code: def test_autolog_logs_expected_data():
      mlflow.paddle.autolog()
  
      with mlflow.start_run() as run:
          train_model()
  
      client = MlflowClient()
      data = client.get_run(run.info.run_id).data
  
      # Testing params are logged
      for param_key, expected_param_value in [
          ("optimizer_name", "Adam"),
          ("learning_rate", "0.01"),
      ]:
          assert param_key in data.params
          assert data.params[param_key] == expected_param_value
  
      # Testing metrics are logged
      for metric_key in [
          "batch_size",
          "loss",
          "step",
          "eval_batch_size",
          "eval_loss",
          "eval_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
  
      # Testing model_summary.txt is saved
      artifacts = client.list_artifacts(run.info.run_id)
      assert any(x.path == "model_summary.txt" for x in artifacts)
  
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 3 for calling a global function with example usage: test_autolog_logs_expected_data() without return types