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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 _objective(self, x):
 """ The objective function passed to the optimizer. It combines the likelihood and the priors. Failures are handled robustly. The algorithm will try several times to return the objective, and will raise the original exception if the objective cannot be computed. :param x: the parameters of the model. :parameter type: np.array """

        try:
            self.optimizer_array = x
            obj = self.objective_function()
            self._fail_count = 0
        except (LinAlgError, ZeroDivisionError, ValueError):#pragma: no cover
            if self._fail_count >= self._allowed_failures:
                raise
            self._fail_count += 1
            return np.inf
        return obj