<｜begin▁of▁sentence｜>### Instruction:
Given a fast_gnp_random_graph with 100 nodes and a probability of edge creation of 0.1, can you use the Node2Vec model to get node embeddings, considering a biased random walk with walk_number=10, walk_length=80, return parameter p=0.5, in-out parameter q=2, and embedding dimensions of 64?
### Response:
import networkx as nx
from karateclub import Node2Vec

# Create a sample graph
G = nx.fast_gnp_random_graph(100, 0.1)

# Instantiate Node2Vec with specific parameters
node2vec = Node2Vec(walk_number=10, walk_length=80, p=0.5, q=2, dimensions=64, workers=4, window_size=5, epochs=1, learning_rate=0.05, min_count=1, seed=42)

# Fit the model
node2vec.fit(G)

# Get the embeddings
embeddings = node2vec.get_embedding()

print(embeddings)
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