code: def train_func(config):
      for epoch in range(1, config["epochs"] + 1):
          # Model training here
          # ...
  
          # Report metrics and save a checkpoint
          metrics = {"metric": "my_metric"}
          if epoch % CHECKPOINT_FREQ == 0:
              with tempfile.TemporaryDirectory() as tempdir:
                  # Save a checkpoint in tempdir.
                  train.report(
                      metrics,
                      checkpoint=Checkpoint.from_directory(tempdir),
                  )
          else:
              train.report(metrics)
  
imports: import os
  import torch
  from torch import nn
  from ray import train, tune
  import random
  import os
  import tempfile
  from ray import train, tune
  from ray.train import Checkpoint
label_desc: Use image node_id 4 for calling a global function with example usage: train_func(config) without return types