docstr: This property provides access to the ``LightningModule`` object that is wrapped by Flash for backbones
  provided by PyTorch Forecasting.
  
  This can be used with
  :func:`~flash.core.integrations.pytorch_forecasting.transforms.convert_predictions` to access the visualization
  features built in to PyTorch Forecasting.
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code: def pytorch_forecasting_model(self) -> LightningModule:
      """This property provides access to the ``LightningModule`` object that is wrapped by Flash for backbones
      provided by PyTorch Forecasting.
  
      This can be used with
      :func:`~flash.core.integrations.pytorch_forecasting.transforms.convert_predictions` to access the visualization
      features built in to PyTorch Forecasting.
  
      """
      if not isinstance(self.adapter, PyTorchForecastingAdapter):
          raise AttributeError(
              "The `pytorch_forecasting_model` attribute can only be accessed for backbones provided by PyTorch "
              "Forecasting."
          )
      return self.adapter.backbone
  
file_path: lightning-flash/src/flash/tabular/forecasting/model.py
imports: from typing import Any, Callable, Dict, List, Optional, Union
  import torchmetrics
  from pytorch_lightning import LightningModule
  from flash.core.adapter import AdapterTask
  from flash.core.integrations.pytorch_forecasting.adapter import PyTorchForecastingAdapter
  from flash.core.integrations.pytorch_forecasting.backbones import PYTORCH_FORECASTING_BACKBONES
  from flash.core.registry import FlashRegistry
  from flash.core.utilities.types import LR_SCHEDULER_TYPE, OPTIMIZER_TYPE
label_desc: Use image node_id 2 for calling the TabularForecaster obj's underlying member method code with example usage: obj.pytorch_forecasting_model() and returns: self