def forward(self, input_coords, input_shape=None):
        """Positionally encode points that are normalized to [0,1]."""
        coordinates = input_coords.clone()

        if input_shape is not None:
            coordinates[:, :, :, 0] = coordinates[:, :, :, 0] / input_shape[1]
            coordinates[:, :, :, 1] = coordinates[:, :, :, 1] / input_shape[0]

        # assuming coords are in [0, 1]^2 square and have d_1 x ... x d_n x 2 shape
        coordinates = 2 * coordinates - 1
        coordinates = coordinates.to(self.positional_embedding.dtype)
        coordinates = coordinates @ self.positional_embedding
        coordinates = 2 * np.pi * coordinates
        # outputs d_1 x ... x d_n x channel shape
        return torch.cat([torch.sin(coordinates), torch.cos(coordinates)], dim=-1)
Positionally encode points that are normalized to [0,1].
dpkd/transformers/src/transformers/models/sam/modeling_sam.py