code: def __init__(self, n_in, n_out=1, bias_x=True, bias_y=True):
      super(Biaffine, self).__init__()
  
      self.n_in = n_in
      self.n_out = n_out
      self.bias_x = bias_x
      self.bias_y = bias_y
      self.weight = nn.Parameter(
          torch.Tensor(n_out, n_in + bias_x, n_in + bias_y)
      )
      self.init_weights()
  
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 1 to create a new Biaffine object from inherited base classes: nn with example: obj = Biaffine(n_in, n_out, bias_x, bias_y)