code: def test_bidaf_trilinear_similarity(self):
      linear = LinearMatrixAttention(2, 2, combination="x,y,x*y")
      linear._weight_vector = Parameter(
          torch.FloatTensor([-0.3, 0.5, 2.0, -1.0, 1, 1])
      )
      linear._bias = Parameter(torch.FloatTensor([0.0]))
      output = linear(
          torch.FloatTensor([[[0, 0], [4, 5]], [[-7, -8], [10, 11]]]),
          torch.FloatTensor([[[1, 2], [4, 5]], [[7, 8], [10, 11]]]),
      )
  
      assert_almost_equal(
          output.data.numpy(),
          numpy.array(
              [
                  [
                      [0 + 0 + 2 + -2 + 0 + 0, 0 + 0 + 8 + -5 + 0 + 0],
                      [
                          -1.2 + 2.5 + 2 + -2 + 4 + 10,
                          -1.2 + 2.5 + 8 + -5 + 16 + 25,
                      ],
                  ],
                  [
                      [
                          2.1 + -4 + 14 + -8 + -49 + -64,
                          2.1 + -4 + 20 + -11 + -70 + -88,
                      ],
                      [
                          -3 + 5.5 + 14 + -8 + 70 + 88,
                          -3 + 5.5 + 20 + -11 + 100 + 121,
                      ],
                  ],
              ]
          ),
          decimal=2,
      )
  
file_path: allennlp/tests/modules/matrix_attention/linear_matrix_attention_test.py
imports: import numpy
  from numpy.testing import assert_almost_equal
  import torch
  from torch.nn import Parameter
  from allennlp.common import Params
  from allennlp.common.testing.test_case import AllenNlpTestCase
  from allennlp.modules.matrix_attention import LinearMatrixAttention
  from allennlp.modules.matrix_attention.matrix_attention import MatrixAttention
label_desc: Use image node_id 3 for calling the TestLinearMatrixAttention obj's underlying member method code with example usage: obj.test_bidaf_trilinear_similarity() without return types