* if the {@code RateLimiter} is left <i>unused</i> for a duration of {@code warmupPeriod}, it
   * will gradually return to its "cold" state, i.e. it will go through the same warming up process
   * as when it was first created.
   *
   * <p>The returned {@code RateLimiter} is intended for cases where the resource that actually

## Pydantic `Settings` { #pydantic-settings }

Fortunately, Pydantic provides a great utility to handle these settings coming from environment variables with [Pydantic: Settings management](https://docs.pydantic.dev/latest/concepts/pydantic_settings/).

### Install `pydantic-settings` { #install-pydantic-settings }

import javax.net.ssl.X509TrustManager
import okhttp3.internal.tls.CertificateChainCleaner

/**
 * Observes, modifies, and potentially short-circuits requests going out and the corresponding
 * responses coming back in. Typically interceptors add, remove, or transform headers on the request
 * or response.
 *
 * Implementations of this interface throw [IOException] to signal connectivity failures. This

            startAnotherThread()
          }

          return readyTask
        }

        // Notify the coordinator of a task that's coming up soon.
        coordinatorWaiting -> {
          if (minDelayNanos < coordinatorWakeUpAt - now) {
            backend.coordinatorNotify(this@TaskRunner)
          }
          return null
        }

* **Define requirements**: from request path parameters, query parameters, headers, bodies, dependencies, etc.
* **Convert data**: from the request to the required type.
* **Validate data**: coming from each request:
    * Generating **automatic errors** returned to the client when the data is invalid.
* **Document** the API using OpenAPI:
    * which is then used by the automatic interactive documentation user interfaces.

* Validation of data:
    * Automatic and clear errors when the data is invalid.
    * Validation even for deeply nested JSON objects.
* <dfn title="also known as: serialization, parsing, marshalling">Conversion</dfn> of input data: coming from the network to Python data and types. Reading from:
    * JSON.
    * Path parameters.
    * Query parameters.
    * Cookies.
    * Headers.
    * Forms.
    * Files.

You will get completion in code you might even consider impossible before. As for example, the `price` key inside a JSON body (that could have been nested) that comes from a request.

No more typing the wrong key names, coming back and forth between docs, or scrolling up and down to find if you finally used `username` or `user_name`.

### Short { #short }

          state = STATE_READ_RESPONSE_HEADERS
          responseBuilder
        }

        statusLine.code in (102 until 200) -> {
          // Processing and Early Hints will mean a second headers are coming.
          // Treat others the same for now
          state = STATE_READ_RESPONSE_HEADERS
          responseBuilder
        }

        else -> {
          state = STATE_OPEN_RESPONSE_BODY

* Validation of data:
    * Automatic and clear errors when the data is invalid.
    * Validation even for deeply nested JSON objects.
* <dfn title="also known as: serialization, parsing, marshalling">Conversion</dfn> of input data: coming from the network to Python data and types. Reading from:
    * JSON.
    * Path parameters.
    * Query parameters.
    * Cookies.
    * Headers.
    * Forms.
    * Files.

And be compatible with all of them at the same time.

///

Create a utility function to hash a password coming from the user.

And another utility to verify if a received password matches the hash stored.

And another one to authenticate and return a user.