code: def pagerank(
      G,
      alpha=0.85,
      personalization=None,
      max_iter=100,
      tol=1.0e-6,
      nstart=None,
      weight="weight",
      dangling=None,
  ):
      """
      Calls the cugraph pagerank algorithm taking in a networkX object.
      In future releases it will maintain compatibility but will migrate more
      of the workflow to the GPU.
  
      Parameters
      ----------
      G : networkx.Graph
  
      alpha : float, optional (default=0.85)
          The damping factor alpha represents the probability to follow an
          outgoing edge, standard value is 0.85.
          Thus, 1.0-alpha is the probability to “teleport” to a random vertex.
          Alpha should be greater than 0.0 and strictly lower than 1.0.
  
      personalization : dictionary, optional (default=None)
          dictionary comes from networkx is converted to a dataframe
          containing the personalization information.
  
      max_iter : int, optional (default=100)
          The maximum number of iterations before an answer is returned. This can
          be used to limit the execution time and do an early exit before the
          solver reaches the convergence tolerance.
          If this value is lower or equal to 0 cuGraph will use the default
          value, which is 100.
  
      tol : float, optional (default=1e-05)
          Set the tolerance the approximation, this parameter should be a small
          magnitude value.
          The lower the tolerance the better the approximation. If this value is
          0.0f, cuGraph will use the default value which is 1.0E-5.
          Setting too small a tolerance can lead to non-convergence due to
          numerical roundoff. Usually values between 0.01 and 0.00001 are
          acceptable.
  
      nstart : dictionary, optional (default=None)
          dictionary containing the initial guess vertex and value for pagerank.
          Will be converted to a Dataframe before calling the cugraph algorithm
          nstart['vertex'] : cudf.Series
              Subset of vertices of graph for initial guess for pagerank values
          nstart['values'] : cudf.Series
              Pagerank values for vertices
  
      weight: str, optional (default=None)
          This parameter is here for NetworkX compatibility and not
          yet supported in this algorithm
  
      dangling : dict, optional (default=None)
          This parameter is here for NetworkX compatibility and ignored
  
      Returns
      -------
          PageRank : dictionary
                 A dictionary of nodes with the PageRank as value
  
      """
      local_pers = None
      local_nstart = None
      if personalization is not None:
          local_pers = create_cudf_from_dict(personalization)
      if nstart is not None:
          local_nstart = create_cudf_from_dict(nstart)
      return cugraph.pagerank(
          G,
          alpha=alpha,
          personalization=local_pers,
          max_iter=max_iter,
          tol=tol,
          nstart=local_nstart,
          weight=weight,
          dangling=dangling,
      )
  
imports: import cugraph
  import cugraph.utilities
  import cudf
  import numpy
label_desc: Use image node_id 2 for calling a global function with example usage: pagerank(G, alpha, personalization, max_iter, tol, nstart, weight, dangling) and returns: cugraph