code: def __init__(
      self,
      batch_frequency,  # Frequency of batches on which to log images
      max_images,  # Maximum number of images to log
      clamp=True,  # Whether to clamp pixel values to [-1,1]
      increase_log_steps=True,  # Whether to increase frequency of log steps exponentially
      rescale=True,  # Whether to rescale pixel values to [0,1]
      disabled=False,  # Whether to disable logging
      log_on_batch_idx=False,  # Whether to log on batch index instead of global step
      log_first_step=False,  # Whether to log on the first step
      log_images_kwargs=None,
  ):  # Additional keyword arguments to pass to log_images method
      super().__init__()
      self.rescale = rescale
      self.batch_freq = batch_frequency
      self.max_images = max_images
      self.logger_log_images = {
          # Dictionary of logger classes and their corresponding logging methods
          pl.loggers.CSVLogger: self._testtube,
      }
      # Create a list of exponentially increasing log steps, starting from 1 and ending at batch_frequency
      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
  import datetime
  import glob
  import os
  import sys
  import time
  from functools import partial
  import lightning.pytorch
  import numpy
  import torch
  import torchvision
  from ldm.models.diffusion.ddpm import LatentDiffusion
  from lightning.pytorch import seed_everything
  from lightning.pytorch.callbacks import Callback, LearningRateMonitor, ModelCheckpoint
  from lightning.pytorch.loggers import TensorBoardLogger, WandbLogger
  from lightning.pytorch.strategies import ColossalAIStrategy, DDPStrategy
  from lightning.pytorch.trainer import Trainer
  from lightning.pytorch.utilities import rank_zero_info, rank_zero_only
  from omegaconf import OmegaConf
  from packaging import version
  from PIL import Image
  from prefetch_generator import BackgroundGenerator
  from torch.utils.data import DataLoader, Dataset
  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)