* followed by <dfn>flattening</dfn> and <dfn>resolution</dfn>.
 * The version selection is done for each dependency during the collection phase.
 * The flatten phase keeps only a single version per ({@code groupId}, {@code artifactId}) pair.
 * The resolution phase actually downloads the dependencies (or artifacts) that have been computed.
 *
 * @since 4.0.0
 */
@Experimental
@Immutable

    expectAdded(e0());
  }

  @CollectionFeature.Require(absent = SUPPORTS_ADD)
  @CollectionSize.Require(absent = ZERO)
  /*
   * absent = ZERO isn't required, since unmodList.add() must
   * throw regardless, but it keeps the method name accurate.
   */
  public void testAdd_unsupportedPresent() {
    assertThrows(UnsupportedOperationException.class, () -> getList().add(e0()));
  }

///

/// tip | Порада

Якщо вам цікаво, "AlexNet", "ResNet" та "LeNet" — це просто назви ML моделей <abbr title="Технічно, архітектури Deep Learning моделей">Machine Learning</abbr>.

///


### Оголосіть *параметр шляху*

Потім створіть *параметр шляху* з анотацією типу, використовуючи створений вами клас enum (`ModelName`):

///

/// tip | Dica



///

	Se você está se perguntando, "AlexNet", "ResNet", e "LeNet" são apenas nomes de <abbr title="técnicamente, modelos de arquitetura de Deep Learning">modelos</abbr> de Machine Learning (aprendizado de máquina).

### Declare um *parâmetro de rota*

Logo, crie um *parâmetro de rota* com anotações de tipo usando a classe enum que você criou (`ModelName`):

            call.executeAsync()
          }
        }
      }

      assertThat(call.canceled).isTrue()
      assertThat(call.responseClosed).isTrue()
    }
  }

  /** A call that keeps track of whether its response body is closed. */
  private class ClosableCall : FailingCall() {
    private val response =
      Response
        .Builder()
        .request(Request("https://example.com/".toHttpUrl()))

designs the management of shared state is built into the system, TensorFlow enables developers to experiment with novel optimizations and training algorithms. TensorFlow supports a variety of applications, with a focus on training and inference on deep neural networks. Several Google services use TensorFlow in production, we have released it as an open-source project, and it has become widely used for machine learning research. In this paper, we describe the TensorFlow dataflow model and demonstrate...

	</mime-type>
  <mime-type type="application/davmount+xml">
    <glob pattern="*.davmount"/>
  </mime-type>
  <mime-type type="application/x-dbf">
    <!-- this looks unique/precise. It isn't. False positives are common. Keep priority low. -->
    <magic priority="40">

    assertThat(request.requestLine).isEqualTo(
      "GET /a/deep/path?key=foo%20bar HTTP/1.1",
    )
    val requestUrl = request.url
    assertThat(requestUrl.scheme).isEqualTo("http")
    assertThat(requestUrl.host).isEqualTo(server.hostName)
    assertThat(requestUrl.port).isEqualTo(server.port)
    assertThat(requestUrl.encodedPath).isEqualTo("/a/deep/path")
    assertThat(requestUrl.queryParameter("key")).isEqualTo("foo bar")

          sb.append(DELIMITER_STRING);
        }
        sb.append(comp);
      }
      dummy ^= sb.toString().length();
    }
    return dummy;
  }

  /**
   * Similar to the above, but keeps a boolean flag rather than checking for the string accumulated
   * so far being empty. As a result, it does not have the above-mentioned bug.
   */
  @Benchmark
  int booleanIfFirst(int reps) {
    int dummy = 0;

  }

  @ListFeature.Require(absent = SUPPORTS_ADD_WITH_INDEX)
  @CollectionSize.Require(absent = ZERO)
  /*
   * absent = ZERO isn't required, since unmodList.add() must
   * throw regardless, but it keeps the method name accurate.
   */
  public void testAddAtIndex_unsupportedPresent() {
    assertThrows(UnsupportedOperationException.class, () -> getList().add(0, e0()));
    expectUnchanged();
  }