assertEquals(10, ticker.read());
    assertEquals(20, ticker.read());

    for (TimeUnit timeUnit : TimeUnit.values()) {
      ticker.setAutoIncrementStep(0, timeUnit);
      assertEquals(
          "Expected no auto-increment when setting autoIncrementStep to 0 " + timeUnit,
          30,
          ticker.read());
    }
  }

  public void testAutoIncrement_negative() {
    FakeTicker ticker = new FakeTicker();

margin-top:-3rem!important}.mr-n5,.mx-n5{margin-right:-3rem!important}.mb-n5,.my-n5{margin-bottom:-3rem!important}.ml-n5,.mx-n5{margin-left:-3rem!important}.m-auto{margin:auto!important}.mt-auto,.my-auto{margin-top:auto!important}.mr-auto,.mx-auto{margin-right:auto!important}.mb-auto,.my-auto{margin-bottom:auto!important}.ml-auto,.mx-auto{margin-left:auto!important}@media (min-width:576px){.m-sm-0{margin:0!important}.mt-sm-0,.my-sm-0{margin-top:0!important}.mr-sm-0,.mx-sm-0{margin-right:0!import...

						aria-expanded="false" aria-label="Toggle navigation">
						<span class="navbar-toggler-icon"></span>
					</button>
					<div class="collapse navbar-collapse" id="navbar">
						<div class="mr-auto"></div>
						<ul class="nav navbar-nav">
							<c:if test="${eoled}">
								<li class="nav-item" data-toggle="tooltip" data-placement="left" title="<la:message key="labels.eol_error" />">

    public static final String OK = "ok";

    public static final String FAIL = "fail";

    public static final String STOP = "stop";

    public static final String AUTO = "auto";

    public static final String NONE = "none";

    public static final String ITEM_LABEL = "label";

    public static final String ITEM_VALUE = "value";

    public static final String ITEM_NAME = "name";

						<la:message key="labels.facet_label_reset" />
					</la:link>
				</div>
			</c:if>
		</c:if>
	</aside>
</div>
<div class="row">
	<nav id="subfooter" class="mx-auto">
		<ul class="pagination">
			<c:if test="${existPrevPage}">
				<li class="page-item"><la:link styleClass="page-link" aria-label="Previous"

  int num_retvals = 1;
  std::vector<AbstractTensorHandle*> outputs(1);
  GradientRegistry registry;
  s = RegisterGradients(&registry);
  ASSERT_EQ(errors::OK, s.code()) << s.message();
  auto tape = std::make_unique<Tape>(/*persistent=*/false);
  s = Execute(check_numerics_op.get(), ctx.get(), absl::MakeSpan(outputs),
              &num_retvals, &forward_op, tape.get(), registry);

            if (commandLine.getOptionValue(CLIManager.COLOR) != null) {
                return Optional.of(commandLine.getOptionValue(CLIManager.COLOR));
            } else {
                return Optional.of("auto");
            }
        }
        return Optional.empty();
    }

    @Override
    public Optional<Boolean> help() {
        if (commandLine.hasOption(CLIManager.HELP)) {

## <a href="https://github.com/tiangolo/uvicorn-gunicorn-fastapi-docker" class="external-link" target="_blank">tiangolo/uvicorn-gunicorn-fastapi</a>

Cette image est dotée d'un mécanisme d'"auto-tuning", de sorte qu'il vous suffit d'ajouter votre code pour obtenir automatiquement des performances très élevées. Et sans faire de sacrifices.

    numerical_grad.reset(numerical_grad_raw);
  }

  TF_Tensor* numerical_tensor;
  s = GetValue(numerical_grad.get(), &numerical_tensor);
  ASSERT_EQ(errors::OK, s.code()) << s.message();
  auto num_elem_numerical = TF_TensorElementCount(numerical_tensor);
  ASSERT_EQ(num_elem_numerical, num_grad);

  float* dnumerical = new float[num_elem_numerical]{0};
  memcpy(&dnumerical[0], TF_TensorData(numerical_tensor),

Então eu contribuí com ele, para deixá-lo completamente de acordo com o JSON Schema, para dar suporte a diferentes maneiras de definir declarações de restrições, e melhorar o suporte a editores (conferências de tipos, auto completações) baseado nos testes em vários editores.

Durante o desenvolvimento, eu também contribuí com o <a href="https://www.starlette.io/" class="external-link" target="_blank">**Starlette**</a>, outro requisito chave.