<SYSTEM_TASK:>
Solve the following problem using Python, implementing the functions described below, one line at a time
<END_TASK>
<USER_TASK:>
Description:
 def _adjust_inferential_results_for_parameter_constraints(self, constraints):
 """ Ensure that parameters that were constrained during estimation do not have any values showed for inferential results. After all, no inference was performed. Parameters constraints : list of ints, or None. If list, should contain the positions in the array of all estimated parameters that were constrained to their initial values. Returns ------- None. """

        if constraints is not None:
            # Ensure the model object has inferential results
            inferential_attributes = ["standard_errors",
                                      "tvalues",
                                      "pvalues",
                                      "robust_std_errs",
                                      "robust_t_stats",
                                      "robust_p_vals"]
            assert all([hasattr(self, x) for x in inferential_attributes])
            assert hasattr(self, "params")

            all_names = self.params.index.tolist()

            for series in [getattr(self, x) for x in inferential_attributes]:
                for pos in constraints:
                    series.loc[all_names[pos]] = np.nan

        return None