def forward(
        self, x_in: tuple[torch.Tensor, Optional[torch.Tensor], Optional[torch.Tensor]]
    ) -> torch.Tensor:
        """Naive model forward pass.

        Parameters
        ----------
        x_in
            comes as tuple `(x_past, x_future, x_static)` where `x_past` is the input/past chunk and `x_future`
            is the output/future chunk. Input dimensions are `(batch_size, time_steps, components)`

        Returns
        -------
        torch.Tensor
            The output Tensor of shape `(batch_size, output_chunk_length, output_dim, nr_params)`
        """
        return self._forward(x_in)
Naive model forward pass.

        Parameters
        ----------
        x_in
            comes as tuple `(x_past, x_future, x_static)` where `x_past` is the input/past chunk and `x_future`
            is the output/future chunk. Input dimensions are `(batch_size, time_steps, components)`

        Returns
        -------
        torch.Tensor
            The output Tensor of shape `(batch_size, output_chunk_length, output_dim, nr_params)`
        
darts/models/forecasting/global_baseline_models.py