"schema": {
                                    "type": "array",
                                    "items": {"type": "string"},
                                    "default": [],
                                    "title": "X Tag",
                                },
                            },
                        ],
                        "responses": {

// Free-version will be added if fi.VersionID has transitioned.
func (x *xlMetaV2) AddFreeVersion(fi FileInfo) error {
	var uv uuid.UUID
	var err error
	switch fi.VersionID {
	case "", nullVersionID:
	default:
		uv, err = uuid.Parse(fi.VersionID)
		if err != nil {
			return err
		}
	}

	for i, version := range x.versions {
		if version.header.VersionID != uv || version.header.Type != ObjectType {
			continue

            (this.testFramework.options as JUnitPlatformOptions).includeEngines("cross-version-test-engine")
        }
        if (executer == defaultExecuter) {
            // The test task with the default executer runs with 'check'
            tasks.named("check").configure { dependsOn(testTask) }
        }
    }
}

fun createAggregateTasks(sourceSet: SourceSet) {

In both cases, it will have:

* An optional `q` query parameter that is a `str`.
* A `skip` query parameter that is an `int`, with a default of `0`.
* A `limit` query parameter that is an `int`, with a default of `100`.

In both cases the data will be converted, validated, documented on the OpenAPI schema, etc.

## Use it { #use-it }

Now you can declare your dependency using this class.

  - `failurePolicy` default changed from `Ignore` to `Fail` for v1
  - `matchPolicy` default changed from `Exact` to `Equivalent` for v1
  - `timeout` default changed from `30s` to `10s` for v1
  - `sideEffects` default value is removed, and the field made required, and only `None` and `NoneOnDryRun` are permitted for v1

Import `BaseSettings` from Pydantic and create a sub-class, very much like with a Pydantic model.

The same way as with Pydantic models, you declare class attributes with type annotations, and possibly default values.

You can use all the same validation features and tools you use for Pydantic models, like different data types and additional validations with `Field()`.

{* ../../docs_src/settings/tutorial001_py39.py hl[2,5:8,11] *}

package org.apache.maven.model.interpolation;

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

import java.util.Properties;

import org.apache.maven.model.building.ModelBuildingRequest;

/**
 * Maven default implementation of the {@link ModelVersionProcessor} to support
 * <a href="https://maven.apache.org/maven-ci-friendly.html">CI Friendly Versions</a>
 *
 * @deprecated use {@code org.apache.maven.api.services.ModelBuilder} instead

   *
   * <p>As of Guava 20.0, this value is optional. We encourage authors who wish to describe why an
   * API is {@code @GwtIncompatible} to instead leave an implementation comment.
   */
  String value() default "";

        super();
        setLeaseKey(key);
        setLeaseState(leaseState);
        this.cacheScope = scope;
        this.maxCacheAge = 30000;  // 30 seconds default
        this.notificationEnabled = true;
    }
    
    @Override
    public void encode(byte[] buffer, int offset) {
        super.encode(buffer, offset);
        

### Configuration cache enabled by default

The build of Gradle enables the configuration cache by default as a dogfooding experiment.