code: def __init__(
      self,
      diffusion_path,
      num_classes,
      ckpt_path=None,
      pool="attention",
      label_key=None,
      diffusion_ckpt_path=None,
      scheduler_config=None,
      weight_decay=1.0e-2,
      log_steps=10,
      monitor="val/loss",
      *args,
      **kwargs,
  ):
      super().__init__(*args, **kwargs)
      self.num_classes = num_classes
      # get latest config of diffusion model
      diffusion_config = natsorted(
          glob(
              os.path.join(diffusion_path, "configs", "*-project.yaml")
          )
      )[-1]
      self.diffusion_config = OmegaConf.load(diffusion_config).model
      self.diffusion_config.params.ckpt_path = diffusion_ckpt_path
      self.load_diffusion()
  
      self.monitor = monitor
      self.numd = (
          self.diffusion_model.first_stage_model.encoder.num_resolutions
          - 1
      )
      self.log_time_interval = (
          self.diffusion_model.num_timesteps // log_steps
      )
      self.log_steps = log_steps
  
      self.label_key = (
          label_key
          if not hasattr(self.diffusion_model, "cond_stage_key")
          else self.diffusion_model.cond_stage_key
      )
  
      assert (
          self.label_key is not None
      ), "label_key neither in diffusion model nor in model.params"
  
      if self.label_key not in __models__:
          raise NotImplementedError()
  
      self.load_classifier(ckpt_path, pool)
  
      self.scheduler_config = scheduler_config
      self.use_scheduler = self.scheduler_config is not None
      self.weight_decay = weight_decay
  
imports: import os
  from copy import deepcopy
  from glob import glob
  import lightning.pytorch
  import torch
  from einops import rearrange
  from ldm.lr_scheduler import LambdaLinearScheduler
  from ldm.models.diffusion.ddpm import LatentDiffusion
  from ldm.modules.diffusionmodules.openaimodel import EncoderUNetModel, UNetModel
  from ldm.util import default, ismap, log_txt_as_img
  from natsort import natsorted
  from omegaconf import OmegaConf
  from torch.nn import functional
  from torch.optim import AdamW
  from torch.optim.lr_scheduler import LambdaLR
label_desc: Use image node_id 1 to create a new NoisyLatentImageClassifier object from inherited base classes: pl with example: obj = NoisyLatentImageClassifier(diffusion_path, num_classes, ckpt_path, pool, label_key, diffusion_ckpt_path, scheduler_config, weight_decay, log_steps, monitor)