code: def test_register_calls_model_data_source_not_supported(
      sagemaker_session,
  ):
      source_dir = "s3://blah/blah/blah"
      t = Model(
          entry_point=ENTRY_POINT_INFERENCE,
          role=ROLE,
          sagemaker_session=sagemaker_session,
          source_dir=source_dir,
          image_uri=IMAGE_URI,
          model_data={
              "S3DataSource": {
                  "S3Uri": "s3://bucket/model/prefix/",
                  "S3DataType": "S3Prefix",
                  "CompressionType": "None",
              }
          },
      )
  
      with pytest.raises(
          ValueError,
          match="SageMaker Model Package currently cannot be created with ModelDataSource.",
      ):
          t.register(
              SUPPORTED_CONTENT_TYPES,
              SUPPORTED_RESPONSE_MIME_TYPES,
              SUPPORTED_REALTIME_INFERENCE_INSTANCE_TYPES,
              SUPPORTED_BATCH_TRANSFORM_INSTANCE_TYPES,
              marketplace_cert=True,
              description=MODEL_DESCRIPTION,
              model_package_name=MODEL_NAME,
              validation_specification=VALIDATION_SPECIFICATION,
          )
  
imports: from __future__ import absolute_import
  import random
  from unittest.mock import MagicMock
  import pytest
  from mock import Mock, patch
  import sagemaker
  from sagemaker.async_inference import AsyncInferenceConfig
  from sagemaker.model import FrameworkModel, Model
  from sagemaker.huggingface.model import HuggingFaceModel
  from sagemaker.jumpstart.constants import JUMPSTART_GATED_AND_PUBLIC_BUCKET_NAME_SET, JUMPSTART_RESOURCE_BASE_NAME
  from sagemaker.jumpstart.enums import JumpStartTag
  from sagemaker.mxnet.model import MXNetModel
  from sagemaker.pytorch.model import PyTorchModel
  from sagemaker.session_settings import SessionSettings
  from sagemaker.sklearn.model import SKLearnModel
  from sagemaker.tensorflow.model import TensorFlowModel
  from sagemaker.xgboost.model import XGBoostModel
  from sagemaker.enums import EndpointType
  from sagemaker.compute_resource_requirements.resource_requirements import ResourceRequirements
  from sagemaker.workflow.properties import Properties
  from tests.unit import _test_default_bucket_and_prefix_combinations, DEFAULT_S3_BUCKET_NAME, DEFAULT_S3_OBJECT_KEY_PREFIX_NAME, SAGEMAKER_CONFIG_EDGE_PACKAGING_JOB
label_desc: Use image node_id 40 for calling a global function with example usage: test_register_calls_model_data_source_not_supported(sagemaker_session) without return types