code: def write_logs(self, loss, logits, targets):
      log_prefix = "train" if self.training else "val"
      log = {}
      log[f"{log_prefix}/loss"] = loss.mean()
      log[f"{log_prefix}/acc@1"] = self.compute_top_k(
          logits, targets, k=1, reduction="mean"
      )
      log[f"{log_prefix}/acc@5"] = self.compute_top_k(
          logits, targets, k=5, reduction="mean"
      )
  
      self.log_dict(
          log,
          prog_bar=False,
          logger=True,
          on_step=self.training,
          on_epoch=True,
      )
      self.log(
          "loss", log[f"{log_prefix}/loss"], prog_bar=True, logger=False
      )
      self.log(
          "global_step",
          self.global_step,
          logger=False,
          on_epoch=False,
          prog_bar=True,
      )
      lr = self.optimizers().param_groups[0]["lr"]
      self.log(
          "lr_abs",
          lr,
          on_step=True,
          logger=True,
          on_epoch=False,
          prog_bar=True,
      )
  
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 11 for calling the NoisyLatentImageClassifier obj's underlying member method code with example usage: obj.write_logs(loss, logits, targets) without return types