<｜begin▁of▁sentence｜>### Instruction:
As a nutrition coach, you work with a client who is interested in optimizing their dietary habits and you gather data on their current nutrition habits and health history. To tailor your advice effectively, you could integrate network analysis by using the 'GeoScattering' model to analyze the dietary patterns within a social network of their friends and family. This could help in obtaining a graph-level feature embedding that leverages geometric scattering with wavelet transforms to identify key dietary influencers within their social circle and recommend personalized dietary improvements.
### Response:
import networkx as nx
from karateclub import GeoScattering
import matplotlib.pyplot as plt

# Create sample graphs
G1 = nx.erdos_renyi_graph(n=50, p=0.1)
G2 = nx.erdos_renyi_graph(n=50, p=0.1)

# List of graphs to embed
graphs = [G1, G2]

# Initialize GeoScattering with default parameters
model = GeoScattering()

# Fit the model
model.fit(graphs)

# Obtain the embedding
embedding = model.get_embedding()

# Print embedding
print(embedding)
<|EOT|>
