class_docstr: A wrapper around learning rate schedulers that require metrics,
  At the moment there is only a single instance of this lrs. It is the ReduceLROnPlateau
code: def step(self, metric, epoch=None):
      if metric is None:
          raise ConfigurationError(
              "The reduce_on_plateau learning rate scheduler requires "
              "a validation metric to compute the schedule and therefore "
              "must be used with a validation dataset."
          )
      self.lr_scheduler.step(metric, epoch)
  
imports: from __future__ import absolute_import
  import torch.optim.lr_scheduler
  from allennlp.common.checks import ConfigurationError
  from allennlp.common.params import Params
  from allennlp.common.registrable import Registrable
label_desc: Use image node_id 2 for calling the LearningRateWithMetricsWrapper obj's underlying member method code with example usage: obj.step(metric, epoch) without return types