def METHOD_NAME(self):
    model = BackboneModel()
    configure_list = {
        'conv1':{'weight_bits':8, 'output_bits':8},
        'conv2':{'weight_bits':32, 'output_bits':32},
        'fc1':{'weight_bits':16, 'output_bits':16},
        'fc2':{'weight_bits':8, 'output_bits':8}
    }
    optimizer = torch.optim.SGD(model.parameters(), lr=0.01, momentum=0.5)
    model.to(self.device)
    for epoch in range(1):
        print('# Epoch {} #'.format(epoch))
        self._train(model, optimizer)
        self._test(model)
    batch_size = 32
    input_shape = (batch_size, 1, 28, 28)
    calibration_path = "calibration.cache"
    onnx_path = "default_model.onnx"
    engine = ModelSpeedupTensorRT(model, input_shape, config=configure_list, calib_data_loader=self.train_loader, batchsize=batch_size)
    engine.compress()
    self._test_trt(engine)
    os.remove(calibration_path)
    os.remove(onnx_path)