Scope root_;
};

TEST_F(MathGradTest, MatMulGrad_NoTranspose) {
  TestMatMulGrad<float>(false, false);
}

TEST_F(MathGradTest, MatMulComplexGrad_NoTranspose) {
  TestMatMulGrad<complex64>(false, false);
}

TEST_F(MathGradTest, MatMulGrad_TransposeX) {
  TestMatMulGrad<float>(true, false);
}

TEST_F(MathGradTest, MatMulComplexGrad_TransposeX) {

}

TEST_F(ArrayGradTest, UnstackGrad_Axis1) {
  TensorShape x_shape({4, 2, 3});
  auto x = Placeholder(scope_, DT_FLOAT, Placeholder::Shape(x_shape));
  // Unstacking the second dimension results in 2 outputs.
  std::vector<TensorShape> y_shapes(2, TensorShape({4, 3}));
  auto y = Unstack(scope_, x, 2, Unstack::Axis(1));
  RunTest({x}, {x_shape}, y.output, y_shapes);
}

TEST_F(ArrayGradTest, IdentityGrad) {
  TensorShape shape({5, 2});

using ::testing::IsNull;
using ::testing::Ne;
using ::testing::NotNull;
using ::testing::Test;

class CreateI8F32UniformQuantizedTypeTest : public Test {
 protected:
  CreateI8F32UniformQuantizedTypeTest() : ctx_() {
    ctx_.loadDialect<quant::QuantizationDialect>();
  }

  MLIRContext ctx_;
};

TEST_F(CreateI8F32UniformQuantizedTypeTest, I8StorageTypeSucceeds) {
  const UniformQuantizedType quantized_type =

  EXPECT_TRUE(failed(TryCast<DotGeneralOp>(nullptr, /*name=*/"nullptr")));
}

TEST_F(AttrsAndConstraintsTest, I64ValueInI32RangeAreCastedCorrectly) {
  EXPECT_TRUE(succeeded(CastI64ToI32(llvm::minIntN(32))));
  EXPECT_TRUE(succeeded(CastI64ToI32(llvm::maxIntN(32))));
}

TEST_F(AttrsAndConstraintsTest, CastingFailsForI64ValueOutOfI32Range) {
  EXPECT_TRUE(failed(CastI64ToI32(llvm::minIntN(32) - 10)));

#include "xla/stream_executor/stream_executor.h"
#include "tensorflow/core/lib/core/status_test_util.h"
#include "tensorflow/core/platform/test.h"
#include "tensorflow/core/protobuf/error_codes.pb.h"
#include "tsl/platform/statusor.h"

namespace stream_executor {
namespace {

/*** Registration tests ***/
TEST(StreamExecutor, SuccessfulRegistration) {
  auto plugin_init = [](SE_PlatformRegistrationParams* const params,

using ::testing::ElementsAre;
using ::testing::HasSubstr;
using ::testing::Key;
using ::testing::SizeIs;
using ::tsl::testing::StatusIs;

class CalibrationStatisticsSaverTest : public OpsTestBase {};

TEST_F(CalibrationStatisticsSaverTest, MissingOutputPath) {
  std::vector<std::string> ids{"1"};
  std::vector<int32_t> calibration_methods{
      CalibrationOptions::CALIBRATION_METHOD_AVERAGE_MIN_MAX};

//               "output(s)");
// }

// TODO(skyewm): test bad body output shape

TEST_F(CApiWhileLoopTest, NullName) {
  Init(1);
  CreateCondGraph();
  params_->body_outputs[0] = params_->body_inputs[0];
  params_->name = nullptr;
  ExpectError(TF_INVALID_ARGUMENT, "TF_WhileParams `name` field is null");
}

TEST_F(CApiWhileLoopTest, WrongGraph) {
  Init(1);
  CreateCondGraph();

```

Verify that expected image eg: `user1/examples-bookinfo-*:test1.0` is updated in `platform/kube/bookinfo*.yaml` files.

## Tests

Test that the bookinfo samples work with the latest image eg: `user1/examples-bookinfo-*:test1.0` that you pushed.

```bash
$ cd ../../

        binary1.name >> "test1"

        expect:
        container.add(binary1)
        container.realizeNow()
        def p = container.getByName("test1")

        p.present
        p.get() == binary1
    }

    def "querying the result of get by name fails when not realized"() {
        def binary1 = Stub(SwiftBinary)
        binary1.name >> "test1"

        given:

};

// This should only have unit tests. End to end tests should be done with
// FILECHECK and MLIR tests.
class Tf2XlaRewriterTest : public ::testing::Test {
 public:
  void SetUp() override {
    tensorflow::XlaOpRegistry::RegisterCompilationKernels();
  }

  Status CreateMlirModule(std::string module_string = kMlirModuleStr) {
    TF_ASSIGN_OR_RETURN(