<｜begin▁of▁sentence｜>### Instruction:
Imagine you're in the workshop, surrounded by the intricate grain patterns of various woods, each representing a member of our local karate club. You've been entrusted with designing a collection of wooden figurines, each set symbolizing a unique faction within the club. Your challenge is to carve out four distinct sets, akin to unearthing hidden communities in the shared space of the club.

Now, there's a blueprint for this task, reminiscent of a design etched into a precious piece of lumber. This blueprint is a GML file—let's call it "ZacharysKarateClub.gml"—detailing the intricate connections between the club members, like the intertwining fibers within the wood. 

Can you, as an artisan, deftly wield the asynchronous fluid communities technique to reveal the four separate factions within our figurines, just as if you were unraveling the story contained within the rings of a mighty oak? Remember, each delicately carved set of figurines must reflect the natural divisions, the communities, that exist within the warm camaraderie of the karate club.
### Response:
import networkx as nx
from networkx.algorithms.community.asyn_fluid import asyn_fluidc

# Create a graph
G = nx.karate_club_graph()

# Set the number of communities to find
k = 4

# Run the asynchronous fluid communities algorithm
communities = asyn_fluidc(G, k)

# Print the communities
for community in communities:
    print(sorted(community))
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