visibility = ["//visibility:public"],
)

cc_library(
    name = "c_api_macros_hdrs",
    hdrs = [
        "c_api_macros.h",
    ],
    visibility = ["//visibility:public"],
)

cc_library(
    name = "c_api_macros",
    hdrs = [
        "c_api_macros.h",
        "c_api_macros_internal.h",
    ],
    copts = tf_copts(),
    visibility = ["//visibility:public"],
    deps = [

    deps = [
        ":immediate_execution_context",
        "//tensorflow/c:conversion_macros",
    ],
)

cc_library(
    name = "tfe_cancellation_manager_internal",
    hdrs = ["tfe_cancellation_manager_internal.h"],
    visibility = [
        "//tensorflow:internal",
    ],
    deps = [
        "//tensorflow/c:conversion_macros",
        "//tensorflow/core:framework",
    ],
)

cc_library(

Con **FastAPI** obtienes todas las características de **Pydantic** (dado que FastAPI está basado en Pydantic para todo el manejo de datos):

* **Sin dificultades para entender**:
    * No necesitas aprender un nuevo micro-lenguaje de definición de schemas.
    * Si sabes tipos de Python, sabes cómo usar Pydantic.

با FastAPI شما تمام ویژگی های Pydantic را دراختیار دارید (زیرا FastAPI برای تمام بخش مدیریت دیتا بر اساس Pydantic عمل میکند):

* **خبری از گیج شدن نیست**:
    * هیچ <abbr title="micro-language">زبان خردی</abbr> برای یادگیری تعریف طرحواره های جدید وجود ندارد.
    * اگر تایپ های پایتون را میشناسید، نحوه استفاده از Pydantic را نیز میدانید.

Com **FastAPI** você terá todos os recursos do **Pydantic** (já que FastAPI utiliza o Pydantic para todo o controle dos dados):

* **Sem pegadinhas**:
    * Sem novas definições de esquema de micro-linguagem para aprender.
    * Se você conhece os tipos do Python, você sabe como usar o Pydantic.

Ce découplage des parties, et le fait d'être un "micro-framework" qui puisse être étendu pour couvrir exactement ce
qui est nécessaire, était une caractéristique clé que je voulais conserver.

Compte tenu de la simplicité de Flask, il semblait bien adapté à la création d'API. La prochaine chose à trouver était un "Django REST Framework" pour Flask.

!!! check "A inspiré **FastAPI** à"

Given the simplicity of Flask, it seemed like a good match for building APIs. The next thing to find was a "Django REST Framework" for Flask.

!!! check "Inspired **FastAPI** to"
    Be a micro-framework. Making it easy to mix and match the tools and parts needed.

    Have a simple and easy to use routing system.


### <a href="https://requests.readthedocs.io" class="external-link" target="_blank">Requests</a>

    Kullanıcılar için otomatik API dökümantasyonu sunan bir web arayüzüne sahip olmalı.

### <a href="https://flask.palletsprojects.com" class="external-link" target="_blank">Flask</a>

Flask bir <abbr title="Mikro Framework: Micro Framework">mikro framework</abbr> olduğundan Django gibi framework'lerin aksine veritabanı entegrasyonu gibi Django ile gelen pek çok özelliği direkt barındırmaz.

E ele gera _schemas_ OpenAPI.

É assim como funciona no Flask, Starlette, Responder etc.

Mas então, nós temos novamente o problema de ter uma micro-sintaxe, dentro de uma string Python (um grande YAML).