code: def should_stop_early(args, valid_loss):
      # skip check if no validation was done in the current epoch
      if valid_loss is None:
          return False
      if args.patience <= 0:
          return False
  
      def is_better(a, b):
          return (
              a > b if args.maximize_best_checkpoint_metric else a < b
          )
  
      prev_best = getattr(should_stop_early, "best", None)
      if prev_best is None or is_better(valid_loss, prev_best):
          should_stop_early.best = valid_loss
          should_stop_early.num_runs = 0
          return False
      else:
          should_stop_early.num_runs += 1
          if should_stop_early.num_runs >= args.patience:
              logger.info(
                  "early stop since valid performance hasn't improved for last {} runs".format(
                      args.patience
                  )
              )
              return True
          else:
              return False
  
imports: import argparse
  import logging
  import math
  import os
  import random
  import sys
  import numpy
  import torch
  from fairseq import checkpoint_utils, distributed_utils, options, quantization_utils, tasks, utils
  from fairseq.data import iterators
  from fairseq.logging import meters, metrics, progress_bar
  from fairseq.model_parallel.megatron_trainer import MegatronTrainer
  from .ls_fs_gcq_trainer import LSTrainer
label_desc: Use image node_id 2 for calling a global function with example usage: should_stop_early(args, valid_loss) and returns: False, False, unknown, False, True, False