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
code: def on_train_begin(self, logs=None):
      self._reset()
  
imports: from .None import Callback
label_desc: Use image node_id 3 for calling the ReduceLROnPlateau obj's underlying member method code with example usage: obj.on_train_begin(logs) without return types