<｜begin▁of▁sentence｜>### Instruction:
As an archivist, let's imagine that you are responsible for organizing and preserving the historical documents and records of a large university. One day, you come across a collection of data related to a random network graph representing connections between 100 individuals with 500 interactions. You are interested in using the Role2Vec model to generate embeddings for the nodes in the graph, with the goal of gaining insights into the network's structure and dynamics.

Your task involves customizing some parameters of the Role2Vec model provided by the networkx library's API to generate the node embeddings effectively. This will allow you to analyze and interpret the network's characteristics and roles more comprehensively.
### Response:
import networkx as nx
from karateclub import Role2Vec

# Create a graph
G = nx.gnm_random_graph(100, 500)

# Initialize Role2Vec model with custom parameters
model = Role2Vec(walk_number=10,
               walk_length=80,
               dimensions=128,
               workers=4,
               window_size=5,
               epochs=1,
               learning_rate=0.05,
               down_sampling=0.0001,
               min_count=5,
               wl_iterations=2,
               seed=42,
               erase_base_features=False)

# Fit the model
model.fit(G)

# Get the embeddings
embeddings = model.get_embedding()

print('Embeddings shape:', embeddings.shape)
print(embeddings)
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