KNeighborsTimeSeriesClassifier with DTW slowness

Greetings
As part of testing of different machine learning models I test the KNN model together with DTW and encounter a great slowness in the predictor function of the classifier, I would be very grateful if you could explain to me why there is such a great slowness and how to calibrate the classifier in such a way that it is not slow, attach the code That I run
Thank you
Lior

import numpy as np
from tslearn.generators import random_walk_blobs
from tslearn.neighbors import KNeighborsTimeSeriesClassifier, KNeighborsTimeSeries
import time

np.random.seed(0)
n_ts_per_blob, sz = 20000, 100 #, 1, 1 # , d, n_blobs
X, y = random_walk_blobs(n_ts_per_blob=n_ts_per_blob, sz=sz)
print(y)
print(X.shape)
print(len(y))

start_time = time.time()
knn = KNeighborsTimeSeriesClassifier(n_neighbors=1,metric="dtw",metric_params=dict({"global_constraint":"sakoe_chiba","sakoe_chiba_radius":3}),n_jobs=-1)
end_time = time.time()
print("Creating the Classifier took: {} seconds".format(end_time - start_time))

start_time = time.time()
knn.fit(X,y)
end_time = time.time()
print("FIT function took: {} seconds".format(end_time - start_time))

start_time = time.time()
y_predict = knn.predict(X)
end_time = time.time()
print("Predict function took: {} seconds".format(end_time - start_time))

Start by running the supplied Python reproduction with KNeighborsTimeSeriesClassifier, DTW, the Sakoe-Chiba constraint, and n_jobs=-1. Trace the classifier's fit and predict paths to identify the source of the reported prediction slowness; done means explaining the cause and documenting or validating a concrete calibration or change with timing results.