class_docstr: Wrapper for PyTorchVideo datasets.
class_docstr_tok: ["Wrapper","for","PyTorchVideo","datasets","."]
docstr: Construct the dataset.
  
  Args:
      num_vidoes (int): number of videos in the dataset.
      clips_per_video (int): number of clips per video in the dataset.
      dataset (torch.utils.data.IterableDataset): a PyTorchVideo dataset.
docstr_tok: ["Construct","the","dataset",".","Args",":","num_vidoes","(","int",")",":","number","of","videos","in","the","dataset",".","clips_per_video","(","int",")",":","number","of","clips","per","video","in","the","dataset",".","dataset","(","torch.utils.data.IterableDataset",")",":","a","PyTorchVideo","dataset","."]
code: def __init__(
      self, num_videos, clips_per_video, crops_per_clip, dataset
  ):
      """
      Construct the dataset.
  
      Args:
          num_vidoes (int): number of videos in the dataset.
          clips_per_video (int): number of clips per video in the dataset.
          dataset (torch.utils.data.IterableDataset): a PyTorchVideo dataset.
      """
      self._clips_per_video = clips_per_video
      self._crops_per_clip = crops_per_clip
      self._num_videos = num_videos
      self.dataset = dataset
  
imports: import functools
  import os
  from typing import Dict
  import torch
  from torch.utils.data import DistributedSampler, RandomSampler, SequentialSampler
  from torchvision.transforms import Compose, Lambda
  from torchvision.transforms._transforms_video import NormalizeVideo, RandomCropVideo, RandomHorizontalFlipVideo
  import slowfast.utils.logging
  from pytorchvideo.data import Charades, LabeledVideoDataset, SSv2, make_clip_sampler
  from pytorchvideo.data.labeled_video_paths import LabeledVideoPaths
  from pytorchvideo.transforms import ApplyTransformToKey, RandomShortSideScale, ShortSideScale, UniformCropVideo, UniformTemporalSubsample
  from .None import utils
  from .build import DATASET_REGISTRY
label_desc: Use image node_id 1 to create a new PTVDatasetWrapper object from inherited base classes: torch.utils.data with example: obj = PTVDatasetWrapper(num_videos, clips_per_video, crops_per_clip, dataset)