<｜begin▁of▁sentence｜>### Instruction:
As a grant coordinator, you might encounter a research project involving the analysis of graphs representing complex networks. In this scenario, the team may want to utilize the FGSD model to calculate the histogram of the Moore-Penrose spectral features for a list of graphs and use this as a representation for the entire set of graphs. This analysis could potentially be used to support a grant application for a project focusing on network analysis and modeling.

For example, imagine a research team is applying for a grant to study communication networks in social media platforms. They have collected a list of graphs representing interactions between users and want to demonstrate the complexity and diversity of these networks using advanced mathematical models. The team is interested in using the FGSD model to create a robust representation of the graphs and highlight the unique spectral features in order to support their grant application.
### Response:
import networkx as nx
from karateclub import FGSD

# Step 1: Create graphs
G1 = nx.gnm_random_graph(100, 200)
G2 = nx.gnm_random_graph(100, 300)
graphs = [G1, G2]

# Step 2: Initialize FGSD model with custom parameters
model = FGSD(hist_bins=200, hist_range=20, seed=42)

# Step 3: Fit the model on the graphs
model.fit(graphs)

# Step 4: Get embeddings
embeddings = model.get_embedding()

print(embeddings)
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