code: def getControls(self):
          '''
          Calculates consumption for the representative agent using the consumption functions.
          Takes the weighted average of cLvl across perceived Markov states.
  
          Parameters
          ----------
          None
  
          Returns
          -------
          None
          '''
          StateCount = self.MrkvArray.shape[0]
          t = self.t_cycle[0]
  
          cNrmNow = np.zeros(StateCount) # Array of chosen cNrm by Markov state
          for i in range(StateCount):
              cNrmNow[i] = self.solution[t].cFunc[i](self.mNrmNow[i])
          self.cNrmNow = cNrmNow
          self.cLvlNow = np.dot(cNrmNow*self.pLvlNow,self.MrkvPcvdf getControls(self):
          '''
          Calculates consumption for the representative agent using the consumption functions.
          Takes the weighted average of cLvl across perceived Markov states.
  
          Parameters
          ----------
          None
  
          Returns
          -------
          None
          '''
          StateCount = self.MrkvArray.shape[0]
          t = self.t_cycle[0]
  
          cNrmNow = np.zeros(StateCount) # Array of chosen cNrm by Markov state
          for i in range(StateCount):
              cNrmNow[i] = self.solution[t].cFunc[i](self.mNrmNow[i])
          self.cNrmNow = cNrmNow
          self.cLvlNow = np.dot(cNrmNow*self.pLvlNow,self.MrkvPcvd)
docs: Calculates consumption for the representative agent using the consumption functions.
          Takes the weighted average of cLvl across perceived Markov states.
  
          Parameters
          ----------
          None
  
          Returns
          -------
          None