docstr: The input is a video tensor.
  
  Args:
      video (torch.Tensor): Input video tensor with shape (T, C, H, W).
docstr_tok: ["The","input","is","a","video","tensor",".","Args",":","video","(","torch.Tensor",")",":","Input","video","tensor","with","shape","(","T",",","C",",","H",",","W",")","."]
code: def __call__(self, video: torch.Tensor) -> torch.Tensor:
      """
      The input is a video tensor.
  
      Args:
          video (torch.Tensor): Input video tensor with shape (T, C, H, W).
      """
      if torch.rand(1).item() > self.prob:
          return video
      magnitude = self._get_magnitude()
      level_args = (
          self.level_fn(magnitude, self.level_paras)
          if self.level_fn is not None
          else ()
      )
      return self.transform_fn(
          video, *level_args, **self.transform_hparas
      )
  
imports: from typing import Any, Callable, Dict, List, Optional, Tuple, Union
  import torch
  import torchvision
  from torch import Tensor
  from torchvision.transforms.functional import InterpolationMode
label_desc: Use image node_id 3 for calling the AugmentTransform obj's underlying member method code with example usage: obj.__call__(video) and returns: self, video