class_docstr: Log images at end of each validatin step
code: def _compute_input_output_batch(
      self, pl_module: pl.LightningModule, batch: VideoData
  ):
      batch = VideoDataset(
          video_tensor=(batch.video_tensor).to(device=pl_module.device)
      )
      outputs = torch.clip(pl_module(batch)[0], min=0, max=1.0)
  
      return {
          "image": make_grid(batch.video_tensor[0], nrow=7),
          "image_compressed": make_grid(outputs[0], nrow=7),
      }
  
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 3 for calling the WandbImageCallback obj's underlying member method code with example usage: obj._compute_input_output_batch(pl_module, batch) and returns: dict