}

  static class MeanAndVariance {
    private final double mean;
    private final double variance;

    MeanAndVariance(double mean, double variance) {
      this.mean = mean;
      this.variance = variance;
    }

    @Override
    public int hashCode() {
      return Double.hashCode(mean) * 31 + Double.hashCode(variance);
    }
  }

  enum VarianceAlgorithm {
    DO_NOT_COMPUTE {
      @Override

   * either the {@code x} or {@code y} data must have a non-zero population variance (i.e. {@code
   * xStats().populationVariance() > 0.0 || yStats().populationVariance() > 0.0}). The result is
   * guaranteed to be horizontal if there is variance in the {@code x} data but not the {@code y}
   * data, and vertical if there is variance in the {@code y} data but not the {@code x} data.
   *

   * either the {@code x} or {@code y} data must have a non-zero population variance (i.e. {@code
   * xStats().populationVariance() > 0.0 || yStats().populationVariance() > 0.0}). The result is
   * guaranteed to be horizontal if there is variance in the {@code x} data but not the {@code y}
   * data, and vertical if there is variance in the {@code y} data but not the {@code x} data.
   *

    return Math.sqrt(populationVariance());
  }

  /**
   * Returns the <a href="http://en.wikipedia.org/wiki/Variance#Sample_variance">unbiased sample
   * variance</a> of the values. If this dataset is a sample drawn from a population, this is an
   * unbiased estimator of the population variance of the population. The count must be greater than
   * one.
   *

    return Math.sqrt(populationVariance());
  }

  /**
   * Returns the <a href="http://en.wikipedia.org/wiki/Variance#Sample_variance">unbiased sample
   * variance</a> of the values. If this dataset is a sample drawn from a population, this is an
   * unbiased estimator of the population variance of the population. The count must be greater than
   * one.
   *

      if (values.hasAnyNonFinite()) {
        assertWithMessage("population variance of " + values).that(populationVariance).isNaN();
        assertWithMessage("population variance by addAll(Stats) of " + values)
            .that(populationVarianceByAddAllStats)
            .isNaN();
      } else {
        assertWithMessage("population variance of " + values)
            .that(populationVariance)
            .isWithin(ALLOWED_ERROR)

    boolean longWait = NANOSECONDS.toSeconds(thread.timeSpentBlocked) >= 5;
    // Count how long it actually took to return; we'll accept any number between the expected delay
    // and the approximate actual delay, to be robust to variance in thread scheduling.
    char overWaitNanosFirstDigit =
        Long.toString(thread.timeSpentBlocked - MILLISECONDS.toNanos(longWait ? 5000 : 3000))
            .charAt(0);
    if (overWaitNanosFirstDigit < '4') {

    boolean longWait = NANOSECONDS.toSeconds(thread.timeSpentBlocked) >= 5;
    // Count how long it actually took to return; we'll accept any number between the expected delay
    // and the approximate actual delay, to be robust to variance in thread scheduling.
    char overWaitNanosFirstDigit =
        Long.toString(thread.timeSpentBlocked - MILLISECONDS.toNanos(longWait ? 5000 : 3000))
            .charAt(0);
    if (overWaitNanosFirstDigit < '4') {

      "buildId": "${maven.build.version}"
    }
  },
  "variants": [
    {
      "name": "${variant.jvmEnvironmentVariantName}ApiElements",
      "attributes": {
        "org.gradle.category": "library",
        "org.gradle.dependency.bundling": "external",
        "org.gradle.jvm.version": "8",
        "org.gradle.jvm.environment": "${variant.jvmEnvironment}",
        "org.gradle.libraryelements": "jar",

        checkArgument(!variable.equalsType(type), "Type variable %s bound to itself", variable);
        builder.put(variable, type);
      }
      return new TypeTable(builder.buildOrThrow());
    }

    final Type resolve(TypeVariable<?> var) {
      TypeTable unguarded = this;
      TypeTable guarded =
          new TypeTable() {
            @Override