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

    So, you might still need to use Pydantic models.

    But if you have a bunch of dataclasses laying around, this is a nice trick to use them to power a web API using FastAPI. 🤓

## Dataclasses in `response_model`

You can also use `dataclasses` in the `response_model` parameter:

This has two implementations:
* The Credentials controller is responsible for reading TLS certificates, stored as Secrets.
* The Kubernetes Service Discovery controller is a bit of a monolith, and spins off a bunch of other sub-controllers in addition to the core service discovery controller.

Because of the monolithic complexity it helps to see this magnified a bit:

```mermaid
graph BT
    mcsc("Multicluster Secret")

    timeToFirstByte()
  }

  /**
   * Test to confirm that events are reported at the time they occur and no earlier and no later.
   * This inserts a bunch of synthetic 250 ms delays into both client and server and confirms that
   * the same delays make it back into the events.
   *

config_setting(
    name = "no_xla_deps_in_cuda",
    define_values = {"no_xla_deps_in_cuda": "true"},
    visibility = ["//visibility:public"],
)

# Crosses between framework_shared_object and a bunch of other configurations
# due to limitations in nested select() statements.
config_setting(
    name = "framework_shared_object",
    define_values = {"framework_shared_object": "true"},