code: def on_validation_batch_end(
      self,
      trainer,
      pl_module,
      outputs,
      batch,
      batch_idx,
      dataloader_idx,
  ):
      if not self.disabled and pl_module.global_step > 0:
          self.log_img(pl_module, batch, batch_idx, split="val")
      if hasattr(pl_module, "calibrate_grad_norm"):
          if (
              pl_module.calibrate_grad_norm and batch_idx % 25 == 0
          ) and batch_idx > 0:
              self.log_gradients(
                  trainer, pl_module, batch_idx=batch_idx
              )
  
imports: import argparse, os, sys, datetime, glob, importlib, csv
  import numpy
  import time
  import torch
  import torchvision
  import pytorch_lightning
  from packaging import version
  from omegaconf import OmegaConf
  from torch.utils.data import random_split, DataLoader, Dataset, Subset
  from functools import partial
  from PIL import Image
  from pytorch_lightning import seed_everything
  from pytorch_lightning.trainer import Trainer
  from pytorch_lightning.callbacks import ModelCheckpoint, Callback, LearningRateMonitor
  from pytorch_lightning.utilities.distributed import rank_zero_only
  from pytorch_lightning.utilities import rank_zero_info
  from ldm.data.base import Txt2ImgIterableBaseDataset
  from ldm.util import instantiate_from_config
label_desc: Use image node_id 7 for calling the ImageLogger obj's underlying member method code with example usage: obj.on_validation_batch_end(trainer, pl_module, outputs, batch, batch_idx, dataloader_idx) without return types