/**
 * A decoded [mapping table] that can perform the [mapping step] of IDNA processing.
 *
 * This implementation is optimized for readability over efficiency.
 *
 * This implements non-transitional processing by preserving deviation characters.
 *
 * This implementation's STD3 rules are configured to `UseSTD3ASCIIRules=false`. This is
 * permissive and permits the `_` character.
 *

  // absence of the evicted pod.  So for example you can prevent all voluntary
  // evictions by specifying "100%".
  // +optional
  optional k8s.io.apimachinery.pkg.util.intstr.IntOrString minAvailable = 1;

  // Label query over pods whose evictions are managed by the disruption
  // budget.
  // A null selector will match no pods, while an empty ({}) selector will select

  /**
   * Returns an output {@code Future} to use in place of the given {@code input}. The output {@code
   * Future} need not be {@linkplain Future#isDone done}, making {@code AsyncFunction} suitable for
   * asynchronous derivations.
   *
   * <p>Throwing an exception from this method is equivalent to returning a failing {@code Future}.
   */
  ListenableFuture<O> apply(@ParametricNullness I input) throws Exception;

  /**
   * Returns the <a
   * href="http://en.wikipedia.org/wiki/Standard_deviation#Corrected_sample_standard_deviation">
   * corrected sample standard deviation</a> of the values. If this dataset is a sample drawn from a
   * population, this is an estimator of the population standard deviation of the population which
   * is less biased than {@link #populationStandardDeviation()} (the unbiased estimator depends on

  // absence of the evicted pod.  So for example you can prevent all voluntary
  // evictions by specifying "100%".
  // +optional
  optional k8s.io.apimachinery.pkg.util.intstr.IntOrString minAvailable = 1;

  // Label query over pods whose evictions are managed by the disruption
  // budget.
  // A null selector selects no pods.

   * R*R)} of the population standard deviation of {@code y}, where {@code R} is the Pearson's
   * correlation coefficient (as given by {@link #pearsonsCorrelationCoefficient()}).
   *
   * <p>The corresponding root-mean-square error in {@code x} as a function of {@code y} is a
   * fraction {@code sqrt(1/(R*R) - 1)} of the population standard deviation of {@code x}. This fit

                  }
                });

    // To start, fill up the cache.
    // Each miss both increments the counter and causes the map to grow by one,
    // so until evictions begin, the size of the map is the greatest return
    // value seen so far
    while (cache.getUnchecked(nextRandomKey()) < maximumSize) {}

    requests.set(0);
    misses.set(0);
  }

  @Benchmark

  /**
   * Returns the <a
   * href="http://en.wikipedia.org/wiki/Standard_deviation#Corrected_sample_standard_deviation">
   * corrected sample standard deviation</a> of the values. If this dataset is a sample drawn from a
   * population, this is an estimator of the population standard deviation of the population which
   * is less biased than {@link #populationStandardDeviation()} (the unbiased estimator depends on

   * R*R)} of the population standard deviation of {@code y}, where {@code R} is the Pearson's
   * correlation coefficient (as given by {@link #pearsonsCorrelationCoefficient()}).
   *
   * <p>The corresponding root-mean-square error in {@code x} as a function of {@code y} is a
   * fraction {@code sqrt(1/(R*R) - 1)} of the population standard deviation of {@code x}. This fit

                  }
                });

    // To start, fill up the cache.
    // Each miss both increments the counter and causes the map to grow by one,
    // so until evictions begin, the size of the map is the greatest return
    // value seen so far
    while (cache.getUnchecked(nextRandomKey()) < maximumSize) {}

    requests.set(0);
    misses.set(0);
  }

  @Benchmark