// whereas console failure logging based on the _thrown exception_ happens up outside session scope. It would be better to refactor so that a result can be returned from here
                throw UncheckedException.throwAsUncheckedException(t);
            } else {

/**
 * An implementation of a workspace reader that knows how to search the Maven reactor for artifacts, either as packaged
 * jar if it has been built, or only compile output directory if packaging hasn't happened yet.
 *
 */
@Named(ReactorReader.HINT)
@SessionScoped
class ReactorReader implements MavenWorkspaceReader {
    public static final String HINT = "reactor";

    public static final String PROJECT_LOCAL_REPO = "project-local-repo";

	MOVD	$16, R10
	MOVD	$32, R14
	MOVD	$48, R15
	ANDCC	$63, R6, R9	// Check for tailing bytes for later
	PCALIGN $16
	// Case for >= 64 bytes
	// Process 64 bytes per iteration
	// Load 4 vectors of a and b
	// XOR the corresponding vectors
	// from a and b and store the result
loop64:
	LXVD2X	(R4)(R8), VS32
	LXVD2X	(R4)(R10), VS34
	LXVD2X	(R4)(R14), VS36
	LXVD2X	(R4)(R15), VS38
	LXVD2X	(R5)(R8), VS33
	LXVD2X	(R5)(R10), VS35

	resp, err := ecw.KV.Get(ctx, key, opts...)
	ecw.getCallCounter++
	if err != nil {
		return nil, err
	}

	if len(ecw.getReactors) > 0 {
		reactor := ecw.getReactors[0]
		ecw.getReactors = ecw.getReactors[1:]
		reactor()
	}

	return resp, nil
}

func initStoreData(ctx context.Context, store storage.Interface) ([]interface{}, error) {

    if (!(quantizeOps(rewriter, op, quantizable_ops))) {
      return failure();
    }

    // 3. Apply post-processing required for each inference type.
    // TODO(b/212514817): refactor mode checking to improve code quality
    if (quant_specs_.inference_type == tensorflow::DT_QINT8 &&
        (setAsymmetricQuantizeInputAttr(rewriter, quantizable_ops))) {
      return failure();
    }

    auto preceding_op = operand.getDefiningOp();
    if (!preceding_op || !IsOperationFoldable(preceding_op)) {
      return false;
    }
  }

  return true;
}

// TODO: b/289744814 - Refactor to have a single source of truth of TF Quant
// specs.
absl::flat_hash_set<int> GetQuantizableOperands(Operation* op) {
  absl::flat_hash_set<int> quantizable_operands;

 * but does not want to test impact of re-instrumenting jars.
 *
 * This mutator could be also moved to the gradle-profiler.
 * TODO Refactor gradle-profiler `AbstractCacheCleanupMutator` so that it can clean up files in `caches/<gradle-version>/<cache-dir>`
 */
class ClearArtifactTransformCacheWithoutInstrumentedJarsMutator extends AbstractCleanupMutator {

    /**

        String version = model.getVersion();
        String modVersion = replaceCiFriendlyVersion(context, version);
        builder.version(modVersion);
    }

    //
    // Infer inner reactor dependencies version
    //
    void handleReactorDependencies(ModelTransformerContext context, Model model, Path pomFile, Model.Builder builder) {
        List<Dependency> newDeps = new ArrayList<>();

  // Prepares OpKernelContext params common to all the ops.
  // Emits an error on failure.
  mlir::LogicalResult PrepareParams();

  // Given the required_consts, it will fill the 3 output vectors with
  // their respective data.
  // Expressions: Output XLA expressions as required by the compiled kernel.
  // Tensors: Vector of tensors that back the TensorValue inputs

	type User struct {
		ID         int32 `gorm:"primaryKey"`
		Name       string
		CreatedBy  int32
		Creators   []User      `gorm:"foreignKey:CreatedBy"`
		AnotherPro interface{} `gorm:"-"`
	}

	checkStructRelation(t, &User{}, Relation{
		Name: "Creators", Type: schema.HasMany, Schema: "User", FieldSchema: "User",
		References: []Reference{{"ID", "User", "CreatedBy", "User", "", true}},
	})