code: def test_decorator_step_data_referenced_by_other_steps(
      pipeline_session,
      role,
      pipeline_name,
      region_name,
      dummy_container_without_error,
      sklearn_latest_version,
  ):
      os.environ["AWS_DEFAULT_REGION"] = region_name
      processing_job_instance_counts = 2
  
      @step(
          name="step1",
          role=role,
          image_uri=dummy_container_without_error,
          instance_type=INSTANCE_TYPE,
          keep_alive_period_in_seconds=60,
      )
      def func(var: int):
          return 1, var
  
      step_output = func(processing_job_instance_counts)
  
      script_path = os.path.join(DATA_DIR, "dummy_script.py")
      input_file_path = os.path.join(DATA_DIR, "dummy_input.txt")
      inputs = [
          ProcessingInput(
              source=input_file_path,
              destination="/opt/ml/processing/inputs/",
          ),
      ]
  
      sklearn_processor = SKLearnProcessor(
          framework_version=sklearn_latest_version,
          role=role,
          instance_type=INSTANCE_TYPE,
          instance_count=step_output[1],
          command=["python3"],
          sagemaker_session=pipeline_session,
          base_job_name="test-sklearn",
      )
  
      step_args = sklearn_processor.run(
          inputs=inputs,
          code=script_path,
      )
      process_step = ProcessingStep(
          name="MyProcessStep",
          step_args=step_args,
      )
  
      pipeline = Pipeline(
          name=pipeline_name,
          steps=[process_step],
          sagemaker_session=pipeline_session,
      )
  
      try:
          _, execution_steps = create_and_execute_pipeline(
              pipeline=pipeline,
              pipeline_name=pipeline_name,
              region_name=region_name,
              role=role,
              no_of_steps=2,
              last_step_name=process_step.name,
              execution_parameters=dict(),
              step_status="Succeeded",
              wait_duration=1000,  # seconds
          )
  
          execution_proc_job = pipeline_session.describe_processing_job(
              execution_steps[0]["Metadata"]["ProcessingJob"][
                  "Arn"
              ].split("/")[-1]
          )
          assert (
              execution_proc_job["ProcessingResources"][
                  "ClusterConfig"
              ]["InstanceCount"]
              == processing_job_instance_counts
          )
      finally:
          try:
              pipeline.delete()
          except Exception:
              pass
  
imports: from __future__ import absolute_import
  import json
  import numpy
  import pytest
  import os
  import random
  from sagemaker import get_execution_role, utils
  from sagemaker.config import load_sagemaker_config
  from sagemaker.processing import ProcessingInput
  from sagemaker.remote_function.errors import RemoteFunctionError
  from sagemaker.sklearn import SKLearnProcessor
  from sagemaker.remote_function.core.serialization import CloudpickleSerializer
  from sagemaker.s3 import S3Uploader
  from sagemaker.s3_utils import s3_path_join
  from sagemaker.workflow.conditions import ConditionLessThan
  from sagemaker.workflow.fail_step import FailStep
  from sagemaker.workflow.function_step import step
  from sagemaker.workflow.condition_step import ConditionStep
  from sagemaker.workflow.conditions import ConditionEquals
  from sagemaker.workflow.pipeline import Pipeline
  from sagemaker.workflow.parameters import ParameterString, ParameterInteger, ParameterFloat, ParameterBoolean
  from sagemaker.workflow.step_outputs import get_step
  from sagemaker.workflow.steps import ProcessingStep
  from tests.integ.sagemaker.workflow.helpers import create_and_execute_pipeline, wait_pipeline_execution
  from tests.integ import DATA_DIR
label_desc: Use image node_id 15 for calling a global function with example usage: test_decorator_step_data_referenced_by_other_steps(pipeline_session, role, pipeline_name, region_name, dummy_container_without_error, sklearn_latest_version) and returns: int,  var