<｜begin▁of▁sentence｜>### Instruction:
In your role as a Diversity and Inclusion Specialist, you've been asked to map out the social network in your organization of 250 employees to better understand and promote diversity and inclusion. For this task, you want to create a network model where each node represents an employee, and the links between them depict their interactions. 

While creating your model, you decide to use NetworkX's LFR_benchmark_graph function to generate a graph that mirrors the connections in your organization. You hypothesize that the degrees of connection follow a power law distribution with an exponent of 3.0, signifying that few employees have many connections, while many employees have few connections. Similarly, you believe that the community size – groups of employees that interact more frequently with each other – also follows a power law distribution, but with an exponent of 1.5.

In your model, you're assuming that 10% of the connections, or edges, are inter-community, meaning they cross between different groups. You also presume that each employee has, on average, connections with 5 others, and that each group in your organization, or community in your model, has to contain at least 20 employees.

The challenge now is to figure out how to determine the number of communities that the LFR_benchmark_graph function creates based on these parameters in your model. It's also essential to consider and include any data files such as a gml file, if applicable, in your analysis.
### Response:
from networkx.generators.community import LFR_benchmark_graph

# Parameters for the LFR benchmark graph
tau1 = 3.0  # 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
n = 250     # Number of nodes

# Create the LFR benchmark graph
G = LFR_benchmark_graph(n, tau1, tau2, mu, average_degree=5, min_community=20, seed=10)

# Retrieve the communities from the node attributes
c=communities = {frozenset(G.nodes[v]["community"]) for v in G}
print("Number of communities:", len(communities))
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