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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 model_prior(self, t):
 """Returns the prior probability of the HMM Runs forward-backward without any data, showing the prior distribution of the model (for comparison with a posterior). Parameters t: int Number of timepoints Returns ------- segments : time by event ndarray segments[t,e] = prior probability that timepoint t is in event e test_ll : float Log-likelihood of model (data-independent term)"""


        lg, test_ll = self._forward_backward(np.zeros((t, self.n_events)))
        segments = np.exp(lg)

        return segments, test_ll