docstr: This LightningModule implementation constructs a PyTorchVideo ResNet,
  defines the train and val loss to be trained with (cross_entropy), and
  configures the optimizer.
code: def __init__(self, args):
      """
      This LightningModule implementation constructs a PyTorchVideo ResNet,
      defines the train and val loss to be trained with (cross_entropy), and
      configures the optimizer.
      """
      self.args = args
      super().__init__()
      self.train_accuracy = pytorch_lightning.metrics.Accuracy()
      self.val_accuracy = pytorch_lightning.metrics.Accuracy()
  
      #############
      # PTV Model #
      #############
  
      # Here we construct the PyTorchVideo model. For this example we're using a
      # ResNet that works with Kinetics (e.g. 400 num_classes). For your application,
      # this could be changed to any other PyTorchVideo model (e.g. for SlowFast use
      # create_slowfast).
      if self.args.arch == "video_resnet":
          self.model = pytorchvideo.models.resnet.create_resnet(
              input_channel=3,
              model_num_class=400,
          )
          self.batch_key = "video"
      elif self.args.arch == "audio_resnet":
          self.model = (
              pytorchvideo.models.resnet.create_acoustic_resnet(
                  input_channel=1,
                  model_num_class=400,
              )
          )
          self.batch_key = "audio"
      else:
          raise Exception("{self.args.arch} not supported")
  
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 VideoClassificationLightningModule object from inherited base classes: pytorch_lightning with example: obj = VideoClassificationLightningModule(args)