class_docstr: Base module of Semantic Head.
  
  Args:
      num_classes (int): the number of classes.
      seg_rescale_factor (float): the rescale factor for ``gt_sem_seg``,
          which equals to ``1 / output_strides``. The output_strides is
          for ``seg_preds``. Defaults to  1 / 4.
      init_cfg (Optional[Union[:obj:`ConfigDict`, dict]]): the initialization
          config.
      loss_seg (Union[:obj:`ConfigDict`, dict]): the loss of the semantic
          head.
docstr: Placeholder of forward function.
  
  Args:
      x (Tensor): Feature maps.
  
  Returns:
      Dict[str, Tensor]: A dictionary, including features
          and predicted scores. Required keys: 'seg_preds'
          and 'feats'.
docstr_tok: ["Placeholder","of","forward","function",".","Args",":","x","(","Tensor",")",":","Feature","maps",".","Returns",":","Dict","[","str",",","Tensor","]",":","A","dictionary",",","including","features","and","predicted","scores",".","Required","keys",":","'seg_preds'","and","'feats","'","."]
code: def forward(
      self, x: Union[Tensor, Tuple[Tensor]]
  ) -> Dict[str, Tensor]:
      """Placeholder of forward function.
  
      Args:
          x (Tensor): Feature maps.
  
      Returns:
          Dict[str, Tensor]: A dictionary, including features
              and predicted scores. Required keys: 'seg_preds'
              and 'feats'.
      """
      pass
  
file_path: mmdetection/mmdet/models/seg_heads/base_semantic_head.py
imports: from abc import ABCMeta, abstractmethod
  from typing import Dict, List, Tuple, Union
  import torch.nn.functional
  from mmengine.model import BaseModule
  from torch import Tensor
  from mmdet.registry import MODELS
  from mmdet.structures import SampleList
  from mmdet.utils import ConfigType, OptMultiConfig
label_desc: Use image node_id 2 for calling the BaseSemanticHead obj's underlying member method code with example usage: obj.forward(x) without return types