class_docstr: Callback for auto-logging metrics and parameters.
code: def on_eval_end(self, logs=None):
      eval_logs = {
          "eval_"
          + key: (metric[0] if isinstance(metric, list) else metric)
          for key, metric in logs.items()
      }
      self._log_metrics(eval_logs, self.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 6 for calling the __MLflowPaddleCallback obj's underlying member method code with example usage: obj.on_eval_end(logs) without return types