code: def test_multi_model_predict_call():
      sagemaker_session = empty_sagemaker_session()
      predictor = Predictor(ENDPOINT, sagemaker_session)
  
      data = "untouched"
      result = predictor.predict(data, target_model="model.tar.gz")
  
      assert (
          sagemaker_session.sagemaker_runtime_client.invoke_endpoint.called
      )
  
      expected_request_args = {
          "Accept": DEFAULT_ACCEPT,
          "Body": data,
          "ContentType": DEFAULT_CONTENT_TYPE,
          "EndpointName": ENDPOINT,
          "TargetModel": "model.tar.gz",
      }
      (
          call_args,
          kwargs,
      ) = (
          sagemaker_session.sagemaker_runtime_client.invoke_endpoint.call_args
      )
      assert kwargs == expected_request_args
  
      assert result == RETURN_VALUE
  
imports: from __future__ import absolute_import
  import io
  import json
  import pytest
  from mock import Mock, call, patch
  from sagemaker.deserializers import CSVDeserializer, PandasDeserializer
  from sagemaker.enums import EndpointType
  from sagemaker.model_monitor.model_monitoring import DEFAULT_REPOSITORY_NAME
  from sagemaker.predictor import Predictor
  from sagemaker.serializers import JSONSerializer, CSVSerializer
  from sagemaker.compute_resource_requirements.resource_requirements import ResourceRequirements
label_desc: Use image node_id 5 for calling a global function with example usage: test_multi_model_predict_call() without return types