code: def compute_top_k(self, logits, labels, k, reduction="mean"):
      _, top_ks = torch.topk(logits, k, dim=1)
      if reduction == "mean":
          return (
              (top_ks == labels[:, None])
              .float()
              .sum(dim=-1)
              .mean()
              .item()
          )
      elif reduction == "none":
          return (top_ks == labels[:, None]).float().sum(dim=-1)
  
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 9 for calling the NoisyLatentImageClassifier obj's underlying member method code with example usage: obj.compute_top_k(logits, labels, k, reduction) and returns: unknown, unknown