code: def forward(self, x, top_module=None, yield_bbox_towers=False):
      logits = []
      bbox_reg = []
      ctrness = []
      top_feats = []
      bbox_towers = []
      for l, feature in enumerate(x):
          feature = self.share_tower(feature)
          cls_tower = self.cls_tower(feature)
          bbox_tower = self.bbox_tower(feature)
          if yield_bbox_towers:
              bbox_towers.append(bbox_tower)
  
          logits.append(self.cls_logits(cls_tower))
          ctrness.append(self.ctrness(bbox_tower))
          reg = self.bbox_pred(bbox_tower)
          if self.scales is not None:
              reg = self.scales[l](reg)
          # Note that we use relu, as in the improved FCOS, instead of exp.
          bbox_reg.append(F.relu(reg))
          if top_module is not None:
              top_feats.append(top_module(bbox_tower))
      return logits, bbox_reg, ctrness, top_feats, bbox_towers
  
imports: import logging
  import math
  from typing import List, Dict
  import torch
  from torch import nn
  from torch.nn import functional
  from ...nn.shape_spec import ShapeSpec
  from ...nn.deform_conv import DeformConvWithChangeableStride
  from ...nn.group_norm import NaiveGroupNorm
  from ...data.structures import Instances, Boxes
  from ...nn.focal_loss import sigmoid_focal_loss_jit
  from ...nn.nms import ml_nms
  from ...utils.loss import IOULoss
  from ...utils.comm import get_world_size, reduce_sum, compute_locations
label_desc: Use image node_id 2 for calling the FCOSHead obj's underlying member method code with example usage: obj.forward(x, top_module, yield_bbox_towers) and returns: logits,  bbox_reg,  ctrness,  top_feats,  bbox_towers