old_contents: #   Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
  #
  # Licensed under the Apache License, Version 2.0 (the "License");
  # you may not use this file except in compliance with the License.
  # You may obtain a copy of the License at
  #
  #     http://www.apache.org/licenses/LICENSE-2.0
  #
  # Unless required by applicable law or agreed to in writing, software
  # distributed under the License is distributed on an "AS IS" BASIS,
  # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
  # See the License for the specific language governing permissions and
  # limitations under the License.
  
  from __future__ import print_function
  
  import unittest
  import numpy as np
  from op_test import OpTest
  
  
  class TestBilinearTensorProductOp(OpTest):
      def setUp(self):
          self.op_type = "bilinear_tensor_product"
          batch_size = 6
          size0 = 30
          size1 = 20
          size2 = 100
          a = np.random.random((batch_size, size0)).astype("float64")
          b = np.random.random((batch_size, size1)).astype("float64")
          w = np.random.random((size2, size0, size1)).astype("float64")
          bias = np.random.random((1, size2)).astype("float64")
          output = np.zeros((batch_size, size2)).astype("float64")
          for i in range(size2):
              w_i = w[i, :, :]
              output[:, i] = np.sum(np.matmul(a, w_i) * b, axis=1)
          self.inputs = {
              'X': a,
              'Y': b,
              'Weight': w,
              'Bias': bias,
          }
          self.outputs = {'Out': output + bias}
  
      def test_check_output(self):
          self.check_output()
  
      def test_check_grad_normal(self):
          self.check_grad(['X', 'Y', 'Weight', 'Bias'], 'Out')
  
  
  if __name__ == "__main__":
      unittest.main()
  
new_contents: #   Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
  #
  # Licensed under the Apache License, Version 2.0 (the "License");
  # you may not use this file except in compliance with the License.
  # You may obtain a copy of the License at
  #
  #     http://www.apache.org/licenses/LICENSE-2.0
  #
  # Unless required by applicable law or agreed to in writing, software
  # distributed under the License is distributed on an "AS IS" BASIS,
  # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
  # See the License for the specific language governing permissions and
  # limitations under the License.
  
  from __future__ import print_function
  
  import unittest
  import numpy as np
  from op_test import OpTest
  
  
  class TestBilinearTensorProductOp(OpTest):
      def setUp(self):
          self.op_type = "bilinear_tensor_product"
          batch_size = 6
          size0 = 5
          size1 = 4
          size2 = 5
          a = np.random.random((batch_size, size0)).astype("float64")
          b = np.random.random((batch_size, size1)).astype("float64")
          w = np.random.random((size2, size0, size1)).astype("float64")
          bias = np.random.random((1, size2)).astype("float64")
          output = np.zeros((batch_size, size2)).astype("float64")
          for i in range(size2):
              w_i = w[i, :, :]
              output[:, i] = np.sum(np.matmul(a, w_i) * b, axis=1)
          self.inputs = {
              'X': a,
              'Y': b,
              'Weight': w,
              'Bias': bias,
          }
          self.outputs = {'Out': output + bias}
  
      def test_check_output(self):
          self.check_output()
  
      def test_check_grad_normal(self):
          self.check_grad(['X', 'Y', 'Weight', 'Bias'], 'Out')
  
  
  if __name__ == "__main__":
      unittest.main()
  
subject: fix test_bilinear_tensor_product_op timeout (#22120)