code: def test_register_model(
      estimator, model_metrics, drift_check_baselines
  ):
      model_data = f"s3://{BUCKET}/model.tar.gz"
      register_model = RegisterModel(
          name="RegisterModelStep",
          estimator=estimator,
          model_data=model_data,
          content_types=["content_type"],
          response_types=["response_type"],
          inference_instances=["inference_instance"],
          transform_instances=["transform_instance"],
          image_uri="012345678901.dkr.ecr.us-west-2.amazonaws.com/my-custom-image-uri",
          model_package_group_name="mpg",
          model_metrics=model_metrics,
          drift_check_baselines=drift_check_baselines,
          approval_status="Approved",
          description="description",
          display_name="RegisterModelStep",
          depends_on=["TestStep"],
          tags=[{"Key": "myKey", "Value": "myValue"}],
          sample_payload_url="s3://test-bucket/model",
          task="IMAGE_CLASSIFICATION",
          framework="TENSORFLOW",
          framework_version="2.9",
          nearest_model_name="resnet50",
          data_input_configuration='{"input_1":[1,224,224,3]}',
      )
      assert ordered(register_model.request_dicts()) == ordered(
          [
              {
                  "Name": "RegisterModelStep-RegisterModel",
                  "Type": "RegisterModel",
                  "DependsOn": ["TestStep"],
                  "DisplayName": "RegisterModelStep",
                  "Description": "description",
                  "Arguments": {
                      "InferenceSpecification": {
                          "Containers": [
                              {
                                  "Image": "012345678901.dkr.ecr.us-west-2.amazonaws.com/my-custom-image-uri",
                                  "ModelDataUrl": f"s3://{BUCKET}/model.tar.gz",
                              }
                          ],
                          "SupportedContentTypes": ["content_type"],
                          "SupportedRealtimeInferenceInstanceTypes": [
                              "inference_instance"
                          ],
                          "SupportedResponseMIMETypes": [
                              "response_type"
                          ],
                          "SupportedTransformInstanceTypes": [
                              "transform_instance"
                          ],
                      },
                      "ModelApprovalStatus": "Approved",
                      "SkipModelValidation": "None",
                      "ModelMetrics": {
                          "Bias": {},
                          "Explainability": {},
                          "ModelQuality": {
                              "Statistics": {
                                  "ContentType": "text/csv",
                                  "S3Uri": f"s3://{BUCKET}/metrics.csv",
                              },
                          },
                      },
                      "DriftCheckBaselines": {
                          "ModelQuality": {
                              "Constraints": {
                                  "ContentType": "text/csv",
                                  "S3Uri": f"s3://{BUCKET}/constraints_metrics.csv",
                              }
                          }
                      },
                      "ModelPackageDescription": "description",
                      "ModelPackageGroupName": "mpg",
                      "Tags": [{"Key": "myKey", "Value": "myValue"}],
                      "SamplePayloadUrl": "s3://test-bucket/model",
                      "Task": "IMAGE_CLASSIFICATION",
                  },
              },
          ]
      )
  
imports: from __future__ import absolute_import
  import json
  import os
  import tempfile
  import shutil
  import pytest
  from sagemaker.drift_check_baselines import DriftCheckBaselines
  from sagemaker.workflow._utils import REPACK_SCRIPT_LAUNCHER
  from sagemaker.workflow.condition_step import ConditionStep
  from sagemaker.workflow.conditions import ConditionEquals
  from sagemaker.workflow.model_step import ModelStep, _CREATE_MODEL_NAME_BASE, _REPACK_MODEL_NAME_BASE
  from sagemaker.workflow.parameters import ParameterString
  from sagemaker.workflow.pipeline import Pipeline, PipelineGraph
  from sagemaker.workflow.utilities import list_to_request
  from tests.unit import DATA_DIR
  from sagemaker import PipelineModel
  from sagemaker.estimator import Estimator
  from sagemaker.model import Model, FrameworkModel
  from sagemaker.tensorflow import TensorFlow
  from sagemaker.inputs import CreateModelInput, TransformInput
  from sagemaker.model_metrics import MetricsSource, ModelMetrics
  from sagemaker.workflow.properties import Properties
  from sagemaker.workflow.steps import CreateModelStep
  from sagemaker.workflow.step_collections import EstimatorTransformer, StepCollection, RegisterModel
  from sagemaker.workflow.retry import StepRetryPolicy, StepExceptionTypeEnum
  from tests.unit.sagemaker.workflow.helpers import ordered, CustomStep
  from tests.unit.sagemaker.workflow.conftest import IMAGE_URI, ROLE, BUCKET, REGION
label_desc: Use image node_id 14 for calling a global function with example usage: test_register_model(estimator, model_metrics, drift_check_baselines) without return types