new_field = create_model_field(name=field.name, type_=use_type)
    new_field.has_alias = field.has_alias  # type: ignore[attr-defined]
    new_field.alias = field.alias  # type: ignore[misc]
    new_field.class_validators = field.class_validators  # type: ignore[attr-defined]
    new_field.default = field.default  # type: ignore[misc]
    new_field.required = field.required  # type: ignore[misc]

==============================================================================*/
#include "tensorflow/c/eager/gradients.h"

#include <memory>

#include "absl/container/flat_hash_set.h"
#include "absl/types/span.h"
#include "tensorflow/c/eager/abstract_context.h"
#include "tensorflow/c/eager/abstract_tensor_handle.h"
#include "tensorflow/c/eager/c_api_experimental.h"
#include "tensorflow/c/eager/c_api_test_util.h"

  }

  /**
   * Returns a new array of the given length with the same type as a reference array.
   *
   * @param reference any array of the desired type
   * @param length the length of the new array
   */
  /*
   * The new array contains nulls, even if the old array did not. If we wanted to be accurate, we
   * would declare a return type of `@Nullable T[]`. However, we've decided not to think too hard

  }

  /**
   * Returns a new array of the given length with the same type as a reference array.
   *
   * @param reference any array of the desired type
   * @param length the length of the new array
   */
  /*
   * The new array contains nulls, even if the old array did not. If we wanted to be accurate, we
   * would declare a return type of `@Nullable T[]`. However, we've decided not to think too hard

limitations under the License.
==============================================================================*/
#include "tensorflow/c/eager/gradient_checker.h"

#include <memory>

#include "absl/types/span.h"
#include "tensorflow/c/eager/abstract_tensor_handle.h"
#include "tensorflow/c/eager/c_api_unified_experimental.h"
#include "tensorflow/c/eager/unified_api_testutil.h"
#include "tensorflow/c/experimental/ops/math_ops.h"

///

## Use `Annotated` in the type for the `q` parameter

Remember I told you before that `Annotated` can be used to add metadata to your parameters in the [Python Types Intro](../python-types.md#type-hints-with-metadata-annotations){.internal-link target=_blank}?

Now it's the time to use it with FastAPI. 🚀

We had this type annotation:

//// tab | Python 3.10+

```Python

   *
   * <blockquote>
   *
   * "Some collection implementations have restrictions on the elements that they may contain. For
   * example, some implementations prohibit null elements, and some have restrictions on the types
   * of their elements."
   *
   * </blockquote>
   */
  RESTRICTS_ELEMENTS,

  /**
   * Indicates that a collection has a well-defined ordering of its elements. The ordering may

 */

package jcifs.smb1.netbios;

import java.io.IOException;
import java.io.InputStream;

public abstract class SessionServicePacket { 

    // session service packet types 
    static final int SESSION_MESSAGE = 0x00; 
    static final int SESSION_REQUEST = 0x81; 
    public static final int POSITIVE_SESSION_RESPONSE = 0x82; 
    public static final int NEGATIVE_SESSION_RESPONSE = 0x83; 

Jeśli potrzebujesz szybkiego przypomnienia jak używać deklaracji typów w Pythonie (nawet jeśli nie używasz FastAPI), sprawdź krótki samouczek: [Python Types](python-types.md){.internal-link target=_blank}.

Wystarczy, że napiszesz standardowe deklaracje typów Pythona:

```Python
from datetime import date

from pydantic import BaseModel

# Zadeklaruj parametr jako str

vous verrez comme réponse :

```JSON
{"item_id":"foo"}
```

## Paramètres de chemin typés

Vous pouvez déclarer le type d'un paramètre de chemin dans la fonction, en utilisant les annotations de type Python :


```Python hl_lines="7"
{!../../docs_src/path_params/tutorial002.py!}
```

Ici, `item_id` est déclaré comme `int`.

/// check | "vérifier"