enabled = enableTriggers
        }
    } else if (stage.trigger != Trigger.never) {
        triggers.schedule {
            if (stage.trigger == Trigger.weekly) {
                schedulingPolicy = weekly {
                    dayOfWeek = ScheduleTrigger.DAY.Saturday
                    hour = 1
                }
            } else {
                schedulingPolicy = daily {

                        schedulingPolicy = daily {
                            this.hour = triggerHour
                        }
                    } else {
                        schedulingPolicy = weekly {
                            this.dayOfWeek = ScheduleTrigger.DAY.Saturday
                            this.hour = triggerHour
                        }
                    }
                    triggerBuild = always()

                PerformanceTestCoverage(5, PerformanceTestType.per_week, Os.LINUX, numberOfBuckets = 20, oldUuid = "PerformanceTestExperimentLinux"),
                PerformanceTestCoverage(8, PerformanceTestType.per_week, Os.WINDOWS, numberOfBuckets = 5),
                PerformanceTestCoverage(9, PerformanceTestType.per_week, Os.MACOS, numberOfBuckets = 5)
            )
        ),
        Stage(

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

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

## Deeply nested models

You can define arbitrarily deeply nested models:

=== "Python 3.10+"

    ```Python hl_lines="7  12  18  21  25"
    {!> ../../../docs_src/body_nested_models/tutorial007_py310.py!}
    ```

=== "Python 3.9+"

* 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.

-   You make the change and submit it for the review again.
-   This cycle repeats itself until the PR gets approved.
-   Note: As a friendly reminder, we may reach out to you if the PR is awaiting
    your response for more than 2 weeks.

**4. Approved**

-   Once the PR is approved, it gets `kokoro:force-run` label applied and it
    initiates CI/CD tests.
-   We can't move forward if these tests fail.

> What’s the history of this project? It seems to have come from nowhere to awesome in a few weeks [...]

Here's a little bit of that history.

## Alternatives

    * 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.

[TF 2.0 Testing User Group](https://groups.google.com/a/tensorflow.org/forum/?utm_medium=email&utm_source=footer#!forum/testing).
We have a support mailing list as well as weekly testing meetings, and would
love to hear your migration feedback and questions.

## Bug Fixes and Other Changes

*   `tf.contrib`:

                },
                "x-aperture-labs-portal": "blue"
            }
        }
    }
}
```

### 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.