code: def forward(self, x, y, z):
      h = self.dropout(self.mlp1(x))
      t = self.dropout(self.mlp2(y))
      o1 = self.biaffine(h, t)
  
      z = self.dropout(self.mlp_rel(z))
      o2 = self.linear(z)
      return o1 + o2
  
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 2 for calling the CoPredictor obj's underlying member method code with example usage: obj.forward(x, y, z) and returns: unknown