class_docstr: YOLACT segmentation head used in https://arxiv.org/abs/1904.02689.
  
  Apply a semantic segmentation loss on feature space using layers that are
  only evaluated during training to increase performance with no speed
  penalty.
  
  Args:
      in_channels (int): Number of channels in the input feature map.
      num_classes (int): Number of categories excluding the background
          category.
      loss_segm (dict): Config of semantic segmentation loss.
docstr: Initialize layers of the head.
docstr_tok: ["Initialize","layers","of","the","head","."]
code: def _init_layers(self):
      """Initialize layers of the head."""
      self.segm_conv = nn.Conv2d(
          self.in_channels, self.num_classes, kernel_size=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 YOLACTSegmHead obj's underlying member method code with example usage: obj._init_layers() without return types