def forward(ctx, points_dist: torch.Tensor,
                num_points: int) -> torch.Tensor:
        """
        Args:
            points_dist (Tensor): (B, N, N) Distance between each point pair.
            num_points (int): Number of points in the sampled set.

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

        B, N, _ = points_dist.size()
        output = points_dist.new_zeros([B, num_points], dtype=torch.int32)
        temp = points_dist.new_zeros([B, N]).fill_(1e10)

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

        Args:
            points_dist (Tensor): (B, N, N) Distance between each point pair.
            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