code: def configure_optimizers(self):
      optimizer = torch.optim.Adam(self.parameters(), lr=0.001)
      scheduler = StepLR(optimizer, step_size=25, gamma=0.25)
      return [optimizer], [scheduler]
  
imports: import argparse
  import glob
  import os
  import os.path
  import time
  from typing import List, NamedTuple, Optional
  import numpy
  import torch
  import torch.nn.functional
  from ogb.lsc import MAG240MDataset, MAG240MEvaluator
  import pytorch_lightning
  from pytorch_lightning import LightningDataModule, LightningModule, Trainer, seed_everything
  from pytorch_lightning.callbacks import ModelCheckpoint
  from torchmetrics import Accuracy
  from torch import Tensor
  from torch.nn import BatchNorm1d, Dropout, Linear, ModuleList, ReLU, Sequential
  from torch.optim.lr_scheduler import StepLR
  from torch_geometric.data import NeighborSampler
  from torch_geometric.nn import GATConv, SAGEConv
  from torch_sparse import SparseTensor
  from tqdm.auto import tqdm
  from root import ROOT
label_desc: Use image node_id 6 for calling the RGNN obj's underlying member method code with example usage: obj.configure_optimizers() and returns: list,  list