depth.times { target.append('    ') }
            }

            target.append("<${element.tagName}")
            for (int i = 0; i < element.attributes.length; i++) {
                Attr attr = element.attributes.item(i)
                target.append(" $attr.name=\"$attr.value\"")
            }

            element.childNodes.findAll { it instanceof Text }.each {

plugins {
  kotlin("jvm")
  id("com.github.johnrengelman.shadow")
}

tasks.compileJava {
  options.isWarnings = false
}

tasks.jar {
  manifest {
    attributes("Main-Class" to "okhttp3.sample.SampleServer")
  }
}

dependencies {
  implementation(projects.mockwebserver)
}

tasks.shadowJar {
  mergeServiceFiles()

# Reference

Here's the reference or code API, the classes, functions, parameters, attributes, and
all the FastAPI parts you can use in your applications.

If you want to **learn FastAPI** you are much better off reading the

# `APIRouter` class

Here's the reference information for the `APIRouter` class, with all its parameters, attributes and methods.

You can import the `APIRouter` class directly from `fastapi`:

```python
from fastapi import APIRouter
```

::: fastapi.APIRouter
    options:
        members:
            - websocket
            - include_router
            - get
            - put
            - post
            - delete

            sitemapSet.setType(SitemapSet.URLSET);
        }

        @Override
        public void startElement(final String uri, final String localName, final String qName, final Attributes attributes) {
            if (URL_ELEMENT.equals(qName)) {
                sitemapUrl = new SitemapUrl();
            } else if (LOC_ELEMENT.equals(qName) || LASTMOD_ELEMENT.equals(qName) || CHANGEFREQ_ELEMENT.equals(qName)

                        final Node node = nodeList.item(i);
                        final NamedNodeMap attributes = node.getAttributes();
                        if (attributes != null) {
                            final Node classAttr = attributes.getNamedItem("class");
                            if (classAttr != null) {
                                final String value = classAttr.getNodeValue();

<a href="https://docs.pydantic.dev/" class="external-link" target="_blank">Pydantic</a> est une bibliothèque Python pour effectuer de la validation de données.

Vous déclarez la forme de la donnée avec des classes et des attributs.

Chaque attribut possède un type.

                }
                final NamedNodeMap attributes = imgNode.getAttributes();
                final String thumbnailUrl = getThumbnailSrc(responseData.getUrl(), attributes);
                final Integer height = getAttributeAsInteger(attributes, "height");
                final Integer width = getAttributeAsInteger(attributes, "width");
                if (!fessConfig.isThumbnailHtmlImageUrl(thumbnailUrl)) {

// Return an allreduce op adding up input tensor `in` from `group_size` workers.
TFE_Op* AllReduceOp(TFE_Context* ctx, TFE_TensorHandle* in, int group_size);

// Return a SendOp op `op_name` with send input tensor `in` and attributes
// `send_device`, `recv_device`, and `send_device_incarnation` set.
TFE_Op* SendOp(TFE_Context* ctx, TFE_TensorHandle* in,
               const std::string& op_name, const std::string& send_device,

///

////

/// info

In Pydantic Version 1 erfolgte die Konfiguration in einer internen Klasse `Config`, in Pydantic Version 2 erfolgt sie in einem Attribut `model_config`. Dieses Attribut akzeptiert ein `dict`. Um automatische Codevervollständigung und Inline-Fehlerberichte zu erhalten, können Sie `SettingsConfigDict` importieren und verwenden, um dieses `dict` zu definieren.

///