<｜begin▁of▁sentence｜>### Instruction:
As a brand strategist, I understand the importance of understanding the community structure within a network to effectively reach and engage with target audiences. Let's say a client in the energy sector has a power grid network data file, power.gml, containing information about the network's nodes and connections. The client wants to identify distinct communities within this network to better understand the interconnections and optimize their outreach strategies.

I need to utilize the sbm_dl function from the cdlib library, which offers community detection capabilities, to infer non-overlapping communities within the power grid network. Once the communities are identified, I'll provide a JSON representation of the detected communities, allowing the client to visualize and analyze the network's structure.

Through this process, the client can gain insights into the distinct clusters of nodes within the power grid network, aiding in the development of targeted communication strategies and resource allocation for each community.
### Response:
import networkx as nx
from cdlib import algorithms

# Read the power grid graph
G = nx.read_gml('power.gml')

# Perform community detection using the sbm_dl algorithm
coms = algorithms.sbm_dl(G)

# Convert the NodeClustering object to json
coms_json = coms.to_json()

# Print the json representation of the detected communities
print(coms_json)
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