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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 _kmeans(y, k=5):
 """ Helper function to do kmeans in one dimension """


    y = y * 1.  # KMEANS needs float or double dtype
    centroids = KMEANS(y, k)[0]
    centroids.sort()
    try:
        class_ids = np.abs(y - centroids).argmin(axis=1)
    except:
        class_ids = np.abs(y[:, np.newaxis] - centroids).argmin(axis=1)

    uc = np.unique(class_ids)
    cuts = np.array([y[class_ids == c].max() for c in uc])
    y_cent = np.zeros_like(y)
    for c in uc:
        y_cent[class_ids == c] = centroids[c]
    diffs = y - y_cent
    diffs *= diffs

    return class_ids, cuts, diffs.sum(), centroids