sequence {
            analysisSession.useSitePackageProvider.getSubPackageFqNames(fqName, analysisSession.targetPlatform, nameFilter).forEach {
                yield(analysisSession.firSymbolBuilder.createPackageSymbol(fqName.child(it)))
            }
        }
    }

  %tmp2 = "tfl.custom_tf"(%tmp1, %tmp1) ({
    ^bb0(%arg0: tensor<1xf32>, %arg1: tensor<1xf32>) :
      %wrap_result = "tf.AddV2"(%arg0, %arg1) { device = "" } : (tensor<1xf32>, tensor<1xf32>) -> tensor<1xf32>
      "tfl.yield"(%wrap_result): (tensor<1xf32>)->()
      }): (tensor<1xf32>, tensor<1xf32>) -> (tensor<1xf32>)
  %tmp3 = "tfl.add"(%const, %tmp2) { fused_activation_function = "NONE" } : (tensor<1xf32>, tensor<1xf32>) -> (tensor<1xf32>)

module {
func.func @simpleWhile(%arg0: tensor<i32>) -> tensor<i32> {
  %0 = "tfl.while"(%arg0) ({
  ^bb0(%block: tensor<i32>):
    "tfl.yield"(%block) : (tensor<i32>) -> ()
  },{
  ^bb0(%block: tensor<i32>):
    "tfl.yield"(%block) : (tensor<i32>) -> ()
  }) {tac.device = "CPU", fused_activation_function = "RELU6", tac.inference_type = "FLOAT"} : (tensor<i32>) -> tensor<i32>
  func.return %0 : tensor<i32>
}

// N is used to define functions based on cSHAKE, it can be empty when plain cSHAKE is
// desired. S is a customization byte string used for domain separation - two cSHAKE
// computations on same input with different S yield unrelated outputs.
// When N and S are both empty, this is equivalent to NewShake128.
func NewCShake128(N, S []byte) ShakeHash {
	if len(N) == 0 && len(S) == 0 {
		return NewShake128()
	}

    %1 = func.call @WhileOp_cond(%arg2, %arg3) : (tensor<*xi32>, tensor<*xf32>) -> tensor<i1>
    "tfl.yield"(%1) : (tensor<i1>) -> ()
  },  {
  ^bb0(%arg2: tensor<*xi32>, %arg3: tensor<*xf32>):
    %1:2 = func.call @WhileOp_body(%arg2, %arg3) : (tensor<*xi32>, tensor<*xf32>) -> (tensor<*xi32>, tensor<*xf32>)
    "tfl.yield"(%1#0, %1#1) : (tensor<*xi32>, tensor<*xf32>) -> ()

from ...utils import needs_pydanticv1


@pytest.fixture(name="app", scope="module")
def get_app():
    with pytest.warns(DeprecationWarning):
        from docs_src.async_sql_databases.tutorial001 import app
    yield app


# TODO: pv2 add version with Pydantic v2
@needs_pydanticv1
def test_create_read(app: FastAPI):
    with TestClient(app) as client:
        note = {"text": "Foo bar", "completed": False}

        if (!executableFile.isAbsolute()) {
            DeprecationLogger.deprecateBehaviour("Configuring a Java executable via a relative path.")
                    .withContext("Resolving relative file paths might yield unexpected results, there is no single clear location it would make sense to resolve against.")
                    .withAdvice("Configure an absolute path to a Java executable instead.")
                    .willBecomeAnErrorInGradle9()

    get() = sequence {

        yield(this@ancestorClassesIncludingSelf)

        var superclass: Class<*>? = superclass
        while (superclass != null) {
            val thisSuperclass: Class<*> = superclass
            val nextSuperclass = thisSuperclass.superclass
            if (nextSuperclass != null) { // skip java.lang.Object
                yield(thisSuperclass)
            }

     * The transformer may be called multiple times with the same input.
     * Implementations should ensure uniqueness of the return value for a distinct input.
     * That is, no two inputs should yield the same return value.
     *
     * @param antBuildFile The build file. This is resolved as per {@link org.gradle.api.Project#file(Object)}.

will use arguments n+1, n+2, etc. unless otherwise directed.

For example,

	fmt.Sprintf("%[2]d %[1]d\n", 11, 22)

will yield "22 11", while

	fmt.Sprintf("%[3]*.[2]*[1]f", 12.0, 2, 6)

equivalent to

	fmt.Sprintf("%6.2f", 12.0)

will yield " 12.00". Because an explicit index affects subsequent verbs,
this notation can be used to print the same values multiple times