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.
docstr: Transform data into SFA words.
  
  Parameters
  ----------
  X : pandas DataFrame or 3d numpy array, input time series.
  y : array_like, target values (optional, ignored).
  
  Returns
  -------
  List of dictionaries containing SFA words
docstr_tok: ["Transform","data","into","SFA","words",".","Parameters","--","--","--","--","--","X",":","pandas","DataFrame","or","3d","numpy","array",",","input","time","series",".","y",":","array_like",",","target","values","(","optional",",","ignored",")",".","Returns","--","--","--","-","List","of","dictionaries","containing","SFA","words"]
code: def _transform(self, X, y=None):
      """Transform data into SFA words.
  
      Parameters
      ----------
      X : pandas DataFrame or 3d numpy array, input time series.
      y : array_like, target values (optional, ignored).
  
      Returns
      -------
      List of dictionaries containing SFA words
      """
      from numba import NumbaTypeSafetyWarning, types
      from numba.typed import Dict
  
      X = X.squeeze(1)
  
      with warnings.catch_warnings():
          warnings.simplefilter(
              "ignore", category=NumbaTypeSafetyWarning
          )
          transform = Parallel(n_jobs=self.n_jobs)(
              delayed(self._transform_case)(
                  X[i, :],
                  supplied_dft=self.binning_dft[i]
                  if self.keep_binning_dft
                  else None,
              )
              for i in range(X.shape[0])
          )
  
      dim, words = zip(*transform)
      if self.save_words:
          self.words = list(words)
  
      # can't pickle typed dict
      if self.typed_dict and self.n_jobs != 1:
          nl = [None] * len(dim)
          for i, pdict in enumerate(dim):
              ndict = (
                  Dict.empty(
                      key_type=types.UniTuple(types.int64, 2),
                      value_type=types.uint32,
                  )
                  if self.levels > 1
                  else Dict.empty(
                      key_type=types.int64, value_type=types.uint32
                  )
              )
              for key, val in pdict.items():
                  ndict[key] = val
              nl[i] = pdict
          dim = nl
  
      bags = (
          pd.DataFrame() if self.return_pandas_data_series else [None]
      )
      bags[0] = list(dim)
  
      return bags
  
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 3 for calling the SFA obj's underlying member method code with example usage: obj._transform(X, y) and returns: bags