assertWithMessage("Pearson's correlation coefficient of " + values)
            .that(pearsonsCorrelationCoefficient)
            .isNaN();
        assertWithMessage("Pearson's correlation coefficient by addAll(PairedStats) of " + values)
            .that(pearsonsCorrelationCoefficient)
            .isNaN();
      } else {
        assertWithMessage("Pearson's correlation coefficient of " + values)

        assertWithMessage("Pearson's correlation coefficient of " + values)
            .that(pearsonsCorrelationCoefficient)
            .isNaN();
        assertWithMessage("Pearson's correlation coefficient by addAll(PairedStats) of " + values)
            .that(pearsonsCorrelationCoefficient)
            .isNaN();
      } else {
        assertWithMessage("Pearson's correlation coefficient of " + values)

    checkState(count() > 1);
    return sumOfProductsOfDeltas / (count() - 1);
  }

  /**
   * Returns the <a href="http://mathworld.wolfram.com/CorrelationCoefficient.html">Pearson's or
   * product-moment correlation coefficient</a> of the values. The count must greater than one, and
   * the {@code x} and {@code y} values must both have non-zero population variance (i.e. {@code

    checkState(count() > 1);
    return sumOfProductsOfDeltas / (count() - 1);
  }

  /**
   * Returns the <a href="http://mathworld.wolfram.com/CorrelationCoefficient.html">Pearson's or
   * product-moment correlation coefficient</a> of the values. The count must greater than one, and
   * the {@code x} and {@code y} values must both have non-zero population variance (i.e. {@code

      if (values.hasAnyNonFinite()) {
        assertWithMessage("Pearson's correlation coefficient of " + values)
            .that(pearsonsCorrelationCoefficient)
            .isNaN();
      } else {
        assertWithMessage("Pearson's correlation coefficient of " + values)
            .that(pearsonsCorrelationCoefficient)
            .isWithin(ALLOWED_ERROR)
            .of(

      if (values.hasAnyNonFinite()) {
        assertWithMessage("Pearson's correlation coefficient of " + values)
            .that(pearsonsCorrelationCoefficient)
            .isNaN();
      } else {
        assertWithMessage("Pearson's correlation coefficient of " + values)
            .that(pearsonsCorrelationCoefficient)
            .isWithin(ALLOWED_ERROR)
            .of(

				log.Errorf("failed to handle conn: %v", err)
			}
		}()
	}
}

// ZDS protocol is very simple, for every message sent, and ack is sent.
// the ack only has temporal correlation (i.e. it is the first and only ack msg after the message was sent)
// All this to say, that we want to make sure that message to ztunnel are sent from a single goroutine
// so we don't mix messages and acks.
// nolint: unparam

                    getAllErrorCorrections().forEach((correction) => {
                        correction.acceptation = reason;
                        result.acceptedApiChanges.push(correction);
                    });
                    // Sort the array in place by type, then member

	} else {
		sn = sn[:maxNanoSecondDigits] // Right truncate
	}
	nsecs, _ := strconv.ParseInt(sn, 10, 64) // Must succeed
	if len(ss) > 0 && ss[0] == '-' {
		return time.Unix(secs, -1*nsecs), nil // Negative correction
	}
	return time.Unix(secs, nsecs), nil
}

// formatPAXTime converts ts into a time of the form %d.%d as described in the
// PAX specification. This function is capable of negative timestamps.

*   Other:
    *   Add `tf.contrib.distributions.Kumaraswamy`.
    *   `RetryingFileSystem::FlushCaches()` calls the base FileSystem's
        `FlushCaches()`.
    *   Add `auto_correlation` to distributions.
    *   Add `tf.contrib.distributions.Autoregressive`.
    *   Add SeparableConv1D layer.
    *   Add convolutional Flipout layers.