Extract high likelihood genes identified in latent time

Hello, I was performing trajectory analysis on my dataset with tutorial from: Sam Morabito | RNA velocity analysis with scVelo

By the end of the session the author did:

top_genes = adata_subset.var['fit_likelihood'].sort_values(ascending=False).index[:300]
scv.pl.heatmap(adata_subset, var_names=top_genes, sortby='latent_time', col_color='celltype', n_convolve=100)

The graph seems like to be drawn from a list of genes that contributes greatly to the latent time calculation (I hope I’m interpreting this correctly). I am hoping that if I could extract the list of genes with relation to which cell cluster latent time they are contributing to, I might find some more information on the differentiation signaling of my dataset.

However, adata.var[‘fit_likelihood’].sort_values(ascending=False) gives me a list of genes with likelihood and adata.obs[‘latent_time’] gives me a list of cells with latent time. I’m not sure how should I rearrange them to get what I want?

Thank you!