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 init_from_checkpoint(self, load):
      if "best_iteration" in load:
          self.best_monitored_iteration = load["best_iteration"]
  
      if "best_metric_value" in load:
          self.best_monitored_value = load["best_metric_value"]
  
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 4 for calling the EarlyStopping obj's underlying member method code with example usage: obj.init_from_checkpoint(load) without return types