def _build_inference_dataset(
        self,
        n: int,
        series: Sequence[TimeSeries],
        past_covariates: Optional[Sequence[TimeSeries]],
        future_covariates: Optional[Sequence[TimeSeries]],
        stride: int = 0,
        bounds: Optional[np.ndarray] = None,
    ) -> TorchInferenceDataset:
        """
        Models can override this method to return a custom `TorchInferenceDataset`.
        """
        return SequentialTorchInferenceDataset(
            series=series,
            past_covariates=past_covariates,
            future_covariates=future_covariates,
            n=n,
            stride=stride,
            bounds=bounds,
            input_chunk_length=self.input_chunk_length,
            output_chunk_length=self.output_chunk_length,
            output_chunk_shift=self.output_chunk_shift,
            use_static_covariates=self.uses_static_covariates,
        )

        Models can override this method to return a custom `TorchInferenceDataset`.
        
darts/models/forecasting/torch_forecasting_model.py