code: def __init__(
      self,
      num_classes=80,
      proposal_embedding_dim=256,
      feedforward_channels=2048,
      dynamic_feature_channels=64,
      roi_resolution=7,
      num_attn_heads=8,
      dropout=0.0,
      num_ffn_fcs=2,
      num_cls_fcs=1,
      num_reg_fcs=3,
  ):
      super(DIIHead, self).__init__()
  
      self.num_classes = num_classes
      self.d_model = proposal_embedding_dim
  
      self.attention = MultiHeadAttention(
          self.d_model, num_attn_heads, dropout
      )
      self.attention_norm = nn.LayerNorm(self.d_model)
  
      self.instance_interactive_conv = DynamicConv(
          self.d_model,
          dynamic_feature_channels,
          roi_resolution=roi_resolution,
          with_proj=True,
      )
      self.instance_interactive_conv_dropout = nn.Dropout(dropout)
      self.instance_interactive_conv_norm = nn.LayerNorm(self.d_model)
  
      self.ffn = FFN(
          self.d_model, feedforward_channels, num_ffn_fcs, dropout
      )
      self.ffn_norm = nn.LayerNorm(self.d_model)
  
      self.cls_fcs = nn.LayerList()
      for _ in range(num_cls_fcs):
          self.cls_fcs.append(
              nn.Linear(self.d_model, self.d_model, bias_attr=False)
          )
          self.cls_fcs.append(nn.LayerNorm(self.d_model))
          self.cls_fcs.append(nn.ReLU())
      self.fc_cls = nn.Linear(self.d_model, self.num_classes)
  
      self.reg_fcs = nn.LayerList()
      for _ in range(num_reg_fcs):
          self.reg_fcs.append(
              nn.Linear(self.d_model, self.d_model, bias_attr=False)
          )
          self.reg_fcs.append(nn.LayerNorm(self.d_model))
          self.reg_fcs.append(nn.ReLU())
      self.fc_reg = nn.Linear(self.d_model, 4)
  
      self._init_weights()
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import copy
  import paddle
  from paddle import nn
  from ppdet.core.workspace import register
  from ppdet.modeling import initializer
  from .roi_extractor import RoIAlign
  from ..bbox_utils import delta2bbox_v2
  from ..cls_utils import _get_class_default_kwargs
  from ..layers import MultiHeadAttention
label_desc: Use image node_id 1 to create a new DIIHead object from inherited base classes: nn with example: obj = DIIHead(num_classes, proposal_embedding_dim, feedforward_channels, dynamic_feature_channels, roi_resolution, num_attn_heads, dropout, num_ffn_fcs, num_cls_fcs, num_reg_fcs)