go with normal convtranspose1d, causal may be not possible?

        x = F.pad(x, (self.causal_padding, 0))

        return self.conv(x)

class CausalConvTranspose1d(nn.Module):

    """ unsure if this module is correct """

    def __init__(self, chan_in, chan_out, kernel_size, stride, **kwargs):

        super().__init__()

        self.upsample_factor = stride

        self.padding = kernel_size - 1

        self.conv = nn.ConvTranspose1d(chan_in, chan_out, kernel_size, stride, **kwargs)

    def forward(self, x):

        n = x.shape[-1]

        x = F.pad(x, (self.padding, 0))

        out = self.conv(x)

        out = out[..., :(n * self.upsample_factor)]

        return out

def ResidualUnit(chan_in, chan_out, dilation, kernel_size = 7):

    return Residual(nn.Sequential(

        CausalConv1d(chan_in, chan_out, kernel_size, dilation = dilation),

    output_padding = 0 if even_stride else 1

    return nn.Sequential(

        CausalConvTranspose1d(chan_in, chan_out, 2 * stride, stride = stride),

        nn.ConvTranspose1d(chan_in, chan_out, 2 * stride, stride = stride, padding = padding, output_padding = output_padding),

        ResidualUnit(chan_out, chan_out, 1),

        ResidualUnit(chan_out, chan_out, 3),

        ResidualUnit(chan_out, chan_out, 9),