class_docstr: Early Stopping to terminate training early under certain conditions
  
  EarlyStopping callback to exit the training loop if training or
      validation loss does not improve by a certain amount for a certain
      number of epochs
  
  :param monitor: (string in {'val_loss', 'loss'}):
      whether to monitor train or val loss
  :param min_delta: (float):
      minimum change in monitored value to qualify as improvement.
      This number should be positive.
  :param patience: (int):
      number of epochs to wait for improvment before terminating.
      the counter be reset after each improvment
code: def on_train_end(self, logs=None):
      if self.stopped_epoch > 0:
          print(
              f"\nTerminated Training for Early Stopping at Epoch: {self.stopped_epoch}"
          )
  
imports: from .None import Callback
label_desc: Use image node_id 4 for calling the EarlyStopping obj's underlying member method code with example usage: obj.on_train_end(logs) without return types