Hello,

I am estimating a nested_block_model with bipartite data and am getting a strange result. There are about 4,000 nodes in the network and I am estimating a model by:

```
state_dc  = minimize_nested_blockmodel_dl(G, state_args=dict(deg_corr=True,
                                            clabel=bipart.a, pclabel=bipart.a,
                                            recs=[G.ep.weight],
                                            rec_types=["discrete-poisson"]))


for i in range(1000): # this should be sufficiently large
    state_dc.multiflip_mcmc_sweep(beta=np.inf, niter=10)
```

The results find 13 nested levels, but after level 5 there are only 2 nonempty blocks found in each additional level. I'm not sure if this is really a problem, or I can just ignore the levels where nothing really changes. Any advice/help would be greatly appreciated. Thank you