code: def train(args):
      trainer = pytorch_lightning.Trainer.from_argparse_args(args)
      classification_module = VideoClassificationLightningModule(args)
      data_module = KineticsDataModule(args)
      trainer.fit(classification_module, data_module)
  
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 a global function with example usage: train(args) without return types