code: def test_model_registration_with_tuning_model(
      pipeline_session,
      role,
      cpu_instance_type,
      pipeline_name,
      region_name,
  ):
      base_dir = os.path.join(
          DATA_DIR, "pipeline/model_step/pytorch_mnist"
      )
      entry_point = "mnist.py"
      input_path = pipeline_session.upload_data(
          path=os.path.join(base_dir, "training"),
          key_prefix="integ-test-data/pytorch_mnist/training",
      )
      inputs = TrainingInput(s3_data=input_path)
  
      instance_count = ParameterInteger(
          name="InstanceCount", default_value=1
      )
      instance_type = "ml.m5.xlarge"
  
      # If image_uri is not provided, the instance_type should not be a pipeline variable
      # since instance_type is used to retrieve image_uri in compile time (PySDK)
      pytorch_estimator = PyTorch(
          entry_point=entry_point,
          source_dir=base_dir,
          role=role,
          framework_version="1.10",
          py_version="py38",
          instance_count=instance_count,
          instance_type=instance_type,
          sagemaker_session=pipeline_session,
          enable_sagemaker_metrics=True,
          max_retry_attempts=3,
      )
  
      min_batch_size = ParameterString(
          name="MinBatchSize", default_value="64"
      )
      max_batch_size = ParameterString(
          name="MaxBatchSize", default_value="128"
      )
      hyperparameter_ranges = {
          "batch-size": IntegerParameter(
              min_batch_size, max_batch_size
          ),
      }
      tuner = HyperparameterTuner(
          estimator=pytorch_estimator,
          objective_metric_name="test:acc",
          objective_type="Maximize",
          hyperparameter_ranges=hyperparameter_ranges,
          metric_definitions=[
              {
                  "Name": "test:acc",
                  "Regex": "Overall test accuracy: (.*?);",
              }
          ],
          max_jobs=2,
          max_parallel_jobs=2,
      )
      step_tune = TuningStep(
          name="my-tuning-step",
          tuner=tuner,
          inputs=inputs,
      )
      model = PyTorchModel(
          image_uri=pytorch_estimator.training_image_uri(),
          role=role,
          model_data=step_tune.get_top_model_s3_uri(
              top_k=0,
              s3_bucket=pipeline_session.default_bucket(),
          ),
          entry_point=entry_point,
          source_dir=base_dir,
          framework_version="1.10",
          py_version="py38",
          sagemaker_session=pipeline_session,
      )
      step_model_regis_args = model.register(
          content_types=["text/csv"],
          response_types=["text/csv"],
          inference_instances=["ml.t2.medium", "ml.m5.large"],
          transform_instances=["ml.m5.large"],
          model_package_group_name=f"{pipeline_name}TestModelPackageGroup",
      )
      step_register_best = ModelStep(
          name="my-model-regis",
          step_args=step_model_regis_args,
      )
  
      pipeline = Pipeline(
          name=pipeline_name,
          parameters=[instance_count, min_batch_size, max_batch_size],
          steps=[step_tune, step_register_best],
          sagemaker_session=pipeline_session,
      )
  
      try:
          response = pipeline.create(role)
          create_arn = response["PipelineArn"]
          assert re.match(
              rf"arn:aws:sagemaker:{region_name}:\d{{12}}:pipeline/{pipeline_name}",
              create_arn,
          )
  
          execution = pipeline.start(parameters={})
          assert re.match(
              rf"arn:aws:sagemaker:{region_name}:\d{{12}}:pipeline/{pipeline_name}/execution/",
              execution.arn,
          )
          wait_pipeline_execution(execution=execution)
          execution_steps = execution.list_steps()
  
          for step in execution_steps:
              assert not step.get("FailureReason", None)
              assert step["StepStatus"] == "Succeeded"
              if _REGISTER_MODEL_NAME_BASE in step["StepName"]:
                  assert step["Metadata"]["RegisterModel"]
              if _REPACK_MODEL_NAME_BASE in step["StepName"]:
                  _verify_repack_output(step, pipeline_session)
          assert len(execution_steps) == 3
      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 19 for calling a global function with example usage: test_model_registration_with_tuning_model(pipeline_session, role, cpu_instance_type, pipeline_name, region_name) without return types