callback2.context = &callback2_invoked;
  callback2.callback = [](void* context) {
    *reinterpret_cast<bool*>(context) = true;
  };
  TFE_CancellationToken token2 = TFE_CancellationManagerGetToken(c_mgr);
  EXPECT_TRUE(TFE_CancellationManagerRegisterCallback(c_mgr, token2, &callback2,
                                                      "callback2"));

  TFE_CancellationToken token3 = TFE_CancellationManagerGetToken(c_mgr);

                                   TF_Status* status) {
  bool downloaded_block = false;
  auto reconcile_state = MakeCleanup([this, &downloaded_block, &key, &block] {
    // Perform this action in a cleanup callback to avoid locking mu_ after
    // locking block->mu.
    if (downloaded_block) {
      absl::MutexLock l(&mu_);
      // Do not update state if the block is already to be evicted.
      if (block->timestamp != 0) {

  cat > $BATS_TEST_TMPDIR/ignore_deps_for_these_packages <<EOF
//tensorflow/lite
//tensorflow/compiler/mlir/lite
//tensorflow/compiler/mlir/tfrt
//tensorflow/core/runtime_fallback
//tensorflow/core/tfrt
//tensorflow/python/kernel_tests/signal
//tensorflow/examples
//tensorflow/tools/android
//tensorflow/python/eager/benchmarks
EOF

    branches:
      - master
      
permissions: {}

jobs:
  create-issue-on-pr-rollback:
    runs-on: ubuntu-latest
    permissions:
      contents: read
      issues: write
      pull-requests: read
    if: |
      github.repository == 'tensorflow/tensorflow' &&
      startsWith(github.event.head_commit.message, 'Rollback of PR #')
    steps:
      - name: Checkout repo

///
/// This class should be shared by read-only random access files on a remote
/// filesystem (e.g. GCS).
class RamFileBlockCache {
 public:
  /// The callback executed when a block is not found in the cache, and needs to
  /// be fetched from the backing filesystem. This callback is provided when the
  /// cache is constructed. It returns total bytes read ( -1 in case of errors

    const TFE_CancelCallback* c_callback, const char* callback_name) {
  tensorflow::CancelCallback callback = [callback = c_callback->callback,
                                         context = c_callback->context]() {
    callback(context);
  };
  return tensorflow::unwrap(cancellation_manager)
      ->RegisterCallbackWithErrorLogging(token, callback, callback_name);
}

  const uint64_t max_age_;

  /// The maximum number of entries in the cache. A value of 0 means there is no
  /// limit on entry count.
  const size_t max_entries_;

  /// The callback to read timestamps.
  std::function<uint64_t()> timer_seconds_;

  /// Guards access to the cache and the LRU list.
  absl::Mutex mu_;

  /// The cache (a map from string key to Entry).

    TFE_CancellationManager*);
TF_CAPI_EXPORT extern bool TFE_CancellationManagerRegisterCallback(
    TFE_CancellationManager*, TFE_CancellationToken token,
    const TFE_CancelCallback* c_callback, const char* callback_name);
TF_CAPI_EXPORT extern bool TFE_CancellationManagerDeregisterCallback(
    TFE_CancellationManager*, TFE_CancellationToken token);
TF_CAPI_EXPORT extern bool TFE_CancellationManagerTryDeregisterCallback(

  // threadpool, so that we avoid the possibility of running the runner_ in the
  // threadpool of GPU event mgr, as that can trigger more callbacks to be
  // scheduled on that same threadpool, causing a deadlock in cases where the
  // caller of event_mgr->ThenExecute() blocks on the completion of the callback
  // (as in the case of ConstOp kernel creation on GPU, which involves copying a
  // CPU tensor to GPU).

  // If false, all activations are deleted in the first call to ComputeGradient.
  // Else, only when this is destructed.
  bool persistent_;
};

// Describes a callback for special-cased and more efficient jvp computation.
//
// Could just be a simple typedef in ForwardAccumulator, but MSVC chokes on
// that.
template <typename Gradient>
class ForwardFunction