* consistent hashing</a> for more information.
   */
  public static int consistentHash(long input, int buckets) {
    checkArgument(buckets > 0, "buckets must be positive: %s", buckets);
    LinearCongruentialGenerator generator = new LinearCongruentialGenerator(input);
    int candidate = 0;
    int next;

    // Jump from bucket to bucket until we go out of range
    while (true) {

				"us-west-1",
				"s3",
				"ABCD"),
			expectedCredentials: credentialHeader{},
			expectedErrCode:     ErrInvalidRequestVersion,
		},
		// Test Case - 9.
		// Test case with right inputs. Expected to return a valid CredentialHeader.
		// "aws4_request" is the valid request version.
		{
			inputCredentialStr: generateCredentialStr(
				"Z7IXGOO6BZ0REAN1Q26I",
				sampleTimeStr,
				"us-west-1",

```json
{
    "detail": [
        {
            "type": "extra_forbidden",
            "loc": ["header", "tool"],
            "msg": "Extra inputs are not permitted",
            "input": "plumbus",
        }
    ]
}
```

## Desativar conversão de underscores { #disable-convert-underscores }

```json
{
    "detail": [
        {
            "type": "extra_forbidden",
            "loc": ["header", "tool"],
            "msg": "Extra inputs are not permitted",
            "input": "plumbus",
        }
    ]
}
```

## Desactivar la conversión de guiones bajos { #disable-convert-underscores }

```

他们将收到一个**错误**响应，告诉他们查询参数 `tool` 是不允许的：

```json
{
    "detail": [
        {
            "type": "extra_forbidden",
            "loc": ["query", "tool"],
            "msg": "Extra inputs are not permitted",
            "input": "plumbus"
        }
    ]
}
```

## 总结

你可以使用 **Pydantic 模型**在 **FastAPI** 中声明**查询参数**。😎

/// tip

剧透警告：你也可以使用 Pydantic 模型来声明 cookie 和 headers，但你将在本教程的后面部分阅读到这部分内容。🤫

```json
{
    "detail": [
        {
            "type": "extra_forbidden",
            "loc": ["cookie", "santa_tracker"],
            "msg": "Extra inputs are not permitted",
            "input": "good-list-please",
        }
    ]
}
```

## 요약

        SmbSession result = transport.getSmbSession(ctx);
        assertSame(session, result);
        verify(transport).getSmbSession(eq(ctx));
    }

    // Edge inputs: null context returns null session per mock setup
    @Test
    @DisplayName("getSmbSession(ctx) with null context returns null as stubbed")
    void getSmbSession_byContext_null() {

```json
{
    "detail": [
        {
            "type": "extra_forbidden",
            "loc": ["cookie", "santa_tracker"],
            "msg": "Extra inputs are not permitted",
            "input": "good-list-please",
        }
    ]
}
```

## Summary { #summary }

    their input and output keys silently ignored and rewritten to 'inputs' and
    'outputs'. If a model was exported with different names before 1.2, and is
    now served with tensorflow/serving, it will accept requests using 'inputs'
    and 'outputs'. Starting at 1.2, such a model will accept the keys specified
    during export. Therefore, inference requests using 'inputs' and 'outputs'

   * (key) bits 'affects' a strictly smaller set of output bits. Funneling is bad because it can
   * result in more-than-ideal collisions for a non-uniformly distributed key space. In practice,
   * most key spaces are ANYTHING BUT uniformly distributed. A bit(i) in the input is said to
   * 'affect' a bit(j) in the output if two inputs, identical but for bit(i), will differ at output