code: def __init__(
      self,
      anchors: Union[List[int], Tuple[int, ...]],
      num_classes: int = 80,
      device: str = "cpu",
  ):
      super().__init__()
  
      # Network Layers
      self.conv1 = ConvLayer(3, 16)
      self.maxpool1 = nn.MaxPool2d(2, 2)
      self.conv2 = ConvLayer(16, 32)
      self.maxpool2 = nn.MaxPool2d(2, 2)
      self.conv3 = ConvLayer(32, 64)
      self.maxpool3 = nn.MaxPool2d(2, 2)
      self.conv4 = ConvLayer(64, 128)
      self.maxpool4 = nn.MaxPool2d(2, 2)
      self.conv5 = ConvLayer(128, 256)
      self.maxpool5 = nn.MaxPool2d(2, 2)
      self.conv6 = ConvLayer(256, 512)
      self.zeropad = nn.ZeroPad2d((0, 1, 0, 1))
      self.maxpool6 = nn.MaxPool2d(2, 1)
      self.conv7 = ConvLayer(512, 1024)
      self.conv8 = ConvLayer(1024, 256, 1, 1)
      self.conv9 = ConvLayer(256, 512)
      self.conv10 = ConvLayer(
          512,
          (3 * (5 + num_classes)),
          1,
          1,
          use_batch_norm=False,
          activation="linear",
      )
      self.yolo1 = DetectionLayer(
          num_classes=num_classes,
          anchors=anchors,
          anchor_masks=(3, 4, 5),
          device=device,
          layer=1,
      )
      # self.__route_layer(conv8)
      self.conv11 = ConvLayer(256, 128, 1, 1)
      self.upsample1 = nn.Upsample(
          scale_factor=2,
          mode="nearest"
          # align_corners=True
      )
      # self.__route_layer(upsample1, conv5)
      self.conv12 = ConvLayer(384, 256)
      self.conv13 = ConvLayer(
          256,
          (3 * (5 + num_classes)),
          1,
          1,
          use_batch_norm=False,
          activation="linear",
      )
      self.yolo2 = DetectionLayer(
          num_classes=num_classes,
          anchors=anchors,
          anchor_masks=(0, 1, 2),
          device=device,
          layer=2,
      )
  
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 1 to create a new YoloV3Tiny object from inherited base classes: nn with example: obj = YoloV3Tiny(anchors, num_classes, device)