allDriveLabels...)
	driveReadsKBPerSecMD = NewGaugeMD(driveReadsKBPerSec,
		"Kilobytes read per second on a drive",
		allDriveLabels...)
	driveReadsAwaitMD = NewGaugeMD(driveReadsAwait,
		"Average time for read requests served on a drive",
		allDriveLabels...)
	driveWritesPerSecMD = NewGaugeMD(driveWritesPerSec,
		"Writes per second on a drive",
		allDriveLabels...)
	driveWritesKBPerSecMD = NewGaugeMD(driveWritesKBPerSec,

jobs:
  main:
    permissions:
      contents: read
      pull-requests: write
    runs-on: ubuntu-latest
    steps:
      - name: Check if PRs have merge conflicts
        uses: eps1lon/actions-label-merge-conflict@v3
        with:
          dirtyLabel: "conflicts"
          repoToken: "${{ secrets.GITHUB_TOKEN }}"

name: Check pulls metadata

on:
  pull_request_target:
    types: [ closed, unlabeled, milestoned, demilestoned ]

permissions: {}

jobs:
  check_pull_metadata:
    permissions:
      issues: write
      pull-requests: write
    runs-on: ubuntu-latest
    steps:
      # Check that PRs have proper metadata: labels and milestone
      # https://github.com/gradle/issue-management-action/blob/main/src/pull-metadata.ts

# Usando Dataclasses { #using-dataclasses }

FastAPI está construido sobre **Pydantic**, y te he estado mostrando cómo usar modelos de Pydantic para declarar requests y responses.

Pero FastAPI también soporta el uso de <a href="https://docs.python.org/3/library/dataclasses.html" class="external-link" target="_blank">`dataclasses`</a> de la misma manera:

{* ../../docs_src/dataclasses_/tutorial001_py310.py hl[1,6:11,18:19] *}

Notice that the *path operations* define the models they use for request payload and response payload, using the models `Item` and `ResponseMessage`.

### API Docs { #api-docs }

If you go to `/docs`, you will see that it has the **schemas** for the data to be sent in requests and received in responses:

<img src="/img/tutorial/generate-clients/image01.png">

 *
 * @since 4.0.0
 */
@Experimental
public interface Parser {
    /**
     * Parses the given ParserRequest to create an {@link InvokerRequest}.
     * This method does interpret tool arguments.
     *
     * @param parserRequest the request containing all necessary information for parsing

Aber jedes Mal, wenn wir ausführen:

```Python
Settings()
```

	return claims.Map(), ErrNone
}

// Check request auth type verifies the incoming http request
//   - validates the request signature
//   - validates the policy action if anonymous tests bucket policies if any,
//     for authenticated requests validates IAM policies.
//
// returns APIErrorCode if any to be replied to the client.

		"Total number of invalid requests", "type")

	apiRequestsWaitingTotalMD = NewGaugeMD(apiRequestsWaitingTotal,
		"Total number of requests in the waiting queue", "type")
	apiRequestsIncomingTotalMD = NewGaugeMD(apiRequestsIncomingTotal,
		"Total number of incoming requests", "type")

	apiRequestsInFlightTotalMD = NewGaugeMD(apiRequestsInFlightTotal,
		"Total number of requests currently in flight", "name", "type")

* Validieren, dass es eine `item_id` im Pfad für `GET`- und `PUT`-Requests gibt.
* Validieren, ob die `item_id` vom Typ `int` für `GET`- und `PUT`-Requests ist.
    * Falls nicht, sieht der Client einen hilfreichen, klaren Fehler.
* Prüfen, ob es einen optionalen Query-Parameter namens `q` (wie in `http://127.0.0.1:8000/items/foo?q=somequery`) für `GET`-Requests gibt.
    * Da der `q`-Parameter mit `= None` deklariert ist, ist er optional.