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Solve the following problem using Python, implementing the functions described below, one line at a time
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Description:

def defBoundary(self):
        '''
        Find the borrowing constraint for each current state and save it as an
        attribute of self for use by other methods.

        Parameters
        ----------
        none

        Returns
        -------
        none
        '''
        self.BoroCnstNatAll          = np.zeros(self.StateCount) + np.nan
        # Find the natural borrowing constraint conditional on next period's state
        for j in range(self.StateCount):
            PermShkMinNext         = np.min(self.IncomeDstn_list[j][1])
            TranShkMinNext         = np.min(self.IncomeDstn_list[j][2])
            self.BoroCnstNatAll[j] = (self.solution_next.mNrmMin[j] - TranShkMinNext)*\
                                     (self.PermGroFac_list[j]*PermShkMinNext)/self.Rfree_list[j]

        self.BoroCnstNat_list   = np.zeros(self.StateCount) + np.nan
        self.mNrmMin_list       = np.zeros(self.StateCount) + np.nan
        self.BoroCnstDependency = np.zeros((self.StateCount,self.StateCount)) + np.nan
        # The natural borrowing constraint in each current state is the *highest*
        # among next-state-conditional natural borrowing constraints that could
        # occur from this current state.
        for i in range(self.StateCount):
            possible_next_states         = self.MrkvArray[i,:] > 0
            self.BoroCnstNat_list[i]     = np.max(self.BoroCnstNatAll[possible_next_states])

            # Explicitly handle the "None" case:            
            if self.BoroCnstArt is None:
                self.mNrmMin_list[i]         = self.BoroCnstNat_list[i]
            else:
                self.mNrmMin_list[i]         = np.max([self.BoroCnstNat_list[i],self.BoroCnstArt])
            self.BoroCnstDependency[i,:] = self.BoroCnstNat_list[i] == self.BoroCnstNatAll