<｜begin▁of▁sentence｜>### Instruction:
As a Video Game Designer working on a new multiplayer online game project for a gaming studio, you are tasked with optimizing the player community experience within the game world. You have access to a network graph data representation of player interactions, stored in a GML file named as-22july06.gml. 

Your goal is to leverage the Leiden community detection algorithm provided by the cdlib library to analyze the player network and identify distinct communities within the game community. Additionally, you are required to calculate the average embeddedness of nodes within each detected community. This metric will help you understand the level of interconnectedness and engagement among players within these communities, ultimately shaping your decision-making process for community-focused gameplay features and dynamics.

Utilizing the functionalities of the Leiden algorithm from the cdlib library, you can efficiently detect communities in the player network based on their interactions and relationships. By computing the average embeddedness within each community, you will gain insights into how closely-knit and interactive these player groups are, allowing you to tailor community management strategies and gameplay content to enhance the overall player experience.
### Response:
import networkx as nx
from cdlib.algorithms import leiden
from cdlib import evaluation

# Create a graph
G = nx.read_gml('as-22july06.gml')

# Apply the Leiden community detection algorithm
communities = leiden(G)

# Compute the average embeddedness of nodes within the community
avg_embeddedness = evaluation.avg_embeddedness(G, communities)

# Output the result
print(avg_embeddedness.score)
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