argument of type 'FakedGensimDict' Error in calculate a metric to evaluate the coherence of each topic

Question

I'm trying to calculate a metric to evaluate the coherence of each topic in my corpus in this code:

import tmtoolkit
from tmtoolkit.topicmod.evaluate import metric_coherence_gensim
def topic_model_coherence_generator(topic_num_start=2,
                                topic_num_end=6,
                                norm_corpus='',
                                cv_matrix='',
                                cv=''):
norm_corpus_tokens = [doc11.split() for doc11 in norm_corpus]
models = []
coherence_scores = []

for i in range(topic_num_start, topic_num_end):
    print(i)
    cur_lda = LatentDirichletAllocation(n_components=i,
                                        max_iter=10000,
                                        random_state=0)
    cur_lda.fit_transform(cv_matrix)
    cur_coherence_score = metric_coherence_gensim(
        measure='c_v',
        top_n=5,
        topic_word_distrib=cur_lda.components_,
        dtm=cv.fit_transform(norm_corpus),
        vocab=np.array(cv.get_feature_names()),
        texts=norm_corpus_tokens)
    models.append(cur_lda)
    coherence_scores.append(np.mean(cur_coherence_score)
return models, coherence_scores

%%time
ts = 2
te = 10
models, coherence_scores = topic_model_coherence_generator(
    ts, te, norm_corpus=norm_corpus, cv=cv, cv_matrix=cv_matrix)

it display this error: TypeError: argument of type 'FakedGensimDict' is not iterable

Can anyone help me to fix this code ??