<｜begin▁of▁sentence｜>### Instruction:
As a knowledgeable assistant with expertise in networkx, I can assist you in understanding how to use the FeatherNode model to generate embeddings for the nodes in a graph. Let's consider a scenario where you're a coach at a sports academy where young athletes are training. You have a network created to represent athlete performance and skills. The graph has 5 nodes, each representing an athlete, and there are 5 edges connecting the athletes based on their competitions and training sessions. Each athlete has an 8-dimensional feature vector representing their skills and performance in various sports.

Your goal is to utilize the FeatherNode model to generate embeddings for each athlete in the graph, which will represent their overall performance and skill set. These embeddings will then be used to analyze and compare the athletes' strengths and identify areas for improvement in their training programs.

By using the FeatherNode model's capabilities, you can efficiently generate embeddings for the athletes' nodes, which will provide valuable insights for optimizing their training regimens and enhancing their overall performance in sports competitions.
### Response:
import networkx as nx
from karateclub import FeatherNode
import numpy as np

# Create a graph with node features
G = nx.Graph()
G.add_nodes_from([(i, {'feature': np.random.rand(8)}) for i in range(5)])
G.add_edges_from([(0,1), (0,2), (2,3), (1,4), (3,4)])

# Gather a Numpy array of the node features
X = np.array([G.nodes[i]['feature'] for i in range(G.number_of_nodes())])

# Initialize the FeatherNode model
model = FeatherNode()

# Fit the model to the graph and the node features
model.fit(G, X)

# Get the resulting node embeddings
embeddings = model.get_embedding()
print(embeddings)
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