docstr: Initializes the optimizer and learning rate scheduler
  
  :return: output - Initialized optimizer and scheduler
docstr_tok: ["Initializes","the","optimizer","and","learning","rate","scheduler",":","return",":","output","-","Initialized","optimizer","and","scheduler"]
code: def configure_optimizers(self):
      """
      Initializes the optimizer and learning rate scheduler
  
      :return: output - Initialized optimizer and scheduler
      """
      self.optimizer = torch.optim.Adam(
          self.parameters(), lr=self.args["lr"]
      )
      self.scheduler = {
          "scheduler": torch.optim.lr_scheduler.ReduceLROnPlateau(
              self.optimizer,
              mode="min",
              factor=0.2,
              patience=2,
              min_lr=1e-6,
              verbose=True,
          ),
          "monitor": "val_loss",
      }
      return [self.optimizer], [self.scheduler]
  
imports: import logging
  import os
  from argparse import ArgumentParser
  import mlflow
  import mlflow.pytorch
  import pytorch_lightning
  import torch
  from pytorch_lightning.callbacks import LearningRateMonitor
  from pytorch_lightning.callbacks import ModelCheckpoint
  from pytorch_lightning.callbacks.early_stopping import EarlyStopping
  from torch.nn import functional
  from torch.utils.data import DataLoader
  from torch.utils.data import random_split
  from torchvision import datasets
  from torchvision import transforms
label_desc: Use image node_id 10 for calling the LightningMNISTClassifier obj's underlying member method code with example usage: obj.configure_optimizers() and returns: list,  list