docstr: Decodes a list of encoded arrays into values in their original domains.
  
  :param encoded_values: encoded slices of numerical time series.
  :param dependency_data: used to determine the correct normalizer for the input.
  
  :return: a list of decoded time series arrays.
docstr_tok: ["Decodes","a","list","of","encoded","arrays","into","values","in","their","original","domains",".",":","param","encoded_values",":","encoded","slices","of","numerical","time","series",".",":","param","dependency_data",":","used","to","determine","the","correct","normalizer","for","the","input",".",":","return",":","a","list","of","decoded","time","series","arrays","."]
code: def decode(self, encoded_values, dependency_data=None) -> List[List]:
      """
      Decodes a list of encoded arrays into values in their original domains.
  
      :param encoded_values: encoded slices of numerical time series.
      :param dependency_data: used to determine the correct normalizer for the input.
  
      :return: a list of decoded time series arrays.
      """
      if not self.is_prepared:
          raise Exception(
              'You need to call "prepare" before calling "encode" or "decode".'
          )
  
      encoded_values = encoded_values.reshape(
          encoded_values.shape[0],
          self.data_window,
          self.sub_encoder.output_size,
      )
  
      ret = []
      for tensor in torch.split(encoded_values, 1, dim=0):
          ret.append(self.decode_one(tensor))
  
      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 5 for calling the TsCatArrayEncoder obj's underlying member method code with example usage: obj.decode(encoded_values, dependency_data) and returns: ret