class_docstr: Fits a normalizer for array data.
  
  To encode, `ArrayEncoder` returns a normalized window of previous data.
  It can be used for generic arrays, as well as for handling historical target values in time series tasks.
  
  Currently supported normalizing strategies are minmax for numerical arrays, and a simple one-hot for categorical arrays. See `lightwood.encoder.helpers` for more details on each approach.
docstr: Encode the properties of a sequence-of-sequence representation
  
  :param column_data: Input column data to be encoded
  :returns: a torch-tensor representing the encoded sequence
docstr_tok: ["Encode","the","properties","of","a","sequence-of-sequence","representation",":","param","column_data",":","Input","column","data","to","be","encoded",":","returns",":","a","torch-tensor","representing","the","encoded","sequence"]
code: def encode(self, column_data: Iterable[Iterable]) -> torch.Tensor:
      """
      Encode the properties of a sequence-of-sequence representation
  
      :param column_data: Input column data to be encoded
      :returns: a torch-tensor representing the encoded sequence
      """
      if not self.is_prepared:
          raise Exception(
              'You need to call "prepare" before calling "encode" or "decode".'
          )
  
      if isinstance(column_data, pd.Series):
          column_data = column_data.values
  
      for i in range(len(column_data)):
          if is_none(column_data[i]):
              column_data[i] = [0] * self.output_size
      column_data = np.array(
          [self._pad_and_strip(list(x)) for x in column_data]
      )
  
      data = torch.cat([self._normalizer.encode(column_data)], dim=-1)
      data[torch.isnan(data)] = 0.0
      data[torch.isinf(data)] = 0.0
  
      return data
  
imports: import torch
  import pandas
  import numpy
  from lightwood.encoder.base import BaseEncoder
  from type_infer.dtype import dtype
  from lightwood.encoder.helpers import MinMaxNormalizer, CatNormalizer
  from lightwood.helpers.general import is_none
  from typing import List, Iterable
label_desc: Use image node_id 4 for calling the ArrayEncoder obj's underlying member method code with example usage: obj.encode(column_data) and returns: data