code: def test_lstm_forecaster_quantization_onnx_tuning(self):
      train_data, val_data, test_data = create_data()
      forecaster = LSTMForecaster(
          past_seq_len=24,
          input_feature_num=2,
          output_feature_num=2,
          loss="mae",
          lr=0.01,
      )
      forecaster.fit(train_data, epochs=2)
      # quantization with tunning
      forecaster.quantize(
          train_data,
          val_data=val_data,
          metric="mse",
          relative_drop=0.8,
          max_trials=3,
          framework="onnxrt_qlinearops",
      )
      pred_q = forecaster.predict_with_onnx(test_data[0], quantize=True)
      eval_q = forecaster.evaluate_with_onnx(test_data, quantize=True)
      with tempfile.TemporaryDirectory() as tmp_dir_name:
          ckpt_name = os.path.join(tmp_dir_name, "fp32_onnx")
          ckpt_name_q = os.path.join(tmp_dir_name, "int_onnx")
          forecaster.export_onnx_file(
              dirname=ckpt_name, quantized_dirname=ckpt_name_q
          )
  
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 12 for calling the TestChronosModelLSTMForecaster obj's underlying member method code with example usage: obj.test_lstm_forecaster_quantization_onnx_tuning() without return types