code: def _parallel_transform_words(X, window_sizes, SFA_transformers, ind):
      # On each dimension, perform SFA
      X_dim = X[:, ind]
  
      bag_all_words = []
      for i in range(len(window_sizes[ind])):
          words = SFA_transformers[ind][i].transform(X_dim)
          bag_all_words.append(words)
  
      return bag_all_words
  
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 2 for calling a global function with example usage: _parallel_transform_words(X, window_sizes, SFA_transformers, ind) and returns: bag_all_words