return tensorflow::CustomDeviceTensorHandle::SummarizeValue(summary);
    }
    TF_Status c_status;
    std::unique_ptr<TF_Buffer, decltype(&TF_DeleteBuffer)> summary_buffer(
        methods_.summarize(data_, &c_status), TF_DeleteBuffer);
    if (!c_status.status.ok()) {
      return c_status.status;
    }
    summary = std::string(reinterpret_cast<const char*>(summary_buffer->data),

                                        TF_Status* status) {
  tensorflow::unwrap(ctx)->SetRunEagerOpAsFunction(enable);
}

void TFE_ContextSetJitCompileRewrite(TFE_Context* ctx, unsigned char enable,
                                     TF_Status* status) {
  tensorflow::unwrap(ctx)->SetJitCompileRewrite(enable);
}

const char* TFE_TensorHandleDeviceType(TFE_TensorHandle* h, TF_Status* status) {
  if (h == nullptr) {

};

using OpPtr = std::unique_ptr<TFE_Op, OpDeleter>;

class StatusDeleter {
 public:
  void operator()(TF_Status* to_delete) const { TF_DeleteStatus(to_delete); }
};

using StatusPtr = std::unique_ptr<TF_Status, StatusDeleter>;

class ExecutorDeleter {
 public:
  void operator()(TFE_Executor* to_delete) const {
    TFE_DeleteExecutor(to_delete);
  }
};

    visibility = ["//tensorflow:internal"],
    deps = [
        ":conversion_macros",
        "//tensorflow/core:framework",
    ],
)

cc_library(
    name = "tf_status",
    srcs = ["tf_status.cc"],
    hdrs = ["tf_status.h"],
    copts = tf_copts(),
    visibility = ["//visibility:public"],
    deps = [
        ":c_api_macros_hdrs",
        ":tf_status_internal",

                                       TF_Status* status) {
  status->status = tensorflow::FindKernelDef(
      tensorflow::DeviceType(device_type), builder->BuildNodeDef(),
      /* def = */ nullptr, /* kernel_class_name = */ nullptr);
}

const char* TF_GetNumberAttrForOpListInput(const char* op_name, int input_index,
                                           TF_Status* status) {
  const tensorflow::OpDef* op_def = nullptr;

void TF_SetTracingImplementation(const char* name, TF_Status* s) {
  tsl::Set_TF_Status_from_Status(s, SetDefaultTracingEngine(name));
}

// Creates a new TensorFlow function, it is an execution context attached to a
// given tracing context.
TF_ExecutionContext* TF_CreateFunction(const char* fn_name, TF_Status* s) {
  return wrap(CreateTracingExecutionContext(fn_name, s));
}

                                                       TF_Status* status) const;

  // Construct a parallel tensor consisting of the scalar values from `values`.
  template <typename DataType>
  std::unique_ptr<ParallelTensor> ScalarsFromSequence(
      absl::Span<const DataType> values, TFE_Context* context,
      TF_Status* status) const;

  // A parallel tensor with scalar integers numbering component devices.

                                              TF_Status* status) {
  Session* session;
  status->status = NewSession(opt->options, &session);
  if (status->status.ok()) {
    return new TF_DeprecatedSession({session});
  } else {
    DCHECK_EQ(nullptr, session);
    return nullptr;
  }
}

void TF_CloseDeprecatedSession(TF_DeprecatedSession* s, TF_Status* status) {
  status->status = s->session->Close();
}

void TF_FunctionToFunctionDef(TF_Function* func, TF_Buffer* output_func_def,
                              TF_Status* status) {
  status->status = MessageToBuffer(func->record->fdef(), output_func_def);
}

TF_Function* TF_FunctionImportFunctionDef(const void* proto, size_t proto_len,
                                          TF_Status* status) {
  tensorflow::FunctionDef fdef;
  bool success = fdef.ParseFromArray(proto, proto_len);

#include "tensorflow/c/eager/parallel_device/parallel_device_testlib.h"
#include "tensorflow/c/eager/tfe_tensorhandle_internal.h"
#include "tensorflow/c/tf_buffer.h"
#include "tensorflow/c/tf_datatype.h"
#include "tensorflow/c/tf_status.h"
#include "tensorflow/c/tf_status_internal.h"
#include "xla/tsl/lib/core/status_test_util.h"
#include "tensorflow/core/platform/test.h"

// NOTE(allenl): These tests currently go through TFE_Execute and so are