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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 label_clusters(image, min_cluster_size=50, min_thresh=1e-6, max_thresh=1, fully_connected=False):
 """ This will give a unique ID to each connected component 1 through N of size > min_cluster_size ANTsR function: `labelClusters` Arguments --------- image : ANTsImage input image e.g. a statistical map min_cluster_size : integer throw away clusters smaller than this value min_thresh : scalar threshold to a statistical map max_thresh : scalar threshold to a statistical map fully_connected : boolean boolean sets neighborhood connectivity pattern Returns ------- ANTsImage Example ------- """

    dim = image.dimension
    clust = threshold_image(image, min_thresh, max_thresh)
    temp = int(fully_connected)
    args = [dim, clust, clust, min_cluster_size, temp]
    processed_args = _int_antsProcessArguments(args)
    libfn = utils.get_lib_fn('LabelClustersUniquely')
    libfn(processed_args)
    return clust