code: def test_log_explanation_doesnt_create_autologged_run():
      try:
          mlflow.sklearn.autolog(disable=False, exclusive=False)
          X, y = sklearn.datasets.load_diabetes(
              return_X_y=True, as_frame=True
          )
          X = X.iloc[:50, :4]
          y = y.iloc[:50]
          model = sklearn.linear_model.LinearRegression()
          model.fit(X, y)
  
          with mlflow.start_run() as run:
              mlflow.shap.log_explanation(model.predict, X)
  
          run_data = MlflowClient().get_run(run.info.run_id).data
          metrics, params, tags = (
              run_data.metrics,
              run_data.params,
              run_data.tags,
          )
          assert not metrics
          assert not params
          assert all("mlflow." in key for key in tags)
          assert "mlflow.autologging" not in tags
      finally:
          mlflow.sklearn.autolog(disable=True)
  
imports: import json
  from unittest import mock
  import numpy
  import pandas
  import pytest
  import shap
  import sklearn
  from numba import njit
  from packaging.version import Version
  from sklearn.datasets import fetch_california_housing, load_diabetes
  import mlflow
  import mlflow.pyfunc.scoring_server
  from mlflow import MlflowClient
  from mlflow.tracking.artifact_utils import _download_artifact_from_uri
  from mlflow.utils import PYTHON_VERSION
  from mlflow.utils.model_utils import _get_flavor_configuration
  from tests.helper_functions import _assert_pip_requirements, _compare_logged_code_paths, _mlflow_major_version_string, pyfunc_serve_and_score_model
label_desc: Use image node_id 6 for calling a global function with example usage: test_log_explanation_doesnt_create_autologged_run() without return types