"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**.

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<a href="https://liblab.com?utm_source=fastapi" target="_blank" title="liblab - Generate SDKs from FastAPI"><img src="https://fastapi.tiangolo.com/img/sponsors/liblab.png"></a>

        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);

* Editor support, including:
    * Completion.
    * Type checks.
* Validation of data:
    * Automatic and clear errors when the data is invalid.
    * Validation even for deeply nested JSON objects.
* <abbr title="also known as: serialization, parsing, marshalling">Conversion</abbr> of input data: coming from the network to Python data and types. Reading from:
    * JSON.
    * Path parameters.

        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());
    }

            System.clearProperty(Constants.FESS_CONFIG_PREFIX + arrayKey);
        }
    }

    // Test property key with dots
    public void test_get_propertyKeyWithDots() {
        String dottedKey = "deeply.nested.property.key";
        String dottedValue = "nested-value";

        System.setProperty(Constants.FESS_CONFIG_PREFIX + dottedKey, dottedValue);

        try {

            }
        }
    }
}
```

### 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.

                props.setProperty(FessEnv.MAIL_RETURN_PATH, "no-reply******@****.***");
                return props;
            }
        };

        assertEquals("smtp-relay.example.com:465", specialMailEnv.getMailSmtpServerMainHostAndPort());
        assertEquals("[テスト環境]", specialMailEnv.getMailSubjectTestPrefix());
        assertEquals("no-reply******@****.***", specialMailEnv.getMailReturnPath());
    }