def METHOD_NAME(self):
    """Basic test of PyTorch quantize::conv3d Node with packed channelwise weights on Glow."""
    with torch.no_grad():
        x = torch.randn([1, 4, 4, 4, 4])
        conv = torch.nn.Conv3d(4, 2, 2, (2, 2, 2), groups=1)
        conv.weight.random_(-1, 1)
        conv.bias.data.random_(-1, 1)
        model = torch.ao.quantization.QuantWrapper(conv)
        model.qconfig = torch.ao.quantization.get_default_qconfig("fbgemm")
        torch.ao.quantization.prepare(model, inplace=True)
        # Calibration
        model.forward(x)
        torch.ao.quantization.convert(model, inplace=True)
        # TODO: acuracy needs to be investigated. Average acuracy is decent
        # but some elements have errors (possibly from rounding differences)
        utils.compare_tracing_methods(
            model,
            x,
            fusible_ops={
                "aten::quantize_per_tensor",
                "quantized::conv3d",
                "aten::dequantize",
            },
        )