code: def test_model_log_load(tmp_path, sklearn_knn_model):
      model_name = f"wheels-test-{random_int()}"
      model_uri = f"models:/{model_name}/1"
      wheeled_model_uri = f"models:/{model_name}/2"
      artifact_path = "model"
  
      # Log a model
      with mlflow.start_run():
          mlflow.sklearn.log_model(
              sk_model=sklearn_knn_model.model,
              artifact_path=artifact_path,
              registered_model_name=model_name,
          )
          model_path = _download_artifact_from_uri(model_uri, tmp_path)
          original_model_config = Model.load(
              os.path.join(model_path, MLMODEL_FILE_NAME)
          ).__dict__
  
      # Re-log with wheels
      with mlflow.start_run():
          WheeledModel.log_model(model_uri=model_uri)
          wheeled_model_path = _download_artifact_from_uri(
              wheeled_model_uri
          )
          wheeled_model_run_id = (
              mlflow.tracking.fluent._get_or_start_run().info.run_id
          )
          wheeled_model_config = Model.load(
              os.path.join(wheeled_model_path, MLMODEL_FILE_NAME)
          ).__dict__
  
      validate_updated_model_file(
          original_model_config, wheeled_model_config
      )
      # Assert correct run_id
      assert wheeled_model_config["run_id"] == wheeled_model_run_id
  
      validate_updated_conda_dependencies(
          model_path, wheeled_model_path
      )
  
      validate_wheeled_dependencies(wheeled_model_path)
  
imports: import json
  import os
  import random
  import re
  from collections import namedtuple
  import numpy
  import pandas
  import pytest
  import sklearn.neighbors
  import yaml
  from sklearn import datasets
  import mlflow
  import mlflow.pyfunc.scoring_server
  from mlflow.exceptions import MlflowException
  from mlflow.models.wheeled_model import _WHEELS_FOLDER_NAME, WheeledModel
  from mlflow.pyfunc.model import MLMODEL_FILE_NAME, Model
  from mlflow.store.artifact.utils.models import _improper_model_uri_msg
  from mlflow.tracking.artifact_utils import _download_artifact_from_uri
  from mlflow.utils.environment import _CONDA_ENV_FILE_NAME, _REQUIREMENTS_FILE_NAME, _is_pip_deps, _mlflow_conda_env
  from tests.helper_functions import _is_available_on_pypi, _mlflow_major_version_string, pyfunc_serve_and_score_model
label_desc: Use image node_id 9 for calling a global function with example usage: test_model_log_load(tmp_path, sklearn_knn_model) without return types