docstr: Encodes a list of time series arrays using the underlying time series numerical encoder.
  
  :param data: list of numerical values to encode. Its length is determined by the tss.window parameter, and all data points belong to the same time series.
  :param dependency_data: dict with values of each group_by column for the time series, used to retrieve the correct normalizer.
  
  :return: list of encoded time series arrays. Tensor is (len(data), N x K)-shaped, where N: self.data_window and K: sub-encoder # of output features.
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code: def encode(
      self,
      data: Iterable[Iterable],
      dependency_data: Optional[Dict[str, str]] = {},
  ) -> torch.Tensor:
      """
      Encodes a list of time series arrays using the underlying time series numerical encoder.
  
      :param data: list of numerical values to encode. Its length is determined by the tss.window parameter, and all data points belong to the same time series.
      :param dependency_data: dict with values of each group_by column for the time series, used to retrieve the correct normalizer.
  
      :return: list of encoded time series arrays. Tensor is (len(data), N x K)-shaped, where N: self.data_window and K: sub-encoder # of output features.
      """  # noqa
      if not self.is_prepared:
          raise Exception(
              'You need to call "prepare" before calling "encode" or "decode".'
          )
  
      ret = []
      for series in data:
          ret.append(self.encode_one(series))
  
      return torch.vstack(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 3 for calling the TsCatArrayEncoder obj's underlying member method code with example usage: obj.encode(data, dependency_data) and returns: torch