code: def configure_optimizers(self):
      optimizer_grouped_parameters = list()
      no_decay = ["bias", "norm"]
  
      optimizer_grouped_parameters.append(
          {
              "params": [
                  p
                  for n, p in self.named_parameters()
                  if not (nd in n for nd in no_decay)
              ],
              "weight_decay": self.hparams.weight_decay,
              "lr": self.hparams.learning_rate,
          }
      )
      optimizer_grouped_parameters.append(
          {
              "params": [
                  p
                  for n, p in self.named_parameters()
                  if (nd in n for nd in no_decay)
              ],
              "weight_decay": 0.0,
              "lr": self.hparams.learning_rate,
          }
      )
  
      if self.hparams.optimizer_type == "radam":
          optimizer = torch.optim.RAdam(
              optimizer_grouped_parameters,
              betas=(self.hparams.beta1, self.hparams.beta2),
              eps=self.hparams.epsilon,
          )
      elif self.hparams.optimizer_type == "adamw":
          optimizer = torch.optim.AdamW(
              optimizer_grouped_parameters,
              betas=(self.hparams.beta1, self.hparams.beta2),
              eps=self.hparams.epsilon,
          )
      elif self.hparams.optimizer_type == "sgd":
          optimizer = torch.optim.SGD(
              optimizer_grouped_parameters, momentum=0.7, dampening=0.1
          )
      # no sgd , adsgd, nadam, rmsprop
      else:
          raise NotImplementedError
  
      if self.hparams.stage in ["search", "finetuning"]:
          return {
              "optimizer": optimizer,
              "lr_scheduler": {
                  "scheduler": torch.optim.lr_scheduler.ExponentialLR(
                      optimizer, gamma=self.hparams.lr_gamma
                  ),
                  "interval": "epoch",
                  "frequency": 1,
                  "name": "learning_rate",
              },
          }
  
imports: from turtle import forward
  from ..transformer.cls_model import Transformer_CLS, Transformer_PSY
  from ..transformer.transformer import AttentionDecoder, AttentionDecoderP, CLS_Decoder_FT2, Deep_Decoder
  from ..transformer.embeddings import Embeddings
  import torch
  import torch.nn
  import logging
label_desc: Use image node_id 3 for calling the SimpleGRU_PSY obj's underlying member method code with example usage: obj.configure_optimizers() and returns: dict