code: def print_rank(args):
      def _print_rank_0(msg):
          if args.local_rank <= 0:
              print(msg)
  
      return _print_rank_0
  
imports: import torch
  from torch.nn import CrossEntropyLoss, MSELoss
  import datasets
  from datasets import load_dataset, load_metric
  import copy
  import os
  import json
  import transformers
  from transformers import AutoConfig, PretrainedConfig
  import huggingface_transformer
  from huggingface_transformer.modeling_bert import BertForSequenceClassification
  import logging
  import numpy
  import math
label_desc: Use image node_id 2 for calling a global function with example usage: print_rank(args) and returns: _print_rank_0