<la:message key="labels.dict_stopwords_title"/>
                        </h1>
                    </div>
                    <div class="col-sm-6">
                        <ol class="breadcrumb float-sm-right">
                            <li class="breadcrumb-item"><la:link href="/admin/dict">
                                <la:message key="labels.dict_list_link"/>
                            </la:link></li>

          new TestHasher().putUnencodedChars(s).hash(),
          new TestHasher().putString(s, UTF_16LE).hash());
    }
  }

  public void testFloat() {
    TestHasher hasher = new TestHasher();
    hasher.putFloat(Float.intBitsToFloat(0x04030201));
    hasher.assertBytes(new byte[] {1, 2, 3, 4});
  }

  public void testDouble() {
    TestHasher hasher = new TestHasher();
    hasher.putDouble(Double.longBitsToDouble(0x0807060504030201L));

		{"DATA", "·A(SB)/4,0", "DATA value must be an immediate constant or address"},
		{"DATA", "·B(SB)/4,$0", ""},
		{"DATA", "·C(SB)/5,$0", "bad int size for DATA argument: 5"},
		{"DATA", "·D(SB)/5,$0.0", "bad float size for DATA argument: 5"},
		{"DATA", "·E(SB)/4,$·A(SB)", "bad addr size for DATA argument: 4"},
		{"DATA", "·F(SB)/8,$·A(SB)", ""},
		{"DATA", "·G(SB)/5,$\"abcde\"", ""},

                } else if (parameterType.equals(Double.TYPE)) {
                    methodKey.append("java.lang.Double");
                } else if (parameterType.equals(Float.TYPE)) {
                    methodKey.append("java.lang.Float");
                } else if (parameterType.equals(Integer.TYPE)) {
                    methodKey.append("java.lang.Integer");
                } else if (parameterType.equals(Long.TYPE)) {

## 响应模型编码参数

你的响应模型可以具有默认值，例如：

{* ../../docs_src/response_model/tutorial004.py hl[11,13:14] *}

* `description: Union[str, None] = None` 具有默认值 `None`。
* `tax: float = 10.5` 具有默认值 `10.5`.
* `tags: List[str] = []` 具有一个空列表作为默认值： `[]`.

但如果它们并没有存储实际的值，你可能想从结果中忽略它们的默认值。

举个例子，当你在 NoSQL 数据库中保存了具有许多可选属性的模型，但你又不想发送充满默认值的很长的 JSON 响应。

### 使用 `response_model_exclude_unset` 参数

Python has support for optional "type hints" (also called "type annotations").

These **"type hints"** or annotations are a special syntax that allow declaring the <abbr title="for example: str, int, float, bool">type</abbr> of a variable.

By declaring types for your variables, editors and tools can give you better support.

  @Override
  int readInt();

  @CanIgnoreReturnValue // to skip some bytes
  @Override
  long readLong();

  @CanIgnoreReturnValue // to skip some bytes
  @Override
  float readFloat();

  @CanIgnoreReturnValue // to skip some bytes
  @Override
  double readDouble();

  @CanIgnoreReturnValue // to skip a line
  @Override
  @Nullable String readLine();

                            <la:message key="labels.dict_stemmeroverride_title"/>
                        </h1>
                    </div>
                    <div class="col-sm-6">
                        <ol class="breadcrumb float-sm-right">
                            <li class="breadcrumb-item"><la:link href="/admin/dict">
                                <la:message key="labels.dict_list_link"/>
                            </la:link></li>

        primitiveTypes.add("byte");
        primitiveTypes.add("short");
        primitiveTypes.add("int");
        primitiveTypes.add("long");
        primitiveTypes.add("char");
        primitiveTypes.add("float");
        primitiveTypes.add("double");
        primitiveTypes.add("void");
    }

    public LinkRenderer(Document document, DslDocModel model) {
        this(document, model, GroovySystem.getVersion(), "8");
    }

		{Name: "FLOAT32", DBName: "ffloat32", BindNames: []string{"FLOAT32"}, DataType: schema.Float, Creatable: true, Updatable: true, Readable: true, Size: 32, Tag: `gorm:"column:ffloat32"`},
		{Name: "FLOAT64", DBName: "ffloat64", BindNames: []string{"FLOAT64"}, DataType: schema.Float, Creatable: true, Updatable: true, Readable: true, Size: 64, Tag: `gorm:"column:ffloat64"`},