code: def make_classification_eval_transform(
      *,
      resize_size: int = 256,
      interpolation=transforms.InterpolationMode.BICUBIC,
      crop_size: int = 224,
      mean: Sequence[float] = IMAGENET_DEFAULT_MEAN,
      std: Sequence[float] = IMAGENET_DEFAULT_STD,
  ) -> transforms.Compose:
      transforms_list = [
          transforms.Resize(resize_size, interpolation=interpolation),
          transforms.CenterCrop(crop_size),
          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 3 for calling a global function with example usage: make_classification_eval_transform() and returns: transforms