<｜begin▁of▁sentence｜>### Instruction:
Given a random graph generated with NetworkX containing 100 nodes and 200 edges, can you use graspologic's node2vec_embed function to generate embeddings for the nodes? Assume that you want to interpolate the walk lengths based on node degree, and you aim to have reproducible results, hence setting a random seed and using one worker.
### Response:
import networkx as nx
from graspologic.embed import node2vec_embed

# Construct a graph
G = nx.gnm_random_graph(100, 200)

# Generate the node2vec embedding
embedding, labels = node2vec_embed(G, num_walks=10, walk_length=40, return_hyperparameter=1, inout_hyperparameter=1, dimensions=128, window_size=2, workers=1, iterations=3, interpolate_walk_lengths_by_node_degree=True, random_seed=42)

# Print the embedding for the first node
print('Embedding for the first node:', embedding[0])
<|EOT|>
