class_docstr: Transform for converting output from dict to a tuple following PySlowFast
  dataset output format.
class_docstr_tok: ["Transform","for","converting","output","from","dict","to","a","tuple","following","PySlowFast","dataset","output","format","."]
code: def forward(self, x: Dict[str, torch.Tensor]):
      index = (
          x["video_index"] * self._num_clips * self._num_crops
          + x["clip_index"] * self._num_crops
          + x["aug_index"]
      )
  
      return x["video"], x["label"], index, {}
  
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 2 for calling the DictToTuple obj's underlying member method code with example usage: obj.forward(x) and returns: x,  x,  index,  dict