code: def forward(self, x, y):
      if self.bias_x:
          x = torch.cat((x, torch.ones_like(x[..., :1])), -1)
      if self.bias_y:
          y = torch.cat((y, torch.ones_like(y[..., :1])), -1)
      # [batch_size, n_out, seq_len, seq_len]
      s = torch.einsum("bxi,oij,byj->boxy", x, self.weight, y)
      # remove dim 1 if n_out == 1
      s = s.permute(0, 2, 3, 1)
  
      return s
  
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 3 for calling the Biaffine obj's underlying member method code with example usage: obj.forward(x, y) and returns: s