//
  // WARNING: The destructor of this class is marked as protected to disallow
  // clients from directly destroying this object since it may manage it's own
  // lifetime through ref counting. Thus this must be allocated on the heap and
  // clients MUST call Release() in order to destroy an instance of this class.
  virtual void Release() = 0;

  // Release any underlying resources, including the interface object.
  //
  // WARNING: The destructor of this class is marked as protected to disallow
  // clients from directly destroying this object since it may manage its own
  // lifetime through ref counting. Thus clients MUST call Release() in order to
  // destroy an instance of this class.
  virtual void Release() = 0;

  // Creates an operation builder and ties it to this context.

#include "xla/pjrt/c/pjrt_c_api.h"
#include "xla/pjrt/pjrt_c_api_client.h"
#include "tensorflow/core/framework/tensor.h"

namespace tensorflow {

StatusOr<PJRT_Buffer*> GetPjRtCBufferFromTensor(const Tensor* tensor);

absl::Status SetPjRtCBufferToTensor(PJRT_Buffer* c_buffer,
                                    xla::PjRtCApiClient* c_api_client,
                                    Tensor* tensor);

  // A single host server def contains a client and the remote host.
  // Example:
  //
  //  Worker1:
  //  cluster { job { name: "client" tasks { key: 0 value: "localhost:14525" } }
  //            job { name: "worker" tasks { key: 1 value: "localhost:14523" } }
  //          } job_name: "worker" task_index: 1 protocol: "grpc"
  //
  //  Worker0:
  //  cluster { job { name: "client" tasks { key: 0 value: "localhost:14526" } }

  server_def.set_task_index(0);
  auto cluster = server_def.mutable_cluster();
  auto client_job = cluster->add_job();
  client_job->set_name("localhost");
  int client_port = tensorflow::testing::PickUnusedPortOrDie();
  client_job->mutable_tasks()->insert(
      {0, strings::StrCat("localhost:", client_port)});
  server_def.set_job_name("localhost");
  auto serialized = server_def.SerializeAsString();

                                            int keep_alive_secs) = 0;

  // Set up a multi-client distributed execution environment. Must be called
  // on all tasks in the cluster. This call internally coordinates with other
  // tasks to initialize the eager context and TF server for multi-client
  // execution.
  virtual Status EnableCollectiveOps(const ServerDef& server_def) = 0;

  if (enable) {
    optimizer_options->set_global_jit_level(tensorflow::OptimizerOptions::ON_1);

    // These XLA flags are needed to trigger XLA properly from C (more generally
    // non-Python) clients. If this API is called again with `enable` set to
    // false, it is safe to keep these flag values as is.
    tensorflow::MarkForCompilationPassFlags* flags =
        tensorflow::GetMarkForCompilationPassFlags();

    RunMetadata run_metadata_proto;
    result = session->Run(run_options_proto, input_pairs, output_tensor_names,
                          target_oper_names, &outputs, &run_metadata_proto);

    // Serialize back to upstream client, who now owns the new buffer
    if (run_metadata != nullptr) {
      status->status = MessageToBuffer(run_metadata_proto, run_metadata);
      if (!status->status.ok()) return;
    }
  } else {

// set XLA flag values to prepare for XLA compilation. Otherwise set
// global_jit_level to OFF.
//
// This and the next API are syntax sugar over TF_SetConfig(), and is used by
// clients that cannot read/write the tensorflow.ConfigProto proto.
// TODO: Migrate to TF_CreateConfig() below.
TF_CAPI_EXPORT extern void TF_EnableXLACompilation(TF_SessionOptions* options,

    CHECK_OK(pjrt::SetPjrtApi(DEVICE_CPU, GetPjrtApi()));
  }
  auto pjrt_client = xla::GetCApiClient(DEVICE_CPU);
  CHECK_OK(pjrt_client.status());
  auto c_api_client = down_cast<xla::PjRtCApiClient*>(pjrt_client->get());
  std::vector<int32_t> data(1, 0);
  xla::Shape shape = xla::ShapeUtil::MakeShape(xla::S32, {1});

  auto buffer = c_api_client->pjrt_c_client()->client->BufferFromHostBuffer(