from itertools import permutations
import sys
from time import time
from random import random, randrange, choice, choices, randint
from heapq import heappop, heappush
from math import exp

START = time()
INF=10**9


def get_time(START):
    return time() - START

def dist(p1,p2):
    x1,y1=p1
    x2,y2=p2
    return (x1-x2)**2 + (y1-y2)**2

def cost(i: int, j: int):
    d = dist(Terminals[i], Terminals[j])
    if i<N: d*=5
    if j<N: d*=5
    return d

def calc(i: int, j: int, ans ):
    d = dist(Terminals[ans[i]], Terminals[ans[j]])
    if ans[i]<N: d*=5
    if ans[j]<N: d*=5
    return d

def dijkstra(s: int, g: int,mind: int):
    _dist = [INF] *(N+M)
    prev = [-1]*(N+M)

    que = [(0,s)]
    _dist[s]=0
    while que:
        d, v = heappop(que)
        if _dist[v] < d:
            continue
        if v==g:
            break
        for nv in range(T):
            nd = dist(Terminals[v],Terminals[nv])
            if v<N: nd*=5
            if nv<N: nd*=5
            nd += d
            # print(nv,nd,file = sys.stderr)s
            if nd < _dist[nv] and nd <= mind:
                prev[nv] = v
                _dist[nv] = nd
                heappush(que,(nd,nv))

    res=[]
    v = g
    while v!=s:
        res.append(v)
        v = prev[v]
    return res[::-1]



class KMEANS:

    def __init__(self):
        self.reps=[]
        self.dists=[]
        self.clusters=[]
        self.keep_flag=True

    # c_data: クラスタリング対象データ
    # k: クラスタ数
    def Clustering(self, c_datas, k):
        # 代表点を初期化
        self.RepInit(c_datas, k)

        while self.keep_flag:
            # 代表点と点の距離を計算
            self.ClusterDist(c_datas)
            # print(self.dists,file=sys.stderr)

            # 所属クラスタを更新
            self.ClusterUpdate()

            # 代表点を更新
            self.RepUpdate(c_datas)

    # クラスタ代表点の初期化 + 代表点と各点の距離と所属クラスタを格納するリストの初期化
    def RepInit(self, c_datas, k):
        self.reps = [(250,250),(500,250),(750,250),(750,500),(750,750),(500,750),(250,750),(250,500)]
        self.dists = [[-1 for j in range(k)] for i in range(len(c_datas))]
        self.clusters=[-1 for i in range(len(c_datas))]

    # 代表点と点の距離を計算
    def ClusterDist(self, c_datas):
        for (i, c_data) in enumerate(c_datas):
            for (j, rep) in enumerate(self.reps):
                # 各点の代表点との距離を計算
                self.dists[i][j] = dist(c_data, rep)

    # 所属クラスタ更新
    def ClusterUpdate(self):
        flag=False
        for (i, dist) in enumerate(self.dists):
            # クラスタ更新があった場合はwhileループのフラグをTrueに維持
            best_dist=INF
            best_v=-1
            for j,d in enumerate(dist):
                if d<best_dist:
                    best_dist = d
                    best_v = j
            if self.clusters[i] != best_v:
                flag=True
            # 距離のリストから最小値の引数を得る
            self.clusters[i] = best_v
        self.keep_flag=flag

     # クラスタの代表点を更新
    def RepUpdate(self, c_datas):
        for c_num in range(len(self.reps)):
            cluster_points=[]
            for i, (cluster, c_data) in enumerate(zip(self.clusters,c_datas)):
                if cluster == c_num:
                    # clauster_pointsにc_numクラスタの点を追加
                    cluster_points.append(c_data)
            if len(cluster_points) is 0:
                cluster_points.append(self.reps[c_num])
            # 点の平均を求め代表点を更新
            self.reps[c_num] = (sum([x for x,y in cluster_points])//len(cluster_points),sum([y for x,y in cluster_points])//len(cluster_points))
    

def calc_score(ans):
    score = 0
    for i in range(len(ans)-1):
        score += calc(i,i+1,ans)
    return score

def calc_score2(ans):
    score = 0
    for i in range(len(ans)-1):
        score += Ecost[ans[i]][ans[i+1]]
    return score


def probability(diff):
    start_temp=50
    end_temp=10
    temp = start_temp + (end_temp - start_temp) * get_time(START) / 0.85
    return exp(diff/temp)

# インプット
N,M=map(int,input().split())
Terminals = [tuple(map(int,input().split())) for _ in range(N)]
KM=KMEANS()
KM.Clustering(Terminals,M)
Stations = KM.reps[:]
print(Stations,file= sys.stderr)
Terminals += Stations
T=len(Terminals)
#各Terminalの移動コストをワーシャルフロイトで計算
Ecost=[[INF]*T for i in range(T)]
for i,t1 in enumerate(Terminals):
    for j,t2 in enumerate(Terminals):
        Ecost[i][j]=cost(i,j)
#ワーシャルフロイト
for k in range(T):
    for i in range(T):
        for j in range(T):
            Ecost[i][j]=min(Ecost[i][j],Ecost[i][k]+Ecost[k][j])

#ステーションを回る順序を全探索で決定
#ステーション毎に貪欲に回る順序を決定
StationCluster=[[] for i in range(M)]
TerminalsStation=[0]*N
for i in range(N):
    best_dist=INF
    best_st=-1
    for j in range(M):
        d = dist(Terminals[i],Terminals[j+N])
        if d<best_dist:
            best_dist=d
            best_st=j
    assert best_st!=-1
    StationCluster[best_st].append(i)
    TerminalsStation[i]=best_st
best_dist = INF
best_per = []
for per in permutations(range(M-1),M-1):
    station_list=[TerminalsStation[0]+N]
    for s in per:
        if s>=TerminalsStation[0]:s+=1
        station_list.append(s+N)
    station_list.append(TerminalsStation[0]+N)
    d = calc_score2(station_list)
    if d<best_dist:
        best_dist=d
        best_per=station_list[:]
print(best_per,file=sys.stderr)        


        


ans = [0]
for i in range(M):
    for v in StationCluster[best_per[i]-N]:
        if v==0:
            continue
        ans.append(v)
ans.append(0)
print(ans, file = sys.stderr)
# print(StationCluster, file = sys.stderr)

Tind=[0]*N
Tinv=[0]*N
for i,x in enumerate(ans[:N]):
    Tind[x]=i
    Tinv[i]=x

# 山登り
n = len(ans)
loop_cnt = 0
update_cnt=0        
best_score = calc_score2(ans)
dx=(10,-10,0,0,5,5,-5,-5)
dy=(0,0,10,-10,5,-5,5,-5)
# print(get_time(START),file=sys.stderr)
while get_time(START) < 0.80:
    loop_cnt+=1
    p=random()
    if p<0.2:
        v1 = randrange(1,n-1)
        v2 = randrange(1,n-1)
        while v1==v2:
            v2 = randrange(1,n-1)

        ans[v1],ans[v2] = ans[v2],ans[v1]
        current_score = calc_score2(ans)
        diff = best_score - current_score
        if diff>0:
            best_score = current_score
            update_cnt += 1
            #print("swap",current_score, file=sys.stderr)
        else:
            ans[v1],ans[v2] = ans[v2],ans[v1]
    else:
        #randomでクラスターを選んでその中でSwap
        v1=randint(1,n-1)
        v2 = choice(StationCluster[TerminalsStation[ans[v1]]])
        while ans[v1]==v2 or v2 == 0:
            v2 = choice(StationCluster[TerminalsStation[ans[v1]]])
        ans[v1],ans[Tind[v2]] = ans[Tind[v2]],ans[v1]
        current_score = calc_score2(ans)
        diff = best_score - current_score
        if diff>0:
            best_score = current_score
            update_cnt += 1
            #print("swap",current_score, file=sys.stderr)
        else:
            ans[v1],ans[Tind[v2]] = ans[Tind[v2]],ans[v1]





# アウトプット
ans.append(0)
output=[0]
for i in range(len(ans)-1):
    output+=dijkstra(ans[i],ans[i+1],Ecost[ans[i]][ans[i+1]])
loopcnt2=0
while get_time(START)<0.95:
    loopcnt2+=1
    v = randrange(N,N+M)
    original = Terminals[v][:]
    best_i = -1
    sub_best_score = best_score
    for i in range(8):
        Terminals[v] = (original[0]+dx[i],original[1]+dy[i])
        current_score = calc_score(output)
        if current_score < sub_best_score:
            sub_best_score = current_score
            best_i = i

    if best_i != -1:
        best_score = sub_best_score
        Terminals[v] = (original[0]+dx[best_i], original[1]+dy[best_i])
        #print("move",best_score, file=sys.stderr)
    else:
        Terminals[v]=original[:]
for pos in Terminals[N:]:
    print(*pos)
print(len(output))
for v in output:
    if v<N:
        print(1,v+1)
    else:
        print(2,v+1-N)

print("Score", 10**9//(1000+calc_score(output)**0.5), file=sys.stderr)
print("Loop", loop_cnt, loopcnt2, update_cnt, file=sys.stderr)
print("time:",get_time(START) * 1000, file=sys.stderr)