* limitations under the License.
 */
package gradlebuild.docs.dsl.source

import com.github.javaparser.JavaParser
import gradlebuild.docs.DocGenerationException
import gradlebuild.docs.model.ClassMetaDataRepository
import gradlebuild.docs.model.SimpleClassMetaDataRepository
import groovy.time.TimeCategory
import groovy.time.TimeDuration
import org.gradle.api.Action
import org.gradle.api.Transformer
import org.gradle.api.file.FileTree

The same way, this database wouldn't receive a Pydantic model (an object with attributes), only a `dict`.

You can use `jsonable_encoder` for that.

It receives an object, like a Pydantic model, and returns a JSON compatible version:

{* ../../docs_src/encoder/tutorial001_py310.py hl[4,21] *}

In this example, it would convert the Pydantic model to a `dict`, and the `datetime` to a `str`.

 * specific language governing permissions and limitations
 * under the License.
 */
package org.apache.maven.classrealm;

import java.util.List;
import java.util.Map;

import org.apache.maven.model.Model;
import org.apache.maven.model.Plugin;
import org.codehaus.plexus.classworlds.realm.ClassRealm;
import org.eclipse.aether.artifact.Artifact;

/**

    @Override
    public ToolchainPrivate createToolchain(ToolchainModel model) throws MisconfiguredToolchainException {
        if (model == null) {
            return null;
        }

        // use DefaultJavaToolChain for compatibility with maven 3.2.3 and earlier

        @SuppressWarnings("deprecation")
        JavaToolchainImpl jtc = new DefaultJavaToolChain(model, logger);

        // populate the provides section

import java.util.List;
import java.util.Map;
import java.util.Properties;

import org.apache.maven.model.Activation;
import org.apache.maven.model.Profile;
import org.apache.maven.model.building.ModelProblem;
import org.apache.maven.model.profile.DefaultProfileActivationContext;
import org.apache.maven.model.profile.ProfileSelector;
import org.apache.maven.profiles.activation.ProfileActivationException;

 */
package org.apache.maven.model.building;

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

import java.io.File;
import java.io.IOException;
import java.io.InputStream;
import java.io.Reader;
import java.util.Map;

import org.apache.maven.model.Model;
import org.apache.maven.model.io.ModelReader;
import org.apache.maven.model.locator.ModelLocator;

 * under the License.
 */
package org.apache.maven.model.interpolation;

import java.util.List;

import org.apache.maven.model.building.ModelProblem.Severity;
import org.apache.maven.model.building.ModelProblem.Version;
import org.apache.maven.model.building.ModelProblemCollector;
import org.apache.maven.model.building.ModelProblemCollectorRequest;
import org.codehaus.plexus.interpolation.ValueSource;

        Files.delete(tempFile);
        try (InputStream expected = Files.newInputStream(beforePomFile)) {
            Model model = new Model(new MavenStaxReader().read(expected));
            MavenProject project = new MavenProject(model);
            project.setOriginalModel(model);
            ConsumerPomArtifactTransformer t = new ConsumerPomArtifactTransformer((s, p, f) -> {
                try (InputStream is = f.openStream()) {

 * specific language governing permissions and limitations
 * under the License.
 */
package org.apache.maven.model.building;

import org.apache.maven.model.DependencyManagement;
import org.apache.maven.model.Model;

/**
 * Describes a tag used by the model builder to access a {@link ModelCache}. This interface basically aggregates a name

{* ../../docs_src/path_params/tutorial005_py39.py hl[1,6:9] *}

/// 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* { #declare-a-path-parameter }

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