// Required.
  optional MatchResources matchConstraints = 2;

  // Validations contain CEL expressions which is used to apply the validation.
  // Validations and AuditAnnotations may not both be empty; a minimum of one Validations or AuditAnnotations is
  // required.
  // +listType=atomic
  // +optional
  repeated Validation validations = 3;

  // Required.
  optional MatchResources matchConstraints = 2;

  // Validations contain CEL expressions which is used to apply the validation.
  // Validations and AuditAnnotations may not both be empty; a minimum of one Validations or AuditAnnotations is
  // required.
  // +listType=atomic
  // +optional
  repeated Validation validations = 3;

    * If you didn't use FastAPI and used Starlette directly (or another tool, like Sanic, Flask, Responder, etc) you would have to implement all the data validation and serialization yourself. So, your final application would still have...

  // Required.
  optional MatchResources matchConstraints = 2;

  // Validations contain CEL expressions which is used to apply the validation.
  // Validations and AuditAnnotations may not both be empty; a minimum of one Validations or AuditAnnotations is
  // required.
  // +listType=atomic
  // +optional
  repeated Validation validations = 3;

    * Si on n'utilisait pas FastAPI mais directement Starlette (ou un outil équivalent comme Sanic, Flask, Responder, etc) il faudrait implémenter la validation des données et la sérialisation par nous-même. Le résultat serait donc le même dans...

  // Required.
  optional MatchResources matchConstraints = 2;

  // Validations contain CEL expressions which is used to apply the validation.
  // Validations and AuditAnnotations may not both be empty; a minimum of one Validations or AuditAnnotations is
  // required.
  // +listType=atomic
  // +optional
  repeated Validation validations = 3;

Another big feature needed by APIs is data validation, making sure that the data is valid, given certain parameters. For example, that some field is an `int`, and not some random string. This is especially useful for incoming data.

Without a data validation system, you would have to do all the checks by hand, in code.

La validation des données est une autre fonctionnalité importante dont ont besoin les API. Elle permet de s'assurer
que les données sont valides, compte tenu de certains paramètres. Par exemple, qu'un champ est un `int`, et non un
string.
Ceci est particulièrement utile pour les données entrantes.

// RUN: tf-opt -verify-input-dialect-to-executor-pass  -split-input-file -verify-diagnostics %s | FileCheck %s
// Tests the VerifyClusteringPass Pass, ensures that an error is thrown when validation fails.

// -----

// CHECK-LABEL: func @testNoClusterFuncOpPasses
func.func @testNoClusterFuncOpPasses(%arg0: tensor<4x?x!tf_type.stringref>) -> tensor<4x2x!tf_type.string> {
  %0 = "tf.Identity"(%arg0) : (tensor<4x?x!tf_type.stringref>) -> tensor<4x2x!tf_type.string>

        - ""
        - -b
        - '*'
        - -d
        - 15090,15021,15020
        - --log_output_level=default:info
        - --run-validation
        - --skip-rule-apply
        image: gcr.io/istio-testing/proxyv2:latest
        name: istio-validation
        resources:
          limits:
            cpu: "2"
            memory: 1Gi
          requests:
            cpu: 100m