class_docstr: This is the default video transform without data augmentation which is useful
  for testing.
  
  It sequentially prepares a torch.Tensor of video data, resize the
  video clip to have the specified short edge, and normalize the pixel values
  by mean subtraction and standard deviation division.
class_docstr_tok: ["This","is","the","default","video","transform","without","data","augmentation","which","is","useful","for","testing",".","It","sequentially","prepares","a","torch.Tensor","of","video","data",",","resize","the","video","clip","to","have","the","specified","short","edge",",","and","normalize","the","pixel","values","by","mean","subtraction","and","standard","deviation","division","."]
docstr: Apply the default transform without data augmentation to video.
  
  Args:
      video: input video that will undergo the transform
docstr_tok: ["Apply","the","default","transform","without","data","augmentation","to","video",".","Args",":","video",":","input","video","that","will","undergo","the","transform"]
code: def __call__(self, video):
      """Apply the default transform without data augmentation to video.
  
      Args:
          video: input video that will undergo the transform
  
      """
      return self._transform(video)
  
imports: import random
  from typing import Any, Callable, Dict, List, Optional, Union
  import torch
  import torchvision.transforms
  import torchvision.transforms._transforms_video
  from .None import build_transforms, ClassyTransform, register_transform
  from .util import ApplyTransformToKey, ImagenetConstants, TupleToMapTransform
label_desc: Use image node_id 2 for calling the VideoDefaultNoAugmentTransform obj's underlying member method code with example usage: obj.__call__(video) and returns: self