def forward(self, x):
        """
        Embeds x to (x, sin(2^k x), cos(2^k x), ...)
        Different from the paper, "x" is also in the output
        See https://github.com/bmild/nerf/issues/12

        Inputs:
            x: (B, self.in_channels)

        Outputs:
            out: (B, self.out_channels)
        """
        out = [x]
        for freq in self.freq_bands:
            for func in self.funcs:
                if self.normalize:
                    out += [func(freq * x) / freq]
                else:
                    out += [func(freq * x)]

        return torch.cat(out, -1)

        Embeds x to (x, sin(2^k x), cos(2^k x), ...)
        Different from the paper, "x" is also in the output
        See https://github.com/bmild/nerf/issues/12

        Inputs:
            x: (B, self.in_channels)

        Outputs:
            out: (B, self.out_channels)
        
mv-diffusion/One-2-3-45/reconstruction/models/embedder.py