code: def get_conditioning(self, batch, k=None):
      if k is None:
          k = self.label_key
      assert k is not None, "Needs to provide label key"
  
      targets = batch[k].to(self.device)
  
      if self.label_key == "segmentation":
          targets = rearrange(targets, "b h w c -> b c h w")
          for down in range(self.numd):
              h, w = targets.shape[-2:]
              targets = F.interpolate(
                  targets, size=(h // 2, w // 2), mode="nearest"
              )
  
          # targets = rearrange(targets,'b c h w -> b h w c')
  
      return targets
  
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 8 for calling the NoisyLatentImageClassifier obj's underlying member method code with example usage: obj.get_conditioning(batch, k) and returns: targets