#     ],
#     deps = [":copybara_config"],
# )
#
# action_config_test(
#     name = "build_cleaner_spec_test",
#     src = "build_cleaner_spec.textproto",
# )
# copybara:uncomment_end

licenses(["notice"])

exports_files([
    "LICENSE",
    # The leakr files are used by //third_party/cloud_tpu and
    # //third_party/tensorboard/google:copybara_config_test.
    "leakr_badwords.dic",
    "leakr_badfiles.dic",

#include "tensorflow/core/platform/logging.h"

namespace tensorflow {

namespace {

// Managing context for the DLManagedTensor, will manage the lifetime of
// DLManagedTensor. When calling DLManagedTensor::deleter, it will notify the
// original framework of destruction, and this context will be deleted also.
struct TfDlManagedTensorCtx {
  TensorReference reference;
  std::vector<int64_t> shape;
  std::vector<int64_t> strides;

    *   Provided a new `experimental_skip_saver` argument which, if specified, will suppress the addition of `SavedModel`-native save and restore ops to the `SavedModel`, for cases where users already build custom save/restore ops and checkpoint formats for the model being saved, and the creation of the SavedModel-native save/restore ops simply cause longer model serialization times.

* Add ops to `tensorflow.raw_ops` that were missing.

  """Set TF_CUDA_COMPUTE_CAPABILITIES."""
  while True:
    native_cuda_compute_capabilities = get_native_cuda_compute_capabilities(
        environ_cp)
    if not native_cuda_compute_capabilities:
      default_cuda_compute_capabilities = _DEFAULT_CUDA_COMPUTE_CAPABILITIES
    else:
      default_cuda_compute_capabilities = native_cuda_compute_capabilities

    ask_cuda_compute_capabilities = (

  tensorflow::Notification done;
  collective_executor_handle->get()->remote_access()->CheckPeerHealth(
      task, timeout_in_ms, [&done, status](const Status& s) {
        status->status = s;
        done.Notify();
      });
  done.WaitForNotification();
}

TF_ShapeAndTypeList* TF_NewShapeAndTypeList(int num_items) {
  TF_ShapeAndTypeList* result = new TF_ShapeAndTypeList;
  result->num_items = num_items;

  std::unique_ptr<Thread> thread_;
};

DeviceThread::~DeviceThread() {
  {
    tensorflow::mutex_lock l(execution_mutex_);
    execution_state_ = ExecutionState::kShuttingDown;
  }
  start_execute_.notify_one();
}

void DeviceThread::AsyncWait(TF_Status* status) {
  tensorflow::mutex_lock l(execution_mutex_);
  TFE_ExecutorWaitForAllPendingNodes(executor_.get(), status);
  TFE_ExecutorClearError(executor_.get());