}

  @Test
  @TestMetadata("multiSmartcastAsReceiver_unstable.kt")
  public void testMultiSmartcastAsReceiver_unstable() {
    runTest("analysis/analysis-api/testData/components/smartCastProvider/smartCastInfo/multiSmartcastAsReceiver_unstable.kt");
  }

  @Test
  @TestMetadata("multiSmartcast_stable.kt")
  public void testMultiSmartcast_stable() {

        val bindingContext = analysisContext.analyze(expression)
        val stableSmartCasts = bindingContext[BindingContext.SMARTCAST, expression]
        val unstableSmartCasts = bindingContext[BindingContext.UNSTABLE_SMARTCAST, expression]

        return when {
            stableSmartCasts != null -> {
                val type = stableSmartCasts.getKtType() ?: return null
                KtSmartCastInfo(type, true, token)

    ```

    </div>

!!! warning
    Remember to remove the `--reload` option if you were using it.

    The `--reload` option consumes much more resources, is more unstable, etc.

    It helps a lot during **development**, but you **shouldn't** use it in **production**.

## Hypercorn with Trio

 *  Fix in okhttp-coroutines: Publish a valid artifact. The coroutines JAR file in 5.0.0-alpha.13
    was corrupt and should not be used.


## Version 5.0.0-alpha.13

_2024-04-16_

 *  Breaking: Tag unstable new APIs as `@ExperimentalOkHttpApi`. We intend to release OkHttp 5.0
    without stabilizing these new APIs first.

    Do not use these experimental APIs in modules that may be executed using a version of OkHttp

    ```

    </div>

!!! warning
     N'oubliez pas de supprimer l'option `--reload` si vous l'utilisiez.

     L'option `--reload` consomme beaucoup plus de ressources, est plus instable, etc.

     Cela aide beaucoup pendant le **développement**, mais vous **ne devriez pas** l'utiliser en **production**.

## Hypercorn avec Trio

Starlette et **FastAPI** sont basés sur

      ASSERT_EQ(errors::OK, status_.code()) << status_.message();
      immediate_execution_ctx_.reset(ctx_raw);
    }

    // Computing numerical gradients with TensorFloat-32 is numerically
    // unstable. Some forward pass tests also fail with TensorFloat-32 due to
    // low tolerances
    enable_tensor_float_32_execution(false);
  }

  AbstractContextPtr immediate_execution_ctx_;
  GradientRegistry registry_;

      ASSERT_EQ(errors::OK, status_.code()) << status_.message();
      immediate_execution_ctx_.reset(ctx_raw);
    }

    // Computing numerical gradients with TensorFloat-32 is numerically
    // unstable. Some forward pass tests also fail with TensorFloat-32 due to
    // low tolerances
    enable_tensor_float_32_execution(false);
  }

  AbstractContextPtr immediate_execution_ctx_;
  GradientRegistry registry_;

	// If the first disconnected before the last connected, we likely had a network issue.
	if delta <= 0 {
		rx = 0
		errStr = "detected network disconnections, possibly an unstable network"
	}

	globalSiteNetPerfRX.Reset()
	return madmin.SiteNetPerfNodeResult{
		Endpoint:        "",
		TX:              r.n,
		TXTotalDuration: duration,
		RX:              uint64(rx),

      ASSERT_EQ(errors::OK, s.code()) << s.message();
      ctx_.reset(ctx_raw);
    }

    // Computing numerical gradients with TensorFloat-32 is numerically
    // unstable. Some forward pass tests also fail with TensorFloat-32 due to
    // low tolerances
    enable_tensor_float_32_execution(false);
  }

  AbstractContextPtr ctx_;

 public:

      ASSERT_EQ(errors::OK, status_.code()) << status_.message();
      immediate_execution_ctx_.reset(ctx_raw);
    }

    // Computing numerical gradients with TensorFloat-32 is numerically
    // unstable. Some forward pass tests also fail with TensorFloat-32 due to
    // low tolerances
    enable_tensor_float_32_execution(false);
  }

  AbstractContextPtr immediate_execution_ctx_;
  GradientRegistry registry_;