code: def _parallel_fit(
      X,
      y,
      ind,
      min_window,
      max_window,
      window_inc,
      word_lengths,
      alphabet_size,
      norm_options,
      anova,
      variance,
      binning_strategies,
      bigrams,
      n_jobs,
      p_threshold,
      feature_selection,
      random_state,
  ):
      if random_state is not None:
          rng = check_random_state(random_state + ind)
      else:
          rng = check_random_state(random_state)
  
      all_words = []
  
      # On each dimension, perform SFA
      X_dim = X[:, ind]
      series_length = X_dim.shape[-1]
  
      # increment window size in steps of 'win_inc'
      win_inc = _compute_window_inc(series_length, window_inc)
      max_window = int(min(series_length, max_window))
  
      if min_window > max_window:
          raise ValueError(
              f"Error in MUSE, min_window ="
              f"{min_window} is bigger"
              f" than max_window ={max_window}."
              f" Try set min_window to be smaller than series length in "
              f"the constructor, but the classifier may not work at "
              f"all with very short series"
          )
  
      SFA_transformers = []
      window_sizes = np.arange(min_window, max_window, win_inc)
      relevant_features_count = 0
  
      for window_size in window_sizes:
          transformer = SFAFast(
              word_length=rng.choice(word_lengths),
              alphabet_size=alphabet_size,
              window_size=window_size,
              norm=rng.choice(norm_options),
              anova=anova,
              variance=variance,
              binning_method=rng.choice(binning_strategies),
              bigrams=bigrams,
              remove_repeat_words=False,
              lower_bounding=False,
              p_threshold=p_threshold,
              feature_selection=feature_selection,
              save_words=False,
              n_jobs=n_jobs,
              return_pandas_data_series=False,
              return_sparse=True,
          )
  
          all_words.append(transformer.fit_transform(X_dim, y))
          SFA_transformers.append(transformer)
          relevant_features_count = transformer.feature_count
  
      return (
          ind,
          all_words,
          SFA_transformers,
          window_sizes,
          relevant_features_count,
      )
  
imports: import math
  import numpy
  from joblib import Parallel, delayed
  from scipy.sparse import hstack
  from sklearn.linear_model import LogisticRegression, RidgeClassifierCV
  from sklearn.utils import check_random_state
  from sktime.classification.base import BaseClassifier
  from sktime.transformations.panel.dictionary_based import SFAFast
  from sktime.utils.warnings import warn
label_desc: Use image node_id 3 for calling a global function with example usage: _parallel_fit(X, y, ind, min_window, max_window, window_inc, word_lengths, alphabet_size, norm_options, anova, variance, binning_strategies, bigrams, n_jobs, p_threshold, feature_selection, random_state) and returns: ind,  all_words,  SFA_transformers,  window_sizes,  relevant_features_count