[Python] Node/edge attributed directed graph embedding

What is the format of node features that graph2vec and GL2vec in karateclub require? It does mention that there should be no string features but with or without it I am running into an error with the following code:

import networkx as nx
import karateclub as kc
import matplotlib.pyplot as plt

G = nx.DiGraph()

G.add_node(0, label = "A", feature=[0.5])
G.add_node(1, label = "B", feature=[1.2])
G.add_node(2, label = "C", feature=[0.8])

G.add_edge(0, 1)
G.add_edge(0, 2)
G.add_edge(1, 2)

nx.draw_networkx(G, with_labels=True)
plt.show()

graphs = [G]

model = kc.graph_embedding.Graph2Vec()
model.fit(graphs)
embeddings = model.get_embedding()
print(embeddings)


Error: RuntimeError: you must first build vocabulary before training the model

I saw an option to build_vocab in word2vec, but how do I do it for graph2vec?

Are there any alternate packages, preferably with simpler implementations like karateclub, that I can use to generate embeddings for a list of directed node/edge attributed networkx graphs without the need to define training/test sets?

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