docstr: A wrapper for MixVideo (CutMix or Mixup) tranform in pytorchvideo.transforms.
  Extends the MixVideo transform to work on a batch dictionary objects.
  
  The dictionary object should consist of keys "video" and "label" representing
  video clips and their associated labels.
docstr_tok: ["A","wrapper","for","MixVideo","(","CutMix","or","Mixup",")","tranform","in","pytorchvideo.transforms",".","Extends","the","MixVideo","transform","to","work","on","a","batch","dictionary","objects",".","The","dictionary","object","should","consist","of","keys","``","video","''","and","``","label","''","representing","video","clips","and","their","associated","labels","."]
code: def __init__(
      self,
      mixup_alpha: float,
      cutmix_prob: float,
      cutmix_alpha: float,
      label_smoothing: float,
  ) -> None:
      """
      A wrapper for MixVideo (CutMix or Mixup) tranform in pytorchvideo.transforms.
      Extends the MixVideo transform to work on a batch dictionary objects.
  
      The dictionary object should consist of keys "video" and "label" representing
      video clips and their associated labels.
      """
  
      self.mix_video_transform = MixVideo(
          mixup_alpha=mixup_alpha,
          cutmix_prob=cutmix_prob,
          cutmix_alpha=cutmix_alpha,
          label_smoothing=label_smoothing,
      )
  
imports: import random
  from typing import Any, Callable, Dict, Iterable, List, Mapping, Sequence
  import hydra
  import torch
  import torchvision
  from PIL import Image, ImageFilter
  from pytorchvideo.transforms import MixVideo
  from torchvision.transforms import Compose
label_desc: Use image node_id 1 to create a new MixVideoBatchWrapper object  with example: obj = MixVideoBatchWrapper(mixup_alpha, cutmix_prob, cutmix_alpha, label_smoothing)