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 signal_transform(
      self, type: str = "spectrogram", sample_rate: int = 48000
  ):
      if type == "spectrogram":
          n_fft = 1024
          win_length = None
          hop_length = 512
  
          transform = torchaudio.transforms.Spectrogram(
              n_fft=n_fft,
              win_length=win_length,
              hop_length=hop_length,
              center=True,
              pad_mode="reflect",
              power=2.0,
          )
      elif type == "melspectrogram":
          n_fft = 1024
          win_length = None
          hop_length = 512
          n_mels = 64
  
          transform = torchaudio.transforms.MelSpectrogram(
              sample_rate=sample_rate,
              n_fft=n_fft,
              win_length=win_length,
              hop_length=hop_length,
              center=True,
              pad_mode="reflect",
              power=2.0,
              norm="slaney",
              onesided=True,
              n_mels=n_mels,
              mel_scale="htk",
          )
      elif type == "mfcc":
          n_fft = 2048
          win_length = None
          hop_length = 512
          n_mels = 256
          n_mfcc = 256
  
          transform = torchaudio.transforms.MFCC(
              sample_rate=sample_rate,
              n_mfcc=n_mfcc,
              melkwargs={
                  "n_fft": n_fft,
                  "n_mels": n_mels,
                  "hop_length": hop_length,
                  "mel_scale": "htk",
              },
          )
      else:
          raise ValueError(type)
  
      return transform
  
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 5 for calling the Ego4dMomentsDataset obj's underlying member method code with example usage: obj.signal_transform(type, sample_rate) and returns: transform