Repository: meta-llama/llama-stack
File: src/llama_stack/core/routers/inference.py (Python)

Code before:
        model_id: str,
        provider_model_id: str | None = None,
        provider_id: str | None = None,
        metadata: dict[str, Any] | None = None,
        model_type: ModelType | None = None,
    ) -> None:
        logger.debug(
            f"InferenceRouter.register_model: {model_id=} {provider_model_id=} {provider_id=} {metadata=} {model_type=}",
        )
        await self.routing_table.register_model(model_id, provider_model_id, provider_id, metadata, model_type)

    def _construct_metrics(
        self,
        prompt_tokens: int,
        completion_tokens: int,
        total_tokens: int,
        fully_qualified_model_id: str,
        provider_id: str,
    ) -> list[MetricEvent]:
        """Constructs a list of MetricEvent objects containing token usage metrics.

        Args:
            prompt_tokens: Number of tokens in the prompt
            completion_tokens: Number of tokens in the completion
            total_tokens: Total number of tokens used
            model_id: The model identifier
            provider_id: The provider identifier

        Returns:
            List of MetricEvent objects with token usage metrics
        """
        span = get_current_span()
        if span is None:
            logger.warning("No span found for token usage metrics")
            return []

        metrics = [
            ("prompt_tokens", prompt_tokens),
            ("completion_tokens", completion_tokens),
            ("total_tokens", total_tokens),
        ]
        metric_events = []
        for metric_name, value in metrics:
            metric_events.append(
                MetricEvent(
                    trace_id=span.trace_id,
                    span_id=span.span_id,
                    metric=metric_name,
                    value=value,
                    timestamp=datetime.now(UTC),
                    unit="tokens",

Review comment (suggestion):
```suggestion
            fully_qualified_model_id: 
```

Code after:
        model_id: str,
        provider_model_id: str | None = None,
        provider_id: str | None = None,
        metadata: dict[str, Any] | None = None,
        model_type: ModelType | None = None,
    ) -> None:
        logger.debug(
            f"InferenceRouter.register_model: {model_id=} {provider_model_id=} {provider_id=} {metadata=} {model_type=}",
        )
        await self.routing_table.register_model(model_id, provider_model_id, provider_id, metadata, model_type)

    def _construct_metrics(
        self,
        prompt_tokens: int,
        completion_tokens: int,
        total_tokens: int,
        fully_qualified_model_id: str,
        provider_id: str,
    ) -> list[MetricEvent]:
        """Constructs a list of MetricEvent objects containing token usage metrics.

        Args:
            prompt_tokens: Number of tokens in the prompt
            completion_tokens: Number of tokens in the completion
            total_tokens: Total number of tokens used
            fully_qualified_model_id:
            provider_id: The provider identifier

        Returns:
            List of MetricEvent objects with token usage metrics
        """
        span = get_current_span()
        if span is None:
            logger.warning("No span found for token usage metrics")
            return []

        metrics = [
            ("prompt_tokens", prompt_tokens),
            ("completion_tokens", completion_tokens),
            ("total_tokens", total_tokens),
        ]
        metric_events = []
        for metric_name, value in metrics:
            metric_events.append(
                MetricEvent(
                    trace_id=span.trace_id,
                    span_id=span.span_id,
                    metric=metric_name,
                    value=value,
                    timestamp=datetime.now(UTC),
                    unit="tokens",