tensorflow::GradientDef gdef;
  gdef.set_function_name(func->record->fdef().signature().name());
  gdef.set_gradient_func(grad->record->fdef().signature().name());
  status->status = g->graph.AddGradientDef(std::move(gdef));
}

int TF_GraphNumFunctions(TF_Graph* g) {
  tensorflow::mutex_lock l(g->mu);
  return g->graph.flib_def().num_functions();
}

int TF_GraphGetFunctions(TF_Graph* g, TF_Function** funcs, int max_func,

  for (const FunctionDef& fdef : fdef_lib.function()) {
    // Make a copy so that we can mutate it.
    FunctionDef fdef_to_load = fdef;
    if (mutate_proto_func) {
      (*mutate_proto_func)(&fdef_to_load);
    }
    VLOG(1) << "Adding func to graph: " << fdef_to_load.DebugString();
    std::vector<char> binary_proto_buf(fdef_to_load.ByteSizeLong());
    fdef_to_load.SerializeToArray(binary_proto_buf.data(),

}

void TFE_ContextAddFunction(TFE_Context* ctx, TF_Function* function,
                            TF_Status* status) {
  auto fdef_or = function->record->mutable_fdef();
  if (!fdef_or.ok()) {
    status->status = fdef_or.status();
    return;
  }

  AnnotateEagerRuntimeConstructionContext(*fdef_or.value());
  status->status = tensorflow::unwrap(ctx)->AddFunctionDefWithStackTraces(

  // Add a function (serialized FunctionDef protocol buffer) so that it can
  // be executed as an op. Return error if the function with the same name
  // already exists.
  virtual Status AddFunctionDef(const FunctionDef& fdef) = 0;

  // Notifies about the function removal.
  virtual Status AddRemoveFunctionNotifier(const string& func,
                                           std::function<void()> notifier) = 0;

		}
		err := s.Err()
		if err != nil {
			t.Errorf("#%d: %v", n, err)
		}
	}
}

var wordScanTests = []string{
	"",
	" ",
	"\n",
	"a",
	" a ",
	"abc def",
	" abc def ",
	" abc\tdef\nghi\rjkl\fmno\vpqr\u0085stu\u00a0\n",
}

// Test that the word splitter returns the same data as strings.Fields.
func TestScanWords(t *testing.T) {
	for n, test := range wordScanTests {

        }

        // workaround to IBM JDK bug
        def createDslDocModelClosure = this.&createDslDocModel.curry(classDocbookDirectory.get().asFile, mainDocbookTemplate, classRepository)

        def doc = mainDocbookTemplate
        use(BuildableDOMCategory) {
            DslDocModel model = createDslDocModelClosure(loadPluginsMetaData())
            def root = doc.documentElement

            ]
        }
    )


def test_query_optional():
    response = client.get("/query/optional")
    assert response.status_code == 200
    assert response.json() == "foo bar"


def test_query_optional_query_baz():
    response = client.get("/query/optional?query=baz")
    assert response.status_code == 200
    assert response.json() == "foo bar baz"


def test_query_optional_not_declared_baz():

      "      value: 'add2:z:0'"
      "    }",
      &def));
  return def.SerializeAsString();
}

void TestFunctionWithPackedInput(const bool remote) {
  tensorflow::ServerDef server_def = GetServerDef(3);

  // This server def has the task index set to 0.
  string serialized = server_def.SerializeAsString();

  server_def.set_task_index(1);
  std::unique_ptr<tensorflow::GrpcServer> worker_server1;

def by_alias_dict():
    return {"alias": "Foo"}


@app.get("/by-alias/model", response_model=Model)
def by_alias_model():
    return Model(alias="Foo")


@app.get("/by-alias/list", response_model=List[Model])
def by_alias_list():
    return [{"alias": "Foo"}, {"alias": "Bar"}]


@app.get("/no-alias/dict", response_model=ModelNoAlias)
def no_alias_dict():
    return {"name": "Foo"}

        def propertiesSection = withCategories { content.section.find { it.title[0].text().trim() == 'Properties' } }
        def propertyDetailsSection = withCategories { content.section.find { it.title[0].text().trim() == 'Property details' } }
        def propertiesTable = withCategories { propertiesSection ? propertiesSection.table[0] : parse('<table/>')}