Each attribute of a Pydantic model has a type.

But that type can itself be another Pydantic model.

So, you can declare deeply nested JSON "objects" with specific attribute names, types and validations.

All that, arbitrarily nested.

### Define a submodel { #define-a-submodel }

For example, we can define an `Image` model:

{* ../../docs_src/body_nested_models/tutorial004_py310.py hl[7:9] *}

        ServerResponseValidator.ValidationStats stats = validator.getStats();
        assertEquals(0, stats.getTotalValidations());
        assertEquals(0, stats.getFailedValidations());

        // Perform some validations
        try {
            validator.validateBuffer(new byte[100], 50, 200);
        } catch (SmbException e) {
            // Ignore
        }

        try {

    * Nombres (`int`, `float`) avec valeur minimale and maximale, etc.

* Validation pour des types plus exotiques, tel que:
    * URL.
    * Email.
    * UUID.
    * ...et autres.

Toutes les validations sont gérées par le bien établi et robuste **Pydantic**.

### Sécurité et authentification

La sécurité et l'authentification sont intégrées. Sans aucun compromis avec les bases de données ou les modèles de données.

### Other (Cleanup or Flake)

     * If no syntax is specified, or if its length is 1 character (interpreted as a Windows drive),
     * the default is a Maven-specific variation of the {@code "glob"} pattern.
     *
     * <p>The default implementation returns an empty list, which means to apply a language-dependent pattern.

border-bottom-width: 2px;\n    }\n    &.border-width-3 {\n      border-bottom-width: 3px;\n    }\n  }\n}\n\n// custom switch color variations\n.custom-switch {\n  @each $name, $color in $theme-colors {\n    @include custom-switch-variant($name, $color);\n  }\n\n  @each $name, $color in $colors {\n    @include custom-switch-variant($name, $color);\n  }\n}\n\n// custom range color variations\n.custom-range {\n  @each $name, $color in $theme-colors {\n    @include custom-range-variant($name, $color);\n  }\n\n ...

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

{* ../../docs_src/settings/tutorial001_py39.py hl[2,5:8,11] *}

/// tip

If you want something quick to copy and paste, don't use this example, use the last one below.

///

	if t, ok := config.Tiers[tierName]; ok {
		return t.Type, true
	}
	return madmin.Unsupported, false
}

// Add adds tier to config if it passes all validations.
func (config *TierConfigMgr) Add(ctx context.Context, tier madmin.TierConfig, ignoreInUse bool) error {
	config.Lock()
	defer config.Unlock()

	// check if tier name is in all caps
	tierName := tier.Name

        ),
    ],
) -> Any:
    """
    Declare a path parameter for a *path operation*.

    Read more about it in the
    [FastAPI docs for Path Parameters and Numeric Validations](https://fastapi.tiangolo.com/tutorial/path-params-numeric-validations/).

    ```python
    from typing import Annotated

    from fastapi import FastAPI, Path

    app = FastAPI()


    @app.get("/items/{item_id}")

			if err = json.Unmarshal(data, &serviceAcctReqs); err != nil {
				writeErrorResponseJSON(ctx, w, importErrorWithAPIErr(ctx, ErrAdminConfigBadJSON, err, allSvcAcctsFile, ""), r.URL)
				return
			}

			// Validations for LDAP enabled deployments.
			if globalIAMSys.LDAPConfig.Enabled() {
				skippedAccessKeys, err := globalIAMSys.NormalizeLDAPAccessKeypairs(ctx, serviceAcctReqs)