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)

<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 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();

                }
                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.

///