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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 _compute(self):
 """Computes the state variable tendencies in time for implicit processes. To calculate the new state the :func:`_implicit_solver()` method is called for daughter classes. This however returns the new state of the variables, not just the tendencies. Therefore, the adjustment is calculated which is the difference between the new and the old state and stored in the object's attribute adjustment. Calculating the new model states through solving the matrix problem already includes the multiplication with the timestep. The derived adjustment is divided by the timestep to calculate the implicit subprocess tendencies, which can be handeled by the :func:`~climlab.process.time_dependent_process.TimeDependentProcess.compute` method of the parent :class:`~climlab.process.time_dependent_process.TimeDependentProcess` class. :ivar dict adjustment: holding all state variables' adjustments of the implicit process which are the differences between the new states (which have been solved through matrix inversion) and the old states. """

        newstate = self._implicit_solver()
        adjustment = {}
        tendencies = {}
        for name, var in self.state.items():
            adjustment[name] = newstate[name] - var
            tendencies[name] = adjustment[name] / self.timestep
        # express the adjustment (already accounting for the finite time step)
        #  as a tendency per unit time, so that it can be applied along with explicit
        self.adjustment = adjustment
        self._update_diagnostics(newstate)
        return tendencies