}

	ts := cpuMetrics.TimesStat
	tot := ts.User + ts.System + ts.Idle + ts.Iowait + ts.Nice + ts.Steal
	cpuUserVal := math.Round(ts.User/tot*100*100) / 100
	m.Set(sysCPUUser, cpuUserVal)
	cpuSystemVal := math.Round(ts.System/tot*100*100) / 100
	m.Set(sysCPUSystem, cpuSystemVal)
	cpuNiceVal := math.Round(ts.Nice/tot*100*100) / 100
	m.Set(sysCPUNice, cpuNiceVal)
	cpuStealVal := math.Round(ts.Steal/tot*100*100) / 100

	// cpu stats
	cpuUser       MetricName = "user"
	cpuSystem     MetricName = "system"
	cpuIOWait     MetricName = "iowait"
	cpuIdle       MetricName = "idle"
	cpuNice       MetricName = "nice"
	cpuSteal      MetricName = "steal"
	cpuLoad1      MetricName = "load1"
	cpuLoad5      MetricName = "load5"
	cpuLoad15     MetricName = "load15"
	cpuLoad1Perc  MetricName = "load1_perc"

// just nice to know that it compiles.
void* create(TF_OpKernelConstruction* ctx) {
  TF_DataType type;
  TF_Status* s = TF_NewStatus();
  TF_OpKernelConstruction_GetAttrType(ctx, "foobar", &type, s);
  TF_DeleteStatus(s);
  return NULL;
}

// A compute function. This will never actually get called in this test, it's
// just nice to know that it compiles.

 * it delegates to other components that potentially encounter problems. Then, the problem reporter can focus on
 * providing a simple error message, leaving the donkey work of creating a nice model problem to this component.
 *
 */
public interface ModelProblemCollector {

    /**
     * The collected problems.
     * @return a list of model problems encountered, never {@code null}
     */

| `minio_node_cpu_avg_iowait_max`      | CPU ioWait time (max).                     |
| `minio_node_cpu_avg_nice`            | CPU nice time.                             |
| `minio_node_cpu_avg_nice_avg`        | CPU nice time (avg).                       |
| `minio_node_cpu_avg_nice_max`        | CPU nice time (max).                       |
| `minio_node_cpu_avg_steal`           | CPU steam time.                            |

# Read the "|" not as "or", but as a "pipe", taking the output of the previous command and passing it to the next command.

# TODO: It would be very nice to avoid having this list somehow, but it needs to be defined in YAML and not in JS for the looping template to work
# Keys like `- build_infrastructure:` do not mean anything, they just need to be unique

flavor.

-   Snapshot API Docs: [guava][guava-snapshot-api-docs]
-   Snapshot API Diffs: [guava][guava-snapshot-api-diffs]

## Learn about Guava

-   Our users' guide, [Guava Explained]
-   [A nice collection](https://www.tfnico.com/presentations/google-guava) of
    other helpful links

## Links

-   [GitHub project](https://github.com/google/guava)

!!! info
    Keep in mind that dataclasses can't do everything Pydantic models can do.

    So, you might still need to use Pydantic models.

    But if you have a bunch of dataclasses laying around, this is a nice trick to use them to power a web API using FastAPI. 🤓

## Dataclasses in `response_model`

You can also use `dataclasses` in the `response_model` parameter:

```Python hl_lines="1  7-13  19"

 * it delegates to other components that potentially encounter problems. Then, the problem reporter can focus on
 * providing a simple error message, leaving the donkey work of creating a nice model problem to this component.
 *
 */
class DefaultModelProblemCollector implements ModelProblemCollector {

    private final ModelBuilderResult result;

    private List<ModelProblem> problems;

| `minio_system_cpu_load`       | `gauge` | CPU load average 1min              | `server` |
| `minio_system_cpu_load_perc`  | `gauge` | CPU load average 1min (percentage) | `server` |
| `minio_system_cpu_nice`       | `gauge` | CPU nice time                      | `server` |
| `minio_system_cpu_steal`      | `gauge` | CPU steal time                     | `server` |
| `minio_system_cpu_system`     | `gauge` | CPU system time                    | `server` |