labels.facet_filetype_pdf=filetype:pdf\t\
labels.facet_filetype_txt=filetype:txt\t\
labels.facet_filetype_others=filetype:others\n\

# ranking
rank.fusion.window_size=200
rank.fusion.rank_constant=20
rank.fusion.threads=-1
rank.fusion.score_field=rf_score

# acl
smb.role.from.file=true
smb.available.sid.types=1,2,4:2,5:1
file.role.from.file=true
ftp.role.from.file=true

# backup

 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND,
 * either express or implied. See the License for the specific language
 * governing permissions and limitations under the License.
 */
package org.codelibs.fess.rank.fusion;

import java.util.HashMap;
import java.util.Locale;
import java.util.Map;

import org.apache.lucene.search.TotalHits.Relation;
import org.codelibs.fess.entity.FacetInfo;

    TFE_Context* ctx, const char* task, int64_t timeout_in_ms,
    TF_Status* status);

// Information about the shape of a Tensor and its type.
struct TF_ShapeAndType {
  // Number of dimensions. -1 indicates unknown rank.
  int num_dims;
  // Array of dimensions. -1 indicates unknown dim.
  int64_t* dims;
  // The data type. May be 0 to denote unknown type.
  TF_DataType dtype;
};

import org.codelibs.fess.mylasta.action.FessUserBean;
import org.codelibs.fess.mylasta.direction.FessConfig;
import org.codelibs.fess.query.QueryFieldConfig;
import org.codelibs.fess.rank.fusion.RankFusionProcessor;
import org.codelibs.fess.util.BooleanFunction;
import org.codelibs.fess.util.ComponentUtil;
import org.codelibs.fess.util.QueryResponseList;
import org.dbflute.optional.OptionalEntity;

import org.codelibs.fess.mylasta.direction.FessProp;
import org.codelibs.fess.query.QueryFieldConfig;
import org.codelibs.fess.query.QueryProcessor;
import org.codelibs.fess.query.parser.QueryParser;
import org.codelibs.fess.rank.fusion.RankFusionProcessor;
import org.codelibs.fess.script.ScriptEngineFactory;
import org.codelibs.fess.sso.SsoManager;
import org.codelibs.fess.thumbnail.ThumbnailManager;
import org.lastaflute.core.message.MessageManager;

        return errors::Unimplemented(absl::StrCat(
            "Computing the shape of a ParallelTensor when the components do "
            "not all have the same rank is not supported. One tensor had "
            "shape ",
            first_shape.DebugString(), " and another had shape ",
            component_shape.DebugString()));
      } else {

                                              const char* attr_name,
                                              const char* value, size_t length);

// Set `num_dims` to -1 to represent "unknown rank".  Otherwise,
// `dims` points to an array of length `num_dims`.  `dims[i]` must be
// >= -1, with -1 meaning "unknown dimension".
TF_CAPI_EXPORT extern void TF_SetAttrShape(TF_OperationDescription* desc,

    /** The key of the configuration. e.g. 200 */
    String RANK_FUSION_window_size = "rank.fusion.window_size";

    /** The key of the configuration. e.g. 20 */
    String RANK_FUSION_rank_constant = "rank.fusion.rank_constant";

    /** The key of the configuration. e.g. -1 */
    String RANK_FUSION_THREADS = "rank.fusion.threads";

    /** The key of the configuration. e.g. rf_score */

But still, the final experience is not the best. 😞

---

This would be the parallel equivalent story for burgers. 🍔

For a more "real life" example of this, imagine a bank.

Up to recently, most of the banks had multiple cashiers 👨‍💼👨‍💼👨‍💼👨‍💼 and a big line 🕙🕙🕙🕙🕙🕙🕙🕙.

All of the cashiers doing all the work with one client after the other 👨‍💼⏯.

// required except where indicated.
typedef struct TFE_CustomDeviceTensorHandleMethods {
  int version = TFE_CUSTOM_DEVICE_VERSION;

  // Computes the rank of the tensor handle.
  //
  // Shapes are specified via callbacks because retrieving the shape of a tensor
  // is a blocking operation for async eager; custom devices should avoid