* 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) {

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

## 요약

    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'

     * for the next update or finish.
     *
     * @param    input    input block
     * @param    offset    start of meaningful bytes in input
     * @param    len        count of bytes in input block to consider
     */
    @Override
    public void engineUpdate(final byte[] input, final int offset, final int len) {
        // make sure we don't exceed input's allocated size/length

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

## Summary { #summary }

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

## Resumen { #summary }

    }

    @ParameterizedTest
    @ValueSource(strings = { "", "password", "pässwörd", "1234567890abcdef" })
    @DisplayName("nTOWFv1 equals getNTHash across inputs")
    void testNTOWFv1_delegatesToGetNTHash(String password) {
        // Act
        byte[] h1 = NtlmUtil.nTOWFv1(password);
        byte[] h2 = NtlmUtil.getNTHash(password);

        // Assert

	}
}

// TestSignV4TrimAll - tests the logic of TrimAll() function
func TestSignV4TrimAll(t *testing.T) {
	testCases := []struct {
		// Input.
		inputStr string
		// Expected result.
		result string
	}{
		{"本語", "本語"},
		{" abc ", "abc"},
		{" a b ", "a b"},
		{"a b ", "a b"},
		{"a  b", "a b"},
		{"a   b", "a b"},

    List<List<Integer>> input = newArrayList(list1, list2);

    FluentIterable<Integer> result = FluentIterable.concat(input);
    assertEquals(asList(1, 4), newArrayList(result));

    // Now change the inputs and see result dynamically change as well

    list1.add(2);
    List<Integer> list3 = newArrayList(3);
    input.add(1, list3);

        byte[] derived = new byte[length];

        // Combine inputs
        byte[] labelBytes = label.getBytes(java.nio.charset.StandardCharsets.UTF_8);
        byte[] input = new byte[baseKey.length + labelBytes.length + (context != null ? context.length : 0)];

        int pos = 0;
        System.arraycopy(baseKey, 0, input, pos, baseKey.length);
        pos += baseKey.length;