}
                    }
                }
            }
        }
    }
}
```

## Types de médias supplémentaires pour la réponse principale

Vous pouvez utiliser ce même paramètre `responses` pour ajouter différents types de médias pour la même réponse principale.

name: 'Auto Assign PR to Author'
on:
  pull_request:
    types: [opened]

permissions: {}

jobs:
  add-reviews:
    permissions:
      contents: read  # for kentaro-m/auto-assign-action to fetch config file
      pull-requests: write  # for kentaro-m/auto-assign-action to assign PR reviewers
    runs-on: ubuntu-latest
    steps:

name: Check pulls metadata

on:
  pull_request_target:
    types: [ closed ]

permissions: {}

jobs:
  check_pull_metadata:
    permissions:
      issues: write
      pull-requests: write
    runs-on: ubuntu-latest
    steps:
      # Check that PRs have proper metadata: labels and milestone
      # https://github.com/gradle/issue-management-action/blob/main/src/pull-metadata.ts

![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.

name: Add to Project

on:
  pull_request_target:
  issues:
    types:
      - opened
      - reopened

jobs:
  add-to-project:
    name: Add to project
    runs-on: ubuntu-latest
    steps:
      - uses: actions/add-to-project@v1.0.2
        with:
          project-url: https://github.com/orgs/fastapi/projects/2

#include <memory>
#include <string>

#include "tensorflow/c/tf_status_helper.h"
#include "tensorflow/core/framework/tensor_shape.h"
#include "tensorflow/core/platform/status.h"
#include "tensorflow/core/platform/types.h"
#include "tensorflow/core/util/tensor_bundle/tensor_bundle.h"
#include "tensorflow/core/util/tensor_slice_reader.h"

namespace tensorflow {
namespace checkpoint {

class TensorSliceReader;

#define TENSORFLOW_C_EAGER_ABSTRACT_TENSOR_HANDLE_H_

#include <memory>

#include "tensorflow/core/framework/full_type.pb.h"
#include "tensorflow/core/framework/tensor_shape.h"
#include "tensorflow/core/framework/types.pb.h"
#include "tensorflow/core/platform/refcount.h"
#include "tensorflow/core/platform/status.h"

namespace tensorflow {

// Abstract interface to a Tensor handle in either tracing or immediate
// execution mode.

 * A {@code PathType} can identify the Java class-path, the Java module-path,
 * or another kind of path for another programming language for example.
 * Path types are often exclusive. For example, a dependency should not be
 * both on the Java class-path and on the Java module-path.
 *
 * @see org.apache.maven.api.services.DependencyResolverResult#getDispatchedPaths()
 *
 * @since 4.0.0

    #
    ; variousMap = map:{
        # o objectTypeTargetList: (NotRequired - Default list:{TABLE;VIEW})
        #  If you want to include other object types in generating target,
        #  you should specify the list of included object types as adding.
        #    e.g. Synonym of Oracle --> list:{TABLE ; VIEW ; SYNONYM}
        #  This is only for the main schema. Additional schemas are unconcerned.

            counter++
        }

        //updating/modifying the metadata and making sure every type reference across the metadata is fully qualified
        //so, the superClassName, interfaces and types needed by declared properties and declared methods will have fully qualified name
        TypeNameResolver resolver = new TypeNameResolver(repository)
        repository.each { name, metaData ->