Complete the sweep$\_$louvain$\_$resolutions function to identify community structures within the graph. Use the Louvain algorithm to find the best partition. Use $10$ different resolution parameters from min$\_$resolution to max$\_$resolution $($inclusive$),$ and compare the result of each partition to the ground truth $($you can find the ground truth in the value field associated with each node in the graph$)$ using the normalized mutual information function. Return the list of resolutions and NMIs from the resulting community assignments. Additionally, complete the plot$\_$nmi$\_$vs$\_$resolution function to display the NMI for each resolution as a line plot.

When I use normalized$\_$mutual$\_$info$\_$score function I run into this error: Found input variables with inconsistent numbers of samples: $[10,9],$ any guidance?

def sweep$\_$louvain$\_$resolutions$($G: nx$.$Graph, min$\_$resolution: int$=1,$ max$\_$resolution: int$=10)->$ Tuple$[$List$[$int$],$ List$[$float$]]$:

$"""$

Inputs:

G: NetworkX graph object

min$\_$resolution : integer

max$\_$resolution : integer

Returns:

Tuple of list of resolutions and list of NMIs

$"""$

resolutions $=[]$

nmis $=[]$

communities $=$ list$($nx$.$community.louvain$\_$communities$($G$))$

for i in range$($min$\_$resolution, max$\_$resolution$+1)$:

res $=$ list$($louvain$\_$communities$($G$,$ resolution$=$i$))$

resolutions.append$($i$)$

nmi$\_$scores $=$ normalized$\_$mutual$\_$info$\_$score$($communities$,$ res$)$

#nmis.append$($nmi$\_$scores$)$

return resolutions, nmis

def plot$\_$nmi$\_$vs$\_$resolution$($resolutions: List$[$int$],$ nmis: List$[$float$],$ save: bool$=$False$)->$ None:

$"""$

Inputs:

min$\_$resolution : integer

max$\_$resolution : integer

save: boolean

Returns:

None

$"""$

if save:

plt$.$savefig$(\text{'}1\_2\_1.$png$\text{'})$

plt$.$show$()$