<｜begin▁of▁sentence｜>### Instruction:
Given the number of nodes (`n_nodes = 6`), a block connectivity matrix (`block_connectivity`), and Dirichlet distribution parameters (`alpha`) for each community, how can you generate a graph using the Mixed Membership Stochastic Block Model (MMSBM) with graspologic, and visualize this graph using networkx and matplotlib?
### Response:
import numpy as np
from graspologic.simulations import mmsbm

# Define the number of nodes and the block connectivity matrix
n_nodes = 6
block_connectivity = np.array([[0.5, 0.1], [0.1, 0.5]])

# Parameter alpha of the Dirichlet distribution
alpha = [0.2, 0.2]

# Use numpy's random default_rng for reproducibility
rng = np.random.default_rng(42)

# Sample the graph using the MMSBM
adj_matrix, labels = mmsbm(n_nodes, block_connectivity, alpha, rng=rng, return_labels=True)

# Use networkx to convert the adjacency matrix to a graph
G = nx.from_numpy_array(adj_matrix)

# Visualize the graph using matplotlib
import matplotlib.pyplot as plt
nx.draw(G, with_labels=True)
plt.show()

# Print sampled adjacency matrix and labels
print('Adjacency Matrix:')
print(adj_matrix)
print('Labels:')
print(labels)
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