docstr: Decode hidden state of RNN into prediction. If n_smaples is given,
  decode not by using actual values but rather by
  sampling new targets from past predictions iteratively
docstr_tok: ["Decode","hidden","state","of","RNN","into","prediction",".","If","n_smaples","is","given",",","decode","not","by","using","actual","values","but","rather","by","sampling","new","targets","from","past","predictions","iteratively"]
code: def decode(
      self,
      input_vector: torch.Tensor,
      target_scale: torch.Tensor,
      decoder_lengths: torch.Tensor,
      hidden_state: HiddenState,
      n_samples: int = None,
  ) -> Tuple[torch.Tensor, bool]:
      """
      Decode hidden state of RNN into prediction. If n_smaples is given,
      decode not by using actual values but rather by
      sampling new targets from past predictions iteratively
      """
      if n_samples is None:
          output, _ = self.decode_all(
              input_vector, hidden_state, lengths=decoder_lengths
          )
          output = self.transform_output(
              output, target_scale=target_scale
          )
      else:
          # run in eval, i.e. simulation mode
          target_pos = self.target_positions
          lagged_target_positions = self.lagged_target_positions
          # repeat for n_samples
          input_vector = input_vector.repeat_interleave(n_samples, 0)
          hidden_state = self.rnn.repeat_interleave(
              hidden_state, n_samples
          )
          target_scale = apply_to_list(
              target_scale, lambda x: x.repeat_interleave(n_samples, 0)
          )
  
          # define function to run at every decoding step
          def decode_one(
              idx,
              lagged_targets,
              hidden_state,
          ):
              x = input_vector[:, [idx]]
              x[:, 0, target_pos] = lagged_targets[-1]
              for lag, lag_positions in lagged_target_positions.items():
                  if idx > lag:
                      x[:, 0, lag_positions] = lagged_targets[-lag]
              prediction, hidden_state = self.decode_all(
                  x, hidden_state
              )
              prediction = apply_to_list(
                  prediction, lambda x: x[:, 0]
              )  # select first time step
              return prediction, hidden_state
  
          # make predictions which are fed into next step
          output = self.decode_autoregressive(
              decode_one,
              first_target=input_vector[:, 0, target_pos],
              first_hidden_state=hidden_state,
              target_scale=target_scale,
              n_decoder_steps=input_vector.size(1),
              n_samples=n_samples,
          )
          # reshape predictions for n_samples:
          # from n_samples * batch_size x time steps to batch_size x time steps x n_samples
          output = apply_to_list(
              output,
              lambda x: x.reshape(
                  -1, n_samples, input_vector.size(1)
              ).permute(0, 2, 1),
          )
      return output
  
imports: from copy import copy, deepcopy
  from typing import Any, Dict, List, Literal, Optional, Tuple, Union
  import numpy
  import pandas
  import torch
  import torch.nn
  from torch.utils.data.dataloader import DataLoader
  from pytorch_forecasting.data.encoders import EncoderNormalizer, MultiNormalizer, NaNLabelEncoder
  from pytorch_forecasting.data.timeseries import TimeSeriesDataSet
  from pytorch_forecasting.metrics import MAE, MAPE, MASE, RMSE, SMAPE, DistributionLoss, Metric, MultiLoss, MultivariateDistributionLoss, NormalDistributionLoss
  from pytorch_forecasting.models.base_model import AutoRegressiveBaseModelWithCovariates, Prediction
  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 6 for calling the DeepAR obj's underlying member method code with example usage: obj.decode(input_vector, target_scale, decoder_lengths, hidden_state, n_samples) and returns: output, prediction,  hidden_state