assertEquals(Integer.class, f.resolveType(Function.class.getTypeParameters()[1]).getType());
    assertEquals(Long.class, f.resolveType(Iterable.class.getTypeParameters()[0]).getType());
  }

  public <E extends Comparable<Iterable<String>> & Iterable<Integer>>
      void testResolveType_fromTypeVariable_onlyDirectBoundsAreUsed() {
    TypeToken<?> e = TypeToken.of(new TypeCapture<E>() {}.capture());

		// gets rejected even when we skip certificate verification. This helps
		// clients detect malformed certificates during testing instead of e.g.
		// a self-signed certificate that works while a comparable certificate
		// issued by a trusted CA fails due to the MinIO server being less strict
		// w.r.t. key usage verification.
		//
		// Basically, MinIO is more consistent (from a client perspective) when

    assertThat(map).hasSize(100_000);
    assertThat(map).containsExactlyEntriesIn(expected).inOrder();
  }

  private static class ClassWithTerribleHashCode implements Comparable<ClassWithTerribleHashCode> {
    private final int value;

    ClassWithTerribleHashCode(int value) {
      this.value = value;
    }

    @Override

    assertEquals(Integer.class, f.resolveType(Function.class.getTypeParameters()[1]).getType());
    assertEquals(Long.class, f.resolveType(Iterable.class.getTypeParameters()[0]).getType());
  }

  public <E extends Comparable<Iterable<String>> & Iterable<Integer>>
      void testResolveType_fromTypeVariable_onlyDirectBoundsAreUsed() {
    TypeToken<?> e = TypeToken.of(new TypeCapture<E>() {}.capture());

Pydantic est une bibliothèque permettant de définir la validation, la sérialisation et la documentation des données (à l'aide de JSON Schema) en se basant sur les Python type hints.

Cela le rend extrêmement intuitif.

Il est comparable à Marshmallow. Bien qu'il soit plus rapide que Marshmallow dans les benchmarks. Et comme il est
basé sur les mêmes type hints Python, le support de l'éditeur est grand.

/// check | **FastAPI** l'utilise pour

HSPLkotlinx/coroutines/internal/Symbol;-><init>(Ljava/lang/String;)V
HSPLkotlinx/coroutines/internal/SystemPropsKt;->compareValues(Ljava/lang/Comparable;Ljava/lang/Comparable;)I
HSPLkotlinx/coroutines/internal/SystemPropsKt;->systemProp$default(Ljava/lang/String;IIIILjava/lang/Object;)I
HSPLkotlinx/coroutines/internal/SystemPropsKt;->systemProp$default(Ljava/lang/String;JJJILjava/lang/Object;)J

* Backend Python <a href="https://github.com/fastapi/fastapi" class="external-link" target="_blank">**FastAPI**</a> :
    * **Rapide** : Très hautes performances, comparables à **NodeJS** ou **Go** (grâce à Starlette et Pydantic).

# JSON Compatible Encoder { #json-compatible-encoder }

There are some cases where you might need to convert a data type (like a Pydantic model) to something compatible with JSON (like a `dict`, `list`, etc).

For example, if you need to store it in a database.

For that, **FastAPI** provides a `jsonable_encoder()` function.

## Using the `jsonable_encoder` { #using-the-jsonable-encoder }

## Convert any Data to JSON-compatible { #convert-any-data-to-json-compatible }

To convert any data to JSON-compatible, read the docs for [Tutorial - JSON Compatible Encoder](../tutorial/encoder.md){.internal-link target=_blank}.

## OpenAPI Metadata - Docs { #openapi-metadata-docs }

        getMockRequest().addHeader("user-agent", "Mozilla/5.0 (compatible; MSIE 9.0; Windows NT 6.0; Trident/5.0)");
        assertEquals(UserAgentType.IE, userAgentHelper.getUserAgentType());
    }

    public void test_getUserAgentType_IE10() {
        getMockRequest().addHeader("user-agent", "Mozilla/5.0 (compatible; MSIE 10.0; Windows NT 6.2; Trident/6.0)");