def remove_cpds(self, *cpds):
        """
        Removes the cpds that are provided in the argument.

        Parameters
        ----------
        *cpds : list, set, tuple (array-like)
            List of CPDs which are to be associated with the model. Each CPD
            should be an instance of `TabularCPD`.

        Examples
        --------
        >>> from pgmpy.models import DynamicBayesianNetwork as DBN
        >>> from pgmpy.factors.discrete import TabularCPD
        >>> dbn = DBN()
        >>> dbn.add_edges_from([(('D',0),('G',0)),(('I',0),('G',0)),(('D',0),('D',1)),(('I',0),('I',1))])
        >>> grade_cpd =  TabularCPD(('G',0), 3, [[0.3,0.05,0.9,0.5],
        ...                                      [0.4,0.25,0.8,0.03],
        ...                                      [0.3,0.7,0.02,0.2]], [('I', 0),('D', 0)],[2,2])
        >>> dbn.add_cpds(grade_cpd)
        >>> dbn.get_cpds()
        [<TabularCPD representing P(('G', 0):3 | ('I', 0):2, ('D', 0):2) at 0x3348ab0>]
        >>> dbn.remove_cpds(grade_cpd)
        >>> dbn.get_cpds()
        []
        """
        for cpd in cpds:
            if isinstance(cpd, (tuple, list)):
                cpd = self.get_cpds(cpd)
            self.cpds.remove(cpd)
Removes the cpds that are provided in the argument.

        Parameters
        ----------
        *cpds : list, set, tuple (array-like)
            List of CPDs which are to be associated with the model. Each CPD
            should be an instance of `TabularCPD`.

        Examples
        --------
        >>> from pgmpy.models import DynamicBayesianNetwork as DBN
        >>> from pgmpy.factors.discrete import TabularCPD
        >>> dbn = DBN()
        >>> dbn.add_edges_from([(('D',0),('G',0)),(('I',0),('G',0)),(('D',0),('D',1)),(('I',0),('I',1))])
        >>> grade_cpd =  TabularCPD(('G',0), 3, [[0.3,0.05,0.9,0.5],
        ...                                      [0.4,0.25,0.8,0.03],
        ...                                      [0.3,0.7,0.02,0.2]], [('I', 0),('D', 0)],[2,2])
        >>> dbn.add_cpds(grade_cpd)
        >>> dbn.get_cpds()
        [<TabularCPD representing P(('G', 0):3 | ('I', 0):2, ('D', 0):2) at 0x3348ab0>]
        >>> dbn.remove_cpds(grade_cpd)
        >>> dbn.get_cpds()
        []