generateTriggers: Boolean = true,
) : BaseGradleBuildType(init = {
        id(stageTriggerId(model, stage, os))
        uuid = stageTriggerUuid(model, stage, os)
        name = stage.stageName.stageName + " (Trigger)" + (os?.asName()?.toCapitalized()?.let { "($it)" } ?: "")
        type = Type.COMPOSITE

        applyDefaultSettings()

        features {
            publishBuildStatusToGithub(model)
        }

And of course, it supports the same:

* data validation
* data serialization
* data documentation, etc.

This works the same way as with Pydantic models. And it is actually achieved in the same way underneath, using Pydantic.

/// info

Keep in mind that dataclasses can't do everything Pydantic models can do.

# GraphQL { #graphql }

As **FastAPI** is based on the **ASGI** standard, it's very easy to integrate any **GraphQL** library also compatible with ASGI.

You can combine normal FastAPI *path operations* with GraphQL on the same application.

/// tip

**GraphQL** solves some very specific use cases.

It has **advantages** and **disadvantages** when compared to common **web APIs**.

                should have all the fields, including the ones that have the same value
                as the default. This is different from `response_model_exclude_unset`
                in that if the fields are set but contain the same default values,
                they will be excluded from the response.

}


def get_openapi_security_definitions(
    flat_dependant: Dependant,
) -> tuple[dict[str, Any], list[dict[str, Any]]]:
    security_definitions = {}
    # Use a dict to merge scopes for same security scheme
    operation_security_dict: dict[str, list[str]] = {}
    for security_dependency in flat_dependant._security_dependencies:
        security_definition = jsonable_encoder(

# restriction that load() statements need to be at the top of .bzl files.
# E.g. we can not retrieve a new repository with http_archive and then load()
# a macro from that repository in the same file.
load("@//tensorflow:workspace3.bzl", "tf_workspace3")

tf_workspace3()

load("@rules_shell//shell:repositories.bzl", "rules_shell_dependencies", "rules_shell_toolchains")

rules_shell_dependencies()

                    },
                }
            },
            "/response_model-no_annotation-return_same_model": {
                "get": {
                    "summary": "Response Model No Annotation Return Same Model",
                    "operationId": "response_model_no_annotation_return_same_model_response_model_no_annotation_return_same_model_get",
                    "responses": {
                        "200": {

import jetbrains.buildServer.configs.kotlin.triggers.schedule

const val NIGHTLY_SNAPSHOT_BUILD_ID = "Promotion_Nightly"

/**
 * 0~23.
 * To avoid nightly promotion jobs running at the same time,
 * we run each branch on different hours.
 * master - 0:00
 * release - 1:00
 * release6x - 2:00
 * release7x - 3:00
 * ...
 * releaseNx - (N-4):00
 */

# See https://github.com/protocolbuffers/upb/blob/9effcbcb27f0a665f9f345030188c0b291e32482/upb/upb.c#L183.
common:rbe_linux_cpu --copt=-Wno-gnu-offsetof-extensions
# Python config is the same across all containers because the binary is the same
common:rbe_linux_cpu --python_path="/usr/bin/python3"
# These you may need to change for your own GCP project.

        ```
        »»»


### Special blocks

There are special blocks of notes, tips and others that look like:

    «««
    /// note
    »»»

To translate it, keep the same line and add the translation after a vertical bar.

For example, if you were translating to Spanish, you would write:

    «««
    /// note | Nota
    »»»

Some examples in Spanish:

    Source: