docstr: Decodes a single window of a time series into its original domain.
  
  :param encoded_value: encoded slice of a numerical time series.
  :param dependency_data: used to determine the correct normalizer for the input.
  
  :return: a list of length TimeseriesSettings.window with decoded values for the forecasted time series.
docstr_tok: ["Decodes","a","single","window","of","a","time","series","into","its","original","domain",".",":","param","encoded_value",":","encoded","slice","of","a","numerical","time","series",".",":","param","dependency_data",":","used","to","determine","the","correct","normalizer","for","the","input",".",":","return",":","a","list","of","length","TimeseriesSettings.window","with","decoded","values","for","the","forecasted","time","series","."]
code: def decode_one(self, encoded_value) -> List:
      """
      Decodes a single window of a time series into its original domain.
  
      :param encoded_value: encoded slice of a numerical time series.
      :param dependency_data: used to determine the correct normalizer for the input.
  
      :return: a list of length TimeseriesSettings.window with decoded values for the forecasted time series.
      """
      ret = []
      for encoded_timestep in torch.split(encoded_value, 1, dim=1):
          ret.extend(
              self.sub_encoder.decode(encoded_timestep.squeeze(1))
          )
      return ret
  
file_path: lightwood/lightwood/encoder/array/ts_cat_array.py
imports: from typing import List, Dict, Iterable, Optional
  import torch
  import torch.nn.functional
  from lightwood.encoder import BaseEncoder
  from lightwood.encoder.categorical import OneHotEncoder
label_desc: Use image node_id 6 for calling the TsCatArrayEncoder obj's underlying member method code with example usage: obj.decode_one(encoded_value) and returns: ret