class_docstr: Log images at end of each validatin step
code: def __init__(self, train_batch: VideoData, eval_batch: VideoData):
      super().__init__()
      self.train_batch = train_batch
      self.eval_batch = eval_batch
      self.key_set = ("image", "image_compressed")
  
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 1 to create a new WandbImageCallback object from inherited base classes: pl with example: obj = WandbImageCallback(train_batch, eval_batch)