// big-endian platform.
  memcpy(*buffer, &num_strings, sizeof(int32_t));

  // Set offset of strings.
  int32_t start = sizeof(int32_t) * (num_strings + 2);
  for (size_t i = 0; i < offset_.size(); i++) {
    // TODO(b/165919229): This code will need changing if/when we port to a
    // big-endian platform.
    int32_t offset = start + offset_[i];
    memcpy(*buffer + sizeof(int32_t) * (i + 1), &offset, sizeof(int32_t));

    ASSERT_EQ(num_elem_numerical, num_elem_analytical);

    float* dnumerical = new float[num_elem_numerical]{0};
    memcpy(&dnumerical[0], TF_TensorData(numerical_tensor),
           TF_TensorByteSize(numerical_tensor));
    float* danalytical = new float[num_elem_analytical]{0};
    memcpy(&danalytical[0], TF_TensorData(analytical_tensor),
           TF_TensorByteSize(analytical_tensor));

  TF_RETURN_IF_ERROR(GetValue(theta, &theta_tensor));

  // Get number of elements and fill data.
  int num_elems = TF_TensorElementCount(theta_tensor);
  vector<float> theta_data(num_elems);
  memcpy(theta_data.data(), TF_TensorData(theta_tensor),
         TF_TensorByteSize(theta_tensor));

  // Initialize space for the numerical gradient.
  vector<float> dtheta_approx(num_elems);

  Status status = XLAShapeToTensorShape(shape_, &tensor_shape);
  if (!status.ok()) {
    done(status);
    return;
  }

  *cpu_tensor = Tensor(dtype, tensor_shape);

  status = stream_->Memcpy(cpu_tensor->data(), device_memory_base_,
                           device_memory_base_.size());
  if (!status.ok()) {
    done(status);
    return;
  }
  status = stream_->RecordEvent(done_event_.get().get());

  xla::Shape shape;
  TF_ASSERT_OK(TensorShapeToXLAShape(DT_FLOAT, TensorShape({2, 2}), &shape));

  // Copy the cpu_tensor to the GPU first before trying to copy it back.
  TF_ASSERT_OK(
      stream->Memcpy(&gpu_dst, origin_cpu_tensor.data(), gpu_dst.size()));
  TF_ASSERT_OK(stream->BlockHostUntilDone());

  TF_ASSERT_OK_AND_ASSIGN(auto se_event, executor->CreateEvent());
  tsl::AsyncValueRef<std::unique_ptr<se::Event>> done_event =

namespace tensorflow {

void XlaHostSendDeviceContext::CopyCPUTensorToDevice(
    const Tensor* cpu_tensor, Device* device, Tensor* device_tensor,
    StatusCallback done, bool sync_dst_compute) const {
  auto status = stream_->Memcpy(device_memory_base_, cpu_tensor->data(),
                                device_memory_base_->size());
  if (!status.ok()) {
    done(status);
    return;
  }
  status = stream_->RecordEvent(done_event_.get().get());

extern "C" {

TF_Buffer* TF_NewBuffer() { return new TF_Buffer{nullptr, 0, nullptr}; }

TF_Buffer* TF_NewBufferFromString(const void* proto, size_t proto_len) {
  void* copy = tensorflow::port::Malloc(proto_len);
  std::memcpy(copy, proto, proto_len);

  TF_Buffer* buf = new TF_Buffer;
  buf->data = copy;
  buf->length = proto_len;
  buf->data_deallocator = [](void* data, size_t length) {
    tensorflow::port::Free(data);
  };

        case FILE -> ComponentUtil.getFessConfig().getCrawlerFileProtocolsAsArray();
        default -> throw new ConstraintDefinitionException("protocolType is emtpy.");
        };
    }

    @Override
    public boolean isValid(final String value, final ConstraintValidatorContext context) {
        if (StringUtil.isNotBlank(value)) {
            return check(protocols, value);