code: def __init__(
      self,
      batch_frequency,
      max_images,
      clamp=True,
      increase_log_steps=True,
      rescale=True,
      disabled=False,
      log_on_batch_idx=False,
      log_first_step=False,
      log_images_kwargs=None,
  ):
      super().__init__()
      self.rescale = rescale
      self.batch_freq = batch_frequency
      self.max_images = max_images
      self.logger_log_images = {
          pl.loggers.TestTubeLogger: self._testtube,
      }
      self.log_steps = [
          2**n for n in range(int(np.log2(self.batch_freq)) + 1)
      ]
      if not increase_log_steps:
          self.log_steps = [self.batch_freq]
      self.clamp = clamp
      self.disabled = disabled
      self.log_on_batch_idx = log_on_batch_idx
      self.log_images_kwargs = (
          log_images_kwargs if log_images_kwargs else {}
      )
      self.log_first_step = log_first_step
  
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 1 to create a new ImageLogger object from inherited base classes: Callback with example: obj = ImageLogger(batch_frequency, max_images, clamp, increase_log_steps, rescale, disabled, log_on_batch_idx, log_first_step, log_images_kwargs)