How to use the chainer.backends.cuda.to_gpu function in chainer

def setup_gpu(self, device=0):
        self.x1 = cuda.to_gpu(self.x1, device)
        self.x2 = cuda.to_gpu(self.x2, device)
        self.y1 = cuda.to_gpu(self.y1, device)
        self.y2 = cuda.to_gpu(self.y2, device)
        self.gx1 = cuda.to_gpu(self.gx1, device)
        self.gx2 = None
        self.gy1 = cuda.to_gpu(self.gy1, device)
        self.gy2 = cuda.to_gpu(self.gy2, device)
        self.f.forward_gpu = mock.MagicMock(return_value=(self.y1, self.y2))
        self.f.backward_gpu = mock.MagicMock(return_value=(self.gx1, self.gx2))

def test_double_backward_gpu(self):
        self.check_double_backward(
            cuda.to_gpu(self.x), cuda.to_gpu(self.gy), cuda.to_gpu(self.ggx))

def test_forward_gpu_train(self):
        self.rnn.to_gpu()
        with chainer.using_config('use_cudnn', 'always'), \
                chainer.using_config('train', True):
            self.check_forward(
                cuda.to_gpu(self.h),
                cuda.to_gpu(self.c),
                [cuda.to_gpu(x) for x in self.xs])

def check_get_item(self, gpu):
        x_data = self.x
        if gpu:
            x_data = cuda.to_gpu(x_data)
        x = chainer.Variable(x_data)
        if len(self.x_shape) > 0:
            slices = slice(2, 5)
            np.testing.assert_equal(cuda.to_cpu(x[slices].data),
                                    cuda.to_cpu(x_data[slices]))
            slices = slice(2, 5),
            np.testing.assert_equal(cuda.to_cpu(x[slices].data),
                                    cuda.to_cpu(x_data[slices]))

def test_double_backward_negative_multi_axis_invert_gpu(self):
        gy = numpy.ones_like(self.x.sum(axis=(-2, 0))) * self.gy
        self.check_double_backward(
            cuda.to_gpu(self.x), cuda.to_gpu(gy), cuda.to_gpu(self.ggx),
            axis=(-2, 0))

def _erf_gpu(x, dtype):
    return cuda.to_gpu(_erf_cpu(cuda.to_cpu(x), dtype))

def setup_gpu(self, device=0):
        self.x1 = cuda.to_gpu(self.x1, device)
        self.x2 = cuda.to_gpu(self.x2, device)
        self.y1 = cuda.to_gpu(self.y1, device)
        self.y2 = cuda.to_gpu(self.y2, device)
        self.gx1 = cuda.to_gpu(self.gx1, device)
        self.gx2 = None
        self.gy1 = cuda.to_gpu(self.gy1, device)
        self.gy2 = cuda.to_gpu(self.gy2, device)
        self.f.forward_gpu = mock.MagicMock(return_value=(self.y1, self.y2))
        self.f.backward_gpu = mock.MagicMock(return_value=(self.gx1, self.gx2))

def test_double_backward_gpu(self):
        self.check_double_backward(
            cuda.to_gpu(self.y), cuda.to_gpu(self.z), cuda.to_gpu(self.gloss),
            cuda.to_gpu(self.ggy), cuda.to_gpu(self.ggz))

def test_deserialize_gpu_strip_slashes(self):
        y = numpy.empty((2, 3), dtype=numpy.float32)
        self.check_deserialize(cuda.to_gpu(y), '/y')