code: def forward(self, roi_features, proposal_features):
      N, num_proposals = proposal_features.shape[:2]
  
      proposal_features = proposal_features + self.attention(
          proposal_features
      )
      attn_features = self.attention_norm(proposal_features)
  
      proposal_features = attn_features.reshape([-1, self.d_model])
      proposal_features_iic = self.instance_interactive_conv(
          proposal_features, roi_features
      )
      proposal_features = (
          proposal_features
          + self.instance_interactive_conv_dropout(
              proposal_features_iic
          )
      )
      obj_features = self.instance_interactive_conv_norm(
          proposal_features
      )
  
      obj_features = self.ffn(obj_features)
      obj_features = self.ffn_norm(obj_features)
  
      cls_feature = obj_features.clone()
      reg_feature = obj_features.clone()
  
      for cls_layer in self.cls_fcs:
          cls_feature = cls_layer(cls_feature)
      class_logits = self.fc_cls(cls_feature)
      for reg_layer in self.reg_fcs:
          reg_feature = reg_layer(reg_feature)
      bbox_deltas = self.fc_reg(reg_feature)
  
      class_logits = class_logits.reshape(
          [N, num_proposals, self.num_classes]
      )
      bbox_deltas = bbox_deltas.reshape([N, num_proposals, 4])
      obj_features = obj_features.reshape(
          [N, num_proposals, self.d_model]
      )
  
      return class_logits, bbox_deltas, obj_features, attn_features
  
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 3 for calling the DIIHead obj's underlying member method code with example usage: obj.forward(roi_features, proposal_features) and returns: class_logits,  bbox_deltas,  obj_features,  attn_features