# CuDNN: https://docs.nvidia.com/deeplearning/sdk/cudnn-install/index.html#ubuntu-network-installation
libcudnn8=8.9.6.50-1+cuda12.2
libcudnn8-dev=8.9.6.50-1+cuda12.2

# This can be removed once NVIDIA publishes a cuda-12.3.2 Docker image.
# For now it ensures that we install at least version 12.3.107 of PTXAS,
# since 12.3.103 has a bug.

# limitations under the License.
# ==============================================================================
# Release jobs are very basic. They don't use any caching or RBE,
# but they do upload logs to resultstore.
# IMPORTANT: trailing slash is required on GCS URIs, as it tells gcloud to
# pretend the path is a directory.
TFCI_ARTIFACT_FINAL_GCS_ENABLE=1

  // When creating file, use the same permissions as original
  mode_t open_mode = mode & (S_IRWXU | S_IRWXG | S_IRWXO);

  // O_WRONLY | O_CREAT | O_TRUNC:
  //   Open file for write and if file does not exist, create the file.
  //   If file exists, truncate its size to 0.
  int dst_fd = open(dst, O_WRONLY | O_CREAT | O_TRUNC, open_mode);
  if (dst_fd < 0) {
    close(src_fd);
    return -1;
  }

# Delete modules that should be hidden from dir().
# Don't fail if these modules are not available.
# For e.g. this file will be originally placed under tensorflow/_api/v1 which
# does not have "python", "core" directories. Then, it will be copied
# to tensorflow/ which does have these two directories.
try:
  del python
except NameError:
  pass
try:
  del core
except NameError:
  pass
try:
  del compiler

# > on the host machine. If we don't have --distinct_host_configuration=false,
# > the core TensorFlow code will be built once for the host and once for the
# > target platform.
# See also https://docs.bazel.build/versions/master/guide.html#build-configurations-and-cross-compilation
build --distinct_host_configuration=false

# Disable clang extension that rejects type definitions within offsetof. 

}  // end namespace tensorflow

// =============================================================================
// Public C API entry points
//
// These are only the generic entry points for the C API. This file does not
// have any visibility into the graph/eager implementation and is only providing
// C bindings to the abstract classes defined in the
// c_api_unified_experimental_internal.h header.
//

      "ApiDefMap is not supported on mobile.");
  return nullptr;
#else
  mutex_lock l(api_def_map->lock);
  if (!api_def_map->update_docs_called) {
    api_def_map->api_def_map.UpdateDocs();
    api_def_map->update_docs_called = true;
  }
  string name_str(name, name_len);
  const auto* api_def = api_def_map->api_def_map.GetApiDef(name_str);
  if (api_def == nullptr) {

                                              int num_inputs,
                                              TF_Status* status);

// Fetches the current number of inputs attached to `op`.
//
// Does not use the operation's definition to determine how many inputs should
// be attached. It is intended for use with TFE_OpGetFlatInput to inspect an
// already-finalized operation.
//

    std::unique_ptr<RandomAccessFile>* result) {
  if (ops_->new_random_access_file == nullptr)
    return errors::Unimplemented(tensorflow::strings::StrCat(
        "Filesystem for ", fname, " does not support NewRandomAccessFile()"));

  UniquePtrTo_TF_Status plugin_status(TF_NewStatus(), TF_DeleteStatus);
  auto file = std::make_unique<TF_RandomAccessFile>();
  std::string translated_name = TranslateName(fname);

# This env is only valid when RBE is available on the selected platform, since
# it enables a derived --config setting.  If RBE is not available (i.e. there
# is no --config setting), bazel would fail and quit. This script does a quick
# check This script checks for such errors early
if ! grep "rbe_$TFCI_BAZEL_TARGET_SELECTING_CONFIG_PREFIX" .bazelrc; then
  cat <<EOF
ERROR: RBE was enabled via the 'rbe' env in the 'TFCI' variable.
       TFCI: $TFCI