code: def test_s2s_forecaster_distributed(self):
      from bigdl.orca import init_orca_context, stop_orca_context
  
      train_data, val_data, test_data = create_data()
      _train_loader, _, _test_loader = create_data(loader=True)
  
      init_orca_context(cores=4, memory="2g")
  
      forecaster = Seq2SeqForecaster(
          past_seq_len=24,
          future_seq_len=5,
          input_feature_num=1,
          output_feature_num=1,
          loss="mae",
          lr=0.01,
          distributed=True,
      )
  
      forecaster.fit(train_data, epochs=2)
      distributed_pred = forecaster.predict(
          test_data[0], acceleration=False
      )
      distributed_eval = forecaster.evaluate(
          val_data, acceleration=False
      )
  
      model = forecaster.get_model()
      assert isinstance(model, torch.nn.Module)
  
      forecaster.to_local()
      local_pred = forecaster.predict(test_data[0], acceleration=False)
      local_eval = forecaster.evaluate(val_data, acceleration=False)
  
      np.testing.assert_almost_equal(
          distributed_pred, local_pred, decimal=5
      )
  
      try:
          import onnx
          import onnxruntime
  
          local_pred_onnx = forecaster.predict_with_onnx(test_data[0])
          distributed_pred_onnx = forecaster.predict_with_onnx(
              _test_loader
          )
          local_eval_onnx = forecaster.evaluate_with_onnx(val_data)
          distributed_eval_onnx = forecaster.evaluate_with_onnx(
              _test_loader
          )
          np.testing.assert_almost_equal(
              distributed_pred, local_pred_onnx, decimal=5
          )
      except ImportError:
          pass
  
      model = forecaster.get_model()
      assert isinstance(model, torch.nn.Module)
  
      with tempfile.TemporaryDirectory() as tmp_dir_name:
          ckpt_name = os.path.join(tmp_dir_name, "checkpoint.ckpt")
          test_pred_save = forecaster.predict(
              test_data[0], acceleration=False
          )
          forecaster.save(ckpt_name)
          forecaster.load(ckpt_name)
          test_pred_load = forecaster.predict(
              test_data[0], acceleration=False
          )
      np.testing.assert_almost_equal(test_pred_save, test_pred_load)
  
      stop_orca_context()
  
imports: import numpy
  import tempfile
  import os
  from bigdl.chronos.utils import LazyImport
  from unittest import TestCase
  import pytest
  from ..None import op_torch, op_distributed, op_inference, op_automl
label_desc: Use image node_id 13 for calling the TestChronosModelSeq2SeqForecaster obj's underlying member method code with example usage: obj.test_s2s_forecaster_distributed() without return types