</p>

<p>
The memory model describes the requirements on program executions,
which are made up of goroutine executions,
which in turn are made up of memory operations.
</p>

<p>
A <i>memory operation</i> is modeled by four details:
</p>
<ul>
	<li>its kind, indicating whether it is an ordinary data read, an ordinary data write,
    or a <i>synchronizing operation</i> such as an atomic data access,

The `app_name` would be `"ChimichangApp"`.

And the `items_per_user` would keep its default value of `50`.

## Settings in another module

You could put those settings in another module file as you saw in [Bigger Applications - Multiple Files](../tutorial/bigger-applications.md){.internal-link target=_blank}.

For example, you could have a file `config.py` with:

```Python

running the `tf.train.Server`.

## Untrusted inputs during training and prediction

TensorFlow supports a wide range of input data formats. For example it can
process images, audio, videos, and text. There are several modules specialized
in taking those formats, modifying them, and/or converting them to intermediate
formats that can be processed by TensorFlow.

These modifications and conversions are handled by a variety of libraries that

# a service account that has the right permissions to be able to do so.
set +x
if [[ -n "${GOOGLE_APPLICATION_CREDENTIALS:-}" ]]; then
  # Python 3.12 removed the module `imp` which is needed by gcloud CLI so we set
  # `CLOUDSDK_PYTHON` to Python 3.11 which is the system Python on TFCI Mac
  # VMs.
  export CLOUDSDK_PYTHON=$(which python3.11)

Let's imagine that loading the model can **take quite some time**, because it has to read a lot of **data from disk**. So you don't want to do it for every request.

///

/// warning

If you do this, you have to make sure each one of your *path operation functions* has a unique name.

Even if they are in different modules (Python files).

///

## Exclude from OpenAPI

///

/// warning | "Attention"

Pour faire cela, vous devez vous assurer que chacun de vos *chemin* ait un nom unique.

Même s'ils se trouvent dans des modules différents (fichiers Python).

///

## Exclusion d'OpenAPI

///

/// warning | "Achtung"

Wenn Sie dies tun, müssen Sie sicherstellen, dass jede Ihrer *Pfadoperation-Funktionen* einen eindeutigen Namen hat.

Auch wenn diese sich in unterschiedlichen Modulen (Python-Dateien) befinden.

///

## Von OpenAPI ausschließen

<img src="/img/tutorial/security/image12.png">

## Check the username

Here's a more complete example.

Use a dependency to check if the username and password are correct.

For this, use the Python standard module <a href="https://docs.python.org/3/library/secrets.html" class="external-link" target="_blank">`secrets`</a> to check the username and password.

	logger.LogIf(ctx, "tier", err, errKind...)
}

func kmsLogIf(ctx context.Context, err error, errKind ...interface{}) {
	logger.LogIf(ctx, "kms", err, errKind...)
}

// KMSLogger permits access to kms module specific logging
type KMSLogger struct{}

// LogOnceIf is the implementation of LogOnceIf, accessible using the Logger interface