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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_segmentation(image, k, kmask=None, mrf=0.1):
 """ K-means image segmentation that is a wrapper around `ants.atropos` ANTsR function: `kmeansSegmentation` Arguments --------- image : ANTsImage input image k : integer integer number of classes kmask : ANTsImage (optional) segment inside this mask mrf : scalar smoothness, higher is smoother Returns ------- ANTsImage Example ------- """

    dim = image.dimension
    kmimage = utils.iMath(image, 'Normalize')
    if kmask is None:
        kmask = utils.get_mask(kmimage, 0.01, 1, cleanup=2)
    kmask = utils.iMath(kmask, 'FillHoles').threshold_image(1,2)
    nhood = 'x'.join(['1']*dim)
    mrf = '[%s,%s]' % (str(mrf), nhood)
    kmimage = atropos(a = kmimage, m = mrf, c = '[5,0]', i = 'kmeans[%s]'%(str(k)), x = kmask)
    kmimage['segmentation'] = kmimage['segmentation'].clone(image.pixeltype)
    return kmimage