class_docstr: ``nn.Module`` wrapper for ``pytorchvideo.transforms.functional.short_side_scale``. The size
  parameter is chosen randomly in [min_size, max_size].
class_docstr_tok: ["``","nn.Module","``","wrapper","for","``","pytorchvideo.transforms.functional.short_side_scale","``",".","The","size","parameter","is","chosen","randomly","in","[","min_size",",","max_size","]","."]
code: def __init__(
      self,
      min_size: int,
      max_size: int,
      interpolation: str = "bilinear",
      backend: str = "pytorch",
  ):
      super().__init__()
      self._min_size = min_size
      self._max_size = max_size
      self._interpolation = interpolation
      self._backend = backend
  
imports: from typing import Callable, Dict, List, Optional, Tuple
  import pytorchvideo.transforms.functional
  import torch
  import torchvision.transforms
label_desc: Use image node_id 1 to create a new RandomShortSideScale object from inherited base classes: torch.nn with example: obj = RandomShortSideScale(min_size, max_size, interpolation, backend)