@BeforeEach
    void setUp() {
        testCache = new TestBufferCacheImpl(5, 1024); // 5 buffers of 1KB each
    }

    @Test
    @DisplayName("BufferCache interface should define correct method signatures")
    void testInterfaceContract() {
        // Given
        BufferCache cache = mockBufferCache;

        // When & Then - verify interface methods exist and can be called

    assertThat(networkA.edgeOrder()).isEqualTo(networkB.edgeOrder());

    assertThat(networkA).isEqualTo(networkB);
  }

  /**
   * In some cases our graph implementations return custom sets that define their own size() and
   * contains(). Verify that these sets are consistent with the elements of their iterator.
   */
  @CanIgnoreReturnValue
  static <T> Set<T> sanityCheckSet(Set<T> set) {

# Parâmetros de Cabeçalho { #header-parameters }

Você pode definir parâmetros de Cabeçalho da mesma maneira que define paramêtros com `Query`, `Path` e `Cookie`.

## Importe `Header` { #import-header }

Primeiro importe `Header`:

{* ../../docs_src/header_params/tutorial001_an_py310.py hl[3] *}

## Declare parâmetros de `Header` { #declare-header-parameters }

If you use any other tool to manage your installations, like `uv`, Poetry, Pipenv, or others, they all have a way that you can use to define specific versions for your packages.

## Available versions { #available-versions }

/// warning

Notice that these parameters are passed directly to the *path operation decorator*, not to your *path operation function*.

///

## Response Status Code { #response-status-code }

You can define the (HTTP) `status_code` to be used in the response of your *path operation*.

You can pass directly the `int` code, like `404`.

                        + "stub-plugin-test, "
                        + "stub-plugin-package, "
                        + "stub-plugin-install], "
                        + "you should define versions in pluginManagement section of your pom.xml or parent");
    }

    @Test
    void testHandleBuildError() throws Exception {}

    @Test
    void testAttachToThread() throws Exception {}

    @Test

{* ../../docs_src/request_forms/tutorial001_an_py39.py hl[3] *}

## `Form`-Parameter definieren { #define-form-parameters }

Erstellen Sie Formular-Parameter, so wie Sie es auch mit `Body` und `Query` machen würden:

{* ../../docs_src/request_forms/tutorial001_an_py39.py hl[9] *}

See the License for the specific language governing permissions and
limitations under the License.
==============================================================================*/
#ifndef TENSORFLOW_C_EAGER_ABSTRACT_CONTEXT_H_
#define TENSORFLOW_C_EAGER_ABSTRACT_CONTEXT_H_

#include <memory>

#include "tensorflow/c/eager/abstract_function.h"
#include "tensorflow/c/eager/abstract_operation.h"

namespace tensorflow {

 *   <li>While waiting for new URLs to be added to the queue ({@link #delayForWaitingNewUrl()})</li>
 * </ul>
 *
 * <p>
 * Subclasses are responsible for implementing the abstract methods to define the actual delay
 * mechanism for each of these stages.
 * </p>
 *
 * <p>
 * The class also provides a mechanism to ignore exceptions that may occur during the delay process.

    * Because pydantic data structures are just instances of classes you define; auto-completion, linting, mypy and your intuition should all work properly with your validated data.
* Validate **complex structures**:
    * Use of hierarchical Pydantic models, Python `typing`’s `List` and `Dict`, etc.
    * And validators allow complex data schemas to be clearly and easily defined, checked and documented as JSON Schema.