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: Callable function which applies the tranform to the input clip.
  
  Args:
      clip (torch.Tensor): input clip tensor
docstr_tok: ["Callable","function","which","applies","the","tranform","to","the","input","clip",".","Args",":","clip","(","torch.Tensor",")",":","input","clip","tensor"]
code: def __call__(self, clip):
      """Callable function which applies the tranform to the input clip.
  
      Args:
          clip (torch.Tensor): input clip tensor
  
      """
      # clip size: C x T x H x W
      if not min(clip.size()[2], clip.size()[3]) == self.size:
          new_h, new_w = _get_rescaled_size(
              self.size, clip.size()[2], clip.size()[3]
          )
          clip = torch.nn.functional.interpolate(
              clip, size=(new_h, new_w), mode=self.interpolation_mode
          )
      return clip
  
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 VideoClipResize obj's underlying member method code with example usage: obj.__call__(clip) and returns: clip