def topk(
        self,
        top_and_bottom_k: int | None,
        transform_activations_fn: Callable[[torch.Tensor], torch.Tensor] | None = None,
        transform_indices_fn: Callable[[NodeIndex], NodeIndex] | None = None,
    ) -> tuple[list[NodeIndex], dict[GroupId, torch.Tensor]]:
        """
        Return all node indices which were in the top or bottom k of any group id (as applicable according to self.exclude_bottom_k_by_group_id)
        Also return the derived scalar values for each group id for each node index, in a dict
        """
        top_node_indices_list = []
        for group_id in self.derived_scalars_by_group_id.keys():
            _, top_node_indices = self._topk_of_group(
                group_id,
                top_and_bottom_k,
                transform_activations_fn,
                transform_indices_fn,
            )
            top_node_indices_list.extend(top_node_indices)

        # Create a list of all node indices across all groups, removing duplicates. We need to maintain
        # a set and a list because we need to maintain the original order (which is why we can't use the
        # list(set()) trick). We need to maintain the original order in order to make the output deterministic.
        # these are the indices of nodes that were in the top-k for at least one kind of derived scalar
        # in parallel construct a matching list of dicts of token information
        all_node_indices = []
        unique_indices = set()
        for node_index in top_node_indices_list:
            if node_index not in unique_indices:
                unique_indices.add(node_index)
                all_node_indices.append(node_index)

        # for nodes that were top-k in at least one kind of derived scalar, convert from the index of the node
        # to the index of the relevant derived scalar for each group id;
        # using the derived scalar indices, access the derived scalar values themselves for all relevant node
        # indices
        activations_by_group_id = self.get_derived_scalars_by_group_id_for_node_indices(
            all_node_indices
        )

        return all_node_indices, activations_by_group_id

        Return all node indices which were in the top or bottom k of any group id (as applicable according to self.exclude_bottom_k_by_group_id)
        Also return the derived scalar values for each group id for each node index, in a dict
        
neuron_explainer/activations/derived_scalars/multi_group.py