class_docstr: This implements RandAugment for video. Assume the input video tensor with shape
  (T, C, H, W).
  
  RandAugment: Practical automated data augmentation with a reduced search space
  (https://arxiv.org/abs/1909.13719)
docstr: Perform RandAugment to the input video tensor.
  
  Args:
      video (torch.Tensor): Input video tensor with shape (T, C, H, W).
docstr_tok: ["Perform","RandAugment","to","the","input","video","tensor",".","Args",":","video","(","torch.Tensor",")",":","Input","video","tensor","with","shape","(","T",",","C",",","H",",","W",")","."]
code: def __call__(self, video: torch.Tensor) -> torch.Tensor:
      """
      Perform RandAugment to the input video tensor.
  
      Args:
          video (torch.Tensor): Input video tensor with shape (T, C, H, W).
      """
      return self.randaug_fn(video)
  
imports: from typing import Any, Dict, Optional
  import torch
  from pytorchvideo.transforms.augmentations import AugmentTransform
  from pytorchvideo.transforms.transforms import OpSampler
label_desc: Use image node_id 2 for calling the RandAugment obj's underlying member method code with example usage: obj.__call__(video) and returns: self