<｜begin▁of▁sentence｜>### Instruction:
Given a number of vertices (10), a block connectivity matrix p indicating probabilities of connections between two communities ([[0.6, 0.2], [0.2, 0.7]]), and a Dirichlet distribution parameter alpha ([0.3, 0.3]), how can you generate a mixed membership stochastic block model graph and the corresponding membership labels for each interaction using graspologic?
### Response:
import numpy as np
from graspologic.simulations import mmsbm

n = 10  # Number of vertices
p = np.array([[0.6, 0.2], [0.2, 0.7]])  # Community connection probabilities
alpha = np.array([0.3, 0.3])  # Dirichlet distribution parameter
rng = np.random.default_rng(42)

adjacency_matrix, labels = mmsbm(n, p, alpha, rng=rng, return_labels=True)

print('Adjacency Matrix:')
print(adjacency_matrix)
print('\nLabels:')
print(labels)
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