code: def test_autoformer_forecaster_tune_save_load(self):
      import bigdl.nano.automl.hpo.space as space
  
      train_data, val_data, _ = create_data(loader=False)
      forecaster = AutoformerForecaster(
          past_seq_len=24,
          future_seq_len=5,
          input_feature_num=2,
          output_feature_num=2,
          label_len=12,
          d_model=space.Categorical(128, 10),
          freq="s",
          loss="mse",
          metrics=["mae", "mse", "mape"],
          seed=1,
          lr=0.01,
      )
      forecaster.tune(train_data, validation_data=val_data, n_trials=2)
      forecaster.fit(train_data, epochs=3, batch_size=32)
      evaluate1 = forecaster.evaluate(val_data)
      with tempfile.TemporaryDirectory() as tmp_dir_name:
          ckpt_name = os.path.join(tmp_dir_name, "tune.ckpt")
          forecaster.save(ckpt_name)
          forecaster.load(ckpt_name)
          evaluate2 = forecaster.evaluate(val_data)
      assert evaluate1[0]["val/loss"] == evaluate2[0]["val_loss"]
  
imports: import numpy
  import pandas
  import tempfile
  import os
  from bigdl.chronos.utils import LazyImport
  from bigdl.chronos.data import TSDataset
  from unittest import TestCase
  import pytest
  from ..None import op_torch, op_automl
label_desc: Use image node_id 11 for calling the TestChronosModelAutoformerForecaster obj's underlying member method code with example usage: obj.test_autoformer_forecaster_tune_save_load() without return types