<｜begin▁of▁sentence｜>### Instruction:
As a network analyst, I can assist you with your inquiry about utilizing the `sbm_dl_nested` function from the `cdlib` library. 

Imagine you are working as a network researcher studying language patterns in a text corpus. You have a graph representing word adjacencies extracted from a dataset, stored in a file named adjnoun.gml. You are interested in performing community detection to identify clusters of words that frequently appear together.

In this context, you are considering applying the `sbm_dl_nested` function from the `cdlib` library to fit a nested non-overlapping stochastic block model by minimizing its description length using an agglomerative heuristic. This approach could help reveal hidden structures or patterns within the network of word adjacencies, facilitating a deeper understanding of the relationships between different words.

By leveraging the functionality provided by the `cdlib` library, you aim to enhance your analysis and gain insights into the underlying community structure of the word network, ultimately advancing your research in computational linguistics.
### Response:
import networkx as nx
from cdlib import algorithms

# Load graph from GML file
G = nx.read_gml('adjnoun.gml')

# Perform community detection using the nested stochastic block model
communities = algorithms.sbm_dl_nested(G)

# Print the detected communities
print(communities.communities)
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