def forward(self, x_in: PLModuleInput) -> torch.Tensor:
        """BlockRNN Module forward.

        Parameters
        ----------
        x_in
            Tuple of Tensors containing the features of the input sequence. The tuple has elements
            (past target, historic future covariates, future covariates, static covariates).
            The shape of the past target is `(batch_size, input_length, input_size)`.

        Returns
        -------
        torch.Tensor
            The BlockRNN output Tensor with shape `(batch_size, output_chunk_length, target_size, nr_params)`.
            It contains the prediction at the last time step of the sequence.
        """
BlockRNN Module forward.

        Parameters
        ----------
        x_in
            Tuple of Tensors containing the features of the input sequence. The tuple has elements
            (past target, historic future covariates, future covariates, static covariates).
            The shape of the past target is `(batch_size, input_length, input_size)`.

        Returns
        -------
        torch.Tensor
            The BlockRNN output Tensor with shape `(batch_size, output_chunk_length, target_size, nr_params)`.
            It contains the prediction at the last time step of the sequence.
        
darts/models/forecasting/block_rnn_model.py