<｜begin▁of▁sentence｜>### Instruction:
As a fashion model, imagine you're preparing for a high-profile photoshoot featuring the latest collection from a renowned designer. The designer has provided you with a set of 100 exquisite garments to showcase, each uniquely connected to the others in a captivating ensemble. However, they've also given you a challenge: you must determine how to flawlessly arrange these outfits to create the most visually stunning display.

To assist you, the designer has also provided a set of 10 distinctive features for each garment, helping you understand their individual characteristics and style elements. Now, using the ASNE (Attentive Structural Network Embedding) model, you need to find the optimal arrangement for these outfits, considering their interconnectedness and distinctive features, ensuring that each ensemble harmonizes seamlessly with the others.

In essence, your task is to use the ASNE model, similar to a specialized tool in your fashion kit, to embed the interconnected garments into a cohesive visual representation, reflecting their relationships and unique attributes while ensuring a breathtaking display for the photoshoot.
### Response:
import networkx as nx
import numpy as np
from karateclub import ASNE
from scipy.sparse import coo_matrix

# Creating a graph
G = nx.gnp_random_graph(100, 0.05)

# Features can be random as an illustration, in practice they should be meaningful
features = np.random.rand(100, 10)
feature_matrix = coo_matrix(features)

# Initializing ASNE with the desired parameters
model = ASNE(dimensions=128, workers=4, epochs=100, down_sampling=0.0001, learning_rate=0.05, min_count=1, seed=42)

# Fit the model
model.fit(G, feature_matrix)

# Get the embedding
embedding = model.get_embedding()
print(embedding)
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