class_docstr: Reduce the learning rate if the train or validation loss plateaus
  
  :param monitor: (string in {'loss', 'val_loss'}):
      which metric to monitor
  :param factor: (float):
      factor to decrease learning rate by
  :param patience: (int):
      number of epochs to wait for loss improvement before reducing lr
  :param epsilon: (float):
      how much improvement must be made to reset patience
  :param cooldown: (int):
      number of epochs to cooldown after a lr reduction
  :param min_lr: (float):
      minimum value to ever let the learning rate decrease to
  :param verbose: (int):
      whether to print reduction to console
docstr: Reset the wait and cooldown counters
docstr_tok: ["Reset","the","wait","and","cooldown","counters"]
code: def _reset(self):
      """
      Reset the wait and cooldown counters
      """
      self.monitor_op = lambda a, b: (a - b) < -self.epsilon
      self.best_loss = 1e15
      self.cooldown_counter = 0
      self.wait = 0
  
imports: from .None import Callback
label_desc: Use image node_id 2 for calling the ReduceLROnPlateau obj's underlying member method code with example usage: obj._reset() without return types