code: def test_processor_input_path_with_pipeline_config(pipeline_session):
      processor = Processor(
          role=ROLE,
          image_uri=CUSTOM_IMAGE_URI,
          instance_count=1,
          instance_type="ml.m4.xlarge",
          sagemaker_session=pipeline_session,
      )
  
      inputs = [
          ProcessingInput(
              input_name="s3_input",
              s3_input=S3Input(
                  local_path="/container/path/",
                  s3_data_type="S3Prefix",
                  s3_input_mode="File",
                  s3_data_distribution_type="FullyReplicated",
                  s3_compression_type="None",
              ),
          )
      ]
  
      step_args = processor.run(
          inputs=inputs,
      )
      # execute process.run() and generate args, S3 paths
      step_args.func(*step_args.func_args, **step_args.func_kwargs)
      pipeline_session.upload_data.assert_called_with(
          path=None,
          bucket="mybucket",
          key_prefix="test-pipeline/test-processing-step/input/s3_input",
          callback=None,
          extra_args=None,
      )
  
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 34 for calling a global function with example usage: test_processor_input_path_with_pipeline_config(pipeline_session) without return types