into.addChild(h)
				// We scanned a folder, optionally send update.
				f.updateCache.deleteRecursive(h)
				f.updateCache.copyWithChildren(&f.newCache, h, folder.parent)
				f.sendUpdate()
			}
		}

		// Transfer existing
		if !into.Compacted {
			for _, folder := range existingFolders {
				h := hashPath(folder.name)
				f.updateCache.copyWithChildren(&f.oldCache, h, folder.parent)
			}

		name            string
		content         string
		meta            map[string]string
		addDeleteMarker bool
	}{
		{testBuckets[0], "unique/folder/", "", nil, true},
		{testBuckets[0], "unique/folder/1.txt", "content", nil, false},
		{testBuckets[1], "unique/folder/1.txt", "content", nil, true},
	}
	for _, object := range testObjects {
		md5Bytes := md5.Sum([]byte(object.content))

	// Start the disk monitoring and connect routine.
	if !globalIsTesting {
		go s.monitorAndConnectEndpoints(ctx, defaultMonitorConnectEndpointInterval)
	}

	return s, nil
}

// cleanup ".trash/" folder every 5m minutes with sufficient sleep cycles, between each
// deletes a dynamic sleeper is used with a factor of 10 ratio with max delay between
// deletes to be 2 seconds.

}

// PutObjectExtractHandler - PUT Object extract is an extended API
// based off from AWS Snowball feature to auto extract compressed
// stream will be extracted in the same directory it is stored in
// and the folder structures will be built out accordingly.
func (api objectAPIHandlers) PutObjectExtractHandler(w http.ResponseWriter, r *http.Request) {
	ctx := newContext(r, w, "PutObjectExtract")

        `Checkpoint` object that is compatible with Keras `model.save_weights()`
        and `model.load_weights`. The checkpoint is also compatible with the
        checkpoint saved in the `variables/` folder in the SavedModel.
    *   When restoring, `save_path` can be a path to a SavedModel. The function
        will automatically find the checkpoint in the SavedModel.

### `tf.data`: