code: def test_passing_different_pipeline_variables_to_function(
      sagemaker_session,
      role,
      pipeline_name,
      region_name,
      dummy_container_without_error,
      sklearn_latest_version,
  ):
      os.environ["AWS_DEFAULT_REGION"] = region_name
  
      param_a = ParameterInteger(name="param_a", default_value=2)
      param_b = ParameterBoolean(name="param_b", default_value=True)
      param_c = ParameterFloat(name="param_c", default_value=2.0)
      param_d = ParameterString(name="param_d", default_value="string")
  
      script_path = os.path.join(DATA_DIR, "dummy_script.py")
  
      sklearn_processor = SKLearnProcessor(
          framework_version=sklearn_latest_version,
          role=role,
          instance_type=INSTANCE_TYPE,
          instance_count=1,
          command=["python3"],
          sagemaker_session=sagemaker_session,
          base_job_name="test-sklearn",
      )
  
      step_sklearn = ProcessingStep(
          name="sklearn-process",
          processor=sklearn_processor,
          code=script_path,
      )
  
      @step(
          role=role,
          instance_type=INSTANCE_TYPE,
          image_uri=dummy_container_without_error,
          keep_alive_period_in_seconds=600,
      )
      def func_1():
          return 1, 2, {"key": 3}
  
      @step(
          role=role,
          instance_type=INSTANCE_TYPE,
          image_uri=dummy_container_without_error,
          keep_alive_period_in_seconds=60,
      )
      def func_2(*args):
          return args
  
      first_output = func_1()
      final_output = func_2(
          param_a,
          param_b,
          param_c,
          param_d,
          step_sklearn.properties.ProcessingJobStatus,
          first_output[2]["key"],
      )
  
      pipeline = Pipeline(
          name=pipeline_name,
          parameters=[param_a, param_b, param_c, param_d],
          steps=[final_output],
          sagemaker_session=sagemaker_session,
      )
  
      try:
          create_and_execute_pipeline(
              pipeline=pipeline,
              pipeline_name=pipeline_name,
              region_name=region_name,
              role=role,
              no_of_steps=3,
              last_step_name="func",
              execution_parameters=dict(param_a=3),
              step_status="Succeeded",
              step_result_type=tuple,
              step_result_value=(
                  3,
                  True,
                  2.0,
                  "string",
                  "Completed",
                  3,
              ),
              wait_duration=600,
          )
  
      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 9 for calling a global function with example usage: test_passing_different_pipeline_variables_to_function(sagemaker_session, role, pipeline_name, region_name, dummy_container_without_error, sklearn_latest_version) and returns: int,  int,  dict, args