TFE_Execute(func, &retvals[0], &num_retvals, status);
  ASSERT_EQ(TF_OK, TF_GetCode(status)) << TF_Message(status);
  ASSERT_EQ(1, num_retvals);
  TF_Tensor* t = TFE_TensorHandleResolve(retvals[0], status);
  ASSERT_EQ(TF_OK, TF_GetCode(status)) << TF_Message(status);
  TFE_DeleteTensorHandle(retvals[0]);
  float sum = 0;
  ASSERT_EQ(sizeof(sum), TF_TensorByteSize(t));
  memcpy(&sum, TF_TensorData(t), TF_TensorByteSize(t));

    status_ = StatusFromTF_Status(status.get());
    ASSERT_EQ(errors::OK, status_.code()) << status_.message();

    {
      AbstractContext* ctx_raw = nullptr;
      status_ =
          BuildImmediateExecutionContext(std::get<1>(GetParam()), &ctx_raw);
      ASSERT_EQ(errors::OK, status_.code()) << status_.message();
      immediate_execution_ctx_.reset(ctx_raw);
    }

  std::stringstream stream;
  stream << x;
  ASSERT_EQ(stream.str(), "42");
}

TEST(OStreamTest, TestFloat32) {
  Float32 x(0.375);  // Exactly representable as a float.
  std::stringstream stream;
  stream << x;
  ASSERT_EQ(stream.str(), "0.375");
}

TEST(OStreamTest, TestString) {
  String s("foo");
  std::stringstream stream;
  stream << s;
  ASSERT_EQ(stream.str(), "foo");
}

    multimap().replaceValues(k0(), values);
    assertGet(k0(), values);
  }

  @MapFeature.Require({SUPPORTS_PUT, SUPPORTS_REMOVE, ALLOWS_NULL_KEYS})
  public void testReplaceValuesWithNullKey() {
    List<V> values = Arrays.asList(v0(), v2(), v3());
    multimap().replaceValues(null, values);
    assertGet(null, values);
  }

  @MapFeature.Require({SUPPORTS_PUT, SUPPORTS_REMOVE})

  ASSERT_EQ(status, kTfLiteOk);

  const uint8_t* buffer = builder.GetBufferPointer();
  const Model* output_model = GetModel(buffer);
  ASSERT_TRUE(output_model);

  ASSERT_EQ(output_model->subgraphs()->size(), model_->subgraphs()->size());
  const auto quantized_graph = output_model->subgraphs()->Get(0);
  // A dequantize op should be added.
  ASSERT_EQ(quantized_graph->operators()->size(),

          tensorflow::unwrap(saved_model));
  tensorflow::Variable* uninitialized_variable;
  ASSERT_EQ(absl::OkStatus(), model_api->GetVariable("uninitialized_variable",
                                                     &uninitialized_variable));
  ASSERT_EQ(tensorflow::DT_FLOAT, uninitialized_variable->dtype());

  ASSERT_EQ(absl::OkStatus(),
            model_api->GetVariable("sub_module.uninitialized_variable",

    int size = getNumElements();

    assertGet(k3(), ImmutableList.<V>of());

    assertTrue(multimap().put(k3(), v3()));

    assertGet(k3(), v3());
    assertEquals(size + 1, multimap().size());
  }

  @MapFeature.Require(SUPPORTS_PUT)
  @CollectionSize.Require(absent = ZERO)
  public void testPutPresent() {
    int size = getNumElements();

    assertGet(k0(), v0());

    int size = getNumElements();

    assertGet(k3(), ImmutableList.<V>of());

    assertTrue(multimap().put(k3(), v3()));

    assertGet(k3(), v3());
    assertEquals(size + 1, multimap().size());
  }

  @MapFeature.Require(SUPPORTS_PUT)
  @CollectionSize.Require(absent = ZERO)
  public void testPutPresent() {
    int size = getNumElements();

    assertGet(k0(), v0());

  (*module_ref)->setAttr("tf.devices", device_dict);

  mlir::TF::RuntimeDevices devices;
  EXPECT_TRUE(mlir::succeeded(GetDevicesFromOp(*module_ref, &devices)));

  ASSERT_EQ(devices.NumDevices(), 1);
  ASSERT_EQ(devices.device_names().size(), 1);
  ASSERT_EQ(DeviceNameUtils::ParsedNameToString(devices.device_names()[0]),
            "/job:worker/replica:0/task:0/device:CPU:0");
}

TEST(DeviceUtilTest, GetGpuDeviceMetadata) {

    extends AbstractMultimapTester<K, V, ListMultimap<K, V>> {

  @Override
  protected void assertGet(K key, V... values) {
    assertGet(key, Arrays.asList(values));
  }

  @Override
  protected void assertGet(K key, Collection<? extends V> values) {
    assertEqualInOrder(values, multimap().get(key));

    if (!values.isEmpty()) {