<｜begin▁of▁sentence｜>### Instruction:
As a lighting technician, you've been asked to set up and control the lighting for a large event taking place in a complex venue. The event organizers have provided you with a detailed floor plan of the venue and a GML file called "as-22july06.gml" representing the layout of the building, including the network of lights and their attributes. They have also assigned random attributes to each light node, such as brightness, color temperature, and power consumption, to account for the unique characteristics of different lighting fixtures.

Using the Eva algorithm in cdlib, you are requested to perform attributed community detection based on these additional node attributes and convert the resulting attributed communities into a JSON representation. This will help optimize the lighting setup and control for different areas of the venue, taking into account the various attributes of the lighting fixtures.

Your expertise in setting up and controlling lighting systems will be instrumental in ensuring that the event's lighting complements the overall atmosphere and meets the specific requirements of the event organizers. With the attributed community detection using additional node attributes, you can enhance the overall lighting experience and contribute to a successful and memorable event.
### Response:
import networkx as nx
from cdlib import algorithms
import random

# Load an Internet graph from the file
G = nx.read_gml('as-22july06.gml')

# Define the attributes for each node
labels = {}
for node in G.nodes():
    labels[node] = {'attribute_name': random.choice(['value1', 'value2', 'value3'])}

# Apply the Eva algorithm for community detection
communities = algorithms.eva(G, labels)

# Convert the result into JSON
json_result = communities.to_json()

print(json_result)
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