class_docstr: RPNHead in the `Sparse R-CNN <https://arxiv.org/abs/2011.12450>`_ .
  
  Unlike traditional RPNHead, this module does not need FPN input, but just
  decode `init_proposal_bboxes` and expand the first dimension of
  `init_proposal_bboxes` and `init_proposal_features` to the batch_size.
  
  Args:
      num_proposals (int): Number of init_proposals. Defaults to 100.
      proposal_feature_channel (int): Channel number of
          init_proposal_feature. Defaults to 256.
      init_cfg (:obj:`ConfigDict` or dict or list[:obj:`ConfigDict` or             dict]): Initialization config dict. Defaults to None.
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docstr: Initialize the init_proposal_bboxes as normalized.
  
  [c_x, c_y, w, h], and we initialize it to the size of  the entire
  image.
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code: def init_weights(self) -> None:
      """Initialize the init_proposal_bboxes as normalized.
  
      [c_x, c_y, w, h], and we initialize it to the size of  the entire
      image.
      """
      super().init_weights()
      nn.init.constant_(self.init_proposal_bboxes.weight[:, :2], 0.5)
      nn.init.constant_(self.init_proposal_bboxes.weight[:, 2:], 1)
  
file_path: mmdetection/mmdet/models/dense_heads/embedding_rpn_head.py
imports: from typing import List
  import torch
  import torch.nn
  from mmengine.model import BaseModule
  from mmengine.structures import InstanceData
  from torch import Tensor
  from mmdet.registry import MODELS
  from mmdet.structures.bbox import bbox_cxcywh_to_xyxy
  from mmdet.structures.det_data_sample import SampleList
  from mmdet.utils import InstanceList, OptConfigType
label_desc: Use image node_id 3 for calling the EmbeddingRPNHead obj's underlying member method code with example usage: obj.init_weights() without return types