchvision import transforms
label_desc: Use image node_id 2 for calling a global function with example usage: make_classification_train_transform() and returns: transforms