code: def __init__(self, block, num_blocks):
      super(RetinaFPN, self).__init__()
      self.in_planes = 64
  
      self.conv1 = nn.Conv2d(
          3, 64, kernel_size=7, stride=2, padding=3, bias=False
      )
      self.bn1 = nn.BatchNorm2d(64)
  
      # Bottom-up layers
      self.layer2 = self._make_layer(block, 64, num_blocks[0], stride=1)
      self.layer3 = self._make_layer(
          block, 128, num_blocks[1], stride=2
      )
      self.layer4 = self._make_layer(
          block, 256, num_blocks[2], stride=2
      )
      self.layer5 = self._make_layer(
          block, 512, num_blocks[3], stride=2
      )
      self.conv6 = nn.Conv2d(
          2048, 256, kernel_size=3, stride=2, padding=1
      )
      self.conv7 = nn.Conv2d(
          256, 256, kernel_size=3, stride=2, padding=1
      )
  
      # Top layer
      self.toplayer = nn.Conv2d(
          2048, 256, kernel_size=1, stride=1, padding=0
      )  # Reduce channels
  
      # Smooth layers
      self.smooth1 = nn.Conv2d(
          256, 256, kernel_size=3, stride=1, padding=1
      )
      self.smooth2 = nn.Conv2d(
          256, 256, kernel_size=3, stride=1, padding=1
      )
  
      # Lateral layers
      self.latlayer1 = nn.Conv2d(
          1024, 256, kernel_size=1, stride=1, padding=0
      )
      self.latlayer2 = nn.Conv2d(
          512, 256, kernel_size=1, stride=1, padding=0
      )
  
file_path: pywick/pywick/models/localization/retina_fpn.py
imports: import torch
  import torch.nn
  import torch.nn.functional
  from torch import Tensor
label_desc: Use image node_id 1 to create a new RetinaFPN object from inherited base classes: nn with example: obj = RetinaFPN(block, num_blocks)