- Sort Score
- Result 10 results
- Languages All
Results 11 - 20 of 211 for model1 (0.15 sec)
-
docs/de/docs/tutorial/extra-models.md
# Extramodelle Fahren wir beim letzten Beispiel fort. Es gibt normalerweise mehrere zusammengehörende Modelle. Insbesondere Benutzermodelle, denn: * Das **hereinkommende Modell** sollte ein Passwort haben können. * Das **herausgehende Modell** sollte kein Passwort haben.
Registered: Mon Jun 17 08:32:26 UTC 2024 - Last Modified: Sat Mar 30 20:26:47 UTC 2024 - 8.7K bytes - Viewed (0) -
platforms/core-configuration/configuration-cache/src/integTest/groovy/org/gradle/internal/cc/impl/isolated/IsolatedProjectsToolingApiCompositeBuildsIntegrationTest.groovy
modelsCreated(":", ":a") } when: executer.withArguments(ENABLE_CLI) def model2 = runBuildAction(new FetchCustomModelForEachProjectInTree()) then: model2.size() == 2 model2[0].message == "It works from project :" model2[1].message == "It works from project :a" and: fixture.assertStateLoaded() when:
Registered: Wed Jun 12 18:38:38 UTC 2024 - Last Modified: Sat Jun 08 11:29:25 UTC 2024 - 6.9K bytes - Viewed (0) -
docs/pt/docs/tutorial/body-nested-models.md
## Modelos aninhados Cada atributo de um modelo Pydantic tem um tipo. Mas esse tipo pode ser outro modelo Pydantic. Portanto, você pode declarar "objects" JSON profundamente aninhados com nomes, tipos e validações de atributos específicos. Tudo isso, aninhado arbitrariamente. ### Defina um sub-modelo Por exemplo, nós podemos definir um modelo `Image`: ```Python hl_lines="9-11"
Registered: Mon Jun 17 08:32:26 UTC 2024 - Last Modified: Thu Apr 18 19:53:19 UTC 2024 - 7.4K bytes - Viewed (0) -
docs/de/docs/reference/openapi/models.md
# OpenAPI-`models` OpenAPI Pydantic-Modelle, werden zum Generieren und Validieren der generierten OpenAPI verwendet.
Registered: Mon Jun 17 08:32:26 UTC 2024 - Last Modified: Sat Mar 30 18:15:17 UTC 2024 - 146 bytes - Viewed (0) -
docs/en/docs/tutorial/extra-models.md
# Extra Models Continuing with the previous example, it will be common to have more than one related model. This is especially the case for user models, because: * The **input model** needs to be able to have a password. * The **output model** should not have a password. * The **database model** would probably need to have a hashed password. !!! danger Never store user's plaintext passwords. Always store a "secure hash" that you can then verify.
Registered: Mon Jun 17 08:32:26 UTC 2024 - Last Modified: Thu Apr 18 19:53:19 UTC 2024 - 7.7K bytes - Viewed (0) -
docs/de/docs/tutorial/body-nested-models.md
Und es wird entsprechend annotiert/dokumentiert. ## Verschachtelte Modelle Jedes Attribut eines Pydantic-Modells hat einen Typ. Aber dieser Typ kann selbst ein anderes Pydantic-Modell sein. Sie können also tief verschachtelte JSON-„Objekte“ deklarieren, mit spezifischen Attributnamen, -typen, und -validierungen.
Registered: Mon Jun 17 08:32:26 UTC 2024 - Last Modified: Fri Mar 22 01:42:11 UTC 2024 - 10.4K bytes - Viewed (0) -
docs/en/docs/tutorial/body-nested-models.md
## Nested Models Each attribute of a Pydantic model has a type. But that type can itself be another Pydantic model. So, you can declare deeply nested JSON "objects" with specific attribute names, types and validations. All that, arbitrarily nested. ### Define a submodel For example, we can define an `Image` model: === "Python 3.10+"
Registered: Mon Jun 17 08:32:26 UTC 2024 - Last Modified: Fri Mar 22 01:42:11 UTC 2024 - 9.5K bytes - Viewed (0) -
platforms/core-configuration/configuration-cache/src/integTest/groovy/org/gradle/internal/cc/impl/isolated/IsolatedProjectsToolingApiStreamingBuildActionIntegrationTest.groovy
and: (model.left as GradleProject).name == "hello-world" (model.right as EclipseProject).gradleProject.name == "hello-world" and: def streamedModels = listener1.models as List<CustomModel> streamedModels.size() == 2 streamedModels[0].value == 1 streamedModels[1].value == 2 when: withIsolatedProjects()
Registered: Wed Jun 12 18:38:38 UTC 2024 - Last Modified: Sat Jun 08 11:29:25 UTC 2024 - 4.1K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/quantization/lite/quantize_model_test.cc
// Unpacks the given flatbuffer model. // // This helper is useful as UnPackTo requires the input to not have any existing // state so directly calling UnPackTo could lead to memory leaks if the model // already had some state. Instead, the returned object from here can be used to // overwrite existing model. ModelT UnPackFlatBufferModel(const Model& flatbuffer_model) { ModelT model; flatbuffer_model.UnPackTo(&model);
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Wed Jun 12 23:15:24 UTC 2024 - 73.9K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/quantization/lite/quantize_weights_test.cc
model_ = input_model_->GetModel(); } std::unique_ptr<FlatBufferModel> input_model_; const Model* model_; bool IsModelInputOrOutput(const Model* model, uint32_t tensor_idx) { for (size_t subgraph_idx = 0; subgraph_idx < model_->subgraphs()->size(); ++subgraph_idx) { const auto subgraph = model->subgraphs()->Get(subgraph_idx);
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Wed Jun 12 23:15:24 UTC 2024 - 32.3K bytes - Viewed (0)