class_docstr: A mask point head use in PointRend.
  
  ``PointHead`` use shared multi-layer perceptron (equivalent to
  nn.Conv1d) to predict the logit of input points. The fine-grained feature
  and coarse feature will be concatenate together for predication.
  
  Args:
      num_fcs (int): Number of fc layers in the head. Default: 3.
      in_channels (int): Number of input channels. Default: 256.
      fc_channels (int): Number of fc channels. Default: 256.
      num_classes (int): Number of classes for logits. Default: 80.
      class_agnostic (bool): Whether use class agnostic classification.
          If so, the output channels of logits will be 1. Default: False.
      coarse_pred_each_layer (bool): Whether concatenate coarse feature with
          the output of each fc layer. Default: True.
      conv_cfg (dict|None): Dictionary to construct and config conv layer.
          Default: dict(type='Conv1d'))
      norm_cfg (dict|None): Dictionary to construct and config norm layer.
          Default: None.
      loss_point (dict): Dictionary to construct and config loss layer of
          point head. Default: dict(type='CrossEntropyLoss', use_mask=True,
          loss_weight=1.0).
code: def forward(self, fine_grained_point_feats, coarse_point_feats):
      x = torch.cat(
          [fine_grained_point_feats, coarse_point_feats], dim=1
      )
      for fc in self.fcs:
          x = fc(x)
          if self.coarse_pred_each_layer:
              x = torch.cat((x, coarse_point_feats), dim=1)
      return self.cls_seg(x)
  
imports: import torch
  import torch.nn
  from annotator.uniformer.mmcv.cnn import ConvModule, normal_init
  from annotator.uniformer.mmcv.ops import point_sample
  from annotator.uniformer.mmseg.models.builder import HEADS
  from annotator.uniformer.mmseg.ops import resize
  from ..losses import accuracy
  from .cascade_decode_head import BaseCascadeDecodeHead
label_desc: Use image node_id 4 for calling the PointHead obj's underlying member method code with example usage: obj.forward(fine_grained_point_feats, coarse_point_feats) and returns: self