limitations under the License.
==============================================================================*/
#ifndef TENSORFLOW_C_EAGER_ABSTRACT_TENSOR_HANDLE_H_
#define TENSORFLOW_C_EAGER_ABSTRACT_TENSOR_HANDLE_H_

#include <memory>

#include "tensorflow/core/framework/full_type.pb.h"
#include "tensorflow/core/framework/tensor_shape.h"
#include "tensorflow/core/framework/types.pb.h"

limitations under the License.
==============================================================================*/
#ifndef TENSORFLOW_C_EAGER_IMMEDIATE_EXECUTION_TENSOR_HANDLE_H_
#define TENSORFLOW_C_EAGER_IMMEDIATE_EXECUTION_TENSOR_HANDLE_H_

#include "tensorflow/c/eager/abstract_tensor_handle.h"
#include "tensorflow/c/tensor_interface.h"
#include "tensorflow/core/framework/types.pb.h"
#include "tensorflow/core/platform/status.h"

See the License for the specific language governing permissions and
limitations under the License.
==============================================================================*/

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

namespace tensorflow {

std::string AbstractTensorHandle::DebugString() const {
  PartialTensorShape shape;
  Status s = Shape(&shape);
  std::string shape_string;
  if (!s.ok()) {

### `response_model` Priority

If you declare both a return type and a `response_model`, the `response_model` will take priority and be used by FastAPI.

package gorm

import "time"

// Model a basic GoLang struct which includes the following fields: ID, CreatedAt, UpdatedAt, DeletedAt
// It may be embedded into your model or you may build your own model without it
//
//	type User struct {
//	  gorm.Model
//	}
type Model struct {
	ID        uint `gorm:"primarykey"`
	CreatedAt time.Time
	UpdatedAt time.Time
	DeletedAt DeletedAt `gorm:"index"`

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

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

namespace tensorflow {

std::string ImmediateExecutionTensorHandle::DebugString() const {
  PartialTensorShape shape;
  std::string shape_string;
  if (Shape(&shape).ok()) {

    }

    @Override
    public boolean isCancelled() {
      return sync.isCancelled();
    }

    @CanIgnoreReturnValue
    @Override
    public boolean cancel(boolean mayInterruptIfRunning) {
      if (!sync.cancel(mayInterruptIfRunning)) {
        return false;
      }
      executionList.execute();
      if (mayInterruptIfRunning) {
        interruptTask();
      }
      return true;

    }

    @Override
    public boolean isCancelled() {
      return sync.isCancelled();
    }

    @CanIgnoreReturnValue
    @Override
    public boolean cancel(boolean mayInterruptIfRunning) {
      if (!sync.cancel(mayInterruptIfRunning)) {
        return false;
      }
      executionList.execute();
      if (mayInterruptIfRunning) {
        interruptTask();
      }
      return true;

    {!> ../../../docs_src/response_model/tutorial001_py39.py!}
    ```

=== "Python 3.8+"

    ```Python hl_lines="17  22  24-27"
    {!> ../../../docs_src/response_model/tutorial001.py!}
    ```

!!! note "Hinweis"
    Beachten Sie, dass `response_model` ein Parameter der „Dekorator“-Methode ist (`get`, `post`, usw.). Nicht der *Pfadoperation-Funktion*, so wie die anderen Parameter.

					continue
				}
				err = resp.Err
			}
			t.Log("Client Context canceled. err state:", err)
			clientCanceled <- time.Now()
		}(t)
		start := time.Now()
		cancel()
		<-serverCanceled
		t.Log("server cancel time:", time.Since(start))
		clientEnd := <-clientCanceled
		if !errors.Is(err, context.Canceled) {
			t.Error("expected context.Canceled, got", err)
		}