class_docstr: Initialize an instance of UAI reader class
  
  Parameters
  ----------
  path : file or str
      Path of the file containing UAI information.
  
  string : str
      String containing UAI information.
  
  Examples
  --------
  >>> from pgmpy.readwrite import UAIReader
  >>> reader = UAIReader('TestUai.uai')
  >>> model = reader.get_model()
  
  Reference
  ---------
  [1] https://uaicompetition.github.io/uci-2022/file-formats/model-format/
  [2] https://forgemia.inra.fr/thomas.schiex/toulbar2/-/blob/master/doc/UAI08Format.txt
docstr: Returns the edges of the network.
  
  Returns
  -------
  set: set containing the edges of the network
  
  Examples
  --------
  >>> from pgmpy.readwrite import UAIReader
  >>> reader = UAIReader('TestUAI.uai')
  >>> reader.get_edges()
  {('var_0', 'var_1'), ('var_0', 'var_2'), ('var_1', 'var_2')}
docstr_tok: ["Returns","the","edges","of","the","network",".","Returns","--","--","--","-","set",":","set","containing","the","edges","of","the","network","Examples","--","--","--","--",">",">",">","from","pgmpy.readwrite","import","UAIReader",">",">",">","reader","=","UAIReader","(","'TestUAI.uai","'",")",">",">",">","reader.get_edges","(",")","{","(","'var_0","'",",","'var_1","'",")",",","(","'var_0","'",",","'var_2","'",")",",","(","'var_1","'",",","'var_2","'",")","}"]
code: def get_edges(self):
      """
      Returns the edges of the network.
  
      Returns
      -------
      set: set containing the edges of the network
  
      Examples
      --------
      >>> from pgmpy.readwrite import UAIReader
      >>> reader = UAIReader('TestUAI.uai')
      >>> reader.get_edges()
      {('var_0', 'var_1'), ('var_0', 'var_2'), ('var_1', 'var_2')}
      """
      edges = []
      for function in range(0, self.no_functions):
          function_variables = self.grammar.parseString(self.network)[
              "fun_" + str(function)
          ]
          if isinstance(function_variables, int):
              function_variables = [function_variables]
          if self.network_type == "BAYES":
              child_var = "var_" + str(function_variables[-1])
              function_variables = function_variables[:-1]
              for var in function_variables:
                  edges.append(("var_" + str(var), child_var))
          elif self.network_type == "MARKOV":
              function_variables = [
                  "var_" + str(var) for var in function_variables
              ]
              edges.extend(list(combinations(function_variables, 2)))
      return set(edges)
  
imports: from itertools import combinations
  import numpy
  from pyparsing import Combine, Literal, Optional, Regex, Word, alphas, nums
  from pgmpy.factors.discrete import DiscreteFactor, TabularCPD
  from pgmpy.models import BayesianNetwork, MarkovNetwork
  from pgmpy.utils import compat_fns
label_desc: Use image node_id 6 for calling the UAIReader obj's underlying member method code with example usage: obj.get_edges() and returns: set