code: def _step(self, state, current=None):
      if state[torchbearer.MODEL].training is False:
          return
  
      if self._newstyle or self._step_on_batch:
          if current is None:
              self._scheduler.step()
          else:
              self._scheduler.step(current)
      else:
          if current is None:
              self._scheduler.step(epoch=state[torchbearer.EPOCH])
          else:
              self._scheduler.step(
                  current, epoch=state[torchbearer.EPOCH]
              )
  
imports: import functools
  import warnings
  import torch
  import torchbearer
  from torchbearer.bases import get_metric, _pytorch_version_lt, _pytorch_version_gt
  from torchbearer.callbacks import Callback
label_desc: Use image node_id 2 for calling the TorchScheduler obj's underlying member method code with example usage: obj._step(state, current) without return types