code: def train(loader, device, model, optimizer, lr_scheduler, criterion):
      model.train()
      for batch_idx, (inputs, targets) in enumerate(loader):
          inputs, targets = inputs.to(device), targets.to(device)
          optimizer.zero_grad()
          outputs = model(inputs)
          loss = criterion(outputs, targets)
          loss.backward()
          optimizer.step()
          lr_scheduler.step()
  
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 4 for calling a global function with example usage: train(loader, device, model, optimizer, lr_scheduler, criterion) without return types