* users to migrate their projects.
     */
    int VALIDATION_LEVEL_MAVEN_2_0 = 20;

    /**
     * Denotes validation as performed by Maven 3.0. This validation level is meant for existing projects.
     */
    int VALIDATION_LEVEL_MAVEN_3_0 = 30;

    /**

But by default, it all **"just works"**.

### Validation

* Validation for most (or all?) Python **data types**, including:
    * JSON objects (`dict`).
    * JSON array (`list`) defining item types.
    * String (`str`) fields, defining min and max lengths.
    * Numbers (`int`, `float`) with min and max values, etc.

* Validation for more exotic types, like:
    * URL.
    * Email.
    * UUID.

        }

        if (request.getValidationLevel() >= ModelBuilderRequest.VALIDATION_LEVEL_MAVEN_2_0) {
            Severity errOn31 = getSeverity(request, ModelBuilderRequest.VALIDATION_LEVEL_MAVEN_3_1);

            validateBannedCharacters(
                    EMPTY, "version", problems, errOn31, Version.V20, m.getVersion(), null, m, ILLEGAL_VERSION_CHARS);

            resultModel = pluginConfigurationExpander.expandPluginConfiguration(resultModel, request, problems);
        }

        result.setEffectiveModel(resultModel);

        // effective model validation
        modelValidator.validateEffectiveModel(resultModel, request, problems);

        if (hasModelErrors(problems)) {
            throw problems.newModelBuilderException();
        }

        return result;

*   Fixes a missing validation which causes `TensorSummaryV2` to crash ([CVE-2022-29193](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2022-29193))
*   Fixes a missing validation which crashes `QuantizeAndDequantizeV4Grad` ([CVE-2022-29192](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2022-29192))

## Dependencies

Used by Pydantic:

* <a href="https://github.com/JoshData/python-email-validator" target="_blank"><code>email_validator</code></a> - for email validation.
* <a href="https://docs.pydantic.dev/latest/usage/pydantic_settings/" target="_blank"><code>pydantic-settings</code></a> - for settings management.

The same way as with Pydantic models, you declare class attributes with type annotations, and possibly default values.

You can use all the same validation features and tools you use for Pydantic models, like different data types and additional validations with `Field()`.

=== "Pydantic v2"

    ```Python hl_lines="2  5-8  11"
    {!> ../../../docs_src/settings/tutorial001.py!}
    ```

=== "Pydantic v1"

## Dépendances facultatives

Utilisées par Pydantic:

* <a href="https://github.com/JoshData/python-email-validator" target="_blank"><code>email_validator</code></a> - pour la validation des adresses email.

Utilisées par Starlette :

* <a href="https://requests.readthedocs.io" target="_blank"><code>requests</code></a> - Obligatoire si vous souhaitez utiliser `TestClient`.

package lifecycle

import (
	"encoding/xml"
	"fmt"
	"testing"
)

// TestInvalidRules checks if Rule xml with invalid elements returns
// appropriate errors on validation
func TestInvalidRules(t *testing.T) {
	invalidTestCases := []struct {
		inputXML    string
		expectedErr error
	}{
		{ // Rule with ID longer than 255 characters
			inputXML: ` <Rule>

## Dependencies

Used by Pydantic:

* <a href="https://github.com/JoshData/python-email-validator" target="_blank"><code>email_validator</code></a> - for email validation.
* <a href="https://docs.pydantic.dev/latest/usage/pydantic_settings/" target="_blank"><code>pydantic-settings</code></a> - for settings management.