class_docstr: Schedule the learning rate according to some function of the
  current epoch index, current learning rate, and current train/val loss.
  
  :param schedule: (callable):
      should return a number of learning rates equal to the number
      of optimizer.param_groups. It should take the epoch index and
      **kwargs (or logs) as argument. **kwargs (or logs) will return
      the epoch logs such as mean training and validation loss from
      the epoch
code: def __init__(self, schedule):
      if isinstance(schedule, dict):
          schedule = self.schedule_from_dict
          self.schedule_dict = schedule
          if any(k < 1.0 for k in schedule.keys()):
              self.fractional_bounds = False
          else:
              self.fractional_bounds = True
      self.schedule = schedule
      super(LRScheduler, self).__init__()
  
imports: import warnings
  from .None import Callback
label_desc: Use image node_id 1 to create a new LRScheduler object from inherited base classes: Callback with example: obj = LRScheduler(schedule)