"linux" : 459,
    "windows" : 914,
    "macOs" : 292
  }, {
    "testProject" : "largeMonolithicGroovyProject",
    "linux" : 1906
  }, {
    "testProject" : "largeMonolithicJavaProject",
    "linux" : 1150
  } ]
}, {
  "scenario" : "org.gradle.performance.regression.java.JavaIncrementalExecutionPerformanceTest.assemble for non-abi change with configuration caching",
  "durations" : [ {

        assertVersionOlder("1.1.2.beta1", "1.2");

        assertVersionOlder("1.0-alpha-1", "1.0");
        assertVersionOlder("1.0-alpha-1", "1.0-alpha-2");
        assertVersionOlder("1.0-alpha-2", "1.0-alpha-15");
        assertVersionOlder("1.0-alpha-1", "1.0-beta-1");

        assertVersionOlder("1.0-beta-1", "1.0-SNAPSHOT");
        assertVersionOlder("1.0-SNAPSHOT", "1.0");

This would allow you to **re-use the model** in **multiple places** and also to declare validations and metadata for all the parameters at once. 😎

/// note

This is supported since FastAPI version `0.115.0`. 🤓

///

## Header Parameters with a Pydantic Model

Declare the **header parameters** that you need in a **Pydantic model**, and then declare the parameter as `Header`:

//// tab | Python 3.10+

mkdocs-material==9.5.18
mdx-include >=1.4.1,<2.0.0
mkdocs-redirects>=1.2.1,<1.3.0
typer == 0.12.5
pyyaml >=5.3.1,<7.0.0
# For Material for MkDocs, Chinese search
jieba==0.42.1
# For image processing by Material for MkDocs
pillow==11.0.0
# For image processing by Material for MkDocs
cairosvg==2.7.1
mkdocstrings[python]==0.26.1
griffe-typingdoc==0.2.7
# For griffe, it formats with black
black==24.3.0
mkdocs-macros-plugin==1.0.5

    void testVersionsEqual() {
        newComparable("1.0-alpha");
        checkVersionsEqual("1", "1");
        checkVersionsEqual("1", "1.0");
        checkVersionsEqual("1", "1.0.0");
        checkVersionsEqual("1.0", "1.0.0");
        checkVersionsEqual("1", "1-0");
        checkVersionsEqual("1", "1.0-0");
        checkVersionsEqual("1.0", "1.0-0");
        // no separator between number and character

numpy >= 2.0.0, < 2.2.0
wheel ~= 0.41.2
h5py >= 3.11.0
lit ~= 17.0.2
opt_einsum == 3.3.0
astunparse == 1.6.3
dill == 0.3.7
astor == 0.7.1
typing_extensions == 4.8.0
gast == 0.4.0
termcolor == 2.3.0
wrapt == 1.16.0
tblib == 2.0.0
ml_dtypes >= 0.4.0, < 0.5.0
# Install tensorboard, and keras
# Note that here we want the latest version that matches TF major.minor version

        assertEquals("8.0 kB", format.formatProgress(_8000_bytes, _8000_bytes));
        assertEquals("8.0/50 kB", format.formatProgress(_8000_bytes, _50_kilobytes));
        assertEquals("16/50 kB", format.formatProgress(2L * _8000_bytes, _50_kilobytes));
        assertEquals("50 kB", format.formatProgress(_50_kilobytes, _50_kilobytes));

        long _500_kilobytes = 500000L;

Isso permitiria que você **reutilizasse o modelo** em **diversos lugares**, e também declarasse validações e metadados de todos os parâmetros de uma única vez. 😎

/// note | Nota

Isso é suportado desde o FastAPI versão `0.115.0`. 🤓

///

## Parâmetros de Consulta com um Modelo Pydantic

Declare os **parâmetros de consulta** que você precisa em um **modelo Pydantic**, e então declare o parâmetro como `Query`:

//// tab | Python 3.10+

This would allow you to **re-use the model** in **multiple places** and also to declare validations and metadata for all the parameters at once. 😎

/// note

This is supported since FastAPI version `0.115.0`. 🤓

///

## Query Parameters with a Pydantic Model

Declare the **query parameters** that you need in a **Pydantic model**, and then declare the parameter as `Query`:

//// tab | Python 3.10+

[versions]
biz-aQute-bnd = "6.4.0"
checkStyle = "10.19.0"
com-squareup-moshi = "1.15.1"
com-squareup-okio = "3.9.1"
de-mannodermaus-junit5 = "1.5.0"
graalvm = "22.3.2"
kotlinx-serialization = "1.6.3"
ksp = "1.9.24-1.0.20"
mockserverClient = "5.15.0"
org-bouncycastle = "1.76"
org-conscrypt = "2.5.2"
org-jetbrains-coroutines = "1.8.1"
org-jetbrains-kotlin = "1.9.25"
org-junit-jupiter = "5.11.0"
retrofit = "2.11.0"