See the License for the specific language governing permissions and
limitations under the License.
==============================================================================*/

#ifndef TENSORFLOW_C_C_OP_REQUIRES_H_
#define TENSORFLOW_C_C_OP_REQUIRES_H_

#include "tensorflow/core/platform/macros.h"

namespace tensorflow {

// Convenience macros for asserting and handling exceptional conditions, for

	// We only get here if defining a macro inside a macro.
	// This imperfect implementation means we cannot tell the difference between
	//	#define A #define B(x) x
	// and
	//	#define A #define B (x) x
	// The first definition of B has an argument, the second doesn't. Because we let
	// text/scanner strip the blanks for us, this is extremely rare, hard to fix, and not worth it.
	return s.pos
}

```console
$ pip install python-multipart
```

///

## Import `File` and `Form` { #import-file-and-form }

{* ../../docs_src/request_forms_and_files/tutorial001_an_py39.py hl[3] *}

## Define `File` and `Form` parameters { #define-file-and-form-parameters }

Create file and form parameters the same way you would for `Body` or `Query`:

{* ../../docs_src/request_forms_and_files/tutorial001_an_py39.py hl[10:12] *}

+#if (!defined __cplusplus || __GNUC_PREREQ (4,3) || defined __clang__) && defined __GNUC__
 # if defined __GNUC_STDC_INLINE__ || defined __cplusplus
 #  define __extern_inline extern __inline __attribute__ ((__gnu_inline__))

You can define Header parameters the same way you define `Query`, `Path` and `Cookie` parameters.

## Import `Header` { #import-header }

First import `Header`:

{* ../../docs_src/header_params/tutorial001_an_py310.py hl[3] *}

## Declare `Header` parameters { #declare-header-parameters }

Then declare the header parameters using the same structure as with `Path`, `Query` and `Cookie`.

So, you can declare deeply nested JSON "objects" with specific attribute names, types and validations.

All that, arbitrarily nested.

### Define a submodel { #define-a-submodel }

For example, we can define an `Image` model:

{* ../../docs_src/body_nested_models/tutorial004_py310.py hl[7:9] *}

### Use the submodel as a type { #use-the-submodel-as-a-type }

See the License for the specific language governing permissions and
limitations under the License.
==============================================================================*/

#ifndef TENSORFLOW_C_C_API_MACROS_INTERNAL_H_
#define TENSORFLOW_C_C_API_MACROS_INTERNAL_H_

#ifdef __cplusplus
#include "tensorflow/core/platform/status.h"

// Macro to verify that the field `struct_size` of STRUCT_OBJ is initialized.

// verification is empty or invalid.
var errBitrotHashAlgoInvalid = StorageErr("bit-rot hash algorithm is invalid")

// errCrossDeviceLink - rename across devices not allowed.
var errCrossDeviceLink = StorageErr("Rename across devices not allowed, please fix your backend configuration")

// errLessData - returned when less data available than what was requested.

# Lifespan Events { #lifespan-events }

You can define logic (code) that should be executed before the application **starts up**. This means that this code will be executed **once**, **before** the application **starts receiving requests**.

The same way, you can define logic (code) that should be executed when the application is **shutting down**. In this case, this code will be executed **once**, **after** having handled possibly **many requests**.

Normalmente, o processo é que **você define** em seu código qual é a mensagem que você irá mandar, o **corpo da sua requisição**.

Você também define de alguma maneira em quais **momentos** a sua aplicação mandará essas requisições ou eventos.

E os **seus usuários** definem de alguma forma (em algum painel por exemplo) a **URL** que a sua aplicação deve enviar essas requisições.