code: def autolog(
      log_models=True,
      log_datasets=True,
      disable=False,
      exclusive=False,
      disable_for_unsupported_versions=False,
      silent=False,
      registered_model_name=None,
      extra_tags=None,
  ):  # pylint: disable=unused-argument
      """
      Enables (or disables) and configures automatic logging from statsmodels to MLflow.
      Logs the following:
  
      - allowlisted metrics returned by method `fit` of any subclass of
        statsmodels.base.model.Model, the allowlisted metrics including: {autolog_metric_allowlist}
      - trained model.
      - an html artifact which shows the model summary.
  
  
      :param log_models: If ``True``, trained models are logged as MLflow model artifacts.
                         If ``False``, trained models are not logged.
                         Input examples and model signatures, which are attributes of MLflow models,
                         are also omitted when ``log_models`` is ``False``.
      :param log_datasets: If ``True``, dataset information is logged to MLflow Tracking.
                           If ``False``, dataset information is not logged.
      :param disable: If ``True``, disables the statsmodels autologging integration. If ``False``,
                      enables the statsmodels autologging integration.
      :param exclusive: If ``True``, autologged content is not logged to user-created fluent runs.
                        If ``False``, autologged content is logged to the active fluent run,
                        which may be user-created.
      :param disable_for_unsupported_versions: If ``True``, disable autologging for versions of
                        statsmodels that have not been tested against this version of the MLflow
                        client or are incompatible.
      :param silent: If ``True``, suppress all event logs and warnings from MLflow during statsmodels
                     autologging. If ``False``, show all events and warnings during statsmodels
                     autologging.
      :param registered_model_name: If given, each time a model is trained, it is registered as a
                                    new model version of the registered model with this name.
                                    The registered model is created if it does not already exist.
      :param extra_tags: A dictionary of extra tags to set on each managed run created by autologging.
      """
      import statsmodels
  
      # Autologging depends on the exploration of the models class tree within the
      # `statsmodels.base.models` module. In order to load / access this module, the
      # `statsmodels.api` module must be imported
      import statsmodels.api
  
      def find_subclasses(klass):
          """
          Recursively return a (non-nested) list of the class object and all its subclasses
          :param klass: the class whose class subtree we want to retrieve
          :return: a list of classes that includes the argument in the first position
          """
          subclasses = klass.__subclasses__()
          if subclasses:
              subclass_lists = [find_subclasses(c) for c in subclasses]
              chain = itertools.chain.from_iterable(subclass_lists)
              return [klass] + list(chain)
          else:
              return [klass]
  
      def overrides(klass, function_name):
          """
          Returns True when the class passed as first argument overrides the function_name
          Based on https://stackoverflow.com/a/62303206/5726057
          :param klass: the class we are inspecting
          :param function_name: a string with the name of the method we want to check overriding
          :return:
          """
          try:
              superclass = inspect.getmro(klass)[1]
              return getattr(klass, function_name) is not getattr(
                  superclass, function_name
              )
          except (IndexError, AttributeError):
              return False
  
      def patch_class_tree(klass):
          """
          Patches all subclasses that override any auto-loggable method via monkey patching using
          the gorilla package, taking the argument as the tree root in the class hierarchy. Every
          auto-loggable method found in any of the subclasses is replaced by the patched version.
          :param klass: root in the class hierarchy to be analyzed and patched recursively
          """
  
          # TODO: add more autologgable methods here (e.g. fit_regularized, from_formula, etc)
          # See https://www.statsmodels.org/dev/api.html
          autolog_supported_func = {"fit": wrapper_fit}
          glob_subclasses = set(find_subclasses(klass))
  
          # Create a patch for every method that needs to be patched, i.e. those
          # which actually override an autologgable method
          patches_list = [
              # Link the patched function with the original via a local variable in the closure
              # to allow invoking superclass methods in the context of the subclass, and not
              # losing the trace of the true original method
              (clazz, method_name, wrapper_func)
              for clazz in glob_subclasses
              for (
                  method_name,
                  wrapper_func,
              ) in autolog_supported_func.items()
              if overrides(clazz, method_name)
          ]
  
          for clazz, method_name, patch_impl in patches_list:
              safe_patch(
                  FLAVOR_NAME,
                  clazz,
                  method_name,
                  patch_impl,
                  manage_run=True,
                  extra_tags=extra_tags,
              )
  
      def wrapper_fit(original, self, *args, **kwargs):
          should_autolog = False
          if AutologHelpers.should_autolog:
              AutologHelpers.should_autolog = False
              should_autolog = True
  
          try:
              if should_autolog:
                  # This may generate warnings due to collisions in already-logged param names
                  log_fn_args_as_params(original, args, kwargs)
  
              # training model
              model = original(self, *args, **kwargs)
  
              if should_autolog:
                  # Log the model
                  if get_autologging_config(
                      FLAVOR_NAME, "log_models", True
                  ):
                      global _save_model_called_from_autolog
                      _save_model_called_from_autolog = True
                      registered_model_name = get_autologging_config(
                          FLAVOR_NAME, "registered_model_name", None
                      )
                      try:
                          log_model(
                              model,
                              artifact_path="model",
                              registered_model_name=registered_model_name,
                          )
                      finally:
                          _save_model_called_from_autolog = False
  
                  # Log the most common metrics
                  if isinstance(
                      model, statsmodels.base.wrapper.ResultsWrapper
                  ):
                      metrics_dict = _get_autolog_metrics(model)
                      mlflow.log_metrics(metrics_dict)
  
                      model_summary = model.summary().as_text()
                      mlflow.log_text(
                          model_summary, "model_summary.txt"
                      )
  
              return model
  
          finally:
              # Clean the shared flag for future calls in case it had been set here ...
              if should_autolog:
                  AutologHelpers.should_autolog = True
  
      patch_class_tree(statsmodels.base.model.Model)
  
imports: import inspect
  import itertools
  import logging
  import os
  from typing import Any, Dict, Optional
  import yaml
  import mlflow
  from mlflow import pyfunc
  from mlflow.exceptions import MlflowException
  from mlflow.models import Model, ModelInputExample, ModelSignature
  from mlflow.models.model import MLMODEL_FILE_NAME
  from mlflow.models.signature import _infer_signature_from_input_example
  from mlflow.models.utils import _save_example
  from mlflow.tracking._model_registry import DEFAULT_AWAIT_MAX_SLEEP_SECONDS
  from mlflow.tracking.artifact_utils import _download_artifact_from_uri
  from mlflow.utils.autologging_utils import autologging_integration, get_autologging_config, log_fn_args_as_params, safe_patch
  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 get_total_file_size, write_to
  from mlflow.utils.model_utils import _add_code_from_conf_to_system_path, _get_flavor_configuration, _validate_and_copy_code_paths, _validate_and_prepare_target_save_path
  from mlflow.utils.requirements_utils import _get_pinned_requirement
  from mlflow.utils.validation import _is_numeric
label_desc: Use image node_id 9 for calling a global function with example usage: autolog(log_models, log_datasets, disable, exclusive, disable_for_unsupported_versions, silent, registered_model_name, extra_tags) and returns: unknown, list, unknown, model, False