code: def forward(self, roi_features, attn_features):
      attn_features = attn_features.reshape([-1, self.d_model])
      attn_features_iic = self.instance_interactive_conv(
          attn_features, roi_features
      )
  
      x = attn_features_iic.transpose([0, 2, 1]).reshape(
          roi_features.shape
      )
  
      for conv in self.convs:
          x = conv(x)
      if self.upsample is not None:
          x = self.upsample(x)
          if self.upsample_method == "deconv":
              x = self.relu(x)
      mask_pred = self.conv_cls(x)
      return mask_pred
  
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 DynamicMaskHead obj's underlying member method code with example usage: obj.forward(roi_features, attn_features) and returns: mask_pred