code: def test_create_and_update_with_parallelism_config(
      sagemaker_session, role, pipeline_name, region_name
  ):
      instance_count = ParameterInteger(
          name="InstanceCount", default_value=2
      )
  
      outputParam = CallbackOutput(
          output_name="output",
          output_type=CallbackOutputTypeEnum.String,
      )
  
      callback_steps = [
          CallbackStep(
              name=f"callback-step{count}",
              sqs_queue_url="https://sqs.us-east-2.amazonaws.com/123456789012/MyQueue",
              inputs={"arg1": "foo"},
              outputs=[outputParam],
          )
          for count in range(500)
      ]
      pipeline = Pipeline(
          name=pipeline_name,
          parameters=[instance_count],
          steps=callback_steps,
          sagemaker_session=sagemaker_session,
      )
  
      try:
          response = pipeline.create(
              role, parallelism_config={"MaxParallelExecutionSteps": 50}
          )
          create_arn = response["PipelineArn"]
          assert re.match(
              rf"arn:aws:sagemaker:{region_name}:\d{{12}}:pipeline/{pipeline_name}",
              create_arn,
          )
          response = pipeline.describe()
          assert (
              response["ParallelismConfiguration"][
                  "MaxParallelExecutionSteps"
              ]
              == 50
          )
  
          pipeline.parameters = [
              ParameterInteger(name="InstanceCount", default_value=1)
          ]
          response = pipeline.upsert(
              role, parallelism_config={"MaxParallelExecutionSteps": 55}
          )
          update_arn = response["PipelineArn"]
          assert re.match(
              rf"arn:aws:sagemaker:{region_name}:\d{{12}}:pipeline/{pipeline_name}",
              update_arn,
          )
  
          response = pipeline.describe()
          assert (
              response["ParallelismConfiguration"][
                  "MaxParallelExecutionSteps"
              ]
              == 55
          )
  
      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 16 for calling a global function with example usage: test_create_and_update_with_parallelism_config(sagemaker_session, role, pipeline_name, region_name) without return types