def METHOD_NAME(self):
    model = XLMProphetNetForConditionalGeneration.from_pretrained(
        "microsoft/xprophetnet-large-wiki100-cased-xglue-ntg"
    )
    model.to(torch_device)
    encoder_ids = torch.tensor([[17, 96208, 103471, 2]]).to(torch_device)
    decoder_prev_ids = torch.tensor(
        [[2, 250, 9953, 34, 69489, 1620, 32, 118424, 624, 210, 105, 2913, 1032, 351]]
    ).to(torch_device)
    output = model(
        input_ids=encoder_ids, attention_mask=None, encoder_outputs=None, decoder_input_ids=decoder_prev_ids
    )
    output_predited_logis = output[0]
    expected_shape = torch.Size((1, 14, 250012))
    self.assertEqual(output_predited_logis.shape, expected_shape)
    # compare the actual values for a slice.
    expected_slice = torch.tensor(
        [[[-9.2253, -9.7173, -6.3529], [-7.6701, -9.0145, -1.9382], [-8.0195, -7.0004, -0.1523]]]
    ).to(torch_device)
    self.assertTrue(torch.allclose(output_predited_logis[:, :3, :3], expected_slice, atol=1e-4))