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

namespace tensorflow {

// Convenience macros for asserting and handling exceptional conditions, for
// C structs, including `TF_OpKernelContext`, `TF_Status`, etc. This is analogus
// to the macros in tensorflow/core/framework/op_requires.h. This is provided
// for plugin OpKernel developer's convenience.

#define C_OPKERNELCONTEXT_REQUIRES_OK(CTX, C_STATUS, __VA_ARGS__) \

Los contenedores de Linux se ejecutan utilizando el mismo núcleo de Linux del host (máquina, máquina virtual, servidor en la nube, etc.). Esto significa que son muy ligeros (en comparación con las máquinas virtuales completas que emulan un sistema operativo completo).

Et ces modèles partagent beaucoup de données et dupliquent des noms et types d'attributs.

Nous pouvons faire mieux.

Nous pouvons déclarer un modèle `UserBase` qui sert de base à nos autres modèles. Ensuite, nous pouvons créer des sous-classes de ce modèle qui héritent de ses attributs (déclarations de type, validation, etc.).

It keeps the function signatures working in other contexts, respects the types, allows reusability.

### In Parameter Declarations

Use `Annotated` for parameter declarations, including `Path`, `Query`, `Header`, etc.:

```python
from typing import Annotated

from fastapi import FastAPI, Path, Query

app = FastAPI()


@app.get("/items/{item_id}")
async def read_item(

       * columns in the first row, the columns in the second row, etc. If a column Comparator is
       * provided but a row Comparator isn't, cellSet() iterates across the rows in the first
       * column, the rows in the second column, etc.
       */
      Comparator<Cell<R, C, V>> comparator =
          (Cell<R, C, V> cell1, Cell<R, C, V> cell2) -> {

But that will only allow certain types of communication, excluding everything that involves credentials: Cookies, Authorization headers like those used with Bearer Tokens, etc.

So, for everything to work correctly, it's better to specify explicitly the allowed origins.

## Use `CORSMiddleware` { #use-corsmiddleware }

// A TF_ExecutionContext stores knowledge about how to execute an operation.
// E.g. it could know whether we're in eager mode or graph mode, keeps track
// of gradient tapes, etc.
typedef struct TF_ExecutionContext TF_ExecutionContext;

// A TF_AbstractTensor is an input to an operation. E.g. it could be a union
// type of eager and graph tensors. It is also the result of executing an
// operation.

export DEBIAN_FRONTEND=noninteractive

# Fetch the NVIDIA key.
apt-key adv --fetch-keys https://developer.download.nvidia.com/compute/cuda/repos/ubuntu2204/x86_64/3bf863cc.pub;

# Set up sources for NVIDIA CUDNN.
cat >/etc/apt/sources.list.d/nvidia.list <<SOURCES
# NVIDIA
deb https://developer.download.nvidia.com/compute/cuda/repos/ubuntu2204/x86_64/ /

For that, you can use the `WSGIMiddleware` and use it to wrap your WSGI application, for example, Flask, Django, etc.

## Using `WSGIMiddleware` { #using-wsgimiddleware }

/// info

This requires installing `a2wsgi` for example with `pip install a2wsgi`.

///

You need to import `WSGIMiddleware` from `a2wsgi`.

) : OsAwareBaseGradleBuildType(os = Os.LINUX, stage = stage, init = {
        id(buildTypeId(model))
        name = "Sanity Check"
        description = "Static code analysis, checkstyle, release notes verification, etc."

        features {
            publishBuildStatusToGithub(model)
        }

        applyDefaults(
            model,
            this,
            "sanityCheck",
            extraParameters =