from .utils import needs_py310


@needs_py310
def test_typing():
    types = {
        list[int]: [1, 2, 3],
        dict[str, list[int]]: {"a": [1, 2, 3], "b": [4, 5, 6]},
        set[int]: [1, 2, 3],  # `set` is converted to `list`
        tuple[int, ...]: [1, 2, 3],  # `tuple` is converted to `list`
    }
    for test_type, expect in types.items():
        app = FastAPI()

        @app.post("/", response_model=test_type)

 * limitations under the License.
 */
package okhttp3.survey.ssllabs

import com.squareup.moshi.Moshi
import okhttp3.Call
import okhttp3.OkHttpClient
import okhttp3.survey.types.Client
import okhttp3.survey.types.SuiteId
import retrofit2.Retrofit
import retrofit2.converter.moshi.MoshiConverterFactory

class SslLabsClient(
  callFactory: Call.Factory,
) {
  private val moshi = Moshi.Builder().build()

 ({{{./apidocs/org/apache/maven/repository/internal/MavenRepositorySystemUtils.html}javadoc}},
 {{{./xref/org/apache/maven/repository/internal/MavenRepositorySystemUtils.html}source}}).

};
#endif  // !defined(IS_MOBILE_PLATFORM) && !defined(IS_SLIM_BUILD)

namespace tensorflow {

// Set the shapes and types of the output's handle.
//
// The lengths of the arrays pointed to by `shapes`, `ranks`, and `types` must
// all be equal to `num_shapes_and_types`. If `ranks[i] != -1`, (i.e., if the
// rank is known), then it must be equal to the length of `shapes[i]`; if

pkg go/ast, func Preorder(Node) iter.Seq[Node] #66339
pkg go/types, method (*Alias) Origin() *Alias #67143
pkg go/types, method (*Alias) Rhs() Type #66559
pkg go/types, method (*Alias) SetTypeParams([]*TypeParam) #67143
pkg go/types, method (*Alias) TypeArgs() *TypeList #67143
pkg go/types, method (*Alias) TypeParams() *TypeParamList #67143
pkg go/types, method (*Func) Signature() *Signature #65772

#ifndef TENSORFLOW_C_EAGER_IMMEDIATE_EXECUTION_OPERATION_H_
#define TENSORFLOW_C_EAGER_IMMEDIATE_EXECUTION_OPERATION_H_

#include <memory>

#include "absl/types/optional.h"
#include "absl/types/span.h"
#include "tensorflow/c/eager/abstract_operation.h"
#include "tensorflow/c/eager/immediate_execution_tensor_handle.h"
#include "tensorflow/c/tensor_interface.h"

    /**
     * Gets the class loader from which to load any types referenced by the configuration. If unset, the class loader of
     * the bean class will be used.
     *
     * @return The class loader to load referenced types from or {@code null} if unset.
     */
    ClassLoader getClassLoader();

    /**
     * Sets the class loader from which to load any types referenced by the configuration. If unset, the class loader of

## Support de l'éditeur

Dans votre éditeur, vous aurez des annotations de types et de l'auto-complétion partout dans votre fonction (ce qui n'aurait pas été le cas si vous aviez utilisé un classique `dict` plutôt qu'un modèle Pydantic) :

<img src="/img/tutorial/body/image03.png">

Et vous obtenez aussi de la vérification d'erreur pour les opérations incorrectes de types :

<img src="/img/tutorial/body/image04.png">

  }

  /**
   * Returns a new bimap with the same mappings as the specified map. If the specified map is an
   * {@code EnumBiMap}, the new bimap has the same types as the provided map. Otherwise, the
   * specified map must contain at least one mapping, in order to determine the key and value types.
   *
   * @param map the map whose mappings are to be placed in this map

  }

  /**
   * Returns a new bimap with the same mappings as the specified map. If the specified map is an
   * {@code EnumBiMap}, the new bimap has the same types as the provided map. Otherwise, the
   * specified map must contain at least one mapping, in order to determine the key and value types.
   *
   * @param map the map whose mappings are to be placed in this map