code: def test_lstm_forecaster_numpy_inference_loader(self):
      train_loader, val_loader, test_loader = create_data(loader=True)
      forecaster = LSTMForecaster(
          past_seq_len=24,
          input_feature_num=2,
          output_feature_num=2,
          loss="mse",
          lr=0.01,
      )
      forecaster.fit(train_loader, epochs=1)
      # onnx model
      forecaster.quantize(
          calib_data=train_loader,
          val_data=val_loader,
          framework="onnxrt_qlinearops",
      )
      q_onnx_numpy_yhat = forecaster.predict_with_onnx(
          data=test_loader, quantize=True
      )
      forecaster.accelerated_model.output_tensors = True
      forecaster.optimized_model_output_tensor = True
      q_onnx_tensor_yhat = forecaster.predict_with_onnx(
          data=test_loader, quantize=True
      )
      np.testing.assert_almost_equal(
          q_onnx_numpy_yhat, q_onnx_tensor_yhat, decimal=5
      )
      # openvino model
      forecaster.quantize(
          calib_data=train_loader,
          val_data=val_loader,
          framework="openvino",
      )
      q_openvino_numpy_yhat = forecaster.predict_with_openvino(
          data=test_loader, quantize=True
      )
      forecaster.accelerated_model.output_tensors = True
      forecaster.optimized_model_output_tensor = True
      q_openvino_tensor_yhat = forecaster.predict_with_openvino(
          data=test_loader, quantize=True
      )
      np.testing.assert_almost_equal(
          q_openvino_numpy_yhat, q_openvino_tensor_yhat, decimal=5
      )
  
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 38 for calling the TestChronosModelLSTMForecaster obj's underlying member method code with example usage: obj.test_lstm_forecaster_numpy_inference_loader() without return types