Modify the genrate$\_$configs$\_$graphs and genrate$\_$sbm$\_$graphs functions to produce $100$ graphs using NetworkX's Configs Model and Stochastic Block Model graph generators. When using the configs$\_$model function, specify create$\_$using$=$nx$.$Graph$()$ to obtain a Graph instead of a MultiGraph.

In this task, you'll be dealing with Hierarchical Random Graphs. A dendrogram, derived from the empirical network, is available in "football$-$hrg$.$gml$"$ created using PyHRG$.$ The dendrogram is represented as a directed graph. Each leaf node $($a node with no outgoing edges$)$ in the dendrogram corresponds to a node in the empirical network. Non$-$leaf nodes contain information about their left$/$right children $("$L$"/"$R$")$ and the probability $($denoted by $"$p$")$ of leaf nodes in the left subtree connecting to leaf nodes in the right subtree, stored as node attributes.

def genrate$\_$configs$\_$graphs$($dgree$\_$sequence: List$[$int$],$ n$\_$graphs: int$=100)->$ List$[$nx$.$Graph$]$:

$"""$

Inputs:

dgree$\_$sequence: List$[$int$]$

n$\_$graphs: int

Returns:

a list of graph

$"""$

return graphs

def genrate$\_$sbm$\_$graphs$($p: np$.$ndarray, sizes: List$[$int$],$ n$\_$graphs: int$=100)->$ List$[$nx$.$Graph$]$:

$"""$

Inputs:

p: np$.$ndarray, element $($r$,$s$)$ gives the density of edges going from the nodes of group r to nodes of group s$.$

dgree$\_$sequence: List$[$int$]$

n$\_$graphs: int

Returns:

a list of graph

$"""$

return graphs