code: def test_autolog_early_stopping_callback():
      mlflow.paddle.autolog()
  
      early_stopping = paddle.callbacks.EarlyStopping(
          "loss", mode="min", patience=1, min_delta=0
      )
      with mlflow.start_run() as run:
          train_model(callbacks=[early_stopping])
  
      client = MlflowClient()
      data = client.get_run(run.info.run_id).data
  
      for param_key in ["monitor", "patience", "min_delta", "baseline"]:
          assert param_key in data.params
          assert data.params[param_key] == str(
              getattr(early_stopping, param_key)
          )
  
      for metric_key in ["stopped_epoch", "best_value"]:
          assert metric_key in data.metrics
          assert float(data.metrics[metric_key]) == getattr(
              early_stopping, metric_key
          )
  
      for metric_key in ["loss", "step"]:
          assert metric_key in data.metrics
          metric_history = client.get_metric_history(
              run.info.run_id, metric_key
          )
          assert len(metric_history) == NUM_EPOCHS
  
file_path: mlflow/tests/paddle/test_paddle_autolog.py
imports: import paddle
  import pytest
  import mlflow
  from mlflow import MlflowClient
label_desc: Use image node_id 4 for calling a global function with example usage: test_autolog_early_stopping_callback() without return types