<｜begin▁of▁sentence｜>### Instruction:
As an insurance adjuster, you're tasked with investigating and evaluating insurance claims to determine coverage and settlement amounts. Suppose you're handling a complex case involving a network of collaborations within the field of astrophysics. You have access to a file named "astro-ph.gml," which contains data representing collaborations among astrophysicists.

Your objective is to utilize a sophisticated analytical tool called the 'sbm_dl_nested' function provided by the Community Detection Library (cdlib). This function employs a nested non-overlapping stochastic block model to detect communities within the astrophysics collaborations network by minimizing its description length.

Once you've applied the 'sbm_dl_nested' function, your next step is to print out the communities that the algorithm has identified within the network. This information will provide valuable insights into the underlying structure and dynamics of collaborations among astrophysicists, aiding you in your assessment of the insurance claim associated with this specialized field.
### Response:
import networkx as nx
from cdlib import algorithms

# Load an astrophysics collaborations graph from a GML file
G = nx.read_gml('astro-ph.gml')

# Use the SBM_DL algorithm for detecting communities
communities = algorithms.sbm_dl_nested(G)

# Output the detected communities
for community in communities.communities:
    print(community)
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