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","]","."]
docstr: Args:
      x (torch.Tensor): video tensor with shape (C, T, H, W).
docstr_tok: ["Args",":","x","(","torch.Tensor",")",":","video","tensor","with","shape","(","C",",","T",",","H",",","W",")","."]
code: def forward(self, x: torch.Tensor) -> torch.Tensor:
      """
      Args:
          x (torch.Tensor): video tensor with shape (C, T, H, W).
      """
      size = torch.randint(
          self._min_size, self._max_size + 1, (1,)
      ).item()
      return pytorchvideo.transforms.functional.short_side_scale(
          x, size, self._interpolation, self._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 2 for calling the RandomShortSideScale obj's underlying member method code with example usage: obj.forward(x) and returns: pytorchvideo