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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 image_to_cluster_images(image, min_cluster_size=50, min_thresh=1e-06, max_thresh=1):
 """ Converts an image to several independent images. Produces a unique image for each connected component 1 through N of size > min_cluster_size ANTsR function: `image2ClusterImages` Arguments --------- image : ANTsImage input image 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 Returns ------- list of ANTsImage types Example ------- """

    if not isinstance(image, iio.ANTsImage):
        raise ValueError('image must be ANTsImage type')

    clust = label_clusters(image, min_cluster_size, min_thresh, max_thresh)
    labs = np.unique(clust[clust > 0])

    clustlist = []
    for i in range(len(labs)):
        labimage = image.clone()
        labimage[clust != labs[i]] = 0
        clustlist.append(labimage)
    return clustlist