<artifactId>modello-maven-plugin</artifactId>
        <configuration>
          <version>1.0.0</version>
          <models>
            <model>src/main/mdo/profiles.mdo</model>
            <model>src/main/mdo/paramdoc.mdo</model>
          </models>
        </configuration>
        <executions>
          <execution>
            <id>modello</id>
            <goals>

import jetbrains.buildServer.configs.kotlin.Project
import model.CIBuildModel
import model.FunctionalTestBucketProvider
import model.Stage
import model.TestCoverage

const val DEFAULT_FUNCTIONAL_TEST_BUCKET_SIZE = 50
const val DEFAULT_LINUX_FUNCTIONAL_TEST_BUCKET_SIZE = 20
const val DEFAULT_MACOS_FUNCTIONAL_TEST_BUCKET_SIZE = 20

class FunctionalTestProject(
    val model: CIBuildModel,

 *   <li>{@link org.apache.maven.api.model.Model} - The root element of a POM file</li>
 *   <li>{@link org.apache.maven.api.model.Dependency} - Represents a project dependency</li>
 *   <li>{@link org.apache.maven.api.model.Plugin} - Represents a Maven plugin configuration</li>
 *   <li>{@link org.apache.maven.api.model.Build} - Contains build configuration information</li>

import common.FlakyTestStrategy
import common.JvmCategory
import common.Os
import common.buildScanTagParam
import common.getBuildScanCustomValueParam
import common.toCapitalized
import model.CIBuildModel
import model.Stage

class SmokeTests(
    model: CIBuildModel,
    stage: Stage,
    testJava: JvmCategory,
    id: String,
    task: String = "smokeTest",
    splitNumber: Int = 1,
    flakyTestStrategy: FlakyTestStrategy,

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

import javax.inject.Inject;

import java.io.File;
import java.util.ArrayList;
import java.util.Arrays;
import java.util.Collection;
import java.util.HashSet;
import java.util.List;
import java.util.Properties;

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

That way, Pydantic will serialize the data into JSON bytes on the Rust side, which will achieve better performance than these custom JSON responses.

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

import org.apache.maven.building.StringSource;

/**
 * Wraps an ordinary {@link CharSequence} as a model source.
 *
 *
 * @deprecated instead use {@link StringSource}
 */
@Deprecated
public class StringModelSource extends StringSource implements ModelSource {

    /**
     * Creates a new model source backed by the specified string.
     *

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

import java.io.File;

import org.apache.maven.toolchain.model.PersistedToolchains;

/**
 * Builds the toolchains model from a previously configured filesystem path to the toolchains file.
 * <strong>Note:</strong> This is an internal component whose interface can change without prior notice.
 *

 -----
 Introduction
 -----
 Hervé Boutemy
 -----
 2006-11-04
 -----

Maven Toolchain Model

 This is the model for Maven toolchain in <<<org.apache.maven.toolchain>>> package,
 delegating content to {{{../../api/maven-api-toolchain/index.html}Maven 4 API immutable toolchain}}. All the effective model
 building logic from multiple toolchains files is done in {{{../maven-toolchain-builder/}Maven Toolchain Builder}}.

## Import `Field` { #import-field }

First, you have to import it:

{* ../../docs_src/body_fields/tutorial001_an_py310.py hl[4] *}


/// warning

Notice that `Field` is imported directly from `pydantic`, not from `fastapi` as are all the rest (`Query`, `Path`, `Body`, etc).

///

## Declare model attributes { #declare-model-attributes }