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 _add_to_pyramid(self, bag, word, last_word, window_ind):
      from sktime.transformations.panel.dictionary_based._sfa_numba import (
          _add_level,
          _add_level_typed,
      )
  
      if self.remove_repeat_words and word == last_word:
          return True
  
      start = 0
      for i in range(self.levels):
          if self.typed_dict:
              new_word, num_quadrants = _add_level_typed(
                  word,
                  start,
                  i,
                  window_ind,
                  self.window_size,
                  self.series_length,
              )
          else:
              new_word, num_quadrants = (
                  _add_level(
                      word,
                      start,
                      i,
                      window_ind,
                      self.window_size,
                      self.series_length,
                      self.level_bits,
                  )
                  if self.word_bits + self.level_bits <= 64
                  else self._add_level_large(
                      word,
                      start,
                      i,
                      window_ind,
                  )
              )
          bag[new_word] = bag.get(new_word, 0) + num_quadrants
          start += num_quadrants
  
      return False
  
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 15 for calling the SFA obj's underlying member method code with example usage: obj._add_to_pyramid(bag, word, last_word, window_ind) and returns: False, True