code: def __init__(
      self,
      d_model,
      num_classes,
      dim_feedforward,
      nhead,
      dropout,
      head_cls,
      head_reg,
      head_dim_dynamic,
      head_num_dynamic,
      scale_clamp: float = _DEFAULT_SCALE_CLAMP,
      bbox_weights=(2.0, 2.0, 1.0, 1.0),
  ):
      super().__init__()
  
      self.d_model = d_model
  
      # dynamic.
      self.self_attn = nn.MultiHeadAttention(
          d_model, nhead, dropout=dropout
      )
      self.inst_interact = DynamicConv(
          d_model, head_dim_dynamic, head_num_dynamic
      )
  
      self.linear1 = nn.Linear(d_model, dim_feedforward)
      self.dropout = nn.Dropout(dropout)
      self.linear2 = nn.Linear(dim_feedforward, d_model)
  
      self.norm1 = nn.LayerNorm(d_model)
      self.norm2 = nn.LayerNorm(d_model)
      self.norm3 = nn.LayerNorm(d_model)
      self.dropout1 = nn.Dropout(dropout)
      self.dropout2 = nn.Dropout(dropout)
      self.dropout3 = nn.Dropout(dropout)
  
      self.activation = nn.ReLU()
  
      # cls.
      num_cls = head_cls
      cls_module = list()
      for _ in range(num_cls):
          cls_module.append(
              nn.Linear(d_model, d_model, bias_attr=False)
          )
          cls_module.append(nn.LayerNorm(d_model))
          cls_module.append(nn.ReLU())
      self.cls_module = nn.LayerList(cls_module)
  
      # reg.
      num_reg = head_reg
      reg_module = list()
      for _ in range(num_reg):
          reg_module.append(
              nn.Linear(d_model, d_model, bias_attr=False)
          )
          reg_module.append(nn.LayerNorm(d_model))
          reg_module.append(nn.ReLU())
      self.reg_module = nn.LayerList(reg_module)
  
      # pred.
      self.class_logits = nn.Linear(d_model, num_classes)
      self.bboxes_delta = nn.Linear(d_model, 4)
      self.scale_clamp = scale_clamp
      self.bbox_weights = bbox_weights
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import math
  import copy
  import paddle
  import paddle.nn
  from ppdet.core.workspace import register
  from ppdet.modeling.heads.roi_extractor import RoIAlign
  from ppdet.modeling.bbox_utils import delta2bbox
  from ..None import initializer
label_desc: Use image node_id 1 to create a new RCNNHead object from inherited base classes: nn with example: obj = RCNNHead(d_model, num_classes, dim_feedforward, nhead, dropout, head_cls, head_reg, head_dim_dynamic, head_num_dynamic, scale_clamp, bbox_weights)