out->options.config.mutable_experimental()
      ->set_disable_optimize_for_static_graph(true);
  return out;
}
void TF_DeleteSessionOptions(TF_SessionOptions* opt) { delete opt; }

void TF_SetTarget(TF_SessionOptions* options, const char* target) {
  options->options.target = target;
}

void TF_SetConfig(TF_SessionOptions* options, const void* proto,

  return static_cast<TF_Operation*>(static_cast<void*>(node));
}

void TF_EnableXLACompilation(TF_SessionOptions* options, unsigned char enable) {
  tensorflow::ConfigProto& config = options->options.config;
  auto* optimizer_options =
      config.mutable_graph_options()->mutable_optimizer_options();
  if (enable) {
    optimizer_options->set_global_jit_level(tensorflow::OptimizerOptions::ON_1);

using tensorflow::unwrap;
using tensorflow::wrap;
using tensorflow::strings::StrCat;

TF_ExecutionContext* TF_NewEagerExecutionContext(TFE_ContextOptions* options,
                                                 TF_Status* s) {
  TFE_Context* c_ctx = TFE_NewContext(options, s);
  if (TF_GetCode(s) != TF_OK) {
    return nullptr;
  }
  return wrap(static_cast<AbstractContext*>(unwrap(c_ctx)));
}

void TF_DefaultThreadOptions(TF_ThreadOptions* options) {
  options->stack_size = 0;
  options->guard_size = 0;
  options->numa_node = -1;
}

TF_Thread* TF_StartThread(const TF_ThreadOptions* options,
                          const char* thread_name, void (*work_func)(void*),
                          void* param) {
  ::tensorflow::ThreadOptions cc_options;
  cc_options.stack_size = options->stack_size;

  ::tensorflow::mutex_lock l(real_data->mu);
  real_data->did_work = true;
}

}  // namespace

TEST(TestEnv, TestThreads) {
  TF_ThreadOptions options;
  TF_DefaultThreadOptions(&options);
  SomeThreadData data;
  TF_Thread* thread =
      TF_StartThread(&options, "SomeThreadName", &SomeThreadFunc, &data);
  TF_JoinThread(thread);
  ::tensorflow::mutex_lock l(data.mu);
  ASSERT_TRUE(data.did_work);

#endif
  } else {
    status->status = tensorflow::errors::InvalidArgument(
        "Unsupported Device Type for dlpack");
  }

  return ctx;
}

// Converts DLDevice to TF device name.
absl::optional<std::string> DeviceNameFromDlContext(const DLDevice& ctx,
                                                    TF_Status* status) {
  switch (ctx.device_type) {
    case DLDeviceType::kDLCPU:
      return "CPU:0";

#include "tensorflow/core/framework/op_def.pb.h"
#include "tensorflow/core/framework/tensor.pb.h"
#include "tensorflow/core/platform/logging.h"
#include "tensorflow/core/platform/strcat.h"
#include "tensorflow/core/public/session_options.h"

using tensorflow::GraphDef;
using tensorflow::NodeDef;

static void BoolDeallocator(void* data, size_t, void* arg) {
  delete[] static_cast<bool*>(data);
}

                 TF_Message(first_bad_status.get()));
  }
}

absl::optional<std::vector<std::unique_ptr<ParallelTensor>>>
ParallelDevice::Join(
    const std::vector<PartialTensorShape>& expected_output_shapes,
    TF_Status* status) const {
  absl::optional<std::vector<std::unique_ptr<ParallelTensor>>> result;
  // Compute per-device per-output tensors

};

absl::optional<std::vector<MaybeParallelTensorOwned>> ExecuteWithSpecialOps(
    const ParallelDevice& parallel_device,
    const std::string& parallel_device_name, TFE_Context* context,
    std::vector<MaybeParallelTensorUnowned> inputs, const char* operation_name,
    const TFE_OpAttrs* attributes, int expected_max_outputs,
    TF_Status* status) {
  absl::optional<std::vector<MaybeParallelTensorOwned>> result;

                                   PJRT_NamedValue* create_options,
                                   int num_options) {
  absl::StatusOr<std::unique_ptr<xla::PjRtClient>> pjrt_client =
      xla::GetCApiClient(device_type, pjrt::ConvertFromPjRtNamedValueList(
                                          create_options, num_options));
  if (!pjrt_client.ok()) {
    status->status = pjrt_client.status();
    return;