<｜begin▁of▁sentence｜>### Instruction:
Imagine you're the volunteer coordinator for our social club's big community event. Picture our club as a network where all members (nodes) are connected through their shared activities and friendships—like a big family tree, but for club relationships! Now, we want to create a fun visual map for this event that illustrates how our members are grouped together—basically, how everyone is connected within our club's social family.

To make our map as true to life as possible, we're planning to use a special way of drawing it up, known as the Laplacian Spectral Embedding (LSE) technique. It's a bit like creating a seating chart that shows who's likely to get along with whom, based on the relationships we've seen during club activities. I need your help to visualize our club's network on a flat poster, using LSE, in a way that shows which members are likely to share the most interests and which ones might be a bit more distant.

Can you assist with setting up this two-dimensional display that accurately groups members into little neighborhoods on our map, so we can really see the different circles of friendship within our club? We'll use this map as a conversation starter and a fun way to show everyone how they contribute to the vibrant community we have.
### Response:
import numpy as np
import networkx as nx
from graspologic.embed import LaplacianSpectralEmbed

G = nx.karate_club_graph()

# Instantiate the LaplacianSpectralEmbed class
lse = LaplacianSpectralEmbed(n_components=2, form='DAD')

# Fit the model to the graph
embedding = lse.fit_transform(G)

# Plot the embedding
import matplotlib.pyplot as plt
plt.scatter(embedding[:, 0], embedding[:, 1])
plt.title('Laplacian Spectral Embedding of Karate Club Graph')
plt.show()
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