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 _get_policy_cls(self):
      if isinstance(self.policy, str):
          return getattr(sys.modules[__name__], self.policy)
      return self.policy
  
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 4 for calling the LRScheduler obj's underlying member method code with example usage: obj._get_policy_cls() and returns: self, getattr