code: def test_tuning_tf_lustre(
      efs_fsx_setup,
      sagemaker_session,
      cpu_instance_type,
      tensorflow_training_latest_version,
      tensorflow_training_latest_py_version,
  ):
      role = efs_fsx_setup["role_name"]
      subnets = [efs_fsx_setup["subnet_id"]]
      security_group_ids = efs_fsx_setup["security_group_ids"]
  
      estimator = TensorFlow(
          entry_point=SCRIPT,
          role=role,
          instance_count=1,
          instance_type=cpu_instance_type,
          sagemaker_session=sagemaker_session,
          framework_version=tensorflow_training_latest_version,
          py_version=tensorflow_training_latest_py_version,
          subnets=subnets,
          security_group_ids=security_group_ids,
      )
  
      hyperparameter_ranges = {"epochs": IntegerParameter(1, 2)}
      objective_metric_name = "accuracy"
      metric_definitions = [
          {
              "Name": objective_metric_name,
              "Regex": "accuracy = ([0-9\\.]+)",
          }
      ]
      tuner = HyperparameterTuner(
          estimator,
          objective_metric_name,
          hyperparameter_ranges,
          metric_definitions,
          max_jobs=MAX_JOBS,
          max_parallel_jobs=MAX_PARALLEL_JOBS,
      )
  
      file_system_fsx_id = efs_fsx_setup["file_system_fsx_id"]
      file_system_input = FileSystemInput(
          file_system_id=file_system_fsx_id,
          file_system_type="FSxLustre",
          directory_path=FSX_DIR_PATH,
      )
  
      with timeout(minutes=TUNING_DEFAULT_TIMEOUT_MINUTES):
          tuning_job_name = unique_name_from_base(
              "test-tuning-tf-script-mode-lustre", max_length=32
          )
          tuner.fit(file_system_input, job_name=tuning_job_name)
          time.sleep(15)
          tuner.wait()
      best_training_job = tuner.best_training_job()
      assert best_training_job
  
file_path: sagemaker-python-sdk/tests/integ/test_tf_efs_fsx.py
imports: from __future__ import absolute_import
  import os
  import time
  import pytest
  from sagemaker.inputs import FileSystemInput
  from sagemaker.parameter import IntegerParameter
  from sagemaker.tensorflow import TensorFlow
  from sagemaker.tuner import HyperparameterTuner
  from sagemaker.utils import unique_name_from_base
  import tests
  from tests.integ import TRAINING_DEFAULT_TIMEOUT_MINUTES, TUNING_DEFAULT_TIMEOUT_MINUTES
  from tests.integ.file_system_input_utils import tear_down, set_up_efs_fsx
  from tests.integ.s3_utils import assert_s3_file_patterns_exist
  from tests.integ.timeout import timeout
label_desc: Use image node_id 5 for calling a global function with example usage: test_tuning_tf_lustre(efs_fsx_setup, sagemaker_session, cpu_instance_type, tensorflow_training_latest_version, tensorflow_training_latest_py_version) without return types