give causal convtranspose another shot

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

        super().__init__()

        self.neg_padding = kernel_size // 2

        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[..., :-self.neg_padding]

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

        return out

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