code: def test_lstm_forecaster_distributed(self):
      from bigdl.orca import init_orca_context, stop_orca_context
  
      init_orca_context(cores=2, memory="2g")
      train_data, val_data, test_data = create_data()
  
      forecaster = LSTMForecaster(
          past_seq_len=10,
          input_feature_num=10,
          output_feature_num=2,
          loss="mae",
          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()
      from bigdl.chronos.model.tf2.VanillaLSTM_keras import LSTMModel
  
      assert isinstance(model, LSTMModel)
  
      with tempfile.TemporaryDirectory() as tmp_file_name:
          name = os.path.join(tmp_file_name, "lstm.ckpt")
          test_pred_save = forecaster.predict(test_data[0])
          forecaster.save(name)
          forecaster.load(name)
          test_pred_load = forecaster.predict(test_data[0])
      np.testing.assert_almost_equal(test_pred_save, test_pred_load)
  
      forecaster.to_local()
      local_pred = forecaster.predict(test_data[0])
      local_eval = forecaster.evaluate(val_data)
  
      np.testing.assert_array_almost_equal(
          distributed_pred, local_pred, decimal=5
      )
      stop_orca_context()
  
imports: import pytest
  import tempfile
  import os
  from unittest import TestCase
  import numpy
  from bigdl.chronos.utils import LazyImport
  from test.bigdl.chronos import op_tf2, op_distributed
label_desc: Use image node_id 8 for calling the TestLSTMForecaster obj's underlying member method code with example usage: obj.test_lstm_forecaster_distributed() without return types