def forward(ctx, features: torch.Tensor,
                indices: torch.Tensor) -> torch.Tensor:
        """
        Args:
            features (Tensor): (B, C, N) features to gather.
            indices (Tensor): (B, M) where M is the number of points.

        Returns:
            Tensor: (B, C, M) where M is the number of points.
        """
        assert features.is_contiguous()
        assert indices.is_contiguous()

        B, npoint = indices.size()
        _, C, N = features.size()
        output = torch.cuda.FloatTensor(B, C, npoint)

        ext_module.gather_points_forward(
            features, indices, output, b=B, c=C, n=N, npoints=npoint)

        ctx.for_backwards = (indices, C, N)
        if torch.__version__ != 'parrots':
            ctx.mark_non_differentiable(indices)
        return output

        Args:
            features (Tensor): (B, C, N) features to gather.
            indices (Tensor): (B, M) where M is the number of points.

        Returns:
            Tensor: (B, C, M) where M is the number of points.
        
ControlNet-v1-1-nightly/annotator/uniformer/mmcv/ops/gather_points.py