code: def replace_config(
      args,
      config,
      model_tmp,
      label_list,
      num_labels=2,
      label_to_id=None,
      is_regression=False,
  ):
      if (
          model_tmp.config.label2id
          != PretrainedConfig(num_labels=num_labels).label2id
          and args.task_name is not None
          and not is_regression
      ):
          # Some have all caps in their config, some don't.
          label_name_to_id = {
              k.lower(): v for k, v in model_tmp.config.label2id.items()
          }
          if list(sorted(label_name_to_id.keys())) == list(
              sorted(label_list)
          ):
              logger.info(
                  f"The configuration of the model provided the following label correspondence: {label_name_to_id}. "
                  "Using it!"
              )
              label_to_id = {
                  i: label_name_to_id[label_list[i]]
                  for i in range(num_labels)
              }
          else:
              logger.warning(
                  "Your model seems to have been trained with labels, but they don't match the dataset: ",
                  f"model labels: {list(sorted(label_name_to_id.keys()))}, dataset labels: {list(sorted(label_list))}."
                  "\nIgnoring the model labels as a result.",
              )
      elif args.task_name is None:
          label_to_id = {v: i for i, v in enumerate(label_list)}
      if label_to_id is not None:
          model_tmp.config.label2id = label_to_id
          model_tmp.config.id2label = {
              id: label for label, id in config.label2id.items()
          }
      elif args.task_name is not None and not is_regression:
          model_tmp.config.label2id = {
              l: i for i, l in enumerate(label_list)
          }
          model_tmp.config.id2label = {
              id: label for label, id in config.label2id.items()
          }
  
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 6 for calling a global function with example usage: replace_config(args, config, model_tmp, label_list, num_labels, label_to_id, is_regression) without return types