code: def __init__(
      self,
      min_size: Union[List[int], Tuple[int], int],
      max_size: int,
      interpolation: InterpolationMode = InterpolationMode.BILINEAR,
  ):
      super().__init__()
      self.min_size = (
          [min_size] if isinstance(min_size, int) else list(min_size)
      )
      self.max_size = max_size
      self.interpolation = interpolation
  
imports: from typing import Dict, List, Optional, Tuple, Union
  import torch
  import torchvision
  from torch import nn, Tensor
  from torchvision import ops
  from torchvision.transforms import functional, InterpolationMode, transforms
label_desc: Use image node_id 1 to create a new RandomShortestSize object from inherited base classes: nn with example: obj = RandomShortestSize(min_size, max_size, interpolation)