* 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.
 *

import common.buildScanTagParam
import model.CIBuildModel
import model.Stage

class CheckLinks(
    model: CIBuildModel,
    stage: Stage,
) : OsAwareBaseGradleBuildType(os = Os.LINUX, stage = stage, failStage = false, init = {
        id("${model.projectId}_CheckLinks")
        name = "CheckLinks"
        description = "Check links in documentations"

        applyDefaults(
            model,
            this,

////

/// info

In Pydantic version 1 the method to get the JSON Schema for a model was called `Item.schema()`, in Pydantic version 2, the method is called `Item.model_json_schema()`.

///

Nevertheless, although we are not using the default integrated functionality, we are still using a Pydantic model to manually generate the JSON Schema for the data that we want to receive in YAML.

			stmt.Context, _ = context.WithTimeout(stmt.Context, db.DefaultContextTimeout)
		}
	}

	// assign model values
	if stmt.Model == nil {
		stmt.Model = stmt.Dest
	} else if stmt.Dest == nil {
		stmt.Dest = stmt.Model
	}

	// parse model values
	if stmt.Model != nil {

import org.apache.maven.artifact.versioning.VersionRange;
import org.apache.maven.bridge.MavenRepositorySystem;
import org.apache.maven.model.Dependency;
import org.apache.maven.model.Plugin;
import org.apache.maven.model.Repository;
import org.apache.maven.model.io.ModelReader;
import org.apache.maven.project.artifact.ArtifactWithDependencies;
import org.apache.maven.settings.Mirror;
import org.apache.maven.settings.Proxy;

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;

        } else {
            str
        }

    override fun createFunctionalTestsFor(
        model: CIBuildModel,
        stage: Stage,
        testCoverage: TestCoverage,
        bucketIndex: Int,
    ): FunctionalTest =
        FunctionalTest(
            model,
            testCoverage.getBucketUuid(model, bucketIndex),
            getName(testCoverage),
            getDescription(testCoverage),

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`.

    @Override
    public Model interpolate(Model model, Map<String, ?> context, boolean strict) throws ModelInterpolationException {
        Properties props = new Properties();
        props.putAll(context);

        return interpolate(model, null, new DefaultProjectBuilderConfiguration().setExecutionProperties(props), true);
    }

    @Override

				})
			}
		}
	})

	var models []User
	if err := DB.Where("name in (?)", []string{"find"}).Find(&models).Error; err != nil || len(models) != 3 {
		t.Errorf("errors happened when query find with in clause: %v, length: %v", err, len(models))
	} else {
		for idx, user := range users {
			t.Run("FindWithInClause#"+strconv.Itoa(idx+1), func(t *testing.T) {
				CheckUser(t, models[idx], user)
			})
		}
	}