code: def trainable(config):
      for epoch in range(1, config["num_epochs"]):
          # Do some training...
  
          with tempfile.TemporaryDirectory() as tempdir:
              torch.save(
                  {"model_state_dict": {"x": 1}},
                  os.path.join(tempdir, "model.pt"),
              )
              train.report(
                  {"score": random.random()},
                  checkpoint=Checkpoint.from_directory(tempdir),
              )
  
imports: import numpy
  from ray import train, tune
  from ray.train import ScalingConfig
  from ray import tune
  import numpy
  import random
  import random
  import numpy
  import torch
  import tensorflow
  import random
  import numpy
  from ray import tune
  from ray import train, tune
  import numpy
  import ray
  import os
  import tempfile
  import torch
  from ray import train, tune
  from ray.train import Checkpoint
  import random
  import ray
label_desc: Use image node_id 7 for calling a global function with example usage: trainable(config) without return types