//
// This is intended to be used when a peer failure is detected.
TF_CAPI_EXPORT extern void TFE_AbortCollectiveOps(TFE_Context* ctx,
                                                  TF_Status* status);

// Checks the health of collective ops peers. Explicit health check is needed in
// multi worker collective ops to detect failures in the cluster.  If a peer is
// down, collective ops may hang.

      if status == InvokeStatus.tests_failed:
        failure_count += 1
        elem_name = 'failure'
      else:
        error_count += 1
        elem_name = 'error'
      if command:
        failure_msg = (f'\nThe command was:\n\n'
                       f'{command}\n\n')
      else:
        failure_msg = ('\nCouldn\'t parse a bazel command '

  TFE_CancellationManager* c_mgr = TFE_NewCancellationManager();
  EXPECT_FALSE(TFE_CancellationManagerIsCancelled(c_mgr));

  TFE_CancelCallback callback1;
  callback1.callback = [](void* context) {
    ADD_FAILURE() << "Callback1 should be deregistered.";
  };
  TFE_CancellationToken token1 = TFE_CancellationManagerGetToken(c_mgr);
  EXPECT_TRUE(TFE_CancellationManagerRegisterCallback(c_mgr, token1, &callback1,

  for (const auto& dt : device_threads_) {
    StatusPtr async_wait_status(TF_NewStatus());
    dt->AsyncWait(async_wait_status.get());
    // Prefer non cancelled errors to uncover real failures.
    if (TF_GetCode(async_wait_status.get()) != TF_OK &&
        (first_bad_status == nullptr ||
         TF_GetCode(first_bad_status.get()) == TF_CANCELLED)) {
      first_bad_status.reset(TF_NewStatus());

    TFE_TensorHandle* h, TF_Status* status);

// Return a pointer to a new TFE_TensorHandle that shares the underlying tensor
// with `h`. On success, `status` is set to OK. On failure, `status` reflects
// the error and a nullptr is returned.
TF_CAPI_EXPORT extern TFE_TensorHandle* TFE_TensorHandleCopySharingTensor(
    TFE_TensorHandle* h, TF_Status* status);

  targets_.clear();
  for (TF_Operation* t : targets) {
    targets_.emplace_back(t);
  }
}

void CSession::Run(TF_Status* s) {
  if (inputs_.size() != input_values_.size()) {
    ADD_FAILURE() << "Call SetInputs() before Run()";
    return;
  }
  ResetOutputValues();
  output_values_.resize(outputs_.size(), nullptr);

  const TF_Output* inputs_ptr = inputs_.empty() ? nullptr : &inputs_[0];

      result of this License or out of the use or inability to use the
      Work (including but not limited to damages for loss of goodwill,
      work stoppage, computer failure or malfunction, or any and all
      other commercial damages or losses), even if such Contributor
      has been advised of the possibility of such damages.

    ASSERT_EQ(errors::OK, status_.code()) << status_.message();
    ASSERT_NO_FATAL_FAILURE(CheckTensorValue(outputs[0], dA_vals[i],
                                             /*dims*/ {3, 3},
                                             /*abs_error*/ 0));
    ASSERT_NO_FATAL_FAILURE(CheckTensorValue(outputs[1], dB_vals[i],
                                             /*dims*/ {3, 3},

                         tensorflow::FunctionLibraryDefinition* flib_def,
                         std::vector<std::string>* control_ret_node_names,
                         bool* control_rets_updated) override {
    // Inject failure to function instantiation if finding a node that contains
    // the given node name (error_node_) and requested device (error_device_).
    for (const auto node : graph->get()->nodes()) {