docs: def add_variables(self, variables, cardinality, inhibitor_probability):
          """
          Adds variables to the NoisyOrModel.
  
          Parameters
          ----------
          variables: list, tuple, dict (array like)
              array containing names of the variables that are to be added.
  
          cardinality: list, tuple, dict (array like)
              array containing integers representing the cardinality
              of the variables.
  
          inhibitor_probability: list, tuple, dict (array_like)
              array containing the inhibitor probabilities corresponding to each variable.
  
          Examples
          --------
          >>> from pgmpy.models import NoisyOrModel
          >>> model = NoisyOrModel(['x1', 'x2', 'x3'], [2, 3, 2], [[0.6, 0.4],
          ...                                                      [0.2, 0.4, 0.7],
          ...                                                      [0.1, 0. 4]])
          >>> model.add_variables(['x4'], [3], [0.1, 0.4, 0.2])
          """
          if len(variables) == 1:
              if not isinstance(inhibitor_probability[0], (list, tuple)):
                  inhibitor_probability = [inhibitor_probability]
  
          if len(variables) != len(cardinality):
              raise ValueError("Size of variables and cardinality should be same")
          elif any(cardinal != len(prob_array) for prob_array, cardinal in zip(inhibitor_probability, cardinality)) or \
                  len(cardinality) != len(inhibitor_probability):
              raise ValueError("Size of variables and inhibitor_probability should be same")
          elif not all(0 <= item <= 1 for item in chain.from_iterable(inhibitor_probability)):
              raise ValueError("Probability values should be between 0 and 1(both inclusive).")
          else:
              self.variables = np.concatenate((self.variables, variables))
              self.cardinality = np.concatenate((self.cardinality, cardinality))
              self.inhibitor_probability.extend(inhibitor_probability)