/*
   * We avoid using a method reference or lambda here for now:
   *
   * - method reference: Inside Google, CacheBuilder is used from the implementation of a custom
   *   ClassLoader that is sometimes used as a system classloader. That's a problem because
   *   method-reference linking tries to look up the system classloader, and it fails because there
   *   isn't one yet.
   *

        if (mojoExecutionConfigurator == null) {
            //
            // The plugin has a custom component configurator but does not have a custom mojo execution configurator
            // so fall back to the default mojo execution configurator.
            //
            mojoExecutionConfigurator = mojoExecutionConfigurators.get("default");

from your test's `tearDown()`.

### API

#### MockResponse

Mock responses default to an empty response body and a `200` status code.
You can set a custom body with a string, input stream or byte array. Also
add headers with a fluent builder API.

### Java
```java
MockResponse response = new MockResponse.Builder()

<img src="/img/tutorial/additional-responses/image01.png">

## Combina responses predefinidos y personalizados { #combine-predefined-responses-and-custom-ones }

Es posible que desees tener algunos responses predefinidos que se apliquen a muchas *path operations*, pero que quieras combinarlos con responses personalizados necesarios por cada *path operation*.

<img src="/img/tutorial/additional-responses/image01.png">

## Vordefinierte und benutzerdefinierte Responses kombinieren { #combine-predefined-responses-and-custom-ones }

Möglicherweise möchten Sie einige vordefinierte Responses haben, die für viele *Pfadoperationen* gelten, Sie möchten diese jedoch mit benutzerdefinierten Responses kombinieren, die für jede *Pfadoperation* erforderlich sind.

    custom_encoder: Annotated[
        Optional[dict[Any, Callable[[Any], Any]]],
        Doc(
            """
            Pydantic's `custom_encoder` parameter, passed to Pydantic models to define
            a custom encoder.
            """
        ),
    ] = None,
    sqlalchemy_safe: Annotated[
        bool,
        Doc(
            """
            Exclude from the output any fields that start with the name `_sa`.

    /**
     * Create path validator with default settings
     */
    public PathValidator() {
        this(260, 255, true, false);
    }

    /**
     * Create path validator with custom settings
     *
     * @param maxPathLength maximum total path length
     * @param maxComponentLength maximum length for path components
     * @param allowUncPaths whether to allow UNC paths

		Password  string `json:"password"`
		Mechanism string `json:"mechanism"`
	} `json:"sasl"`
	// Queue store
	QueueSize int    `json:"queueSize"`
	QueueDir  string `json:"queueDir"`

	// Custom logger
	LogOnce func(ctx context.Context, err error, id string, errKind ...any) `json:"-"`
}

// Target - Kafka target.
type Target struct {
	status int32

	totalMessages  int64
	failedMessages int64

It used custom types in its declarations instead of standard Python types, but it was still a huge step forward.

It also was one of the first frameworks to generate a custom schema declaring the whole API in JSON.

        // Test skip authentication mode
        when(request.getHeader("Authorization")).thenReturn(null);
        when(httpSession.getAttribute("NtlmHttpAuth")).thenReturn(null);

        // Create a custom filter that overrides negotiate to test skipAuthentication
        NtlmHttpFilter customFilter = new NtlmHttpFilter() {
            @Override