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.
docstr: Simulates the learning rate scheduler.
  
  Parameters
  ----------
  steps: int
    Number of steps to simulate
  
  initial_lr: float
    Initial learning rate
  
  Returns
  -------
  lrs: numpy ndarray
    Simulated learning rates
docstr_tok: ["Simulates","the","learning","rate","scheduler",".","Parameters","--","--","--","--","--","steps",":","int","Number","of","steps","to","simulate","initial_lr",":","float","Initial","learning","rate","Returns","--","--","--","-","lrs",":","numpy","ndarray","Simulated","learning","rates"]
code: def simulate(self, steps, initial_lr):
      """
      Simulates the learning rate scheduler.
  
      Parameters
      ----------
      steps: int
        Number of steps to simulate
  
      initial_lr: float
        Initial learning rate
  
      Returns
      -------
      lrs: numpy ndarray
        Simulated learning rates
  
      """
      test = torch.ones(1, requires_grad=True)
      opt = torch.optim.SGD([{"params": test, "lr": initial_lr}])
      policy_cls = self._get_policy_cls()
      sch = policy_cls(opt, **self.kwargs)
  
      lrs = []
      for _ in range(steps):
          opt.step()  # suppress warning about .step call order
          lrs.append(opt.param_groups[0]["lr"])
          sch.step()
  
      return np.array(lrs)
  
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 2 for calling the LRScheduler obj's underlying member method code with example usage: obj.simulate(steps, initial_lr) and returns: np