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.
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docstr: The constructor method of VideoDefaultNoAugmentTransform class.
  
  Args:
      size: the short edge of rescaled video clip
      mean: a 3-tuple denoting the pixel RGB mean
      std: a 3-tuple denoting the pixel RGB standard deviation
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code: def __init__(
      self,
      size: int = VideoConstants.SIZE_RANGE[0],
      mean: List[float] = VideoConstants.MEAN,
      std: List[float] = VideoConstants.STD,
  ):
      """The constructor method of VideoDefaultNoAugmentTransform class.
  
      Args:
          size: the short edge of rescaled video clip
          mean: a 3-tuple denoting the pixel RGB mean
          std: a 3-tuple denoting the pixel RGB standard deviation
  
      """
      self._transform = transforms.Compose(
          # At testing stage, central cropping is not used because we
          # conduct fully convolutional-style testing
          [
              transforms_video.ToTensorVideo(),
              # TODO(zyan3): migrate VideoClipResize to TorchVision
              VideoClipResize(size),
              transforms_video.NormalizeVideo(mean=mean, std=std),
          ]
      )
  
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 1 to create a new VideoDefaultNoAugmentTransform object from inherited base classes: ClassyTransform with example: obj = VideoDefaultNoAugmentTransform(size, mean, std)