All the dependencies we have seen are a fixed function or class.

But there could be cases where you want to be able to set parameters on the dependency, without having to declare many different functions or classes.

Let's imagine that we want to have a dependency that checks if the query parameter `q` contains some fixed content.

But we want to be able to parameterize that fixed content.

## A "callable" instance

        .url(server.url("/"))
        .post(data.toRequestBody("text/plain".toMediaType()))
        .build()
    val call = client.newCall(request1)
    assertFailsWith<SocketTimeoutException> {
      call.execute() // we want this call to throw a SocketTimeoutException
    }
  }

  @Test
  fun chainCanCancelCall() {
    val callRef = AtomicReference<Call>()
    val interceptor =
      Interceptor { chain: Interceptor.Chain ->

#### CRD Watcher

  // (whether or not they contain an Ingress section) are assumed to affect Ingress.
  // If you want to write an egress-only policy, you must explicitly specify policyTypes [ "Egress" ].
  // Likewise, if you want to write a policy that specifies that no egress is allowed,
  // you must specify a policyTypes value that include "Egress" (since such a policy would not include

    return delegate!!.useClientMode
  }

  override fun setNeedClientAuth(need: Boolean) {
    delegate!!.needClientAuth = need
  }

  override fun setWantClientAuth(want: Boolean) {
    delegate!!.wantClientAuth = want
  }

  override fun getNeedClientAuth(): Boolean {
    return delegate!!.needClientAuth
  }

  override fun getWantClientAuth(): Boolean {
    return delegate!!.wantClientAuth

opt_einsum == 3.3.0
astunparse == 1.6.3
dill == 0.3.7
astor == 0.7.1
typing_extensions == 4.8.0
gast == 0.4.0
termcolor == 2.3.0
wrapt == 1.16.0
tblib == 2.0.0

# Install tensorboard, and keras
# Note that here we want the latest version that matches TF major.minor version
# Note that we must use nightly here as these are used in nightly jobs
# For release jobs, we will pin these on the release branch
keras-nightly ~= 3.0.0.dev

# Corps de la requête

Quand vous avez besoin d'envoyer de la donnée depuis un client (comme un navigateur) vers votre API, vous l'envoyez en tant que **corps de requête**.

Le corps d'une **requête** est de la donnée envoyée par le client à votre API. Le corps d'une **réponse** est la donnée envoyée par votre API au client.

Votre API aura presque toujours à envoyer un corps de **réponse**. Mais un client n'a pas toujours à envoyer un corps de **requête**.

Follow the steps from the Veeam documentation for adding MinIO as an object store - <https://helpcenter.veeam.com/docs/backup/vsphere/adding_s3c_object_storage.html?ver=100>

For Veeam Backup with Immutability, choose the amount of days you want to make backups immutable for

![Choose Immutability Days for Object Store](https://raw.githubusercontent.com/minio/minio/master/docs/integrations/veeam/screenshots/object_store_immutable_days.png)

# for the actions to run (there is an implicit AND relation between
# the conditions on each line).  Each condition is specified as a Jinja expression.  You
# can also use the constant `true` if you want to always run the actions.

# Each automation is independent of the others.  Every time one of the `on` conditions match for
# this PR, this automations will have its `if` checked to run.  In a way, the `on` conditions

![ReDoc](https://fastapi.tiangolo.com/img/index/index-06-redoc-02.png)

### En résumé

En résumé, vous déclarez **une fois** les types de paramètres, <abbr title="en anglais : body">le corps</abbr>  de la requête, etc. en tant que paramètres de fonction.

Vous faites cela avec les types Python standard modernes.

Vous n'avez pas à apprendre une nouvelle syntaxe, les méthodes ou les classes d'une bibliothèque spécifique, etc.