//
// We expose this here to allow fine-grained synchronization in multi-threaded
// workloads, which is required since the Python implementation depends on the
// above mutation methods. This allows us to prevent modifications to nodes in
// the graph after the session has been made aware of them.
TF_CAPI_EXPORT extern void TF_ExtendSession(TF_Session* session,
                                            TF_Status* status);

  int num_requests = 0;
  Notification notification;
  auto fetcher = [&num_requests, &notification, block_size](
                     const string& filename, size_t offset, size_t n,
                     char* buffer, TF_Status* status) -> int64_t {
    EXPECT_EQ(n, block_size);
    EXPECT_EQ(offset, 0);
    num_requests++;
    memset(buffer, 'x', n);
    notification.Notify();

    TF_Status* status) {
  tensorflow::EagerContext* context =
      tensorflow::ContextFromInterface(tensorflow::unwrap(ctx));
  auto collective_executor_handle = context->GetCollectiveExecutorHandle();
  tensorflow::Notification done;
  collective_executor_handle->get()->remote_access()->CheckPeerHealth(
      task, timeout_in_ms, [&done, status](const Status& s) {
        status->status = s;
        done.Notify();
      });

# `VERSION` parameter.
# Note: 'Templatizing' this and the other conditional branches would require
# defining several variables (version, os, path) making it difficult to maintain
# and extend for future modifications.
mkdir -p glibc-src
mkdir -p glibc-build
cd glibc-src
wget --retry-connrefused --waitretry=1 --read-timeout=20 --timeout=15 --tries=5 "https://vault.centos.org/centos/7/os/Source/SPackages/glibc-2.17-317.el7.src.rpm"

        "//tensorflow/core/common_runtime/next_pluggable_device/c:tf_rendezvous_c_api",
        "//tensorflow/core/common_runtime/next_pluggable_device/c:tf_rendezvous_c_api_internal",
        "//tensorflow/core/platform:notification",
        "@com_google_absl//absl/status",
        "@com_google_absl//absl/strings",
        "@local_xla//xla/tsl/c:tsl_status_internal",
    ] + select({
        "//tensorflow:android": [

          as modifying the License.

      You may add Your own copyright statement to Your modifications and
      may provide additional or different license terms and conditions
      for use, reproduction, or distribution of Your modifications, or
      for any such Derivative Works as a whole, provided Your use,
      reproduction, and distribution of the Work otherwise complies with

  /// The cache pruning thread that removes files with expired blocks.
  std::unique_ptr<TF_Thread, std::function<void(TF_Thread*)>> pruning_thread_;

  /// Notification for stopping the cache pruning thread.
  absl::Notification stop_pruning_thread_;

  /// Guards access to the block map, LRU list, and cached byte count.
  mutable absl::Mutex mu_;

  /// The block map (map from Key to Block).

process images, audio, videos, and text. There are several modules specialized
in taking those formats, modifying them, and/or converting them to intermediate
formats that can be processed by TensorFlow.

These modifications and conversions are handled by a variety of libraries that
have different security properties and provide different levels of confidence
when dealing with untrusted data. Based on the security history of these

    forward compatible code: by explicitly importing either
    `tensorflow.compat.v1` or `tensorflow.compat.v2`, you can ensure that your
    code works without modifications against an installation of 1.15 or 2.0.
*   EagerTensor now supports numpy buffer interface for tensors.
*   Add toggles `tf.enable_control_flow_v2()` and `tf.disable_control_flow_v2()`