atomic.AddInt64(&lat, latency)
				})
				spent := time.Since(t)
				if spent > 0 && n > 0 {
					// Since we are benchmarking n parallel servers we need to multiply by n.
					// This will give an estimate of the total ops/s.
					latency := float64(atomic.LoadInt64(&lat)) / float64(time.Millisecond)
					b.ReportMetric(float64(n)*float64(ops)/spent.Seconds(), "vops/s")
					b.ReportMetric(latency/float64(ops), "ms/op")

        normalizedOutput
            .replaceAll(/Received \d+ file system events .*\n/, '')
            .replaceAll(/Spent \d+ ms processing file system events since last build\n/, '')
            .replaceAll(/Spent \d+ ms registering watches for file system events\n/, '')
            .replaceAll(/Virtual file system .*\n/, '')
    }

		Estimated total CPU time goroutines spent performing GC
		tasks to assist the GC and prevent it from falling behind the
		application. This metric is an overestimate, and not directly
		comparable to system CPU time measurements. Compare only with
		other /cpu/classes metrics.

	/cpu/classes/gc/mark/dedicated:cpu-seconds
		Estimated total CPU time spent performing GC tasks on processors

{{end}}
</table>

<h3 id="ranges">Special ranges</h3>

The table below describes how much of the traced period each goroutine spent in
certain special time ranges.
If a goroutine has spent no time in any special time ranges, it is excluded from
the table.
For example, how much time it spent helping the GC. Note that these times do
overlap with the times from the first table.

{{end}}
</table>

<h3 id="ranges">Special ranges</h3>

The table below describes how much of the traced period each goroutine spent in
certain special time ranges.
If a goroutine has spent no time in any special time ranges, it is excluded from
the table.
For example, how much time it spent helping the GC. Note that these times do
overlap with the times from the first table.

			expectedValue: `
        # HELP apiserver_authentication_jwt_authenticator_latency_seconds [ALPHA] Latency of jwt authentication operations in seconds. This is the time spent authenticating a token for cache miss only (i.e. when the token is not found in the cache).
        # TYPE apiserver_authentication_jwt_authenticator_latency_seconds histogram

        }
        message.append("]");

        return message.toString();
    }

    /**
     * 0-100, the percentage of time spent on doing the work vs time spent in gc
     */
    private int getCurrentPerformance() {
        long collectionTime = gcInfo.getCollectionTime();
        long allBuildsTime = runningStats.getAllBuildsTime();

Also, the best approach was to use already existing standards.

So, before even starting to code **FastAPI**, I spent several months studying the specs for OpenAPI, JSON Schema, OAuth2, etc. Understanding their relationship, overlap, and differences.

## Design

Then I spent some time designing the developer "API" I wanted to have as a user (as a developer using FastAPI).

// IO wait, currently only network blocking event).
func computePprofIO() computePprofFunc {
	return makeComputePprofFunc(trace.GoWaiting, func(reason string) bool {
		return reason == "network"
	})
}

// computePprofBlock returns a computePprofFunc that generates blocking pprof-like profile
// (time spent blocked on synchronization primitives).

			Namespace:      namespace,
			Subsystem:      subsystem,
			Name:           "jwt_authenticator_latency_seconds",
			Help:           "Latency of jwt authentication operations in seconds. This is the time spent authenticating a token for cache miss only (i.e. when the token is not found in the cache).",
			StabilityLevel: metrics.ALPHA,
			// default histogram buckets with a 1ms starting point