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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 _cum_net_resp(node_lis, instance=0):
 """ Compute the cumulative network response by reading saved energy .npy files. :type node_lis: numpy.ndarray :param node_lis: List of nodes (ints) to read from :type instance: int :param instance: Instance flag for parallel workflows, defaults to 0. :returns: cumulative network response :rtype: numpy.ndarray :returns: node indices for each sample of the cumulative network response. :rtype: list """

    cum_net_resp = np.load('tmp' + str(instance) +
                           '/node_' + str(node_lis[0]) + '.npy')[0]
    os.remove('tmp' + str(instance) + '/node_' + str(node_lis[0]) + '.npy')
    indices = np.ones(len(cum_net_resp)) * node_lis[0]
    for i in node_lis[1:]:
        node_energy = np.load('tmp' + str(instance) + '/node_' +
                              str(i) + '.npy')[0]
        updated_indices = np.argmax([cum_net_resp, node_energy], axis=0)
        temp = np.array([cum_net_resp, node_energy])
        cum_net_resp = np.array([temp[updated_indices[j]][j]
                                 for j in range(len(updated_indices))])
        del temp, node_energy
        updated_indices[updated_indices == 1] = i
        indices = updated_indices
        os.remove('tmp' + str(instance) + '/node_' + str(i) + '.npy')
    return cum_net_resp, indices