code: def shared_step(self, batch, t=None):
      x, *_ = self.diffusion_model.get_input(
          batch, k=self.diffusion_model.first_stage_key
      )
      targets = self.get_conditioning(batch)
      if targets.dim() == 4:
          targets = targets.argmax(dim=1)
      if t is None:
          t = torch.randint(
              0,
              self.diffusion_model.num_timesteps,
              (x.shape[0],),
              device=self.device,
          ).long()
      else:
          t = torch.full(
              size=(x.shape[0],), fill_value=t, device=self.device
          ).long()
      x_noisy = self.get_x_noisy(x, t)
      logits = self(x_noisy, t)
  
      loss = F.cross_entropy(logits, targets, reduction="none")
  
      self.write_logs(loss.detach(), logits.detach(), targets.detach())
  
      loss = loss.mean()
      return loss, logits, x_noisy, targets
  
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 12 for calling the NoisyLatentImageClassifier obj's underlying member method code with example usage: obj.shared_step(batch, t) and returns: loss,  logits,  x_noisy,  targets