code: def optimizer_scheduler(self):
      param_optimizer = list(self.named_parameters())
      no_decay = ["bias", "LayerNorm.weight"]
      optimizer_parameters = [
          {
              "params": [
                  p
                  for n, p in param_optimizer
                  if not any(nd in n for nd in no_decay)
              ],
              "weight_decay": 0.001,
          },
          {
              "params": [
                  p
                  for n, p in param_optimizer
                  if any(nd in n for nd in no_decay)
              ],
              "weight_decay": 0.0,
          },
      ]
      opt = torch.optim.AdamW(
          optimizer_parameters, lr=self.learning_rate
      )
      sch = get_linear_schedule_with_warmup(
          opt,
          num_warmup_steps=0,
          num_training_steps=self.num_train_steps,
      )
  
      return opt, sch
  
file_path: tez/examples/nlp/imdb_sentiment_classification.py
imports: import pandas
  import torch
  import torch.nn
  from sklearn import metrics, model_selection
  from transformers import AutoConfig, AutoModel, AutoTokenizer, get_linear_schedule_with_warmup
  from tez import Tez, TezConfig
  from tez.callbacks import EarlyStopping
label_desc: Use image node_id 2 for calling the IMDBModel obj's underlying member method code with example usage: obj.optimizer_scheduler() and returns: opt,  sch