code: def log_img(self, pl_module, batch, batch_idx, split="train"):
      check_idx = (
          batch_idx if self.log_on_batch_idx else pl_module.global_step
      )
      if (
          self.check_frequency(check_idx)
          and hasattr(  # batch_idx % self.batch_freq == 0
              pl_module, "log_images"
          )
          and callable(pl_module.log_images)
          and self.max_images > 0
      ):
          logger = type(pl_module.logger)
  
          is_train = pl_module.training
          if is_train:
              pl_module.eval()
  
          with torch.no_grad():
              images = pl_module.log_images(
                  batch, split=split, **self.log_images_kwargs
              )
  
          for k in images:
              N = min(images[k].shape[0], self.max_images)
              images[k] = images[k][:N]
              if isinstance(images[k], torch.Tensor):
                  images[k] = images[k].detach().cpu()
                  if self.clamp:
                      images[k] = torch.clamp(images[k], -1.0, 1.0)
  
          self.log_local(
              pl_module.logger.save_dir,
              split,
              images,
              pl_module.global_step,
              pl_module.current_epoch,
              batch_idx,
          )
  
          logger_log_images = self.logger_log_images.get(
              logger, lambda *args, **kwargs: None
          )
          logger_log_images(
              pl_module, images, pl_module.global_step, split
          )
  
          if is_train:
              pl_module.train()
  
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 4 for calling the ImageLogger obj's underlying member method code with example usage: obj.log_img(pl_module, batch, batch_idx, split) without return types