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.AdamW(self.parameters(), lr=self.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 math
  import os
  import lightning
  import pandas
  import torch
  import torch.nn.functional
  import torchtext.datasets
  from lightning.pytorch.callbacks import EarlyStopping, LearningRateMonitor, ModelCheckpoint
  from lightning.pytorch.cli import LightningCLI
  from sklearn.datasets import fetch_20newsgroups
  from sklearn.metrics import accuracy_score
  from sklearn.model_selection import train_test_split
  from torch import nn
  from torch.utils.data import DataLoader
  from torch.utils.data.dataset import random_split
  from torchdata.datapipes.iter import IterDataPipe
  from torchtext.data.functional import to_map_style_dataset
  from torchtext.datasets import AG_NEWS
  from transformers import BertModel, BertTokenizer
  import mlflow.pytorch
label_desc: Use image node_id 8 for calling the BertNewsClassifier obj's underlying member method code with example usage: obj.configure_optimizers() and returns: list,  list