code: def test_create_and_start_without_parallelism_config_override(
      pipeline_session, role, pipeline_name, script_dir
  ):
      sklearn_train = SKLearn(
          framework_version="0.20.0",
          entry_point=os.path.join(script_dir, "train.py"),
          instance_type="ml.m5.xlarge",
          sagemaker_session=pipeline_session,
          role=role,
      )
  
      train_steps = [
          TrainingStep(
              name=f"my-train-{count}",
              display_name="TrainingStep",
              description="description for Training step",
              step_args=sklearn_train.fit(),
          )
          for count in range(2)
      ]
      pipeline = Pipeline(
          name=pipeline_name,
          steps=train_steps,
          sagemaker_session=pipeline_session,
      )
  
      try:
          pipeline.create(
              role, parallelism_config=dict(MaxParallelExecutionSteps=1)
          )
          # No ParallelismConfiguration given in pipeline.start, so it won't override that in pipeline.create
          execution = pipeline.start()
  
          def validate():
              # Only one step would be scheduled initially
              assert len(execution.list_steps()) == 1
  
          retry_with_backoff(validate, num_attempts=4)
  
          wait_pipeline_execution(execution=execution)
  
      finally:
          try:
              pipeline.delete()
          except Exception:
              pass
  
imports: from __future__ import absolute_import
  import json
  import os
  import re
  import tempfile
  import time
  import shutil
  from contextlib import contextmanager
  import pytest
  import pandas
  from sagemaker.utils import retry_with_backoff
  from tests.integ.sagemaker.workflow.helpers import wait_pipeline_execution
  from tests.integ.s3_utils import extract_files_from_s3
  from sagemaker.workflow.model_step import ModelStep, _REGISTER_MODEL_NAME_BASE, _REPACK_MODEL_NAME_BASE
  from sagemaker.parameter import IntegerParameter
  from sagemaker.pytorch import PyTorch, PyTorchModel
  from sagemaker.tuner import HyperparameterTuner
  from tests.integ.timeout import timeout
  from sagemaker.session import Session
  from sagemaker import image_uris
  from sagemaker.estimator import Estimator
  from sagemaker.inputs import CreateModelInput, TrainingInput
  from sagemaker.model import Model
  from sagemaker.model_metrics import MetricsSource, ModelMetrics
  from sagemaker.processing import ProcessingInput, ProcessingOutput, FeatureStoreOutput, ScriptProcessor
  from sagemaker.s3 import S3Uploader
  from sagemaker.session import get_execution_role
  from sagemaker.sklearn.estimator import SKLearn
  from sagemaker.transformer import Transformer
  from sagemaker.sklearn.processing import SKLearnProcessor
  from sagemaker.workflow.conditions import ConditionGreaterThanOrEqualTo, ConditionLessThanOrEqualTo
  from sagemaker.workflow.condition_step import ConditionStep
  from sagemaker.workflow.callback_step import CallbackStep, CallbackOutput, CallbackOutputTypeEnum
  from sagemaker.wrangler.processing import DataWranglerProcessor
  from sagemaker.dataset_definition.inputs import DatasetDefinition, AthenaDatasetDefinition
  from sagemaker.workflow.execution_variables import ExecutionVariables
  from sagemaker.workflow.functions import Join, JsonGet
  from sagemaker.wrangler.ingestion import generate_data_ingestion_flow_from_s3_input
  from sagemaker.workflow.parameters import ParameterInteger, ParameterString
  from sagemaker.workflow.steps import CreateModelStep, ProcessingStep, TrainingStep, TransformStep, TransformInput, PropertyFile, TuningStep
  from sagemaker.workflow.step_collections import RegisterModel
  from sagemaker.workflow.pipeline import Pipeline
  from sagemaker.workflow.utilities import hash_files_or_dirs
  from sagemaker.feature_store.feature_group import FeatureGroup, FeatureDefinition, FeatureTypeEnum
  from tests.integ import DATA_DIR
label_desc: Use image node_id 17 for calling a global function with example usage: test_create_and_start_without_parallelism_config_override(pipeline_session, role, pipeline_name, script_dir) without return types