code: def get_optimizer(self):
      lerning_rate = self.args.lr
      if self.args.lr_decay_rate:
          lerning_rate = tf.keras.optimizers.schedules.ExponentialDecay(
              self.args.lr,
              decay_steps=self.args.lr_decay_steps,
              decay_rate=self.args.lr_decay_rate,
              staircase=True,
          )
  
      if self.args.optimizer == "adam":
          opt = tf.keras.optimizers.Adam(
              learning_rate=lerning_rate,
          )
      elif self.args.optimizer == "adagrad":
          opt = tf.keras.optimizers.legacy.Adagrad(
              learning_rate=lerning_rate,
          )
      else:
          raise ValueError("Invalid optimizer")
  
      return opt
  
imports: import json
  import logging
  import os
  from datetime import datetime
  import merlin.models.tf
  import numpy
  import tensorflow
  from merlin.io.dataset import Dataset
  from merlin.models.tf.logging.callbacks import ExamplesPerSecondCallback, WandbLogger
  from merlin.models.tf.transforms.negative_sampling import InBatchNegatives
  from merlin.schema.tags import Tags
  from .args_parsing import Task, parse_arguments
  from .mtl import get_mtl_loss_weights, get_mtl_prediction_tasks
  from .ranking_models import get_model
label_desc: Use image node_id 8 for calling the RankingTrainEvalRunner obj's underlying member method code with example usage: obj.get_optimizer() and returns: opt