docs: def get_independencies(self, condition=None):
          """
          Returns the independent variables in the joint probability distribution.
          Returns marginally independent variables if condition=None.
          Returns conditionally independent variables if condition!=None
  
          Parameter
          ---------
          condition: array_like
                  Random Variable on which to condition the Joint Probability Distribution.
  
          Examples
          --------
          >>> import numpy as np
          >>> from pgmpy.factors.discrete import JointProbabilityDistribution
          >>> prob = JointProbabilityDistribution(['x1', 'x2', 'x3'], [2, 3, 2], np.ones(12)/12)
          >>> prob.get_independencies()
          (x1 _|_ x2)
          (x1 _|_ x3)
          (x2 _|_ x3)
          """
          JPD = self.copy()
          if condition:
              JPD.conditional_distribution(condition)
          independencies = Independencies()
          for variable_pair in itertools.combinations(list(JPD.variables), 2):
              if (JPD.marginal_distribution(variable_pair, inplace=False) ==
                      JPD.marginal_distribution(variable_pair[0], inplace=False) *
                      JPD.marginal_distribution(variable_pair[1], inplace=False)):
                  independencies.add_assertions(variable_pair)
          return independencies