GridSearchCV with custom Kernel

I have the following code:

import numpy as np
from sklearn import svm
from sklearn import datasets
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score

def tanimotoKernel(xs, ys):
    a = 0
    b = 0
    for x, y in zip(xs, ys):
        a += min(x, y)
        b += max(x, y)
    return a / b

def tanimotoLambdaKernel(xs,ys, gamma = 0.01):
    return np.exp(gamma * tanimotoKernel(xs,ys)) / (np.exp(gamma) - 1)

def GramMatrix(X1, X2, K_function=tanimotoLambdaKernel):
    gram_matrix = np.zeros((X1.shape[0], X2.shape[0]))
    for i, x1 in enumerate(X1):
        for j, x2 in enumerate(X2):
            gram_matrix[i, j] = K_function(x1, x2)
    return gram_matrix

X, y = datasets.load_iris(return_X_y=True)
x_train, x_test, y_train, y_test = train_test_split(X, y)
clf.fit(x_train, y_train)
accuracy_score(clf.predict(x_test), y_test)

clf = svm.SVC(kernel=GramMatrix)

However, I would like to be able to tune the gamma parameter of tanimotoLambdaKernel with GridSearchCV, because I don't want to have to manually test the parameter, check for the accuracy, etc. Is there any way to do this?

from GridSearchCV with custom Kernel