code: def test_coach_mxnet(
      sagemaker_session, coach_mxnet_latest_version, cpu_instance_type
  ):
      estimator = _test_coach(
          sagemaker_session,
          RLFramework.MXNET,
          coach_mxnet_latest_version,
          cpu_instance_type,
      )
      job_name = unique_name_from_base("test-coach-mxnet")
  
      with timeout(minutes=15):
          estimator.fit(wait="False", job_name=job_name)
  
          estimator = RLEstimator.attach(
              estimator.latest_training_job.name,
              sagemaker_session=sagemaker_session,
          )
  
      endpoint_name = "test-mxnet-coach-deploy-{}".format(
          sagemaker_timestamp()
      )
  
      with timeout_and_delete_endpoint_by_name(
          endpoint_name, sagemaker_session
      ):
          predictor = estimator.deploy(
              1,
              cpu_instance_type,
              entry_point="mxnet_deploy.py",
              endpoint_name=endpoint_name,
          )
  
          observation = numpy.asarray([0, 0, 0, 0])
          action = predictor.predict(observation)
  
      assert 0 < action[0][0] < 1
      assert 0 < action[0][1] < 1
  
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 1 for calling a global function with example usage: test_coach_mxnet(sagemaker_session, coach_mxnet_latest_version, cpu_instance_type) without return types