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 list of tuple of child variable and CPD in case of Bayesian
  and list of tuple of scope of variables and values in case of Markov.
  
  Returns
  -------
  list : list of tuples of child variable and values in Bayesian
      list of tuples of scope of variables and values in case of Markov.
  
  Examples
  --------
  >>> from pgmpy.readwrite import UAIReader
  >>> reader = UAIReader('TestUAI.uai')
  >>> reader.get_tables()
  [(['var_0', 'var_1'], ['4.000', '2.400', '1.000', '0.000']),
   (['var_0', 'var_1', 'var_2'],
    ['2.2500', '3.2500', '3.7500', '0.0000', '0.0000', '10.0000',
     '1.8750', '4.0000', '3.3330', '2.0000', '2.0000', '3.4000'])]
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code: def get_tables(self):
      """
      Returns list of tuple of child variable and CPD in case of Bayesian
      and list of tuple of scope of variables and values in case of Markov.
  
      Returns
      -------
      list : list of tuples of child variable and values in Bayesian
          list of tuples of scope of variables and values in case of Markov.
  
      Examples
      --------
      >>> from pgmpy.readwrite import UAIReader
      >>> reader = UAIReader('TestUAI.uai')
      >>> reader.get_tables()
      [(['var_0', 'var_1'], ['4.000', '2.400', '1.000', '0.000']),
       (['var_0', 'var_1', 'var_2'],
        ['2.2500', '3.2500', '3.7500', '0.0000', '0.0000', '10.0000',
         '1.8750', '4.0000', '3.3330', '2.0000', '2.0000', '3.4000'])]
      """
      tables = []
      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])
              values = self.grammar.parseString(self.network)[
                  "fun_values_" + str(function)
              ]
              tables.append((child_var, list(values)))
          elif self.network_type == "MARKOV":
              function_variables = [
                  "var_" + str(var) for var in function_variables
              ]
              values = self.grammar.parseString(self.network)[
                  "fun_values_" + str(function)
              ]
              tables.append((function_variables, list(values)))
      return tables
  
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 7 for calling the UAIReader obj's underlying member method code with example usage: obj.get_tables() and returns: tables