class_docstr: Biaffine Dependency Parser 的子模块, 用于构建预测边的图
class_docstr_tok: ["Biaffine","Dependency","Parser","\u7684\u5b50\u6a21\u5757",",","\u7528\u4e8e\u6784\u5efa\u9884\u6d4b\u8fb9\u7684\u56fe"]
docstr: :param hidden_size: 输入的特征维度
  :param bias: 是否使用bias. Default: ``True``
docstr_tok: [":","param","hidden_size",":","\u8f93\u5165\u7684\u7279\u5f81\u7ef4\u5ea6",":","param","bias",":","\u662f\u5426\u4f7f\u7528bias",".","Default",":","``","True","``"]
code: def __init__(self, hidden_size, bias=True):
      r"""
  
      :param hidden_size: 输入的特征维度
      :param bias: 是否使用bias. Default: ``True``
      """
      super(ArcBiaffine, self).__init__()
      self.U = nn.Parameter(
          torch.Tensor(hidden_size, hidden_size), requires_grad=True
      )
      self.has_bias = bias
      if self.has_bias:
          self.bias = nn.Parameter(
              torch.Tensor(hidden_size), requires_grad=True
          )
      else:
          self.register_parameter("bias", None)
  
imports: from collections import defaultdict
  import numpy
  import torch
  import torch.nn
  import torch.nn.functional
  from ...core.utils import seq_len_to_mask
  from ...embeddings.torch.utils import get_embeddings
  from ...modules.torch.dropout import TimestepDropout
  from ...modules.torch.encoder.transformer import TransformerEncoder
  from ...modules.torch.encoder.variational_rnn import VarLSTM
label_desc: Use image node_id 1 to create a new ArcBiaffine object from inherited base classes: nn with example: obj = ArcBiaffine(hidden_size, bias)