}
    }
  }

  private Type getOnlyParameterType() {
    assertThat(method.getGenericParameterTypes()).hasLength(1);
    return method.getGenericParameterTypes()[0];
  }

  @SuppressWarnings({"rawtypes", "unchecked"})
  private static Type getSupertype(Type type, Class<?> superclass) {
    Class rawType = superclass;
    return TypeToken.of(type).getSupertype(rawType).getType();
  }

    "title": "ValidationError",
    "type": "object",
    "properties": {
        "loc": {
            "title": "Location",
            "type": "array",
            "items": {"anyOf": [{"type": "string"}, {"type": "integer"}]},
        },
        "msg": {"title": "Message", "type": "string"},
        "type": {"title": "Error Type", "type": "string"},
    },
    "required": ["loc", "msg", "type"],
}

{* ../../docs_src/body/tutorial001_py310.py hl[2] *}

## Ihr Datenmodell erstellen { #create-your-data-model }

Dann deklarieren Sie Ihr Datenmodell als eine Klasse, die von `BaseModel` erbt.

Verwenden Sie Standard-Python-Typen für alle Attribute:

{* ../../docs_src/body/tutorial001_py310.py hl[5:9] *}

* Validação de dados
* Documentação automatica

## Tipos especiais e validação { #special-types-and-validation }

Além dos tipos singulares normais como `str`, `int`, `float`, etc. Você também pode usar tipos singulares mais complexos que herdam de `str`.

xlHeaderVersion version == 1:

```
//msgp:tuple xlMetaV2VersionHeader
type xlMetaV2VersionHeader struct {
 VersionID [16]byte  // Version UUID, raw.
 ModTime   int64     // Unix nanoseconds.
 Signature [4]byte   // Signature of metadata.
 Type      uint8     // Type if the version
 Flags     uint8
}
```

The following flags are defined:

```
const (

// Code generated by "stringer -type=TargetType -trimprefix=Target types.go"; DO NOT EDIT.

package loggertypes

import "strconv"

func _() {
	// An "invalid array index" compiler error signifies that the constant values have changed.
	// Re-run the stringer command to generate them again.
	var x [1]struct{}
	_ = x[TargetConsole-1]
	_ = x[TargetHTTP-2]
	_ = x[TargetKafka-3]
}

const _TargetType_name = "ConsoleHTTPKafka"

{* ../../docs_src/response_model/tutorial003_04_py310.py hl[8] *}

... das scheitert, da die Typannotation kein Pydantic-Typ ist, und auch keine einzelne `Response`-Klasse, oder -Unterklasse, es ist eine Union (eines von beiden) von `Response` und `dict`.

### Responsemodell deaktivieren { #disable-response-model }

To show help on setting compression config values.

```bash
~ mc admin config set myminio compression
```

To enable compression for all content, no matter the extension and content type
(except for the default excluded types) set BOTH extensions and mime types to empty.

```bash
~ mc admin config set myminio compression enable="on" extensions="" mime_types=""
```

The compression settings may also be set through environment variables.

    /**
     * Gets a conflict resolver of the specified type.
     *
     * @param type the type of conflict resolver to obtain
     * @return the conflict resolver
     * @throws ConflictResolverNotFoundException
     *          if the specified type was not found
     */
    ConflictResolver getConflictResolver(String type) throws ConflictResolverNotFoundException;

		m.Aggregated.Scanner = &metrics
	}
	if types.Contains(madmin.MetricsOS) {
		metrics := globalOSMetrics.report()
		m.Aggregated.OS = &metrics
	}
	if types.Contains(madmin.MetricsBatchJobs) {
		m.Aggregated.BatchJobs = globalBatchJobsMetrics.report(opts.jobID)
	}
	if types.Contains(madmin.MetricsSiteResync) {
		m.Aggregated.SiteResync = globalSiteResyncMetrics.report(opts.depID)
	}
	if types.Contains(madmin.MetricNet) {