code: def log_images(self, batch, N=8, *args, **kwargs):
      log = dict()
      x = self.get_input(batch, self.diffusion_model.first_stage_key)
      log["inputs"] = x
  
      y = self.get_conditioning(batch)
  
      if self.label_key == "class_label":
          y = log_txt_as_img(
              (x.shape[2], x.shape[3]), batch["human_label"]
          )
          log["labels"] = y
  
      if ismap(y):
          log["labels"] = self.diffusion_model.to_rgb(y)
  
          for step in range(self.log_steps):
              current_time = step * self.log_time_interval
  
              _, logits, x_noisy, _ = self.shared_step(
                  batch, t=current_time
              )
  
              log[f"inputs@t{current_time}"] = x_noisy
  
              pred = F.one_hot(
                  logits.argmax(dim=1), num_classes=self.num_classes
              )
              pred = rearrange(pred, "b h w c -> b c h w")
  
              log[
                  f"pred@t{current_time}"
              ] = self.diffusion_model.to_rgb(pred)
  
      for key in log:
          log[key] = log[key][:N]
  
      return log
  
imports: import os
  import torch
  import pytorch_lightning
  from omegaconf import OmegaConf
  from torch.nn import functional
  from torch.optim import AdamW
  from torch.optim.lr_scheduler import LambdaLR
  from copy import deepcopy
  from einops import rearrange
  from glob import glob
  from natsort import natsorted
  from ldm.modules.diffusionmodules.openaimodel import EncoderUNetModel, UNetModel
  from ldm.util import log_txt_as_img, default, ismap, instantiate_from_config
label_desc: Use image node_id 18 for calling the NoisyLatentImageClassifier obj's underlying member method code with example usage: obj.log_images(batch, N) and returns: log