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./">
     *

  // 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,

```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

    TFE_TensorHandle* input = TFE_OpGetFlatInput(original_op, j, s);
    if (TF_GetCode(s) != TF_OK) return;
    const char* input_device = TFE_TensorHandleDeviceName(input, s);
    if (TF_GetCode(s) != TF_OK) return;
    if (dev->device_name == input_device) {
      LoggedTensor* t = reinterpret_cast<LoggedTensor*>(
          TFE_TensorHandleDevicePointer(input, s));
      if (TF_GetCode(s) != TF_OK) return;

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

## Zusammenfassung { #summary }

Sie können **Pydantic-Modelle** verwenden, um **Query-Parameter** in **FastAPI** zu deklarieren. 😎

/// tip | Tipp

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

## Summary { #summary }

You can use **Pydantic models** to declare **query parameters** in **FastAPI**. 😎

/// tip

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

## Вимкнути перетворення підкреслень { #disable-convert-underscores }