<｜begin▁of▁sentence｜>### Instruction:
Given a fast_gnp_random_graph with 100 nodes and a probability of 0.5 for edge creation, can you use the Node2Vec model to generate node embeddings with the following parameters: 20 walks per node, walk length of 10, return parameter p=1, in-out parameter q=1, embedding dimensionality of 64, using 2 workers for parallelism, window size of 5, 10 training epochs, learning rate of 0.01, and a random seed value of 42?
### Response:
import networkx as nx
from karateclub import Node2Vec

# Creating a sample graph
G = nx.fast_gnp_random_graph(100, 0.5)

# Initializing Node2Vec model with specific parameters
model = Node2Vec(walk_number=20, walk_length=10, p=1, q=1, dimensions=64, workers=2, window_size=5, epochs=10, learning_rate=0.01, min_count=1, seed=42)

# Fitting the model
model.fit(G)

# Getting the node embeddings
embedding = model.get_embedding()

print(embedding)
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