code: def get_labels(data, centroids,K):
      
      distances = np.sqrt(((data - centroids[:, np.newaxis])**2).sum(axis=2))
      return np.argmin(distances, axis=0)
docs: 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)