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)
      return s
  
imports: from dataclasses import dataclass
  from typing import Optional, Tuple
  import torch
  from torch import nn
  from torch.nn import CrossEntropyLoss
  from transformers.utils import ModelOutput
  from .base import PredictionHead
label_desc: Use image node_id 4 for calling the Biaffine obj's underlying member method code with example usage: obj.forward(x, y) and returns: s