You can see those schemas because they were declared with the models in the app.

That information is available in the app's **OpenAPI schema**, and then shown in the API docs.

That same information from the models that is included in OpenAPI is what can be used to **generate the client code**.

### Hey API { #hey-api }

Kubernetes manages stateless Spark and Hive containers elastically on the compute nodes. Spark has native scheduler integration with Kubernetes. Hive, for legacy reasons, uses YARN scheduler on top of Kubernetes.

)

// TestMain initializes the testing framework
func TestMain(m *testing.M) {
	startLockServers()

	// Initialize locker clients for dsync.
	var clnts []NetLocker
	for i := range nodes {
		clnts = append(clnts, newClient(nodes[i].URL))
	}

	ds = &Dsync{
		GetLockers: func() ([]NetLocker, string) { return clnts, uuid.New().String() },
		Timeouts: Timeouts{
			Acquire:         testDrwMutexAcquireTimeout,

{* ../../docs_src/security/tutorial001_an_py39.py hl[12] *}

Но это всё ещё не слишком полезно.

Сделаем так, чтобы она возвращала текущего пользователя.

## Создать модель пользователя { #create-a-user-model }

Сначала создадим Pydantic-модель пользователя.

Точно так же, как мы используем Pydantic для объявления тел запросов, мы можем использовать его где угодно:

import org.apache.maven.api.di.Singleton;
import org.apache.maven.api.model.Build;
import org.apache.maven.api.model.Model;
import org.apache.maven.api.model.Parent;
import org.apache.maven.api.model.Plugin;
import org.apache.maven.api.model.PluginManagement;
import org.apache.maven.api.model.Repository;
import org.apache.maven.api.model.RepositoryPolicy;
import org.apache.maven.api.services.ModelBuilder;

abstract: TensorFlow is a machine learning system that operates at large scale and in heterogeneous environments. TensorFlow uses dataflow graphs to represent computation, shared state, and the operations that mutate that state. It maps the nodes of a dataflow graph across many machines in a cluster, and within a machine across multiple computational devices, including multicore CPUs, general purpose GPUs, and custom-designed ASICs known as Tensor Processing Units (TPUs). This architecture...

import gradlebuild.basics.classanalysis.Attributes.minified
import gradlebuild.basics.transforms.Minify
import org.gradle.api.internal.attributes.AttributesFactory
import org.gradle.internal.component.external.model.DefaultModuleComponentSelector
import org.gradle.kotlin.dsl.support.serviceOf

/**
 * A map from artifact name to a set of class name prefixes that should be kept.

                if annotation_is_pydantic_v1(model):
                    raise PydanticV1NotSupportedError(
                        "pydantic.v1 models are no longer supported by FastAPI."
                        f" In responses={{}}, please update {model}."
                    )
                response_field = create_model_field(
                    name=response_name, type_=model, mode="serialization"
                )

					association.Error = associationDB.Model(nil).Where(clause.IN{Column: column, Values: values}).Delete(reflect.New(rel.FieldSchema.ModelType).Interface()).Error
				}
			}
		case schema.HasOne, schema.HasMany:
			model := reflect.New(rel.FieldSchema.ModelType).Interface()
			tx := association.DB.Model(model)

            <option value="$PROJECT_DIR$/platforms/core-configuration/kotlin-dsl-tooling-models" />
            <option value="$PROJECT_DIR$/platforms/core-configuration/model-core" />
            <option value="$PROJECT_DIR$/platforms/core-configuration/model-groovy" />
            <option value="$PROJECT_DIR$/platforms/core-configuration/model-reflect" />
            <option value="$PROJECT_DIR$/platforms/core-configuration/project-features" />