class_docstr: Biaffine Dependency Parser 的子模块, 用于构建预测边类别的图
class_docstr_tok: ["Biaffine","Dependency","Parser","\u7684\u5b50\u6a21\u5757",",","\u7528\u4e8e\u6784\u5efa\u9884\u6d4b\u8fb9\u7c7b\u522b\u7684\u56fe"]
docstr: :param in1_features: 输入的特征1维度
  :param in2_features: 输入的特征2维度
  :param num_label: 边类别的个数
  :param bias: 是否使用bias. Default: ``True``
docstr_tok: [":","param","in1_features",":","\u8f93\u5165\u7684\u7279\u5f811\u7ef4\u5ea6",":","param","in2_features",":","\u8f93\u5165\u7684\u7279\u5f812\u7ef4\u5ea6",":","param","num_label",":","\u8fb9\u7c7b\u522b\u7684\u4e2a\u6570",":","param","bias",":","\u662f\u5426\u4f7f\u7528bias",".","Default",":","``","True","``"]
code: def __init__(self, in1_features, in2_features, num_label, bias=True):
      r"""
  
      :param in1_features: 输入的特征1维度
      :param in2_features: 输入的特征2维度
      :param num_label: 边类别的个数
      :param bias: 是否使用bias. Default: ``True``
      """
      super(LabelBilinear, self).__init__()
      self.bilinear = nn.Bilinear(
          in1_features, in2_features, num_label, bias=bias
      )
      self.lin = nn.Linear(
          in1_features + in2_features, num_label, bias=False
      )
  
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 LabelBilinear object from inherited base classes: nn with example: obj = LabelBilinear(in1_features, in2_features, num_label, bias)