Method forward

src/model/encodings/positional_encoding.py:28–33

(
        self,
        samples: Float[Tensor, "*batch dim"],
    )

        self.register_buffer("phases", phases, persistent=False)

    def forward(
        self,
        samples: Float[Tensor, "*batch dim"],
    ) -> Float[Tensor, "*batch embedded_dim"]:
        samples = einsum(samples, self.frequencies, "... d, f p -> ... d f p")
        return rearrange(torch.sin(samples + self.phases), "... d f p -> ... (d f p)")

    def d_out(self, dimensionality: int):
        return self.frequencies.numel() * dimensionality