code: def test_pipeline_execution_with_default_experiment_config(
      sagemaker_session,
      smclient,
      role,
      sklearn_latest_version,
      cpu_instance_type,
      pipeline_name,
      athena_dataset_definition,
  ):
      instance_count = ParameterInteger(
          name="InstanceCount", default_value=2
      )
      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/",
          ),
          ProcessingInput(dataset_definition=athena_dataset_definition),
      ]
  
      sklearn_processor = SKLearnProcessor(
          framework_version=sklearn_latest_version,
          role=role,
          instance_type=cpu_instance_type,
          instance_count=instance_count,
          command=["python3"],
          sagemaker_session=sagemaker_session,
          base_job_name="test-sklearn",
      )
  
      step_sklearn = ProcessingStep(
          name="sklearn-process",
          processor=sklearn_processor,
          inputs=inputs,
          code=script_path,
      )
      pipeline = Pipeline(
          name=pipeline_name,
          parameters=[instance_count],
          steps=[step_sklearn],
          sagemaker_session=sagemaker_session,
      )
  
      try:
          pipeline.create(role)
          execution = pipeline.start(parameters={})
  
          wait_pipeline_execution(execution=execution, max_attempts=3)
          execution_steps = execution.list_steps()
          assert len(execution_steps) == 1
          assert execution_steps[0]["StepName"] == "sklearn-process"
  
          execution_id = execution.arn.split("/")[-1]
  
          # trial components
          trial_components = smclient.list_trial_components(
              TrialName=execution_id
          )
          assert len(trial_components["TrialComponentSummaries"]) == 1
  
          # trial details
          trial = smclient.describe_trial(TrialName=execution_id)
          assert pipeline_name == trial["ExperimentName"]
      finally:
          try:
              pipeline.delete()
          except Exception:
              pass
  
imports: from __future__ import absolute_import
  import os
  import time
  import pytest
  from tests.integ.sagemaker.workflow.helpers import wait_pipeline_execution
  from sagemaker.processing import ProcessingInput
  from sagemaker.session import get_execution_role
  from sagemaker.sklearn.processing import SKLearnProcessor
  from sagemaker.dataset_definition.inputs import DatasetDefinition, AthenaDatasetDefinition
  from sagemaker.workflow.execution_variables import ExecutionVariables
  from sagemaker.workflow.functions import Join
  from sagemaker.workflow.parameters import ParameterInteger
  from sagemaker.workflow.pipeline_experiment_config import PipelineExperimentConfig
  from sagemaker.workflow.steps import ProcessingStep
  from sagemaker.workflow.pipeline import Pipeline
  from tests.integ import DATA_DIR
label_desc: Use image node_id 7 for calling a global function with example usage: test_pipeline_execution_with_default_experiment_config(sagemaker_session, smclient, role, sklearn_latest_version, cpu_instance_type, pipeline_name, athena_dataset_definition) without return types