code: def forward(self, x, batch):
      loss = self.loss_fn(
          self.model, self.denoiser, self.conditioner, x, batch
      )
      loss_mean = loss.mean()
      loss_dict = {"loss": loss_mean}
      return loss_mean, loss_dict
  
imports: import math
  from contextlib import contextmanager
  from typing import Any, Dict, List, Optional, Tuple, Union
  import pytorch_lightning
  import torch
  from omegaconf import ListConfig, OmegaConf
  from safetensors.torch import load_file
  from torch.optim.lr_scheduler import LambdaLR
  from ..modules import UNCONDITIONAL_CONFIG
  from ..modules.autoencoding.temporal_ae import VideoDecoder
  from ..modules.diffusionmodules.wrappers import OPENAIUNETWRAPPER
  from ..modules.ema import LitEma
  from ..util import default, disabled_train, get_obj_from_str, instantiate_from_config, log_txt_as_img
label_desc: Use image node_id 7 for calling the DiffusionEngine obj's underlying member method code with example usage: obj.forward(x, batch) and returns: loss_mean,  loss_dict