def _make_update_bias_stats_fn(self, ensemble_stamp, predictions, gradients, bias_stats_accumulator):
    'A method to create the function which updates the bias stats.'

    def _update_bias_stats():
        'A method to update the bias stats.'
        grads_sum = math_ops.reduce_sum(gradients, 0)
        hess = gradients_impl.gradients(grads_sum, predictions, name='Hessians', colocate_gradients_with_ops=False, gate_gradients=0, aggregation_method=None)[0]
        hess_sum = math_ops.reduce_sum(hess, 0)
        partition_ids = math_ops.range(self._logits_dimension)
        feature_ids = array_ops.zeros([self._logits_dimension, 2], dtype=dtypes.int64)
        add_stats_op = bias_stats_accumulator.add(ensemble_stamp, partition_ids, feature_ids, grads_sum, hess_sum)
        return control_flow_ops.group(*[add_stats_op], name='update_bias_stats')
    return _update_bias_stats
A method to create the function which updates the bias stats.
def _make_update_bias_stats_fn(self, ensemble_stamp, predictions, gradients, bias_stats_accumulator):
    

    def _update_bias_stats():
        'A method to update the bias stats.'
        grads_sum = math_ops.reduce_sum(gradients, 0)
        hess = gradients_impl.gradients(grads_sum, predictions, name='Hessians', colocate_gradients_with_ops=False, gate_gradients=0, aggregation_method=None)[0]
        hess_sum = math_ops.reduce_sum(hess, 0)
        partition_ids = math_ops.range(self._logits_dimension)
        feature_ids = array_ops.zeros([self._logits_dimension, 2], dtype=dtypes.int64)
        add_stats_op = bias_stats_accumulator.add(ensemble_stamp, partition_ids, feature_ids, grads_sum, hess_sum)
        return control_flow_ops.group(*[add_stats_op], name='update_bias_stats')
    return _update_bias_stats