TFE_Execute(op, op_outputs.data(), num_outputs, s);
  TFE_DeleteOp(op);
  if (TF_GetCode(s) != TF_OK) return;
  std::vector<TFE_TensorHandle*> unwrapped_outputs;
  unwrapped_outputs.reserve(op_outputs.size());
  for (auto* handle : op_outputs) {
    unwrapped_outputs.push_back(handle);
  }
  for (int i = 0; i < *num_outputs; ++i) {

   */
  @CanIgnoreReturnValue
  public double acquire(int permits) {
    long microsToWait = reserve(permits);
    stopwatch.sleepMicrosUninterruptibly(microsToWait);
    return 1.0 * microsToWait / SECONDS.toMicros(1L);
  }

  /**
   * Reserves the given number of permits from this {@code RateLimiter} for future use, returning

		// of whether we got a lot of requests
		limiter: rate.NewLimiter(rate.Limit(0.2), 1),
		hits:    0,
		in:      in,
	}
}

func (i *installationThrottle) Throttle(ctx context.Context) {
	res := i.limiter.Reserve()
	// Slightly weird usage of the limiter, as we are not strictly using it for limiting
	// First, we get a reservation. This will use up the limit for 5s
	if res.Delay() == 0 {

   */
  @CanIgnoreReturnValue
  public double acquire(int permits) {
    long microsToWait = reserve(permits);
    stopwatch.sleepMicrosUninterruptibly(microsToWait);
    return 1.0 * microsToWait / SECONDS.toMicros(1L);
  }

  /**
   * Reserves the given number of permits from this {@code RateLimiter} for future use, returning

    tensorflow::shape_inference::InferenceContext* ic, int num_dims,
    const int64_t* dims) {
  if (num_dims != -1) {
    std::vector<tensorflow::shape_inference::DimensionHandle> dim_vec;
    dim_vec.reserve(num_dims);
    for (int i = 0; i < num_dims; ++i) {
      dim_vec.push_back(ic->MakeDim(dims[i]));
    }
    return ic->MakeShape(dim_vec);
  } else {
    return ic->UnknownShape();
  }
}

Here I'm also highlighting contributions from sponsors.

  for (int i = 0; i < inputs.size(); i++) {
    input_ids[i] = ToId(inputs[i]);
    input_dtypes[i] = inputs[i]->DataType();
  }
  std::vector<TapeTensor> tape_tensors;
  tape_tensors.reserve(outputs.size());
  for (auto t : outputs) {
    tape_tensors.push_back(TapeTensor(t));
  }
  GradientTape::RecordOperation(
      op_name, tape_tensors, input_ids, input_dtypes,

  return grads;
}

std::vector<string> GetFuncNames(const tensorflow::GraphDef& graph_def) {
  std::vector<string> names;
  auto functions = graph_def.library().function();
  names.reserve(functions.size());
  for (const tensorflow::FunctionDef& func : functions) {
    names.push_back(func.signature().name());
  }
  std::sort(names.begin(), names.end());
  return names;
}

                                               int* num_handles,
                                               TF_Status* status) {
  std::vector<tensorflow::TensorHandle*> tensor_handles;
  tensor_handles.reserve(*num_handles);
  for (int i = 0; i < *num_handles; ++i) {
    tensorflow::ImmediateExecutionTensorHandle* unwrapped_handle =
        tensorflow::unwrap(handles[i]);

	POPCNTW R3, R4                  // 7c6402f4
	POPCNTD R3, R4                  // 7c6403f4

	PASTECC R3, R4                  // 7c23270d
	COPY R3, R4                     // 7c23260c

	// load-and-reserve
	LBAR (R4)(R3*1),$1,R5           // 7ca32069
	LBAR (R4)(R0),$1,R5             // 7ca02069
	LBAR (R4),$0,R5                 // 7ca02068
	LBAR (R3),R5                    // 7ca01868