code: def test_create_model_with_custom_image(sagemaker_session):
      container_log_level = '"logging.INFO"'
      source_dir = "s3://mybucket/source"
      custom_image = "ubuntu:latest"
      sklearn = SKLearn(
          entry_point=SCRIPT_PATH,
          role=ROLE,
          sagemaker_session=sagemaker_session,
          instance_type=INSTANCE_TYPE,
          image_uri=custom_image,
          container_log_level=container_log_level,
          py_version=PYTHON_VERSION,
          base_job_name="job",
          source_dir=source_dir,
      )
  
      sklearn.fit(inputs="s3://mybucket/train", job_name="new_name")
      model = sklearn.create_model()
  
      assert model.image_uri == custom_image
  
imports: from __future__ import absolute_import
  import logging
  import json
  import os
  import pytest
  from mock import Mock
  from mock import patch
  from sagemaker.fw_utils import UploadedCode
  from sagemaker.session_settings import SessionSettings
  from sagemaker.sklearn import SKLearn, SKLearnModel, SKLearnPredictor
label_desc: Use image node_id 10 for calling a global function with example usage: test_create_model_with_custom_image(sagemaker_session) without return types