code: def configure_optimizers(self):
      if self.custom_optimizer is None:
          # Loading from the config
          try:
              self._optimizer = _create_optimizer(
                  self.hparams.optimizer
              )
              opt = self._optimizer(
                  self.parameters(),
                  lr=self.hparams.learning_rate,
                  **self.hparams.optimizer_params,
              )
          except AttributeError as e:
              logger.error(
                  f"{self.hparams.optimizer} is not a valid optimizer defined in the torch.optim module"
              )
              raise e
      else:
          # Loading from custom fit arguments
          self._optimizer = _create_optimizer(self.custom_optimizer)
  
          opt = self._optimizer(
              self.parameters(),
              lr=self.hparams.learning_rate,
              **self.custom_optimizer_params,
          )
      if self.hparams.lr_scheduler is not None:
          try:
              self._lr_scheduler = getattr(
                  torch.optim.lr_scheduler, self.hparams.lr_scheduler
              )
          except AttributeError as e:
              logger.error(
                  f"{self.hparams.lr_scheduler} is not a valid learning rate sheduler defined"
                  f" in the torch.optim.lr_scheduler module"
              )
              raise e
          if isinstance(
              self._lr_scheduler, torch.optim.lr_scheduler._LRScheduler
          ):
              return {
                  "optimizer": opt,
                  "lr_scheduler": self._lr_scheduler(
                      opt, **self.hparams.lr_scheduler_params
                  ),
              }
          return {
              "optimizer": opt,
              "lr_scheduler": self._lr_scheduler(
                  opt, **self.hparams.lr_scheduler_params
              ),
              "monitor": self.hparams.lr_scheduler_monitor_metric,
          }
      else:
          return opt
  
imports: import importlib
  import warnings
  from abc import ABCMeta, abstractmethod
  from functools import partial
  from typing import Any, Callable, Dict, List, Optional, Tuple, Type, Union
  import numpy
  import pytorch_lightning
  import torch
  import torch.nn
  import torchmetrics
  from omegaconf import DictConfig, OmegaConf
  from pandas import DataFrame
  from torch import Tensor
  from torch.optim import Optimizer
  from pytorch_tabular.models.common.heads import blocks
  from pytorch_tabular.models.common.layers import PreEncoded1dLayer
  from pytorch_tabular.utils import get_logger, reset_all_weights
label_desc: Use image node_id 25 for calling the BaseModel obj's underlying member method code with example usage: obj.configure_optimizers() and returns: dict, opt, dict