code: def test_export_pipeline():
      """Assert that exported_pipeline() generated a compile source file as expected given a fixed pipeline."""
  
      pipeline_string = (
          "KNeighborsClassifier(CombineDFs("
          "DecisionTreeClassifier(input_matrix, DecisionTreeClassifier__criterion=gini, "
          "DecisionTreeClassifier__max_depth=8,DecisionTreeClassifier__min_samples_leaf=5,"
          "DecisionTreeClassifier__min_samples_split=5),SelectPercentile(input_matrix, SelectPercentile__percentile=20))"
          "KNeighborsClassifier__n_neighbors=10, "
          "KNeighborsClassifier__p=1,KNeighborsClassifier__weights=uniform"
      )
  
      pipeline = creator.Individual.from_string(
          pipeline_string, tpot_obj._pset
      )
      expected_code = """import numpy as np
  import pandas as pd
  from sklearn.feature_selection import SelectPercentile, f_classif
  from sklearn.model_selection import train_test_split
  from sklearn.neighbors import KNeighborsClassifier
  from sklearn.pipeline import make_pipeline, make_union
  from sklearn.tree import DecisionTreeClassifier
  from tpot.builtins import StackingEstimator
  
  # NOTE: Make sure that the outcome column is labeled 'target' in the data file
  tpot_data = pd.read_csv('PATH/TO/DATA/FILE', sep='COLUMN_SEPARATOR', dtype=np.float64)
  features = tpot_data.drop('target', axis=1)
  training_features, testing_features, training_target, testing_target = \\
              train_test_split(features, tpot_data['target'], random_state=None)
  
  exported_pipeline = make_pipeline(
      make_union(
          StackingEstimator(estimator=DecisionTreeClassifier(criterion="gini", max_depth=8, min_samples_leaf=5, min_samples_split=5)),
          SelectPercentile(score_func=f_classif, percentile=20)
      ),
      KNeighborsClassifier(n_neighbors=10, p=1, weights="uniform")
  )
  
  exported_pipeline.fit(training_features, training_target)
  results = exported_pipeline.predict(testing_features)
  """
      assert expected_code == export_pipeline(
          pipeline, tpot_obj.operators, tpot_obj._pset
      )
  
imports: from tqdm import tqdm
  import numpy
  from os import remove, path
  from tpot import TPOTClassifier, TPOTRegressor
  from tpot.export_utils import export_pipeline, generate_import_code, _indent, generate_pipeline_code, get_by_name, set_param_recursive
  from tpot.operator_utils import TPOTOperatorClassFactory
  from tpot.config.classifier import classifier_config_dict
  from sklearn.datasets import load_digits
  from sklearn.model_selection import train_test_split
  from deap import creator
  from nose.tools import assert_raises, assert_equal, nottest
label_desc: Use image node_id 10 for calling a global function with example usage: test_export_pipeline() without return types