code: def _reset_parameters(self):
      # class and bbox head init
      bias_cls = bias_init_with_prob(0.01)
      linear_init_(self.enc_score_head)
      constant_(self.enc_score_head.bias, bias_cls)
      constant_(self.enc_bbox_head.layers[-1].weight)
      constant_(self.enc_bbox_head.layers[-1].bias)
      for cls_, reg_ in zip(self.dec_score_head, self.dec_bbox_head):
          linear_init_(cls_)
          constant_(cls_.bias, bias_cls)
          constant_(reg_.layers[-1].weight)
          constant_(reg_.layers[-1].bias)
  
      linear_init_(self.enc_output[0])
      xavier_uniform_(self.enc_output[0].weight)
      normal_(self.level_embed.weight)
      if self.learnt_init_query:
          xavier_uniform_(self.tgt_embed.weight)
      xavier_uniform_(self.query_pos_head.layers[0].weight)
      xavier_uniform_(self.query_pos_head.layers[1].weight)
      for l in self.input_proj:
          xavier_uniform_(l[0].weight)
          constant_(l[0].bias)
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import paddle
  import paddle.nn
  import paddle.nn.functional
  from paddle import ParamAttr
  from paddle.regularizer import L2Decay
  from ppdet.core.workspace import register
  from ..layers import MultiHeadAttention
  from .position_encoding import PositionEmbedding
  from ..heads.detr_head import MLP
  from .deformable_transformer import MSDeformableAttention, DeformableTransformerEncoderLayer, DeformableTransformerEncoder
  from ..initializer import linear_init_, constant_, xavier_uniform_, normal_, bias_init_with_prob
  from .utils import _get_clones, get_valid_ratio, get_contrastive_denoising_training_group, get_sine_pos_embed, inverse_sigmoid
label_desc: Use image node_id 2 for calling the DINOTransformer obj's underlying member method code with example usage: obj._reset_parameters() without return types