class_docstr: This class just allows us to implement ``Registrable`` for Pytorch :class:`LRSchedulers`.
code: def from_params(cls, optimizer, params):  # type: ignore
      # pylint: disable=arguments-differ
      scheduler = params.pop_choice(
          "type", LearningRateScheduler.list_available()
      )
  
      schedulers = LearningRateScheduler.by_name(scheduler)(optimizer, **params.as_dict())  # type: ignore
      if isinstance(
          schedulers, torch.optim.lr_scheduler.ReduceLROnPlateau
      ):
          return LearningRateWithMetricsWrapper(schedulers)
      else:
          return LearningRateWithoutMetricsWrapper(schedulers)
  
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 4 for calling the LearningRateScheduler obj's underlying member method code with example usage: obj.from_params(cls, optimizer, params) and returns: LearningRateWithMetricsWrapper, LearningRateWithoutMetricsWrapper