code: def scheduler_creator(optimizer, config):
      """Returns a learning rate scheduler wrapping the optimizer.
      By default a scheduler will take effect automatically every epoch,
      and you can update the scheduler at the right time through the hooks.
      If using a scheduler for validation loss, be sure to call
      ``trainer.update_scheduler(validation_loss)``.
      """
      return torch.optim.lr_scheduler.StepLR(
          optimizer, step_size=5, gamma=0.9
      )
  
file_path: BigDL/python/orca/example/learn/horovod/pytorch_estimator.py
imports: import argparse
  import numpy
  import torch
  import torch.nn
  from bigdl.orca import init_orca_context, stop_orca_context
  from bigdl.orca.learn.pytorch import Estimator
label_desc: Use image node_id 3 for calling a global function with example usage: scheduler_creator(optimizer, config) and returns: torch