class_docstr: To ensure a constant number of samples are retrieved from the dataset we use this
  LimitDataset wrapper. This is necessary because several of the underlying videos
  may be corrupted while fetching or decoding, however, we always want the same
  number of steps per epoch.
code: def __getitem__(self, index):
      return next(self.dataset_iter)
  
imports: import argparse
  import itertools
  import logging
  import os
  import pytorch_lightning
  import pytorchvideo.data
  import pytorchvideo.models.resnet
  import torch
  import torch.nn.functional
  from pytorch_lightning.callbacks import LearningRateMonitor
  from pytorchvideo.transforms import ApplyTransformToKey, Normalize, RandomShortSideScale, RemoveKey, ShortSideScale, UniformTemporalSubsample
  from slurm import copy_and_run_with_config
  from torch.utils.data import DistributedSampler, RandomSampler
  from torchaudio.transforms import MelSpectrogram, Resample
  from torchvision.transforms import CenterCrop, Compose, Lambda, RandomCrop, RandomHorizontalFlip
label_desc: Use image node_id 2 for calling the LimitDataset obj's underlying member method code with example usage: obj.__getitem__(index) and returns: next