EXPECT_THAT(*shape, ElementsAre(-1));
  std::unique_ptr<TFE_Op, decltype(&TFE_DeleteOp)> size_op(
      TFE_NewOp(context.get(), "Size", status.get()), TFE_DeleteOp);
  auto result = parallel_device.Execute(
      context.get(), {unknown_dims_vector.get()}, "Size",
      TFE_OpGetAttrs(size_op.get()), 1, status.get());
  ASSERT_TRUE(TF_GetCode(status.get()) == TF_OK);
  TF_ASSERT_OK((*result)[0]->Shape(&shape));

-#  endif
-# endif
-
-__STRING_INLINE size_t __strcspn_c1 (const char *__s, int __reject);
-__STRING_INLINE size_t
-__strcspn_c1 (const char *__s, int __reject)
-{
-  size_t __result = 0;
-  while (__s[__result] != '\0' && __s[__result] != __reject)
-    ++__result;
-  return __result;
-}
-
-__STRING_INLINE size_t __strcspn_c2 (const char *__s, int __reject1,
-				     int __reject2);

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

static void Int32Deallocator(void* data, size_t, void* arg) {
  delete[] static_cast<int32_t*>(data);
}

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

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

      const ParallelDevice& parallel_device,
      std::vector<TensorHandlePtr> components, absl::Span<const int64_t> shape,
      TF_Status* status);

  size_t num_tensors() const { return tensors_.size(); }
  TFE_TensorHandle* tensor(size_t index) const { return tensors_[index].get(); }

  // If the `shape` argument to `FromTensorHandles` is specified, returns that.
  //

    float value = 0.0f;
    TF_Tensor* t = TFE_TensorHandleResolve(handle_1, status);
    ASSERT_EQ(TF_OK, TF_GetCode(status)) << TF_Message(status);
    ASSERT_EQ(sizeof(float), TF_TensorByteSize(t));
    memcpy(&value, TF_TensorData(t), sizeof(float));
    TF_DeleteTensor(t);
    EXPECT_EQ(1.2f, value);
    TFE_DeleteTensorHandle(handle_1);
    TF_DeleteStatus(status);
    TFE_DeleteTensorHandle(var_handle);
  }

}

const char* TF_GraphDebugString(TF_Graph* graph, size_t* len) {
  tensorflow::mutex_lock c(graph->mu);
  const auto& debug_str = graph->graph.ToGraphDefDebug().DebugString();
  *len = debug_str.size();
  char* ret = static_cast<char*>(malloc(*len + 1));
  memcpy(ret, debug_str.c_str(), *len + 1);
  return ret;
}

char* TF_FunctionDebugString(TF_Function* func, size_t* len) {

      methods));
}

TFE_TensorHandle* TFE_NewTensorHandleFromDeviceMemory(
    TFE_Context* ctx, const char* device_name, TF_DataType dtype,
    const int64_t* dims, int num_dims, void* data, size_t len,
    void (*deallocator)(void* data, size_t len, void* arg),
    void* deallocator_arg, TF_Status* status) {
  tensorflow::Device* device = nullptr;
  tensorflow::EagerContext* context =

  int64_t dims[] = {2, 2};
  double data[] = {1.0, 2.0, 3.0, 4.0};
  TF_Status* status = TF_NewStatus();
  TF_Tensor* t = TFE_AllocateHostTensor(ctx, TF_DOUBLE, &dims[0],
                                        sizeof(dims) / sizeof(int64_t), status);
  memcpy(TF_TensorData(t), &data[0], TF_TensorByteSize(t));
  TFE_TensorHandle* th = TFE_NewTensorHandleFromTensor(ctx, t, status);
  CHECK_EQ(TF_OK, TF_GetCode(status)) << TF_Message(status);

  void* data = tensorflow::port::Malloc(value.length());
  value.copy(static_cast<char*>(data), value.length(), 0);
  buf->data = data;
  buf->length = value.length();
  buf->data_deallocator = [](void* data, size_t length) {
    tensorflow::port::Free(data);
  };
}

TFE_MonitoringStringGauge0* TFE_MonitoringNewStringGauge0(
    const char* name, TF_Status* status, const char* description) {

                                                      size_t proto_len,
                                                      TF_Status* status);

// This API is for experimental usage and may be subject to change.
TF_CAPI_EXPORT extern void TFE_ContextUpdateServerDefWithTimeout(
    TFE_Context* ctx, int keep_alive_secs, const void* proto, size_t proto_len,
    int64_t init_timeout_in_ms, TF_Status* status);