<｜begin▁of▁sentence｜>### Instruction:
A researcher is analyzing a social network (represented as a graph) to discover underlying patterns by embedding the graph into a low-dimensional space. The graph is undirected and each node represents a member of a karate club with edges representing social ties. The researcher wants to use the Laplacian Spectral Embedding method to produce a 2-dimensional representation of the graph. How can they use graspologic's LaplacianSpectralEmbed class to do this, and what does the resulting embedding look like?
### Response:
import networkx as nx
from graspologic.embed import LaplacianSpectralEmbed

# Step 1: Create a graph using networkx
G = nx.karate_club_graph()

# Step 2: Initialize LaplacianSpectralEmbed
lse = LaplacianSpectralEmbed(form='DAD', n_components=2, n_elbows=2, algorithm='randomized')

# Step 3: Fit the model to the graph and transform the graph to obtain embedded coordinates
embedding = lsе.fit_transform(G)

# Step 4: Print result of embedding
print(embedding)
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