&device_tensor, "", device_.get(), &dest_cpu_tensor));

  tensorflow::test::ExpectClose(origin_cpu_tensor, dest_cpu_tensor);
  device_context->Unref();
}

TEST_F(XlaHostSendRecvDeviceContextTest, CopyCPUTensorToDevice) {
  SetDevice("GPU");
  Tensor origin_cpu_tensor(host_allocator_, DT_FLOAT, TensorShape({2, 2}));
  test::FillValues<float>(&origin_cpu_tensor, {1.2, 2.3, 3.4, 4.5});

    }

    static class JavaTestClass {
        public static final PRESERVED_TEST1 = new JavaTestClass('org.gradle.Test1', 'org.gradle.Test1')
        public static final PRESERVED_TEST2 = new JavaTestClass('org.gradle.Test2', 'org.gradle.Test2')

package org.gradle.api.plugins.quality.codenarc

import org.gradle.test.fixtures.file.TestFile


trait CodeNarcTestFixture {
    def goodCode() {
        file("src/main/groovy/org/gradle/class1.java") << "package org.gradle; class class1 { }"
        file("src/test/groovy/org/gradle/testclass1.java") << "package org.gradle; public class testclass1 { @org.junit.Test public void test1() { } }"

        value = "${\\$}";
        assertEquals(value, ResourceUtil.resolve(value));

        System.setProperty("test.dir", "c:\\test1\\test2");

        value = "${test.dir}";
        assertEquals("c:\\test1\\test2", ResourceUtil.resolve(value));

    }

        run 'show'

        then:
        outputContains("files: [test-1.0, test-1.0, test-1.0-classy]")
        outputContains("display-names: [test-1.0 (org:test:1.0), test-1.0 (org:test:1.0), test-1.0-classy (org:test:1.0)]")
        outputContains("ids: [test-1.0 (org:test:1.0), test-1.0 (org:test:1.0), test-1.0-classy (org:test:1.0)]")
        outputContains("names: [test::jar, test::, test::]")
        outputContains("classifiers: [null, null, classy]")

    }
    return absl::OkStatus();
  }

  DialectRegistry registry_;
  MLIRContext context_;
  OwningOpRef<mlir::ModuleOp> mlir_module_;
};

TEST_F(TpuMetadataUtilsTest, SingleDevice) {
  TF_ASSERT_OK_AND_ASSIGN(auto cluster_func_ops,
                          GetClusterFuncOps("basic_cluster.mlir"));

    }
    return absl::OkStatus();
  }

  DialectRegistry registry_;
  MLIRContext context_;
  OwningOpRef<mlir::ModuleOp> mlir_module_;
};

TEST_F(SessionClusterTensorflowDialectTest, ClustersTf) {
  CellReader<int64_t> compilation_status(kCompilationStreamz);

  TF_ASSERT_OK(CreateMlirModule("empty_func.mlir"));

  TF_EXPECT_OK(

  OwningOpRef<mlir::ModuleOp> mlir_module_;
};

TEST_F(TensorflowDialectToExecutorTest, ConvertsToExecutor) {
  CellReader<int64_t> compilation_status(kCompilationStreamz);

  TF_ASSERT_OK(CreateMlirModule("empty_func.mlir"));

  TF_EXPECT_OK(ExportFromTensorflowDialectToExecutor(*mlir_module_));

  EXPECT_EQ(compilation_status.Delta("success"), 1);
}

TEST_F(TensorflowDialectToExecutorTest, ErrorsWhenCannotConvert) {

    }

    def "acquires worker lease and starts worker process on first test"() {
        given:
        def test1 = Mock(TestClassRunInfo)
        def test2 = Mock(TestClassRunInfo)
        def processor = newProcessor()

        when:
        processor.processTestClass(test1)
        processor.processTestClass(test2)

        then:
        1 * workerLeaseRegistry.startWorker()

using ::mlir::ModuleOp;
using ::mlir::OwningOpRef;
using ::mlir::mhlo::test::GetMlirModuleFromString;
using ::tensorflow::monitoring::testing::CellReader;

static constexpr char kHasTpuPartitionedCallStreamzName[] =
    "/tensorflow/core/tf2xla/internal/inference/tpu_partitioned_call";

class InferenceMetricsPassTest : public ::testing::Test {
 protected:
  void CreateModule(const char* module_string) {