# Declare Request Example Data { #declare-request-example-data }

You can declare examples of the data your app can receive.

Here are several ways to do it.

## Extra JSON Schema data in Pydantic models { #extra-json-schema-data-in-pydantic-models }

You can declare `examples` for a Pydantic model that will be added to the generated JSON Schema.

{* ../../docs_src/schema_extra_example/tutorial001_py310.py hl[13:24] *}

    page = context.new_page()
    page.goto("http://localhost:8000/docs")
    page.get_by_role("link", name="/items/").click()
    # Manually add the screenshot
    page.screenshot(path="docs/en/docs/img/tutorial/cookie-param-models/image01.png")

    # ---------------------
    context.close()
    browser.close()


process = subprocess.Popen(
    ["fastapi", "run", "docs_src/cookie_param_models/tutorial001.py"]
)
try:

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

import org.apache.maven.model.Model;

/**
 * Provides the super POM that all models implicitly inherit from.
 *
 * @deprecated use {@code org.apache.maven.api.services.ModelBuilder} instead
 */
@Deprecated(since = "4.0.0")
public interface SuperPomProvider {

    /**

          <velocityBasedir>${project.basedir}/../../src/mdo</velocityBasedir>
          <version>4.1.0</version>
          <models>
            <model>src/main/mdo/maven.mdo</model>
          </models>
          <params>
            <param>forcedIOModelVersion=4.0.0</param>
            <param>packageModelV3=org.apache.maven.model</param>

import org.apache.maven.api.annotations.Nonnull;
import org.apache.maven.api.toolchain.ToolchainModel;

/**
 * Factory interface for creating toolchain instances from configuration models.
 *
 * <p>This factory is responsible for instantiating concrete toolchain implementations
 * based on toolchain model configurations or default settings.</p>
 *
 * @since 4.0.0
 */
@Experimental
@Consumer

        Note that this change may break certain subclassed models. You can
        revert back to the previous behavior by adding upranking yourself in the
        `train_step()`/`test_step()`/`predict_step()` methods, e.g. `if
        x.shape.rank == 1: x = tf.expand_dims(x, axis=-1)`. Functional models as
        well as Sequential models built with an explicit input shape are not
        affected.

from typing import Annotated, Optional

from annotated_doc import Doc
from fastapi.openapi.models import OpenIdConnect as OpenIdConnectModel
from fastapi.security.base import SecurityBase
from starlette.exceptions import HTTPException
from starlette.requests import Request
from starlette.status import HTTP_401_UNAUTHORIZED


class OpenIdConnect(SecurityBase):
    """
    OpenID Connect authentication class. An instance of it would be used as a

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

/**
 * Handles normalization of a model. In this context, normalization is the process of producing a canonical
 * representation for models that physically look different but are semantically equivalent.
 *
 * @deprecated use {@code org.apache.maven.api.services.ModelBuilder} instead
 */
@Deprecated(since = "4.0.0")
public interface ModelNormalizer {

from enum import Enum

from fastapi import FastAPI


class ModelName(str, Enum):
    alexnet = "alexnet"
    resnet = "resnet"
    lenet = "lenet"


app = FastAPI()


@app.get("/models/{model_name}")
async def get_model(model_name: ModelName):
    if model_name is ModelName.alexnet:
        return {"model_name": model_name, "message": "Deep Learning FTW!"}

    if model_name.value == "lenet":

Посмотрим, как это работает, и как это изменить при необходимости.

## Pydantic‑модели для входа и выхода { #pydantic-models-for-input-and-output }

Предположим, у вас есть Pydantic‑модель со значениями по умолчанию, как здесь:

{* ../../docs_src/separate_openapi_schemas/tutorial001_py310.py ln[1:7] hl[7] *}