code: def make_optimizer_scheduler(
      self, params
  ) -> Dict[
      str,
      Union[
          torch.optim.Optimizer, torch.optim.lr_scheduler._LRScheduler
      ],
  ]:
      assert (
          len(self.lr_schedulers) <= 1
      ), "Multiple schedulers for one optimizer is no longer supported"
      # Assuming the classname is the same as the torch class name
      torch_optimizer_class = getattr(torch.optim, type(self).__name__)
      assert is_torch_optimizer(
          torch_optimizer_class
      ), f"{torch_optimizer_class} is not an optimizer."
      filtered_args = {
          k: getattr(self, k)
          for k in inspect.signature(torch_optimizer_class).parameters
          if k != "params" and hasattr(self, k)
      }
      optimizer = torch_optimizer_class(params=params, **filtered_args)
      if len(self.lr_schedulers) == 0:
          return {"optimizer": optimizer}
      else:
          lr_scheduler = self.lr_schedulers[0].make_from_optimizer(
              optimizer
          )
          return {"optimizer": optimizer, "lr_scheduler": lr_scheduler}
  
file_path: ReAgent/reagent/optimizer/optimizer.py
imports: import inspect
  from typing import Dict, List, Union
  import torch
  from reagent.core.dataclasses import dataclass, field
  from reagent.core.registry_meta import RegistryMeta
  from .scheduler import LearningRateSchedulerConfig
  from .utils import is_torch_optimizer
label_desc: Use image node_id 1 for calling the OptimizerConfig obj's underlying member method code with example usage: obj.make_optimizer_scheduler(params) and returns: dict, dict