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
      weight = torch.zeros(
          (n_out, n_in + int(bias_x), n_in + int(bias_y))
      )
      nn.init.xavier_normal_(weight)
      self.weight = nn.Parameter(weight, requires_grad=True)
  
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 Biaffine object from inherited base classes: nn with example: obj = Biaffine(n_in, n_out, bias_x, bias_y)