def add_grabbing_hook_for_backward_pass(
    attached_activation_dict: dict[str, torch.Tensor | None],
    name: str,
    transformer_graph: TransformerHookGraph,
    activation_index_for_backward_pass: ActivationIndex,
    batch_index: int,
    append_to_fwd2: bool = False,
) -> dict[str, torch.Tensor | None]:
    """This is a helper function that returns a TransformerHooks object containing hooks at naturally existing hook locations
    (e.g. MLP post-activations, rather than autoencoder latent activations) for the given
    scalar derivers, and a dictionary mapping each activation location type with a naturally existing hook to the activations requested
    (before it is filled with activations during forward passes)."""

    (
        hook_fn,
        activation_location_type_and_pass_type,
        layer_index,
        attached_activation_dict,
    ) = create_activation_grabbing_hook_fn(
        attached_activation_dict, name, activation_index_for_backward_pass, batch_index=batch_index
    )

    transformer_graph.append(
        activation_location_type_and_pass_type,
        hook_fn,
        layer_indices=layer_index,
        append_to_fwd2=append_to_fwd2,
    )

    return attached_activation_dict
This is a helper function that returns a TransformerHooks object containing hooks at naturally existing hook locations
    (e.g. MLP post-activations, rather than autoencoder latent activations) for the given
    scalar derivers, and a dictionary mapping each activation location type with a naturally existing hook to the activations requested
    (before it is filled with activations during forward passes).
neuron_explainer/activation_server/derived_scalar_computation.py