code: def make_scheduler(optimizer, step_size, gamma=0.1):
      scheduler = torch.optim.lr_scheduler.StepLR(
          optimizer, step_size=step_size, gamma=gamma
      )
      return scheduler
  
file_path: avalanche/examples/clear.py
imports: import json
  from pathlib import Path
  import numpy
  import torch
  import torchvision
  from avalanche.evaluation.metrics import forgetting_metrics, accuracy_metrics, loss_metrics, timing_metrics, cpu_usage_metrics, confusion_matrix_metrics, disk_usage_metrics
  from avalanche.logging import InteractiveLogger, TextLogger, TensorboardLogger
  from avalanche.training.plugins import EvaluationPlugin
  from avalanche.training.plugins.lr_scheduling import LRSchedulerPlugin
  from avalanche.training.supervised import Naive
  from avalanche.benchmarks.classic.clear import CLEAR, CLEARMetric
label_desc: Use image node_id 1 for calling a global function with example usage: make_scheduler(optimizer, step_size, gamma) and returns: scheduler