class_docstr: YOLACT box head used in https://arxiv.org/abs/1904.02689.
  
  Note that YOLACT head is a light version of RetinaNet head.
  Four differences are described as follows:
  
  1. YOLACT box head has three-times fewer anchors.
  2. YOLACT box head shares the convs for box and cls branches.
  3. YOLACT box head uses OHEM instead of Focal loss.
  4. YOLACT box head predicts a set of mask coefficients for each box.
  
  Args:
      num_classes (int): Number of categories excluding the background
          category.
      in_channels (int): Number of channels in the input feature map.
      anchor_generator (dict): Config dict for anchor generator
      loss_cls (dict): Config of classification loss.
      loss_bbox (dict): Config of localization loss.
      num_head_convs (int): Number of the conv layers shared by
          box and cls branches.
      num_protos (int): Number of the mask coefficients.
      use_ohem (bool): If true, ``loss_single_OHEM`` will be used for
          cls loss calculation. If false, ``loss_single`` will be used.
      conv_cfg (dict): Dictionary to construct and config conv layer.
      norm_cfg (dict): Dictionary to construct and config norm layer.
docstr: Initialize layers of the head.
docstr_tok: ["Initialize","layers","of","the","head","."]
code: def _init_layers(self):
      """Initialize layers of the head."""
      self.relu = nn.ReLU(inplace=True)
      self.head_convs = nn.ModuleList()
      for i in range(self.num_head_convs):
          chn = self.in_channels if i == 0 else self.feat_channels
          self.head_convs.append(
              ConvModule(
                  chn,
                  self.feat_channels,
                  3,
                  stride=1,
                  padding=1,
                  conv_cfg=self.conv_cfg,
                  norm_cfg=self.norm_cfg,
              )
          )
      self.conv_cls = nn.Conv2d(
          self.feat_channels,
          self.num_anchors * self.cls_out_channels,
          3,
          padding=1,
      )
      self.conv_reg = nn.Conv2d(
          self.feat_channels, self.num_anchors * 4, 3, padding=1
      )
      self.conv_coeff = nn.Conv2d(
          self.feat_channels,
          self.num_anchors * self.num_protos,
          3,
          padding=1,
      )
  
imports: import numpy
  import torch
  import torch.nn
  import torch.nn.functional
  from mmcv.cnn import ConvModule, xavier_init
  from mmcv.runner import force_fp32
  from mmdet.core import build_sampler, fast_nms, images_to_levels, multi_apply
  from ..builder import HEADS, build_loss
  from .anchor_head import AnchorHead
label_desc: Use image node_id 2 for calling the YOLACTHead obj's underlying member method code with example usage: obj._init_layers() without return types