<｜begin▁of▁sentence｜>### Instruction:
As a sports coach, you work tirelessly to enhance your team's performance, focusing on skill development and strategic refinement. Imagine you're preparing for an upcoming tournament where each team's success hinges not only on individual prowess but also on effective teamwork and synergy.

Now, let's transpose this mindset to a different arena - the realm of data analysis and network optimization. Just as you would dissect a game strategy, we'll delve into the intricacies of a neural network graph representing connections within the C. elegans worm brain. Picture this graph as a team, where nodes symbolize individual players, and edges denote the connections between them.

Your task involves utilizing Python's NetworkX library to import the C. elegans neural network graph from a file named 'celegansneural.gml'. Then, employing the Louvain method, integrated within the Community Discovery Library (cdlib), to detect communities within this network. But, instead of scoring goals, here we aim to calculate the conductance of these communities. Conductance serves as a measure of how much connectivity exists within each community versus connections outside.

Your ultimate goal: to print out the conductance score, akin to evaluating a team's cohesion and collaboration in preparation for the big game.
### Response:
import networkx as nx
from cdlib import algorithms, evaluation

# Load a graph
G = nx.read_gml('celegansneural.gml')

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

# Evaluate the conductance of the partition
conductance_score = evaluation.conductance(G, communities)

# Print the conductance
print(conductance_score.score)
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