* String a1 = peekingIterator.peek(); // returns "a"
   * String a2 = peekingIterator.peek(); // also returns "a"
   * String a3 = peekingIterator.next(); // also returns "a"
   * }</pre>
   *
   * <p>Any structural changes to the underlying iteration (aside from those performed by the
   * iterator's own {@link PeekingIterator#remove()} method) will leave the iterator in an undefined
   * state.
   *

import java.util.ArrayList;
import java.util.Collection;

/**
 * This is the main graph data structure used by the RepositorySystem to present tree and graph objects.
 *
 *
 */
@Deprecated
public class MetadataGraph {
    /** all graph nodes */
    Collection<MetadataGraphNode> nodes;

    /** entry point for tree-like structures */
    MetadataGraphNode entry;

    public MetadataGraph(MetadataGraphNode entry) {

This way, its schema will show up in the API docs user interface:

<img src="/img/tutorial/dataclasses/image01.png">

## Dataclasses in Nested Data Structures

You can also combine `dataclasses` with other type annotations to make nested data structures.

In some cases, you might still have to use Pydantic's version of `dataclasses`. For example, if you have errors with the automatically generated API documentation.

## Importar `Cookie`

Primero importa `Cookie`:

{* ../../docs_src/cookie_params/tutorial001_an_py310.py hl[3]*}

## Declarar parámetros de `Cookie`

Luego declara los parámetros de cookie usando la misma estructura que con `Path` y `Query`.

El primer valor es el valor por defecto, puedes pasar todos los parámetros adicionales de validación o anotación:

{* ../../docs_src/cookie_params/tutorial001_an_py310.py hl[9]*}

    };
  }

  /** Returns the children of the specified node. Must not contain null. */
  public abstract Iterable<T> children(T root);

  /**
   * Returns an unmodifiable iterable over the nodes in a tree structure, using pre-order traversal.
   * That is, each node's subtrees are traversed after the node itself is returned.
   *
   * <p>No guarantees are made about the behavior of the traversal when nodes change while iteration

### Project file structure

Let's say your project file structure looks like this:

```
.
├── app
│   ├── __init__.py
│   ├── main.py
```

Now create a directory to store those static files.

Your new file structure could look like this:

```
.
├── app
│   ├── __init__.py
│   ├── main.py
└── static/
```

    }

    /**
     * Check most classical urls inheritance: directory structure where parent POM in parent directory
     * and child directory == artifactId
     * @throws IOException Model read problem
     */
    @Test
    void testUrls() throws Exception {
        testInheritance("urls");
    }

    /**
     * Flat directory structure: parent & child POMs in sibling directories, child directory == artifactId.

  <groupId>inheritance</groupId>
  <artifactId>parent</artifactId>
  <version>11-SNAPSHOT</version>

  <name>Model urls inheritance test parent</name>
  <description>Flat directory structure case: module = ../child directory path + child directory path != child-artifact-id</description>

  <modules>
    <module>../inheritance</module><!-- current directory == inheritance -->
  </modules>

  </parent>

  <groupId>inheritance</groupId>
  <artifactId>child-artifact-id</artifactId>
  <version>11-SNAPSHOT</version>
  <name>Model urls inheritance test child</name>
  <description>Flat directory structure case: module = ../child-artifact-id + child directory path != child-artifact-id</description>

  <!-- 5 inherited urls with ../${project.artifactId} added to parent -->

It doesn't return a large `str` containing the data in JSON format (as a string). It returns a Python standard data structure (e.g. a `dict`) with values and sub-values that are all compatible with JSON.

/// note

`jsonable_encoder` is actually used by **FastAPI** internally to convert data. But it is useful in many other scenarios.