code: def __init__(
      self,
      *,
      crop_size,
      resize_size=256,
      mean=(0.485, 0.456, 0.406),
      std=(0.229, 0.224, 0.225),
      interpolation=InterpolationMode.BILINEAR,
      backend="pil",
      use_v2=False,
  ):
      T = get_module(use_v2)
      transforms = []
      backend = backend.lower()
      if backend == "tensor":
          transforms.append(T.PILToTensor())
      elif backend != "pil":
          raise ValueError(
              f"backend can be 'tensor' or 'pil', but got {backend}"
          )
  
      transforms += [
          T.Resize(
              resize_size, interpolation=interpolation, antialias=True
          ),
          T.CenterCrop(crop_size),
      ]
  
      if backend == "pil":
          transforms.append(T.PILToTensor())
  
      transforms += [
          T.ToDtype(torch.float, scale=True)
          if use_v2
          else T.ConvertImageDtype(torch.float),
          T.Normalize(mean=mean, std=std),
      ]
  
      if use_v2:
          transforms.append(T.ToPureTensor())
  
      self.transforms = T.Compose(transforms)
  
file_path: vision/references/classification/presets.py
imports: import torch
  from torchvision.transforms.functional import InterpolationMode
label_desc: Use image node_id 1 to create a new ClassificationPresetEval object  with example: obj = ClassificationPresetEval()