code: def decode_all(
      self,
      x: torch.Tensor,
      hidden_state: HiddenState,
      lengths: torch.Tensor = None,
  ):
      decoder_output, hidden_state = self.rnn(
          x, hidden_state, lengths=lengths, enforce_sorted=False
      )
      if isinstance(self.hparams.target, str):  # single target
          output = self.output_projector(decoder_output)
      else:
          output = [
              projector(decoder_output)
              for projector in self.output_projector
          ]
      return output, hidden_state
  
file_path: pytorch-forecasting/pytorch_forecasting/models/rnn/__init__.py
imports: from copy import copy
  from typing import Dict, List, Tuple, Union
  import numpy
  import torch
  import torch.nn
  from pytorch_forecasting.data.encoders import MultiNormalizer, NaNLabelEncoder
  from pytorch_forecasting.data.timeseries import TimeSeriesDataSet
  from pytorch_forecasting.metrics import MAE, MAPE, MASE, RMSE, SMAPE, MultiHorizonMetric, MultiLoss, QuantileLoss
  from pytorch_forecasting.models.base_model import AutoRegressiveBaseModelWithCovariates
  from pytorch_forecasting.models.nn import HiddenState, MultiEmbedding, get_rnn
  from pytorch_forecasting.utils import apply_to_list, to_list
label_desc: Use image node_id 5 for calling the RecurrentNetwork obj's underlying member method code with example usage: obj.decode_all(x, hidden_state, lengths) and returns: output,  hidden_state