class_docstr: Computes attention between two matrices using a bilinear attention function.  This function has
  a matrix of weights `W` and a bias `b`, and the similarity between the two matrices `X`
  and `Y` is computed as `X W Y^T + b`.
  
  Registered as a `MatrixAttention` with name "bilinear".
  
  # Parameters
  
  matrix_1_dim : `int`, required
      The dimension of the matrix `X`, described above.  This is `X.size()[-1]` - the length
      of the vector that will go into the similarity computation.  We need this so we can build
      the weight matrix correctly.
  matrix_2_dim : `int`, required
      The dimension of the matrix `Y`, described above.  This is `Y.size()[-1]` - the length
      of the vector that will go into the similarity computation.  We need this so we can build
      the weight matrix correctly.
  activation : `Activation`, optional (default=`linear`)
      An activation function applied after the `X W Y^T + b` calculation.  Default is
      linear, i.e. no activation.
  use_input_biases : `bool`, optional (default = `False`)
      If True, we add biases to the inputs such that the final computation
      is equivalent to the original bilinear matrix multiplication plus a
      projection of both inputs.
  label_dim : `int`, optional (default = `1`)
      The number of output classes. Typically in an attention setting this will be one,
      but this parameter allows this class to function as an equivalent to `torch.nn.Bilinear`
      for matrices, rather than vectors.
code: def reset_parameters(self):
      torch.nn.init.xavier_uniform_(self._weight_matrix)
      self._bias.data.fill_(0)
  
file_path: allennlp/allennlp/modules/matrix_attention/bilinear_matrix_attention.py
imports: import torch
  from torch.nn.parameter import Parameter
  from allennlp.modules.matrix_attention.matrix_attention import MatrixAttention
  from allennlp.nn import Activation
label_desc: Use image node_id 2 for calling the BilinearMatrixAttention obj's underlying member method code with example usage: obj.reset_parameters() without return types