code: def test_pyspark_processor_configuration_path_pipeline_config(
      exists_mock, isfile_mock, pipeline_session
  ):
      processor = PySparkProcessor(
          role=ROLE,
          image_uri=CUSTOM_IMAGE_URI,
          instance_count=1,
          instance_type="ml.m4.xlarge",
          sagemaker_session=pipeline_session,
      )
  
      (
          extended_inputs,
          extended_outputs,
      ) = processor._extend_processing_args(
          inputs=[],
          outputs=[],
          configuration={
              "Classification": "hadoop-env",
              "Properties": {},
          },
      )
  
      s3_uri = extended_inputs[0].s3_input.s3_uri
      assert (
          s3_uri
          == "s3://mybucket/test-pipeline/test-processing-step/input/conf/config-hash-abcdefg/configuration.json"
      )
  
imports: from __future__ import absolute_import
  import copy
  import pytest
  from mock import Mock, patch, MagicMock
  from packaging import version
  from sagemaker import LocalSession
  from sagemaker.dataset_definition.inputs import S3Input, DatasetDefinition, AthenaDatasetDefinition, RedshiftDatasetDefinition
  from sagemaker.processing import ProcessingInput, ProcessingOutput, Processor, ScriptProcessor, ProcessingJob
  from sagemaker.session_settings import SessionSettings
  from sagemaker.spark.processing import PySparkProcessor
  from sagemaker.sklearn.processing import SKLearnProcessor
  from sagemaker.pytorch.processing import PyTorchProcessor
  from sagemaker.tensorflow.processing import TensorFlowProcessor
  from sagemaker.workflow.pipeline_definition_config import PipelineDefinitionConfig
  from sagemaker.xgboost.processing import XGBoostProcessor
  from sagemaker.mxnet.processing import MXNetProcessor
  from sagemaker.network import NetworkConfig
  from sagemaker.processing import FeatureStoreOutput
  from sagemaker.fw_utils import UploadedCode
  from sagemaker.workflow.pipeline_context import PipelineSession, _PipelineConfig
  from sagemaker.workflow.functions import Join
  from sagemaker.workflow.execution_variables import ExecutionVariables
  from tests.unit import SAGEMAKER_CONFIG_PROCESSING_JOB
label_desc: Use image node_id 37 for calling a global function with example usage: test_pyspark_processor_configuration_path_pipeline_config(exists_mock, isfile_mock, pipeline_session) without return types