YOUR CODE HERE:

# Use the distances to calculate and then store the labels of

# the k$-$nearest neighbors to the ith test point. The function

# numpy.argsort may be useful.

#

# After doing this, find the most common label of the k$-$nearest

# neighbors. Store the predicted label of the ith training example

# as y$\_$pred$[$i$].$ Break ties by choosing the smaller label.

# Find indices of k$-$nearest neighbors using argsort