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.
code: def _preproc_audio(self, audio, audio_fps) -> Dict[str, Any]:
      # convert stero to mono
      # https://github.com/pytorch/audio/issues/363
      waveform_mono = torch.mean(audio, dim=0, keepdim=True)
      return {
          "signal": waveform_mono,
          "spectrogram": self.signal_transform(
              type=self.audio_transform_type,
              sample_rate=audio_fps,
          )(waveform_mono),
          "sampling_rate": audio_fps,
      }
  
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 6 for calling the Ego4dMomentsDataset obj's underlying member method code with example usage: obj._preproc_audio(audio, audio_fps) and returns: dict