<｜begin▁of▁sentence｜>### Instruction:
As a virus researcher specializing in studying the biology, transmission, and evolution of viruses, including infectious diseases, you're faced with the task of analyzing the intricate network of interactions within the American College football community to understand its structure better. To achieve this, you've obtained a dataset stored in a GML file named "football.gml," representing the network graph of American College football teams and their connections.

Now, utilizing the Louvain method available in the Community Detection Library (cdlib), your objective is to dissect this network into distinct communities, mirroring the diverse clusters within the football landscape. Additionally, you're interested in quantifying the average embeddedness score of nodes within each community, providing insights into the cohesion and connectivity within these groups.

Your task involves implementing the Louvain method through the cdlib API and subsequently computing the average embeddedness of nodes within each detected community. This analysis will shed light on the cohesive relationships within the football network, contributing to a deeper understanding of its underlying dynamics.
### Response:
import networkx as nx
from cdlib import algorithms, evaluation

# Read the American College football network
G = nx.read_gml('football.gml')

# Detect communities using the Louvain method
communities = algorithms.louvain(G)

# Calculate the average embeddedness of nodes within each community
avg_embeddedness = evaluation.avg_embeddedness(G, communities)

# Print the average embeddedness score
print(avg_embeddedness.score)
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