How to find overall model coefficients in Gensim's latent dirichlet allocation?

I am using Latent Dirichlet Allocation (LDA) to find topics that occur in my corpus, and I was wondering if there is a way to get the overall model coefficients in Gensim? Here each coefficient would represent the weight of each topic in the overall model. I want to get an idea of which topics are the most important relevant through these weights, in addition to looking at topic vocabularies. Something like this is possible in R, since the model has a coefs attribute ( a coefficient for each of the six topics):


[1] -0.56446635 -0.52604353 -0.43325116 -1.88352347  0.20560172 -0.26902856

One idea I have currently is to use Gensim's get_document_topics(bow) function where bow represents a single document converted into a bow vector. Here is the documentation: Although this is not the same as the model coefficients I was talking about above, it may capture a somewhat similar idea. I can use this function for all documents to get the document's topic distribution, and average all the topic distributions across documents for each topic to get a sense of which topics are the most prevalent. However, this would not be my first go-to option. Please let me know if there is something along the lines of coefs in Gensim's LDA to obtain model coefficients. Thanks!