class_docstr: A video clip transform that is often useful for testing data.
  
  Given an input size, rescale the clip so that its short edge equals to
  the input size while aspect ratio is preserved.
class_docstr_tok: ["A","video","clip","transform","that","is","often","useful","for","testing","data",".","Given","an","input","size",",","rescale","the","clip","so","that","its","short","edge","equals","to","the","input","size","while","aspect","ratio","is","preserved","."]
docstr: The constructor method of VideoClipResize class.
  
  Args:
      size: input size
      interpolation_mode: Default: "bilinear". See valid values in
          (https://pytorch.org/docs/stable/nn.functional.html#torch.nn.
          functional.interpolate)
docstr_tok: ["The","constructor","method","of","VideoClipResize","class",".","Args",":","size",":","input","size","interpolation_mode",":","Default",":","``","bilinear","''",".","See","valid","values","in","(","https",":","\/\/pytorch.org\/docs\/stable\/nn.functional.html","#","torch.nn",".","functional.interpolate",")"]
code: def __init__(self, size: int, interpolation_mode: str = "bilinear"):
      """The constructor method of VideoClipResize class.
  
      Args:
          size: input size
          interpolation_mode: Default: "bilinear". See valid values in
              (https://pytorch.org/docs/stable/nn.functional.html#torch.nn.
              functional.interpolate)
  
      """
      self.interpolation_mode = interpolation_mode
      self.size = size
  
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 VideoClipResize object from inherited base classes: ClassyTransform with example: obj = VideoClipResize(size, interpolation_mode)