class_docstr: Callback for auto-logging metrics and parameters.
code: def _log_metrics(self, logs, current_epoch):
      metrics = {
          key: (metric[0] if isinstance(metric, list) else metric)
          for key, metric in logs.items()
      }
      self.metrics_logger.record_metrics(metrics, current_epoch)
  
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 2 for calling the __MLflowPaddleCallback obj's underlying member method code with example usage: obj._log_metrics(logs, current_epoch) without return types