code: def estimate(self, X, **params):
          
          return super(TRAM, self).estimate(X, **params)
docs: Parameters
          ----------
          X : tuple of (ttrajs, dtrajs, btrajs)
              Simulation trajectories. ttrajs contain the indices of the thermodynamic state, dtrajs
              contains the indices of the configurational states and btrajs contain the biases.
  
              ttrajs : list of numpy.ndarray(X_i, dtype=int)
                  Every element is a trajectory (time series). ttrajs[i][t] is the index of the
                  thermodynamic state visited in trajectory i at time step t.
              dtrajs : list of numpy.ndarray(X_i, dtype=int)
                  dtrajs[i][t] is the index of the configurational state (Markov state) visited in
                  trajectory i at time step t.
              btrajs : list of numpy.ndarray((X_i, T), dtype=numpy.float64)
                  For every simulation frame seen in trajectory i and time step t, btrajs[i][t,k] is the
                  bias energy of that frame evaluated in the k'th thermodynamic state (i.e. at the k'th
                  Umbrella/Hamiltonian/temperature).