static_cast<off_t>(offset))};
    if (r > 0) {
      dst += r;
      offset += static_cast<uint64_t>(r);
      n -= r;  // safe as 0 < r <= n so n will never underflow
      read += r;
    } else if (r == 0) {
      TF_SetStatus(status, TF_OUT_OF_RANGE, "Read fewer bytes than requested");
      break;
    } else if (errno == EINTR || errno == EAGAIN) {
      // Retry

}

// On success, returns a set of TF_Function instances from `text_proto` of
// GraphDef type. These functions must be deleted by calling TF_DeleteFunction.
//
// If `mutate_proto_func` is non-NULL, run it over each FunctionDef proto,
// before creating a TF_Function out of the possibly mutated proto.
static std::vector<UniqueFuncPtr> CreateFunctionsFromTextProto(
    const char* text_proto,

  // TODO(srbs): It seems like this is used only for performance optimization
  // and not for correctness. The only downside of keeping this 1 seems to be
  // that the gradient accumulation is unbounded and we will never
  // aggressively aggregate accumulated gradients to recover memory.
  // Revisit and fix.
  return 1;
}

// Consumes references to the tensors in the gradient_tensors list and returns

  StatusPtr first_bad_status(nullptr);

  for (const auto& dt : device_threads_) {
    StatusPtr async_wait_status(TF_NewStatus());
    dt->AsyncWait(async_wait_status.get());
    // Prefer non cancelled errors to uncover real failures.
    if (TF_GetCode(async_wait_status.get()) != TF_OK &&
        (first_bad_status == nullptr ||
         TF_GetCode(first_bad_status.get()) == TF_CANCELLED)) {
      first_bad_status.reset(TF_NewStatus());

  if (num_dims > TensorShape::MaxDimensions()) {
    return errors::InvalidArgument("Value specified for `", attr_name, "` has ",
                                   num_dims,
                                   " dimensions which is over the limit of ",
                                   TensorShape::MaxDimensions(), ".");
  }
  TensorShapeProto proto;
  if (num_dims < 0) {
    proto.set_unknown_rank(true);
  } else {

              "localhost:", tensorflow::testing::PickUnusedPortOrDie())});

  tensorflow::JobDef* job_def2 = cluster_def->add_job();
  job_def2->set_name("worker");

  // Copy over `host:port` at `task_index`
  for (auto task : cluster_server_def.cluster().job(0).tasks()) {
    if (task.first == task_index) {
      job_def2->mutable_tasks()->insert({task.first, task.second});
    }
  }

}

TEST(CAPI, TensorEncodeDecodeStrings) {
  TestEncodeDecode(__LINE__, {});
  TestEncodeDecode(__LINE__, {"hello"});
  TestEncodeDecode(__LINE__,
                   {"the", "quick", "brown", "fox", "jumped", "over"});

  string big(1000, 'a');
  TestEncodeDecode(__LINE__, {"small", big, "small2"});
}

TEST(CAPI, SessionOptions) {
  TF_SessionOptions* opt = TF_NewSessionOptions();
  TF_DeleteSessionOptions(opt);