assert response.json() == {
        "detail": [
            {
                "type": "missing",
                "loc": ["query", "p"],
                "msg": "Field required",
                "input": IsOneOf(None, {}),
            }
        ]
    }


@pytest.mark.parametrize(
    "path",
    ["/required-list-str", "/model-required-list-str"],
)
def test_required_list_str(path: str):

    assert response.json() == {
        "detail": [
            {
                "type": "missing",
                "loc": ["body", "p"],
                "msg": "Field required",
                "input": None,
            }
        ]
    }


@pytest.mark.parametrize(
    "path",
    [
        "/list-bytes",
        "/list-uploadfile",
    ],
)
def test_list(path: str):

    input.put("foo", 1);
    input.put("bar", 2);
    input.put("foo", 3);
    input.put("foo", 1);
    ImmutableListMultimap<String, Integer> multimap = ImmutableListMultimap.copyOf(input);
    new EqualsTester().addEqualityGroup(input, multimap).testEquals();
  }

  public void testCopyOfEmpty() {
    ArrayListMultimap<String, Integer> input = ArrayListMultimap.create();

                                    /*remote_func_outputs=*/true);
}
TEST(CAPI, RemoteExecuteSilentCopiesLocalAsyncFuncOrdering) {
  // A remote input may be not ready when we start running a function. Test that
  // the function execution should wait until the remote input is ready.
  TestRemoteExecuteSilentCopiesFunc(/*async=*/true, /*remote=*/false,
                                    /*heavy_load_on_streaming_rpc=*/true);
}

     * requires some work.
     */

    return Collector.of(
        ImmutableTableCollectorState<R, C, V>::new,
        (state, input) ->
            state.put(
                rowFunction.apply(input),
                columnFunction.apply(input),
                valueFunction.apply(input),
                mergeFunction),
        (s1, s2) -> s1.combine(s2, mergeFunction),
        state -> state.toTable());
  }

  static <

  static <I extends @Nullable Object, O extends @Nullable Object> ListenableFuture<O> createAsync(
      ListenableFuture<I> input,
      AsyncFunction<? super I, ? extends O> function,
      Executor executor) {
    AsyncTransformFuture<I, O> output = new AsyncTransformFuture<>(input, function);
    input.addListener(output, rejectionPropagatingExecutor(executor, output));
    return output;
  }

            throw new IORuntimeException(e);
        }
    }

    /**
     * Gets a byte array from an {@link InputStream}.
     * <p>
     * The input stream is not closed.
     * </p>
     *
     * @param is the input stream (must not be {@literal null})
     * @return the byte array
     */
    public static final byte[] getBytes(final InputStream is) {
        assertArgumentNotNull("is", is);

    assert response.json() == {
        "detail": [
            {
                "type": "int_parsing",
                "loc": ["body", "age"],
                "msg": "Input should be a valid integer, unable to parse string as an integer",
                "input": "seventy",
            }
        ]
    }


def test_no_data():
    response = client.post("/form/")
    assert response.status_code == 422, response.text

自從 Pydantic v2 發佈後，生成的 OpenAPI 比以往更精確也更正確。😎

實際上，在某些情況下，同一個 Pydantic 模型在 OpenAPI 中會同時有兩個 JSON Schema：分別用於輸入與輸出，這取決於它是否有預設值。

來看看它如何運作，以及若需要時該如何調整。

## 作為輸入與輸出的 Pydantic 模型 { #pydantic-models-for-input-and-output }

假設你有一個帶有預設值的 Pydantic 模型，如下所示：

{* ../../docs_src/separate_openapi_schemas/tutorial001_py310.py ln[1:7] hl[7] *}

### 輸入用模型 { #model-for-input }

如果你把這個模型用作輸入，如下所示：

<div class="screenshot">
<img src="/img/tutorial/separate-openapi-schemas/image03.png">
</div>

### 文档中的输入/输出模型 { #model-for-input-and-output-in-docs }

如果你查看 OpenAPI 中可用的所有 Schema（JSON Schema），你会看到有两个，一个是 `Item-Input`，一个是 `Item-Output`。

对于 `Item-Input`，`description` **不是必填**，没有红色星号。

但对于 `Item-Output`，`description` 是**必填**，带有红色星号。

<div class="screenshot">