code: def get_factors(
      model,
      X_train,
      X_test,
      y_train,
      fnum,
      fname,
      nvalues,
      dtype,
      encoder,
      rounding,
      sentinel,
  ):
      r"""Convert the original feature to a factor.
  
      Parameters
      ----------
      model : alphapy.Model
          Model object with the feature specifications.
      X_train : pandas.DataFrame
          Training dataframe containing the column ``fname``.
      X_test : pandas.DataFrame
          Testing dataframe containing the column ``fname``.
      y_train : pandas.Series
          Training series for target variable.
      fnum : int
          Feature number, strictly for logging purposes
      fname : str
          Name of the text column in the dataframe ``df``.
      nvalues : int
          The number of unique values.
      dtype : str
          The values ``'float64'``, ``'int64'``, or ``'bool'``.
      encoder : alphapy.features.Encoders
          Type of encoder to apply.
      rounding : int
          Number of places to round.
      sentinel : float
          The number to be imputed for NaN values.
  
      Returns
      -------
      all_features : numpy array
          The features that have been transformed to factors.
      all_fnames : list
          The feature names for the encodings.
  
      """
  
      logger.info(
          "Feature %d: %s is a factor of type %s with %d unique values",
          fnum,
          fname,
          dtype,
          nvalues,
      )
      logger.info("Encoding: %s", encoder)
  
      # get feature
      feature_train = X_train[fname]
      feature_test = X_test[fname]
      # convert float to factor
      if dtype == "float64":
          logger.info("Rounding: %d", rounding)
          feature_train = feature_train.apply(
              float_factor, args=[rounding]
          )
          feature_test = feature_test.apply(
              float_factor, args=[rounding]
          )
      # create data frames for the feature
      df_train = pd.DataFrame(feature_train)
      df_test = pd.DataFrame(feature_test)
      # encoders
      enc = None
      try:
          enc = encoder_map[encoder](cols=[fname])
      except:
          raise ValueError("Unknown Encoder %s" % encoder)
      # Transform the train and test features.
      if enc is not None:
          # fit training features
          logger.info("Fitting training features for %s", fname)
          ftrain = enc.fit_transform(df_train, y_train)
          # fit testing features
          logger.info("Transforming testing features for %s", fname)
          ftest = enc.transform(df_test)
          # get feature names
          all_fnames = enc.get_feature_names()
          # concatenate all generated features
          all_features = np.row_stack((ftrain, ftest))
      else:
          all_features = None
          all_fnames = None
          logger.info("Encoding for feature %s failed" % fname)
      return all_features, all_fnames
  
imports: from alphapy.globals import BSEP, LOFF, NULLTEXT
  from alphapy.globals import PSEP, SSEP, USEP
  from alphapy.globals import Encoders
  from alphapy.globals import ModelType
  from alphapy.globals import Scalers
  from alphapy.variables import Variable
  from alphapy.variables import vparse
  import category_encoders
  from importlib import import_module
  import itertools
  import logging
  import math
  import numpy
  import os
  import pandas
  import re
  from scipy import sparse
  import scipy.stats
  from sklearn.cluster import MiniBatchKMeans
  from sklearn.decomposition import PCA
  from sklearn.feature_extraction.text import TfidfVectorizer
  from sklearn.feature_selection import chi2
  from sklearn.feature_selection import f_classif
  from sklearn.feature_selection import f_regression
  from sklearn.feature_selection import SelectFdr
  from sklearn.feature_selection import SelectFpr
  from sklearn.feature_selection import SelectFwe
  from sklearn.feature_selection import SelectKBest
  from sklearn.feature_selection import SelectPercentile
  from sklearn.feature_selection import VarianceThreshold
  from sklearn.impute import SimpleImputer
  from sklearn.manifold import Isomap
  from sklearn.manifold import TSNE
  from sklearn.preprocessing import MinMaxScaler
  from sklearn.preprocessing import PolynomialFeatures
  from sklearn.preprocessing import StandardScaler
  import sys
label_desc: Use image node_id 9 for calling a global function with example usage: get_factors(model, X_train, X_test, y_train, fnum, fname, nvalues, dtype, encoder, rounding, sentinel) and returns: all_features,  all_fnames