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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 log_p_obs(self, obs, out=None, dtype=np.float32):
 """ Returns the element-wise logarithm of the output probabilities for an entire trajectory and all hidden states This is a default implementation that will take the log of p_obs(obs) and should only be used if p_obs(obs) is numerically stable. If there is any danger of running into numerical problems *during* the calculation of p_obs, this function should be overwritten in order to compute the log-probabilities directly. Parameters obs : ndarray((T), dtype=int) a discrete trajectory of length T Return ------ p_o : ndarray (T,N) the log probability of generating the symbol at time point t from any of the N hidden states """

        if out is None:
            return np.log(self.p_obs(obs))
        else:
            self.p_obs(obs, out=out, dtype=dtype)
            np.log(out, out=out)
            return out