class_docstr: Callback that sets the learning rate of each
  parameter group according to some policy.
  
  Parameters
  ----------
  
  policy : str or _LRScheduler class (default='WarmRestartLR')
    Learning rate policy name or scheduler to be used.
  
  monitor : str or callable (default=None)
    Value of the history to monitor or function/callable. In
    the latter case, the callable receives the net instance as
    argument and is expected to return the score (float) used to
    determine the learning rate adjustment.
  
  event_name: str, (default='event_lr')
    Name of event to be placed in history when the scheduler takes a step.
    Pass ``None`` to disable placing events in history.
    **Note:** This feature works only for pytorch version >=1.4
  
  step_every: str, (default='epoch')
    Value for when to apply the learning scheduler step. Can be either 'batch'
     or 'epoch'.
  
  kwargs
    Additional arguments passed to the lr scheduler.
code: def on_batch_end(self, net, training, **kwargs):
      if not training or self.step_every != "batch":
          return
      if (self.event_name is not None) and hasattr(
          self.lr_scheduler_, "get_last_lr"
      ):
          net.history.record_batch(
              self.event_name, self.lr_scheduler_.get_last_lr()[0]
          )
      self._step(net, self.lr_scheduler_)
      self.batch_idx_ += 1
  
imports: import sys
  import warnings
  import numpy
  import torch
  from torch.optim.lr_scheduler import _LRScheduler
  from torch.optim.lr_scheduler import CosineAnnealingLR
  from torch.optim.lr_scheduler import CyclicLR
  from torch.optim.lr_scheduler import ExponentialLR
  from torch.optim.lr_scheduler import LambdaLR
  from torch.optim.lr_scheduler import MultiStepLR
  from torch.optim.lr_scheduler import ReduceLROnPlateau
  from torch.optim.lr_scheduler import StepLR
  from torch.optim.optimizer import Optimizer
  from skorch.callbacks import Callback
label_desc: Use image node_id 9 for calling the LRScheduler obj's underlying member method code with example usage: obj.on_batch_end(net, training) without return types