TensorFlow has several execution modes, with Eager-mode being the default in v2.
Eager mode lets users write imperative-style statements that can be easily
inspected and debugged and it is intended to be used during the development
phase.

As part of the differences that make Eager mode easier to debug, the [shape
inference
functions](https://www.tensorflow.org/guide/create_op#define_the_op_interface)

$ fastapi dev main.py

 ╭────────── FastAPI CLI - Development mode ───────────╮
 │                                                     │
 │  Serving at: http://127.0.0.1:8000                  │
 │                                                     │
 │  API docs: http://127.0.0.1:8000/docs               │
 │                                                     │
 │  Running in development mode, for production use:   │

$ fastapi dev main.py

 ╭────────── FastAPI CLI - Development mode ───────────╮
 │                                                     │
 │  Serving at: http://127.0.0.1:8000                  │
 │                                                     │
 │  API docs: http://127.0.0.1:8000/docs               │
 │                                                     │
 │  Running in development mode, for production use:   │

* **Kein Kopfzerbrechen**:
    * Keine neue Schemadefinition-Mikrosprache zu lernen.
    * Wenn Sie Pythons Typen kennen, wissen Sie, wie man Pydantic verwendet.
* Gutes Zusammenspiel mit Ihrer/Ihrem **<abbr title="Integrated Development Environment – Integrierte Entwicklungsumgebung: Ähnlich einem Code-Editor">IDE</abbr>/<abbr title="Ein Programm, das Fehler im Quellcode sucht">Linter</abbr>/Gehirn**:

    * Farklı bir syntax öğrenmenize gerek kalmaz,
    * Eğer Python typelarını nasıl kullanacağını biliyorsan Pydantic kullanmayı da biliyorsundur.
* Kullandığın geliştirme araçları ile iyi çalışır **<abbr title="Integrated Development Environment, kod editörüne benzer">IDE</abbr>/<abbr title="Code errorlarınızı inceleyen program">linter</abbr>/brain**:

discussion, and please direct specific questions to
[Stack Overflow](https://stackoverflow.com/questions/tagged/tensorflow).**

The TensorFlow project strives to abide by generally accepted best practices in
open-source software development.

## Patching guidelines

Follow these steps to patch a specific version of TensorFlow, for example, to
apply fixes to bugs or security vulnerabilities:

* der Serverworker
* der Uvicorn-Worker
* der Gunicorn-Worker
* der Workerprozess
* die Workerklasse
* die Workload

* das Deployment
* deployen

* das SDK
* das Software Development Kit

* der `APIRouter`
* die `requirements.txt`
* das Bearer-Token
* der Breaking Change
* der Bug
* der Button
* das Callable
* der Code
* der Commit
* der Contextmanager

* **Pas de prise de tête**:
    * Pas de nouveau langage de définition de schéma à apprendre.
    * Si vous connaissez le typage en python vous savez comment utiliser Pydantic.
* Aide votre **<abbr title="Integrated Development Environment, il s'agit de votre éditeur de code">IDE</abbr>/<abbr title="Programme qui analyse le code à la recherche d'erreurs">linter</abbr>/cerveau**:

        <abbr title="Input/Output: disk reading or writing, network communication.">I/O</abbr>
        <abbr title="Content Delivery Network: service, that provides static files.">CDN</abbr>
        <abbr title="Integrated Development Environment: similar to a code editor">IDE</abbr>
        »»»

    Result (German):

        «««

## Version 4.7.0

_2020-05-17_

 *  New: `HandshakeCertificates.Builder.addInsecureHost()` makes it easy to turn off security in
    private development environments that only carry test data. Prefer this over creating an
    all-trusting `TrustManager` because only hosts on the allowlist are insecure. From
    [our DevServer sample][dev_server]:

    ```kotlin