return new DependencyResolvers(variantResolver, componentResolver);
    }

    /**
     * Adapts a {@link ComponentDependencyResolver} to a {@link VariantDependencyResolver}
     * by returning component-precision coordinates.
     */
    private static class VariantResolverAdapter implements VariantDependencyResolver {

        private final ComponentDependencyResolver delegate;

	panic("unreachable")
}

// truncate float literal fv to 32-bit or 64-bit precision
// according to type; return truncated value.
func truncfltlit(v constant.Value, t *types.Type) constant.Value {
	if t.IsUntyped() {
		return v
	}

	return roundFloat(v, t.Size())
}

// truncate Real and Imag parts of Mpcplx to 32-bit or 64-bit
// precision, according to type; return truncated value. In case of

		// When formatting values with prec = -1 (shortest representation),
		// the actually available mantissa precision matters.
		// For denormalized values, that precision is < 53 (SetFloat64 default).
		// Compute and set the actual precision explicitly.
		f := new(Float).SetPrec(actualPrec(test.x)).SetFloat64(test.x)
		got := f.Text(test.format, test.prec)
		if got != test.want {

// a ratVal. The reasoning is that all representations but floatVal are mathematically
// exact, but once that precision is lost (by moving to floatVal), moving back to
// a different representation implies a precision that's not actually there.

type (
	unknownVal struct{}
	boolVal    bool
	stringVal  struct {
		// Lazy value: either a string (l,r==nil) or an addition (l,r!=nil).

                    dependencyResultSerializer.write(encoder, dependency);
                    if (dependency.getFailure() != null) {
                        //by keying the failures only by 'requested' we lose some precision
                        //at edge case we'll lose info about a different exception if we have different failure for the same requested version
                        failures.put(dependency.getRequested(), dependency.getFailure());

     * 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) {

 * thus paying for the cost of the expensive task.
 *
 * @author Dimitris Andreou
 * @since 13.0
 */
// TODO(user): switch to nano precision. A natural unit of cost is "bytes", and a micro precision
// would mean a maximum rate of "1MB/s", which might be small in some cases.
@Beta
@J2ktIncompatible
@GwtIncompatible
@ElementTypesAreNonnullByDefault
public abstract class RateLimiter {

  // A serialized "QuantizationInfo" object to specify value ranges for some of
  // the tensors with known names.
  std::string serialized_quant_stats = "";

  // A bitmask to encode support for reduced precision inference in the model.
  tflite::optimize::ReducedPrecisionSupport support_mask =
      tflite::optimize::ReducedPrecisionSupport::None;

  // Whether to run the passes to propagate the quantization parameters and

}

type Timex struct {
	Modes     uint32
	Pad_cgo_0 [4]byte
	Offset    int64
	Freq      int64
	Maxerror  int64
	Esterror  int64
	Status    int32
	Pad_cgo_1 [4]byte
	Constant  int64
	Precision int64
	Tolerance int64
	Time      Timeval
	Tick      int64
	Ppsfreq   int64
	Jitter    int64
	Shift     int32
	Pad_cgo_2 [4]byte
	Stabil    int64
	Jitcnt    int64
	Calcnt    int64
	Errcnt    int64

                return;
            }

            // If the dependency has artifacts, only map the coordinates to component-level precision.
            // This is so we match the Gradle behavior where an explicit artifact on a dependency
            // that would otherwise map to different coordinates resolves to the declared coordinates.