def METHOD_NAME(self):
    out = bias_act(self.input_tensor, self.bias)
    assert out.shape == (1, 3)
    # test with different dim
    input_tensor = torch.randn((1, 1, 3), requires_grad=True)
    bias = torch.randn(3, requires_grad=True)
    out = bias_act(input_tensor, bias, dim=2)
    assert out.shape == (1, 1, 3)
    # test with different act
    out = bias_act(self.input_tensor, self.bias, act='relu')
    assert out.shape == (1, 3)
    out = bias_act(self.input_tensor, self.bias, act='lrelu')
    assert out.shape == (1, 3)
    out = bias_act(self.input_tensor, self.bias, act='tanh')
    assert out.shape == (1, 3)
    out = bias_act(self.input_tensor, self.bias, act='sigmoid')
    assert out.shape == (1, 3)
    out = bias_act(self.input_tensor, self.bias, act='elu')
    assert out.shape == (1, 3)
    out = bias_act(self.input_tensor, self.bias, act='selu')
    assert out.shape == (1, 3)
    out = bias_act(self.input_tensor, self.bias, act='softplus')
    assert out.shape == (1, 3)
    out = bias_act(self.input_tensor, self.bias, act='swish')
    assert out.shape == (1, 3)
    # test with different alpha
    out = bias_act(self.input_tensor, self.bias, act='lrelu', alpha=0.1)
    assert out.shape == (1, 3)
    # test with different gain
    out1 = bias_act(self.input_tensor, self.bias, act='lrelu', gain=0.2)
    out2 = bias_act(self.input_tensor, self.bias, act='lrelu', gain=0.1)
    assert torch.allclose(out1, out2 * 2)
    # test with different clamp
    out1 = bias_act(self.input_tensor, self.bias, act='lrelu', clamp=0.5)
    out2 = bias_act(self.input_tensor, self.bias, act='lrelu', clamp=0.2)
    assert out1.max() <= 0.5
    assert out2.max() <= 0.5