code: def __init__(
      self,
      in_channels,
      num_chars=92,
      visual_dim=16,
      fusion_dim=1024,
      node_input=32,
      node_embed=256,
      edge_input=5,
      edge_embed=256,
      num_gnn=2,
      num_classes=26,
      bidirectional=False,
  ):
      super().__init__()
  
      self.fusion = Block(
          [visual_dim, node_embed], node_embed, fusion_dim
      )
      self.node_embed = nn.Embedding(num_chars, node_input, 0)
      hidden = node_embed // 2 if bidirectional else node_embed
      self.rnn = nn.LSTM(
          input_size=node_input, hidden_size=hidden, num_layers=1
      )
      self.edge_embed = nn.Linear(edge_input, edge_embed)
      self.gnn_layers = nn.LayerList(
          [GNNLayer(node_embed, edge_embed) for _ in range(num_gnn)]
      )
      self.node_cls = nn.Linear(node_embed, num_classes)
      self.edge_cls = nn.Linear(edge_embed, 2)
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import math
  import paddle
  from paddle import nn
  import paddle.nn.functional
  from paddle import ParamAttr
label_desc: Use image node_id 1 to create a new SDMGRHead object from inherited base classes: nn with example: obj = SDMGRHead(in_channels, num_chars, visual_dim, fusion_dim, node_input, node_embed, edge_input, edge_embed, num_gnn, num_classes, bidirectional)