class_docstr: This LightningDataModule implementation constructs a PyTorchVideo Kinetics dataset for both
  the train and val partitions. It defines each partition's augmentation and
  preprocessing transforms and configures the PyTorch DataLoaders.
code: def __init__(self, args):
      self.args = args
      super().__init__()
  
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 1 to create a new KineticsDataModule object from inherited base classes: pytorch_lightning with example: obj = KineticsDataModule(args)