code: def get_optimizer(self, model):
      no_decay = ["bias", "LayerNorm.weight", "layer_norm.weight"]
      plm_param, task_param = model.get_params(named=True)
  
      grouped_param = [
          {
              "params": [
                  p
                  for n, p in plm_param
                  if not any(nd in n for nd in no_decay)
              ],
              "lr": self.config["plm_learning_rate"],
              "weight_decay": self.config["adam_weight_decay"],
          },
          {
              "params": [
                  p
                  for n, p in plm_param
                  if any(nd in n for nd in no_decay)
              ],
              "lr": self.config["plm_learning_rate"],
              "weight_decay": 0.0,
          },
          {
              "params": [p for n, p in task_param],
              "lr": self.config["task_learning_rate"],
              "weight_decay": 0.0,
          },
      ]
      if self.config["optimizer"].lower() == "adamw":
          # FusedAdam is faster. Requires apex.
          # Otherwise use AdamW
          opt_class = FusedAdam
  
      logger.info(opt_class)
      optimizer = opt_class(
          grouped_param,
          lr=self.config["plm_learning_rate"],
          eps=self.config["adam_eps"],
      )
      return optimizer
  
imports: import sys
  import os
  import logging
  import random
  import numpy
  import torch
  from torch import nn
  from torch.optim import AdamW
  from util.multigpu_fused_adam import FusedAdam
  from torch.optim.lr_scheduler import LambdaLR
  from torch.utils.data import DataLoader
  import time
  from os.path import join
  from datetime import datetime
  import util
  from util.tensorize_coref import CorefDataProcessor, coref_collate_fn
  from util.tensorize_ner import NERDataProcessor, ner_collate_fn
  from util.tensorize_ere import EREDataProcessor, ere_collate_fn
  from metrics import CorefEvaluator, MentionEvaluator
  from models.model_coref import CorefModel
  from models.model_ner import NERModel
  from models.model_ere import EREModel
  import wandb
label_desc: Use image node_id 5 for calling the Runner obj's underlying member method code with example usage: obj.get_optimizer(model) and returns: optimizer