human
Solve the following python programming problem given the following code:
def clustered_sortind(x, k = 10, scorefunc = None) : 
    '
    Uses MiniBatch k-means clustering to cluster matrix into groups.

    Each cluster of rows is then sorted by `scorefunc` -- by default, the max
    peak height when all rows in a cluster are averaged, or
    cluster.mean(axis=0).max().

    Returns the index that will sort the rows of `x` and a list of "breaks".
    `breaks` is essentially a cumulative row count for each cluster boundary.
    In other words, after plotting the array you can use axhline on each
    "break" to plot the cluster boundary.

    If `k` is a list or tuple, iteratively try each one and select the best
    with the lowest mean distance from cluster centers.

    :param x: Matrix whose rows are to be clustered
    :param k: Number of clusters to create or a list of potential clusters.
the
        optimum will be chosen from the list
    :param scorefunc: Optional function for sorting rows within clusters.  Must
        accept a single argument of a NumPy array.
    ' 
    try : 
        from sklearn.cluster import MiniBatchKMeans 
except ImportError : 
        raise ImportError ('please install scikits.learn for clustering.') 
if isinstance (k, int) : 
        best_k = k 
else : 
        mean_dists = { 
            
} 
        for _k in k : 
            mbk = MiniBatchKMeans (init = 'k-means++', n_clusters = _k) 
            mbk.fit (x) 
            mean_dists [score] = mbk.transform (x).mean () 
best_k = sorted (mean_dists.items (), key = (lambda x : x [1])) [(- 1)] [0] 
mbk = MiniBatchKMeans (init = 'k-means++', n_clusters = best_k) 
    mbk.fit (x) 
    k = best_k 
    labels = mbk.labels_ 
    scores = np.zeros (labels.shape, dtype = float) 
    if (not scorefunc) : 
        def scorefunc(x) : 
            return x.mean (axis = 0).max () 
for label in range (k) : 
        ind = (labels == label) 
        score = scorefunc (x [ind, :]) 
        scores [ind] = score 
pos = 0 
    breaks = [] 
    ind = np.argsort (scores) 
    for (k, g) in itertools.groupby (labels...

    
    

    Fix the buggy line: mean_dists [score] = mbk.transform (x).mean ()
assistant
#Here is Python program to fix the buggy line: mean_dists [score] = mbk.

mean_dists [_k] = mbk.transform (x).mean ()