class_docstr: Callback for auto-logging metrics and parameters.
code: def on_train_begin(
      self, logs=None
  ):  # pylint: disable=unused-argument
      params = {
          "optimizer_name": self.model._optimizer.__class__.__name__,
          "learning_rate": self.model._optimizer._learning_rate,
      }
      self.client.log_params(self.run_id, params)
      self.client.flush(synchronous=True)
  
file_path: mlflow/mlflow/paddle/_paddle_autolog.py
imports: import paddle
  import mlflow
  from mlflow.utils.autologging_utils import BatchMetricsLogger, ExceptionSafeAbstractClass, MlflowAutologgingQueueingClient, get_autologging_config
label_desc: Use image node_id 4 for calling the __MLflowPaddleCallback obj's underlying member method code with example usage: obj.on_train_begin(logs) without return types