* **Kodlaması Hızlı**: Geliştirme hızını yaklaşık %200 ile %300 aralığında arttırır. *
* **Daha az hata**: İnsan (geliştirici) kaynaklı hataları yaklaşık %40 azaltır. *
* **Sezgisel**: Muhteşem bir editör desteği. Her yerde <abbr title="Otomatik Tamamlama: auto-complete, autocompletion, IntelliSense">otomatik tamamlama</abbr>. Hata ayıklama ile daha az zaman harcayacaksınız.

* Editor support, including:
    * Completion.
    * Type checks.
* Validation of data:
    * Automatic and clear errors when the data is invalid.
    * Validation even for deeply nested JSON objects.
* <abbr title="also known as: serialization, parsing, marshalling">Conversion</abbr> of input data: coming from the network to Python data and types. Reading from:
    * JSON.
    * Path parameters.
    * Query parameters.

            token,
        )
    }
    add(FirErrors.DATA_CLASS_CONSISTENT_COPY_AND_EXPOSED_COPY_ARE_INCOMPATIBLE_ANNOTATIONS) { firDiagnostic ->
        DataClassConsistentCopyAndExposedCopyAreIncompatibleAnnotationsImpl(
            firDiagnostic as KtPsiDiagnostic,
            token,
        )
    }
    add(FirErrors.DATA_CLASS_CONSISTENT_COPY_WRONG_ANNOTATION_TARGET) { firDiagnostic ->

facility refers to a function or data to be supplied by an Application
that uses the facility (other than as an argument passed when the
facility is invoked), then you may convey a copy of the modified
version:

   a) under this License, provided that you make a good faith effort to
   ensure that, in the event an Application does not supply the
   function or data, the facility still operates, and performs

                    )
                }
            }
        }
        return null
    }

    private data class CompoundArrayAccessPartiallyAppliedSymbols(
        val operationPartiallyAppliedSymbol: KtPartiallyAppliedFunctionSymbol<KtFunctionSymbol>,

```Python
my_user: User = User(id=3, name="John Doe", joined="2018-07-19")

second_user_data = {
    "id": 4,
    "name": "Mary",
    "joined": "2018-11-30",
}

my_second_user: User = User(**second_user_data)
```

!!! info
    `**second_user_data` means:

    Pass the keys and values of the `second_user_data` dict directly as key-value arguments, equivalent to: `User(id=4, name="Mary", joined="2018-11-30")`

*   `tf.data`:
    *   Graduated experimental APIs:
        * [`tf.data.Dataset.ragged_batch`](https://www.tensorflow.org/api_docs/python/tf/data/Dataset/#ragged_batch), which batches elements of `tf.data.Dataset`s into `tf.RaggedTensor`s.
        * [`tf.data.Dataset.sparse_batch`](https://www.tensorflow.org/api_docs/python/tf/data/Dataset/#sparse_batch), which batches elements of `tf.data.Dataset`s into `tf.sparse.SparseTensor`s.

# OpenAPI Webhooks

There are cases where you want to tell your API **users** that your app could call *their* app (sending a request) with some data, normally to **notify** of some type of **event**.

This means that instead of the normal process of your users sending requests to your API, it's **your API** (or your app) that could **send requests to their system** (to their API, their app).

This is normally called a **webhook**.

## Webhooks steps

In your WebSocket route you can `await` for messages and send messages.

```Python hl_lines="48-52"
{!../../../docs_src/websockets/tutorial001.py!}
```

You can receive and send binary, text, and JSON data.

## Try it

If your file is named `main.py`, run your application with:

<div class="termy">

```console
$ fastapi dev main.py

```

But possibly the most important part of the callback is making sure that your API user (the external developer) implements the *external API* correctly, according to the data that *your API* is going to send in the request body of the callback, etc.

So, what we will do next is add the code to document how that *external API* should look like to receive the callback from *your API*.