code: def build_image_logger(data_module: LightningDataModule):
      data_module.setup(stage=None)  # type: ignore
  
      train_sample = next(iter(data_module.train_dataloader()))
      val_sample = next(iter(data_module.val_dataloader()))
      return WandbImageCallback(train_sample, val_sample)
  
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 for calling a global function with example usage: build_image_logger(data_module) and returns: WandbImageCallback