class_docstr: Implement FCOS (https://arxiv.org/abs/1904.01355).
class_docstr_tok: ["Implement","FCOS","(","https",":","\/\/arxiv.org\/abs\/1904.01355",")","."]
code: def forward_head(self, features, top_module=None):
      features = [features[f] for f in self.in_features]
      (
          pred_class_logits,
          pred_deltas,
          pred_centerness,
          top_feats,
          bbox_towers,
      ) = self.fcos_head(features, top_module, self.yield_proposal)
      return (
          pred_class_logits,
          pred_deltas,
          pred_centerness,
          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 FCOS obj's underlying member method code with example usage: obj.forward_head(features, top_module) and returns: pred_class_logits,  pred_deltas,  pred_centerness,  top_feats,  bbox_towers