code: def autolog(
      log_models=True,
      log_datasets=True,
      disable=False,
      exclusive=False,
      disable_for_unsupported_versions=False,
      silent=False,
      registered_model_name=None,
  ):  # pylint: disable=unused-argument
      """
      Enables (or disables) and configures autologging from Gluon to MLflow.
      Logs loss and any other metrics specified in the fit
      function, and optimizer data as parameters. Model checkpoints
      are logged as artifacts to a 'models' directory.
  
      Args:
          log_models: If ``True``, trained models are logged as MLflow model artifacts.
              If ``False``, trained models are not logged.
          log_datasets: If ``True``, dataset information is logged to MLflow Tracking.
              If ``False``, dataset information is not logged.
          disable: If ``True``, disables the MXNet Gluon autologging integration. If ``False``,
              enables the MXNet Gluon autologging integration.
          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.
          disable_for_unsupported_versions: If ``True``, disable autologging for versions of
              gluon that have not been tested against this version of the MLflow client
              or are incompatible.
          silent: If ``True``, suppress all event logs and warnings from MLflow during MXNet Gluon
              autologging. If ``False``, show all events and warnings during MXNet Gluon
              autologging.
          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.
  
      """
  
      from mxnet.gluon.contrib.estimator import Estimator
  
      from mlflow.gluon._autolog import __MLflowGluonCallback
  
      def getGluonCallback(metrics_logger):
          return __MLflowGluonCallback(log_models, metrics_logger)
  
      def fit(original, self, *args, **kwargs):
          # Wrap `fit` execution within a batch metrics logger context.
          run_id = mlflow.active_run().info.run_id
          with batch_metrics_logger(run_id) as metrics_logger:
              mlflowGluonCallback = getGluonCallback(metrics_logger)
              if len(args) >= 4:
                  args = (
                      *args[:3],
                      args[3] + [mlflowGluonCallback],
                      *args[4:],
                  )
              elif "event_handlers" in kwargs:
                  kwargs["event_handlers"] += [mlflowGluonCallback]
              else:
                  kwargs["event_handlers"] = [mlflowGluonCallback]
              result = original(self, *args, **kwargs)
  
          return result
  
      safe_patch(FLAVOR_NAME, Estimator, "fit", fit, manage_run=True)
  
imports: import os
  from typing import Any, Dict, Optional
  import numpy
  import pandas
  import yaml
  from packaging.version import Version
  import mlflow
  from mlflow import pyfunc
  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.artifact_utils import _download_artifact_from_uri
  from mlflow.utils.annotations import deprecated
  from mlflow.utils.autologging_utils import autologging_integration, batch_metrics_logger, 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 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
label_desc: Use image node_id 7 for calling a global function with example usage: autolog(log_models, log_datasets, disable, exclusive, disable_for_unsupported_versions, silent, registered_model_name) and returns: __MLflowGluonCallback, result