code: def autolog(
      disable=False, silent=False
  ):  # pylint: disable=unused-argument
      """
      Enables (or disables) and configures logging of Spark datasource paths, versions
      (if applicable), and formats when they are read. This method is not threadsafe and assumes a
      `SparkSession
      <https://spark.apache.org/docs/latest/api/python/reference/pyspark.sql/api/pyspark.sql.SparkSession.html>`_
      already exists with the
      `mlflow-spark JAR
      <https://www.mlflow.org/docs/latest/tracking.html#spark>`_
      attached. It should be called on the Spark driver, not on the executors (i.e. do not call
      this method within a function parallelized by Spark). This API requires Spark 3.0 or above.
  
      Datasource information is cached in memory and logged to all subsequent MLflow runs,
      including the active MLflow run (if one exists when the data is read). Note that autologging of
      Spark ML (MLlib) models is not currently supported via this API. Datasource autologging is
      best-effort, meaning that if Spark is under heavy load or MLflow logging fails for any reason
      (e.g., if the MLflow server is unavailable), logging may be dropped.
  
      For any unexpected issues with autologging, check Spark driver and executor logs in addition
      to stderr & stdout generated from your MLflow code - datasource information is pulled from
      Spark, so logs relevant to debugging may show up amongst the Spark logs.
  
      .. code-block:: python
          :caption: Example
  
          import mlflow.spark
          import os
          import shutil
          from pyspark.sql import SparkSession
  
          # Create and persist some dummy data
          # Note: On environments like Databricks with pre-created SparkSessions,
          # ensure the org.mlflow:mlflow-spark:2.22.0 is attached as a library to
          # your cluster
          spark = (
              SparkSession.builder.config("spark.jars.packages", "org.mlflow:mlflow-spark:2.22.0")
              .master("local[*]")
              .getOrCreate()
          )
          df = spark.createDataFrame(
              [(4, "spark i j k"), (5, "l m n"), (6, "spark hadoop spark"), (7, "apache hadoop")],
              ["id", "text"],
          )
          import tempfile
  
          tempdir = tempfile.mkdtemp()
          df.write.csv(os.path.join(tempdir, "my-data-path"), header=True)
          # Enable Spark datasource autologging.
          mlflow.spark.autolog()
          loaded_df = spark.read.csv(
              os.path.join(tempdir, "my-data-path"), header=True, inferSchema=True
          )
          # Call toPandas() to trigger a read of the Spark datasource. Datasource info
          # (path and format) is logged to the current active run, or the
          # next-created MLflow run if no run is currently active
          with mlflow.start_run() as active_run:
              pandas_df = loaded_df.toPandas()
  
      :param disable: If ``True``, disables the Spark datasource autologging integration.
                      If ``False``, enables the Spark datasource autologging integration.
      :param silent: If ``True``, suppress all event logs and warnings from MLflow during Spark
                     datasource autologging. If ``False``, show all events and warnings during Spark
                     datasource autologging.
      """
      from pyspark import __version__ as pyspark_version
      from pyspark.sql import SparkSession
  
      from mlflow._spark_autologging import (
          _listen_for_spark_activity,
          _stop_listen_for_spark_activity,
      )
      from mlflow.utils._spark_utils import _get_active_spark_session
  
      # Check if environment variable PYSPARK_PIN_THREAD is set to false.
      # The "Pin thread" concept was introduced since Pyspark 3.0.0 and set to default to true
      # since Pyspark 3.2.0 (https://issues.apache.org/jira/browse/SPARK-35303). When pin thread
      # is enabled, Pyspark manages Python and JVM threads in a 1:1, meaning that when one thread
      # is terminated, the corresponding thread in the other side will be terminated as well.
      # However, this causes an issue in spark autologging as our event listener thread may be
      # terminated before receiving the datasource event.
      # Hence, we have to disable it and decouple the thread management between Python and JVM.
      if (
          Version(pyspark_version) >= Version("3.2.0")
          and os.environ.get("PYSPARK_PIN_THREAD", "").lower()
          != "false"
      ):
          _logger.warning(
              "With Pyspark >= 3.2, PYSPARK_PIN_THREAD environment variable must be set to false "
              "for Spark datasource autologging to work."
          )
  
      def __init__(original, self, *args, **kwargs):
          original(self, *args, **kwargs)
  
          _listen_for_spark_activity(self._sc)
  
      safe_patch(
          FLAVOR_NAME,
          SparkSession,
          "__init__",
          __init__,
          manage_run=False,
      )
  
      def patched_session_stop(original, self, *args, **kwargs):
          _stop_listen_for_spark_activity(self.sparkContext)
          original(self, *args, **kwargs)
  
      safe_patch(
          FLAVOR_NAME,
          SparkSession,
          "stop",
          patched_session_stop,
          manage_run=False,
      )
  
      active_session = _get_active_spark_session()
      if active_session is not None:
          # We know SparkContext exists here already, so get it
          sc = active_session.sparkContext
  
          if disable:
              _stop_listen_for_spark_activity(sc)
          else:
              _listen_for_spark_activity(sc)
  
imports: import logging
  import os
  import posixpath
  import re
  import shutil
  from typing import Any, Dict, Optional
  import pandas
  import yaml
  from packaging.version import Version
  import mlflow
  from mlflow import environment_variables, mleap, pyfunc
  from mlflow.environment_variables import MLFLOW_DFS_TMP
  from mlflow.exceptions import MlflowException
  from mlflow.models import Model, ModelInputExample, ModelSignature, infer_signature
  from mlflow.models.model import MLMODEL_FILE_NAME
  from mlflow.models.signature import _LOG_MODEL_INFER_SIGNATURE_WARNING_TEMPLATE
  from mlflow.models.utils import _Example, _save_example
  from mlflow.protos.databricks_pb2 import INVALID_PARAMETER_VALUE
  from mlflow.store.artifact.databricks_artifact_repo import DatabricksArtifactRepository
  from mlflow.tracking._model_registry import DEFAULT_AWAIT_MAX_SLEEP_SECONDS
  from mlflow.tracking.artifact_utils import _download_artifact_from_uri, _get_root_uri_and_artifact_path
  from mlflow.utils import _get_fully_qualified_class_name, databricks_utils
  from mlflow.utils.autologging_utils import autologging_integration, safe_patch
  from mlflow.utils.class_utils import _get_class_from_string
  from mlflow.utils.docstring_utils import LOG_MODEL_PARAM_DOCS, format_docstring
  from mlflow.utils.environment import _CONDA_ENV_FILE_NAME, _CONSTRAINTS_FILE_NAME, _PYTHON_ENV_FILE_NAME, _REQUIREMENTS_FILE_NAME, _mlflow_conda_env, _process_conda_env, _process_pip_requirements, _PythonEnv, _validate_env_arguments
  from mlflow.utils.file_utils import TempDir, get_total_file_size, shutil_copytree_without_file_permissions, write_to
  from mlflow.utils.model_utils import _add_code_from_conf_to_system_path, _get_flavor_configuration_from_uri, _validate_and_copy_code_paths
  from mlflow.utils.requirements_utils import _get_pinned_requirement
  from mlflow.utils.uri import append_to_uri_path, dbfs_hdfs_uri_to_fuse_path, generate_tmp_dfs_path, get_databricks_profile_uri_from_artifact_uri, is_databricks_acled_artifacts_uri, is_local_uri, is_valid_dbfs_uri
label_desc: Use image node_id 17 for calling a global function with example usage: autolog(disable, silent) without return types