How to use the aif360.metrics.utils.compute_num_instances function in aif360

def num_instances(self, privileged=None):
        """Compute the number of instances, :math:`n`, in the dataset conditioned
        on protected attributes if necessary.

        Args:
            privileged (bool, optional): Boolean prescribing whether to
                condition this metric on the `privileged_groups`, if `True`, or
                the `unprivileged_groups`, if `False`. Defaults to `None`
                meaning this metric is computed over the entire dataset.

        Raises:
            AttributeError: `privileged_groups` or `unprivileged_groups` must be
                must be provided at initialization to condition on them.
        """
        condition = self._to_condition(privileged)
        return utils.compute_num_instances(self.dataset.protected_attributes,
            self.dataset.instance_weights,
            self.dataset.protected_attribute_names, condition=condition)

"""
        # Dirichlet smoothing parameters
        if concentration < 0:
            raise ValueError("Concentration parameter must be non-negative.")
        num_classes = 2  # binary label dataset
        dirichlet_alpha = concentration / num_classes

        # compute counts for all intersecting groups, e.g. black-women, white-man, etc
        intersect_groups = np.unique(self.dataset.protected_attributes, axis=0)
        num_intersects = len(intersect_groups)
        counts_pos = np.zeros(num_intersects)
        counts_total = np.zeros(num_intersects)
        for i in range(num_intersects):
            condition = [dict(zip(self.dataset.protected_attribute_names,
                                  intersect_groups[i]))]
            counts_total[i] = utils.compute_num_instances(
                    self.dataset.protected_attributes,
                    self.dataset.instance_weights,
                    self.dataset.protected_attribute_names, condition=condition)
            counts_pos[i] = utils.compute_num_pos_neg(
                    self.dataset.protected_attributes, labels,
                    self.dataset.instance_weights,
                    self.dataset.protected_attribute_names,
                    self.dataset.favorable_label, condition=condition)

        # probability of y given S (p(y=1|S))
        return (counts_pos + dirichlet_alpha) / (counts_total + concentration)