But if we use the same model for another *path operation*, we could be sending our user's passwords to every client.

/// danger

Never store the plain password of a user or send it in a response like this, unless you know all the caveats and you know what you are doing.

///

## Add an output model { #add-an-output-model }

    * Es wird auch von automatisch Client-Code-generierenden Tools verwendet.

Aber am wichtigsten:

* Es wird die Ausgabedaten auf das **limitieren und filtern**, was im Rückgabetyp definiert ist.
    * Das ist insbesondere für die **Sicherheit** wichtig, mehr dazu unten.

## `response_model`-Parameter { #response-model-parameter }

        <field>
          <name>project</name>
          <version>1.0.0</version>
          <description>Snippets of POM xml files used to supplement the data model.</description>
          <type>DOM</type>
        </field>
      </fields>
    </class>
  </classes>

	VIOTAM		V2, V3				// d7212852
	VIOTAM		V2, V0, V3			// d7212850
	VIDV		V3				// d7a10852
	VIDV		V0, V3				// d7a10850

	// 31.16.1: Integer Scalar Move Instructions
	VMVXS		V2, X10				// 57252042
	VMVSX		X10, V2				// 57610542

	// 31.16.2: Floating-Point Scalar Move Instructions
	VFMVFS		V2, F10				// 57152042
	VFMVSF		F10, V2				// 57510542

	// 31.16.3: Vector Slide Instructions

            <ul>
              <li>{@code ${project.build.outputDirectory}} (typically {@code target/classes}) if {@code scope} is "main",</li>
              <li>{@code ${project.build.testOutputDirectory}} (typically {@code target/test-classes}) if {@code scope} is "test",</li>
              <li>{@code ${project.build.directory}} (typically {@code target}) otherwise.</li>
            </ul>

        tensorflow::io::JoinPath("tensorflow", "c", "test_op1.so"));
    TF_Library* lib = TF_LoadLibrary(lib_path.c_str(), status);
    TF_Code code = TF_GetCode(status);
    string status_msg(TF_Message(status));
    TF_DeleteStatus(status);
    ASSERT_EQ(TF_OK, code) << status_msg;

    // Test op list.
    TF_Buffer op_list_buf = TF_GetOpList(lib);
    tensorflow::OpList op_list;

		Code:  ErrSiteReplicationInvalidRequest,
	}
	errSRResyncCanceled = SRError{
		Cause: errors.New("site replication resync is already canceled"),
		Code:  ErrSiteReplicationInvalidRequest,
	}
	errSRNoResync = SRError{
		Cause: errors.New("no resync in progress"),
		Code:  ErrSiteReplicationInvalidRequest,
	}
	errSRResyncToSelf = SRError{
		Cause: errors.New("invalid peer specified - cannot resync to self"),

## ドキュメントを見る

自動ドキュメントを見ると、入力モデルと出力モデルがそれぞれ独自のJSON Schemaを持っていることが確認できます。

<img src="https://fastapi.tiangolo.com/img/tutorial/response-model/image01.png">

そして、両方のモデルは、対話型のAPIドキュメントに使用されます:

<img src="https://fastapi.tiangolo.com/img/tutorial/response-model/image02.png">

## レスポンスモデルのエンコーディングパラメータ

レスポンスモデルにはデフォルト値を設定することができます:

{* ../../docs_src/response_model/tutorial004.py hl[11,13,14] *}

# Response Status Code { #response-status-code }

The same way you can specify a response model, you can also declare the HTTP status code used for the response with the parameter `status_code` in any of the *path operations*:

* `@app.get()`
* `@app.post()`
* `@app.put()`
* `@app.delete()`
* etc.

{* ../../docs_src/response_status_code/tutorial001_py39.py hl[6] *}

/// note

erasure-code.jpg...