class_docstr: Init method for the base class of Elimination Orders.
  
  Parameters
  ----------
  model: BayesianNetwork instance
      The model on which we want to compute the elimination orders.
docstr: Return edges needed to be added to the graph if a node is removed.
  
  Parameters
  ----------
  node: string (any hashable python object)
      Node to be removed from the graph.
docstr_tok: ["Return","edges","needed","to","be","added","to","the","graph","if","a","node","is","removed",".","Parameters","--","--","--","--","--","node",":","string","(","any","hashable","python","object",")","Node","to","be","removed","from","the","graph","."]
code: def fill_in_edges(self, node):
      """
      Return edges needed to be added to the graph if a node is removed.
  
      Parameters
      ----------
      node: string (any hashable python object)
          Node to be removed from the graph.
      """
      return combinations(self.bayesian_model.neighbors(node), 2)
  
imports: from abc import abstractmethod
  from itertools import combinations
  import numpy
  from tqdm.auto import tqdm
  from pgmpy import config
  from pgmpy.models import BayesianNetwork
label_desc: Use image node_id 4 for calling the BaseEliminationOrder obj's underlying member method code with example usage: obj.fill_in_edges(node) and returns: combinations