class_docstr: Log images at end of each validatin step
code: def on_validation_end(self, trainer, pl_module: pl.LightningModule):
      self.log_images(
          trainer,
          "train_images",
          self._compute_input_output_batch(pl_module, self.train_batch),
          pl_module.global_step,
      )
      self.log_images(
          trainer,
          "eval_images",
          self._compute_input_output_batch(pl_module, self.eval_batch),
          pl_module.global_step,
      )
  
imports: import hashlib
  from pathlib import Path
  from typing import Dict
  import hydra
  import pytorch_lightning
  import torch
  import torch.distributed
  import wandb
  from datamodules.video_data_api import VideoData, VideoDataset
  from model_lightning import VCTModule
  from omegaconf import DictConfig
  from pytorch_lightning import LightningDataModule, Trainer, seed_everything
  from pytorch_lightning.callbacks import LearningRateMonitor, ModelCheckpoint
  from pytorch_lightning.loggers.wandb import WandbLogger
  from torch import Tensor
  from torchvision.utils import make_grid
  from utils.hydra_tools import OmegaConf
label_desc: Use image node_id 4 for calling the WandbImageCallback obj's underlying member method code with example usage: obj.on_validation_end(trainer, pl_module) without return types