code: def forward(self, x, top_module=None, yield_kpts_towers=False):
      logits = []
      bbox_reg = []
      kpts_reg = []
      kpts_locator_reg = []
      ctrness = []
      top_feats = []
      kpt_towers = []
      all_sampler_features = []
      hms = None
      hms_offset = None
  
      for l, feature in enumerate(x):
          feature = self.share_tower(feature)
          cls_tower = self.cls_tower(feature)
          kpt_tower = self.kpt_tower(feature)
          if yield_kpts_towers:
              kpt_towers.append(kpt_tower)
  
          if self.enable_hm_branch and l == 0:
              hm_tower = self.hm_tower(feature)
              hms = self.hm_pred(hm_tower)
              if self.predict_hm_offset:
                  hms_offset = self.hm_offset_pred(hm_tower)
  
          logits.append(self.cls_logits(cls_tower))
  
          if self.enable_bbox_branch:
              bbox_tower = self.bbox_tower(feature)
              cur_bbox_reg = self.bbox_pred(bbox_tower)
  
          if self.center_branch == "cls":
              center_tower = cls_tower
          elif self.center_branch == "bbox":
              center_tower = bbox_tower
          else:
              center_tower = kpt_tower
          ctrness.append(self.ctrness(center_tower))
  
          cur_kpt_locator_offset = self.locator(kpt_tower)
          sampler_feature = self.sampler(kpt_tower)
          b, _, h, w = sampler_feature.shape
          sampler_feature = sampler_feature.reshape(
              b, self.groups, self.seperate_conv_channel, h, w
          )
          all_sampler_features.append(sampler_feature)
  
          cur_kpt_reg = []
          for group_id in range(self.groups):
              per_group_kpt_bases = cur_kpt_locator_offset[
                  :, 2 * group_id : 2 * group_id + 2
              ]
              if l == 0:
                  sampler_features = all_sampler_features[0][
                      :, group_id
                  ]
                  sampler_features_stride = self.fpn_strides[0]
              else:
                  sampler_features = all_sampler_features[l - 1][
                      :, group_id
                  ]
                  sampler_features_stride = self.fpn_strides[l - 1]
  
              per_group_kps_offsets = self.predictor[group_id](
                  per_group_kpt_bases,
                  sampler_features,
                  sampler_features_stride,
                  self.fpn_strides[l],
              ).reshape(b, self.kpt_per_group[group_id], -1, h, w)
              per_group_kps_offsets = torch.cat(
                  [
                      per_group_kps_offsets[:, :, 0:2, :, :]
                      / float(
                          self.fpn_strides[l] / sampler_features_stride
                      )
                      + per_group_kpt_bases[:, None],
                      per_group_kps_offsets[:, :, 2:, :, :],
                  ],
                  dim=2,
              )
              # per_group_kps_offsets[:, :, 0:2, :, :] /= float(self.fpn_strides[l] / sampler_features_stride)
              # per_group_kps_offsets[:, :, 0:2, :, :] += per_group_kpt_bases[:, None]
              cur_kpt_reg.append(per_group_kps_offsets)
  
          cur_kpt_reg = torch.cat(cur_kpt_reg, dim=1)
          kpt_index = self.kpt_index.to(cur_kpt_reg.device)[
              None, :, None, None, None
          ].repeat(b, 1, cur_kpt_reg.shape[2], h, w)
          cur_kpt_reg = cur_kpt_reg.gather(1, kpt_index)
  
          if self.scales is not None:
              if self.enable_bbox_branch:
                  cur_bbox_reg = self.scales[l](cur_bbox_reg)
              if self.loss_on_locator:
                  cur_kpt_locator_offset = self.scales[l](
                      cur_kpt_locator_offset
                  )
              cur_kpt_reg[:, :, 0:2, :, :] = self.scales[l](
                  cur_kpt_reg[:, :, 0:2, :, :]
              )
          cur_kpt_reg = cur_kpt_reg.reshape(b, -1, h, w)
  
          # Note that we use relu, as in the improved FCOS, instead of exp.
          if self.enable_bbox_branch:
              bbox_reg.append(F.relu(cur_bbox_reg))
          if self.loss_on_locator:
              kpts_locator_reg.append(cur_kpt_locator_offset)
          kpts_reg.append(cur_kpt_reg)
  
          if top_module is not None:
              top_feats.append(top_module(kpt_tower))
  
      return (
          logits,
          bbox_reg,
          kpts_reg,
          kpts_locator_reg,
          ctrness,
          top_feats,
          kpt_towers,
          hms,
          hms_offset,
      )
  
imports: import math
  from typing import List, Dict
  import torch
  from torch import nn
  from torch.nn import functional
  from torch.autograd import Variable
  import numpy
  from ...utils.comm import compute_locations
  from ...nn.shape_spec import ShapeSpec
  from ...nn.batch_norm import NaiveSyncBatchNorm
  from ...nn.deform_conv import DeformConvWithChangeableStride
  from ...nn.group_norm import NaiveGroupNorm
  from .directpose_outputs import DirectPoseOutputs
label_desc: Use image node_id 2 for calling the DIRECTPOSEHead obj's underlying member method code with example usage: obj.forward(x, top_module, yield_kpts_towers) and returns: logits,  bbox_reg,  kpts_reg,  kpts_locator_reg,  ctrness,  top_feats,  kpt_towers,  hms,  hms_offset