code: def test_model_custom_serialization(
      sagemaker_session,
      pytorch_inference_version,
      pytorch_inference_py_version,
      gpu_pytorch_instance_type,
  ):
      model = PyTorchModel(
          MODEL_DATA,
          role=ROLE,
          entry_point=SCRIPT_PATH,
          framework_version=pytorch_inference_version,
          py_version=pytorch_inference_py_version,
          sagemaker_session=sagemaker_session,
      )
      custom_serializer = Mock()
      custom_deserializer = Mock()
      predictor = model.deploy(
          1,
          gpu_pytorch_instance_type,
          serializer=custom_serializer,
          deserializer=custom_deserializer,
      )
      assert isinstance(predictor, PyTorchPredictor)
      assert predictor.serializer is custom_serializer
      assert predictor.deserializer is custom_deserializer
  
imports: from __future__ import absolute_import
  import logging
  import json
  import os
  import pytest
  from mock import ANY, MagicMock, Mock, patch
  from packaging.version import Version
  from sagemaker import image_uris
  from sagemaker.pytorch import defaults
  from sagemaker.pytorch import PyTorch, PyTorchPredictor, PyTorchModel
  from sagemaker.instance_group import InstanceGroup
  from sagemaker.session_settings import SessionSettings
label_desc: Use image node_id 15 for calling a global function with example usage: test_model_custom_serialization(sagemaker_session, pytorch_inference_version, pytorch_inference_py_version, gpu_pytorch_instance_type) without return types