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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 _grads(self, x):
 """ Gets the gradients from the likelihood and the priors. Failures are handled robustly. The algorithm will try several times to return the gradients, and will raise the original exception if the objective cannot be computed. :param x: the parameters of the model. :type x: np.array """

        try:
            # self._set_params_transformed(x)
            self.optimizer_array = x
            self.obj_grads = self._transform_gradients(self.objective_function_gradients())
            self._fail_count = 0
        except (LinAlgError, ZeroDivisionError, ValueError): #pragma: no cover
            if self._fail_count >= self._allowed_failures:
                raise
            self._fail_count += 1
            self.obj_grads = np.clip(self._transform_gradients(self.objective_function_gradients()), -1e100, 1e100)
        return self.obj_grads