*
     * Unfortunately, we don't distinguish between these two cases in our public API: Joiner.on(...)
     * and Joiner.on(...).useForNull(...) both declare the same return type: plain Joiner. To ensure
     * that users *can* pass null arguments to Joiner, we annotate it as if it always tolerates null
     * inputs, rather than as if it never tolerates them.
     *

  @SafeVarargs
  public static <T extends @Nullable Object> Iterable<T> concat(Iterable<? extends T>... inputs) {
    return FluentIterable.concat(inputs);
  }

  /**
   * Combines multiple iterables into a single iterable. The returned iterable has an iterator that
   * traverses the elements of each iterable in {@code inputs}. The input iterators are not polled
   * until necessary.
   *

    }

    @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

            // Then
            assertNotNull(result, "Result should never be null");
            if (input == null || input.isEmpty()) {
                assertEquals(0, result.length, "Null or empty input should produce empty array");
            } else {
                assertTrue(result.length > 0, "Non-empty input should produce non-empty array");
            }
        }
    }

    @Nested

     *
     * Unfortunately, we don't distinguish between these two cases in our public API: Joiner.on(...)
     * and Joiner.on(...).useForNull(...) both declare the same return type: plain Joiner. To ensure
     * that users *can* pass null arguments to Joiner, we annotate it as if it always tolerates null
     * inputs, rather than as if it never tolerates them.
     *

Therefore, if you run a `tf.train.Server` in your network, anybody with access
to the network can execute arbitrary code with the privileges of the user
running the `tf.train.Server`.

## Untrusted inputs during training and prediction

TensorFlow supports a wide range of input data formats. For example it can
process images, audio, videos, and text. There are several modules specialized
in taking those formats, modifying them, and/or converting them to intermediate

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

## Заключение

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

## Resumen

クエリパラメータ `tool` が許可されていないことを通知する**エラー**レスポンスが返されます。

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

## まとめ

**FastAPI**では、**クエリパラメータ**を宣言するために、**Pydanticモデル**を使用できます。😎

/// tip | 豆知識

        // Assert
        assertNotNull(resp, "Instance should not be null");
    }

    /**
     * All wire‑format methods should return zero regardless of inputs.
     */
    @Nested
    @DisplayName("Wire format method behaviour")
    class WireFormatMethods {
        @ParameterizedTest
        @ValueSource(ints = { 0, 1, -1, Integer.MAX_VALUE, Integer.MIN_VALUE })