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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 generate_synthetic_state_trajectory(self, nsteps, initial_Pi=None, start=None, stop=None, dtype=np.int32):
 """Generate a synthetic state trajectory. Parameters nsteps : int Number of steps in the synthetic state trajectory to be generated. initial_Pi : np.array of shape (nstates,), optional, default=None The initial probability distribution, if samples are not to be taken from the intrinsic initial distribution. start : int starting state. Exclusive with initial_Pi stop : int stopping state. Trajectory will terminate when reaching the stopping state before length number of steps. dtype : numpy.dtype, optional, default=numpy.int32 The numpy dtype to use to store the synthetic trajectory. Returns ------- states : np.array of shape (nstates,) of dtype=np.int32 The trajectory of hidden states, with each element in range(0,nstates). Examples -------- Generate a synthetic state trajectory of a specified length. """

        # consistency check
        if initial_Pi is not None and start is not None:
            raise ValueError('Arguments initial_Pi and start are exclusive. Only set one of them.')

        # Generate first state sample.
        if start is None:
            if initial_Pi is not None:
                start = np.random.choice(range(self._nstates), size=1, p=initial_Pi)
            else:
                start = np.random.choice(range(self._nstates), size=1, p=self._Pi)

        # Generate and return trajectory
        from msmtools import generation as msmgen
        traj = msmgen.generate_traj(self.transition_matrix, nsteps, start=start, stop=stop, dt=1)
        return traj.astype(dtype)