* Retrieve the stored data.
* Put that data in a Pydantic model.
* Generate a `dict` without default values from the input model (using `exclude_unset`).
    * This way you can update only the values actually set by the user, instead of overriding values already stored with default values in your model.
* Create a copy of the stored model, updating its attributes with the received partial updates (using the `update` parameter).

    private final Document document;
    private final DslDocModel model;
    private final Set<String> primitiveTypes = new HashSet<String>();
    private final String groovyVersion;
    private final String javaVersion;

    public LinkRenderer(Document document, DslDocModel model, String groovyVersion, String javaVersion) {
        this.document = document;
        this.model = model;
        this.groovyVersion = groovyVersion;

) : PerformanceTestBuildSpec,
    PerformanceTestProjectSpec {
    override fun asConfigurationId(
        model: CIBuildModel,
        bucket: String,
    ) = "${asConfigurationId(model)}$bucket"

    override fun asConfigurationId(model: CIBuildModel) = "${model.projectId}_${oldUuid ?: "PerformanceTest$uuid"}"

    override fun asName(): String = "${type.displayName} - ${os.asName()}"

 * under the License.
 */
package org.apache.maven.configuration;

import java.util.Objects;

import org.apache.maven.model.Build;
import org.apache.maven.model.Model;
import org.apache.maven.model.Plugin;
import org.apache.maven.model.PluginExecution;
import org.apache.maven.model.PluginManagement;

/**
 * A basic bean configuration request.
 *
 */

Use base64 only if you definitely need to include binary data in JSON, and you can't use files for that.

## Pydantic `bytes` { #pydantic-bytes }

You can declare a Pydantic model with `bytes` fields, and then use `val_json_bytes` in the model config to tell it to use base64 to *validate* input JSON data, as part of that validation it will decode the base64 string into bytes.

## Additional Response with `model` { #additional-response-with-model }

You can pass to your *path operation decorators* a parameter `responses`.

It receives a `dict`: the keys are status codes for each response (like `200`), and the values are other `dict`s with the information for each of them.

Each of those response `dict`s can have a key `model`, containing a Pydantic model, just like `response_model`.

import common.setArtifactRules
import jetbrains.buildServer.configs.kotlin.ParameterDisplay
import jetbrains.buildServer.configs.kotlin.ReuseBuilds
import model.CIBuildModel
import model.PerformanceTestType
import projects.PerformanceTestProject

class PerformanceTestsPass(
    model: CIBuildModel,
    performanceTestProject: PerformanceTestProject,
) : OsAwareBaseGradleBuildType(
        os = Os.LINUX,

    async def get_model_c() -> ModelC:
        return ModelC(username="test-user", password="test-password")

    @app.get("/model/{name}", response_model=ModelA)
    async def get_model_a(name: str, model_c=Depends(get_model_c)):
        return {
            "name": name,
            "description": "model-a-desc",
            "foo": model_c,
            "tags": {"key1": "value1", "key2": "value2"},
        }

 * @see Source
 */
public interface ModelSource extends Source {

    /**
     * Returns the model identifier in the format {@code groupId:artifactId:version}
     * if this source represents a resolved artifact with known coordinates.
     * <p>
     * This method is primarily used by resolved sources to provide the model ID
     * without requiring the XML to be parsed. For build sources, this typically

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

/**
 * Caches auxiliary data used during model building like already processed raw/effective models. The data in the cache
 * is meant for exclusive consumption by the model builder and is opaque to the cache implementation. The cache key is