code: def test_custom_image_estimator_deploy(
      sagemaker_session,
      pytorch_inference_version,
      pytorch_inference_py_version,
  ):
      custom_image = "mycustomimage:latest"
      pytorch = _pytorch_estimator(
          sagemaker_session,
          framework_version=pytorch_inference_version,
          py_version=pytorch_inference_py_version,
      )
      pytorch.fit(inputs="s3://mybucket/train", job_name="new_name")
      model = pytorch.create_model(image_uri=custom_image)
      assert model.image_uri == custom_image
  
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 27 for calling a global function with example usage: test_custom_image_estimator_deploy(sagemaker_session, pytorch_inference_version, pytorch_inference_py_version) without return types