class_docstr: Implementation of `DETR: End-to-End Object Detection with Transformers.
  
  <https://arxiv.org/pdf/2005.12872>`_.
  
  Code is modified from the `official github repo
  <https://github.com/facebookresearch/detr>`_.
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)
  
file_path: mmdetection/mmdet/models/detectors/detr.py
imports: from typing import Dict, Tuple
  import torch
  import torch.nn.functional
  from torch import Tensor, nn
  from mmdet.registry import MODELS
  from mmdet.structures import OptSampleList
  from ..layers import DetrTransformerDecoder, DetrTransformerEncoder, SinePositionalEncoding
  from .base_detr import DetectionTransformer
label_desc: Use image node_id 2 for calling the DETR obj's underlying member method code with example usage: obj.init_weights() without return types