code: def configure_optimizers(self):
      optimizer = RMSpropTF(
          self.parameters(),
          lr=self.hparams.learning_rate,
          weight_decay=self.hparams.weight_decay,
          momentum=0.9,
          alpha=0.9,
          eps=1.0,
      )
      return {
          "optimizer": optimizer,
          "lr_scheduler": CosineAnnealingLR(
              optimizer, self.hparams.max_epochs
          ),
      }
  
imports: import click
  import nni
  import nni.retiarii.evaluator.pytorch.lightning
  import torch.nn
  import torchmetrics
  from nni.retiarii import model_wrapper, serialize
  from nni.retiarii.experiment.pytorch import RetiariiExperiment, RetiariiExeConfig
  from nni.retiarii.nn.pytorch import NasBench101Cell
  from nni.retiarii.strategy import Random
  from pytorch_lightning.callbacks import LearningRateMonitor
  from timm.optim import RMSpropTF
  from torch.optim.lr_scheduler import CosineAnnealingLR
  from torchvision import transforms
  from torchvision.datasets import CIFAR10
  from base_ops import Conv3x3BnRelu, Conv1x1BnRelu, Projection
label_desc: Use image node_id 5 for calling the NasBench101TrainingModule obj's underlying member method code with example usage: obj.configure_optimizers() and returns: dict