class_docstr: A regression head for use in RetinaNet.
  
  Args:
      in_channels (int): number of channels of the input feature
      num_anchors (int): number of anchors to be predicted
class_docstr_tok: ["A","regression","head","for","use","in","RetinaNet",".","Args",":","in_channels","(","int",")",":","number","of","channels","of","the","input","feature","num_anchors","(","int",")",":","number","of","anchors","to","be","predicted"]
code: def forward(self, x: List[Tensor]) -> Tensor:
      all_bbox_regression = []
  
      # 遍历每个预测特征层
      for features in x:
          bbox_regression = self.conv(features)
          bbox_regression = self.bbox_reg(bbox_regression)
  
          # Permute bbox regression output from (N, 4 * A, H, W) to (N, HWA, 4).
          N, _, H, W = bbox_regression.shape
          # [N, 4 * A, H, W] -> [N, A, 4, H, W]
          bbox_regression = bbox_regression.view(N, -1, 4, H, W)
          # [N, A, 4, H, W] -> [N, H, W, A, 4]
          bbox_regression = bbox_regression.permute(0, 3, 4, 1, 2)
          # [N, H, W, A, 4] -> [N, HWA, 4]
          bbox_regression = bbox_regression.reshape(N, -1, 4)
  
          all_bbox_regression.append(bbox_regression)
  
      return torch.cat(all_bbox_regression, dim=1)
  
imports: import math
  import warnings
  from collections import OrderedDict
  from typing import Dict, List, Tuple, Optional, Union
  import torch
  from torch import nn, Tensor
  from .None import det_utils
  from .anchor_utils import AnchorsGenerator
  from .None import boxes
  from .losses import sigmoid_focal_loss
  from .transform import GeneralizedRCNNTransform
label_desc: Use image node_id 3 for calling the RetinaNetRegressionHead obj's underlying member method code with example usage: obj.forward(x) and returns: torch