It could be just one or two lines of code, like:

```Python
callback_url = "https://example.com/api/v1/invoices/events/"
httpx.post(callback_url, json={"description": "Invoice paid", "paid": True})
```

But possibly the most important part of the callback is making sure that your API user (the external developer) implements the *external API* correctly, according to the data that *your API* is going to send in the request body of the callback, etc.

     * For example, an artifact of type {@value org.apache.maven.api.Type#JAR} can be placed
     * either on the class-path or on the module-path. The project needs to make a choice
     * (possibly using heuristic rules), then add the dependency in only one of the paths
     * identified by {@link PathType}.
     *
     * @return file paths to place on the different tool options
     */
    @Nonnull

Import `BaseSettings` from Pydantic and create a sub-class, very much like with a Pydantic model.

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()`.

//// tab | Pydantic v2

{* ../../docs_src/configure_swagger_ui/tutorial003.py hl[3] *}

## Otros parámetros de Swagger UI

Para ver todas las demás configuraciones posibles que puedes usar, lee la <a href="https://swagger.io/docs/open-source-tools/swagger-ui/usage/configuration/" class="external-link" target="_blank">documentación oficial de los parámetros de Swagger UI</a>.

Because of that, the JSON Schema for a model can be different depending on if it's used for **input or output**:

* for **input** the `description` will **not be required**
* for **output** it will be **required** (and possibly `None`, or in JSON terms, `null`)

### Model for Output in Docs { #model-for-output-in-docs }

You can check the output model in the docs too, **both** `name` and `description` are marked as **required** with a **red asterisk**:

import org.junit.runners.JUnit4;

/**
 * Unit test for {@link FinalizableReferenceQueue}.
 *
 * @author Bob Lee
 */
// - depends on details of GC and classloading
// - .class files aren't available
// - possibly no real concept of separate ClassLoaders?
@AndroidIncompatible
@GwtIncompatible
@RunWith(JUnit4.class)
@NullUnmarked
public class FinalizableReferenceQueueTest {

  private @Nullable FinalizableReferenceQueue frq;

import org.junit.runners.JUnit4;

/**
 * Unit test for {@link FinalizableReferenceQueue}.
 *
 * @author Bob Lee
 */
// - depends on details of GC and classloading
// - .class files aren't available
// - possibly no real concept of separate ClassLoaders?
@AndroidIncompatible
@GwtIncompatible
@RunWith(JUnit4.class)
@NullUnmarked
public class FinalizableReferenceQueueTest {

  private @Nullable FinalizableReferenceQueue frq;

     * repository. It is worth to point out that merging does not simply choose one of the input repositories and
     * discards the others but actually combines their possibly different policies.
     *
     * @param repositories The original repositories, may be {@code null}.
     * @return The effective repositories or {@code null} if the input was {@code null}.
     */

be reported to TensorFlow’s Project Stewards, Thea Lamkin (******@****.***) and Joana Carrasqueira (******@****.***). The Project Steward will determine whether the Code of Conduct was violated, and will issue an appropriate sanction, possibly including a written warning or expulsion from the project, project sponsored spaces, or project forums. We ask that you make a good-faith effort to resolve your conflict via the conflict resolution policy before submitting a report.

Violations...

// GraphDef type. These functions must be deleted by calling TF_DeleteFunction.
//
// If `mutate_proto_func` is non-NULL, run it over each FunctionDef proto,
// before creating a TF_Function out of the possibly mutated proto.
static std::vector<UniqueFuncPtr> CreateFunctionsFromTextProto(
    const char* text_proto,
    std::function<void(FunctionDef*)>* mutate_proto_func, TF_Status* status) {
  tensorflow::GraphDef gdef;