code: def forward(self, x: torch.Tensor) -> torch.Tensor:
      y = self.conv2(self.conv1(x))
      # shortcut1
      y = self.conv5(
          self.__shortcut_layer(self.conv4(self.conv3(y)), y)
      )
      y2 = self.conv7(self.conv6(y))
      # shortcut2
      y = self.__shortcut_layer(y2, y)
      y2 = self.conv9(self.conv8(y))
      # shortcut3
      y2 = self.conv10(self.__shortcut_layer(y2, y))
      y = self.conv12(self.conv11(y2))
      # shortcut4
      y2 = self.__shortcut_layer(y, y2)
      y = self.conv14(self.conv13(y2))
      # shortcut5
      y2 = self.__shortcut_layer(y, y2)
      y = self.conv16(self.conv15(self.__shortcut_layer(y2, y)))
      # shortcut6
      y2 = self.__shortcut_layer(y, y2)
      y = self.conv18(self.conv17(y2))
      # shortcut7
      y2 = self.__shortcut_layer(y, y2)
      y = self.conv20(self.conv19(y2))
      # shortcut8
      y2 = self.__shortcut_layer(y, y2)
      y = self.conv22(self.conv21(y2))
      # shortcut9
      y2 = self.__shortcut_layer(y, y2)
      y = self.conv24(self.conv23(y2))
      # shortcut10
      y2 = self.__shortcut_layer(y, y2)
      y = self.conv26(self.conv25(y2))
      # shortcut11
      r1 = self.__shortcut_layer(y, y2)  # route_layer
      y = self.conv27(r1)
      y2 = self.conv29(self.conv28(y))
      # shortcut12
      y = self.__shortcut_layer(y2, y)
      y2 = self.conv31(self.conv30(y))
      # shortcut13
      y = self.__shortcut_layer(y2, y)
      y2 = self.conv33(self.conv32(y))
      # shortcut14
      y = self.__shortcut_layer(y2, y)
      y2 = self.conv35(self.conv34(y))
      # shortcut15
      y = self.__shortcut_layer(y2, y)
      y2 = self.conv37(self.conv36(y))
      # shortcut16
      y = self.__shortcut_layer(y2, y)
      y2 = self.conv39(self.conv38(y))
      # shortcut17
      y = self.__shortcut_layer(y2, y)
      y2 = self.conv41(self.conv40(y))
      # shortcut18
      y = self.__shortcut_layer(y2, y)
      y2 = self.conv43(self.conv42(y))
      # shortcut19
      r2 = self.__shortcut_layer(y2, y)  # route_layer
      y2 = self.conv44(r2)
      y = self.conv46(self.conv45(y2))
      # shortcut20
      y2 = self.__shortcut_layer(y, y2)
      y = self.conv48(self.conv47(y2))
      # shortcut21
      y2 = self.__shortcut_layer(y, y2)
      y = self.conv50(self.conv49(y2))
      # shortcut22
      y2 = self.__shortcut_layer(y, y2)
      y = self.conv52(self.conv51(y2))
      # shortcut23
      y2 = self.__shortcut_layer(y, y2)
      y = self.conv54(self.conv53(y2))
      r3 = self.conv57(self.conv56(self.conv55(y)))  # route_layer
      y = self.conv59(self.conv58(r3))
  
      # first detection layer
      out = self.yolo1(y)
      y = self.conv60(self.__route_layer(r3))
      y = self.conv62(
          self.conv61(self.__route_layer(self.upsample1(y), r2))
      )
      r4 = self.conv65(self.conv64(self.conv63(y)))  # route_layer
      y = self.conv67(self.conv66(r4))
  
      # second detection layer
      out = torch.cat([out, self.yolo2(y)], dim=1)
      y = self.conv68(self.__route_layer(r4))
      y = self.conv70(
          self.conv69(self.__route_layer(self.upsample2(y), r1))
      )
      y = self.conv75(
          self.conv74(self.conv73(self.conv72(self.conv71(y))))
      )
  
      # third detection layer
      out = torch.cat([out, self.yolo3(y)], dim=1)
  
      return out
  
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 5 for calling the YoloV3 obj's underlying member method code with example usage: obj.forward(x) and returns: out