<｜begin▁of▁sentence｜>### Instruction:
As a seasoned Meeting, Convention, and Event Planner, I'm currently brainstorming a concept for an engaging event centered around the fascinating social dynamics of a local karate club. I'm intrigued by the idea of using advanced analytical techniques to delve into the club's intricate community network. Specifically, I'm captivated by the potential of utilizing graspologic's HierarchicalClusters class with its hierarchical Leiden algorithm function. This sophisticated tool could assist me in effectively dissecting the club's social structure by dividing the network graph into distinct layers of community clusters. 

Could you provide guidance on how one might deploy this graspologic API to perform a nuanced analysis of the karate club's social network? I am particularly interested in understanding how to adeptly apply its hierarchical Leiden algorithm to obtain a multi-level view of the community structure, from the initial to the final stages of hierarchical clustering, for the purpose of enriching the event's narrative and experience.
### Response:
import networkx as nx
from graspologic.partition import HierarchicalClusters, hierarchical_leiden

# Step 1: Construct a social network graph using networkx
G = nx.karate_club_graph()

# Step 2: Apply hierarchical Leiden algorithm to partition the graph
partitions = hierarchical_leiden(G)

# Step 3: Create the HierarchicalClusters instance
cluster_hierarchy = HierarchicalClusters(partitions)

# Step 4: Retrieve the initial and final level of hierarchical clustering
initial_clustering = cluster_hierarchy.first_level_hierarchical_clustering()
final_clustering = cluster_hierarchy.final_level_hierarchical_clustering()

# Step 5: Output results
print("Initial clustering:", initial_clustering)
print("Final clustering:", final_clustering)
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