class_docstr: Symbolic Fourier Approximation (SFA) Transformer.
  
  Overview: for each series:
      run a sliding window across the series
      for each window
          shorten the series with DFT
          discretise the shortened series into bins set by MFC
          form a word from these discrete values
  by default SFA produces a single word per series (window_size=0)
  if a window is used, it forms a histogram of counts of words.
  
  Parameters
  ----------
  word_length:         int, default = 8
      length of word to shorten window to (using PAA)
  
  alphabet_size:       int, default = 4
      number of values to discretise each value to
  
  window_size:         int, default = 12
      size of window for sliding. Input series
      length for whole series transform
  
  norm:                boolean, default = False
      mean normalise words by dropping first fourier coefficient
  
  binning_method:      {"equi-depth", "equi-width", "information-gain", "kmeans"},
                          default="equi-depth"
      the binning method used to derive the breakpoints.
  
  anova:               boolean, default = False
      If True, the Fourier coefficient selection is done via a one-way
      ANOVA test. If False, the first Fourier coefficients are selected.
      Only applicable if labels are given
  
  bigrams:             boolean, default = False
      whether to create bigrams of SFA words
  
  skip_grams:          boolean, default = False
      whether to create skip-grams of SFA words
  
  remove_repeat_words: boolean, default = False
      whether to use numerosity reduction (default False)
  
  levels:              int, default = 1
      Number of spatial pyramid levels
  
  save_words:          boolean, default = False
      whether to save the words generated for each series (default False)
  
  return_pandas_data_series:          boolean, default = False
      set to true to return Pandas Series as a result of transform.
      setting to true reduces speed significantly but is required for
      automatic test.
  
  n_jobs:              int, optional, default = 1
      The number of jobs to run in parallel for both `transform`.
      ``-1`` means using all processors.
  
  Attributes
  ----------
  words: []
  breakpoints: = []
  num_insts = 0
  num_atts = 0
  
  References
  ----------
  .. [1] Schäfer, Patrick, and Mikael Högqvist. "SFA: a symbolic fourier approximation
  and  index for similarity search in high dimensional datasets." Proceedings of the
  15th international conference on extending database technology. 2012.
code: def _shorten_case(self, word_len, i):
      from numba import types
      from numba.typed import Dict
  
      from sktime.transformations.panel.dictionary_based._sfa_numba import (
          _shorten_words,
      )
  
      if self.typed_dict:
          new_bag = (
              Dict.empty(
                  key_type=types.Tuple((types.int64, types.int16)),
                  value_type=types.uint32,
              )
              if self.levels > 1
              else Dict.empty(
                  key_type=types.int64, value_type=types.uint32
              )
          )
      else:
          new_bag = {}
  
      last_word = -1
      repeat_words = 0
  
      for window, word in enumerate(self.words[i]):
          new_word = _shorten_words(word, word_len)
  
          repeat_word = (
              self._add_to_pyramid(
                  new_bag,
                  new_word,
                  last_word,
                  window - int(repeat_words / 2),
              )
              if self.levels > 1
              else self._add_to_bag(new_bag, new_word, last_word)
          )
  
          if repeat_word:
              repeat_words += 1
          else:
              last_word = new_word
              repeat_words = 0
  
          if self.bigrams:
              if window - self.window_size >= 0:
                  bigram = self._create_bigram_words(
                      new_word,
                      _shorten_words(
                          self.words[i][window - self.window_size],
                          word_len,
                      ),
                  )
  
                  if self.levels > 1:
                      if self.typed_dict:
                          bigram = (bigram, -1)
                      else:
                          bigram = (bigram << self.level_bits) | 0
                  new_bag[bigram] = new_bag.get(bigram, 0) + 1
  
          if self.skip_grams:
              # creates skip-grams, skipping every (s-1)-th word in-between
              for s in range(2, 4):
                  if window - s * self.window_size >= 0:
                      skip_gram = self._create_bigram_words(
                          new_word,
                          _shorten_words(
                              self.words[i][
                                  window - s * self.window_size
                              ],
                              word_len,
                          ),
                      )
  
                      if self.levels > 1:
                          if self.typed_dict:
                              skip_gram = (skip_gram, -1)
                          else:
                              skip_gram = (
                                  skip_gram << self.level_bits
                              ) | 0
                      new_bag[skip_gram] = new_bag.get(skip_gram, 0) + 1
  
      # can't pickle typed dict
      if self.typed_dict and self.n_jobs != 1:
          pdict = dict()
          for key, val in new_bag.items():
              pdict[key] = val
          new_bag = pdict
  
      return (
          pd.Series(new_bag)
          if self.return_pandas_data_series
          else new_bag
      )
  
imports: import math
  import sys
  import warnings
  import numpy
  import pandas
  from joblib import Parallel, delayed
  from sklearn.feature_selection import f_classif
  from sklearn.preprocessing import KBinsDiscretizer
  from sklearn.tree import DecisionTreeClassifier
  from sktime.transformations.base import BaseTransformer
  from sktime.utils.validation.panel import check_X
label_desc: Use image node_id 13 for calling the SFA obj's underlying member method code with example usage: obj._shorten_case(word_len, i) and returns: unknown