Generate random graph by changing degree distribution of another graph

I have created a random graph of 50 nodes as follows. Where, I have specified the that every node must have degree 6.

> set.seed(1234)
> g = sample_degseq(out.deg = rep(6,50), method = "simple.no.multiple")
> degree(g)
 [1] 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6
[32] 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6
> plot(g, vertex.label= NA, edge.arrow.size=0.02,vertex.size = 25.5, xlab = "Random Network: G(N,p) model")
> g
IGRAPH 1a84fc6 U--- 50 150 -- Degree sequence random graph
+ attr: name (g/c), method (g/c)
+ edges from 1a84fc6:
 [1]  1--19  1--27  1--37  1--38  1--46  1--47  2--13  2--15  2--24
[10]  2--36  2--46  2--47  3--12  3--15  3--18  3--45  3--49  3--50
[19]  4--23  4--24  4--31  4--40  4--41  4--47  5--17  5--21  5--26
[28]  5--28  5--35  5--46  6--11  6--31  6--36  6--40  6--48  6--49
[37]  7-- 9  7--20  7--22  7--26  7--32  7--41  8--16  8--18  8--27
[46]  8--37  8--40  8--49  9--12  9--18  9--19  9--22  9--35 10--18
[55] 10--21 10--33 10--40 10--41 10--50 11--19 11--25 11--27 11--30
[64] 11--47 12--30 12--40 12--42 12--43 13--16 13--21 13--25 13--26
+ ... omitted several edges

Now, I want to make another 4 random graphs from above random graph by changing its degree distribution. I want to keep the same topology, as of above graph. The new 4 random graphs should have the following degree distribution.

• Uniform Erdos-Renyi random graph = uniform degree distribution
• Poisson Erdos-Renyi random graph = Poisson degree distribution
• Scale – free random graph (Barabasi – Albert graph) = Power-law degree distribution
• Small world random graph (Watts – Strogatz graph) = small world properties such as short average path lengths and high clustering.

Can anyone help me to create 4 random graphs from original graph by changing its degree distribution?