<a href="https://github.com/scalar/scalar/?utm_source=fastapi&utm_medium=website&utm_campaign=main-badge" target="_blank" title="Scalar: Beautiful Open-Source API References from Swagger/OpenAPI files"><img src="https://fastapi.tiangolo.com/img/sponsors/scalar.svg"></a>

You can use **type annotations** the same way you would for input data in function **parameters**, you can use Pydantic models, lists, dictionaries, scalar values like integers, booleans, etc.

=== "Python 3.10+"

    ```Python hl_lines="16  21"
    {!> ../../../docs_src/response_model/tutorial001_01_py310.py!}
    ```

=== "Python 3.9+"

*   For C++ API users: `IsLegacyScalar` and `IsLegacyVector` are now gone from
    `TensorShapeUtils` since TensorFlow is scalar strict within Google (for
    example, the shape argument to `tf.reshape` can't be a scalar anymore). The
    open source release was already scalar strict, so outside Google `IsScalar`
    and `IsVector` are exact replacements.
*   The following files are being removed from `tensorflow/core/public/`:

* [Building Groovy Libraries](https://docs.gradle.org/current/samples/sample_building_groovy_libraries.html)
* [Building Scala Applications](https://docs.gradle.org/current/samples/sample_building_scala_applications.html)
* [Building Scala Libraries](https://docs.gradle.org/current/samples/sample_building_scala_libraries.html)
* [Building Kotlin JVM Applications](https://docs.gradle.org/current/samples/sample_building_kotlin_applications.html)

import org.gradle.buildinit.plugins.internal.modifiers.Language.SCALA
import org.gradle.buildinit.plugins.internal.modifiers.Language.SWIFT
import org.gradle.buildinit.plugins.internal.modifiers.ModularizationOption
import org.gradle.docs.samples.Dsl

plugins {
    id("org.gradle.samples")
}

val singleProjectSampleLanguages = listOf(JAVA, GROOVY, SCALA, KOTLIN, SWIFT, CPP)

    Bu tanımlamayla birlikte, **FastAPI** size otomatik istek <abbr title="HTTP isteği ile birlikte gelen string'i Python verisine dönüştürme">"ayrıştırma"</abbr> özelliği sağlar.

## Veri Doğrulama

  // This can not be 0 if MaxSurge is 0.
  // Defaults to 25%.
  // Example: when this is set to 30%, the old ReplicaSet can be scaled down to 70% of desired pods
  // immediately when the rolling update starts. Once new pods are ready, old ReplicaSet
  // can be scaled down further, followed by scaling up the new ReplicaSet, ensuring
  // that the total number of pods available at all times during the update is at

  optional ScaleSpec spec = 2;

  // current status of the scale. More info: https://git.k8s.io/community/contributors/devel/sig-architecture/api-conventions.md#spec-and-status. Read-only.
  // +optional
  optional ScaleStatus status = 3;
}

// describes the attributes of a scale subresource
message ScaleSpec {
  // desired number of instances for the scaled object.
  // +optional
  optional int32 replicas = 1;
}

streaming compression due to its stability and performance.

This algorithm is specifically optimized for machine generated content.
Write throughput is typically at least 500MB/s per CPU core,
and scales with the number of available CPU cores.
Decompression speed is typically at least 1GB/s.

This means that in cases where raw IO is below these numbers

#### Optimizations

A naive implementation would simply regenerate all resources, of all subscribed types, for each client, on any configuration change. However, this scales poorly. As a result, we have many levels of optimizations to avoid doing this work.