//  [python]
def prune(self, sparsity, prunen=0, prunem=0, blocksize=128, percdamp=.01):
        """The implementation for SparseGPT."""
        with torch_setting(dtype=torch.float):
            # Converted from https://github.com/ist-daslab/sparsegpt

            assert self.hessian is not None
            W: torch.Tensor = self.weight_matrix.float()  # out in

            H = self.hessian.float().to(W.device)

            dead = torch.diag(H) == 0
            H[dead, dead] = 1
            W[:, dead] = 0

            Losses = torch.zeros(self.rows, device=W.device)

            damp = percdamp * torch.mean(torch.diag(H))
            diag = torch.arange(self.columns, device=W.device)
            H[diag, diag] += damp
            H = torch.linalg.cholesky(H)
            H = torch.cholesky_inverse(H)
            H = torch.linalg.cholesky(H, upper=True)
            Hinv = H

            mask = None

            for i1 in range(0, self.columns, blocksize):
                i2 = min(i1 + blocksize, self.columns)
                count = i2 - i1

                W1 = W[:, i1:i2].clone()
                Q1 = torch.zeros_like(W1)
                Err1 = torch.zeros_like(W1)
                Losses1 = torch.zeros_like(W1)
                Hinv1 = Hinv[i1:i2, i1:i2]

                if prunen == 0:
                    if mask is not None:
                        mask1 = mask[:, i1:i2]
                    else:
                        tmp = W1**2 / (torch.diag(Hinv1).reshape((1, -1)))**2
                        thresh = torch.sort(tmp.flatten())[0][int(tmp.numel() *
                                                                  sparsity)]
                        mask1 = tmp <= thresh
                else:
                    mask1 = torch.zeros_like(W1) == 1

                for i in range(count):
                    w = W1[:, i]
                    d = Hinv1[i, i]

                    if prunen != 0 and i % prunem == 0:
                        tmp = W1[:, i:(i + prunem)]**2 / (torch.diag(Hinv1)[i:(
                            i + prunem)].reshape((1, -1)))**2
                        mask1.scatter_(
                            1, i +
                            torch.topk(tmp, prunen, dim=1, largest=False)[1],
                            True)

                    q = w.clone()
                    q[mask1[:, i]] = 0

                    Q1[:, i] = q
                    Losses1[:, i] = (w - q)**2 / d**2

                    err1 = (w - q) / d
                    W1[:,
                       i:] -= err1.unsqueeze(1).matmul(Hinv1[i,
                                                             i:].unsqueeze(0))
                    Err1[:, i] = err1

                W[:, i1:i2] = Q1
                Losses += torch.sum(Losses1, 1) / 2

                W[:, i2:] -= Err1.matmul(Hinv[i1:i2, i2:])

            if W.device.type == 'cuda':
                torch.cuda.synchronize()
            from .sparse24_utils import is_weight_sparse_24
            if prunen == 2 and prunem == 4:
                assert is_weight_sparse_24(
                    W, -1), f'Weight dose not satisfy 24 with shape {W.shape}'
            error = torch.sum(Losses)

            if torch.isnan(error).any():
                raise Exception('get nan error')
            else:
                self.weight_matrix = W.data

            return error.item()