Project: 007_Spread_Preference_Annotation_Direct_Preference_Jud | Function: confidence_update (iclr)

system: You are an expert Python programmer implementing algorithms from scientific papers.
Complete the masked function based on the algorithm description.
Follow Python best practices and maintain consistent code style.
Return **only** the complete Python function code.

user: Complete this Python function:

**DESCRIPTION:** Confidence threshold updating mechanism for noise detection in self-refinement process, tracking preference margins to identify unreliable samples

**KEY TERMS:** {
  "margin": "preference margin scores",
  "threshold": "τ (noise detection threshold)",
  "confidence": "updated confidence threshold",
  "rewards/mix_margin": "preference prediction margins"
}

**CONTEXT CODE:**         self,
        dataloader: DataLoader,
        description: str,
        prediction_loss_only: Optional[bool] = None,
        ignore_keys: Optional[List[str]] = None,
        metric_key_prefix: str = "eval",
    ) -> EvalLoopOutput:
        """
        Overriding built-in evaluation loop to store metrics for each batch.
        Prediction/evaluation loop, shared by `Trainer.evaluate()` and `Trainer.predict()`.

        Works both with or without labels.
        """

        # Sample and save to game log if requested (for one batch to save time)
        if self.generate_during_eval:
            # Generate random indices within the range of the total number of samples
            num_samples = len(dataloader.dataset)
            random_indices = random.sample(range(num_samples), k=self.args.eval_batch_size)

            # Use dataloader.dataset.select to get the random batch without iterating over the DataLoader
            random_batch_dataset = dataloader.dataset.select(random_indices)
            random_batch = self.data_collator(random_batch_dataset)
            random_batch = self._prepare_inputs(random_batch)
            policy_output_decoded, ref_output_decoded = self.get_batch_samples(self.model, random_batch)
            self.log(
                {
                    "game_log": wandb.Table(
                        columns=["Prompt", "Policy", "Ref Model"],
                        rows=[
                            [prompt, pol[len(prompt) :], ref[len(prompt) :]]
                            for prompt, pol, ref in zip(
                                random_batch["prompt"], policy_output_decoded, ref_output_decoded
                            )
                        ],
                    )
                }
            )
            self.state.log_history.pop()

        # Base evaluation
        initial_output = super().evaluation_loop(
            dataloader, description, prediction_loss_only, ignore_keys, metric_key_prefix
        )

        return initial_output




    def confidence_update(self):
        raise NotImplementedError("Function implementation has been masked for testing")
    def log(self, logs: Dict[str, float]) -> None:
        """
        Log `logs` on the various objects watching training, including stored metrics.

        Args:
            logs (`Dict[str, float]`):
                The values to log.
        """

        # logs either has 'loss' or 'eval_loss'
        train_eval = "train" if "loss" in logs else "eval"
        # Add averaged stored metrics to logs
        for key, metrics in self._stored_metrics[train_eval].items():
            logs[key] = torch.tensor(metrics).mean().item()
        del self._stored_metrics[train_eval]
        return super().log(logs)

    @wraps(Trainer.push_to_hub)
    def push_to_hub(self, commit_message: Optional[str] = "End of training", blocking: bool = True, **kwargs) -> str:
        """
        Overwrite the `push_to_hub` method in order to force-add the tag "sft" when pushing the
        model on the Hub. Please refer to `~transformers.Trainer.push_to_hub` for more details.
        """
        kwargs = trl_sanitze_kwargs_for_tagging(model=self.model, tag_names=self._tag_names, kwargs=kwargs)

        return super().push_to_hub(commit_message=commit_message, blocking=blocking, **kwargs)


Complete the function implementation replacing "raise NotImplementedError".