code: def test_autoformer_forecaster_multi_objective_tune(self):
      import bigdl.nano.automl.hpo.space as space
  
      train_data, val_data, test_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,
          freq="s",
          loss="mse",
          metrics=["mae", "mse", "mape"],
          lr=space.Real(0.001, 0.01, log=True),
      )
      forecaster.tune(
          train_data,
          validation_data=val_data,
          target_metric=["mse", "latency"],
          directions=["minimize", "minimize"],
          direction=None,
          n_trials=2,
      )
      forecaster.fit(
          train_data, epochs=3, batch_size=32, use_trial_id=0
      )
      evaluate = forecaster.evaluate(val_data)
  
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 8 for calling the TestChronosModelAutoformerForecaster obj's underlying member method code with example usage: obj.test_autoformer_forecaster_multi_objective_tune() without return types