def forward(ctx, points_xyz: torch.Tensor,
                num_points: int) -> torch.Tensor:
        """
        Args:
            points_xyz (Tensor): (B, N, 3) where N > num_points.
            num_points (int): Number of points in the sampled set.

        Returns:
             Tensor: (B, num_points) indices of the sampled points.
        """
        assert points_xyz.is_contiguous()

        B, N = points_xyz.size()[:2]
        output = torch.cuda.IntTensor(B, num_points)
        temp = torch.cuda.FloatTensor(B, N).fill_(1e10)

        ext_module.furthest_point_sampling_forward(
            points_xyz,
            temp,
            output,
            b=B,
            n=N,
            m=num_points,
        )
        if torch.__version__ != 'parrots':
            ctx.mark_non_differentiable(output)
        return output

        Args:
            points_xyz (Tensor): (B, N, 3) where N > num_points.
            num_points (int): Number of points in the sampled set.

        Returns:
             Tensor: (B, num_points) indices of the sampled points.
        
ControlNet-v1-1-nightly/annotator/uniformer/mmcv/ops/furthest_point_sample.py