code: def _fit_tuner(sagemaker_session, tuner, tuning_job_base_name=None):
      training_inputs = _create_training_inputs(sagemaker_session)
      if tuning_job_base_name is None:
          tuning_job_base_name = "test-multi-algo-tuning"
      job_name = utils.unique_name_from_base(
          tuning_job_base_name, max_length=32
      )
  
      with timeout(minutes=TUNING_DEFAULT_TIMEOUT_MINUTES):
          tuner.fit(
              inputs={
                  ESTIMATOR_FM: training_inputs,
                  ESTIMATOR_KNN: training_inputs,
              },
              include_cls_metadata={},
              job_name=job_name,
          )
          tuner.wait()
  
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 6 for calling a global function with example usage: _fit_tuner(sagemaker_session, tuner, tuning_job_base_name) without return types