// No error when we need to create the Endpoints but the correct
			// EndpointSlice already exists
			expectedError:  nil,
			expectedResult: endpoints1,
			expectCreate:   []runtime.Object{endpoints1},
			initialState:   []runtime.Object{epSlice1},
			namespaceParam: endpoints1.Namespace,
			endpointsParam: endpoints1,
		},
		"existing-endpointslice-correct": {

        cost == remoteAnswer

        where:
        remoteAnswer << MetadataFetchingCost.values()
    }

    def "estimates cost for missing metadata is correct (remote says #remoteAnswer, must refresh = #mustRefreshMissingModule)"() {
        def module = Mock(ModuleComponentIdentifier)
        def localAccess = repo.localAccess

// CHECK: return %[[DEQUANT]] : tensor<1x3x3x4xf32>

  // The following uniform_quantize & uniform_dequantize functions do NOT have
  // the correct body. Only the type signatures matter for testing.
  func.func private @uniform_quantize_0(%arg0: tensor<1x3x3x4xf32>, %arg1: tensor<1x1x1x1xf32>, %arg2: tensor<1x1x1x1xi8>) -> tensor<1x3x3x4xi8> {

const (
	export checkMode = 1 << iota
	rawFormat
	normNumber
	idempotent
	allowTypeParams
)

// format parses src, prints the corresponding AST, verifies the resulting
// src is syntactically correct, and returns the resulting src or an error
// if any.
func format(src []byte, mode checkMode) ([]byte, error) {
	// parse src
	f, err := parser.ParseFile(fset, "", src, parser.ParseComments)
	if err != nil {

    ],
    defines = if_llvm_x86_available(["TF_LLVM_X86_AVAILABLE=1"]),
    deps = [
        ":tfcompile_lib",
        "//tensorflow/core:lib",
        "//tensorflow/core:protos_all_cc",
        "//tensorflow/core:test",
        "//tensorflow/core:test_main",
        "//tensorflow/core/platform:resource_loader",
        "@com_google_absl//absl/strings",
        "@llvm-project//llvm:Support",  # fixdeps: keep

// attached as an attribute.
std::unique_ptr<OperationPass<func::FuncOp>>
CreateMaterializePassthroughOpPass();

// Replicates the TensorList init op by undoing some CSE needed for correct
// shape assignment in shape_inference.
std::unique_ptr<OperationPass<func::FuncOp>>
CreateReplicateTensorListInitOpsPass();

// Performs Shape Inference on the TensorFlow dialect using the global registry.

    // Currently flaky with CC enabled
    def "producer task followed by a destroyer task followed by a producer with a dependency on the first producer are run in the correct order"() {
        def foo = subproject(':foo')
        def bar = subproject(':bar')

        def cleanFooLocal = foo.task('cleanFooLocalState').destroys('build/foo-local')

If you added the return type annotation, tools and editors would complain with a (correct) error telling you that your function is returning a type (e.g. a dict) that is different from what you declared (e.g. a Pydantic model).

    //If you're changing this you will also need to change: src/snippets/modelRules/basicRuleSourcePlugin/basicRuleSourcePlugin-model-task.out
    def "displays a report in the correct format"() {
        given:
        settingsFile << "rootProject.name = 'test'"
        buildFile << """

@Managed
public interface PasswordCredentials {
    String getUsername()
    String getPassword()

	case *Alias:
		// TODO(gri) is this correct?
		assert(expanding == nil) // Alias instances cannot be reached from Named types

		tparams := orig.TypeParams()
		// TODO(gri) investigate if this is needed (type argument and parameter count seem to be correct here)
		if !check.validateTArgLen(pos, orig.String(), tparams.Len(), len(targs)) {
			return Typ[Invalid]
		}