<｜begin▁of▁sentence｜>### Instruction:
Given a list of two GNP (Erdős-Rényi) random graphs with 100 nodes and a 0.5 probability for edge creation, using a custom Invariant Graph Embedding (IGE) model configuration with feature_embedding_dimensions, spectral_embedding_dimensions, histogram_bins, and a seed, how can you compute the embeddings for these graphs?
### Response:
import networkx as nx
from karateclub import IGE
import numpy as np

# Step 1: Create graphs
G1 = nx.gnp_random_graph(100, 0.5, seed=42)
G2 = nx.gnp_random_graph(100, 0.5, seed=42)

# Step 2: Initialize Invariant Graph Embedding model with custom parameters
model = IGE(feature_embedding_dimensions=[3, 5], spectral_embedding_dimensions=[10, 20], histogram_bins=[10, 20], seed=42)

# Step 3: Fit the model
model.fit([G1, G2])

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

print(embeddings)
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