docstr: In charge of applying the recurrent logic for non-recurrent models.
  Should be overwritten by recurrent models.
docstr_tok: ["In","charge","of","applying","the","recurrent","logic","for","non-recurrent","models",".","Should","be","overwritten","by","recurrent","models","."]
code: def _get_batch_prediction(
      self, n: int, input_batch: Tuple, roll_size: int
  ) -> torch.Tensor:
      """
      In charge of applying the recurrent logic for non-recurrent models.
      Should be overwritten by recurrent models.
      """
      pass
  
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 12 for calling the PLForecastingModule obj's underlying member method code with example usage: obj._get_batch_prediction(n, input_batch, roll_size) without return types