code: def test_inference_with_untrained_model_and_empty_string(model_name):
      data = "".encode("utf8")
  
      response = requests.post(
          url=f"http://localhost:8080/predictions/{model_name}",
          data=data,
      )
  
      assert response.status_code == 200
  
      result_entries = json.loads(response.text)
  
      assert "Negative" in result_entries
      assert "Positive" in result_entries
  
      # We're using an untrained model for the unit test, so results do not make sense but should be consistent
      assert (
          float(result_entries["Negative"])
          == EXPECTED_RESULT_EMPTY_STRING[0]["0"]
      )
      assert (
          float(result_entries["Positive"])
          == EXPECTED_RESULT_EMPTY_STRING[0]["1"]
      )
  
imports: import importlib
  import json
  import os
  import shutil
  import sys
  import time
  from argparse import Namespace
  from pathlib import Path
  import pytest
  import requests
  import test_utils
  import torch
  from model_archiver import ModelArchiverConfig
  from test_utils import REPO_ROOT
  from ts.torch_handler.unit_tests.test_utils.mock_context import MockContext
label_desc: Use image node_id 10 for calling a global function with example usage: test_inference_with_untrained_model_and_empty_string(model_name) without return types