code: def __shortcut_layer(
      self,
      y1: torch.Tensor,
      y2: torch.Tensor,
      activation: str = "linear",
  ) -> torch.Tensor:
      actv = noop if activation == "linear" else nn.LeakyReLU(0.1)
      return actv(y1 + y2)
  
imports: from typing import Union, List, Tuple, Optional
  import torch
  import torch.nn
  import numpy
  from .utils import transform_prediction
label_desc: Use image node_id 4 for calling the YoloV3 obj's underlying member method code with example usage: obj.__shortcut_layer(y1, y2, activation) and returns: actv