is_path_param: bool,
) -> ParamDetails:
    field_info = None
    depends = None
    type_annotation: Any = Any
    use_annotation: Any = Any
    if is_typealiastype(annotation):
        # unpack in case PEP 695 type syntax is used
        annotation = annotation.__value__
    if annotation is not inspect.Signature.empty:
        use_annotation = annotation
        type_annotation = annotation

{
    'username': 'john',
    'password': 'secret',
    'email': '******@****.***',
    'full_name': None,
}
```

#### Bir `dict`'i Unpack Etmek { #unpacking-a-dict }

`user_dict` gibi bir `dict` alıp bunu bir fonksiyona (ya da sınıfa) `**user_dict` ile gönderirsek, Python bunu "unpack" eder. Yani `user_dict` içindeki key ve value'ları doğrudan key-value argümanları olarak geçirir.

    'password': 'secret',
    'email': '******@****.***',
    'full_name': None,
}
```

#### `dict` 언패킹 { #unpacking-a-dict }

`user_dict`와 같은 `dict`를 함수(또는 클래스)에 `**user_dict`로 전달하면, Python은 이를 "언팩(unpack)"합니다. 이 과정에서 `user_dict`의 키와 값을 각각 키-값 인자로 직접 전달합니다.

따라서, 위에서 생성한 `user_dict`를 사용하여 다음과 같이 작성하면:

```Python
UserInDB(**user_dict)
```

다음과 같은 결과를 생성합니다:

```Python
UserInDB(
    username="john",

        }

        iterator = topMetaIterator.next();

        if (iterator instanceof ConcatenatedIterator) {
          // Instead of taking linear time in the number of nested concatenations, unpack
          // them into the queue
          @SuppressWarnings("unchecked")
          ConcatenatedIterator<T> topConcat = (ConcatenatedIterator<T>) iterator;
          iterator = topConcat.iterator;

        }

        iterator = topMetaIterator.next();

        if (iterator instanceof ConcatenatedIterator) {
          // Instead of taking linear time in the number of nested concatenations, unpack
          // them into the queue
          @SuppressWarnings("unchecked")
          ConcatenatedIterator<T> topConcat = (ConcatenatedIterator<T>) iterator;
          iterator = topConcat.iterator;

            "names (",
            n->name(),
            "). This is a bug. Please file an issue at "
            "https://github.com/tensorflow/tensorflow/issues.");
      }
    }
  }

  // Unpack the results from grad_outputs_arg.
  TFOutputsFromOutputs(dy_arg, dy);
#endif  // defined(IS_MOBILE_PLATFORM) || defined(IS_SLIM_BUILD)
}

// TF_Session functions ----------------------------------------------

    ----     ------   ----                ----               -------
    Warning  Failed   7m11s (x2 over 7m33s) kubelet          spec.containers{nginx}: Failed to pull image "nginx": failed to pull and unpack image... ([#133627](https://github.com/kubernetes/kubernetes/pull/133627), [@itzPranshul](https://github.com/itzPranshul)) [SIG CLI]

are deprecated in favor of `tf.multiply`, `tf.subtract` and `tf.negative`. *
`tf.pack` and `tf.unpack` are deprecated in favor of `tf.stack` and
`tf.unstack`. * `TensorArray.pack` and `TensorArray.unpack` are getting
deprecated in favor of `TensorArray.stack` and `TensorArray.unstack`. * The
following Python functions have had their arguments changed to use `axis` when

limitations under the License.
==============================================================================*/

#include "tensorflow/c/eager/dlpack.h"

#include <string>

#include "include/dlpack/dlpack.h"  // from @dlpack
#include "tensorflow/c/eager/c_api.h"
#include "tensorflow/c/eager/c_api_experimental.h"
#include "tensorflow/c/eager/tfe_tensorhandle_internal.h"

                                            TF_Status* status);

// Converts eager tensor handle to DLPack (DLManagedTensor*), and return the
// void* for further PyCapsule construction.
TF_CAPI_EXPORT extern void* TFE_HandleToDLPack(TFE_TensorHandle* h,
                                               TF_Status* status);

// Converts DLPack (DLManagedTensor*) to eager tensor handle.
TF_CAPI_EXPORT extern TFE_TensorHandle* TFE_HandleFromDLPack(void* dlm,