code: def get_grouped_params(
      model,
      args,
      no_decay=["bias", "ln_1.weight", "ln_2.weight", "ln_f.weight"],
  ):
      params_with_wd, params_without_wd = [], []
      for n, p in model.named_parameters():
          if any(nd in n for nd in no_decay):
              params_without_wd.append(p)
          else:
              params_with_wd.append(p)
      return [
          {"params": params_with_wd, "weight_decay": args.weight_decay},
          {"params": params_without_wd, "weight_decay": 0.0},
      ]
  
imports: import logging
  import os
  import time
  from argparse import Namespace
  from pathlib import Path
  import datasets
  import torch
  from datasets import load_dataset
  from torch.optim import AdamW
  from torch.utils.data import IterableDataset
  from torch.utils.data.dataloader import DataLoader
  from torch.utils.data.datapipes.iter.combinatorics import ShufflerIterDataPipe
  import transformers
  from accelerate import Accelerator, DistributedType
  from arguments import TrainingArguments
  from huggingface_hub import Repository
  from transformers import AutoModelForCausalLM, AutoTokenizer, HfArgumentParser, get_scheduler, set_seed
label_desc: Use image node_id 3 for calling a global function with example usage: get_grouped_params(model, args, no_decay) and returns: list