<｜begin▁of▁sentence｜>### Instruction:
Given the parameters of a desired graph (number of nodes, power law exponents for degree and community size distribution, fraction of inter-community edges, average degree, minimum community size, and random seed), how can you generate an LFR benchmark graph that fulfills these requirements and extract the resulting communities?
### Response:
import networkx as nx
from networkx.generators.community import LFR_benchmark_graph

# Generate an LFR benchmark graph
n = 100 # number of nodes
tau1 = 3 # power law exponent for the degree distribution
tau2 = 1.5 # power law exponent for the community size distribution
mu = 0.1 # fraction of inter-community edges

G = LFR_benchmark_graph(n, tau1, tau2, mu, average_degree=5, min_community=20, seed=42)

# Extract the communities from the graph
communities = {frozenset(G.nodes[v]['community']) for v in G}

print('Generated graph has %d nodes and %d communities.' % (G.number_of_nodes(), len(communities)))
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