code: def _run_processing_job(
      sagemaker_session,
      instance_type,
      sklearn_version,
      py_version,
      wait=True,
  ):
      with timeout(minutes=TRAINING_DEFAULT_TIMEOUT_MINUTES):
          code_path = os.path.join(
              DATA_DIR, "dummy_code_bundle_with_reqs"
          )
          entry_point = "main_script.py"
  
          processor = SKLearnProcessor(
              framework_version=sklearn_version,
              py_version=py_version,
              role=ROLE,
              instance_count=1,
              instance_type=instance_type,
              sagemaker_session=sagemaker_session,
              base_job_name="test-sklearn",
          )
  
          processor.run(
              code=entry_point,
              source_dir=code_path,
              inputs=[],
              wait=wait,
          )
          return processor.latest_job.name
  
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 9 for calling a global function with example usage: _run_processing_job(sagemaker_session, instance_type, sklearn_version, py_version, wait) and returns: processor