code: def test_lstm_forecaster_set_thread_num(self):
      train_data, val_data, test_data = create_data()
      forecaster = LSTMForecaster(
          past_seq_len=24,
          input_feature_num=2,
          output_feature_num=2,
          loss="mse",
          lr=0.01,
      )
      forecaster.fit(train_data, epochs=1)
      original_thread = torch.get_num_threads()
      assert forecaster.thread_num == original_thread
  
      pred = forecaster.predict_with_onnx(test_data[0])
      current_thread = torch.get_num_threads()
      assert current_thread == 1
      assert forecaster.thread_num == 1
      assert forecaster.optimized_model_thread_num == 1
  
      num = max(1, original_thread // 2)
      forecaster.quantize(train_data, thread_num=num)
      pred = forecaster.predict(test_data[0], quantize=True)
      current_thread = torch.get_num_threads()
      assert current_thread == num
      assert forecaster.thread_num == num
      assert forecaster.optimized_model_thread_num == num
  
      # if set `optimize=False`, keep the current thread num
      num = max(1, current_thread // 2)
      forecaster.optimize(
          train_data=train_data,
          validation_data=val_data,
          batch_size=32,
          thread_num=num,
      )
      pred = forecaster.predict(test_data[0], acceleration=False)
      new_current_thread = torch.get_num_threads()
      assert new_current_thread == current_thread
      assert forecaster.thread_num == current_thread
      assert forecaster.optimized_model_thread_num == num
  
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 35 for calling the TestChronosModelLSTMForecaster obj's underlying member method code with example usage: obj.test_lstm_forecaster_set_thread_num() without return types