@@ -0,0 +1,1030 @@
+import numpy as np%0Aimport pytest%0Afrom keras import backend as K%0A%0Afrom matchzoo import layers%0A%0A%0Adef test_matching_layers():%0A    s1_value = np.array(%5B%5B%5B1, 2%5D, %5B2, 3%5D, %5B3, 4%5D%5D,%0A                         %5B%5B0.1, 0.2%5D, %5B0.2, 0.3%5D, %5B0.3, 0.4%5D%5D%0A                        %5D)%0A    s2_value = np.array(%5B%5B%5B1, 2%5D, %5B2, 3%5D%5D,%0A                         %5B%5B0.1, 0.2%5D, %5B0.2, 0.3%5D%5D%0A                        %5D)%0A    s3_value = np.array(%5B%5B%5B1, 2%5D, %5B2, 3%5D%5D,%0A                         %5B%5B0.1, 0.2%5D, %5B0.2, 0.3%5D%5D,%0A                         %5B%5B0.1, 0.2%5D, %5B0.2, 0.3%5D%5D%0A                        %5D)%0A    s1_tensor = K.variable(s1_value)%0A    s2_tensor = K.variable(s2_value)%0A    s3_tensor = K.variable(s3_value)%0A    for matching_type in %5B'dot', 'mul', 'plus', 'minus', 'concat'%5D:%0A        model = layers.MatchingLayer(matching_type=matching_type)(%5Bs1_tensor, s2_tensor%5D)%0A        ret = K.eval(model)%0A    with pytest.raises(ValueError):%0A        layers.MatchingLayer(matching_type='error')%0A    with pytest.raises(ValueError):%0A        layers.MatchingLayer()(%5Bs1_tensor, s3_tensor%5D)%0A

fix coverage