def METHOD_NAME(self):
    """Basic test of the PyTorch quantize::conv3d Node with unpacked weights on Glow."""
    def test_f(a, w, b):
        qu = torch.nn.quantized.Quantize(1 / 16, 0, torch.quint8)
        qi = torch.nn.quantized.Quantize(1 / 16, 0, torch.qint8)
        dq = torch.nn.quantized.DeQuantize()
        conv = torch.nn.quantized.functional.conv3d
        return dq(conv(qu(a), qi(w), b))
    # TODO
    # Due to the quantization error between
    # PyTorch and Glow, we would like to use some
    # determined test data instead of random ones
    # x = torch.randn([3, 3, 3, 3])
    # w = torch.randn([3, 3, 3, 3])
    # b = torch.randn([3])
    x = torch.tensor([[[[[5.0, 6.0], [7.0, 8.0]]]]])
    w = torch.tensor([[[[[2.0]]]]])
    b_zero = torch.zeros(1)
    b = torch.randn(1)
    utils.compare_tracing_methods(
        UnpackedConv3dModel(
            torch.nn.quantized.Quantize(1 / 16, 0, torch.quint8),
            torch.nn.quantized.Quantize(1 / 16, 0, torch.qint8),
        ),
        x,
        w,
        b,
        fusible_ops={
            "aten::quantize_per_tensor",
            "glow::unpacked_quantized_conv3d",
            "aten::dequantize",
        },
        skip_to_glow=True,
    )
    utils.compare_tracing_methods(
        UnpackedConv3dModel(
            torch.nn.quantized.Quantize(1 / 16, 0, torch.quint8),
            torch.nn.quantized.Quantize(1 / 16, 0, torch.qint8),
        ),
        x,
        w,
        b_zero,
        fusible_ops={
            "aten::quantize_per_tensor",
            "glow::unpacked_quantized_conv3d",
            "aten::dequantize",
        },
        skip_to_glow=True,
    )