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tensorflow/c/c_api_experimental.cc
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src/main/resources/fess_config.properties
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
Properties - Registered: Mon Apr 29 08:04:11 GMT 2024 - Last Modified: Thu Apr 11 02:34:53 GMT 2024 - 30.6K bytes - Viewed (1) -
docs/de/docs/index.md
* 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 zusΓ€tzliche Funktionen (dank Starlette) wie: * **WebSockets** * extrem einfache Tests auf Basis von `httpx` und `pytest` * **CORS**
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tensorflow/c/c_api.cc
} tensorflow::shape_inference::ShapeHandle shape = ic->output(output.index); int rank = -1; if (ic->RankKnown(shape)) { rank = ic->Rank(shape); } if (num_dims != rank) { status->status = InvalidArgument("Expected rank is ", num_dims, " but actual rank is ", rank); return; } if (num_dims == 0) { // Output shape is a scalar.
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src/main/java/org/codelibs/fess/mylasta/direction/FessConfig.java
/** 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 */
Java - Registered: Mon Apr 29 08:04:11 GMT 2024 - Last Modified: Thu Apr 11 02:34:53 GMT 2024 - 459.2K bytes - Viewed (4) -
docs/en/docs/async.md
For a more "real life" example of this, imagine a bank. Up to recently, most of the banks had multiple cashiers π¨βπΌπ¨βπΌπ¨βπΌπ¨βπΌ and a big line ππππππππ. All of the cashiers doing all the work with one client after the other π¨βπΌβ―. And you have to wait π in the line for a long time or you lose your turn. You probably wouldn't want to take your crush π with you to do errands at the bank π¦. ### Burger Conclusion
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RELEASE.md
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
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tensorflow/c/c_api_test.cc
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);
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pom.xml
<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>
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