Help:      "Distribution of time to last byte for objects downloaded from warm tier",
		Type:      gaugeMetric,
	}

	// {minio_node}_{tier}_{requests_success}
	tierRequestsSuccessMD = MetricDescription{
		Namespace: nodeMetricNamespace,
		Subsystem: tierSubsystem,
		Name:      tierRequestsSuccess,
		Help:      "Number of requests to download object from warm tier that were successful",
		Type:      counterMetric,

// since most usage will be by language specific wrappers.
//
// Conventions:
// * We use the prefix TF_ for everything in the API.
// * Objects are always passed around as pointers to opaque structs
//   and these structs are allocated/deallocated via the API.
// * TF_Status holds error information.  It is an object type
//   and therefore is passed around as a pointer to an opaque
//   struct as mentioned above.

        if (typeof options.startDate === 'object')
            this.startDate = moment(options.startDate);

        if (typeof options.endDate === 'object')
            this.endDate = moment(options.endDate);

        if (typeof options.minDate === 'object')
            this.minDate = moment(options.minDate);

        if (typeof options.maxDate === 'object')
            this.maxDate = moment(options.maxDate);

	// Key specified in Form data
	keyName := "test/object"

	var opts ObjectOptions

	// The final name of the upload object
	targetObj := keyName + "/upload.txt"

	// The url of success_action_redirect field
	redirectURL, err := url.Parse("http://www.google.com?query=value")
	if err != nil {
		t.Fatal(err)
	}

	// Register the API end points with Erasure/FS object layer.

So, if we create a Pydantic object `user_in` like:

```Python
user_in = UserIn(username="john", password="secret", email="******@****.***")
```

and then we call:

```Python
user_dict = user_in.dict()
```

we now have a `dict` with the data in the variable `user_dict` (it's a `dict` instead of a Pydantic model object).

And if we call:

```Python
print(user_dict)
```

/// note | "Detalhes técnicos"

Na realidade, `Query`, `Path` e outros que você verá em seguida, criam objetos de subclasses de uma classe `Param` comum, que é ela mesma uma subclasse da classe `FieldInfo` do Pydantic.

E `Field` do Pydantic retorna uma instância de `FieldInfo` também.

`Body` também retorna objetos de uma subclasse de `FieldInfo` diretamente. E tem outras que você verá mais tarde que são subclasses da classe `Body`.

        {
            "url": "http://example.com/dave.jpg",
            "name": "The Baz"
        }
    ]
}
```

/// info | "informação"

Note como o campo `images` agora tem uma lista de objetos de image.

///

## Modelos profundamente aninhados

Você pode definir modelos profundamente aninhados de forma arbitrária:

```Python hl_lines="9  14  20  23  27"

#include "tensorflow/c/c_api.h"
#include "tensorflow/c/c_api_macros.h"

#ifdef __cplusplus
extern "C" {
#endif

typedef struct TFE_ContextOptions TFE_ContextOptions;

// Return a new options object.
TF_CAPI_EXPORT extern TFE_ContextOptions* TFE_NewContextOptions(void);

// Set the config in TF_ContextOptions.options.
// config should be a serialized tensorflow.ConfigProto proto.

	if !o.isSorted() {
		t.Fatal("Expected sorted objects")
	}

	// Swap first and last
	o[0], o[len(o)-1] = o[len(o)-1], o[0]
	if o.isSorted() {
		t.Fatal("Expected unsorted objects")
	}

	sorted := o.sort()
	if !o.isSorted() {
		t.Fatal("Expected sorted o objects")
	}
	if !sorted.entries().isSorted() {
		t.Fatal("Expected sorted wrapped objects")
	}
	want := loadMetacacheSampleNames

		args.EventName == event.ObjectRemovedNoOP

	if !isRemovedEvent {
		newEvent.S3.Object.ETag = args.Object.ETag
		newEvent.S3.Object.Size = args.Object.Size
		newEvent.S3.Object.ContentType = args.Object.ContentType
		newEvent.S3.Object.UserMetadata = make(map[string]string, len(args.Object.UserDefined))
		for k, v := range args.Object.UserDefined {
			if stringsHasPrefixFold(strings.ToLower(k), ReservedMetadataPrefixLower) {