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Solve the following problem using Python, implementing the functions described below, one line at a time
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Description:

def get_initial_centroids(self):
        '''Randomly choose k data points as initial centroids'''
        if self.seed is not None: # useful for obtaining consistent results
            np.random.seed(self.seed)
        n = self.data.shape[0] # number of data points
        
        # Pick K indices from range [0, N).
        rand_indices = np.random.randint(0, n, self.k)
    
        # Keep centroids as dense format, as many entries will be nonzero due to averaging.
        # As long as at least one document in a cluster contains a word,
        # it will carry a nonzero weight in the TF-IDF vector of the centroid.
        centroids = self.data[rand_indices,:].toarray()
        self.centroids=centroids
        return centroids