text: import math
  from torch import nn
  
  
  class RetinaNetHead(nn.Module):
      """
      Adds a RetinaNet head with classification and regression heads
      """
  
      def __init__(self, in_channels, num_classes, aspect_ratios, scales_per_octave,
                   num_convs, prior_prob):
          """
          Arguments:
              in_channels (int): number of channels of the input feature
              num_anchors (int): number of anchors to be predicted
          """
          super(RetinaNetHead, self).__init__()
          # TODO: Implement the sigmoid version first.
          num_classes = num_classes - 1
          num_anchors = len(aspect_ratios) * scales_per_octave
  
          cls_tower = []
          bbox_tower = []
          for i in range(num_convs):
              cls_tower.append(nn.Conv2d(in_channels, in_channels, 3, 1, 1))
              cls_tower.append(nn.ReLU(inplace=True))
              bbox_tower.append(nn.Conv2d(in_channels, in_channels, 3, 1, 1))
              bbox_tower.append(nn.ReLU(inplace=True))
  
          self.cls_tower = nn.Sequential(*cls_tower)
          self.bbox_tower = nn.Sequential(*bbox_tower)
          self.cls_logits = nn.Conv2d(in_channels, num_anchors * num_classes, 3, 1, 1)
          self.bbox_pred = nn.Conv2d(in_channels, num_anchors * 4, 3, 1, 1)
  
          # retinanet_bias_init
          self._weight_init()
          bias_value = -math.log((1 - prior_prob) / prior_prob)
          nn.init.constant_(self.cls_logits.bias, bias_value)
  
      def _weight_init(self):
          for m in self.modules():
              if isinstance(m, nn.Conv2d):
                  nn.init.normal_(m.weight, std=0.01)
                  nn.init.constant_(m.bias, 0)
  
      def forward(self, x):
          logits = []
          bbox_reg = []
          for feature in x:
              logits.append(self.cls_logits(self.cls_tower(feature)))
              bbox_reg.append(self.bbox_pred(self.bbox_tower(feature)))
          return logits, bbox_reg
  