docstr: Converts output of MixedCovariatesDataset (training dataset) into an input/past- and
  output/future chunk.
  
  Parameters
  ----------
  input_batch
      ``(past_target, past_covariates, historic_future_covariates, future_covariates, static_covariates)``.
  
  Returns
  -------
  tuple
      ``(x_past, x_future, x_static)`` the input/past and output/future chunks.
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code: def _process_input_batch(
      self, input_batch
  ) -> Tuple[
      torch.Tensor, Optional[torch.Tensor], Optional[torch.Tensor]
  ]:
      """
      Converts output of MixedCovariatesDataset (training dataset) into an input/past- and
      output/future chunk.
  
      Parameters
      ----------
      input_batch
          ``(past_target, past_covariates, historic_future_covariates, future_covariates, static_covariates)``.
  
      Returns
      -------
      tuple
          ``(x_past, x_future, x_static)`` the input/past and output/future chunks.
      """
      (
          past_target,
          past_covariates,
          historic_future_covariates,
          future_covariates,
          static_covariates,
      ) = input_batch
      dim_variable = 2
  
      x_past = torch.cat(
          [
              tensor
              for tensor in [
                  past_target,
                  past_covariates,
                  historic_future_covariates,
              ]
              if tensor is not None
          ],
          dim=dim_variable,
      )
      return x_past, future_covariates, static_covariates
  
imports: from abc import ABC, abstractmethod
  from functools import wraps
  from typing import Any, Dict, Optional, Sequence, Tuple, Union
  import pytorch_lightning
  import torch
  import torch.nn
  import torchmetrics
  from joblib import Parallel, delayed
  from darts.logging import get_logger, raise_if, raise_log
  from darts.models.components.layer_norm_variants import RINorm
  from darts.timeseries import TimeSeries
  from darts.utils.likelihood_models import Likelihood
  from darts.utils.timeseries_generation import _build_forecast_series
  from darts.utils.torch import MonteCarloDropout
label_desc: Use image node_id 2 for calling the PLMixedCovariatesModule obj's underlying member method code with example usage: obj._process_input_batch(input_batch) and returns: x_past,  future_covariates,  static_covariates