def test_predict_likelihood_parameters_multivariate_torch_models(
            self, lkl_config
        ):
            """Checking convergence of model for each metric is too time consuming, making sure that the dimensions
            of the predictions contain the proper elements for multivariate inputs.
            """
            lkl, lkl_params, comp_names = lkl_config
            # fix seed to avoid values outside of distribution's support

            seed = 142857
            torch.manual_seed(seed=seed)
            kwargs = {
                "likelihood": lkl,
                "n_epochs": 1,
                "random_state": seed,
                **tfm_kwargs,
            }

            n_times = 5
            n_comp = 2
            n_samples = 1
            if isinstance(lkl, QuantileRegression):
                values = np.random.normal(
                    loc=0, scale=1, size=(n_times, n_comp, n_samples)
                )
            else:
                values = lkl._distr_from_params(lkl_params).sample((
                    n_times,
                    n_comp,
                    n_samples,
                ))
            ts = TimeSeries.from_values(
                values, columns=[f"dummy_{i}" for i in range(values.shape[1])]
            )

            # [DualCovariatesModule, PastCovariatesModule, MixedCovariatesModule]
            models = [
                RNNModel(4, "RNN", training_length=4, **kwargs),
                TCNModel(4, 1, **kwargs),
                DLinearModel(4, 1, **kwargs),
            ]

            for model in models:
                model.fit(ts)
                pred_lkl_params = model.predict(
                    n=1, num_samples=1, predict_likelihood_parameters=True
                )
                # check the dimensions
                assert pred_lkl_params.values().shape[1] == n_comp * len(lkl_params)
                # check the component names
                assert list(pred_lkl_params.components) == comp_names, (
                    f"Components names are not matching; expected {comp_names} "
                    f"but received {list(pred_lkl_params.components)}"
                )
Checking convergence of model for each metric is too time consuming, making sure that the dimensions
            of the predictions contain the proper elements for multivariate inputs.
            
darts/tests/models/forecasting/test_probabilistic_models.py