code: def test_deploy_update_predictor_with_other_model(
      tfs_model,
      resources,
      resources_update,
      xgboost_model,
  ):
      endpoint_name = sagemaker.utils.unique_name_from_base(
          "multi-different-model-endpoint"
      )
      predictor_to_update = tfs_model.deploy(
          1,
          "ml.m5.4xlarge",
          endpoint_name=endpoint_name,
          endpoint_type=EndpointType.INFERENCE_COMPONENT_BASED,
          resources=resources,
      )
  
      input_data = {"instances": [1.0, 2.0, 5.0]}
      expected_result = {"predictions": [3.5, 4.0, 5.5]}
  
      result = predictor_to_update.predict(input_data)
      assert expected_result == result
  
      models, _ = predictor_to_update.list_related_models()
      assert models and len(models) > 0 and len(models) == 1
  
      xgboost_predictor = xgboost_model.deploy(
          1,
          "ml.m5.4xlarge",
          endpoint_name=endpoint_name,
          endpoint_type=EndpointType.INFERENCE_COMPONENT_BASED,
          resources=resources,
      )
      xgboost_predictor.serializer = CSVSerializer()
      xgboost_predictor.deserializer = CSVDeserializer()
      xgb_result = xgboost_predictor.predict(TEST_CSV_DATA)
      assert xgb_result
  
      # update Tensorflow predictor to Xgboost predictor
      predictor_to_update.update_predictor(
          model_name=xgboost_model.name,
          resources=resources_update,
      )
  
      # predictor_to_update is now a Xgboost predictor
      predictor_to_update.serializer = CSVSerializer()
      predictor_to_update.deserializer = CSVDeserializer()
      xgb_result_2 = predictor_to_update.predict(TEST_CSV_DATA)
      assert xgb_result == xgb_result_2
  
      # delete predictor
      predictor_to_update.delete_predictor(wait=True)
      xgboost_predictor.delete_predictor(wait=True)
  
      # delete endpoint
      predictor_to_update.delete_endpoint()
  
imports: from __future__ import absolute_import
  import os
  import sagemaker.predictor
  import sagemaker.utils
  import pytest
  from sagemaker import image_uris
  from sagemaker.compute_resource_requirements.resource_requirements import ResourceRequirements
  from sagemaker.enums import EndpointType
  from sagemaker.model import Model
  from sagemaker.serializers import CSVSerializer
  from sagemaker.deserializers import CSVDeserializer
  from sagemaker.predictor import Predictor
  from sagemaker.tensorflow.model import TensorFlowModel
  from tests.integ import DATA_DIR
label_desc: Use image node_id 8 for calling a global function with example usage: test_deploy_update_predictor_with_other_model(tfs_model, resources, resources_update, xgboost_model) without return types