docstr: This function is called in the inner loop of the evaluation cycle. For this
  simple example it's mostly the same as the training loop but with a different
  metric name.
code: def validation_step(self, batch, batch_idx):
      """
      This function is called in the inner loop of the evaluation cycle. For this
      simple example it's mostly the same as the training loop but with a different
      metric name.
      """
      x = batch[self.batch_key]
      y_hat = self.model(x)
      loss = F.cross_entropy(y_hat, batch["label"])
      acc = self.val_accuracy(F.softmax(y_hat, dim=-1), batch["label"])
      self.log("val_loss", loss)
      self.log(
          "val_acc",
          acc,
          on_step=True,
          on_epoch=True,
          prog_bar=True,
          sync_dist=True,
      )
      return loss
  
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 5 for calling the VideoClassificationLightningModule obj's underlying member method code with example usage: obj.validation_step(batch, batch_idx) and returns: loss