code: def autolog(
      log_every_n_epoch=1,
      log_models=True,
      disable=False,
      exclusive=False,
      silent=False,
      registered_model_name=None,
      extra_tags=None,
  ):  # pylint: disable=unused-argument
      """
      Enables (or disables) and configures autologging from PaddlePaddle to MLflow.
  
      Autologging is performed when the `fit` method of `paddle.Model`_ is called.
  
      .. _paddle.Model:
          https://www.paddlepaddle.org.cn/documentation/docs/en/api/paddle/Model_en.html
  
      Args:
          log_every_n_epoch: If specified, logs metrics once every `n` epochs. By default, metrics
              are logged after every epoch.
          log_models: If ``True``, trained models are logged as MLflow model artifacts.
              If ``False``, trained models are not logged.
          disable: If ``True``, disables the PaddlePaddle autologging integration.
              If ``False``, enables the PaddlePaddle 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.
          silent: If ``True``, suppress all event logs and warnings from MLflow during PyTorch
              Lightning autologging. If ``False``, show all events and warnings during
              PaddlePaddle 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
  
          import paddle
          import mlflow
          from mlflow import MlflowClient
  
  
          def show_run_data(run_id):
              run = mlflow.get_run(run_id)
              print(f"params: {run.data.params}")
              print(f"metrics: {run.data.metrics}")
              client = MlflowClient()
              artifacts = [f.path for f in client.list_artifacts(run.info.run_id, "model")]
              print(f"artifacts: {artifacts}")
  
  
          class LinearRegression(paddle.nn.Layer):
              def __init__(self):
                  super().__init__()
                  self.fc = paddle.nn.Linear(13, 1)
  
              def forward(self, feature):
                  return self.fc(feature)
  
  
          train_dataset = paddle.text.datasets.UCIHousing(mode="train")
          eval_dataset = paddle.text.datasets.UCIHousing(mode="test")
          model = paddle.Model(LinearRegression())
          optim = paddle.optimizer.SGD(learning_rate=1e-2, parameters=model.parameters())
          model.prepare(optim, paddle.nn.MSELoss(), paddle.metric.Accuracy())
          mlflow.paddle.autolog()
          with mlflow.start_run() as run:
              model.fit(train_dataset, eval_dataset, batch_size=16, epochs=10)
          show_run_data(run.info.run_id)
  
      .. code-block:: text
          :caption: Output
  
          params: {
              "learning_rate": "0.01",
              "optimizer_name": "SGD",
          }
          metrics: {
              "loss": 17.482044,
              "step": 25.0,
              "acc": 0.0,
              "eval_step": 6.0,
              "eval_acc": 0.0,
              "eval_batch_size": 6.0,
              "batch_size": 4.0,
              "eval_loss": 24.717455,
          }
          artifacts: [
              "model/MLmodel",
              "model/conda.yaml",
              "model/model.pdiparams",
              "model/model.pdiparams.info",
              "model/model.pdmodel",
              "model/requirements.txt",
          ]
      """
      import paddle
  
      from mlflow.paddle._paddle_autolog import patched_fit
  
      safe_patch(
          FLAVOR_NAME,
          paddle.Model,
          "fit",
          patched_fit,
          manage_run=True,
          extra_tags=extra_tags,
      )
  
imports: import logging
  import os
  from typing import Any, Dict, Optional
  import yaml
  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._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, 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 8 for calling a global function with example usage: autolog(log_every_n_epoch, log_models, disable, exclusive, silent, registered_model_name, extra_tags) without return types