code: def test_deploy_best_candidate(
      sagemaker_session, cpu_instance_type, test_session_job_name
  ):
      auto_ml_utils.create_auto_ml_job_if_not_exist(
          sagemaker_session, test_session_job_name
      )
  
      auto_ml = AutoML(
          role=ROLE,
          target_attribute_name=TARGET_ATTRIBUTE_NAME,
          sagemaker_session=sagemaker_session,
      )
      best_candidate = auto_ml.best_candidate(
          job_name=test_session_job_name
      )
      endpoint_name = unique_name_from_base(
          "sagemaker-auto-ml-best-candidate-test"
      )
  
      with timeout(minutes=AUTO_ML_DEFAULT_TIMEMOUT_MINUTES):
          auto_ml.deploy(
              candidate=best_candidate,
              initial_instance_count=INSTANCE_COUNT,
              instance_type=cpu_instance_type,
              endpoint_name=endpoint_name,
          )
  
      endpoint_status = (
          sagemaker_session.sagemaker_client.describe_endpoint(
              EndpointName=endpoint_name
          )["EndpointStatus"]
      )
      assert endpoint_status == "InService"
      sagemaker_session.sagemaker_client.delete_endpoint(
          EndpointName=endpoint_name
      )
  
imports: from __future__ import absolute_import
  import os
  import pytest
  from botocore.exceptions import ClientError
  import tests.integ
  from sagemaker import AutoML, AutoMLInput, CandidateEstimator
  from sagemaker.utils import unique_name_from_base
  from tests.integ import AUTO_ML_DEFAULT_TIMEMOUT_MINUTES, DATA_DIR, auto_ml_utils
  from tests.integ.timeout import timeout
  from tests.conftest import CUSTOM_S3_OBJECT_KEY_PREFIX
label_desc: Use image node_id 12 for calling a global function with example usage: test_deploy_best_candidate(sagemaker_session, cpu_instance_type, test_session_job_name) without return types