import org.testcontainers.containers.MockServerContainer
import org.testcontainers.junit.jupiter.Container
import org.testcontainers.junit.jupiter.Testcontainers
import org.testcontainers.utility.DockerImageName

@Testcontainers
class BasicMockServerTest {
  @Container
  val mockServer: MockServerContainer = MockServerContainer(MOCKSERVER_IMAGE)

  val client =
    OkHttpClient.Builder()

import org.testcontainers.containers.MockServerContainer
import org.testcontainers.containers.Network
import org.testcontainers.junit.jupiter.Container
import org.testcontainers.junit.jupiter.Testcontainers
import org.testcontainers.utility.DockerImageName

@Testcontainers
class SocksProxyTest {
  val network: Network = Network.newNetwork()

  @Container
  val mockServer: MockServerContainer =
    MockServerContainer(MOCKSERVER_IMAGE)

    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.

=== "Python 3.10+"

### Other utility functions

Any other utility function that you call directly can be created with normal `def` or `async def` and FastAPI won't affect the way you call it.

This is in contrast to the functions that FastAPI calls for you: *path operation functions* and dependencies.

Because it's a Python exception, you don't `return` it, you `raise` it.

!!! tip
    You could adopt ideas from the section about SQLAlchemy ORM ([SQL (Relational) Databases](../tutorial/sql-databases.md){.internal-link target=_blank}), like using utility functions to perform operations in the database, independent of your **FastAPI** code.

under the `kubelet` process. On systems with `journalctl` the following is an example command line
to view the last 1000 `kubelet` logs via the `less` utility to allow for `vi`-style searching:

```console
$ journalctl -t kubelet -n 1000 | less
```

#### GKE via Stackdriver Log Viewer

Import `models` (the SQLAlchemy models) and `schemas` (the Pydantic *models* / schemas).

Create utility functions to:

* Read a single user by ID and by email.
* Read multiple users.
* Read multiple items.

```Python hl_lines="1  3  6-7  10-11  14-15  27-28"
{!../../../docs_src/sql_databases/sql_app/crud.py!}

That means that any value read in Python from an environment variable will be a `str`, and any conversion to a different type or validation has to be done in code.

## Pydantic `Settings`

Fortunately, Pydantic provides a great utility to handle these settings coming from environment variables with <a href="https://docs.pydantic.dev/latest/usage/pydantic_settings/" class="external-link" target="_blank">Pydantic: Settings management</a>.

 *   Add `show_trainable` option to `tf.keras.utils.model_to_dot` to display layer trainable status in model plots.
 *   Add ability to save a `tf.keras.utils.FeatureSpace` object, via `feature_space.save("myfeaturespace.keras")`, and reload it via `feature_space = tf.keras.models.load_model("myfeaturespace.keras")`.
*   Added utility `tf.keras.utils.to_ordinal` to convert class vector to ordinal regression / classification matrix.