code: def configure_optimizers(self):
      lr = self.learning_rate
      params = list(self.model.parameters())
      for embedder in self.conditioner.embedders:
          if embedder.is_trainable:
              params = params + list(embedder.parameters())
      opt = self.instantiate_optimizer_from_config(
          params, lr, self.optimizer_config
      )
      if self.scheduler_config is not None:
          scheduler = instantiate_from_config(self.scheduler_config)
          print("Setting up LambdaLR scheduler...")
          scheduler = [
              {
                  "scheduler": LambdaLR(
                      opt, lr_lambda=scheduler.schedule
                  ),
                  "interval": "step",
                  "frequency": 1,
              }
          ]
          return [opt], scheduler
      return opt
  
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 14 for calling the DiffusionEngine obj's underlying member method code with example usage: obj.configure_optimizers() and returns: opt, list,  scheduler