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;
};

     * @return The value of found property. (NotNull: if not found, exception but basically no way)
     * @throws NumberFormatException When the property is not integer.
     */
    Integer getRankFusionWindowSizeAsInteger();

    /**
     * Get the value for the key 'rank.fusion.rank_constant'. <br>
     * The value is, e.g. 20 <br>
     * comment: Rank constant for rank fusion.

 * 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.ArrayList;
import java.util.Collections;
import java.util.HashMap;
import java.util.List;
import java.util.Map;

import org.apache.lucene.search.TotalHits.Relation;

                                              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,

        ComponentUtil.getLanguageHelper().updateDocument(map);
    }

    /**
     * Adds a boost value to the document for search relevance scoring.
     * The boost value affects how highly the document will rank in search results.
     *
     * @param map the document data map to add the boost value to
     * @param documentBoost the boost value to apply to the document
     */

					<groupId>org.opensearch.plugin</groupId>
					<artifactId>aggs-matrix-stats-client</artifactId>
				</exclusion>
				<exclusion>
					<groupId>org.opensearch.plugin</groupId>
					<artifactId>rank-eval-client</artifactId>
				</exclusion>
				<exclusion>
					<groupId>org.opensearch.plugin</groupId>
					<artifactId>lang-expression</artifactId>
				</exclusion>
				<exclusion>

        as `tf.tpu.experimental.embedding.serving_embedding_lookup` which can
        take arbitrary rank of dense and sparse tensor. For ragged tensor,
        though the input tensor remains to be rank 2, the activations now can be
        rank 2 or above by specifying the output shape in the feature config or
        via the build method.

*   Add

  ASSERT_EQ(TF_OK, TF_GetCode(s)) << TF_Message(s);
  EXPECT_EQ(dims[0], returned_dims[0]);
  EXPECT_EQ(dims[1], returned_dims[1]);

  // Try to set 'unknown' with unknown rank on the shape and see that
  // it doesn't change.
  TF_GraphSetTensorShape(graph, feed_out_0, /*dims=*/nullptr, -1, s);
  EXPECT_EQ(TF_OK, TF_GetCode(s)) << TF_Message(s);

* Fortgeschrittenere (aber ebenso einfache) Techniken zur Deklaration **tief verschachtelter JSON-Modelle** (dank Pydantic).
* **GraphQL** Integration mit <a href="https://strawberry.rocks" class="external-link" target="_blank">Strawberry</a> und anderen Bibliotheken.
* Viele zusätzliche Funktionen (dank Starlette) wie:
    * **WebSockets**
    * extrem einfache Tests auf Basis von `httpx` und `pytest`
    * **CORS**

{* ../../docs_src/dataclasses/tutorial001.py hl[1,7:12,19:20] *}

Das ist dank **Pydantic** ebenfalls möglich, da es <a href="https://pydantic-docs.helpmanual.io/usage/dataclasses/#use-of-stdlib-dataclasses-with-basemodel" class="external-link" target="_blank">`dataclasses` intern unterstützt</a>.