class_docstr: A PageRankModel object contains the pagerank value for each vertex.
  The pagerank value characterizes the importance of a vertex
  in the graph using the following recursive definition:
  
      .. math::
        pr(i) =  reset_probability + (1-reset_probability) \sum_{j\in N(i)} pr(j) / out_degree(j)
  
  where :math:`N(i)` is the set containing all vertices :math:`j` such that
  there is an edge going from :math:`j` to :math:`i`. Self edges (i.e., edges
  where the source vertex is the same as the destination vertex) and repeated
  edges (i.e., multiple edges where the source vertices are the same and the
  destination vertices are the same) are treated like normal edges in the
  above recursion.
  
  Currently, edge weights are not taken into account when computing the
  PageRank.
  
  Below is a list of queryable fields for this model:
  
  +-------------------+-----------------------------------------------------------+
  | Field             | Description                                               |
  +===================+===========================================================+
  | reset_probability | The probability of random jumps to any node in the graph  |
  +-------------------+-----------------------------------------------------------+
  | graph             | A new SGraph with the pagerank as a vertex property       |
  +-------------------+-----------------------------------------------------------+
  | delta             | Total changes in pagerank during the last iteration       |
  |                   | (the L1 norm of the changes)                              |
  +-------------------+-----------------------------------------------------------+
  | pagerank          | An SFrame with each vertex's pagerank                     |
  +-------------------+-----------------------------------------------------------+
  | num_iterations    | Number of iterations                                      |
  +-------------------+-----------------------------------------------------------+
  | threshold         | The convergence threshold in L1 norm                      |
  +-------------------+-----------------------------------------------------------+
  | training_time     | Total training time of the model                          |
  +-------------------+-----------------------------------------------------------+
  | max_iterations    | The maximum number of iterations to run                   |
  +-------------------+-----------------------------------------------------------+
  
  
  This model cannot be constructed directly.  Instead, use
  :func:`turicreate.pagerank.create` to create an instance
  of this model. A detailed list of parameter options and code samples
  are available in the documentation for the create function.
  
  See Also
  --------
  create
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code: def _load_version(cls, state, version):
      assert version == 0
      return cls(state["model"])
  
file_path: turicreate/src/python/turicreate/toolkits/graph_analytics/pagerank.py
imports: from __future__ import print_function
  from __future__ import division
  from __future__ import absolute_import
  import turicreate
  from turicreate.data_structures.sgraph import SGraph
  from turicreate.toolkits.graph_analytics._model_base import GraphAnalyticsModel
label_desc: Use image node_id 8 for calling the PagerankModel obj's underlying member method code with example usage: obj._load_version(cls, state, version) and returns: cls