code: def get_column_transformer(
      flow,
      component_map,
      unsupported_ops,
      not_sk_ops,
      numeric_columns,
      nominal_columns,
  ):
      col_transformer_id = flow.flow_id
      transformers = []
      for key in flow.components:
          comp_id = flow.components[key].flow_id
          comp_class = flow.components[key].class_name
          if comp_class == "sklearn.pipeline.Pipeline":
              comp = get_pipeline(
                  flow.components[key],
                  component_map,
                  unsupported_ops,
                  not_sk_ops,
                  numeric_columns,
                  nominal_columns,
              )
          else:
              comp = get_sk_op(comp_class, unsupported_ops, not_sk_ops)
  
          if key == "numeric" and len(numeric_columns) > 0:
              transformers.append((key, comp, numeric_columns))
          elif key == "nominal" and len(nominal_columns) > 0:
              transformers.append((key, comp, nominal_columns))
          else:
              transformers.append((key, comp, []))
  
          component_map[comp_id] = comp
  
      col_transformer = ColumnTransformer(transformers)
      component_map[col_transformer_id] = col_transformer
      return col_transformer
  
imports: from packaging.version import Version, parse
  import openml
  import sklearn
  import operator
  import keyword
  import re
  from pathlib import Path
  import numpy
  import random
  import os
  import joblib
  import warnings
  from sklearn.datasets import fetch_openml
  from sklearn.metrics import accuracy_score
  from sklearn.model_selection import train_test_split
  from sklearn.compose import ColumnTransformer
  from sklearn.decomposition import PCA, TruncatedSVD, FastICA, KernelPCA
  from sklearn.ensemble import RandomForestClassifier, GradientBoostingClassifier
  from sklearn.feature_extraction.text import CountVectorizer, TfidfVectorizer
  from sklearn.feature_extraction import FeatureHasher
  from sklearn.feature_selection import SelectKBest, VarianceThreshold, SelectPercentile
  from sklearn.linear_model import LogisticRegression, SGDClassifier, LogisticRegressionCV
  from sklearn.naive_bayes import BernoulliNB
  from sklearn.neural_network import MLPClassifier
  from sklearn.preprocessing import OneHotEncoder, RobustScaler, MaxAbsScaler, MinMaxScaler, StandardScaler, Normalizer, Binarizer, KBinsDiscretizer, PolynomialFeatures, LabelEncoder
  from sklearn.impute import SimpleImputer
  from sklearn.svm import LinearSVC, SVC, NuSVC
  from sklearn.tree import DecisionTreeClassifier
  from sklearn.impute import MissingIndicator, SimpleImputer
  from sklearn.ensemble import ExtraTreesClassifier, AdaBoostClassifier
  from sklearn.ensemble._hist_gradient_boosting.gradient_boosting import HistGradientBoostingClassifier
  from sklearn.pipeline import FeatureUnion
  from sklearn.naive_bayes import GaussianNB, MultinomialNB
  from sklearn.neighbors import KNeighborsClassifier, KNeighborsRegressor
  from hummingbird.ml.supported import sklearn_operator_list
label_desc: Use image node_id 2 for calling a global function with example usage: get_column_transformer(flow, component_map, unsupported_ops, not_sk_ops, numeric_columns, nominal_columns) and returns: col_transformer