"name": "The Baz"
        }
    ]
}
```

/// info

Notice how the `images` key now has a list of image objects.

///

## Deeply nested models { #deeply-nested-models }

You can define arbitrarily deeply nested models:

{* ../../docs_src/body_nested_models/tutorial007_py310.py hl[7,12,18,21,25] *}

/// info

Apart from all the fancy words used here, the **Dependency Injection** system is quite simple.

Just functions that look the same as the *path operation functions*.

But still, it is very powerful, and allows you to declare arbitrarily deeply nested dependency "graphs" (trees).

/// tip

All this might not seem as useful with these simple examples.

But you will see how useful it is in the chapters about **security**.

        assertEquals(longMessage, exception.getMessage());
        assertNull(exception.getCause());
    }

    public void test_getCause_withNestedExceptions() {
        // Test deeply nested exception causes
        Exception level3 = new Exception("Level 3: Database connection failed");
        Exception level2 = new Exception("Level 2: User lookup failed", level3);

        assertEquals("original2", retrievedValues[1]);

        // Modify retrieved array
        retrievedValues[0] = "modified1";

        // Verify the parameter's values can still be modified (not deeply immutable)
        assertEquals("modified1", param.getValues()[0]);

        // This shows that the class returns the same array reference
        assertSame(retrievedValues, param.getValues());
    }

            }
        }
    }
}
```

### Custom OpenAPI *path operation* schema { #custom-openapi-path-operation-schema }

The dictionary in `openapi_extra` will be deeply merged with the automatically generated OpenAPI schema for the *path operation*.

So, you could add additional data to the automatically generated schema.

    * Use of hierarchical Pydantic models, Python `typing`’s `List` and `Dict`, etc.
    * And validators allow complex data schemas to be clearly and easily defined, checked and documented as JSON Schema.
    * You can have deeply **nested JSON** objects and have them all validated and annotated.
* **Extensible**:
    * Pydantic allows custom data types to be defined or you can extend validation with methods on a model decorated with the validator decorator.

          accountId: ${{ secrets.CLOUDFLARE_ACCOUNT_ID }}
          command: pages deploy ./site --project-name=${{ env.PROJECT_NAME }} --branch=${{ env.BRANCH }}
      - name: Comment Deploy
        run: python ./scripts/deploy_docs_status.py
        env:
          GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
          DEPLOY_URL: ${{ steps.deploy.outputs.deployment-url }}
          COMMIT_SHA: ${{ github.event.workflow_run.head_sha }}

     * @throws ArtifactDeployerException if the deployment failed
     */
    void deploy(@Nonnull ArtifactDeployerRequest request);

    /**
     * @param session the repository session
     * @param repository the repository to deploy to
     * @param artifacts the collection of artifacts to deploy
     * @throws ArtifactDeployerException if the deployment failed

        String extension = artifact.getArtifactHandler().getExtension();
        File source = new File(basedir, finalName + "." + extension);
        deploy(source, artifact, deploymentRepository, localRepository);
    }

    @Override
    public void deploy(
            File source, Artifact artifact, ArtifactRepository deploymentRepository, ArtifactRepository localRepository)
            throws ArtifactDeploymentException {

# Lasta Di properties, you can set container's options
# _/_/_/_/_/_/_/_/_/_/

# location of smart-deploy mode e.g. maihama_env.properties: lasta_di.smart.deploy.mode
#smart.deploy.mode.location = maihama_env.properties: lasta_di.smart.deploy.mode

# package for smart deploy target e.g. org.docksidestage.app
smart.package1 = org.codelibs.fess.crawler