def _process_input_batch(self, input_batch: TorchBatch) -> PLModuleInput:
        """Processes module input batch.

        Converts output of a dataset into a tuple of tensors (past target + past cov + historic future cov
        (concatenated), future cov, static cov)

        Parameters
        ----------
        input_batch
            ``(past target, past cov, historic future cov, future cov, static cov)``.

        Returns
        -------
        tuple
            ``(x_past, x_future, x_static)`` the past, future, and static features.
        """
        (
            past_target,
            past_covariates,
            historic_future_covariates,
            future_covariates,
            static_covariates,
        ) = input_batch
        dim_comp = 2

        x_past = torch.cat(
            [
                tensor
                for tensor in [
                    past_target,
                    past_covariates,
                    historic_future_covariates,
                ]
                if tensor is not None
            ],
            dim=dim_comp,
        )
        return x_past, future_covariates, static_covariates
Processes module input batch.

        Converts output of a dataset into a tuple of tensors (past target + past cov + historic future cov
        (concatenated), future cov, static cov)

        Parameters
        ----------
        input_batch
            ``(past target, past cov, historic future cov, future cov, static cov)``.

        Returns
        -------
        tuple
            ``(x_past, x_future, x_static)`` the past, future, and static features.
        
darts/models/forecasting/pl_forecasting_module.py