class_docstr: Time series encoder. This module can learn features for any `order_by` temporal column, both with and without accompanying target data.
  
  The backbone of this encoder is either a recurrent neural network or a transformer; both structured in an encoder-decoder fashion.
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docstr: Decodes a single time series from its encoded representation.
  :param hidden: time series embedded representation tensor, with size self.output_size
  :param steps: as in decode(), defines how many values to output when reconstructing
  :return: decoded time series list
docstr_tok: ["Decodes","a","single","time","series","from","its","encoded","representation",".",":","param","hidden",":","time","series","embedded","representation","tensor",",","with","size","self.output_size",":","param","steps",":","as","in","decode","(",")",",","defines","how","many","values","to","output","when","reconstructing",":","return",":","decoded","time","series","list"]
code: def _decode_one(self, hidden, steps):
      """
      Decodes a single time series from its encoded representation.
      :param hidden: time series embedded representation tensor, with size self.output_size
      :param steps: as in decode(), defines how many values to output when reconstructing
      :return: decoded time series list
      """
      self._decoder.eval()
      with torch.no_grad():
          ret = []
          next_tensor = torch.full(
              (1, 1, self._n_dims), self._sos, dtype=torch.float32
          ).to(self.device)
          timesteps = steps if steps else self._max_ts_length
          for _ in range(timesteps):
              next_tensor, hidden = self._decoder.forward(
                  next_tensor, hidden
              )
              ret.append(next_tensor)
          return torch.stack(ret)
  
imports: import time
  from math import gcd
  from typing import List
  from copy import deepcopy
  import numpy
  import pandas
  import torch
  import torch.nn
  from torch import optim
  from type_infer.dtype import dtype
  from lightwood.helpers.log import log
  from lightwood.encoder.base import BaseEncoder
  from lightwood.helpers.device import get_device_from_name
  from lightwood.helpers.torch import LightwoodAutocast
  from lightwood.encoder.datetime import DatetimeNormalizerEncoder
  from lightwood.encoder.time_series.helpers.rnn_helpers import EncoderRNNNumerical, DecoderRNNNumerical
  from lightwood.encoder.helpers import MinMaxNormalizer, CatNormalizer
  from lightwood.encoder.time_series.helpers.transformer_helpers import TransformerEncoder, get_chunk, len_to_mask
label_desc: Use image node_id 9 for calling the TimeSeriesEncoder obj's underlying member method code with example usage: obj._decode_one(hidden, steps) and returns: torch