if (fflush(posix_file->handle) != 0)
    TF_SetStatusFromIOError(status, errno, posix_file->filename);
}

static void Sync(const TF_WritableFile* file, TF_Status* status) {
  // For historical reasons, this does the same as `Flush` at the moment.
  // TODO(b/144055243): This should use `fsync`/`sync`.
  Flush(file, status);
}

static void Close(const TF_WritableFile* file, TF_Status* status) {

// executed in sequence. Assigning nodes to different executors allows executing
// nodes in parallel.
// in_flight_nodes_limit: when is_async is true, this value controls the
// maximum number of in flight async nodes. Enqueuing of additional async ops
// after the limit is reached blocks until some inflight nodes finishes.
// The effect is bounding the memory held by inflight TensorHandles that are

}

// Note: this function looks up a thread local policy. So it should be called in
// the appropriate client thread. In particular, in async mode, it may not be
// safe to call this function from the async EagerExecutor threads.
extern TFE_ContextDevicePlacementPolicy TFE_ContextGetDevicePlacementPolicy(
    TFE_Context* ctx) {
  return static_cast<TFE_ContextDevicePlacementPolicy>(

      tensorflow::unwrap(cancellation_manager));
  status->status = absl::OkStatus();
}

TFE_Executor* TFE_NewExecutor(bool is_async, bool enable_streaming_enqueue,
                              int in_flight_nodes_limit) {
  return new TFE_Executor(is_async, enable_streaming_enqueue,
                          in_flight_nodes_limit);
}

void TFE_DeleteExecutor(TFE_Executor* executor) { delete executor; }

    "if_google",
    "if_oss",
    "if_xla_available",
    "tf_cc_shared_object",
    "tf_custom_op_library_additional_deps_impl",
    "tf_monitoring_python_deps",
    "tf_native_cc_binary",
    "tsl_async_value_deps",
)
load(
    "//tensorflow:tensorflow.default.bzl",
    "ADDITIONAL_API_INDEXABLE_SETTINGS",
    "tf_cc_shared_library",
)
load(
    "@local_xla//xla/tsl/mkl:build_defs.bzl",
    "if_mkl_ml",
)

*   `tf.distribute`:
    *   Support added for global sync `BatchNormalization` by using the newly
        added `tf.keras.layers.experimental.SyncBatchNormalization` layer. This
        layer will sync `BatchNormalization` statistics every step across all
        replicas taking part in sync training.
    *   Performance improvements for GPU multi-worker distributed training using

    visibility = ["//visibility:public"],
    deps = [
        "//tensorflow/compiler/jit:pjrt_tensor_buffer_util",
        "//tensorflow/core:framework",
        "//tensorflow/core/tfrt/common:async_value_tensor",
        "//tensorflow/core/tfrt/common:global_state",
        "//tensorflow/core/tfrt/common:pjrt_state",
        "//tensorflow/core/tfrt/common:pjrt_util",
        "@com_google_absl//absl/status",

# "/path/bar". This script uses "gsutil rsync" instead, which acts on directory
# contents. About arguments to gsutil:
# "gsutil -m rsync" runs in parallel.
# "gsutil rsync -r" is recursive and makes directories work.
# "gsutil rsync -d" is "sync and delete files from destination if not present in source"

DOWNLOADS="$(mktemp -d)"
mkdir -p "$DOWNLOADS"
gsutil -m rsync -r "$TFCI_ARTIFACT_STAGING_GCS_URI" "$DOWNLOADS"

                        const int in_flight_nodes_limit)
      : status_(TF_NewStatus()),
        // If the context's default exector is set to async, re-using that in
        // each thread would cause collectives to deadlock. For consistency we
        // create a new sync executor for every thread.
        //
        // TODO(allenl): We should have an async API that works with the

libomp-17-dev
libssl-dev
libtool
libxml2-dev
libxslt1-dev
libzmq3-dev
mlocate
moreutils
openjdk-11-jdk
openjdk-11-jre-headless
openssl
patchelf
pkg-config
python3-dev
python3-setuptools
rsync
software-properties-common
sudo
swig
unzip
vim
wget
zip