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.
code: def _pad_and_strip(self, array: List[object]):
      if len(array) < self.output_size:
          array = array + [0] * (self.output_size - len(array))
      if len(array) > self.output_size:
          array = array[: self.output_size]
      return array
  
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 2 for calling the ArrayEncoder obj's underlying member method code with example usage: obj._pad_and_strip(array) and returns: array