Cleanup(Cleanup&& src)  // NOLINT
      : released_(src.is_released()), f_(src.release()) {}

  // Implicitly move-constructible from any compatible Cleanup<G>.
  // The source will be released as if src.release() were called.
  // A moved-from Cleanup can be safely destroyed or reassigned.
  template <typename G>
  Cleanup(Cleanup<G>&& src)  // NOLINT

#include <fstream>
#include <string>
#include <utility>

TempFile::TempFile(const std::string& temp_file_name, std::ios::openmode mode)
    : std::fstream(temp_file_name, mode), name_(temp_file_name) {}

TempFile::TempFile(TempFile&& rhs)
    : std::fstream(std::move(rhs)), name_(std::move(rhs.name_)) {}

TempFile::~TempFile() {
  std::fstream::close();
  std::remove(name_.c_str());
}

    (*var_to_data_type_map)[key] = DataType(entry.dtype());
  }
  // The returned pointers are owned by the caller.
  return std::make_pair(std::move(var_to_shape_map),
                        std::move(var_to_data_type_map));
}

}  // namespace checkpoint

                               file.get(), plugin_status.get());

  if (TF_GetCode(plugin_status.get()) == TF_OK)
    *result = std::make_unique<ModularRandomAccessFile>(
        translated_name, std::move(file), random_access_file_ops_.get());

  return StatusFromTF_Status(plugin_status.get());
}

Status ModularFileSystem::NewWritableFile(
    const std::string& fname, TransactionToken* token,

void RamFileBlockCache::Trim() {
  while (!lru_list_.empty() && cache_size_ > max_bytes_) {
    RemoveBlock(block_map_.find(lru_list_.back()));
  }
}

/// Move the block to the front of the LRU list if it isn't already there.
void RamFileBlockCache::UpdateLRU(const Key& key,
                                  const std::shared_ptr<Block>& block,

      std::move(maybe_parallel_results.value()));
  std::vector<MaybeParallelTensorOwned> result_content;
  result_content.reserve(parallel_results.size());
  for (std::unique_ptr<ParallelTensor>& parallel_result : parallel_results) {
    result_content.push_back(
        MaybeParallelTensorOwned(std::move(parallel_result)));
  }
  result.emplace(std::move(result_content));
  return result;
}

      const char* description, LabelDesc&&... label) {
    sampler = absl::WrapUnique(tensorflow::monitoring::Sampler<NumLabels>::New(
        {name, description, label...}, std::move(buckets)));
  }

  std::unique_ptr<tensorflow::monitoring::Sampler<NumLabels>> sampler;
};

struct TFE_MonitoringSampler0 : TFE_MonitoringSampler<0> {
  using TFE_MonitoringSampler::TFE_MonitoringSampler;

      xla::PjRtClient::HostBufferSemantics::kImmutableOnlyDuringCall, nullptr,
      c_api_client->pjrt_c_client()->client->addressable_devices()[0]);
  CHECK_OK(buffer.status());

  return new PJRT_Buffer{std::move(*buffer), c_api_client->pjrt_c_client()};
}

TEST(TensorPjRtBufferUtilTest, GetPjRtCBufferFromTensorNoBuffer) {
  auto allocator = std::make_unique<AsyncValueAllocator>();

    for (int j = 0; j < underlying_devices_.size(); ++j) {
      components.push_back(std::move(per_device_output_tensors[j][i]));
    }
    if (expected_output_shapes[i].IsFullyDefined()) {
      per_device_outputs.push_back(ParallelTensor::FromTensorHandles(
          *this, std::move(components),
          absl::Span<const int64_t>(expected_output_shapes[i].dim_sizes()),
          status));
    } else {

      : device_(device),
        tensors_(std::move(tensors)),
        shape_(std::vector<int64_t>(shape.begin(), shape.end())),
        dtype_(dtype) {}
  ParallelTensor(const ParallelDevice& device,
                 std::vector<TensorHandlePtr> tensors, const TF_DataType dtype)
      : device_(device),
        tensors_(std::move(tensors)),
        shape_(absl::nullopt),
        dtype_(dtype) {}