code: def load_classifier(self, ckpt_path, pool):
      model_config = deepcopy(
          self.diffusion_config.params.unet_config.params
      )
      model_config.in_channels = (
          self.diffusion_config.params.unet_config.params.out_channels
      )
      model_config.out_channels = self.num_classes
      if self.label_key == "class_label":
          model_config.pool = pool
  
      self.model = __models__[self.label_key](**model_config)
      if ckpt_path is not None:
          print(
              "#####################################################################"
          )
          print(f'load from ckpt "{ckpt_path}"')
          print(
              "#####################################################################"
          )
          self.init_from_ckpt(ckpt_path)
  
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 4 for calling the NoisyLatentImageClassifier obj's underlying member method code with example usage: obj.load_classifier(ckpt_path, pool) without return types