Scikit-learn uses a KD Tree or Ball Tree to compute nearest neighbors in O[N log(N)] time. Your algorithm is a direct approach that requires O[N^2] time, and also uses nested for-loops within Python generator expressions which will add significant computational overhead compared to optimized code.

If you'd like to compute weighted k-neighbors classification using a fast O[N log(N)] implementation, you can use sklearn.neighbors.KNeighborsClassifier with the weighted minkowski metric, setting p=2 (for euclidean distance) and setting w to your desired weights. For example:

from sklearn.neighbors import KNeighborsClassifier

model = KNeighborsClassifier(metric='wminkowski', p=2,
                             metric_params=dict(w=weights))
model.fit(X_train, y_train)
y_predicted = model.predict(X_test)

This should work:

# compute inverses of distances
# suppress division by 0 warning, 
# replace np.inf with a very large number
with np.errstate(divide='ignore'):
    dinv = np.nan_to_num(1 / distances)
    
# an array with distinct class labels
distinct_labels = np.array(list(set(labels)))
# an array with labels of neighbors
neigh_labels = labels[indices]
# compute the weighted score for each potential label
weighted_scores = ((neigh_labels[:, :, np.newaxis] == distinct_labels) * dinv[:, :, np.newaxis]).sum(axis=1)
# choose the label with the highest score
predictions = distinct_labels[weighted_scores.argmax(axis=1)]

Consider a simple example with three classifications (red green blue) and the six nearest neighbors denoted by R, G, B. I'll make this linear to simplify visualization and arithmetic

R B G x G R R

The points listed with distance are

class dist
  R     3
  B     2
  G     1
  G     1
  R     2
  R     3

Thus, if we're using unweighted nearest neighbours, the simple "voting" algorithm is 3-2-1 in favor of Red. However, with the weighted influences, we have ...

red_total   = 1/3^2 + 1/2^2 + 1/3^2  = 1/4 + 2/9 ~=  .47
blue_total  = 1/2^2 ..............................=  .25
green_total = 1/1^2 + 1/1^2 ......................= 2.00

... and x winds up as Green due to proximity.

That lower-delta function is merely the classification function; in this simple example, it returns red | green | blue. In a more complex example, ... well, I'll leave that to later tutorials.