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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 pagerank(graph, damping_factor=0.85, max_iterations=100, min_delta=0.00001):
 """ Compute and return the PageRank in an directed graph. @type graph: digraph @param graph: Digraph. @type damping_factor: number @param damping_factor: PageRank dumping factor. @type max_iterations: number @param max_iterations: Maximum number of iterations. @type min_delta: number @param min_delta: Smallest variation required to have a new iteration. @rtype: Dict @return: Dict containing all the nodes PageRank. """

    
    nodes = graph.nodes()
    graph_size = len(nodes)
    if graph_size == 0:
        return {}
    min_value = (1.0-damping_factor)/graph_size #value for nodes without inbound links
    
    # itialize the page rank dict with 1/N for all nodes
    pagerank = dict.fromkeys(nodes, 1.0/graph_size)
        
    for i in range(max_iterations):
        diff = 0 #total difference compared to last iteraction
        # computes each node PageRank based on inbound links
        for node in nodes:
            rank = min_value
            for referring_page in graph.incidents(node):
                rank += damping_factor * pagerank[referring_page] / len(graph.neighbors(referring_page))
                
            diff += abs(pagerank[node] - rank)
            pagerank[node] = rank
        
        #stop if PageRank has converged
        if diff < min_delta:
            break
    
    return pagerank