<｜begin▁of▁sentence｜>### Instruction:
Imagine you're devising a training program to understand how two different packs of dogs might interact within a shared space, where Pack A and Pack B represent two different communities. You decide to represent the likelihood of interactions within and between the packs using a set of probabilities—a bark matrix, so to speak. This matrix, called 'p', suggests that members within Pack A have a certain chance of interacting denoted by p[0][0], while pups within Pack B have a different likelihood of roughhousing, marked as p[1][1]. For now, you decide that cross-pack sniff-outs are off the table, hence a zero chance for inter-pack mingling.

To figure out the social structure, you've heard about this nifty tool - a networkx function tailored for creating pretend groups of pups based on the Mixed Membership Stochastic Block Model (MMSBM). This model simulates a park where each pooch might have a preference for hanging with Pack B, all thanks to a specific 'alpha' parameter that makes them rather partial to one group. This situation mirrors your intent to understand the furry dynamics within that shared space.

Could you pawsibly use the MMSBM.NetworkX function for this behavior simulation, generating what you might call a 'sniff network', where the adjacency matrix would lay out all the potential play-bows and tail wags within and between the pack members? Plus, you'd like to have a membership list - a tag for each doggo, indicating which pack they're wagging their tails with during each interaction. How would you unleash this function to get both the adjacency play matrix and the pack tags, ensuring the doggos’ interactions stay true to the probabilities you’ve mapped out?
### Response:
from graspologic.simulations import mmsbm
import numpy as np

# Seed for reproducibility
rng = np.random.default_rng(1)
np.random.seed(1)

# Number of nodes in the graph
n = 6

# Block connectivity matrix indicating the probability of connection between communities
p = [[0.5, 0], [0, 1]]

# Dirichlet parameter for sampling membership vectors
alpha = [0.05, 1000]

# Generate a graph from MMSBM
A, labels = mmsbm(n, p, alpha, rng=rng, return_labels=True)

# Display the adjacency matrix and labels
print('Adjacency Matrix:\n', A)
print('Labels:\n', labels)
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