code: def test_async_fit(
      sagemaker_session,
      cpu_instance_type,
      sklearn_latest_version,
      sklearn_latest_py_version,
  ):
      endpoint_name = unique_name_from_base(
          "test-sklearn-attach-deploy"
      )
  
      with timeout(minutes=5):
          training_job_name = _run_mnist_training_job(
              sagemaker_session,
              cpu_instance_type,
              sklearn_version=sklearn_latest_version,
              wait=False,
          )
  
          print(
              "Waiting to re-attach to the training job: %s"
              % training_job_name
          )
          time.sleep(20)
  
      with timeout_and_delete_endpoint_by_name(
          endpoint_name, sagemaker_session
      ):
          print("Re-attaching now to: %s" % training_job_name)
          estimator = SKLearn.attach(
              training_job_name=training_job_name,
              sagemaker_session=sagemaker_session,
          )
          predictor = estimator.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 7 for calling a global function with example usage: test_async_fit(sagemaker_session, cpu_instance_type, sklearn_latest_version, sklearn_latest_py_version) without return types