How to perform Grid Search on NLP CRF model

I am trying to perform hyperparameter tuning on sklearn_crfsuite.CRF model. When I try to execute below code, it doesn't give any exception but it probably fails to perform fit. And due to which, if I try to get best estimator from grid search, it doesn't work.

%%time
# define fixed parameters and parameters to search
crf = sklearn_crfsuite.CRF(
    algorithm='lbfgs',
    max_iterations=100,
    all_possible_transitions=True
)
params_space = {
    c1: [0,0.05,0.1, 0.25,0.5,1],
    c2: [0,0.05,0.1, 0.25,0.5,1]
}

# use the same metric for evaluation
f1_scorer = make_scorer(metrics.flat_f1_score,
                        average='weighted', labels=labels)

# search
grid_search = GridSearchCV(estimator=crf,
                           param_grid=params_space,
                           cv=3,
                           n_jobs=-1, verbose=1,scoring=f1_scorer)

#grid_search.fit(X_train, Y_train)
#above code throws exception, which seems to be a open bug in latest version of scikit-learn 0.24.0 or later.
#github link for bug: https://github.com/TeamHG-Memex/sklearn-crfsuite/issues/60
try:
    grid_search.fit(X_train, Y_train)
except AttributeError as e:
     if 'CRF' object has no attribute 'keep_tempfiles' not in str(e):
        raise

Any help would be appreciated, how can I perform hyperparameter tuning here?

I took reference using this tutorial, but stuck in same situation.