code: def resume_from_checkpoint(
      checkpoint, model, optimizer, lr_scheduler
  ):
      if checkpoint is None:
          return 1
  
      try:
          state_dict = torch.load(f"{checkpoint}/checkpoint.pth")
      except FileNotFoundError:
          return 1
  
      model.load_state_dict(state_dict["model"])
      optimizer.load_state_dict(state_dict["optimizer"])
      lr_scheduler.load_state_dict(state_dict["lr_scheduler"])
      return state_dict["epoch"] + 1
  
file_path: orion/examples/tutorials/code_2_hyperband_checkpoint.py
imports: import os
  import numpy
  import torch
  import torch.nn
  import torch.optim
  import torchvision
  import torchvision.models
  import torchvision.transforms
  from torch.utils.data import SubsetRandomSampler
  from orion.client import build_experiment
label_desc: Use image node_id 3 for calling a global function with example usage: resume_from_checkpoint(checkpoint, model, optimizer, lr_scheduler) and returns: unknown, int, int