code: def __init__(
      self,
      *,
      crop_size,
      resize_size,
      mean=(0.43216, 0.394666, 0.37645),
      std=(0.22803, 0.22145, 0.216989),
      hflip_prob=0.5,
  ):
      trans = [
          transforms.ConvertImageDtype(torch.float32),
          # We hard-code antialias=False to preserve results after we changed
          # its default from None to True (see
          # https://github.com/pytorch/vision/pull/7160)
          # TODO: we could re-train the video models with antialias=True?
          transforms.Resize(resize_size, antialias=False),
      ]
      if hflip_prob > 0:
          trans.append(transforms.RandomHorizontalFlip(hflip_prob))
      trans.extend(
          [
              transforms.Normalize(mean=mean, std=std),
              transforms.RandomCrop(crop_size),
              ConvertBCHWtoCBHW(),
          ]
      )
      self.transforms = transforms.Compose(trans)
  
file_path: vision/references/video_classification/presets.py
imports: import torch
  from torchvision.transforms import transforms
  from transforms import ConvertBCHWtoCBHW
label_desc: Use image node_id 1 to create a new VideoClassificationPresetTrain object  with example: obj = VideoClassificationPresetTrain()