#     visibility = ["//visibility:public"],
# )
# copybara:uncomment_end

# 'enable_registration_v2' opts-in to a different implementation of op and
# kernel registration - REGISTER_OP, REGISTER_KERNEL_BUILDER, etc.
#
# This setting is currently experimental. The 'v2' implementation does _not_
# correspond to a particular, finalized design; rather, it relates to
# developing one.
#

#else   // !defined(IS_MOBILE_PLATFORM)
  tensorflow::ServerDef server_def;
  if (!server_def.ParseFromArray(proto, proto_len)) {
    status->status = tensorflow::errors::InvalidArgument(
        "Invalid tensorflow.ServerDef protocol buffer");
    return;
  }
  status->status =
      tensorflow::unwrap(ctx)->GetDistributedManager()->SetOrUpdateServerDef(
          server_def, /*reset_context=*/true, keep_alive_secs,

        based server to register tf.function methods and a GRPC client to invoke
        remote registered methods. RPC APIs are intended for multi-client setups
        i.e. server and clients are started in separate binaries independently.

    *   Example usage to create server: ```python server =
        tf.distribute.experimental.rpc.Server.create("grpc", "127.0.0.1:1234")

        with:
          push: ${{ contains(github.event.pull_request.labels.*.name, 'build and push to gcr.io for staging') }}
          context: ./tensorflow/tools/tf_sig_build_dockerfiles
          target: devel
          build-args: |
            PYTHON_VERSION=${{ matrix.python-version }}
            CACHEBUSTER=${{ steps.date.outputs.DATE }}
          tags: |

  tensorflow::ServerDef server_def;
  if (!server_def.ParseFromArray(proto, proto_len)) {
    status->status = tensorflow::errors::InvalidArgument(
        "Invalid tensorflow.ServerDef protocol buffer");
    return;
  }
  status->status =
      tensorflow::unwrap(ctx)
          ->GetDistributedManager()
          ->InitializeLocalOnlyContext(server_def, keep_alive_secs);

  const std::string bucket;
  const std::string object;
  gcs::Client* gcs_client;  // not owned
  TempFile outfile;
  bool sync_need;
  // `offset` tells us how many bytes of this file are already uploaded to
  // server. If `offset == -1`, we always upload the entire temporary file.
  int64_t offset;
} GCSFile;

static void SyncImpl(const std::string& bucket, const std::string& object,

#include "tensorflow/core/platform/env.h"
#include "tensorflow/core/platform/logging.h"
#include "tensorflow/core/platform/resource_loader.h"
#include "tensorflow/core/platform/test.h"
#include "tensorflow/core/protobuf/tensorflow_server.pb.h"

namespace tensorflow {
namespace {

TEST(CAPI_EXPERIMENTAL, GetServerDefTest) {
  const string expected_text_proto(R"(cluster {
  job {
    name: "worker"
    tasks {
      key: 0

tblib==2.0.0 \
    --hash=sha256:9100bfa016b047d5b980d66e7efed952fbd20bd85b56110aaf473cb97d18709a \
    --hash=sha256:a6df30f272c08bf8be66e0775fad862005d950a6b8449b94f7c788731d70ecd7
    # via -r requirements.in
tensorboard-data-server==0.7.2 \
    --hash=sha256:7e0610d205889588983836ec05dc098e80f97b7e7bbff7e994ebb78f578d0ddb \
    --hash=sha256:9fe5d24221b29625dbc7328b0436ca7fc1c23de4acf4d272f1180856e32f9f60 \

Michael Hudgins <******@****.***> 1695048765 +0000

  VLOG(1) << "Enqueuing is done.";
}

TF_Buffer* TFE_GetServerDef(const char* text_proto, TF_Status* status) {
  tensorflow::ServerDef server_def;
  if (!tensorflow::protobuf::TextFormat::ParseFromString(text_proto,
                                                         &server_def)) {
    status->status = tensorflow::errors::Internal(
        "Invalid text proto for ServerDef: ", text_proto);
    return nullptr;
  }