of `Policy` optionally took in a `tf.compat.v1.mixed_precision.LossScale` in the constructor, which defaulted to a dynamic loss scale for the `"mixed_float16"` policy and no loss scale for other policies. In `Model.compile`, if the model's policy had a loss scale, the optimizer would be wrapped with a `LossScaleOptimizer`. With the non-experimental `Policy`, there is no loss scale associated with the `Policy`, and `Model.compile` wraps the optimizer with a `LossScaleOptimizer` if and only if the...

  @BeforeExperiment
  void setUp() {
    for (int i = 0; i < ARRAY_SIZE; i++) {
      longs[i] = random();
      divisors[i] = randomDivisor(longs[i]);
      decimalStrings[i] = UnsignedLongs.toString(longs[i]);
      binaryStrings[i] = UnsignedLongs.toString(longs[i], 2);
      hexStrings[i] = UnsignedLongs.toString(longs[i], 16);
      prefixedHexStrings[i] = "0x" + hexStrings[i];
    }
  }

  @Benchmark

= vendor/github.com/go-logr/zapr licensed under: =

                                 Apache License
                           Version 2.0, January 2004
                        http://www.apache.org/licenses/

   TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION

   1. Definitions.

      "License" shall mean the terms and conditions for use, reproduction,
      and distribution as defined by Sections 1 through 9 of this document.

  @BeforeExperiment
  void setUp() {
    for (int i = 0; i < ARRAY_SIZE; i++) {
      longs[i] = random();
      divisors[i] = randomDivisor(longs[i]);
      decimalStrings[i] = UnsignedLongs.toString(longs[i]);
      binaryStrings[i] = UnsignedLongs.toString(longs[i], 2);
      hexStrings[i] = UnsignedLongs.toString(longs[i], 16);
      prefixedHexStrings[i] = "0x" + hexStrings[i];
    }
  }

  @Benchmark

Por exemplo, vamos adicionar <a href="https://github.com/Rebilly/ReDoc/blob/master/docs/redoc-vendor-extensions.md#x-logo" class="external-link" target="_blank">Extensão OpenAPI do ReDoc para incluir um logo personalizado</a>.

### **FastAPI** Normal

Primeiro, escreva toda a sua aplicação **FastAPI** normalmente:

{* ../../docs_src/extending_openapi/tutorial001.py hl[1,4,7:9] *}

    }

    @Override
    public void info(final String message, final Throwable t) {
        logger.logp(Level.INFO, sourceClass, null, message, t);
    }

    @Override
    public boolean isDebugEnabled() {
        return logger.isLoggable(Level.FINE);
    }

    @Override
    public void debug(final String message) {
        logger.logp(Level.FINE, sourceClass, null, message);

    assertThat(Longs.lastIndexOf(ARRAY1, 2L)).isEqualTo(-1);
    assertThat(Longs.lastIndexOf(ARRAY234, 1L)).isEqualTo(-1);
    assertThat(Longs.lastIndexOf(new long[] {-1L}, -1L)).isEqualTo(0);
    assertThat(Longs.lastIndexOf(ARRAY234, 2L)).isEqualTo(0);
    assertThat(Longs.lastIndexOf(ARRAY234, 3L)).isEqualTo(1);
    assertThat(Longs.lastIndexOf(ARRAY234, 4L)).isEqualTo(2);

    }
  }

  // Relies on the correctness of log2(BigInteger, {HALF_UP,HALF_DOWN}).
  public void testLog2HalfEven() {
    for (BigInteger x : POSITIVE_BIGINTEGER_CANDIDATES) {
      int halfEven = BigIntegerMath.log2(x, HALF_EVEN);
      // Now figure out what rounding mode we should behave like (it depends if FLOOR was
      // odd/even).
      boolean floorWasEven = (BigIntegerMath.log2(x, FLOOR) & 1) == 0;

    return result;
  }

  /**
   * Generates a number in [0, 2^numBits) with an exponential distribution. The floor of the log2 of
   * the result is chosen uniformly at random in [0, numBits), and then the result is chosen in that
   * range uniformly at random. Zero is treated as having log2 == 0.
   */
  static BigInteger randomNonNegativeBigInteger(int numBits) {
    int digits = RANDOM_SOURCE.nextInt(numBits);

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