code: def _sample_tiling(input_data_tuple, batch_sample_size):
      tiled_input_data = []
      for tensor in input_data_tuple:
          if tensor is not None:
              tiled_input_data.append(
                  tensor.tile((batch_sample_size, 1, 1))
              )
          else:
              tiled_input_data.append(None)
      return tuple(tiled_input_data)
  
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 13 for calling the PLForecastingModule obj's underlying member method code with example usage: obj._sample_tiling(input_data_tuple, batch_sample_size) and returns: tuple