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 kwargs(self):
      # These are the parameters that are passed to the
      # scheduler. Parameters that don't belong there must be
      # excluded.
      excluded = ("policy", "monitor", "event_name", "step_every")
      kwargs = {
          key: val
          for key, val in vars(self).items()
          if not (key in excluded or key.endswith("_"))
      }
      return kwargs
  
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 5 for calling the LRScheduler obj's underlying member method code with example usage: obj.kwargs() and returns: kwargs