How to tune an weighted voting ensemble method?

I am working on kidney cancer patients’ data with 5 unbalanced labels. These codes are contained of Normalization, Oversampling on Feature Engineering part. A list of 9 ordinary Machine Learning methods is provided which are used for the classification task. Then, I take advantage of two kinds of ensemble methods of hard voting and weighted voting methods. 10-fold CV has is exploited to validate results.

methods = ['Support Vector Machine', 'Logistic Regression', 'K Neighbors Classifier', 'Random Forest', 
          'Gaussian Naive Bayes', 'Linear Discriminant Analysis', 'Decision Tree', 'Gradient Boosting',
          'soft_VotingClassifier','hard_VotingClassifier']

I would like to know how to tune weights for the soft voting method one?
Here, are the code and results I have right now:

from sklearn import preprocessing
from sklearn.linear_model import LogisticRegression
from sklearn.neighbors import KNeighborsClassifier
from sklearn.ensemble import RandomForestClassifier
from sklearn.naive_bayes import GaussianNB
from sklearn.discriminant_analysis import LinearDiscriminantAnalysis
from sklearn.tree import DecisionTreeClassifier
from sklearn.svm import SVC
from sklearn.ensemble import GradientBoostingClassifier
from sklearn.ensemble import VotingClassifier

if method == 'soft_VotingClassifier':
    cl1 = LogisticRegression()
    cl2 = KNeighborsClassifier(n_neighbors=10)
    cl3 = RandomForestClassifier(max_depth=35)
    cl4 = GaussianNB()
    cl5 = LinearDiscriminantAnalysis()
    cl6 = DecisionTreeClassifier()
    cl7 = SVC(C=0.1, gamma=0.0001, kernel='poly')
    cl8 = GradientBoostingClassifier()
    estimator = [(method[0],cl1), (method[1],cl2), (method[2],cl3), (method[3],cl4),
                 (method[4],cl5), (method[5],cl6), (method[6],cl7), (method[7],cl8)]
    eclf = VotingClassifier(estimators=estimator,
    voting='soft', weights=[5, 5, 10, 5, 6, 8, 4, 10])

if method == 'hard_VotingClassifier':
    cl1 = LogisticRegression()
    cl2 = KNeighborsClassifier(n_neighbors=10)
    cl3 = RandomForestClassifier(max_depth=35)
    cl4 = GaussianNB()
    cl5 = LinearDiscriminantAnalysis()
    cl6 = DecisionTreeClassifier()
    cl7 = SVC(kernel='linear',gamma='scale')
    cl8 = GradientBoostingClassifier()
    estimator = [(method[0],cl1), (method[1],cl2), (method[2],cl3), (method[3],cl4),
                 (method[4],cl5), (method[5],cl6), (method[6],cl7), (method[7],cl8)]
    eclf = VotingClassifier(estimators=estimator, voting='hard') 

Confusion Matrix results on test data:

[50  5  1  4  2]
[ 0 13  1  0  3]
[ 0  1  2  1  1]
[ 4  0  0  2  0]
[ 0  3  0  0  2]

Accuracy result on test data:

For those kind people who want to check my code or even run them, I gonna put the repository link.