code: def test_tcn_forecaster_distributed(self):
      train_data, val_data, test_data = create_data()
  
      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])
      distributed_eval = forecaster.evaluate(val_data)
  
      model = forecaster.get_model()
      assert isinstance(model, torch.nn.Module)
  
      forecaster.to_local()
      local_pred = forecaster.predict(test_data[0])
      local_eval = forecaster.evaluate(val_data)
  
      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])
          local_eval_onnx = forecaster.evaluate_with_onnx(val_data)
          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)
  
      stop_orca_context()
  
file_path: analytics-zoo/pyzoo/test/zoo/chronos/forecaster/test_seq2seq_forecaster.py
imports: import numpy
  import tempfile
  import os
  import torch
  from zoo.chronos.forecaster.seq2seq_forecaster import Seq2SeqForecaster
  from zoo.orca import init_orca_context, stop_orca_context
  from unittest import TestCase
  import pytest
label_desc: Use image node_id 9 for calling the TestChronosModelTCNForecaster obj's underlying member method code with example usage: obj.test_tcn_forecaster_distributed() without return types