def test_post_body_form(client: TestClient):
    response = client.post("/login/", data={"username": "Foo", "password": "secret"})
    assert response.status_code == 200
    assert response.json() == {"username": "Foo", "password": "secret"}


@needs_pydanticv1
def test_post_body_extra_form(client: TestClient):
    response = client.post(
        "/login/", data={"username": "Foo", "password": "secret", "extra": "extra"}
    )

    return HTMLResponse(html)


@app.websocket("/ws")
async def websocket_endpoint(websocket: WebSocket):
    await websocket.accept()
    while True:
        data = await websocket.receive_text()

        read_with_orm_mode = getattr(_get_model_config(res), "read_with_orm_mode", None)
        if read_with_orm_mode:
            # Let from_orm extract the data from this model instead of converting
            # it now to a dict.
            # Otherwise, there's no way to extract lazy data that requires attribute
            # access instead of dict iteration, e.g. lazy relationships.
            return res
        return _model_dump(

                                                            <la:message key="labels.webauth_any"/>
                                                        </c:if> ${f:h(data.hostname)}: <c:if
                                                                test="${data.port==null||data.port==''}">

                                            <c:forEach var="data" varStatus="s"
                                                       items="${stemmerOverrideItemItems}">
                                                <tr
                                                        data-href="${contextPath}/admin/dict/stemmeroverride/details/${f:u(dictId)}/4/${f:u(data.id)}">

   * data, and vertical if there is variance in the {@code y} data but not the {@code x} data.
   *
   * <p>This fit minimizes the root-mean-square error in {@code y} as a function of {@code x}. This
   * error is defined as the square root of the mean of the squares of the differences between the
   * actual {@code y} values of the data and the values predicted by the fit for the {@code x}

                                            <c:forEach var="data" varStatus="s"
                                                       items="${scheduledJobItems}">
                                                <tr
                                                        data-href="${contextPath}/admin/scheduler/details/4/${f:u(data.id)}">
                                                    <td>${f:h(data.name)}</td>

	if err != nil {
		return errInvalidArgument
	}
	data := &syscall.Win32finddata{}
	handle, err := syscall.FindFirstFile(globAllP, data)
	if err != nil {
		if err = syscallErrToFileErr(dirPath, err); err == errFileNotFound {
			return nil
		}
		return err
	}
	defer syscall.FindClose(handle)

	for ; ; err = syscall.FindNextFile(handle, data) {
		if err != nil {
			if err == syscall.ERROR_NO_MORE_FILES {

		} else {
			names = append(names, name)
		}
		var data [][]string
		for _, attr := range names {
			value, err := getxattr(path, attr)
			if err != nil {
				data = append(data, []string{attr, errors.Unwrap(err).Error()})
			} else {
				data = append(data, []string{attr, fmt.Sprintf("%d", value)})
			}
		}
		table.AppendBulk(data) // Add Bulk Data
		table.Render()
	}

        try (final InputStream in = ResourceUtil.getResourceAsStream("extractor/eml/sample1.eml")) {
            ExtractData data = emlExtractor.getText(in, null);
            final String content = data.getContent();
            logger.info(content);
            assertTrue(content.contains("プレイステーション"));
            assertTrue(data.getValues("Subject")[0].contains("ダイジェスト"));
        }
    }