code: def test_forecaster_from_tsdataset_data_loader_onnx(self):
      train, test = create_tsdataset(roll=False)
      train.gen_dt_feature(one_hot_features=["WEEK"])
      test.gen_dt_feature(one_hot_features=["WEEK"])
      loader = train.to_torch_data_loader(lookback=24, horizon=1)
      test_loader = test.to_torch_data_loader(lookback=24, horizon=1)
      lstm = LSTMForecaster.from_tsdataset(train)
  
      lstm.fit(loader, epochs=2, batch_size=32)
      yhat = lstm.predict(test, acceleration=False)
      res = lstm.evaluate(test_loader, acceleration=False)
      lstm.quantize(
          calib_data=loader, metric="mse", framework="pytorch_fx"
      )
      q_yhat = lstm.predict(test, quantize=True, acceleration=False)
      q_res = lstm.evaluate(
          test_loader, quantize=True, acceleration=False
      )
      lstm.quantize(
          calib_data=loader, metric="mse", framework="onnxrt_qlinearops"
      )
      q_onnx_yhat = lstm.predict_with_onnx(test, quantize=True)
      q_onnx_res = lstm.evaluate_with_onnx(test_loader, quantize=True)
      onnx_yhat = lstm.predict_with_onnx(test)
      onnx_res = lstm.evaluate_with_onnx(test_loader)
      assert (
          onnx_yhat.shape
          == q_yhat.shape
          == yhat.shape
          == q_onnx_yhat.shape
      )
  
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 23 for calling the TestChronosModelLSTMForecaster obj's underlying member method code with example usage: obj.test_forecaster_from_tsdataset_data_loader_onnx() without return types