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
      """
      Enable automatic logging from Fastai 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.
  
      MLflow will also log the parameters of the
      `EarlyStoppingCallback <https://docs.fast.ai/callback.tracker.html#EarlyStoppingCallback>`_
      and `OneCycleScheduler <https://docs.fast.ai/callback.schedule.html#ParamScheduler>`_ callbacks
  
      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 Fastai autologging integration. If ``False``, enables
              the Fastai 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 fastai
              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 Fastai
              autologging. If ``False``, show all events and warnings during Fastai 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.
          extra_tags: A dictionary of extra tags to set on each managed run created by
              autologging.
  
  
      .. code-block:: python
          :caption: Example
  
          # This is a modified example from
          # https://github.com/mlflow/mlflow/tree/master/examples/fastai
          # demonstrating autolog capabilities.
  
          import fastai.vision as vis
          import mlflow.fastai
          from mlflow import MlflowClient
  
  
          def print_auto_logged_info(r):
              tags = {k: v for k, v in r.data.tags.items() if not k.startswith("mlflow.")}
              artifacts = [f.path for f in MlflowClient().list_artifacts(r.info.run_id, "model")]
              print(f"run_id: {r.info.run_id}")
              print(f"artifacts: {artifacts}")
              print(f"params: {r.data.params}")
              print(f"metrics: {r.data.metrics}")
              print(f"tags: {tags}")
  
  
          def main(epochs=5, learning_rate=0.01):
              # Download and untar the MNIST data set
              path = vis.untar_data(vis.URLs.MNIST_SAMPLE)
  
              # Prepare, transform, and normalize the data
              data = vis.ImageDataBunch.from_folder(
                  path, ds_tfms=(vis.rand_pad(2, 28), []), bs=64
              )
              data.normalize(vis.imagenet_stats)
  
              # Create CNN the Learner model
              model = vis.cnn_learner(data, vis.models.resnet18, metrics=vis.accuracy)
  
              # Enable auto logging
              mlflow.fastai.autolog()
  
              # Start MLflow session
              with mlflow.start_run() as run:
                  model.fit(epochs, learning_rate)
  
              # fetch the auto logged parameters, metrics, and artifacts
              print_auto_logged_info(mlflow.get_run(run_id=run.info.run_id))
  
  
          main()
  
      .. code-block:: text
          :caption: output
  
          run_id: 5a23dcbcaa334637814dbce7a00b2f6a
          artifacts: ['model/MLmodel', 'model/conda.yaml', 'model/model.fastai']
          params: {'wd': 'None',
                   'bn_wd': 'True',
                   'opt_func': 'Adam',
                   'epochs': '5', '
                   train_bn': 'True',
                   'num_layers': '60',
                   'lr': '0.01',
                   'true_wd': 'True'}
          metrics: {'train_loss': 0.024,
                    'accuracy': 0.99214,
                    'valid_loss': 0.021}
          # Tags model summary omitted too long
          tags: {...}
  
      .. figure:: ../_static/images/fastai_autolog.png
  
          Fastai autologged MLflow entities
      """
      from fastai.callback.all import (
          EarlyStoppingCallback,
          TrackerCallback,
      )
      from fastai.callback.hook import (
          _print_shapes,
          find_bs,
          layer_info,
          module_summary,
      )
      from fastai.learner import Learner
  
      def getFastaiCallback(metrics_logger, is_fine_tune=False):
          from mlflow.fastai.callback import __MlflowFastaiCallback
  
          return __MlflowFastaiCallback(
              metrics_logger=metrics_logger,
              log_models=log_models,
              is_fine_tune=is_fine_tune,
          )
  
      def _find_callback_of_type(callback_type, callbacks):
          for callback in callbacks:
              if isinstance(callback, callback_type):
                  return callback
          return None
  
      def _log_early_stop_callback_params(callback):
          if callback:
              try:
                  earlystopping_params = {
                      "early_stop_monitor": callback.monitor,
                      "early_stop_min_delta": callback.min_delta,
                      "early_stop_patience": callback.patience,
                      "early_stop_comp": callback.comp.__name__,
                  }
                  mlflow.log_params(earlystopping_params)
              except Exception:
                  return
  
      def _log_model_info(learner):
          # The process executed here, are incompatible with TrackerCallback
          # Hence it is removed and add again after the execution
          remove_cbs = [
              cb
              for cb in learner.cbs
              if isinstance(cb, TrackerCallback)
          ]
          if remove_cbs:
              learner.remove_cbs(remove_cbs)
  
          xb = learner.dls.train.one_batch()[: learner.dls.train.n_inp]
          infos = layer_info(learner, *xb)
          bs = find_bs(xb)
          inp_sz = _print_shapes((x.shape for x in xb), bs)
          mlflow.log_param("input_size", inp_sz)
          mlflow.log_param("num_layers", len(infos))
  
          summary = module_summary(learner, *xb)
  
          # Add again TrackerCallback
          if remove_cbs:
              learner.add_cbs(remove_cbs)
  
          with tempfile.TemporaryDirectory() as tempdir:
              summary_file = os.path.join(tempdir, "module_summary.txt")
              with open(summary_file, "w") as f:
                  f.write(summary)
              mlflow.log_artifact(local_path=summary_file)
  
      def _run_and_log_function(
          self,
          original,
          args,
          kwargs,
          unlogged_params,
          is_fine_tune=False,
      ):
          # Check if is trying to fit while fine tuning or not
          mlflow_cbs = [
              cb for cb in self.cbs if cb.name == "___mlflow_fastai"
          ]
          fit_in_fine_tune = (
              original.__name__ == "fit"
              and len(mlflow_cbs) > 0
              and mlflow_cbs[0].is_fine_tune
          )
  
          if not fit_in_fine_tune:
              log_fn_args_as_params(
                  original, list(args), kwargs, unlogged_params
              )
  
          run_id = mlflow.active_run().info.run_id
          with batch_metrics_logger(run_id) as metrics_logger:
              if not fit_in_fine_tune:
                  early_stop_callback = _find_callback_of_type(
                      EarlyStoppingCallback, self.cbs
                  )
                  _log_early_stop_callback_params(early_stop_callback)
  
                  # First try to remove if any already registered callback
                  self.remove_cbs(mlflow_cbs)
  
                  # Log information regarding model and data without bar and print-out
                  with self.no_bar(), self.no_logging():
                      _log_model_info(learner=self)
  
                  mlflowFastaiCallback = getFastaiCallback(
                      metrics_logger=metrics_logger,
                      is_fine_tune=is_fine_tune,
                  )
  
                  # Add the new callback
                  self.add_cb(mlflowFastaiCallback)
  
              result = original(self, *args, **kwargs)
  
          return result
  
      def fit(original, self, *args, **kwargs):
          unlogged_params = [
              "self",
              "cbs",
              "learner",
              "lr",
              "lr_max",
              "wd",
          ]
          return _run_and_log_function(
              self,
              original,
              args,
              kwargs,
              unlogged_params,
              is_fine_tune=False,
          )
  
      safe_patch(
          FLAVOR_NAME,
          Learner,
          "fit",
          fit,
          manage_run=True,
          extra_tags=extra_tags,
      )
  
      def fine_tune(original, self, *args, **kwargs):
          unlogged_params = [
              "self",
              "cbs",
              "learner",
              "lr",
              "lr_max",
              "wd",
          ]
          return _run_and_log_function(
              self,
              original,
              args,
              kwargs,
              unlogged_params,
              is_fine_tune=True,
          )
  
      safe_patch(
          FLAVOR_NAME,
          Learner,
          "fine_tune",
          fine_tune,
          manage_run=True,
          extra_tags=extra_tags,
      )
  
imports: import logging
  import os
  import tempfile
  from pathlib import Path
  from typing import Any, Dict, Optional
  import numpy
  import pandas
  import yaml
  import mlflow.tracking
  from mlflow import pyfunc
  from mlflow.models import Model, ModelInputExample, ModelSignature
  from mlflow.models.model import MLMODEL_FILE_NAME
  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, batch_metrics_logger, 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
label_desc: Use image node_id 8 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: __MlflowFastaiCallback, None, result, _run_and_log_function, _run_and_log_function, callback