class_docstr: Base class for DenseHeads.
  
  1. The ``init_weights`` method is used to initialize densehead's
  model parameters. After detector initialization, ``init_weights``
  is triggered when ``detector.init_weights()`` is called externally.
  
  2. The ``loss`` method is used to calculate the loss of densehead,
  which includes two steps: (1) the densehead model performs forward
  propagation to obtain the feature maps (2) The ``loss_by_feat`` method
  is called based on the feature maps to calculate the loss.
  
  .. code:: text
  
  loss(): forward() -> loss_by_feat()
  
  3. The ``predict`` method is used to predict detection results,
  which includes two steps: (1) the densehead model performs forward
  propagation to obtain the feature maps (2) The ``predict_by_feat`` method
  is called based on the feature maps to predict detection results including
  post-processing.
  
  .. code:: text
  
  predict(): forward() -> predict_by_feat()
  
  4. The ``loss_and_predict`` method is used to return loss and detection
  results at the same time. It will call densehead's ``forward``,
  ``loss_by_feat`` and ``predict_by_feat`` methods in order.  If one-stage is
  used as RPN, the densehead needs to return both losses and predictions.
  This predictions is used as the proposal of roihead.
  
  .. code:: text
  
  loss_and_predict(): forward() -> loss_by_feat() -> predict_by_feat()
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docstr: Initialize the weights.
docstr_tok: ["Initialize","the","weights","."]
code: def init_weights(self) -> None:
      """Initialize the weights."""
      super().init_weights()
      # avoid init_cfg overwrite the initialization of `conv_offset`
      for m in self.modules():
          # DeformConv2dPack, ModulatedDeformConv2dPack
          if hasattr(m, "conv_offset"):
              constant_init(m.conv_offset, 0)
  
imports: import copy
  from abc import ABCMeta, abstractmethod
  from inspect import signature
  from typing import List, Optional, Tuple
  import torch
  from mmcv.ops import batched_nms
  from mmengine.config import ConfigDict
  from mmengine.model import BaseModule, constant_init
  from mmengine.structures import InstanceData
  from torch import Tensor
  from mmdet.structures import SampleList
  from mmdet.structures.bbox import cat_boxes, get_box_tensor, get_box_wh, scale_boxes
  from mmdet.utils import InstanceList, OptMultiConfig
  from ..test_time_augs import merge_aug_results
  from ..utils import filter_scores_and_topk, select_single_mlvl, unpack_gt_instances
label_desc: Use image node_id 2 for calling the BaseDenseHead obj's underlying member method code with example usage: obj.init_weights() without return types