code: def __init__(
      self,
      cls_num,
      hid_size,
      biaffine_size,
      channels,
      ffnn_hid_size,
      dropout=0,
  ):
      super().__init__()
      self.mlp1 = MLP(
          n_in=hid_size, n_out=biaffine_size, dropout=dropout
      )
      self.mlp2 = MLP(
          n_in=hid_size, n_out=biaffine_size, dropout=dropout
      )
      self.biaffine = Biaffine(
          n_in=biaffine_size, n_out=cls_num, bias_x=True, bias_y=True
      )
      self.mlp_rel = MLP(channels, ffnn_hid_size, dropout=dropout)
      self.linear = nn.Linear(ffnn_hid_size, cls_num)
      self.dropout = nn.Dropout(dropout)
  
imports: import torch
  import torch.nn
  import torch.nn.functional
  from torch.nn.utils.rnn import pack_padded_sequence, pad_packed_sequence
  from transformers import AutoModel
label_desc: Use image node_id 1 to create a new CoPredictor object from inherited base classes: nn with example: obj = CoPredictor(cls_num, hid_size, biaffine_size, channels, ffnn_hid_size, dropout)