/*
 * Licensed to the Apache Software Foundation (ASF) under one
 * or more contributor license agreements.  See the NOTICE file
 * distributed with this work for additional information
 * regarding copyright ownership.  The ASF licenses this file
 * to you under the Apache License, Version 2.0 (the
 * "License"); you may not use this file except in compliance
 * with the License.  You may obtain a copy of the License at
 *

//
// This program is distributed in the hope that it will be useful
// but WITHOUT ANY WARRANTY; without even the implied warranty of
// MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
// GNU Affero General Public License for more details.
//
// You should have received a copy of the GNU Affero General Public License
// along with this program.  If not, see <http://www.gnu.org/licenses/>.

package event

  public void testClearPropagatesToGet() {
    for (K key : sampleKeys()) {
      resetContainer();
      Collection<V> collection = multimap().get(key);
      multimap().clear();
      assertEmpty(collection);
    }
  }

  @MapFeature.Require(SUPPORTS_REMOVE)
  @CollectionSize.Require(absent = ZERO)
  public void testClearPropagatesToAsMapGet() {
    for (K key : sampleKeys()) {
      resetContainer();

 *
 * Unless required by applicable law or agreed to in writing, software distributed under the License
 * is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express
 * or implied. See the License for the specific language governing permissions and limitations under
 * the License.
 */

package com.google.common.util.concurrent;

import static com.google.common.collect.Lists.newArrayListWithCapacity;

 *
 * This library is distributed in the hope that it will be useful,
 * but WITHOUT ANY WARRANTY; without even the implied warranty of
 * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the GNU
 * Lesser General Public License for more details.
 *
 * You should have received a copy of the GNU Lesser General Public
 * License along with this library; if not, write to the Free Software

/*
 * Licensed to the Apache Software Foundation (ASF) under one
 * or more contributor license agreements.  See the NOTICE file
 * distributed with this work for additional information
 * regarding copyright ownership.  The ASF licenses this file
 * to you under the Apache License, Version 2.0 (the
 * "License"); you may not use this file except in compliance
 * with the License.  You may obtain a copy of the License at
 *

                          std::vector<int64_t> shape,
                          std::vector<int64_t> strides) {
  size_t num_elements = 1;
  for (int i = 0; i < static_cast<int32_t>(shape.size()); ++i) {
    num_elements *= shape[i];
  }
  std::vector<float> data(num_elements);
  for (size_t j = 0; j < num_elements; ++j) {
    data[j] = j;
  }
  DLManagedTensor dlm_in = {};
  DLTensor* dltensor_in = &dlm_in.dl_tensor;

        // Act
        handle.close();

        // Assert
        // Verify sendrecv was called once for open and once for close
        verify(mockDcerpcHandle, times(1)).sendrecv(any(MsrpcLsarOpenPolicy2.class));
        verify(mockDcerpcHandle, times(1)).sendrecv(any(MsrpcLsarClose.class));
    }

    @Test
    void close_shouldThrowSmbExceptionOnRpcError() throws IOException {
        // Arrange

    @DisplayName("Interaction: consumer is invoked for each found mapping")
    void interactionWithConsumerIsAsExpected() {
        // Arrange: choose a small set of known DOS codes
        List<Integer> inputs = Arrays.asList(0x00000000, 0x00050001, 0x007b0001, 0x0BADF00D);
        @SuppressWarnings("unchecked")
        BiConsumer<Integer, Integer> consumer = mock(BiConsumer.class);

        // Act: for each input, if a mapping exists, notify the consumer

# 입력과 출력에 대해 OpenAPI 스키마를 분리할지 여부 { #separate-openapi-schemas-for-input-and-output-or-not }

**Pydantic v2**가 릴리스된 이후, 생성되는 OpenAPI는 이전보다 조금 더 정확하고 **올바르게** 만들어집니다. 😎

실제로 어떤 경우에는, 같은 Pydantic 모델에 대해 OpenAPI 안에 **두 개의 JSON Schema**가 생기기도 합니다. **기본값(default value)**이 있는지 여부에 따라, 입력용과 출력용으로 나뉩니다.

이것이 어떻게 동작하는지, 그리고 필요하다면 어떻게 변경할 수 있는지 살펴보겠습니다.

## 입력과 출력을 위한 Pydantic 모델 { #pydantic-models-for-input-and-output }

예를 들어, 다음처럼 기본값이 있는 Pydantic 모델이 있다고 해보겠습니다: