code: def py_spark_processor(sagemaker_session) -> PySparkProcessor:
      spark = PySparkProcessor(
          base_job_name="sm-spark",
          role="AmazonSageMaker-ExecutionRole",
          framework_version="2.4",
          instance_count=1,
          instance_type="ml.c5.xlarge",
          image_uri="790336243319.dkr.ecr.us-west-2.amazonaws.com/sagemaker-spark:0.1",
          sagemaker_session=sagemaker_session,
      )
  
      return spark
  
imports: from __future__ import absolute_import
  import pathlib
  import subprocess
  import tempfile
  from io import BytesIO
  from typing import Generator
  from unittest.case import TestCase
  from unittest.mock import MagicMock, Mock, call, mock_open, patch
  import pytest
  from sagemaker.processing import ProcessingInput, ProcessingOutput
  from sagemaker.session_settings import SessionSettings
  from sagemaker.spark.processing import PySparkProcessor, SparkJarProcessor, _SparkProcessorBase, _HistoryServer, FileType, SparkConfigUtils
label_desc: Use image node_id 5 for calling a global function with example usage: py_spark_processor(sagemaker_session) and returns: spark