code: def test_deploy_model(
      sklearn_training_job,
      sagemaker_session,
      cpu_instance_type,
      sklearn_latest_version,
      sklearn_latest_py_version,
  ):
      endpoint_name = unique_name_from_base("test-sklearn-deploy-model")
      with timeout_and_delete_endpoint_by_name(
          endpoint_name, sagemaker_session
      ):
          desc = (
              sagemaker_session.sagemaker_client.describe_training_job(
                  TrainingJobName=sklearn_training_job
              )
          )
          model_data = desc["ModelArtifacts"]["S3ModelArtifacts"]
          script_path = os.path.join(
              DATA_DIR, "sklearn_mnist", "mnist.py"
          )
          model = SKLearnModel(
              model_data,
              ROLE,
              entry_point=script_path,
              framework_version=sklearn_latest_version,
              sagemaker_session=sagemaker_session,
          )
          predictor = model.deploy(
              1, cpu_instance_type, endpoint_name=endpoint_name
          )
          _predict_and_assert(predictor)
  
imports: from __future__ import absolute_import
  import os
  import time
  import pytest
  import numpy
  from sagemaker.serverless import ServerlessInferenceConfig
  from sagemaker.sklearn import SKLearn, SKLearnModel, SKLearnProcessor
  from sagemaker.utils import unique_name_from_base
  from tests.integ import DATA_DIR, TRAINING_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_deploy_model(sklearn_training_job, sagemaker_session, cpu_instance_type, sklearn_latest_version, sklearn_latest_py_version) without return types