bean.setSpecialCron("invalid");

        // Validate default group
        Set<ConstraintViolation<GroupTestBean>> violations = validator.validate(bean, Default.class);
        assertEquals(1, violations.size());
        assertTrue(violations.iterator().next().getPropertyPath().toString().contains("defaultCron"));

        // Validate special group
        violations = validator.validate(bean, SpecialGroup.class);

            yield cls.validate

        @classmethod
        def validate(cls, v: Any) -> str:
            logger.warning(
                "email-validator not installed, email fields will be treated as str.\n"
                "To install, run: pip install email-validator"
            )
            return str(v)

        @classmethod
        def _validate(cls, __input_value: Any, _: Any) -> str:

* Validate **complex structures**:
    * Use of hierarchical Pydantic models, Python `typing`’s `List` and `Dict`, etc.
    * And validators allow complex data schemas to be clearly and easily defined, checked and documented as JSON Schema.
    * You can have deeply **nested JSON** objects and have them all validated and annotated.
* **Extensible**:

            return 4000;
        }
    }

    /**
     * Parses and validates field filter parameters from the request.
     * Only configured label values are accepted to prevent query injection.
     *
     * @param request the HTTP request
     * @return a map of field names to their validated filter values
     */
    protected Map<String, String[]> parseFieldFilters(final HttpServletRequest request) {

   */
  public final Iterable<N> depthFirstPostOrder(Iterable<? extends N> startNodes) {
    ImmutableSet<N> validated = validate(startNodes);
    return () -> newTraversal().postOrder(validated.iterator());
  }

  abstract Traversal<N> newTraversal();

  @SuppressWarnings("CheckReturnValue")
  private ImmutableSet<N> validate(Iterable<? extends N> startNodes) {
    ImmutableSet<N> copy = ImmutableSet.copyOf(startNodes);

    # For the test client
    "httpx >=0.23.0,<1.0.0",
    # For templates
    "jinja2 >=3.1.5",
    # For forms and file uploads
    "python-multipart >=0.0.18",
    # To validate email fields
    "email-validator >=2.0.0",
    # Uvicorn with uvloop
    "uvicorn[standard] >=0.12.0",
    # # Settings management
    "pydantic-settings >=2.0.0",
    # # Extra Pydantic data types
    "pydantic-extra-types >=2.0.0",

     * Reloads the document index by closing and reopening it.
     *
     * @param form the action form (validated but not used for configuration)
     * @return HTML response redirecting to the maintenance page
     */
    @Execute
    @Secured({ ROLE })
    public HtmlResponse reloadDocIndex(final ActionForm form) {
        validate(form, messages -> {}, this::asIndexHtml);
        verifyToken(this::asIndexHtml);

And then in our code, we parse that YAML content directly, and then we are again using the same Pydantic model to validate the YAML content:

{* ../../docs_src/path_operation_advanced_configuration/tutorial007_py310.py hl[24:31] *}

/// tip

Here we reuse the same Pydantic model.

But the same way, we could have validated it in some other way.

    //                                                                           =========
    /**
     * Validates document fields according to index field requirements.
     *
     * @param doc the document to validate
     * @param throwError consumer to handle validation errors
     */

            throw new SsoLoginException("unexpected set of artifacts received");
        }
    }

    /**
     * Validates the OAuth2 state parameter.
     * @param session The HTTP session containing stored state data.
     * @param state The state parameter to validate.
     * @return The validated state data.
     */
    protected StateData validateState(final HttpSession session, final String state) {