class_docstr: Implementation of `Deformable DETR: Deformable Transformers for
  End-to-End Object Detection <https://arxiv.org/abs/2010.04159>`_
  
  Code is modified from the `official github repo
  <https://github.com/fundamentalvision/Deformable-DETR>`_.
  
  Args:
      decoder (:obj:`ConfigDict` or dict, optional): Config of the
          Transformer decoder. Defaults to None.
      bbox_head (:obj:`ConfigDict` or dict, optional): Config for the
          bounding box head module. Defaults to None.
      with_box_refine (bool, optional): Whether to refine the references
          in the decoder. Defaults to `False`.
      as_two_stage (bool, optional): Whether to generate the proposal
          from the outputs of encoder. Defaults to `False`.
      num_feature_levels (int, optional): Number of feature levels.
          Defaults to 4.
docstr: Initialize weights for Transformer and other components.
docstr_tok: ["Initialize","weights","for","Transformer","and","other","components","."]
code: def init_weights(self) -> None:
      """Initialize weights for Transformer and other components."""
      super().init_weights()
      for coder in self.encoder, self.decoder:
          for p in coder.parameters():
              if p.dim() > 1:
                  nn.init.xavier_uniform_(p)
      for m in self.modules():
          if isinstance(m, MultiScaleDeformableAttention):
              m.init_weights()
      if self.as_two_stage:
          nn.init.xavier_uniform_(self.memory_trans_fc.weight)
          nn.init.xavier_uniform_(self.pos_trans_fc.weight)
      else:
          xavier_init(
              self.reference_points_fc, distribution="uniform", bias=0.0
          )
      normal_(self.level_embed)
  
imports: import math
  from typing import Dict, Tuple
  import torch
  import torch.nn.functional
  from mmcv.cnn.bricks.transformer import MultiScaleDeformableAttention
  from mmengine.model import xavier_init
  from torch import Tensor, nn
  from torch.nn.init import normal_
  from mmdet.registry import MODELS
  from mmdet.structures import OptSampleList
  from mmdet.utils import OptConfigType
  from ..layers import DeformableDetrTransformerDecoder, DeformableDetrTransformerEncoder, SinePositionalEncoding
  from .base_detr import DetectionTransformer
label_desc: Use image node_id 3 for calling the DeformableDETR obj's underlying member method code with example usage: obj.init_weights() without return types