import numpy as np
import itertools
import random

N, M = map(int, input().split())
AB = [np.array(list(map(int, input().split()))) for _ in range(N)]
AB = np.array(AB)

class KMeans:

  def __init__(self, n_clusters, max_iter = 10, random_seed = 0):
    self.n_clusters = n_clusters
    self.max_iter = max_iter

  def fit(self, X):
    self.labels_ = []
    now = 0
    while len(self.labels_) < len(X):
      self.labels_.append(now)
      now += 1
      now %= self.n_clusters
    random.shuffle(self.labels_)
    labels_prev = [0]*len(X)
    count = 0
    self.cluster_centers_ = [(0, 0)] * self.n_clusters

    while (not (self.labels_ == labels_prev) and count < self.max_iter):
      syuukei = [[] for _ in range(self.n_clusters)]
      for i in range(len(X)):
        syuukei[self.labels_[i]].append(X[i])
      for i,l in enumerate(syuukei):
        if l:
          x, y = sum(x for x,y in l)//len(l), sum(y for x,y in l)//len(l)
        else:
          x, y = random.randint(0, 1000), random.randint(0, 1000)
        self.cluster_centers_[i] = (x, y)

      labels_prev = self.labels_[:]
      for i in range(len(X)):
        min = -1
        dist = 10**18
        for j in range(self.n_clusters):
          tmp = (X[i][0] - self.cluster_centers_[j][0])**2 + (X[i][1] - self.cluster_centers_[j][1])**2
          if tmp < dist:
            dist = tmp
            self.labels_[i] = j

      count += 1

  def predict(self, X):
    dist = ((X[:, :, np.newaxis] - self.cluster_centers_.T[np.newaxis, :, :]) ** 2).sum(axis = 1)
    labels = dist.argmin(axis = 1)
    return labels


model = KMeans(8)
model.fit(AB)
labels = model.labels_
centers = model.cluster_centers_


ans = [(1, 0)]
L = sorted(enumerate(labels), key=lambda x: x[1])
for i in range(L[-1][1]+1):
  for j, cluster_number in L:
    if cluster_number == i:
      ans.append((1, j))
      ans.append((2, i))
ans.append((1, 0))

for c,d in centers:
  print(c, d)
print(len(ans))
for a,b in ans:
  print(a, b+1)