class_docstr: Provides early stopping functionality. Keeps track of an early stop criteria,
  and if it doesn't improve over time restores last best performing
  parameters.
code: def get_info(self):
      return {
          "best_update": self.best_monitored_update,
          "best_iteration": self.best_monitored_iteration,
          f"best_{self.metric}": f"{self.best_monitored_value:.6f}",
      }
  
file_path: mmf/mmf/utils/early_stopping.py
imports: import numpy
  import torch
  from mmf.utils.distributed import is_main, is_xla
label_desc: Use image node_id 5 for calling the EarlyStopping obj's underlying member method code with example usage: obj.get_info() and returns: dict