code: def test_ray_tf(
      sagemaker_session,
      ray_tensorflow_latest_version,
      cpu_instance_type,
  ):
      source_dir = os.path.join(DATA_DIR, "ray_cartpole")
      cartpole = "train_ray.py"
  
      estimator = RLEstimator(
          entry_point=cartpole,
          source_dir=source_dir,
          toolkit=RLToolkit.RAY,
          framework=RLFramework.TENSORFLOW,
          toolkit_version=ray_tensorflow_latest_version,
          sagemaker_session=sagemaker_session,
          role="SageMakerRole",
          instance_type=cpu_instance_type,
          instance_count=1,
      )
      job_name = unique_name_from_base("test-ray-tf")
  
      with timeout(minutes=15):
          estimator.fit(job_name=job_name)
  
      with pytest.raises(NotImplementedError) as e:
          estimator.deploy(1, cpu_instance_type)
      assert (
          "Automatic deployment of Ray models is not currently available"
          in str(e.value)
      )
  
file_path: sagemaker-python-sdk/tests/integ/test_rl.py
imports: from __future__ import absolute_import
  import os
  import numpy
  import pytest
  from sagemaker.rl import RLEstimator, RLFramework, RLToolkit
  from sagemaker.utils import sagemaker_timestamp, unique_name_from_base
  from tests.integ import DATA_DIR, RL_SUPPORTED_REGIONS, test_region
  from tests.integ.timeout import timeout, timeout_and_delete_endpoint_by_name
label_desc: Use image node_id 4 for calling a global function with example usage: test_ray_tf(sagemaker_session, ray_tensorflow_latest_version, cpu_instance_type) without return types