def construct_zero_loss_fn() -> Callable[[torch.Tensor], torch.Tensor]:
    """This loss function is used for running a backward pass that will be interrupted
    by ablating some desired parameters. Parameters downstream of the ablated parameters
    will have a gradient of 0, and parameters upstream of the ablated parameters will
    in general have a non-zero gradient."""

    def loss_fn_for_backward_pass(output_logits: torch.Tensor) -> torch.Tensor:
        return 0.0 * output_logits.sum()

    return loss_fn_for_backward_pass
This loss function is used for running a backward pass that will be interrupted
    by ablating some desired parameters. Parameters downstream of the ablated parameters
    will have a gradient of 0, and parameters upstream of the ablated parameters will
    in general have a non-zero gradient.
neuron_explainer/activation_server/derived_scalar_computation.py