In skopt.Optimizer docs it said:

• a (lower_bound, upper_bound, "prior") tuple (for Real dimensions), ...

however if lower_bound and upper_bound are integers it generate an Integer dimension
a reproduceable example:

from skopt import BayesSearchCV
from sklearn.datasets import load_iris
from sklearn.svm import SVC

X, y = load_iris(return_X_y=True)

searchcv = BayesSearchCV(
    SVC(gamma='scale'),
    search_spaces={'C': (1, 100, 'log-uniform')},
    n_iter=10,
    cv=5,
    scoring='f1_macro',
    random_state=42
)
searchcv.fit(X, y)
print(searchcv.cv_results_['param_C'])

it shows a list of integer values.
Another bug:
with the same code above, when replacing (1, 100, 'log-uniform') with (1.1, 100, 'log-uniform'), it produce a ValueError: All values shouldbe greater than 0.041393