def get_labels(data, centroids,K):
distances = np.sqrt(((data - centroids[:, np.newaxis])**<NUM_LIT:2>).sum(axis=<NUM_LIT:2>))<EOL>return np.argmin(distances, axis=<NUM_LIT:0>)<EOL>
Returns a label for each piece of data in the dataset

Parameters
------------
data: array-like, shape= (m_samples,n_samples)
K: integer
    number of K clusters  
centroids: array-like, shape=(K, n_samples)     

returns
-------------
labels: array-like, shape (1,n_samples)