code: def forward(self, x: torch.Tensor) -> torch.Tensor:
      y = self.maxpool2(self.conv2(self.maxpool1(self.conv1(x))))
      y = self.maxpool4(self.conv4(self.maxpool3(self.conv3(y))))
      r1 = self.conv5(y)  # route layer
      y = self.zeropad(self.conv6(self.maxpool5(r1)))
      y = self.conv7(self.maxpool6(y))
      r2 = self.conv8(y)  # route layer
      y = self.conv10(self.conv9(r2))
  
      # first detection layer
      out = self.yolo1(y)
      y = self.conv11(self.__route_layer(r2))
      y = self.__route_layer(self.upsample1(y), r1)
      y = self.conv13(self.conv12(y))
  
      # second detection layer
      out = torch.cat([out, self.yolo2(y)], 1)
  
      return out
  
file_path: ImageAI/imageai/yolov3/tiny_yolov3.py
imports: from typing import Union, List, Tuple, Optional
  import torch
  import torch.nn
  import numpy
  from .yolov3 import DetectionLayer, ConvLayer
label_desc: Use image node_id 4 for calling the YoloV3Tiny obj's underlying member method code with example usage: obj.forward(x) and returns: out