class_docstr: Hierarchical Attention Network (RNN baseline model)
code: def configure_optimizers(self):
      """"""
      optimizer = torch.optim.RAdam(
          self.parameters(),
          lr=self.hparams.learning_rate,
          weight_decay=self.hparams.weight_decay,
          betas=(self.hparams.beta1, self.hparams.beta2),
          eps=self.hparams.epsilon,
      )
  
      return {
          "optimizer": optimizer,
          "lr_scheduler": {
              "scheduler": torch.optim.lr_scheduler.StepLR(
                  optimizer, step_size=1, gamma=0.9
              ),
              "interval": "epoch",
              "frequency": 1,
              "name": "learning_rate",
          },
      }
  
imports: from turtle import hideturtle
  import pytorch_lightning
  import torch
  import torch.nn.functional
  import torch.nn
  from pytorch_lightning import seed_everything
  from pathlib import Path
  from src.transformer.transformer_utils import *
  from src.transformer.metrics import CorrectedBAcc, CorrectedF1, CorrectedMCC, AUL
  import torchmetrics
label_desc: Use image node_id 12 for calling the FFN obj's underlying member method code with example usage: obj.configure_optimizers() and returns: dict