但是这个类型本身可以是另一个 Pydantic 模型。

因此，你可以声明拥有特定属性名称、类型和校验的深度嵌套的 JSON 对象。

上述这些都可以任意的嵌套。

### 定义子模型

例如，我们可以定义一个 `Image` 模型：

//// tab | Python 3.10+

```Python hl_lines="7-9"
{!> ../../docs_src/body_nested_models/tutorial004_py310.py!}
```

////

//// tab | Python 3.9+

```Python hl_lines="9-11"
{!> ../../docs_src/body_nested_models/tutorial004_py39.py!}
```

## Dieselben Eingabedaten zurückgeben

Im Folgenden deklarieren wir ein `UserIn`-Modell; es enthält ein Klartext-Passwort:

//// tab | Python 3.10+

```Python hl_lines="7  9"
{!> ../../docs_src/response_model/tutorial002_py310.py!}
```

////

//// tab | Python 3.8+

```Python hl_lines="9  11"
{!> ../../docs_src/response_model/tutorial002.py!}
```

////

  public void testToImmutableRangeSet_java7_combine() {
    Range<Integer> rangeOne = Range.closedOpen(1, 3);
    Range<Integer> rangeTwo = Range.closedOpen(7, 9);
    Range<Integer> rangeThree = Range.closedOpen(4, 5);
    Range<Integer> rangeFour = Range.closedOpen(6, 7);

    ImmutableRangeSet.Builder<Integer> zis =

0000400 e1 42 1b d2 61 d6 79 ec fd 3e 44 28 a4 51 6c 5c
0000410 fc d2 72 ca ba 82 18 46 16 61 cd 93 a9 0f d1 24
0000420 17 99 e2 2c 71 16 84 0c c8 7a 13 0f 9a 5e c5 f0
0000430 79 64 e2 12 4d c8 82 a1 81 19 2d aa 44 6d 87 54
0000440 84 71 c1 f6 d4 ca 25 8c 77 b9 08 c7 c8 5e 10 8a
0000450 8f 61 ed 8c ba 30 1f 79 9a c7 60 34 2b b9 8c f8
0000460 18 a6 83 1b e3 9f ad 79 fe fd 1b 8b f1 fc 41 6f

## Получить и вернуть один и тот же тип данных

Здесь мы объявили модель `UserIn`, которая хранит пользовательский пароль в открытом виде:

//// tab | Python 3.10+

```Python hl_lines="7  9"
{!> ../../docs_src/response_model/tutorial002_py310.py!}
```

////

//// tab | Python 3.8+

```Python hl_lines="9  11"
{!> ../../docs_src/response_model/tutorial002.py!}
```

////

    RangeMap<Integer, Integer> sub = rangeMap.subRangeMap(Range.closed(5, 11));
    assertEquals(
        ImmutableMap.of(Range.closedOpen(5, 7), 1, Range.closed(9, 10), 2), sub.asMapOfRanges());
    sub.remove(Range.closed(7, 9));
    assertEquals(
        ImmutableMap.of(Range.closedOpen(5, 7), 1, Range.openClosed(9, 10), 2),
        sub.asMapOfRanges());
    assertEquals(

pkg debug/elf, const EM_XIMO16 Machine
pkg debug/elf, const EM_XTENSA = 94
pkg debug/elf, const EM_XTENSA Machine
pkg debug/elf, const EM_Z80 = 220
pkg debug/elf, const EM_Z80 Machine
pkg debug/elf, const EM_ZSP = 79
pkg debug/elf, const EM_ZSP Machine
pkg debug/elf, const R_RISCV_32 = 1
pkg debug/elf, const R_RISCV_32 R_RISCV
pkg debug/elf, const R_RISCV_64 = 2
pkg debug/elf, const R_RISCV_64 R_RISCV

pkg debug/dwarf, const AttrAccessibility = 50
pkg debug/dwarf, const AttrAddrClass = 51
pkg debug/dwarf, const AttrAllocated = 78
pkg debug/dwarf, const AttrArtificial = 52
pkg debug/dwarf, const AttrAssociated = 79
pkg debug/dwarf, const AttrBaseTypes = 53
pkg debug/dwarf, const AttrBitOffset = 12
pkg debug/dwarf, const AttrBitSize = 13
pkg debug/dwarf, const AttrByteSize = 11
pkg debug/dwarf, const AttrCallColumn = 87

pkg debug/elf, const R_ARM_LDRS_PC_G2 = 66
pkg debug/elf, const R_ARM_LDRS_PC_G2 R_ARM
pkg debug/elf, const R_ARM_LDRS_SB_G0 = 78
pkg debug/elf, const R_ARM_LDRS_SB_G0 R_ARM
pkg debug/elf, const R_ARM_LDRS_SB_G1 = 79
pkg debug/elf, const R_ARM_LDRS_SB_G1 R_ARM
pkg debug/elf, const R_ARM_LDRS_SB_G2 = 80
pkg debug/elf, const R_ARM_LDRS_SB_G2 R_ARM
pkg debug/elf, const R_ARM_LDR_PC_G1 = 62
pkg debug/elf, const R_ARM_LDR_PC_G1 R_ARM

              }
            ]
          }
        ]
      },
      "gridPos": {
        "h": 6,
        "w": 6,
        "x": 18,
        "y": 42
      },
      "id": 79,
      "options": {
        "legend": {
          "calcs": [],
          "displayMode": "list",
          "placement": "bottom",
          "showLegend": true
        },
        "tooltip": {