loadWildcards();
        loadJdk();
        loadProjectLibraries();
    }

    @Override
    protected void store(Node xml) {
        storeModulePaths();
        storeWildcards();
        storeJdk();
        storeBytecodeLevels();
        storeVcs();
        storeProjectLibraries();
    }

    private void loadModulePaths() {
        for (Node moduleNode : getChildren(findOrCreateModules(), "module")) {

        store.move("/a", a)
        store.move("b", b)

        then:
        def storedA = store.get("a")
        storedA.file.text == "abc"
        storedA.file == fsBase.file("a")
        !a.exists()

        def storedB = store.get("b")
        storedB.file.text == "def"
        storedB.file == fsBase.file("b")
        !b.exists()
    }

    def "can move directory to filestore"() {

Anže Sodja <******@****.***> 1717576978 +0200

    public static final BuildCacheRemoteStoreBuildOperationType.Result NOT_STORED = new StoreOperationResult(false);

    private final boolean stored;

    private StoreOperationResult(boolean stored) {
        this.stored = stored;
    }

    @Override
    public boolean isStored() {
        return stored;
    }

}

// Unix returns the time stored in ts as seconds plus nanoseconds.
func (ts *Timespec) Unix() (sec int64, nsec int64) {
	return int64(ts.Sec), int64(ts.Nsec)
}

// Unix returns the time stored in tv as seconds plus nanoseconds.
func (tv *Timeval) Unix() (sec int64, nsec int64) {
	return int64(tv.Sec), int64(tv.Usec) * 1000
}

// Nano returns the time stored in ts as nanoseconds.
func (ts *Timespec) Nano() int64 {

    mlir::ModuleOp module, const GraphExportConfig& configs);

// Converts an MLIR module to TensorFlow graph and FunctionLibraryDefinition.
// The "main" function of the module is stored in the graph and the rest of
// functions are stored in the library. Control ret nodes are stored separately
// in `control_ret_nodes`.
Status ConvertMlirToGraph(mlir::ModuleOp module,
                          const GraphExportConfig& configs,

    mlir::ModuleOp module, const GraphExportConfig& configs);

// Converts an MLIR module to TensorFlow graph and FunctionLibraryDefinition.
// The "main" function of the module is stored in the graph and the rest of
// functions are stored in the library. Control ret nodes are stored separately
// in `control_ret_nodes`.
Status ConvertMlirToGraph(mlir::ModuleOp module,
                          const GraphExportConfig& configs,

// a Content-Disposition of "form-data".
// It stores up to maxMemory bytes + 10MB (reserved for non-file parts)
// in memory. File parts which can't be stored in memory will be stored on
// disk in temporary files.
// It returns [ErrMessageTooLarge] if all non-file parts can't be stored in
// memory.
func (r *Reader) ReadForm(maxMemory int64) (*Form, error) {
	return r.readForm(maxMemory)
}

var (

* (Optionally) use `PATCH` instead of `PUT`.
* Retrieve the stored data.
* Put that data in a Pydantic model.
* Generate a `dict` without default values from the input model (using `exclude_unset`).
    * This way you can update only the values actually set by the user, instead of overriding values already stored with default values in your model.

import java.util.stream.Stream;

public interface VirtualFileSystem {

    /**
     * Returns the snapshot stored at the absolute path if it exists in the VFS.
     */
    Optional<FileSystemLocationSnapshot> findSnapshot(String absolutePath);

    /**
     * Returns the metadata stored at the absolute path if it exists.
     */
    Optional<MetadataSnapshot> findMetadata(String absolutePath);

    /**