code: def log_metrics(step, metrics):
      logger.info(f"Step {step}: {metrics}")
      if accelerator.is_main_process:
          accelerator.log(metrics, step)
  
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 4 for calling a global function with example usage: log_metrics(step, metrics) without return types