How to use the skypy.deref_json function in skypy

def skypy_main():

    wc_source = skypy.sync_exec("grab", {"urls":["http://www.gutenberg.org/cache/epub/4908/pg4908.html"], "version":0}, 1)
    wc_input = skypy.deref_json(wc_source[0]) # Yields a single reference
    wc_result = skypy.sync_exec("stdinout", {"inputs": [wc_input], "command_line":["wc", "-c"]}, 1)
    return skypy.deref_json(wc_result[0])

def skypy_main():

    wc_source = skypy.spawn_exec("grab", {"urls":["http://www.gutenberg.org/cache/epub/4908/pg4908.html"], "version":0}, 1)
    wc_input = skypy.deref_json(wc_source[0]) # Yields a single reference
    wc_result = skypy.spawn_exec("stdinout", {"inputs": [wc_input], "command_line":["wc", "-c"]}, 1)
    return skypy.deref_json(wc_result[0])

def skypy_main():

    wc_source = skypy.spawn_exec("grab", {"urls":["http://www.gutenberg.org/cache/epub/4908/pg4908.html"], "version":0}, 1)
    wc_input = skypy.deref_json(wc_source[0]) # Yields a single reference
    wc_result = skypy.spawn_exec("stdinout", {"inputs": [wc_input], "command_line":["wc", "-c"]}, 1)
    return skypy.deref_json(wc_result[0])

return {"cluster" : result[0], "converged" : skypy.deref(result[1])}
	
	new_clusters_and_decisions = mapreduce(data_chunks, kmeans_map, kmeans_reduce, num_reducers)

	new_clusters = [x["decision"] for x in new_clusters_and_decisions]
        converged = True
        for x in new_clusters_and_decisions:
            if not x["converged"]:
                converged = False
                break

	return {"converged" : converged, "clusters" : new_clusters[0]}

    # TODO: Something about this
    input_vectors = skypy.deref_json(grab(vector_in))
    input_clusters = (skypy.deref_json(grab(cluster_in)))[0]

    r = 1
    i = 0

    old_clusters = input_clusters
    converged = False
    while i < 10 and not converged:
        result = kmeans_iteration(input_vectors, old_clusters, convergence_delta, r)
        converged = result["converged"]
        old_clusters = result["clusters"]
	i += 1

    return old_clusters

def skypy_main():

    wc_source = skypy.sync_exec("grab", {"urls":["http://www.gutenberg.org/cache/epub/4908/pg4908.html"], "version":0}, 1)
    wc_input = skypy.deref_json(wc_source[0]) # Yields a single reference
    wc_result = skypy.sync_exec("stdinout", {"inputs": [wc_input], "command_line":["wc", "-c"]}, 1)
    return skypy.deref_json(wc_result[0])

def skypy_main():
    sw_ret = skypy.sync_exec("swi", sw_file_ref=skypy.package_lookup("sw_main"))
    sw_str = skypy.deref_json(sw_ret)
    return "SW returned: %s" % str(sw_str)

2)
            return {"cluster" : result[0], "converged" : skypy.deref(result[1])}
	
	new_clusters_and_decisions = mapreduce(data_chunks, kmeans_map, kmeans_reduce, num_reducers)

	new_clusters = [x["decision"] for x in new_clusters_and_decisions]
        converged = True
        for x in new_clusters_and_decisions:
            if not x["converged"]:
                converged = False
                break

	return {"converged" : converged, "clusters" : new_clusters[0]}

    # TODO: Something about this
    input_vectors = skypy.deref_json(grab(vector_in))
    input_clusters = (skypy.deref_json(grab(cluster_in)))[0]

    r = 1
    i = 0

    old_clusters = input_clusters
    converged = False
    while i < 10 and not converged:
        result = kmeans_iteration(input_vectors, old_clusters, convergence_delta, r)
        converged = result["converged"]
        old_clusters = result["clusters"]
	i += 1

    return old_clusters

def grab(url):
    ref = (skypy.spawn_exec("grab", {"urls": [url], "version": 0}, 1))[0]
    return (skypy.deref_json(ref))