memcpy(TF_TensorData(t.get()), &value, TF_TensorByteSize(t.get()));

  std::unique_ptr<TFE_TensorHandle, decltype(&TFE_DeleteTensorHandle)>
      value_handle(TFE_NewTensorHandle(t.get(), status),
                   TFE_DeleteTensorHandle);
  if (TF_GetCode(status) != TF_OK) return nullptr;

  TFE_OpAddInput(op, value_handle.get(), status);

  virtual AbstractTensorInterface* CreateUint64Scalar(uint64 value) = 0;
  virtual AbstractTensorInterface* CreateInt32Scalar(int32_t value) = 0;
  virtual AbstractTensorInterface* CreateFloatScalar(float value) = 0;
  virtual AbstractTensorInterface* CreateDoubleScalar(double value) = 0;
  virtual AbstractTensorInterface* CreateHalfScalar(Eigen::half value) = 0;

  }
  Status SetAttrInt(const char* attr_name, int64_t value) override {
    op_->node_builder.Attr(attr_name, static_cast<int64_t>(value));
    return absl::OkStatus();
  }
  Status SetAttrFloat(const char* attr_name, float value) override {
    op_->node_builder.Attr(attr_name, value);
    return absl::OkStatus();
  }
  Status SetAttrBool(const char* attr_name, bool value) override {
    op_->node_builder.Attr(attr_name, value);

  const char* block_size_env = std::getenv(kBlockSize);
  if (block_size_env && absl::SimpleAtoi(block_size_env, &value)) {
    block_size = value * 1024 * 1024;
  }
  const char* max_bytes_env = std::getenv(kMaxCacheSize);
  if (max_bytes_env && absl::SimpleAtoi(max_bytes_env, &value)) {
    max_bytes = static_cast<size_t>(value * 1024 * 1024);
  }
  const char* max_staleness_env = std::getenv(kMaxStaleness);

  DLTensor* dl_tensor = &dlmt->dl_tensor;
  absl::optional<std::string> device_name =
      DeviceNameFromDlContext(dl_tensor->device, status);
  if (!device_name.has_value()) {
    status->status =
        tensorflow::errors::InvalidArgument("Unsupported Device Type");
    return nullptr;
  }
  TF_DataType dtype;
  Status s = TfDataTypeFormDlDataType(dl_tensor->dtype, &dtype);

namespace {

TEST(CAPI_EXPERIMENTAL, GetServerDefTest) {
  const string expected_text_proto(R"(cluster {
  job {
    name: "worker"
    tasks {
      key: 0
      value: "tpuserver:0"
    }
    tasks {
      key: 1
      value: "localhost:1"
    }
  }
}
job_name: "worker"
task_index: 1
protocol: "grpc"
)");

  TF_Status* status = TF_NewStatus();

#define VALIDATE_STRUCT_SIZE(STRUCT_NAME, STRUCT_OBJ, SIZE_VALUE_NAME) \
  do {                                                                 \
    if (STRUCT_OBJ.struct_size == 0) {                                 \
      return absl::Status(absl::StatusCode::kFailedPrecondition,       \
                          "struct_size field in " #STRUCT_NAME         \
                          " must be set to " #SIZE_VALUE_NAME ".");    \

  TFE_TensorHandle* var_handle1 = TestVariable(ctx, 2.0, dev1_name);
  EXPECT_NE(var_handle1, nullptr);

  TFE_TensorHandle* value_handle = nullptr;
  ReadVariable(ctx, var_handle1, &value_handle);
  CheckTFE_TensorHandleHasFloats(value_handle, {2});
  TFE_DeleteTensorHandle(value_handle);

  // Start a new worker to replace task:1
  ReplaceTaskInServerDef(&server_def, 1);
  server_def.set_task_index(1);

                                                   unsigned char enable);

// Set XLA's internal BuildXlaOpsPassFlags.tf_xla_enable_lazy_compilation to the
// value of 'enabled'. Also returns the original value of that flag.
//
// Use in tests to allow XLA to fallback to TF classic. This has global effect.
TF_CAPI_EXPORT unsigned char TF_SetXlaEnableLazyCompilation(
    unsigned char enable);

  // Create a tensor on the first parallel device
  TensorHandlePtr value_one(FloatTensorHandle(1., status.get()));
  TensorHandlePtr value_two(FloatTensorHandle(2., status.get()));
  ASSERT_EQ(TF_GetCode(status.get()), TF_OK) << TF_Message(status.get());
  std::array<TFE_TensorHandle*, 2> components{value_one.get(), value_two.get()};
  TensorHandlePtr first_combined_value = CreatePerDeviceValues(