class_docstr: S2Anet head
  Args:
      stacked_convs (int): number of stacked_convs
      feat_in (int): input channels of feat
      feat_out (int): output channels of feat
      num_classes (int): num_classes
      anchor_strides (list): stride of anchors
      anchor_scales (list): scale of anchors
      anchor_ratios (list): ratios of anchors
      target_means (list): target_means
      target_stds (list): target_stds
      align_conv_type (str): align_conv_type ['Conv', 'AlignConv']
      align_conv_size (int): kernel size of align_conv
      use_sigmoid_cls (bool): use sigmoid_cls or not
      reg_loss_weight (list): loss weight for regression
class_docstr_tok: ["S2Anet","head","Args",":","stacked_convs","(","int",")",":","number","of","stacked_convs","feat_in","(","int",")",":","input","channels","of","feat","feat_out","(","int",")",":","output","channels","of","feat","num_classes","(","int",")",":","num_classes","anchor_strides","(","list",")",":","stride","of","anchors","anchor_scales","(","list",")",":","scale","of","anchors","anchor_ratios","(","list",")",":","ratios","of","anchors","target_means","(","list",")",":","target_means","target_stds","(","list",")",":","target_stds","align_conv_type","(","str",")",":","align_conv_type","[","'Conv","'",",","'AlignConv","'","]","align_conv_size","(","int",")",":","kernel","size","of","align_conv","use_sigmoid_cls","(","bool",")",":","use","sigmoid_cls","or","not","reg_loss_weight","(","list",")",":","loss","weight","for","regression"]
code: def get_bboxes(self, head_outs):
      perd_bboxes_list, pred_scores_list = head_outs
      batch = paddle.shape(pred_scores_list[0])[0]
      bboxes, bbox_num = [], []
      for i in range(batch):
          pred_scores_per_image = [t[i] for t in pred_scores_list]
          pred_bboxes_per_image = [t[i] for t in perd_bboxes_list]
          bbox_per_image, bbox_num_per_image = self.get_bboxes_single(
              pred_scores_per_image, pred_bboxes_per_image
          )
          bboxes.append(bbox_per_image)
          bbox_num.append(bbox_num_per_image)
  
      bboxes = paddle.concat(bboxes)
      bbox_num = paddle.concat(bbox_num)
      return bboxes, bbox_num
  
imports: import paddle
  from paddle import ParamAttr
  import paddle.nn
  import paddle.nn.functional
  from paddle.nn.initializer import Normal, Constant
  from ppdet.core.workspace import register
  from ppdet.modeling.proposal_generator.target_layer import RBoxAssigner
  from ppdet.modeling.proposal_generator.anchor_generator import S2ANetAnchorGenerator
  from ppdet.modeling.layers import AlignConv
  from ..cls_utils import _get_class_default_kwargs
  import numpy
label_desc: Use image node_id 3 for calling the S2ANetHead obj's underlying member method code with example usage: obj.get_bboxes(head_outs) and returns: bboxes,  bbox_num