Is it possible to add categorical data to time-series classification

I have a dataset, however each time-series has additional category features.
Is it possible to take them into account when making classification?
For example:

from tslearn.utils import to_time_series_dataset
X = to_time_series_dataset([[1, 2, 3, 4], [1, 2, 3], [2, 5, 6, 7, 8, 9]])
y = [0, 0, 1]
features_vec = ['a','c','q']
X = X.add_features_data(features_vec) #the first time-series has the category 'a', the second 'c' and the third 'q'
from tslearn.neighbors import KNeighborsTimeSeriesClassifier
knn = KNeighborsTimeSeriesClassifier(n_neighbors=2)
knn.fit(X, y)

The issue names no implementation file, test, or entry point. First inspect the time-series classifier APIs and data representations to determine whether categorical metadata can be supported; done means an agreed design, implementation, and coverage for fitting and predicting with the additional categories.