class_docstr: Ego4d video/audio/imu dataset for the moments benchmark:
  `<https://ego4d-data.org/docs/benchmarks/episodic-memory/>`
  
  This dataset handles the parsing of frames, loading and clip sampling for the
  videos.
  
  IO utilizing :code:`iopath.common.file_io.PathManager` to support
  non-local storage uri's.
docstr: This function contains example transforms using both PyTorchVideo and
  TorchVision in the same callable. For 'train' model, we use augmentations (prepended
  with 'Random'), for 'val' we use the respective deterministic function
code: def _video_transform(self):
      """
      This function contains example transforms using both PyTorchVideo and
      TorchVision in the same callable. For 'train' model, we use augmentations (prepended
      with 'Random'), for 'val' we use the respective deterministic function
      """
  
      assert (
          self.video_means
          and self.video_stds
          and self.video_min_short_side_scale > 0
          and self.video_crop_size > 0
      )
  
      video_transforms = ApplyTransformToKey(
          key="video",
          transform=Compose(
              # pyre-fixme
              [Div255(), Normalize(self.video_means, self.video_stds)]
              + [  # pyre-fixme
                  RandomShortSideScale(
                      min_size=self.video_min_short_side_scale,
                      max_size=self.video_max_short_side_scale,
                  ),
                  RandomCrop(self.video_crop_size),
                  RandomHorizontalFlip(p=0.5),
              ]
              if self.training
              else [
                  ShortSideScale(self.video_min_short_side_scale),
                  CenterCrop(self.video_crop_size),
              ]
          ),
      )
      return Compose([video_transforms])
  
imports: import csv
  import json
  import logging
  import os
  from bisect import bisect_left
  from collections import defaultdict
  from typing import Any, Callable, Dict, List, Optional, Set, Tuple, Type
  import numpy
  import torch
  import torch.autograd.profiler
  import torch.utils.data
  import torchaudio
  from iopath.common.file_io import g_pathmgr
  from pytorchvideo.data import LabeledVideoDataset
  from pytorchvideo.data.clip_sampling import ClipSampler
  from pytorchvideo.data.ego4d.utils import Ego4dImuDataBase, get_label_id_map, MomentsClipSampler
  from pytorchvideo.data.utils import get_logger
  from pytorchvideo.data.video import VideoPathHandler
  from pytorchvideo.transforms import ApplyTransformToKey, Div255, Normalize, RandomShortSideScale, ShortSideScale
  from torchvision.transforms import CenterCrop, Compose, RandomCrop, RandomHorizontalFlip
label_desc: Use image node_id 4 for calling the Ego4dMomentsDataset obj's underlying member method code with example usage: obj._video_transform() and returns: Compose