* gigantic heap, in which case we scale by heap size.
   */
  private static long timeoutSeconds() {
    // This class can make no hard guarantees.  The methods in this class are inherently flaky, but
    // we try hard to make them robust in practice.  We could additionally try to add in a system
    // load timeout multiplier.  Or we could try to use a CPU time bound instead of wall clock time

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                withFirEntry("fir", firFunctionSymbol.firSymbol.fir)
            }
        else firFunctionSymbol.valueParameters[valueParameterReference.parameterIndex]
    )

    // Util function to avoid hard coding names of the classes. Type inference will do a better job figuring out the best type to cast to.
    // This visitor isn't type-safe anyway
    private inline fun <reified T> ConeContractDescriptionElement.accept() =

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   */
  /*
   * The new array contains nulls, even if the old array did not. If we wanted to be accurate, we
   * would declare a return type of `@Nullable T[]`. However, we've decided not to think too hard
   * about arrays for now, as they're a mess. (We previously discussed this in the review of
   * ObjectArrays, which is the main caller of this method.)
   */

   * gigantic heap, in which case we scale by heap size.
   */
  private static long timeoutSeconds() {
    // This class can make no hard guarantees.  The methods in this class are inherently flaky, but
    // we try hard to make them robust in practice.  We could additionally try to add in a system
    // load timeout multiplier.  Or we could try to use a CPU time bound instead of wall clock time

Bit Rot, also known as data rot or silent data corruption is a data loss issue faced by disk drives today. Data on the drive may silently get corrupted without signaling an error has occurred, making bit rot more dangerous than a permanent hard drive failure.

MinIO's erasure coded backend uses high speed [HighwayHash](https://github.com/minio/highwayhash) checksums to protect against Bit Rot.

## How are drives used for Erasure Code?

   There used to be two different mechanisms - one for regular S3 objects and one for IAM data.
- Reduced server startup time. For IAM encryption with the root credentials, MinIO had
   to use a memory-hard function (Argon2) that (on purpose) consumes a lot of memory and CPU.
   The new KMS-based approach can use a key derivation function that is orders of magnitudes
   cheaper w.r.t. memory and CPU.

                        }
                    }
                }
            }
        }
    }
}

// run the parallel ITs
parallel(runITsTasks)

// JENKINS-34376 seems to make it hard to detect the aborted builds
} catch (org.jenkinsci.plugins.workflow.steps.FlowInterruptedException e) {
    echo "[FAILURE-002] FlowInterruptedException ${e}"
    // this ambiguous condition means a user probably aborted

		}
	}
	return nil
}

// bitrotSelfTest performs a self-test to ensure that bitrot
// algorithms compute correct checksums. If any algorithm
// produces an incorrect checksum it fails with a hard error.
//
// bitrotSelfTest tries to catch any issue in the bitrot implementation
// early instead of silently corrupting data.
func bitrotSelfTest() {
	checksums := map[BitrotAlgorithm]string{