code: def test_deploy_multi_models_without_endpoint_name(
      tfs_model, resources
  ):
      input_data = {"instances": [1.0, 2.0, 5.0]}
      expected_result = {"predictions": [3.5, 4.0, 5.5]}
  
      tfs_predictor1 = tfs_model.deploy(
          1,
          "ml.m5.large",
          endpoint_type=EndpointType.INFERENCE_COMPONENT_BASED,
          resources=resources,
      )
  
      result = tfs_predictor1.predict(input_data)
      assert expected_result == result
  
      endpoint_name = tfs_predictor1.endpoint
  
      tfs_predictor2 = tfs_model.deploy(
          1,
          "ml.m5.large",
          endpoint_name=endpoint_name,
          endpoint_type=EndpointType.INFERENCE_COMPONENT_BASED,
          resources=resources,
      )
  
      result_2 = tfs_predictor2.predict(input_data)
      assert expected_result == result_2
  
      models1, _ = tfs_predictor1.list_related_models()
      assert models1 and len(models1) > 0 and len(models1) == 2
  
      models2, _ = tfs_predictor2.list_related_models()
      assert models2 and len(models2) > 0 and len(models2) == 2
  
      # update endpoint(for instance scaling)
      tfs_predictor1.update_endpoint(max_instance_count=5)
  
      result = tfs_predictor1.predict(input_data)
      assert expected_result == result
  
      result_2 = tfs_predictor2.predict(input_data)
      assert expected_result == result_2
  
      # delete predictors
      tfs_predictor1.delete_predictor(wait=True)
      tfs_predictor2.delete_predictor(wait=True)
  
      # delete endpoint
      tfs_predictor1.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 9 for calling a global function with example usage: test_deploy_multi_models_without_endpoint_name(tfs_model, resources) without return types