Do graph_tool have Communities Louvain algorithm implementation in?

I'm working on code translating from Networkx to Igraph. This code use community detection based on Louvain Community detection algorithm. I din't find it on graph_tool documentation, do someone know if it exist? I readed the documentation of graph tool library.

This is covered in the FAQ: https://graph-tool.skewed.de/static/doc/faq.html#is-it-possible-to-perform-modularity-maximization-with-graph-tool

I quote the text below:

Is it possible to perform modularity maximization with graph-tool?

It is in fact possible to perform modularity maximization with graph-tool. For that you need to use the ModularityState object instead of BlockState, as documented in the section Inferring modular network structure, e.g.

g = gt.collection.data["football"]

state = gt.minimize_blockmodel_dl(g, state=gt.ModularityState)

However, please note the following admonition:

Danger: Using modularity maximization is almost always a terrible idea.

Modularity maximization is a substantially inferior method to the inference-based ones that are implemented in graph-tool, since it does not possess any kind of statistical regularization. Among many other problems, the method tends to massively overfit empirical data.

For a more detailed explanation see “Modularity maximization considered harmful”, as well as [peixoto-descriptive-2021].

Do not use this approach in the analysis of networks without understanding the consequences. This algorithm is included only for comparison purposes. In general, the inference-based approaches based on BlockState, NestedBlockState, and PPBlockState should be universally preferred.