class_docstr: RepPoint head.
  
  Args:
      point_feat_channels (int): Number of channels of points features.
      gradient_mul (float): The multiplier to gradients from
          points refinement and recognition.
      point_strides (Iterable): points strides.
      point_base_scale (int): bbox scale for assigning labels.
      loss_cls (dict): Config of classification loss.
      loss_bbox_init (dict): Config of initial points loss.
      loss_bbox_refine (dict): Config of points loss in refinement.
      use_grid_points (bool): If we use bounding box representation, the
      reppoints is represented as grid points on the bounding box.
      center_init (bool): Whether to use center point assignment.
      transform_method (str): The methods to transform RepPoints to bbox.
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code: def loss_single(
      self,
      cls_score,
      pts_pred_init,
      pts_pred_refine,
      labels,
      label_weights,
      bbox_gt_init,
      bbox_weights_init,
      bbox_gt_refine,
      bbox_weights_refine,
      stride,
      num_total_samples_init,
      num_total_samples_refine,
  ):
      # classification loss
      labels = labels.reshape(-1)
      label_weights = label_weights.reshape(-1)
      cls_score = cls_score.permute(0, 2, 3, 1).reshape(
          -1, self.cls_out_channels
      )
      cls_score = cls_score.contiguous()
      loss_cls = self.loss_cls(
          cls_score,
          labels,
          label_weights,
          avg_factor=num_total_samples_refine,
      )
  
      # points loss
      bbox_gt_init = bbox_gt_init.reshape(-1, 4)
      bbox_weights_init = bbox_weights_init.reshape(-1, 4)
      bbox_pred_init = self.points2bbox(
          pts_pred_init.reshape(-1, 2 * self.num_points), y_first=False
      )
      bbox_gt_refine = bbox_gt_refine.reshape(-1, 4)
      bbox_weights_refine = bbox_weights_refine.reshape(-1, 4)
      bbox_pred_refine = self.points2bbox(
          pts_pred_refine.reshape(-1, 2 * self.num_points),
          y_first=False,
      )
      normalize_term = self.point_base_scale * stride
      loss_pts_init = self.loss_bbox_init(
          bbox_pred_init / normalize_term,
          bbox_gt_init / normalize_term,
          bbox_weights_init,
          avg_factor=num_total_samples_init,
      )
      loss_pts_refine = self.loss_bbox_refine(
          bbox_pred_refine / normalize_term,
          bbox_gt_refine / normalize_term,
          bbox_weights_refine,
          avg_factor=num_total_samples_refine,
      )
      return loss_cls, loss_pts_init, loss_pts_refine
  
imports: import numpy
  import torch
  import torch.nn
  from mmcv.cnn import ConvModule, bias_init_with_prob, normal_init
  from mmcv.ops import DeformConv2d
  from mmdet.core import PointGenerator, build_assigner, build_sampler, images_to_levels, multi_apply, multiclass_nms, unmap
  from ..builder import HEADS, build_loss
  from .anchor_free_head import AnchorFreeHead
label_desc: Use image node_id 13 for calling the RepPointsHead obj's underlying member method code with example usage: obj.loss_single(cls_score, pts_pred_init, pts_pred_refine, labels, label_weights, bbox_gt_init, bbox_weights_init, bbox_gt_refine, bbox_weights_refine, stride, num_total_samples_init, num_total_samples_refine) and returns: loss_cls,  loss_pts_init,  loss_pts_refine