SAE lens error term

    # Basic Forward Pass Functionality.
    def forward(
        self,
        x: torch.Tensor,
    ) -> torch.Tensor:
        feature_acts = self.encode(x)
        sae_out = self.decode(feature_acts)

        # TEMP
        if self.use_error_term:
            with torch.no_grad():
                # Recompute everything without hooks to get true error term
                # Otherwise, the output with error term will always equal input, even for causal interventions that affect x_reconstruct
                # This is in a no_grad context to detach the error, so we can compute SAE feature gradients (eg for attribution patching). See A.3 in https://arxiv.org/pdf/2403.19647.pdf for more detail
                # NOTE: we can't just use `sae_error = input - x_reconstruct.detach()` or something simpler, since this would mean intervening on features would mean ablating features still results in perfect reconstruction.
                with _disable_hooks(self):
                    feature_acts_clean = self.encode(x)
                    x_reconstruct_clean = self.decode(feature_acts_clean)
                sae_error = self.hook_sae_error(x - x_reconstruct_clean)
            sae_out = sae_out + sae_error
        return self.hook_sae_output(sae_out)