class_docstr: Wrapper for Ego4D IMU data loads, assuming one csv per video_uid at the provided path.
code: def _load_csv(self, csv_path: str) -> List[Dict[str, Any]]:
      with g_pathmgr.open(csv_path, "r") as f:
          reader = csv.DictReader(f)
          data = []
          for row in reader:
              data.append(row)
      return data
  
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 3 for calling the Ego4dImuData obj's underlying member method code with example usage: obj._load_csv(csv_path) and returns: data