}
	}
	return b.String()
}

// ResyncDecision is a struct representing a map with target's individual resync decisions
type ResyncDecision struct {
	targets map[string]ResyncTargetDecision
}

// Empty returns true if no targets with resync decision present
func (r ResyncDecision) Empty() bool {
	return r.targets == nil
}

func (r ResyncDecision) mustResync() bool {

Given these constraints, we cannot always follow best practices.  This ADR proposes the way we should handle the use of Provider APIs in the gradle/gradle codebase.

## Decision

### Types of properties that should not use lazy types

The guidelines below do not apply to all properties. There are cases where the lazy types should not be used:

#### Non-calculated values

		writeErrorResponse(ctx, w, toAPIError(ctx, err), r.URL)
		return
	}

	_, isEncrypted := crypto.IsEncrypted(mi.UserDefined)

	// Read compression metadata preserved in the init multipart for the decision.
	_, isCompressed := mi.UserDefined[ReservedMetadataPrefix+"compression"]
	// Compress only if the compression is enabled during initial multipart.
	var idxCb func() []byte
	if isCompressed {

// CheckPreconditionFn returns true if precondition check failed.
type CheckPreconditionFn func(o ObjectInfo) bool

// EvalMetadataFn validates input objInfo and GetObjectInfo error and returns an updated metadata and replication decision if any
type EvalMetadataFn func(o *ObjectInfo, gerr error) (ReplicateDecision, error)

// EvalRetentionBypassFn validates input objInfo and GetObjectInfo error and returns an error if retention bypass is not allowed.

   * one from the pool. We must take the pooled connection because by taking it from the pool, we've
   * fully acquired it.
   *
   * This test yields threads to force the decision of plan1 to be deliberate and not lucky. In
   * particular, we set up this sequence of events:
   *
   *  1. take plan 0
   *  3. plan 0 connects
   *  4. finish taking plan 1
   *

    feature.
*   Full new features (e.g., a new op implementing a cutting-edge algorithm)
    typically will live in
    [tensorflow/addons](https://github.com/tensorflow/addons) to get some
    airtime before a decision is made regarding whether they are to be migrated
    to the core.
*   As every PR requires several CPU/GPU hours of CI testing, we discourage
    submitting PRs to fix one typo, one warning,etc. We recommend fixing the

write to the author to ask for permission.  For software which is
copyrighted by the Free Software Foundation, write to the Free
Software Foundation; we sometimes make exceptions for this.  Our
decision will be guided by the two goals of preserving the free status
of all derivatives of our free software and of promoting the sharing
and reuse of software generally.

                            NO WARRANTY

     * int.
     */
    checkNonNegative("a", a);
    checkNonNegative("b", b);
    if (a == 0) {
      // 0 % b == 0, so b divides a, but the converse doesn't hold.
      // BigInteger.gcd is consistent with this decision.
      return b;
    } else if (b == 0) {
      return a; // similar logic
    }
    /*
     * Uses the binary GCD algorithm; see http://en.wikipedia.org/wiki/Binary_GCD_algorithm. This is

                return sid.revision == this.revision;
            }
        }
        return false;
    }

    /**
     * Return the numeric representation of this sid such as
     * {@code S-1-5-21-1496946806-2192648263-3843101252-1029}.
     */
    @Override
    public String toString() {
        StringBuilder ret = new StringBuilder("S-").append(this.revision & 0xFF).append("-");

     * Computes the quantile value of the given dataset.
     *
     * @param dataset the dataset to do the calculation on, which must be non-empty, which will be
     *     cast to doubles (with any associated lost of precision), and which will not be mutated by
     *     this call (it is copied instead)
     * @return the quantile value
     */
    public double compute(Collection<? extends Number> dataset) {