if (charset.equals(UTF_8)) {
      int utf16Length = input.length();
      int h1 = seed;
      int i = 0;
      int len = 0;

      // This loop optimizes for pure ASCII.
      while (i + 4 <= utf16Length) {
        char c0 = input.charAt(i);
        char c1 = input.charAt(i + 1);
        char c2 = input.charAt(i + 2);
        char c3 = input.charAt(i + 3);

        // ensure that bouncycastle is on classpath for the all of test types, must happen in evaluateAfter since the rest tests explicitly
        // set the class path to help maintain pure black box testing, and here we are adding to that classpath
        tasks.withType(Test).configureEach { Test test ->
          test.setClasspath(test.getClasspath().plus(extraFipsJars))
        }
      }

/// info | Info

Beachten Sie, wie `Offer` eine Liste von `Item`s hat, die ihrerseits eine optionale Liste von `Image`s haben.

///

## Bodys aus reinen Listen { #bodies-of-pure-lists }

Wenn das äußerste Element des JSON-Bodys, das Sie erwarten, ein JSON-`array` (eine Python-`list`) ist, können Sie den Typ im Funktionsparameter deklarieren, mit der gleichen Syntax wie in Pydantic-Modellen:

```Python

    // Warning to maintainers: this implementation is highly optimized.
    int utf16Length = sequence.length();
    int utf8Length = utf16Length;
    int i = 0;

    // This loop optimizes for pure ASCII.
    while (i < utf16Length && sequence.charAt(i) < 0x80) {
      i++;
    }

    // This loop optimizes for chars less than 0x800.
    for (; i < utf16Length; i++) {
      char c = sequence.charAt(i);

    // Warning to maintainers: this implementation is highly optimized.
    int utf16Length = sequence.length();
    int utf8Length = utf16Length;
    int i = 0;

    // This loop optimizes for pure ASCII.
    while (i < utf16Length && sequence.charAt(i) < 0x80) {
      i++;
    }

    // This loop optimizes for chars less than 0x800.
    for (; i < utf16Length; i++) {
      char c = sequence.charAt(i);

/// info | Bilgi

`Offer`’ın bir `Item` list’i olduğuna, `Item`’ların da opsiyonel bir `Image` list’ine sahip olduğuna dikkat edin.

///

## Sadece list olan body’ler { #bodies-of-pure-lists }

Beklediğiniz JSON body’nin en üst seviye değeri bir JSON `array` (Python’da `list`) ise, tipi Pydantic modellerinde olduğu gibi fonksiyonun parametresinde tanımlayabilirsiniz:

```Python
images: list[Image]

import com.google.errorprone.annotations.Immutable;
import java.nio.ByteBuffer;
import java.nio.charset.Charset;
import org.jspecify.annotations.Nullable;

/**
 * A hash function is a collision-averse pure function that maps an arbitrary block of data to a
 * number called a <i>hash code</i>.
 *
 * <h3>Definition</h3>
 *
 * <p>Unpacking this definition:
 *
 * <ul>

  :Darwin*arm64) distributionPlatform=darwin-aarch64 ;;
  :Linux*x86_64*) distributionPlatform=linux-amd64 ;;
  *)
    echo "Cannot detect native platform for mvnd on $(uname)-$(uname -m), use pure java version" >&2
    distributionPlatform=linux-amd64
    ;;
  esac
  distributionUrl="${distributionUrl%-bin.*}-$distributionPlatform.zip"
  ;;
maven-mvnd-*) MVN_CMD=mvnd.sh _MVNW_REPO_PATTERN=/maven/mvnd/ ;;

你可以定義任意深度的巢狀模型：

{* ../../docs_src/body_nested_models/tutorial007_py310.py hl[7,12,18,21,25] *}

/// info

請注意，`Offer` 具有一個 `Item` 的列表，而每個 `Item` 又有一個可選的 `Image` 列表。

///

## 純列表的本文 { #bodies-of-pure-lists }

如果你期望的 JSON 本文頂層值是一個 JSON `array`（Python 的 `list`），可以像在 Pydantic 模型中那樣，直接在函式參數上宣告其型別：

```Python
images: list[Image]
```

如下所示：

/// info | Información

Observa cómo `Offer` tiene una lista de `Item`s, que a su vez tienen una lista opcional de `Image`s

///

## Cuerpos de listas puras { #bodies-of-pure-lists }

Si el valor superior del cuerpo JSON que esperas es un `array` JSON (una `list` en Python), puedes declarar el tipo en el parámetro de la función, al igual que en los modelos Pydantic:

```Python