code: def test_create_model(
      name_from_base,
      sagemaker_session,
      chainer_version,
      chainer_py_version,
  ):
      container_log_level = '"logging.INFO"'
      source_dir = "s3://mybucket/source"
      base_job_name = "job"
  
      chainer = Chainer(
          entry_point=SCRIPT_PATH,
          role=ROLE,
          sagemaker_session=sagemaker_session,
          instance_count=INSTANCE_COUNT,
          instance_type=INSTANCE_TYPE,
          framework_version=chainer_version,
          container_log_level=container_log_level,
          py_version=chainer_py_version,
          base_job_name=base_job_name,
          source_dir=source_dir,
      )
  
      chainer.fit(inputs="s3://mybucket/train", job_name="new_name")
  
      model_name = "model_name"
      name_from_base.return_value = model_name
      model = chainer.create_model()
  
      assert model.sagemaker_session == sagemaker_session
      assert model.framework_version == chainer_version
      assert model.py_version == chainer.py_version
      assert model.entry_point == SCRIPT_PATH
      assert model.role == ROLE
      assert model.name == model_name
      assert model.container_log_level == container_log_level
      assert model.source_dir == source_dir
      assert model.vpc_config is None
  
      name_from_base.assert_called_with(base_job_name)
  
imports: from __future__ import absolute_import
  import logging
  import json
  import os
  from distutils.util import strtobool
  import pytest
  from mock import MagicMock, Mock, ANY
  from mock import patch
  from sagemaker.chainer import defaults
  from sagemaker.chainer import Chainer
  from sagemaker.chainer import ChainerPredictor, ChainerModel
  from sagemaker.session_settings import SessionSettings
label_desc: Use image node_id 7 for calling a global function with example usage: test_create_model(name_from_base, sagemaker_session, chainer_version, chainer_py_version) without return types