std::unique_ptr<const void*[]>* ptrs,
                          std::unique_ptr<size_t[]>* lens) {
  ptrs->reset(new const void*[v.size()]);
  lens->reset(new size_t[v.size()]);
  for (size_t i = 0; i < v.size(); ++i) {
    (*ptrs)[i] = v[i].data();
    (*lens)[i] = v[i].size();
  }
}

class CApiColocationTest : public ::testing::Test {
 protected:

      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 =

static int64_t Read(const TF_RandomAccessFile* file, uint64_t offset, size_t n,
                    char* buffer, TF_Status* status) {
  auto posix_file = static_cast<PosixFile*>(file->plugin_file);
  char* dst = buffer;
  int64_t read = 0;

  while (n > 0) {
    // Some platforms, notably macs, throw `EINVAL` if `pread` is asked to read
    // more than fits in a 32-bit integer.
    size_t requested_read_length;
    if (n > INT32_MAX)

  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) {

}

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) {

  const auto& s = attr->s();
  std::memcpy(value, s.data(), std::min<size_t>(s.length(), max_length));
}

void TF_OperationGetAttrStringList(TF_Operation* oper, const char* attr_name,
                                   void** values, size_t* lengths,
                                   int max_values, void* storage,
                                   size_t storage_size, TF_Status* status) {

                                                      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);

      retvals[i] = new GraphTensor({operation, i}, g_);
    }
    return absl::OkStatus();
  }

  Status SetAttrString(const char* attr_name, const char* data,
                       size_t length) override {
    tensorflow::StringPiece s(data, length);
    op_->node_builder.Attr(attr_name, s);
    return absl::OkStatus();
  }
  Status SetAttrInt(const char* attr_name, int64_t value) override {

    fprintf(stderr, "TF_GetLocalTempDirectories returned no results\n");
    return 1;
  }

  char file_name[100];
  time_t t = time(NULL);
  snprintf(file_name, sizeof(file_name), "test-%d-%ld.txt", getpid(), t);

  size_t length = 2 + strlen(path) + strlen(file_name);
  char* full_path = malloc(length);
  snprintf(full_path, length, "%s/%s", path, file_name);

  TF_WritableFileHandle* h;

             AbstractContext* ctx,
             absl::Span<AbstractTensorHandle* const> inputs,
             absl::Span<AbstractTensorHandle*> outputs) -> Status {
    Tape tape(/*persistent=*/false);
    for (size_t i{}; i < inputs.size(); ++i) {
      tape.Watch(inputs[i]);
    }
    std::vector<AbstractTensorHandle*> temp_outputs(1);
    AbstractContextPtr tape_ctx(new TapeContext(ctx, &tape, grad_registry));
    TF_RETURN_IF_ERROR(