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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 top_path(sources, sinks, net_flux):
 """ Use the Dijkstra algorithm for finding the shortest path connecting a set of source states from a set of sink states. Parameters sources : array_like, int One-dimensional list of nodes to define the source states. sinks : array_like, int One-dimensional list of nodes to define the sink states. net_flux : np.ndarray, shape = [n_states, n_states] Net flux of the MSM Returns ------- top_path : np.ndarray Array corresponding to the top path between sources and sinks. It is an array of states visited along the path. flux : float Flux traveling through this path -- this is equal to the minimum flux over edges in the path. See Also -------- msmbuilder.tpt.paths : function for calculating many high flux paths through a network. References .. [1] Weinan, E. and Vanden-Eijnden, E. Towards a theory of transition paths. J. Stat. Phys. 123, 503-523 (2006). .. [2] Metzner, P., Schutte, C. & Vanden-Eijnden, E. Transition path theory for Markov jump processes. Multiscale Model. Simul. 7, 1192-1219 (2009). .. [3] Berezhkovskii, A., Hummer, G. & Szabo, A. Reactive flux and folding pathways in network models of coarse-grained protein dynamics. J. Chem. Phys. 130, 205102 (2009). .. [4] Dijkstra, E. W. A Note on Two Problems in Connexion with Graphs. Numeriche Mathematik 1, 269-271 (1959). .. [5] Noe, Frank, et al. "Constructing the equilibrium ensemble of folding pathways from short off-equilibrium simulations." PNAS 106.45 (2009):
 19011-19016. """

    sources = np.array(sources, dtype=np.int).reshape((-1,))
    sinks = np.array(sinks, dtype=np.int).reshape((-1,))

    n_states = net_flux.shape[0]

    queue = list(sources)
    # nodes to check (the "queue")
    # going to use list.pop method so I can't keep it as an array

    visited = np.zeros(n_states).astype(np.bool)
    # have we already checked this node?

    previous_node = np.ones(n_states).astype(np.int) * -1
    # what node was found before finding this one

    min_fluxes = np.ones(n_states) * -1 * np.inf
    # what is the flux of the highest flux path
    # from this node to the source set.

    min_fluxes[sources] = np.inf
    # source states are connected to the source
    # so this distance is zero which means the flux is infinite

    while len(queue) > 0: # iterate until there's nothing to check anymore

        test_node = queue.pop(min_fluxes[queue].argmax())
        # find the node in the queue that has the
        # highest flux path to it from the source set

        visited[test_node] = True

        if np.all(visited[sinks]):
            # if we've visited all of the sink states, then we just have to choose
            # the path that goes to the sink state that is closest to the source
            break

        # if test_node in sinks: # I *think* we want to break ... or are there paths we still
        # need to check?
        # continue
        # I think if sinks is more than one state we have to check everything

        # now update the distances for each neighbor of the test_node:
        neighbors = np.where(net_flux[test_node, :] > 0)[0]
        if len(neighbors) == 0:
            continue

        new_fluxes = net_flux[test_node, neighbors].flatten()
        # flux from test_node to each neighbor

        new_fluxes[np.where(new_fluxes > min_fluxes[test_node])] = min_fluxes[test_node]
        # previous step to get to test_node was lower flux, so that is still the path flux

        ind = np.where((1 - visited[neighbors]) & (new_fluxes > min_fluxes[neighbors]))
        min_fluxes[neighbors[ind]] = new_fluxes[ind]

        previous_node[neighbors[ind]] = test_node
        # each of these neighbors came from this test_node
        # we don't want to update the nodes that have already been visited

        queue.extend(neighbors[ind])

    top_path = []
    # populate the path in reverse
    top_path.append(int(sinks[min_fluxes[sinks].argmax()]))
    # find the closest sink state

    while previous_node[top_path[-1]] != -1:
        top_path.append(previous_node[top_path[-1]])

    return np.array(top_path[::-1]), min_fluxes[top_path[0]]