//  [python]
def test_transformerdecoderlayer_gelu(self):
        # this is a deterministic test for TransformerDecoderLayer with gelu activation
        d_model = 4
        nhead = 2
        dim_feedforward = 16
        dropout = 0.0
        bsz = 2
        seq_length = 5
        tgt_length = 3
        activation = "gelu"

        for batch_first in (True, False):
            def perm_fn(x):
                return x.transpose(1, 0) if batch_first else x

            model = nn.TransformerDecoderLayer(d_model, nhead, dim_feedforward, dropout, activation,
                                               batch_first=batch_first)

            # set constant weights of the model
            for idx, p in enumerate(model.parameters()):
                x = p.data
                sz = x.view(-1).size(0)
                shape = x.shape
                x = torch.cos(torch.arange(0, sz).float().view(shape))
                p.data.copy_(x)

            # deterministic input
            decoder_input = torch.tensor([[[20., 30., 40., 50.]]])
            memory_input = torch.tensor([[[60., 70., 80., 90.]]])
            result = model(decoder_input, memory_input)
            ref_output = torch.tensor([[[2.306435, 0.095946, -0.675796, 0.10687]]])
            self.assertEqual(tuple(result.shape), tuple(ref_output.shape))
            torch.testing.assert_allclose(result, ref_output)

            # deterministic input
            decoder_input = perm_fn(torch.tensor([[[9., 10., 11., 12.]],
                                                  [[11., 12., 13., 14.]]]))
            memory_input = perm_fn(torch.tensor([[[1., 2., 3., 4.]]]))
            result = model(decoder_input, memory_input)
            ref_output = perm_fn(torch.tensor([[[2.415448, 0.054389, -0.610932, -0.0156613]],
                                               [[2.415448, 0.054389, -0.610932, -0.0156613]]]))
            self.assertEqual(tuple(result.shape), tuple(ref_output.shape))
            torch.testing.assert_allclose(result, ref_output)

            # deterministic input
            decoder_input = perm_fn(torch.tensor([[[1., 2., 3., 4.]],
                                                  [[5., 6., 7., 8.]]]))
            memory_input = perm_fn(torch.tensor([[[9., 10., 11., 12.]],
                                                 [[11., 12., 13., 14.]]]))
            result = model(decoder_input, memory_input)
            ref_output = perm_fn(torch.tensor([[[2.338531, 0.087709, -0.65776, 0.080646]],
                                               [[2.338531, 0.087709, -0.65776, 0.080646]]]))
            self.assertEqual(tuple(result.shape), tuple(ref_output.shape))
            torch.testing.assert_allclose(result, ref_output)

            # deterministic input
            decoder_input = perm_fn(torch.tensor([[[0.4517, 0.6793, 0.5313, 0.0034],
                                                   [0.2678, 0.3677, 0.4459, 0.7166]],
                                                  [[0.8100, 0.3716, 0.4096, 0.1976],
                                                   [0.6958, 0.8844, 0.6081, 0.8315]],
                                                  [[0.0494, 0.9343, 0.5955, 0.3830],
                                                   [0.5404, 0.3464, 0.9378, 0.6200]]]))
            memory_input = perm_fn(torch.tensor([[[0.7462, 0.6653, 0.5679, 0.4891],
                                                  [0.5387, 0.1655, 0.3565, 0.0471]],
                                                 [[0.8335, 0.2799, 0.5031, 0.2947],
                                                  [0.1402, 0.0318, 0.7636, 0.1346]],
                                                 [[0.6333, 0.9344, 0.1376, 0.9938],
                                                  [0.8924, 0.2872, 0.6692, 0.2944]],
                                                 [[0.9897, 0.6915, 0.3154, 0.1733],
                                                  [0.8645, 0.3513, 0.3064, 0.0767]],
                                                 [[0.8117, 0.2366, 0.4838, 0.7881],
                                                  [0.3718, 0.4945, 0.9511, 0.0864]]]))
            result = model(decoder_input, memory_input)
            ref_output = perm_fn(torch.tensor([[[2.42049104, 0.03443088, -0.60793706, -0.05436271],
                                                [2.42210631, 0.03546578, -0.60679895, -0.05357488]],
                                               [[2.41907674, 0.0336104, -0.60892977, -0.05490462],
                                                [2.42216881, 0.03586554, -0.6067524, -0.05289126]],
                                               [[2.42205716, 0.03488046, -0.60683681, -0.05460596],
                                                [2.42240309, 0.0354595, -0.60659063, -0.05378816]]]))
            self.assertEqual(tuple(result.shape), tuple(ref_output.shape))
            torch.testing.assert_allclose(result, ref_output)