docstr: We use the SGD optimizer with per step cosine annealing scheduler.
code: def configure_optimizers(self):
      """
      We use the SGD optimizer with per step cosine annealing scheduler.
      """
      optimizer = torch.optim.SGD(
          self.parameters(),
          lr=self.args.lr,
          momentum=self.args.momentum,
          weight_decay=self.args.weight_decay,
      )
      scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(
          optimizer, self.args.max_epochs, last_epoch=-1
      )
      return [optimizer], [scheduler]
  
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 6 for calling the VideoClassificationLightningModule obj's underlying member method code with example usage: obj.configure_optimizers() and returns: list,  list