class_docstr: Wrapper for PyTorchVideo datasets.
class_docstr_tok: ["Wrapper","for","PyTorchVideo","datasets","."]
docstr: Returns:
      (torch.utils.data.Sampler): video sampler for the dataset.
docstr_tok: ["Returns",":","(","torch.utils.data.Sampler",")",":","video","sampler","for","the","dataset","."]
code: def sampler(self):
      """
      Returns:
          (torch.utils.data.Sampler): video sampler for the dataset.
      """
      return self.dataset.video_sampler
  
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 3 for calling the PTVDatasetWrapper obj's underlying member method code with example usage: obj.sampler() and returns: self