def _maybe_decode(
        self, ds_index: DerivedScalarIndex, derived_scalars: torch.Tensor
    ) -> torch.Tensor:
        """decodes if ds_index.dst is an autoencoder latent type, and if so, returns the
        derived_scalars decoded by the decoder weight for the corresponding autoencoder. Otherwise, returns the
        derived_scalars unchanged."""
        if ds_index.dst.is_autoencoder_latent:
            assert self._multi_autoencoder_context is not None
            assert derived_scalars.ndim == 0
            autoencoder = self._multi_autoencoder_context.get_autoencoder(
                ds_index.layer_index, node_type=ds_index.dst.node_type
            )
            assert Dimension.AUTOENCODER_LATENTS in ds_index.tensor_index_by_dim
            indices_for_decoder = (
                ds_index.tensor_index_by_dim[Dimension.AUTOENCODER_LATENTS],
                None,
            )
            slices_for_decoder: tuple[slice | int | None, ...] = tuple(
                slice(None) if index is None else index for index in indices_for_decoder
            )
            decoder_weight = get_decoder_weight(autoencoder)[slices_for_decoder]
            assert decoder_weight.ndim == 1
            derived_scalars = derived_scalars * decoder_weight
        return derived_scalars
decodes if ds_index.dst is an autoencoder latent type, and if so, returns the
        derived_scalars decoded by the decoder weight for the corresponding autoencoder. Otherwise, returns the
        derived_scalars unchanged.
neuron_explainer/activations/derived_scalars/postprocessing.py