int num_dims = TF_NumDims(t);
  for (int i = 0; i < num_dims; i++) {
    out_dims[i] = TF_Dim(t, i);
  }
}

// Runs model as is if output is a scalar,
// else sums the output tensor before returning.
absl::Status RunAndMaybeSum(AbstractContext* ctx, Model forward,
                            absl::Span<AbstractTensorHandle* const> inputs,
                            absl::Span<AbstractTensorHandle*> outputs,

import gradlebuild.docs.XmlSpecification
import gradlebuild.docs.dsl.source.TypeNameResolver
import gradlebuild.docs.dsl.source.model.ClassMetaData
import gradlebuild.docs.dsl.source.model.EnumConstantMetaData
import gradlebuild.docs.dsl.source.model.MethodMetaData
import gradlebuild.docs.model.ClassMetaDataRepository

class JavadocLinkConverterTest extends XmlSpecification {
    final LinkRenderer linkRenderer = Mock()

	"gorm.io/gorm/utils"
)

func SetupUpdateReflectValue(db *gorm.DB) {
	if db.Error == nil && db.Statement.Schema != nil {
		if !db.Statement.ReflectValue.CanAddr() || db.Statement.Model != db.Statement.Dest {
			db.Statement.ReflectValue = reflect.ValueOf(db.Statement.Model)
			for db.Statement.ReflectValue.Kind() == reflect.Ptr {
				db.Statement.ReflectValue = db.Statement.ReflectValue.Elem()
			}

 * under the License.
 */
package org.apache.maven.repository.internal.relocation;

import javax.inject.Named;
import javax.inject.Singleton;

import org.apache.maven.model.DistributionManagement;
import org.apache.maven.model.Model;
import org.apache.maven.model.Relocation;
import org.apache.maven.repository.internal.MavenArtifactRelocationSource;
import org.apache.maven.repository.internal.RelocatedArtifact;

///

### Das `Settings`-Objekt erstellen

Importieren Sie `BaseSettings` aus Pydantic und erstellen Sie eine Unterklasse, ganz ähnlich wie bei einem Pydantic-Modell.

Auf die gleiche Weise wie bei Pydantic-Modellen deklarieren Sie Klassenattribute mit Typannotationen und möglicherweise Defaultwerten.

///

/// tip

If you are wondering, "AlexNet", "ResNet", and "LeNet" are just names of Machine Learning <abbr title="Technically, Deep Learning model architectures">models</abbr>.

///

### Declare a *path parameter*

Then create a *path parameter* with a type annotation using the enum class you created (`ModelName`):

```Python hl_lines="16"

```

Vous pouvez retourner un dictionnaire (`dict`), une liste (`list`), des valeurs seules comme des chaines de caractères (`str`) et des entiers (`int`), etc.

Vous pouvez aussi retourner des models **Pydantic** (qui seront détaillés plus tard).

Il y a de nombreux autres objets et modèles qui seront automatiquement convertis en JSON. Essayez d'utiliser vos favoris, il est fort probable qu'ils soient déjà supportés.

**FastAPI** faz uso dele para que você veja o erro no seu log, caso você utilize um modelo de Pydantic em `response_model`, e seus dados tenham erro.

Contudo, o cliente ou usuário não terão acesso a ele. Ao contrário, o cliente receberá um "Internal Server Error" com o HTTP status code `500`.

     * @param result The artifact descriptor result, never {@code null}.
     * @param model The artifact model, never {@code null}.
     * @return The {@link Artifact} to relocate to, or {@code null} if no relocation wanted.
     */
    Artifact relocatedTarget(RepositorySystemSession session, ArtifactDescriptorResult result, Model model)
            throws ArtifactDescriptorException;

  "AS IS" BASIS, 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.

-->

<model xmlns="http://codehaus-plexus.github.io/MODELLO/2.0.0" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"
  xsi:schemaLocation="http://codehaus-plexus.github.io/MODELLO/2.0.0 https://codehaus-plexus.github.io/modello/xsd/modello-2.0.0.xsd"