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 check_IMU(self, input_dict: Dict[str, Any]) -> bool:
      if (
          len(input_dict["imu"]["signal"].shape) != 2
          or input_dict["imu"]["signal"].shape[0] == 0
          or input_dict["imu"]["signal"].shape[0] < 200
          or input_dict["imu"]["signal"].shape[1] != 6
      ):
          log.warning(f"Problematic Sample: {input_dict}")
          return True
      else:
          return False
  
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 2 for calling the Ego4dMomentsDataset obj's underlying member method code with example usage: obj.check_IMU(input_dict) and returns: True, False