code: def __init__(
      self,
      d_model=256,
      n_head=8,
      dim_feedforward=1024,
      dropout=0.0,
      activation="relu",
      n_levels=4,
      n_points=4,
      weight_attr=None,
      bias_attr=None,
  ):
      super(DINOTransformerEncoderLayer, self).__init__()
      # self attention
      self.self_attn = MSDeformableAttention(
          d_model, n_head, n_levels, n_points, 1.0
      )
      self.dropout1 = nn.Dropout(dropout)
      self.norm1 = nn.LayerNorm(
          d_model,
          weight_attr=ParamAttr(regularizer=L2Decay(0.0)),
          bias_attr=ParamAttr(regularizer=L2Decay(0.0)),
      )
      # ffn
      self.linear1 = nn.Linear(
          d_model, dim_feedforward, weight_attr, bias_attr
      )
      self.activation = getattr(F, activation)
      self.dropout2 = nn.Dropout(dropout)
      self.linear2 = nn.Linear(
          dim_feedforward, d_model, weight_attr, bias_attr
      )
      self.dropout3 = nn.Dropout(dropout)
      self.norm2 = nn.LayerNorm(
          d_model,
          weight_attr=ParamAttr(regularizer=L2Decay(0.0)),
          bias_attr=ParamAttr(regularizer=L2Decay(0.0)),
      )
      self._reset_parameters()
  
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
  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 1 to create a new DINOTransformerEncoderLayer object from inherited base classes: nn with example: obj = DINOTransformerEncoderLayer(d_model, n_head, dim_feedforward, dropout, activation, n_levels, n_points, weight_attr, bias_attr)