<｜begin▁of▁sentence｜>### Instruction:
Given a network of books about US politics represented as a graph which you can read from a GML file named polbooks.gml, and assuming each node (representing a book) has an associated attribute named 'attribute_name' with possible values 'Value1', 'Value2', or 'Value3', can you use the I-Louvain algorithm in cdlib to perform attribute-aware community detection? Additionally, can you output a JSON representation of the resulting clustering?
### Response:
import networkx as nx
from cdlib import algorithms
import random

# Create a graph from a GML file
G = nx.read_gml('polbooks.gml')

# Define node attributes
labels = {}
for node in G.nodes():
    labels[node] = {'attribute_name': random.choice(['Value1', 'Value2', 'Value3'])}

# Apply the I-Louvain community detection algorithm considering node attributes
communities = algorithms.ilouvain(G, labels)

# Export the clustering
clustering_json = communities.to_json()
print(clustering_json)
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