Smb2LockResponse response = req.createResponse(mockContext, req);
            assertNotNull(response);
        }
    }

    @Nested
    @DisplayName("Integration Tests")
    class IntegrationTests {

        @Test
        @DisplayName("Should handle complete lock request workflow")
        void testCompleteLockWorkflow() {

# AssumeRoleWithWebIdentity [![Slack](https://slack.min.io/slack?type=svg)](https://slack.min.io)

## Introduction

MinIO supports the standard AssumeRoleWithWebIdentity STS API to enable integration with OIDC/OpenID based identity provider environments. This allows the generation of temporary credentials with pre-defined access policies for applications/users to interact with MinIO object storage.

            // Then
            assertEquals(fileFsFullSizeInfo.getCapacity(), fileFsFullSizeInfo.getFree());
        }
    }

    @Nested
    @DisplayName("Integration Tests")
    class IntegrationTests {

        @Test
        @DisplayName("Should work as AllocInfo implementation")
        void shouldWorkAsAllocInfoImplementation() throws SMBProtocolDecodingException {

# AssumeRoleWithLDAPIdentity [![Slack](https://slack.min.io/slack?type=svg)](https://slack.min.io)

## Introduction

MinIO provides a custom STS API that allows integration with LDAP based corporate environments including Microsoft Active Directory. The MinIO server uses a separate LDAP service account to lookup user information. The login flow for a user is as follows:

- User provides their AD/LDAP username and password to the STS API.

![Swagger UI interaction](https://fastapi.tiangolo.com/img/index/index-04-swagger-03.png)

* Cliquez ensuite sur le bouton "Execute", l'interface utilisateur communiquera avec votre API, enverra les paramètres, obtiendra les résultats et les affichera à l'écran :

![Swagger UI interaction](https://fastapi.tiangolo.com/img/index/index-05-swagger-04.png)

just getting started, GitHub has a
[how-to](https://help.github.com/articles/using-pull-requests/).

TensorFlow team members will be assigned to review your pull requests. Once the
pull requests are approved and pass continuous integration checks, a TensorFlow
team member will apply `ready to pull` label to your change. This means we are
working on getting your pull request submitted to our internal repository. After

![Swagger UI interaction](https://fastapi.tiangolo.com/img/index/index-04-swagger-03.png)

* Then click on the "Execute" button, the user interface will communicate with your API, send the parameters, get the results and show them on the screen:

![Swagger UI interaction](https://fastapi.tiangolo.com/img/index/index-05-swagger-04.png)

![Swagger UI Interaktion](https://fastapi.tiangolo.com/img/index/index-04-swagger-03.png)

* Klicken Sie dann auf den Button „Execute“, die Benutzeroberfläche wird mit Ihrer API kommunizieren, die Parameter senden, die Ergebnisse erhalten und sie auf dem Bildschirm anzeigen:

![Swagger UI Interaktion](https://fastapi.tiangolo.com/img/index/index-05-swagger-04.png)

It had a dependency injection system. It required pre-registration of components, as other tools discussed above. But still, it was a great feature.

I was never able to use it in a full project, as it didn't have security integration, so, I couldn't replace all the features I was having with the full-stack generators based on Flask-apispec. I had in my backlog of projects to create a pull request adding that functionality.

But then, the project's focus shifted.

 * - Document search with pagination and faceting
 * - Scroll search for large result sets
 * - Document retrieval by ID
 * - Bulk document updates
 * - Search parameter serialization/deserialization for cookies
 * - Integration with search engines and logging systems
 */
public class SearchHelper {

    // ===================================================================================