* `COMPATIBLE_TLS` is a secure configuration that connects to secure–but not current–HTTPS servers.
 * `CLEARTEXT` is an insecure configuration that is used for `http://` URLs.

These loosely follow the model set in [Google Cloud Policies](https://cloud.google.com/load-balancing/docs/ssl-policies-concepts). We [track changes](../security/tls_configuration_history.md) to this policy.

一つ前の章では、（依存性注入システムに基づいた）セキュリティシステムは、 *path operation 関数* に `str` として `token` を与えていました:

{* ../../docs_src/security/tutorial001_an_py310.py hl[12] *}

しかし、それはまだそんなに有用ではありません。

現在のユーザーを取得するようにしてみましょう。

## ユーザーモデルの作成 { #create-a-user-model }

まずは、Pydanticのユーザーモデルを作成しましょう。

ボディを宣言するのにPydanticを使用するのと同じやり方で、Pydanticを別のどんなところでも使うことができます:

{* ../../docs_src/security/tutorial002_an_py310.py hl[5,12:6] *}

### Memory per Process { #memory-per-process }

Now, when the program loads things in memory, for example, a machine learning model in a variable, or the contents of a large file in a variable, all that **consumes a bit of the memory (RAM)** of the server.

        if isinstance(obj, classes_tuple):
            return encoder(obj)
    if is_pydantic_v1_model_instance(obj):
        raise PydanticV1NotSupportedError(
            "pydantic.v1 models are no longer supported by FastAPI."
            f" Please update the model {obj!r}."
        )
    try:
        data = dict(obj)
    except Exception as e:
        errors: list[Exception] = []
        errors.append(e)
        try:

Temporarily adopting this point of view (of the *external developer*) can help you feel like it's more obvious where to put the parameters, the Pydantic model for the body, for the response, etc. for that *external API*.

///

### Create a callback `APIRouter` { #create-a-callback-apirouter }

First create a new `APIRouter` that will contain one or more callbacks.

  public static final String SEC_CH_UA_ARCH = "Sec-CH-UA-Arch";

  /**
   * The HTTP <a href="https://wicg.github.io/ua-client-hints/#sec-ch-ua-model">{@code
   * Sec-CH-UA-Model}</a> header field name.
   *
   * @since 30.0
   */
  public static final String SEC_CH_UA_MODEL = "Sec-CH-UA-Model";

  /**
   * The HTTP <a href="https://wicg.github.io/ua-client-hints/#sec-ch-ua-platform">{@code

        chatApiManager.sendSseEvent(writer, "error", Map.of("phase", "generation", "message", "Model unavailable"));

        final String output = stringWriter.toString();
        assertTrue(output.contains("event: error"));
        assertTrue(output.contains("\"phase\":\"generation\""));
        assertTrue(output.contains("\"message\":\"Model unavailable\""));
    }

    @Test
    public void test_sendSseEvent_done() {

{* ../../docs_src/path_params/tutorial005_py310.py hl[1,6:9] *}

/// tip

If you are wondering, "AlexNet", "ResNet", and "LeNet" are just names of Machine Learning <dfn title="Technically, Deep Learning model architectures">models</dfn>.

///

### Declare a *path parameter* { #declare-a-path-parameter }

Then create a *path parameter* with a type annotation using the enum class you created (`ModelName`):

import java.util.List;
import java.util.Map;
import java.util.Objects;
import java.util.concurrent.ConcurrentHashMap;
import java.util.stream.Collectors;
import java.util.stream.Stream;

import org.apache.maven.api.model.Model;
import org.apache.maven.api.services.Lookup;
import org.apache.maven.eventspy.EventSpy;
import org.apache.maven.execution.ExecutionEvent;
import org.apache.maven.execution.MavenSession;

                // drop through...
            } catch (final LinkageError e) {
                // drop through...
            }
        }

        try {
            // last chance - classic model
            return Class.forName(name);
        } catch (final Exception e) {
            throw new TypeNotPresentException(name, e);
        } catch (final LinkageError e) {