## Nested Models

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

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

//// tab | Python 3.10+

```Python hl_lines="7-9"

Navbar\n// Navbar brand\n// Navbar nav\n// Navbar text\n// Navbar divider\n// Responsive navbar\n// Navbar position\n// Navbar themes\n\n\n// Navbar\n//\n// Provide a static navbar from which we expand to create full-width, fixed, and\n// other navbar variations.\n\n.navbar {\n  position: relative;\n  display: flex;\n  flex-wrap: wrap; // allow us to do the line break for collapsing content\n  align-items: center;\n  justify-content: space-between; // space out brand from logo\n  padding: $navbar-padding-y...

	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

### Other (Cleanup or Flake)

        ),
    ],
) -> 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}")

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

//// tab | Pydantic v2

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

////

//// tab | Pydantic v1

/// info

    checkArgument(parts.size() <= MAX_PARTS, "Domain has too many parts: '%s'", name);
    checkArgument(validateSyntax(parts), "Not a valid domain name: '%s'", name);
  }

  /**
   * Internal constructor that skips validations when creating an instance from parts of an
   * already-validated InternetDomainName, as in {@link ancestor}.
   */
  private InternetDomainName(String name, ImmutableList<String> parts) {

			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)

    checkArgument(parts.size() <= MAX_PARTS, "Domain has too many parts: '%s'", name);
    checkArgument(validateSyntax(parts), "Not a valid domain name: '%s'", name);
  }

  /**
   * Internal constructor that skips validations when creating an instance from parts of an
   * already-validated InternetDomainName, as in {@link ancestor}.
   */
  private InternetDomainName(String name, ImmutableList<String> parts) {

/// check | "Inspired **FastAPI** to"

Define extra validations for data types using the "default" value of model attributes. This improves editor support, and it was not available in Pydantic before.