private static void assertMacHashing(
      byte[] input, String algorithm, SecretKey key, HashFunction hashFunc) throws Exception {
    Mac mac = Mac.getInstance(algorithm);
    mac.init(key);
    mac.update(input);

    assertEquals(HashCode.fromBytes(mac.doFinal()), hashFunc.hashBytes(input));
    assertEquals(HashCode.fromBytes(mac.doFinal(input)), hashFunc.hashBytes(input));
  }

  // watched inputs.
  void Watch(const AbstractTensorHandle*);
  // Records an operation with given inputs and outputs
  // on the tape and marks all its outputs as watched if at
  // least one input of the op is watched and has a trainable dtype.
  // op_name is optional and is used for debugging only.
  void RecordOperation(absl::Span<AbstractTensorHandle* const> inputs,

    return new FunctionForMapNoDefault<>(map);
  }

  /**
   * Returns a function which performs a map lookup with a default value. The function created by
   * this method returns {@code defaultValue} for all inputs that do not belong to the map's key
   * set. See also {@link #forMap(Map)}, which throws an exception in this case.
   *
   * <p>Prefer to write the lambda expression {@code k -> map.getOrDefault(k, defaultValue)}

  @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.
   *

  public void testExtensiveWithAbsolutePrefix() throws IOException {
    // Inputs are /b/c/<every possible 10-character string of characters "a./">
    // Expected outputs are from realpath -s.
    doExtensiveTest("testdata/simplifypathwithabsoluteprefixtests.txt");
  }

  public void testExtensiveNoPrefix() throws IOException {
    /*
     * Inputs are <every possible 10-character string of characters "a./">
     *

  public void testExtensiveWithAbsolutePrefix() throws IOException {
    // Inputs are /b/c/<every possible 10-character string of characters "a./">
    // Expected outputs are from realpath -s.
    doExtensiveTest("testdata/simplifypathwithabsoluteprefixtests.txt");
  }

  public void testExtensiveNoPrefix() throws IOException {
    /*
     * Inputs are <every possible 10-character string of characters "a./">
     *

```json
{
    "detail": [
        {
            "type": "extra_forbidden",
            "loc": ["body", "extra"],
            "msg": "Extra inputs are not permitted",
            "input": "Mr. Poopybutthole"
        }
    ]
}
```

## Итоги { #summary }

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

## Resumen { #summary }

Puedes usar **modelos de Pydantic** para declarar **parámetros query** en **FastAPI**. 😎

/// tip | Consejo

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": ["query", "tool"],
            "msg": "Extra inputs are not permitted",
            "input": "plumbus"
        }
    ]
}
```

## Özet { #summary }

**FastAPI** içinde **query parameter**’ları tanımlamak için **Pydantic model**’leri kullanabilirsiniz. 😎

/// tip | İpucu