tf_executor.graph {
      %out, %ctl = tf_executor.island wraps "tf.Const"() {device = "", value = dense<["test_1"]> : tensor<1x!tf_type.string>} : () -> tensor<1x!tf_type.string>
      %out_0, %ctl_1 = tf_executor.island wraps "tf.Const"() {device = "", value = dense<[1]> : tensor<1xi64>} : () -> tensor<1xi64>

class BaseTest : public ::testing::Test {
  // You can inherit all the usual members for a non-parameterized test
  // fixture here.
};

class DerivedTest : public BaseTest, public ::testing::WithParamInterface<int> {
  // The usual test fixture members go here too.
};

TEST_F(BaseTest, HasFoo) {
  // This is an ordinary non-parameterized test.
}

class BaseTest : public ::testing::Test {
  // You can inherit all the usual members for a non-parameterized test
  // fixture here.
};

class DerivedTest : public BaseTest, public ::testing::WithParamInterface<int> {
  // The usual test fixture members go here too.
};

TEST_F(BaseTest, HasFoo) {
  // This is an ordinary non-parameterized test.
}

// 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>

// 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>

  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,