code: def test_multi_estimator_tuning_autotune(
      sagemaker_session,
      estimator_fm,
      estimator_knn,
      data_set,
      cpu_instance_type,
  ):
      tuner = HyperparameterTuner.create(
          base_tuning_job_name=BASE_TUNING_JOB_NAME,
          estimator_dict={
              ESTIMATOR_FM: estimator_fm,
              ESTIMATOR_KNN: estimator_knn,
          },
          objective_metric_name_dict={
              ESTIMATOR_FM: OBJECTIVE_METRIC_NAME_FM,
              ESTIMATOR_KNN: OBJECTIVE_METRIC_NAME_KNN,
          },
          hyperparameter_ranges_dict={
              ESTIMATOR_FM: HYPER_PARAMETER_RANGES_FM,
              ESTIMATOR_KNN: HYPER_PARAMETER_RANGES_KNN,
          },
          strategy=STRATEGY,
          objective_type=OBJECTIVE_TYPE,
          max_jobs=MAX_JOBS,
          max_parallel_jobs=MAX_PARALLEL_JOBS,
          tags=TAGS,
          autotune=True,
          hyperparameters_to_keep_static_dict={
              ESTIMATOR_FM: ["num_factors", "predictor_type"],
              ESTIMATOR_KNN: ["predictor_type", "mini_batch_size"],
          },
      )
      tuning_job_base_name = "test-multi-autotune"
      _fit_tuner(
          sagemaker_session,
          tuner,
          tuning_job_base_name=tuning_job_base_name,
      )
  
      _retrieve_analytics(
          sagemaker_session, tuner.latest_tuning_job.name
      )
  
      tuner_attached = _attach_tuner(
          sagemaker_session, tuner.latest_tuning_job.name
      )
  
      _deploy_and_predict(
          sagemaker_session, tuner_attached, data_set, cpu_instance_type
      )
  
imports: from __future__ import absolute_import
  import json
  import os
  import pytest
  from sagemaker import image_uris, utils
  from sagemaker.analytics import HyperparameterTuningJobAnalytics
  from sagemaker.deserializers import JSONDeserializer
  from sagemaker.estimator import Estimator
  from sagemaker.serializers import SimpleBaseSerializer
  from sagemaker.tuner import ContinuousParameter, IntegerParameter, HyperparameterTuner
  from tests.integ import datasets, DATA_DIR, TUNING_DEFAULT_TIMEOUT_MINUTES
  from tests.integ.timeout import timeout, timeout_and_delete_endpoint_by_name
label_desc: Use image node_id 5 for calling a global function with example usage: test_multi_estimator_tuning_autotune(sagemaker_session, estimator_fm, estimator_knn, data_set, cpu_instance_type) without return types