Help with the f1 function of the k-cluster with fixed cardinality problem

from mip import *

# creating model
model = Model(sense=MAXIMIZE)

# cluster index
K: int = 2

# total number of items
N = 5

# number of items per cluster k
Mk = 2

# similarity between items i and j
s = [
    [0, 0.2, 0.5, 0.8, 0.3],
    [0.2, 0, 0.6, 0.4, 0.7],
    [0.5, 0.6, 0, 0.9, 0.1],
    [0.8, 0.4, 0.9, 0, 0.2],
    [0.3, 0.7, 0.1, 0.2, 0]
]

# binary variable  indicating whether item i is in cluster k(=1) or not(=0),(i = 1,...N; k = 1,...K_num )
xi = [[model.add_var('xi({})({})'.format(i, k), var_type=BINARY) for i in range(1, N)] for k in range(1, K)]

# binary variable  indicating whether item j is in cluster k(=1) or not(=0),(j = 1,...N; k = 1,...K_num )
xj = [[model.add_var('xj({})({})'.format(j, k), var_type=BINARY) for j in range(1, N)] for k in range(1, K)]

# binary variable indicating whether items i and j are in the same cluster k(=1) or not (=0)
y = [[[model.add_var('y({})({})({})'.format(i, j, k), var_type=BINARY) for i in range(1, N - 1) for j in range(i + 1, N)]]for k in range(1, K)]

# objective function: maximize the total similarity
model.objective = maximize(
    xsum(s[i][j] * y[i][j][k] for k in range(1, K-1) for i in range(1, N - 1) for j in range(i + 1, N)))

# constraint: each item to belong to one cluster at most
model.add_constr(xsum(xi[i][k] for k in range(1, K - 1) for i in range(1,N-1) ) <= 1)

for k in range(1, K-1):
    for j in range(1, N-1):
        for i in range(1, j):
            model.add_constr(y <= xi)

for k in range(1, K - 1):
    for i in range(1, N - 1):
        for j in range(i+1, N-1):
            model.add_constr(y <= xj)
# model += y <= xj

# constraint: do not allow violation of the number of items in each cluster
for k in range(1, K - 1):
    model.add_constr(xsum(xi) == Mk)


# optimizing
model.optimize()
model.write("prova.lp")

# objective value
print(model.objective_value)