/*ret_def=*/{{"out", "out:output:0"}});
  *fdef_lib.add_function() = std::move(func);
  return fdef_lib;
}

// This tests a rewrite that only makes sense and is active in a CUDA-enabled
// build.  Specifically we check that we insert an IdentityN op to avoid extra
// device-to-host copies.
TEST_F(BuildXlaOpsTest, NoDeviceToHostCopiesForClustersWithInt32Inputs) {
  const char* kXlaDeviceName = "/job:worker/replica:0/task:0/device:GPU:0";

        assertContentType("text/x-csrc", "extractor/program/test.c", "test.c");
        assertContentType("text/x-c++src", "extractor/program/test.cpp", "test.cpp");
        assertContentType("text/x-chdr", "extractor/program/test.h", "test.h");
        assertContentType("text/x-c++hdr", "extractor/program/test.hpp", "test.hpp");
        assertContentType("text/x-java-source", "extractor/program/test.java", "test.java");

      /*use_pjrt_tensor_buffer=*/false, execute_outputs, context_.get()));

  Tensor* expected = CreateHostTensor<int32>(TensorShape({1, 3}), {5, 7, 9});
  test::ExpectTensorEqual<int32>(*expected, *GetOutput(0));
}

TEST_F(PjRtExecutionUtilTest, PopulateCtxOutputsDynamicShape) {
  XlaOpRegistry::RegisterCompilationKernels();
  TF_EXPECT_OK(NodeDefBuilder("testWhere", "Where")

    }

    def "can register progress loggers for different test workers"() {
        def testWorkerProgressLogger1 = Mock(ProgressLogger)
        def testWorkerProgressLogger2 = Mock(ProgressLogger)

        given:
        def testEvent1 = new TestEvent('Gradle Test Executor 1', 'org.gradle.Test1')
        def testEvent2 = new TestEvent('Gradle Test Executor 2', 'org.gradle.Test2')
        def testDescriptor1 = createTestDescriptor(testEvent1)

def test_multiple_path():
    app = FastAPI()

    @app.get("/test1")
    @app.get("/test2")
    async def test(var: Annotated[str, Query()] = "bar"):
        return {"foo": var}

    client = TestClient(app)
    response = client.get("/test1")
    assert response.status_code == 200
    assert response.json() == {"foo": "bar"}

    response = client.get("/test1", params={"var": "baz"})
    assert response.status_code == 200

        def notRequired = mavenRepo.module('test', 'dont-include-me', '1.0')
        def m1 = mavenRepo.module('test', 'test1', '1.0').publish()
        def m2 = mavenRepo.module('test', 'test2', '1.0').publish()
        def m3 = mavenRepo.module('test', 'test3', '1.0').publish()
        def m4 = mavenRepo.module('test', 'test4', '1.0').publish()
        def m5 = mavenRepo.module('test', 'test5', '1.0')
            .dependsOn(m1, scope: 'compile')

// This header implements typed tests and type-parameterized tests.

// Typed (aka type-driven) tests repeat the same test for types in a
// list.  You must know which types you want to test with when writing
// typed tests. Here's how you do it:

#if 0

// First, define a fixture class template.  It should be parameterized
// by a type.  Remember to derive it from testing::Test.
template <typename T>

		}
		return true
	}

	tests := []struct {
		name    string
		Resp    *http.Response
		HttpErr error
		Modify  bool
		Object  runtime.Object

		ExpectObject runtime.Object
		Err          bool
		Req          func(*http.Request) bool
	}{
		{
			name:    "test1",
			HttpErr: errors.New("failure"),
			Err:     true,
		},
		{
			name: "test1",
			Resp: &http.Response{

  EXPECT_EQ(histogram.num(), 1);
  EXPECT_EQ(compilation_status.Delta("kOldBridgeNoMlirSuccess"), 1);
}

TEST_F(CompileTFGraphTest, SuccessfullyCompilesWithManualSharding) {
  // MLIR module from failing test.
  constexpr char kSupportedManualSharding[] = R"(

#include "tensorflow/core/lib/monitoring/cell_reader.h"
#include "tensorflow/core/platform/test.h"
#include "tensorflow/core/public/session_options.h"

namespace tensorflow {

using ::testing::_;
using ::testing::NiceMock;
using ::testing::Return;
using ::testing::Test;

constexpr char kOk[] = "OK";
constexpr char kInvalidArgument[] = "INVALID_ARGUMENT";
constexpr char kSuccess[] = "kSuccess";